Water Energy Nexus of China’s Recent Energy Development … · Case study of Jiangsu UK Foreign and Commonwealth Office, China 21 April 2016 Draft report on Jiangsu Coal Power Water

Water Energy Nexus of China’s Recent Energy Development Strategy: A Case study of Jiangsu UK Foreign and Commonwealth Office, China

21 April 2016 Draft report on Jiangsu Coal Power Water energy Nexus


Atkins 21 April 2016 | Ref: 5140036 | Version 1.0

Notice

This document and its contents have been prepared and are intended solely for UK Foreign and Commonwealth Office, China’s information and use in relation to the SPF project study on the Jiangsu Coal Power Water Energy Nexus.

WS Atkins International Ltd assumes no responsibility to any other party in respect of or arising out of or in connection with this document and/or its contents.

This document has 44 pages including the cover.

Document history

Document ref: 5140036

Revision Purpose description Originated Checked Reviewed Authorised Date

Rev 1.0 Draft SBS SBS 22/4/2016



Table of contents

Chapter Pages

1. National Context of Jiangsu Power Sector 5 1.1. Introduction 5 1.2. Jiangsu’s Energy production balance 6 1.3. Coal power station permitting and authorisation 10 1.4. Conclusion 11

2. PowerWEN Model 12 2.1. Modelling approach 12 2.2. Power Capacity modelling 12 2.3. Water Use modelling 16 2.4. Coal use and greenhouse gas emissions 21 2.5. Sensitivity testing 22 2.6. Analysing CGE Scenarios 22 2.7. Analysis of water saving technology scenarios 23 2.8. Conclusions 25

3. Model operation Manual 27 3.1. Structure of model 27 3.2. Data Preparation for PowerWEN Model 33 3.3. Summary of Data Used in the model 36 3.4. Model Files and Copyright 36

Appendices 37

Appendix A. Power Sector Permit Approvals 2015 38 A.1. Permit approvals 38

Tables Table 2-1 2013 Jiangsu statistical yearbook data on power capacity and energy produced with calculation of utilisation 16 Table 2-2 Example of coefficients for water use - Water withdrawal (m3/MWh) Mean figures 17 Table 2-3 Coefficients for Coal consumption (Kg/MWh) (after Zhang et al 2016) 21

Figures Figure 1-1 Coal production and coal consumption by Province in China, 2010 6 Figure 1-2 Electricity production and Electricity consumption by province 2010 7 Figure 1-3 Planned and completed ultra high voltage long distance transmission lines 7 Figure 1-4 Nuclear power development plans 8 Figure 1-5 Hydropower, wind and solar development and potential in China 9 Figure 1-6 Projections of energy mix in Jiangsu, from NJU CGE model report 10 Figure 2-1 Jiangsu Province with major power stations sized by capacity and coloured by cooling type. 13 Figure 2-2 Capacity of major and minor power stations in Jiangsu Municipalities (2013 data) 14 Figure 2-3 Capacity of known and planned power stations compared to CGE model of Jiangsu Thermal power production from 2007 to 2030, BAU Scenario. 14 Figure 2-4 Capacity of known and planned power stations compared to CGE model of Jiangsu Thermal power production from 2007 to 2030, EE-3% Scenario. 15 Figure 2-5 Power station capacity in Jiangsu municipalities with correction to match CGE model 15 Figure 2-6 PowerWEN GIS output showing power stations sized by capacity and coloured by boiler type 17 Figure 2-7 Sankey Diagram of Power Sector Water Use in Jiangsu 18



Figure 2-8 PowerWEN GIS outputs thematically representing power sector Fresh water withdrawals and consumption and seawater use across Jiangsu, 2015 scenario 18 Figure 2-9 Water resources use (based on withdrawals) estimated for 2007-2030 under BAU showing compliance with three redlines allocations 19 Figure 2-10 Water use compliance with redline allocations by municipality 19 Figure 2-11 Water use by power sector in Suzhou municipality compared to 3 red lines allocations 20 Figure 2-12 PowerWEN analysis of water withdrawal by Jiangsu power sector under different scenarios for the CGE model for 2015 situation and in comparison to redline allocations 2007 to 2030. 23 Figure 2-13 Control Panel: Percentage reduction in cooling water use for technology scenarios. 23 Figure 2-14 Map of China coloured by river basin water stress (after WRI Aqueduct), indication of the areas of China where Dry cooling is required, and charts of the impact of cooling method on Water and coal consumption and water withdrawal 25 Figure 3-1 Control panel with default settings 27 Figure 3-2 Control panel with main output graphics 28 Figure 3-3 Control panel calculation of Multi Year results 29 Figure 3-4 Layout of Nodes sheet of PowerWEN model 31 Figure 3-5 PowerWEN Schematic GIS system 32 Figure 3-6 Summary of data used in Jiangsu Water Energy Model 36 Figure A-1 Permits Approved 2015 39


Atkins 21 April 2016 | Ref: 5140036 | Version 1.0 5

1. National Context of Jiangsu Power Sector

1.1. Introduction Electrical generation capacity has grown rapidly during China’s industrialisation with consequences for water use and greenhouse gas emissions, this report examines this as a case study in Jiangsu Province in Eastern China. Currently most of the energy in Jiangsu is derived from burning coal, but it is the intention that due to the negative environmental impacts of coal combustion there is to be a transition away from coal thermal power production in the future.

The aim of this project is to develop and pilot decision support tools for policy makers to better understand the water-carbon trade-off challenges caused by Jiangsu’s future energy development strategy, so support better energy and water resource policy planning and promote its adoption by the Department of Water Resources (DWR) and Department of Environmental Protection (DEP) of Jiangsu Province.

Economic development and rising demand for better living standards will continue to drive China’s demand for energy. Under the triple pressures of energy security, air pollution control and climate change, China plans to optimize its energy consumption structure by decreasing the share of coal consumption and increasing the shares of natural gas, nuclear and renewable energy consumption. Energy production is highly water intensive. The changing energy portfolios will change the risks facing the water resources and the environment. The current water-energy decision landscape in China is highly fragmented. At the provincial level, Department of Water Resources (DWR), Department of Environmental Protection (DEP) and Development and Reform Commission (DRC) are responsible for water withdrawal, wastewater discharge and energy saving respectively.

As the largest electricity producer in China, Jiangsu is also one of the most progressive provinces in China in low carbon development. This project will take Jiangsu as a case study and develop a decision support tool for local policy makers to understand the water intensity of the current energy system and assess the future water and GHG impact of energy strategies that could be deployed.

The UK has a water-efficient energy sector and is advanced in exploring modelling and transition of UK Water-Energy Nexus. The UK is already well advanced in its translon away from coal dependence with complete phasing out of coal power station planned for the 2020s. This project brings together UK and Chinese experts in the development of decision support tools for policy makers to make them aware of the water-carbon trade-off challenges faced by changing energy portfolios and how these may help bridge the highly fragmented water-energy decision landscape in Jiangsu.

The project carried out a water flow analysis of Jiangsu’s current and future energy sector through data collection and scenario analysis. The scope of water flow includes water abstraction and consumption. The scenario analysis of future water demand of the energy sector contains variables of energy portfolio, generation technology, cooling method and so on. The project used a top down Computable General Equilibrium (CGE) model (a type of economic model that uses economic data to estimate how an economy might react to changes in policy, technology or other external factors) developed by Nanjing University and a bottom up PowerWEN model developed by Atkins (representing the actual power stations in Jiangsu to better understand the situation and future scenarios.

The analytical tools provide a visual and dynamic platform to display the water-requirements, water intensity, and resilience of the energy production system under different energy production strategies. We consider this in the context of the “Three Red Lines” policies for water allocation, water saving and pollution load allocation. The case study report, training, training materials, and workshop supported local policy makers to have a better understanding of the water-energy nexus in the energy sector and how different energy development strategies will impact local water security. The dissemination of this decision support tool among Jiangsu DWR, DEP and relevant city-level officers will enable policy makers and other stakeholders to incorporate water into energy policy discussions and enhance the communication among multiple stakeholders.



The project ran from May 2015 to April 2016 and was sponsored by the UK Foreign and Commonwealth Office SPF programme. The project was delivered by a partnership of Nanjing University and W S Atkins International Consultants.

1.2. Jiangsu’s Energy production balance China has abundant fossil fuel resources, abundant hydropower and other renewable energy resources but these are mostly far from the main population centres. The main demand centres for energy are on the East and south coastal areas. These have developed rapidly mostly by building coal power stations and transporting the coal by rail and sea to be burnt locally to produce the power they need.

The consequence of this has been severe problems with air pollution in the main population areas and also a large inventory of older plants with low efficiency and high water consumption. The national policy is to address this by moving coal power production and other heavy polluting industries away from the main cities and increasing development of lower carbon renewable and nuclear energy sources.

Figure 1-1 Coal production and coal consumption by Province in China, 2010

Jiangsu Province is the second largest consumer of coal in China (after Shandong), and also the second largest producer of Electricity (after Guangdong). But coal production in the province itself is tiny so this has to be transported. However coal is heavy and expensive to transport such that the cost of coal delivered to a power plant in Jiangsu is nearly twice that in Shanxi or Inner Mongolia near the minehead.

Jiangsu Province

Primary raw coal production (circles) :

Inner Mongolia 561 Million tons, 2010;

Jiangsu 15 million tons)

vs

Raw Coal Consumption (colour

intensity): Shangdong 266 million tons,

Jiangsu 165 million tons

after NRDC www.chinaenergymap.com)

原煤产量：内蒙古5.61亿吨；江苏0.15亿吨。原煤消

耗：山东2.66亿吨；江苏1.65亿吨（2010）。

http://www.chinaenergymap.com/



Figure 1-2 Electricity production and Electricity consumption by province 2010

The ever increasing transport of coal from the coal production areas in north western China to the energy hungry factories of the East and south is now being reversed with the construction of Ultra High Voltage AC and DC power grid connectors that allow the power to be produced near the minehead or from remote hydropower, wind and solar renewable sites and transported efficiently to the centres of consumption.

Figure 1-3 Planned and completed ultra high voltage long distance transmission lines

Nuclear

There are also plans for continuing the massive development of nuclear power in China. Jiangsu has one of the major existing Nuclear plants in China at Tianwan. A 2 X 990 MWe plant based on Russian VVER technology, is expected to have additional units constructed in the coming years for a further 2 X 1060 MWe by 2018 and then another 2 X 1080 MWE capacity by 2020.

Jiangsu Consumption 2010 was 386.4

TWh out of National 4,242 TWh, second

greatest only to Guangdong. Installed

Generating capacity was 60 GW (similar

to Shangdong in 2010) but total

generation was 330 TWh, significantly

more than any other province, indicating

better utilisation of available capacity

(Jiangsu was 63% utilisation compared to

national average of 56%, in 2010).

China’s total thermal generation was

3,416 Twh, with 684 of Hydro and 74

TWh of nuclear.

after NRDC www.chinaenergymap.com)

1000 KV AC

规划的1000千伏交流电网

800 KV DC Transmission lines

规划的800千伏直流电网

Source: https://worldmap.harvard.edu/chinamap/




Figure 1-4 Nuclear power development plans

There are also national plans for developing lower carbon natural and shale gas reserves, increasing imports from Russia and of LNG and of synthesising Natural gas from Coal in SNG plants.

1.2.1. Renewables development

Jiangsu has potential and plans for the further development of renewable and lower carbon energy sources.

Hydropower

Jiangsu has limited potential for major hydropower itself but with many very large dams being constructed further west on the upper Yangtze and also in neighbouring countries such as Myanmar there is great potential for the importation of primary hydropower. Pumped storage hydropower schemes are under construction in Jiangsu, Wikipedia power stations in Jiangsu page reports 3 pumped storage schemes, Shahe (100MW) and Yixing (1000MW) which have been constructed and Liyang (1500 MW) under construction. These should provide significant load balancing capacity and therefore support more renewables development.

Wind

The main wind development to date in China has been of Onshore turbines and located in the windy uplands and plateaus of North China. These being distant from the main demand centres. However there is massive wind potential offshore and Jiangsu itself has huge potential. Offshore arrays are currently being constructed in Binhai and Rudong in Jiangsu with plans being developed for more than 10GW of capacity in these.

Solar

Due to the relatively cloudy weather conditions in Jiangsu the solar potential is lower than other areas in China. Thus while local roof mounted systems ae likely to proliferate, large scale solar farms are more likely to be located in more distant western areas with greater insolation potential.

Biomass, Waste to energy and Combined heat and power

Most of the minor power stations are related to biomass or municipal solid waste incineration or local combined heat and power many more of these have been approved in 2015. See Appendix A.

Natural Gas

In recent years there has been an increase in the number of natural gas power production plants approved and constructed. Some but not all of these have been included in the PowerWEN model so far.

Source http://www.forbes.com/sites/jamesconca/2014/12/31/2014-the-year-in-energy/



Figure 1-5 Hydropower, wind and solar development and potential in China

Overall Trends

These changes in the energy mix will greatly influence the plans for development of the power sector in Jiangsu. Based on the projections developed by NJU for the CGE model the projected mix is as shown in Figure 1-6. It is how these trends / plans will be implemented and the consequences for water policy that are the focus of this report.

Hydropower

development

Circles indicate installed

capacity 2010.

Colours of provinces

indicate electricity

demand

Wind potential and

development

Circles indicate installed

capacity 2010.

Solar power potential

across China

Source: www.chinaenergymap.com




Figure 1-6 Projections of energy mix in Jiangsu, from NJU CGE model report

Thus by 2017 the reliance on coal is expected to drop from current 70% to 65% and by 2030 to 50% from coal combustion. The total power capacity will have grown from the current 90 GW or so to over 145 GW, but most of this will have to be from nuclear, renewable or imported sources.

1.3. Coal power station permitting and authorisation In March 2015 the permitting of new power stations was decentralised, with the NDRC’s National Energy Administration (NEA) delegating the approval process to provincial governments. The result was a very sharp increase in the number of new installations approved for construction despite there already being over capacity in the market1. This was largely motivated by local economic interests. Existing plants are already having to run at partial production with the average number of operational hours in 2015 falling to an all time low of 4,329 hours compared to a design target of 5500 hours2. This is also seen in Jiangsu with the approval of 21 new projects with a total capacity of 5.7 GW (of which 5.35 GW are for a few large coal and gas installations, see Appendix A. This is despite the 13th Five year plan emphasising the transition to clean and renewable energy. Due to economic restructuring there was also an overall reduction in coal consumption in China in 2015. On April 6 2016 the NEA issued directives to halt the progress of construction of new plants in 13 provinces where there is overcapacity and to delay construction in a further 15 provinces. In the remaining provinces preference is to be given to cleaner power sources than coal to meet capacity shortfalls. Jiangsu is among those provinces where both current projects are halted and new projects suspended. This construction suspension directive was issued more recently than the permits to proceed listed in Appendix A. It is not yet clear if these directives apply only to the larger coal plants or also to small CHP, biomass, MSW or other power infrastructure.

Given the national trends outlined in 1.1 above it is hard to see how there is a case for the construction of further coal capacity in Jiangsu in the coming years. However standard economic modelling and forecasting of a business as usual case does project that there will be further capacity commissioned.

There are also rules concerning the decommissioning of older plant. It was reported by a major power plant construction contractor3 that for every 1.0 GW of new high efficiency capacity that is constructed, the power company must demonstrate that they have decommissioned 0.6 GW of older less efficient plant and have compensated the workers in that plant for the economic and social costs of early decommissioning.

1 Greenpeace, “Is China doubling down on its coal power bubble?” February 2016 2 China Puts an Emergency Stop on Coal Power Construction, 7 April 2016, http://thediplomat.com/2016/04/china-puts-an-

emergency-stop-on-coal-power-construction/ 3 SEPCOIII, Personal communication March 2016

Wind

Hydropower

Nuclear

Natural Gas

Crude Oil

Coal

http://thediplomat.com/2016/04/china-puts-an-emergency-stop-on-coal-power-construction/

http://thediplomat.com/2016/04/china-puts-an-emergency-stop-on-coal-power-construction/



1.4. Conclusion It may be expected that the power mix in Jiangsu will change over the coming few decades with a steady move away from using coal as the primary energy source. The total energy production infrastructure will increase, but most of this increase will come from other sources. Some new, higher efficiency plant may be constructed but at the same time much of the older, less efficient plant may be decommissioned.

This report presents tool able to explore these scenarios. The emphasis is on examining the business as usual case, but the tools are flexible, can look at other cases and can also incorporate decommissioning of old plant and changes in energy production source mixes in order to work out the water resources, GHG and other environmental and economic impacts.



2. PowerWEN Model

2.1. Modelling approach The modelling work in this project consists of two parts which are brought together in the PowerWEN model worksheet. These are:

The top down CGE model for making general predictions of the power production and consumption in Jiangsu, together with coal use and GHG impacts, now and into the future under various possible scenarios.

The Bottom up model of the actual power stations and their water use, coal use and GHG impacts based on information about the different power stations that have been built or planned in Jiangsu over the years.

This project has produced software model to link the different power stations power production to expected water use and emissions based on the application of standard coefficients of water use for different types of plant or cooling systems.

The reason for doing this is that it allows decision makers to see how actual decisions about what plant to build, or shut down, where will have what impacts on the meeting of likely demand requirements under different policy situations and to illustrate the consequences for meeting water resources use targets.

The exercise of pulling together the information also helps to put all of the data into context and also to clarify which are the key and important assumptions or decisions that are made and what is the scale of their impact.

The CGE model is detailed elsewhere in project reports, therefore for this manual on the PowerWEN model we will focus on the bottom up modelling and how the results and outputs of the CGE model can be incorporated to with individual station data to attain greater insight and understanding.

The scope of the model is to cover the province of Jiangsu and to do so at scales of Whole Province, each Municipality and individual installations and their component generation units installed at each site over time. The initial version of the model focusses on the main coal power stations, though does also include many of the gas fuelled stations and the Nuclear station. The minor CHP, Biomass and MSW or power co-production associated with various industrial processes is included in the model but is dealt with in a collective manner.

To explain the model out puts we describe how the power production capacity is calculated over time and compared to scenarios in CGE, then how water use and regulatory limits are dealt with and finally look at GHG emissions. The model can also look at the economic costs and benefits of constructing the power stations applying the cost equations listed in the CGE model, however this is not currently fully implemented. Estimates are made of water resource fees costs.

2.2. Power Capacity modelling The primary data for the PowerWEN model is a list of the major power stations in Jiangsu derived from internet searches – this details 45 installations and their component generation units past present and planned future. There is also statistical year book data for 2013 which indicates the total power capacity and energy output for each of the 13 municipalities. There is also a list provided by the local government of data about all of the thermal power plants in Jiangsu in 2012, this lists 187 plants, their power production and water use. Most of there are small combined heat and power or municipal waste incineration units. Thus the total power production situation is divided into the bulk of power produced in a few major plants and a large number of minor power plants that produce a smaller fraction of the power. The amount of minor power plant production in each municipality is estimated from the 2012 data and applied in a proportional manner to any other year under consideration.

Different power plants have different boiler systems and different types of cooling systems which greatly affect their water use. Coefficients of water and coal use have been derived for China that are considered appropriate to each type of system. This are derived in the model from work published in 2016 by Zhang et



al4. The source of water may be from a river or from the sea and the cooling process may be by direct cooling – using heat exchanger to transfer heat from the power plant condensers direct to the river water and pumping back to the river; or by wet evaporative cooling using cooling towers to evaporate water and so cool water in a loop to then cool the steam turbine’s condensers and other processes. Figure 2-1 shows the location of the major power stations sized by capacity (MW of potential production capacity) and coloured according to which cooling system is employed. The background in this is from the GIS of Jiangsu showing the main river, roads and water bodies in the 13 municipalities of the province.

Figure 2-1 Jiangsu Province with major power stations sized by capacity and coloured by cooling type.

The capacity split between the Major and minor power stations in each municipalities can be seen in Figure

2.

4 “Managing Scarce Water Resources in China’s Coal Power Industry” Chao Zhang, Lijin Zhong, Xiaotian Fu, Zhongnan Zhao, Environmental Management, 2016

Direct

Wet

Dry

Direct Saline

Legend Cooling type



Figure 2-2 Capacity of major and minor power stations in Jiangsu Municipalities (2013 data)

The CGE model simulates and predicts the power production in each municipality for each year from 2007 to 2030. This is done in an analogue manner with incremental increases in capacity each year. In reality new plant will be built occasionally and then commissioned as a major new block of power in that municipality. Also there may be much more development in one municipality compared to others because of local factors.

The PowerWEN model can compare for each year of scenarios the known capacity – or in future the planned capacity. This shows how scenarios may match to reality or planned plant construction. Note that the information about planned new plant becomes uncertain after the mid 2020s so the difference between the planned development and the CGE model becomes greater in more distant future. This is illustrated in

Figure 2-3 Capacity of known and planned power stations compared to CGE model of Jiangsu Thermal power production from 2007 to 2030, BAU Scenario.

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acit

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(GW

十亿

瓦特

)

Capacity容量Major 大型电站

Minor 小型电站

CGE 模型的运用

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120

140

160

2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029

Capacity 容量 GW

CGE 模型 PowerWEN 模型CGE Scenario CGE

BAU



In the Business as usual scenario, the CGE prediction is seen to move ahead of actual and planned capacity. In Figure 2-4 we illustrate for one of the reduced scenarios to show how predicted demand for new capacity would be much less and fall lower than the actual and planned capacity. Note that based on the recent directives halting new plant construction, outlined in Chapter 1, the curve of actual production may now change to a line involving much less growth.

Figure 2-4 Capacity of known and planned power stations compared to CGE model of Jiangsu Thermal power production from 2007 to 2030, EE-3% Scenario.

The capacity in each municipality can be compared to the CGE model and a correction applied to calculate the additional capacity that can be added or deducted from the actual capacities in that municipality for that year. Figure 2-5 Power station capacity in Jiangsu municipalities with correction to match CGE modelIllustrates how this addition and deduction of CGE model capacities can be applied.

Figure 2-5 Power station capacity in Jiangsu municipalities with correction to match CGE model

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2007 2009 2011 2013 2015 2017 2019 2021 2023 2025 2027 2029

Capacity 容量 GW


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From the capacity of the power station (in MW) so an estimate can be made of the actual power produced (in MWh). This takes account of the utilisation of the station, i.e. the number of hours it is actually run and whether it is running at 100 % or less. Estimates of utilisation are derived for each municipality, based on the 2013 year book data for Jiangsu which gives both capacity and total power produced.

Table 2-1 2013 Jiangsu statistical yearbook data on power capacity and energy produced with calculation of utilisation

Region Installed

capacity（

10000 KW）

Generated Power (100 million KWH)

Utilisation

Nanjing 756 429.81 65%

Wuxi 492 293.94 68%

Xuzhou 904 506.24 64%

Changzhou 244 116.48 54%

Suzhou 1586 811.89 58%

Nantong 470 275.89 67%

Lianyungang 366 257.31 80%

Huaian 234 129.81 63%

Yancheng 239 156.1 75%

Yangzhou 384 207.33 62%

Zhenjiang 518 309.91 68%

Taizhou 200 121.32 69%

Suqian 27 17.78 75%

Whole Province 6420 3633.81 65%

This utilisation factor is then applied to the capacities of all major and minor power stations in the municipalities in a constant manner for all years to convert capacities to power production. It is from the power production that water use and GHG emissions etc are calculated.

2.3. Water Use modelling Based on the coefficients for water use for each MWh of power produced the amount of water withdrawn and amount consumed may be calculated. From this can also be estimated the amount returned to the river (Withdrawn – Consumed). For sea water cooled stations then there will still be some freshwater consumed while the amount of sea water abstracted and returned can also be calculated, this is separated as it would have no impact on overall water resources use.

The amount of water use depends upon the overall cooling approach (direct once through cooling, wet evaporative, dry or seawater) the technologies employed in each phase of the power station, this considers the boiler technology (subcritical, super critical or ultra-supercritical) also the scale of the unit with smaller units being less efficient. There are also differences for gas fired stations and for municipal solid waste or biomass burning stations or nuclear stations.

The model has a full set of reference values from Zhang et al 2016, which provides maximum and minimum estimates of consumption in Chinese thermal stations. An example selection is shown in Table 2-2 for the coefficients for water withdrawal. Sensitivity testing can be run in the model to see the effect of different estimates of water use coefficients.



Table 2-2 Example of coefficients for water use - Water withdrawal (m3/MWh) Mean figures

Once through cooling

一旦经过冷却

Wet tower

cooling湿式冷

却

Dry cooling 干式

冷却

Saline海水冷却

ultrasupercritical 82.8 2.11 0.31 0.31

supercritical 100.6 2.061 0.334 0.334

>300MW subcritical 103.1 2.37 0.367 0.367

=< 300 subcritical 103.1 2.7 0.59 0.59

<100 MW 103.1 3.09 1 1

Gas 100.6 4.54 0 0

Combined Cycle Gas Turbine

34.07 0.946 0 0

Nuclear 178 0 0 1.514

MSW 0 7.95 0 0

Biomass 0 4.54 0 0

Waste Heat 0 9.61 0 0

The amount of water use is calculated based on the capacity and processes of each phase of a power station, dates are checked to establish if the phase is in operation at the time of the reference year, then the use for each phase is summed to give a total.

Different Boiler technologies are used in different phases of a power station development with larger and more recent phases using more advanced supercritical or ultra supercritical boilers and the older phases being sub critical. Smaller minor power stations may be burning MSW in a sub critical process, nuclear stations are assumed to be sub-critical as are gas. Figure 2-6 illustrates the different power station types / boiler technologies between Phase 1 and the final planned phase of each station.

Figure 2-6 PowerWEN GIS output showing power stations sized by capacity and coloured by boiler type

To better understand the water use by source, technology and municipality a Sankey diagram analysis can be used. Various Sankey diagram drawing packages were linked to the model to draw representative diagrams. Software issues limit how well these can be represented so far but

Figure 2-7 shows the representative water use by the power sector in Jiangsu broken down by municipality based on an application of the Captain Sankey tool linked to the PowerWEN model.

Legend Super

Sub

SSub

Gas

Nuclear

MSW

Legend Super

Sub

SSub

Gas

Nuclear

MSW

Phase 12015

Latest Phase2025



Figure 2-7 Sankey Diagram of Power Sector Water Use in Jiangsu

The spatial distribution of water withdrawals and consumption can also be analysed using the GIS in the PowerWEN model. Figure 2-8 shows the power stations thematically sized by fresh water withdrawals, this emphasises the direct abstractions from the Yangtze river. The scale of withdrawals for direct cooling is vastly greater than that for wet cooling, while the consumption in wet cooled stations is greater than in Direct cooled ones.

Figure 2-8 PowerWEN GIS outputs thematically representing power sector Fresh water withdrawals and consumption and seawater use across Jiangsu, 2015 scenario

2.3.1. Compliance with 3 red lines water resources allocations The water withdrawal figure is used to estimate compliance with the three redlines policy limits for water resources use. However since most of the water withdrawn for cooling is then returned to the river it is not reasonable to consider that the full withdrawal figure be used in the estimate of water use. The exact method of calculation does not appear yet to be firmly defined and following workshops it was decided to use a factor of 50% of the withdrawal as counting towards the water resources allocations. The power sector water use must be added on to the other water use in the province and each municipality for agriculture, urban water, environmental flows and other industry etc. Some data on this water use by municipality was provided by the water resources department for a mixed variety of years, though for some municipalities the data were censored so estimates were made based on known water resources and utilisation from 2013 statistical

SourceCooling Technology

Municipality Return flows

Direct

Wet

Dry

Direct Saline

Draw Nodes Clear Nodes Draw Reaches Water Withdrawal Water Consumption Seawater Withdrawal



yearbook. The result is that a picture can be built of the water use compared to the redlines limit – this is shown below in Figure 1-1 for the whole province for the period 2007 to 2030 assuming BAU development and continuation of current boiler and cooling technologies.

In the model this can also be estimated for each municipality. Figure 2-10 below shows the comparison for each Municipality of the estimate of total water use (2015) – including the power sector (at 50% of total withdrawal) compared to the 2013 estimate of typical water resources and the 2015 redline water use allocation

Figure 2-9 Water resources use (based on withdrawals) estimated for 2007-2030 under BAU showing compliance with three redlines allocations

Figure 2-10 Water use compliance with redline allocations by municipality

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Water Withdrawal 取水量 BCM

CGE 模型

PowerWEN 模型

2014年农业，城镇，环境，服务业，以及电力行业总用水量 Total Water Use (Agri, Urban, Env, Service but ignoring other industry) based on 2014 data Plus Power sector

Redline Allocation

2014年除电力行业外其他行业的总用水量 Total Water use by Other Sectors (Agri, Urban, Environment etc) (2014)

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南京无锡徐州常州苏州南通连云港淮安盐城扬州镇江泰州宿迁全省

Three Red Line Limits

Water resource (billion ton) 2013 Total water use

Total water consumption redline in 2015



This can also be analysed over time for each municipality as illustrated in Figure 2-11for Suzhou. Note in this that the total water withdrawal estimated by the PowerWEN model is higher than the water use compared to the redline allocation because of the 50% water withdrawal factor that has been applied to abstractions by the power sector.

Figure 2-11 Water use by power sector in Suzhou municipality compared to 3 red lines allocations

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2.4. Coal use and greenhouse gas emissions The same principles of allocating a coefficient per MWh of energy produced can be used to estimate the Coal consumption and greenhouse gas emissions for each power station and for each municipality or for Jiangsu as a whole.

Table 2-3 Coefficients for Coal consumption (Kg/MWh) (after Zhang et al 2016)

Once through cooling

一旦经过冷却

Wet tower

cooling湿式冷却

Dry cooling 干

式冷却 Saline海水冷却

Operating hours

运行时间

ultrasupercritical 超超临

界 291.2 295 311.9 291 5679

supercritical 超临界 305.6 305.6 326.7 304.2 5312

>300MW subcritical

>300MW的亚临界 327 323.6 334.3 322.5 5207

<= 300 subcritical

<= 300MW的亚临界 333.2 363.4 341.2 331.1 4726

<100 MW <100MW的亚

临界 366.5 399.7 375.3 364.2 4296

Gas 燃气 183.12 181.216 187.208 180.6

Combined Cycle Gas

Turbine 联合循环燃气轮

机

Not specified Not specified Not specified Not specified Not specified

Nuclear 核能 Not specified Not specified Not specified Not specified Not specified

MSW 市政固废 Not specified Not specified Not specified Not specified Not specified

Biomass 生物能 Not specified Not specified Not specified Not specified Not specified

Waste Heat 余热 Not specified Not specified Not specified Not specified Not specified

From the coal use the GHG emissions can be calculated by applying a factor of 2.7 kgCO2e/kg coal5. Ideally this would vary based on the type of coal used (Anthracite, Bitumous, lignite etc) but these details were not available. Currently the MSW and the Biomass fuelled plants (which are the small / minor plants) are considered as of zero CoO2 emission on the basis that this is compensatory.

The GHG emissions will also be influenced by pollution control measures such as the flue gas desulphurization which if present will increase CO2 emissions by about 2%6

Estimated GHG Emissions from power stations in Jiangsu estimated for 2015 and projected for 2025

5 2010 Guidelines to Defra / DECC's GHG Conversion Factors for Company Reporting, Version 1.2.1 FINAL Updated: 06/10/2010. 6 "Power Generation from Coal: Measuring and Reporting Efficiency Performance and CO2 Emissions" OECD/IEA, 2010, International Energy Agency, https://www.iea.org/ciab/papers/power_generation_from_coal.pdf

Table 3.11

https://www.iea.org/ciab/papers/power_generation_from_coal.pdf



2.4.1. Other outputs of PowerWEN Model The Power Wen model also estimates the Cost of the Water resources fees applicable to each power station This based on 0.2 RMB/m3 for 10% of the flow for direct cooled stations, no charge for wet cooled or seawater cooled stations7

It is also intended to add to the PowerWEN model the components of the Cost model used in the CGE component so that comparative capital and operational cost information can be generated for different scenarios. However at this time there is not sufficient confidence in the data underlying this to make useable.

The PowerWEN model includes an estimate of the Thermal impact of discharges to the Yangtze river from the direct cooling to the river water – this is made for estimated annual average flows of 700 bcm and for a 10% low flow situation. This is a very simple model based on an estimate of heat wasted to cooling system and specific heat capacity of the various water flows with no consideration of heat dissipation or geographic differences nor of tidal influence. The outcome is an estimate of the cooling water causing a 0.3 degree rise in temperature under average flow conditions in 2015 rising to 0.4 degrees by 2025. Under 10% low flows would be a 3 to 4 degree increase in temperature of the river. This very simple model is for indication of scale of impact only and is far too simple for any decision influencing purposes.

2.5. Sensitivity testing For the water use it is possible to set the model to use the maximum or minimum in the range of water use coefficients provided or a percentage of the mean value to represent impacts of possible improvements in cooling technology or operational practices.

This is a major assumption – for the mean of water use coefficients the level of water consumption under a 2015 BAU scenario is 0.55 bcm; at maximum limit of coefficients is 0.72 bcm and at the lower range 0.43. For withdrawal annual estimates are mean 23.15 bcm, max 27.11 and min 19.46 bcm.

2.6. Analysing CGE Scenarios The PowerWEN model has been set up with the same assumptions for the split of cooling technologies for the CGE model with 41.5 as subcritical wet cooled, 50% as supercritical direct cooled and 8.5% as super critical direct saline cooled. In the normal settings of the PowerWEN model the major and minor power station estimates will take priority and the CGE component will only be for the balancing amount to match the CGE model but it is also possible to switch off the major and minor power stations and so just analyse CGE

7 "From Managing Scarce Water Resources in China’s Coal Power Industry” Chao Zhang, Lijin Zhong, Xiaotian Fu, Zhongnan Zhao, 2016.

Direct

Wet

Dry

Direct Saline

Direct

Wet

Dry

Direct Saline

PowerWEN GHG, kgCO2e, 2015 PowerWEN GHG, kgCO2e, 2025



model scenarios and to vary the percentage allocations to different cooling and boiler technologies to explore the water resources impact.

Figure 2-12 PowerWEN analysis of water withdrawal by Jiangsu power sector under different scenarios for the CGE model for 2015 situation and in comparison to redline allocations 2007 to 2030.

2.7. Analysis of water saving technology scenarios There is a specific option in the control panel for the cooling coefficients sensitivity selection that allows for the entry of a percentage change in the cooling water used coefficient. Thus for example there could be exploration of the effect of a 90% reduction in water use across all installations (current and planned) on the compliance with a 3 redlines water allocation target.

Figure 2-13 Control Panel: Percentage reduction in cooling water use for technology scenarios.

How might such water saving be achieved?

We can draw on UK experience of water saving in the power sector. This is most effectively implemented by undertaking clean technology and water saving audits of the facility, tracking the water use and generation of water and air pollution at each stage of the processes, building a water balance for the whole plant and

TRUE

CGE Scenario CGE情境 BAU

Part 1 % 42%

CGE Boiler CGE锅炉种类 Sub

CGE Cooling Scenario冷却方式 Wet

Part 2 % 50%

CGE Boiler CGE锅炉种类 Super

CGE Cooling Scenario冷却方式 Direct

Part 3 % 9%


CGE Cooling Scenario冷却方式 Direct Saline

Seawater % 海水利用百分比 100%

Include CGE Model

是否考虑CGE模型

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/年)

CGE Water WithdrawCGE 1 Sub Wet 0.415

CGE 2 Super Direct 0.5

CGE 3 Super Direct Saline 0.085

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CGE Water WithdrawCGE 1 Sub Wet 0.315

CGE 2 Super Wet 0.4

CGE 3 Super Direct Saline 0.285

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PowerWEN 模型


Redline Allocation


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PowerWEN 模型


Redline Allocation


Percent Mean平均百分比Cooling % 冷却效率 90%

Cooling Estimate

冷却效率估计

TRUE

CGE Scenario CGE情境 BAU

Part 1 % 32%



Part 2 % 40%



Part 3 % 29%




Include CGE Model




exploring the options for water saving at each stage. An example of undertaking this process in China was performed as a part of the EU-China River Basin Management Programme and reported in the “T-089 Clean Technology for water resources protection: A Summary Handbook” publication8 with detailed reports of the 2009 audit of the 4.8GW Tuoketuo power station in Inner Mongolia (then the largest coal thermal station in China)9. This showed that very significant savings in water use could be achieved such that with the implementation of the water saving methods the construction of an additional 2 X 600 MW dry cooled units could be undertaken without requiring additional abstraction permits.

Water “Saving” in the context of a power station depends upon whether the counting is being done on the amount of water abstracted or the net amount of water actually used, i.e considering flows returned to the river from which it was abstracted. For direct cooled stations this is a very high proportion. For wet cooled stations the amount of water returned to the river depends largely on the recycle rate in the cooling towers. The use of more effective biocides and non-chemical biological protection can achieve protection against the build-up of biological material in the towers without excessive concentration of chemicals and minerals in the recirculated water.

It is also possible to make better use of wastewater streams from the boiler make up or wet cooling systems for secondary water consuming tasks such as coal dust suppression, ash cooling and flue gas desulphurization. This can reduce the amount of water abstracted but will also reduce any return flows to the river.

The only way to dramatically reduce the overall water use is to use dry cooling methods that transfer significant portions of the heat direct to the atmosphere using radiators and fans. In water scarce China this is the cooling method that is required for all new plant. However there is an impact on the efficiency of the plant in part due to the increased electrical drain to run fans, but most significantly result of the less effective cooling resulting in higher back pressure in the condenser and so less operation of the steam turbines. This will increase the coal consumption per MW generated and so increase the GHG emissions and running costs of the plant.

Figure 2-14 illustrates this using data on basin water stress produced by the WRI aqueduct program to show the areas where since 2008 the NEA has required that all new major power plants employ dry cooling to reduce water resources impact, despite the negative GHG and immediate economic impacts this causes. This demonstrates a case of water resources management policy taking precedence over short term Energy development economics.

8 RBMP, Koning and Spooner, 2012, available from www.CEWP.org, 9 See RBMP reports “T-042 Water Saving and Pollution Reduction Audit at Inner Mongolia Datang International Tuoketuo Power Generation Co ltd, 2010; “T-047 Audit at Tuoketuo Power Generation Co Ltd”, 2010 and “T-052 Water Saving and Pollution Reduction at Tuoketuo Summary Report”, 2010, all Jan Koning, Liu Canqi and S Spooner.

http://www.cewp.org/



Figure 2-14 Map of China coloured by river basin water stress (after WRI Aqueduct), indication of the areas of China where Dry cooling is required, and charts of the impact of cooling method on Water and coal consumption and water withdrawal

Dry Cooling 干式冷却

Wet Cooling 湿式冷却

2.3 2.4 2.5 2.6 2.7 2.8 2.9 3.0

Direct Wet Dry

Subcritical Ultrasupercritical

1.0

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Direct Wet Dry

Subcritical

Ultrasupercritical

-

5.0

10.0

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Subcritical ultrasupercritical

Coal Used 煤炭用量 Water Withdrawal 取水量 Water Consumption 耗水量(百万吨/千兆瓦/年) (百万m3 /千兆瓦/年) (百万m3 /千兆瓦/年)



2.8. Conclusions Through this project a set of models have been created that can project the energy production mix for Jiangsu and also analyse the current and planned actual production mix and work out the consequences of different energy generation decisions on the water use and GHG production at provincial and municipal levels.

These decision support tools can help in understanding the consequences of decisions that will work towards the goals of transitioning to sustainable and lower carbon energy production within the environmental resource limits of the province and the local areas of the power stations.

By far the largest factor influencing the water use of a power station is the cooling method used – Direct once through (from a River or the Sea); Wet evaporative; or Dry cooling. If using direct cooling there is an additional limiting factor of the receiving waterbody’s capacity to absorb the heat. Currently in Jiangsu most of the power is generated from Direct cooled stations abstracting water from the Yangtze.

When deciding on the water resources impact of a power production facility it depends very much on how you count return flows. If return flows are not counted then direct cooling has a very large water resources impact on paper, but in reality most of the water taken from the river is returned (slightly warmer) and is available to another user. Likewise for wet cooled systems, though the water use is much lower certain water saving advances, such as increasing recirculation in the cooling towers will reduce the water withdrawals, but will also reduce returns and may result in more concentrated pollution in those return flows.

Dry cooling does dramatically reduce water resources impact, but at a cost of greater capital and operational cost and higher GHG emissions. Currently Jiangsu does not use dry cooling nor are there any published plans to do so.

An important decision in the regulation of the water resources impact of the power sector is the way regulations are written to account for return flows to the river. Not considering the return flows could lead to perverse outcomes such as favouring wet cooling over direct cooling in order to reduce the withdrawals from a river when the net impact of evaporative cooling will be to actually consume far more of the water resource.

The re-use ability of the return flows will of course depend upon the quality of the flows. Thus ensuring good wastewater treatment processes in the plant water cycle will ensure that the net impact on water resources is minimised.



3. Model operation Manual

3.1. Structure of model The PowerWEN model is an excel spreadsheet with a degree of automation to provide a reasonably user friendly interface for the visualisation of data, study of different scenarios production of graphics, analysis of multi-year scenarios and basic GIS presentation and interrogation of data.

The main components of the model, with the worksheet names, are:

“Control”, the control panel where the scenarios are set up and the results graphics presented

“Schematic”, the GIS Schematic which allows presentation of some of the results and interrogation of the data about each powerstation.

“Nodes”, The lists of the power stations and the main calculations of water and coal consumption.

“Coefficients”, The list of the Main coefficients for water consumption / MWh, coal consumption and efficiency

“GHG”, some additional coefficients related to Greenhouse gas emissions.

“Water Resources”, Data from statistical Yearbooks on Water resources, three redlines allocations, power capacity and production, water consumption etc by municipality. There are also some graphs generated of this page for specific outputs related to water resources

“ThermalStat2012”, summary data of 187 major and minor power stations in Jiangsu, with power production and water use data. Also some calculations for assigning the proportion of minor compared to major power stations in each municipality.

“Lookups”, some look up tables for relating the main list of stations in the Nodes sheet to the ThermalStat2012 lists and sorting out some of the units conversions.

“CGE 13 Cities” A list of the outputs from the CGE model for each Municipality that is linked to the rest of the PowerWEN model for comparative purposes.

“CGE Jiangsu Province”, a list of the outputs of the CGE model at province level (not quite the same as the sum of the provinces for some reason), not used in rest of model.

“About” some info on the development of the model over time, what was done, by whom.

“Power_Mun, Power_County, Power_WRI” a set of tables generated by the QGIS system for looking up and relating each power station to the Municipality and county that it is in and to the BasinID of the WRI Aqueduct model.

Much of the model will be self-explanatory to someone with reasonable experience and understanding of Excel spreadsheets. The following give some information on key elements of operation.

3.1.1. Control Sheet Figure 3-1 Control panel with default settings

Inputs 输入

Reference Year 参考年份 2025

TRUE

Cooling Scenario 冷却方式 Current

TRUE

Boiler Type 锅炉种类 SSub

TRUE

CGE Scenario CGE情境 ET-h

Part 1 % 42%



Part 2 % 50%



Part 3 % 9%




Mean平均Cooling % 冷却效率 100%

50%

Control Panel 控制面板

Include Major Stations

是否考虑大型电站

Include Minor Stations

是否考虑小型电站

Include CGE Model


Cooling Estimate

冷却效率估计

Three red lines Direct consumption factor “三条红线”取水量因子

Select year and setup for model

输入年份以及模型条件



It is from the control panel that the main model set up factors can be set for major, minor and CGE based power stations. The settings here are picked up in the nodes sheet where the main calculations are performed.

The main input on the control panel is to set the scenario year which determines what data are selected in the model. Certain options in the control panel are then provided to set up specific scenarios for example “what would happen if all major power stations switched to wet cooling?”

For Minor power stations the cooling method is assumed to be wet cooled (set in the Nodes sheet) but the boiler technology can be varied in the control panel (this has little influence). For the CGE model a combination of 3 different cooling and boiler technologies can be set. The sensitivity testing for the water use coefficients can be set up in the control panel and also options for testing the effect of increased water use efficiency technologies. Finally there is the option to set a factor for the water withdrawals when counted against three redline water allocation targets.

There are some further options that can be set in the nodes sheet itself, for the control panel can be easily re-configured to set different scenarios.

Figure 3-2 Control panel with main output graphics

The control panel also has some of the graphic outputs for the model. What is displayed depends on the setting in the drop down box in the results area. The results can be prepared for the whole province or just for one municipality. Results are presented for the selected year in the top right of the control panel and can then be calculated for multiple years below.

Figure 3-3 is shown the area of the control panel that is used to calculate and display multi year results. This is achieved using a TABLE function which automatically runs multiple calculations of the whole spreadsheet taking results for each year that is entered, and to get the lines with or without the CGE model correction the runs are made with the CGE model switched on and switched off. The running of this calculation takes a few seconds and can make the spreadsheet very slow and unreactive if the entire table calculations are run every time any cell is altered. It is therefore good practice to switch the calculation of the spreadsheet to be Automatic but without tables (in excel menus select File>options>Formulas>Calculation options). In this setting the tables will re-calculate only when the f9 key is pressed or the recalculate tables and charts button pressed.

Inputs 输入

Reference Year 参考年份 2025

TRUE

Cooling Scenario 冷却方式 Current

TRUE

Boiler Type 锅炉种类 SSub

TRUE

CGE Scenario CGE情境 ET-h

Part 1 % 42%



Part 2 % 50%



Part 3 % 9% 100%




Mean平均Cooling % 冷却效率 100%

50%

Results 结果 Capacity容量

Total

全省

Nanjing

南京

Wuxi

无锡

Xuzhou

徐州

Changzhou

常州

Suzhou

苏州

Nantong

南通

Lianyungang

连云港

Huaian

淮安

Yancheng

盐城

Yangzhou

扬州

Zhenjiang

镇江

Taizhou

泰州

Suqian

宿迁

0 Major 大型电站 69.79 9.14 5.45 8.55 2.30 13.57 6.70 2.66 2.82 3.41 3.72 8.15 3.32 0.00

Minor 小型电站 13.08 1.88 1.34 1.95 0.93 4.35 0.03 0.01 0.09 0.23 0.89 1.26 0.13 0.00

CGE 模型的运用 -11.18 -2.13 0.13 0.94 -0.38 -0.93 -0.85 -1.73 -0.22 -1.26 -0.51 -3.44 -0.89 0.09

Total 总计 71.69 8.89 6.92 11.44 2.85 16.99 5.87 0.94 2.69 2.38 4.10 5.97 2.56 0.09

Total 全省

0

Table Calculations of multi-year results 多年计算结果MULTI Year Results CGE 模型PowerWEN 模型 CGE 模型PowerWEN 模型 CGE 模型 PowerWEN 模型

Capacity 容量 GW Water Withdrawal 取水量 BCM2014年农业，城镇，环境，服务业，以及电力行业总用水量 Total Water Use (Agri, Urban, Env, Service but ignoring other industry) based on 2014 data Plus Power sectorRedline Allocation2014年除电力行业外其他行业的总用水量 Total Water use by Other Sectors (Agri, Urban, Environment etc) (2014)Water consumption 用水量 BCM

Control Panel 控制面板

Capacity容量 (GW

十亿瓦特)

Include Major Stations

是否考虑大型电站

Include Minor Stations

是否考虑小型电站

Include CGE Model


Cooling Estimate

冷却效率估计

Three red lines Direct consumption factor “三条红线”取水量因子

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CGE 模型的运用

Select what to display on chart here 选择希望显

示的参量

Select year and setup for model

输入年份以及模型边界条件

Multi Year Graphics

with water resources / 3 redlines comparisons

Press F9 to Update

下拉查看多年水资源与



In the same area can also be seen the crude calculations for thermal impact on the Yangtze.

Figure 3-3 Control panel calculation of Multi Year results

3.1.2. Nodes Sheet The Nodes Sheet holds a list of all of the power stations and their phases, links to water consumption and GHG emissions etc. It uses lookup functions and offset functions to automatically adjust the data that it analyses in response to instructions from the Control sheet. Figure 3-4 shows the general layout of the Nodes spreadsheet.

Generally it should not be necessary to edit this sheet except for the changing of the details of the major power stations or the addition or deletion of whole power stations or phases of them. Most of the columns are formulae, so the only columns to edit are the names, latitude and longitude (although this originally derived by formulae from the web sourced locations) and the details of each phase of each power station. Where a value has been estimated (or guessed) it is marked in red.

Rows can be copied and inserted as required to add new stations. The stations can also be viewed from the schematic. Clicking on a node for a power station will bring up a dialogue box with key information. From this by pressing the “Show” button you will be taken to the relevant entry in the Nodes spreadsheet.

Note that the reason for the power stations spreadsheet being referred to as nodes is that it is derived from other WEN models which can also include links between nodes. For example this is implemented in the Qingdao Water resources WEN model.

3.1.3. Schematic Sheet To represent the power stations in a full GIS system it is possible to link a separate spreadsheet to the Nodes spreadsheet and then connect to that using a GIS system such as ArcMap or QGIS. For QGIS this

Table Calculations of multi-year results 多年计算结果MULTI Year Results CGE 模型PowerWEN 模型 CGE 模型PowerWEN 模型 CGE 模型 PowerWEN 模型

Capacity 容量 GW Water Withdrawal 取水量 BCM2014年农业，城镇，环境，服务业，以及电力行业总用水量 Total Water Use (Agri, Urban, Env, Service but ignoring other industry) based on 2014 data Plus Power sectorRedline Allocation2014年除电力行业外其他行业的总用水量 Total Water use by Other Sectors (Agri, Urban, Environment etc) (2014)Water consumption 用水量 BCM

37.41 TRUE FALSE 11.34 TRUE FALSE 0.25 TRUE FALSE

2007 39.3204 37.407 2007 12.096 11.3361 2007 0.261782 0.2477

2008 42.0681 44.548 2008 13.506 14.0773 2008 0.273315 0.2839

2009 45.1958 46.473 2009 14.536 14.7204 2009 0.290442 0.2939

2010 53.2549 49.021 2010 17.319 15.9682 2010 0.327036 0.3019

2011 61.6349 61.392 2011 18.658 18.4684 2011 0.37219 0.3687

2012 68.6679 69.519 2012 18.338 18.5612 2012 0.437479 0.4416

2013 76.9612 73.835 2013 20.317 19.5867 2013 0.485749 0.4722

2014 85.8672 80.176 2014 23.214 21.7732 35.39 2014 0.518409 0.4916

2015 90.6582 82.87 2015 25.079 23.0773 47.93 50.8 35.39 2015 0.532846 0.4957

2016 98.536 88.522 2016 27.418 24.7449 49.10 50.8 35.39 2016 0.573186 0.5235

2017 104.454 93.407 2017 27.762 24.8067 49.27 50.8 35.39 2017 0.62364 0.5688

2018 107.865 96.11 2018 28.815 25.7843 49.80 50.8 35.39 2018 0.655188 0.5989

2019 112.773 100.88 2019 29.753 26.6448 50.27 50.8 35.39 2019 0.670019 0.6123

2020 117.449 109.03 2020 29.857 27.7184 50.32 52.415 35.39 2020 0.718174 0.6785

2021 121.379 109.03 2021 30.988 27.7184 50.88 52.415 35.39 2021 0.739172 0.6785

2022 125.015 114.51 2022 32.516 29.7421 51.65 52.415 35.39 2022 0.748016 0.6965

2023 129.123 119.55 2023 33.351 31.0031 52.06 52.415 35.39 2023 0.754508 0.7109

2024 132.108 124.16 2024 33.717 31.9106 52.25 52.415 35.39 2024 0.758399 0.7249

2025 135.973 124.16 2025 34.829 31.9106 52.80 52.415 35.39 2025 0.77905 0.7249

2026 138.537 124.16 2026 35.567 31.9106 53.17 52.415 35.39 2026 0.792747 0.7249

2027 141.562 124.16 2027 36.437 31.9106 53.61 52.415 35.39 2027 0.808912 0.7249

2028 144.837 124.16 2028 37.379 31.9106 54.08 52.415 35.39 2028 0.826409 0.7249

2029 147.813 124.16 2029 38.236 31.9106 54.51 52.415 35.39 2029 0.842312 0.7249

2030 149.889 124.16 2030 38.833 31.9106 54.81 52.415 35.39 2030 0.8534 0.7249

Water consumption data

35.39 BCM 2014年除电力行业外其他行业的总用水量 Total Water use by Other Sectors (Agri, Urban, Environment etc) (2014)

11.34 BCM 电力行业总用水量 Total Water use by Power Sector 2007

46.73 BCM 结合三条红线取水量因子的电力行业总用水量 Total Water use by Power Sector With Redline factor on Direct withrawals 2007

46.73 BCM 2014年农业，城镇，环境，服务业，以及电力行业总用水量 Total Water Use (Agri, Urban, Env, Service but ignoring other industry) based on 2014 data Plus Power sector

Total Energy to Yangtze and Temperature Change 冷却水排放对长江的影响

Flow in Yangtze 长江流量 Annual Total 700 bcm/year 1 kWh = 3.6 MJ

10% Low Flow 70

Flow from powerstations to yangtze 发电站入长江的冷却水流量 10.7352 bcm/year

Power produced by these stations 发电站发电量 104.281 TWh/year

% total capacity lost as heat through cooling 冷却消耗的总装机容量百分比 75%

Temperature rise 温度上升:

Specific heat Cap 比热容 4.186 Mj/m3

1.16278 kWh/m3

1.16278 GWh/Mm3

1.16278 TWh/Bcm

Temperature increase in cooling water 冷却水上升温度 8.35404 deg C

Temperature increase in river 河水上升温度 0.12812 deg C

Low Flows increase 缓流时的河水上升温度 1.28118 deg C

These numbers are estimates from 2013 data and where in red are gueses based on total consumption data

Estimated non industrialWater Use (2013) 35.39 2.70 1.45 2.28 3.48 2.22 1.47 1.64 2.99 4.79 4.09 2.40 3.36 2.50

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CGE 模型

PowerWEN 模型

2014年农业，城镇，环境，服务业，以及电力行业总用水量 Total Water Use (Agri, Urban, Env, Service

but ignoring other industry) based on 2014 data Plus Power sector

Redline Allocation

2014年除电力行业外其他行业的总用水量 Total Water use by Other Sectors (Agri, Urban, Environment etc)

(2014)

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Water consumption 用水量 BCM

CGE 模型 PowerWEN 模型

下拉查看多年水资源与



linked sheet needs to be in CSV format. Initially for the PowerWEN model this was the method used for the GIS linkages, however later in the development cycle an excel based GIS system developed by S Spooner of Atkins was incorporated to the PowerWEN spreadsheet model. This is much easier to use and requires no external or proprietary software.

The Schematic sheet is an interactive GIS sheet which is automated using the visual basic for applications (VBA) programming language built in to excel. The background picture of Jiangsu with municipalities and roads etc is derived from the QGIS system developed for the project. The PowerWEN power stations are then drawn over this as Excel shape objects.

To draw new shapes press the draw nodes button. The power stations will appear as little circles. These can then be thematically sized and coloured by pressing the “Size Nodes” button. There is a drop down box to select what feature to base the thematic mapping on – e.g. capacity, water use, GHGs etc.

Nodes are drawn for each major power station. For the Minor and the CGE power station entries in the Nodes sheet a node is also drawn in the centre of the relevant municipality. At first these will be over laping, but after thematic mapping it may be possible to differentiate the Minor and the CGE nodes.

Each time a different theme is selected it is only necessary to press the “Size Nodes” button. The “Draw Nodes” button only need be used when nodes have been added or deleted or moved in the Nodes worksheet.

There are a lot of other boxes on the sheet for sizing and scaling the drawing, most of these do not require user intervention and are protected. It is possible to select to add labels to each node, in which case the nodes will be drawn with the Chinese name of the station next to it. There are also boxes where a scale factor and max and min sizes can be entered for the thematic mapping.

The colouring of the nodes is set in the legend. Changing the colour of the circle in the legend with change the colour of that type of node in the main drawing the next time the nodes are re-sized.

When clicking on a station a dialog box will appear listing the key information about that location. An example of this can be seen in Figure 3-5. In this information about the node is displayed. In the highlighted cells this information can be edited and if the update button is pressed then the Nodes sheet will be updated with the new values. The Show button will take you to the relevant entry in the nodes sheet to see this. To see the effect of any changes on the thematic mapping, press the Size Nodes button.



Figure 3-4 Layout of Nodes sheet of PowerWEN model

Major 大型发电站 69,786 69.8 397,756 272,930 55.10 GW 59% Direct 281,661,886 340,894,594 22,883,831,809 3,502,070,722 123,079 336,326

Minor 小型发电站 13,084 13.1 58,856 48,800 12.67 8% Direct Saline 359,419,231 154,788,505 193,485,631 - 26 70,313

CGE 一般均衡模型 7,788 7.8 39,007 100% 37,163,122 2,001,502,906 332,437,805 12,212 33,631

77 NumNodes Total 总计 90,658 90.7 495,618 321,730 67.76 Number Capacity Type Cool Date Number Capacity Type Cool Date Number Capacity Type Cool Date Number CapacityType Cool Date Number CapacityType Cool Date 641,081,117 532,846,221 m3/year 25,078,820,346 m3/y 3,834,508,526 m3/y 161,333 435,598 440,270 419,437 6,362,529 15.2 59791.66906 45,092,785

Max 35 122 - 5,000 1 26,324 17,978 4,352 - - 8 4,352 - 2,013 4 3,969 - 2,015 4 3,969 - 2,015 3 1,000 - 2,017 3 1,260 - 2,023 148,880,455 48,125,455 48,125,455 23,734,387 18,480,000 1,439,778 - 2,471,143,367 1,480,388,618 1,117,896,029 ########### 1,439,778 - 1,592,032,754 ###### ###### ###### ###### - 8,269 8,097 4,148 4,923 1,311 - - 22,772 95,796 1,426,760 1,049 2,460,431,728 - - 4,942,287

Min 31 117 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1,998 0 1 1 2,005 1 200 1 2,013 1 200 1 2,017 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

NodesTab Column Numbers 2 3 4 5 6 7 8 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76

WRI MW % GWh GWh MW Phase 1 Phase 2 Phase 3 Under construction Planned Total Total P1 P2 P3 UC PL Total P1 P2 P3 UC PL Total P1 P2 P3 UC PL Total P1 P2 P3 UC PL 2012 Water Use

Name Name_CNMunicipality District BasinID Lat Lon NodeCode Total_Capacity_GWUtilizationEnergy_GWh2012_Energy_GWh2012_Capacity_GWCooling_TypeCooling_ObservedP1_NumberP1_CapacityP1_TypeP1_CoolP1_DateP2_Number P2_CapacityP2_TypeP2_CoolP2_DateP3_NumberP3_CapacityP3_TypeP3_CoolP3_DateUC_NumberUC_CapacityUC_TypeUC_CoolUC_DatePL_NumberPL_CapacityPL_Type PL_Cool PL_Date2012Total_Wcon Total_Wcon P1_Wcon P2_Wcon P3_Wcon UC_Wcon PL_Wcon Total_WDraw P1_WDraw P2_WDraw P3_WDraw UC_WDraw PL_WDraw Total_Seawater P1_SeawaterP2_SeawaterP3_SeawaterUC_SeawaterPL_SeawaterTotal_Coal P1_CoalP2_CoalP3_Coal UC_CoalPL_CoalDesulphurizationTotal GHG Withdrawn Recycled R/c RateModel Return Cooling Source Water_Resource_Fees

Major Power Stations 大型电站 Direct

SINOPEC Yangzi Petrochemical Power Station扬子石化分公司热电厂Nanjing Nanjing 5784 32.253 118.79 MAJ_01_01 360 65% 2,047 1,400 246 Wet Wet 6 60 SSub Wet 8,005,018 4,420,903 4,420,903 5,526,129 5,526,129 - - 744 744 No FGD 2,008 8,005 4,316 0.5 1,105,226 Wet Other -

Nanjing Chemical Industry Campus Power Station南京化学工业园热电厂Nanjing Nanjing 5784 32.272 118.82 MAJ_01_02 710 65% 4,037 4,037 710 Wet Wet 2 55 SSub Wet 2 300 Sub Wet 2004 16,088,757 7,797,981 1,350,831 6,447,150 9,773,061 1,688,539 8,084,521 - - - 1331 227 1104 No FGD 3,594 16,089 526 0.0 1,975,079 Wet Yangtze -

Huaneng Nanjing Power Station华能南京发电厂Nanjing Nanjing 5784 32.207 118.75 MAJ_01_03 640 65% 3,639 3,809 670 Direct Direct 2 320 Sub Direct 1994 27,717 1,248,041 1,248,041 375,139,987 375,139,987 - - 1190 1190 No FGD 3,213 28 2 0.1 373,891,946 Direct Yangtze 750,280

Banqiao Power Station 板桥电厂Nanjing Nanjing 5784 31.948 118.63 MAJ_01_04 2,070 65% 11,769 6,449 1,273 Direct Direct 2 135 SSub Direct 2 300 Sub Direct 2 600 Sub Direct 2010 4,363,595 853,480 1,170,038 2,340,077 1,213,343,396 158,262,182 351,693,738 703,387,476 - - - - 3858 511 1115 2231 FGD 10,625 41,979 3,670 0.1 1,208,979,802 Direct Yangtze 2,426,687

Jinling Power Station 金陵电厂Nanjing Nanjing 5784 32.172 119.02 MAJ_01_05 2,840 65% 16,146 11,538 2,136 Direct Direct 2 390 Sub Direct 2005 2 1030 Ultra Direct 2010 4,191,330 1,521,050 2,670,280 1,426,935,088 457,201,860 969,733,229 - - - 4861 1450 3410 FGD 13,386 95,796 876 0.0 1,422,743,758 Direct Yangtze 2,853,870

Datang Nanjing Power Station大唐南京发电厂Nanjing Nanjing 5784 32.213 119.21 MAJ_01_06 1,320 65% 7,505 8,304 1,461 Direct Direct 2 660 Super Direct 2010 2,590,445 2,101,293 2,101,293 754,964,676 754,964,676 - - 2293 2293 No FGD 6,192 2,590 346 0.1 752,863,383 Direct Yangtze 1,509,929

Nanjing Thermal Power Station南京电厂Nanjing Nanjing 5784 32.05 118.77 MAJ_01_07 1,200 65% 6,822 3,496 615 Wet Wet 2 600 Sub Wet 2011 2,223,745 12,894,300 12,894,300 16,169,043 16,169,043 - - 2208 2208 No FGD 5,961 2,224 797 0.4 3,274,743 Wet Yangtze -

Ligang Power Station 利港电厂Wuxi Jiangyin 5784 31.941 120.08 MAJ_02_08 3,960 68% 23,659 17,978 3,009 Direct Direct 2 350 Sub Direct 1993 2 370 Sub Direct 1998 4 630 Sub Direct 2007 7,631,818 8,114,895 1,434,451 1,516,420 5,164,024 2,439,200,151 431,171,744 455,810,129 ########### - - - - 7736 1368 1446 4923 FGD 21,306 7,632 4,974 0.7 2,431,085,256 Direct Yangtze 4,878,400

Xiagang Power Station 夏港电厂Wuxi Jiangyin 5784 31.919 120.2 MAJ_02_09 1,216 68% 7,265 7,172 1,328 Direct Direct 4 139 SSub Direct 2 330 Sub Direct 1,711,532 3,199,382 1,846,899 1,352,482 749,006,915 342,473,557 406,533,359 - - - 2396 1107 1289 No FGD 6,470 1,712 3,168 1.9 745,807,534 Direct Yangtze 1,498,014

Yixing Power Station 宜兴电厂Wuxi Yixing 3462 31.371 119.77 MAJ_02_10 270 68% 1,613 2,642 442 Wet Wet 2 135 SSub Wet 420,000 3,484,264 3,484,264 4,355,330 4,355,330 - - 586 586 No FGD 1,583 420 7,530 17.9 871,066 Wet Other -

Xuzhou Power Station 徐州电厂Xuzhou Tongshan 6245 34.386 117.26 MAJ_03_11 2,660 64% 14,896 2,400 1,173 Wet Wet 2 330 Sub Wet 1987 2 1000 Sub Wet 2011 - 28,153,440 6,985,440 21,168,000 35,303,520 8,759,520 26,544,000 - - - 4820 1196 3624 FGD 13,275 - - 7,150,080 Wet Other -

Pengcheng Power Station 彭城电厂Xuzhou Tongshan 6245 34.378 117.18 MAJ_03_12 3,280 64% 18,368 13,893 2,481 Wet Wet 2 300 SSub Wet 1996 2 340 Sub Wet 2004 2 1000 Super Wet 2010 36,890,000 32,934,720 7,257,600 7,197,120 18,480,000 41,180,160 9,072,000 9,024,960 23,083,200 - - - - 5876 1221 1232 3423 FGD 16,183 36,890 1,426,760 38.7 8,245,440 Wet Other -

Kanshan Power Station 阚山电厂Xuzhou Tongshan 6245 34.407 117.58 MAJ_03_13 1,200 64% 6,720 5,856 1,877 Wet Wet 2 600 Sub Wet 2007 2 1000 Ultra Wet 2019 4,547,620 12,700,800 12,700,800 - 15,926,400 15,926,400 - - - - 2175 2175 0 No FGD 5,871 4,548 315,220 69.3 3,225,600 Wet Other -

Xutang Power Station 徐塘电厂Xuzhou Pizhou 6245 34.349 117.93 MAJ_03_14 1,200 64% 6,720 5,532 1,573 Wet Wet 4 300 Sub Wet 2004 2 1000 Super Wet 2016 12,301,000 12,700,800 12,700,800 - 15,926,400 15,926,400 - - - - 2175 2175 0 No FGD 5,871 12,301 1,117,990 90.9 3,225,600 Wet Other -

Datun Mine 大屯能源大屯热电联产上大压小新建项目Xuzhou Pei 6245 34.798 116.91 MAJ_03_15 100 64% 560 2,293 409 Wet Wet 1 100 Sub Wet 2009 2 350 Super Wet 2015 13,100,000 1,058,400 1,058,400 - 1,327,200 1,327,200 - - - - 181 181 0 No FGD 489 13,100 218,410 16.7 268,800 Wet Other -

Xuzhou Huamei Coal Mine Power Station徐矿综合利用发电厂Xuzhou Xuzhou 6245 34.318 117.12 MAJ_03_16 110 64% 616 3,494 624 Wet Wet 2 55 Sub Wet 2005 2 350 Super Wet 2015 329 1,164,240 1,164,240 - 1,459,920 1,459,920 - - - - 199 199 0 No FGD 538 0 9 27.4 295,680 Wet Other -

Changzhou Power Station 常州电厂Changzhou Wujin 5784 31.958 119.99 MAJ_04_17 1,200 54% 5,729 7,820 1,638 Direct Direct 2 600 Sub Direct 2006 2 660 Super Direct 2015 2,774,000 1,964,884 1,964,884 - 590,610,885 590,610,885 - - - - 1873 1873 0 No FGD 5,058 2,774 4,495 1.6 588,646,001 Direct Yangtze 1,181,222

Qishuyan Power Station 戚墅堰电厂Changzhou Changzhou 3462 31.736 120.04 MAJ_04_18 1,100 54% 5,251 3,795 795 Wet Wet 2 200 SSub Wet 2 350 Sub Wet 2005 2 220 Gas Wet 2016 4,560,630 10,440,236 4,124,538 6,315,698 - 13,075,357 5,155,672 7,919,685 - - - - - 1775 694 1081 No FGD 4,793 4,561 201,262 44.1 2,635,121 Wet Other -

Wangting Power Station 望亭电厂Suzhou Wuxian 3462 31.444 120.44 MAJ_05_19 2,700 58% 13,822 - - Wet Wet 2 300 SSub Wet 1996 2 390 Gas Wet 2005 2 660 Sub Wet 2010 18,147,500 30,425,910 6,634,360 11,020,409 12,771,142 42,435,330 8,292,950 18,127,773 16,014,607 - - - - 3303 1116 2187 FGD 9,096 18,148 500,985 27.6 12,009,420 Wet Other -

Shazhou Power Station 沙洲电厂Suzhou Shazhou 5784 31.991 120.68 MAJ_05_20 1,200 58% 6,143 8,025 1,702 Direct Direct 2 600 Sub Direct 2008 2 1000 Ultra Direct 2018 5,670,000 2,107,023 2,107,023 - 633,335,629 633,335,629 - - - - 2009 2009 0 No FGD 5,424 5,670 563,890 99.5 631,228,606 Direct Yangtze 1,266,671

Zhangjiagang Power Station 张家港电厂Suzhou Shazhou 3547 31.887 120.6 MAJ_05_21 1,200 58% 6,143 4,040 1,314 Wet Wet 2 250 Sub Wet 2003 2 350 Gas Wet 2003 2 1000 Ultra Wet 2019 3,484,750 14,727,664 4,837,554 9,890,110 - 22,334,654 6,066,139 16,268,515 - - - - - 828 828 0 No FGD 2,236 3,485 106,984 30.7 7,606,990 Wet Other -

Changshu Power Station 常熟电厂Suzhou Changshu 5784 31.757 120.98 MAJ_05_22 5,000 58% 25,596 12,087 2,361 Direct Direct 4 300 SSub Direct 1995 3 600 Sub Direct 2006 2 1000 Ultra Direct 2013 770,000 8,910,314 3,415,467 3,160,535 2,334,312 2,431,062,806 633,335,629 950,003,444 847,723,733 - - - - 8041 2047 3013 2981 FGD 22,146 770 50,000 64.9 2,422,152,492 Direct Yangtze 4,862,126

Taicang Power Station 太仓电厂Suzhou Taicang 5784 31.657 121.18 MAJ_05_23 1,900 58% 9,726 11,672 2,280 Direct Direct 2 320 Sub Direct 2004 2 630 Super Direct 2006 3,700,000 2,929,766 1,123,746 1,806,020 986,656,233 337,779,002 648,877,231 - - - 3042 1071 1971 FGD 8,379 3,700 80,000 21.6 983,726,467 Direct Yangtze 1,973,312

Taicanggang Power Station 太仓港电厂Suzhou Taicang 5784 31.585 121.26 MAJ_05_24 1,570 58% 8,037 8,633 1,686 Direct Direct 2 135 Sub Direct 2003 2 330 Sub Direct 2004 2 320 Sub Direct 2005 2 1200 Ultra Direct 2021 1,244,360 2,756,689 474,080 1,158,863 1,123,746 - 828,614,115 142,500,517 348,334,596 337,779,002 - - - - - - 2628 452 1105 1071 0 FGD 7,238 1,244 80,000 64.3 825,857,426 Direct Yangtze 1,657,228

Lvsigang Power Station 大唐国际吕四港发电厂Nantong Qidong 5747 32.059 121.73 MAJ_06_25 2,640 67% 15,497 15,754 2,684 Direct SalineDirect Saline 4 660 Sub Direct Saline2010 2 1060 Ultra Direct Saline 2018 1,350,000 6,462,166 6,462,166 - 5,687,326 5,687,326 - 1,592,032,754 ###### - 4998 4998 0 FGD 13,764 1,350 6,535 4.8 774,840- Direct SalineSea -

Huaneng Nantong Power Station南通电厂Nantong Nantong 5784 32.033 120.77 MAJ_06_26 3,400 67% 19,958 8,406 1,432 Direct Direct 2 350 Sub Direct 1989 2 350 Sub Direct 1999 2 1000 Ultra Direct 2014 2,144,803 5,495,494 1,409,387 1,409,387 2,676,720 1,819,347,800 423,637,900 423,637,900 972,072,000 - - - - 6106 1344 1344 3419 No FGD 16,486 2,145 77,232 36.0 1,813,852,306 Direct Yangtze 3,638,696

Tianshenggang Power Station天生港电厂Nantong Nantong 5784 32.035 120.75 MAJ_06_27 660 67% 3,874 4,060 692 Direct Direct 2 330 Sub Direct 2000 5,282,300 1,328,851 1,328,851 399,430,020 399,430,020 - - 1267 1267 No FGD 3,421 5,282 20,182 3.8 398,101,169 Direct Yangtze 798,860

Xinhai Power Station 新海电厂LianyungangLianyungang 6245 34.578 119.13 MAJ_07_28 2,660 80% 18,701 4,401 626 Wet Wet 2 330 Sub Wet 2003 2 1000 Ultra Wet 2012 1 1030 Ultra Wet 2017 11,180,000 32,504,018 8,769,631 23,734,387 - 40,664,822 10,996,839 29,667,984 - - - - - 5649 1502 4148 0 FGD 15,558 11,180 575,641 51.5 8,160,805 Wet Other -

Huaneng Huaiyin Power Station华能国际电厂Huaian Huaiyin 6245 33.595 118.96 MAJ_08_29 1,760 63% 9,763 7,152 1,289 Wet Wet 2 220 SSub Wet 1995 4 330 Sub Wet 2011 33,469,730 19,112,026 5,272,283 13,839,743 23,944,952 6,590,354 17,354,598 - - - 3257 887 2370 No FGD 8,793 33,470 609,289 18.2 4,832,926 Wet Other -

Huaiyin Power Station 淮阴电厂Huaian Huaiyin 6245 33.587 119.01 MAJ_08_30 660 63% 3,661 4,075 735 Wet Wet 1 330 Sub Wet 2008 1 330 Sub Wet 2011 51,337,692 6,919,872 3,459,936 3,459,936 8,677,299 4,338,650 4,338,650 - - - 1185 592 592 No FGD 3,199 51,338 14,257 0.3 1,757,428 Wet Other -

Guoxin Huaian Gas 国信淮安燃气发电厂Huaian Huaiyin 6245 33.587 119.01 MAJ_08_31 400 63% 2,219 1,900 507 Wet Wet 2 200 Gas Wet 2011 2,679,562 6,124,369 6,124,369 10,074,144 10,074,144 - - 0 No FGD - 2,680 - 0.0 3,949,774 Wet Other -

Chenjiagang Power Station 陈家港电厂Yancheng Xiangshui 6245 34.424 119.8 MAJ_09_32 1,320 75% 8,621 66 40 Direct SalineDirect Saline 2 660 Super Direct Saline2011 1 1000 Ultra Direct Saline2016 1,652,400 2,879,555 2,879,555 - 2,879,555 2,879,555 - 864,435,558 ###### - 2623 2623 0 No FGD 7,081 1,652 85 0.1 - Direct SalineSea -

Yancheng Power Station 盐城电厂Yancheng Yiancheng 5747 33.4 120.12 MAJ_09_33 500 75% 3,266 1,122 181 Direct Direct 4 125 SSub Direct 2004 2 350 Super Direct 2017 61,536 1,815,724 1,815,724 - 336,692,678 336,692,678 - - - - 1088 1088 0 No FGD 2,938 62 64,551 1049.0 334,876,954 Direct Other 673,385

Sheyanggang Power Station射阳港电厂Yancheng Sheyang 5784 33.818 120.47 MAJ_09_34 1,590 75% 10,385 3,800 687 Direct SalineDirect Saline 0 125 SSub Direct Saline1994 2 135 Sub Direct Saline2003 1 660 Super Direct Saline2011 1 660 Super Direct Saline2013 2 1000 Ultra Direct Saline 2022 190 3,614,923 - 735,368 1,439,778 1,439,778 - 3,526,750 - 647,195 1,439,778 1,439,778 - 1,045,602,409 - ###### ###### ###### - 3191 0 569 1311 1311 0 No FGD 8,617 0 19,000 88,174- Direct SalineSea -

Yanweigang 燕尾港电厂Yancheng Xiangshui 6245 34.424 119.8 MAJ_09_35 - 75% - 5,957 1,637 Direct SalineDirect Saline 2 1000 Ultra Direct Saline 2023 1,539,874 - - - - - - 0 0 No FGD - 1,540 836 0.5 - Direct SalineSea -

Yangzhou Power Station 扬州电厂Yangzhou Yangzhou 5747 32.429 119.48 MAJ_10_36 660 62% 3,563 8,314 1,540 Wet Wet 0 220 SSub Wet 1990 2 330 Sub Wet 2005 8,086,636 6,734,985 - 6,734,985 8,445,458 - 8,445,458 - - - 1153 0 1153 No FGD 3,113 8,087 105,810 13.1 1,710,473 Wet Other -

Yangzhou No2 Power Station扬州第二电厂Yangzhou Hanjiang 5784 32.268 119.42 MAJ_10_37 2,460 62% 13,282 7,293 1,351 Direct Direct 2 630 Sub Direct 1998 2 600 Super Direct 2007 2 1000 Ultra Direct 2023 7,093,853 4,147,572 2,333,434 1,814,138 - 1,353,184,598 701,390,911 651,793,688 - - - - - 4205 2225 1980 0 FGD 11,579 7,094 92,820 13.1 1,349,037,027 Direct Yangtze 2,706,369

Yizheng Natural Gas Power Station仪征燃气电厂Yangzhou Yizheng 5784 32.255 119.14 MAJ_10_38 - 62% - - Wet Wet 3 200 Gas Wet 2016 3 200 Gas Wet 2019 - - - - - - - - - 0 No FGD - - Wet Yangtze -

SINOPEC Yizheng Chemical Fiber Power Station仪征化纤自备电厂Yangzhou Yizheng 5784 32.276 119.11 MAJ_10_39 600 62% 3,240 0 0 Wet Wet 6 100 SSub Wet 668,674 6,997,388 6,997,388 8,746,734 8,746,734 - - 1177 1177 No FGD 3,179 669 2,740 4.1 1,749,347 Wet Other -

Jurong Power Station 句容电厂Zhenjiang Jurong 5784 32.194 119.25 MAJ_11_40 2,000 68% 11,966 2,000 Direct Direct 2 1000 Ultra Direct 2013 2 1000 Ultra Direct 2019 2,728,165 2,728,165 - 990,754,749 990,754,749 - - - - 3484 3484 0 FGD 9,596 988,026,584 Direct Yangtze 1,981,509

Zhenjiang Power Station 镇江电厂Zhenjiang Dantu 5784 32.186 119.27 MAJ_11_41 1,750 68% 10,470 9,724 1,625 Direct Direct 2 135 SSub Direct 2000 2 140 Sub Direct 2004 2 600 Sub Direct 2009 2 400 Gas Direct 2017 2 1000 Ultra Direct 2021 3,924,868 3,935,259 898,141 574,590 2,462,528 - - 1,079,450,034 166,543,719 172,712,005 740,194,309 - - - - - - - - 3434 538 548 2348 0 No FGD 9,271 3,925 - 0.0 1,075,514,775 Direct Yangtze 2,158,900

Jianbi Power Station 谏壁电厂Zhenjiang Zhenjiang 5784 32.177 119.58 MAJ_11_42 4,400 68% 26,324 16,400 2,741 Direct Direct 8 300 SSub Direct 2004 2 1000 Ultra Direct 2012 2 1260 Ultra Direct 2022 3,712,258 10,711,638 7,983,473 2,728,165 - 2,471,143,367 1,480,388,618 990,754,749 - - - - - 8269 4784 3484 0 FGD 22,772 3,712 50,135 13.5 2,460,431,728 Direct Yangtze 4,942,287

Taizhou Power Station 泰州电厂Taizhou Taixing 5784 32.186 119.91 MAJ_12_43 2,000 69% 12,132 12,294 2,333 Direct Direct 1 1000 Ultra Direct 2007 1 1000 Ultra Direct 2009 2 1000 Ultra Direct 2015 1,003,509 2,766,096 1,383,048 1,383,048 - 1,004,529,600 502,264,800 502,264,800 - - - - - 3533 1766 1766 0 FGD 9,729 1,004 4,928 4.9 1,001,763,504 Direct Yangtze 2,009,059

Jingjiang Power Station 靖江电厂Taizhou Jingjiang 5784 32.031 120.38 MAJ_12_44 1,320 69% 8,007 5,848 964 Direct Direct 1 660 Ultra Direct 2013 1 660 Ultra Direct 2014 584,780 1,825,623 912,812 912,812 662,989,536 331,494,768 331,494,768 - - - 2332 1166 1166 No FGD 6,296 585 26,280 44.9 661,163,913 Direct Yangtze 1,325,979

Guodian Suqian 国电宿迁Suqian Suqian MAJ_13_45 - 75% - 228 Wet Wet 2 660 Super Wet 2017 - - - - - - 0 0 FGD - - Wet Other -

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69786 -

Minor Power stations 小型电站 Sum Major MW -

Nanjing Minor 南京 Nanjing 1 9,140 31.930 118.842 MIN_01_01 1,875 65% 9,096 7,077 1,459 Wet Wet 1 1,875 SSub Wet 20.5% 148,880,455 23,031,667 23,031,667 28,789,584 28,789,584 3875 3875 No FGD 10,462 Wet Other -

Wuxi Minor 无锡 Wuxi 2 5,446 31.527 120.076 MIN_02_02 1,343 68% 7,074 6,423 1,343 Wet Wet 1 1,343 SSub Wet 24.7% 36,827,528 17,332,071 17,332,071 21,665,089 21,665,089 2916 2916 No FGD 7,873 Wet Other -

Xuzhou Minor 徐州 Xuzhou 3 8,550 34.360 117.512 MIN_03_03 1,953 64% 9,072 8,635 1,953 Wet Wet 1 1,953 SSub Wet 22.8% 21,844,863 23,622,249 23,622,249 29,527,811 29,527,811 3974 3974 No FGD 10,730 Wet Other -

Changzhou Minor 常州 Changzhou 4 2,300 31.627 119.638 MIN_04_04 931 54% 3,584 3,791 931 Wet Wet 1 931 SSub Wet 40.5% 22,526,280 9,602,120 9,602,120 12,002,650 12,002,650 1615 1615 No FGD 4,362 Wet Other -

Suzhou Minor 苏州 Suzhou 5 13,570 31.376 120.642 MIN_05_05 4,352 58% 19,212 15,515 4,352 Wet Wet 1 4,352 SSub Wet 32.1% 40,034,793 48,125,455 48,125,455 60,156,819 60,156,819 8097 8097 No FGD 21,861 Wet Other -

Nantong Minor 南通 Nantong 6 6,700 32.190 120.994 MIN_06_06 28 67% 142 102 28 Wet Wet 1 28 SSub Wet 0.4% 17,882,489 360,304 360,304 450,380 450,380 61 61 No FGD 164 Wet Other -

Lianyungang Minor 连云港 Lianyungang 7 2,660 34.534 119.131 MIN_07_07 9 80% 37 9 9 Wet Wet 1 9 SSub Wet 0.3% 599,584 130,890 130,890 163,612 163,612 22 22 No FGD 59 Wet Other -

Huaian Minor 淮安 Huaian 8 2,820 33.357 118.963 MIN_08_08 86 63% 360 323 86 Wet Wet 1 86 SSub Wet 3.0% 7,828,665 1,026,694 1,026,694 1,283,368 1,283,368 173 173 No FGD 466 Wet Other -

Yancheng Minor 盐城 Yancheng 9 3,410 33.518 120.173 MIN_09_09 226 75% 1,086 810 226 Wet Wet 1 226 SSub Wet 6.6% 39,845,196 3,194,556 3,194,556 3,993,195 3,993,195 537 537 No FGD 1,451 Wet Other -

Yangzhou Minor 扬州 Yangzhou 10 3,720 32.739 119.471 MIN_10_10 888 62% 3,879 3,014 888 Wet Wet 1 888 SSub Wet 23.9% 8,414,300 10,360,210 10,360,210 12,950,263 12,950,263 1743 1743 No FGD 4,706 Wet Other -

Zhenjiang Minor 镇江 Zhenjiang 11 8,150 32.018 119.455 MIN_11_11 1,262 68% 4,761 2,551 1,262 Wet Wet 1 1,262 SSub Wet 15.5% 1,316,358 16,307,623 16,307,623 20,384,529 20,384,529 2744 2744 No FGD 7,408 Wet Other -

Taizhou Minor 泰州 Taizhou 12 3,320 32.571 120.055 MIN_12_12 129 69% 555 551 129 Wet Wet 1 129 SSub Wet 3.9% 10,627,669 1,694,664 1,694,664 2,118,331 2,118,331 285 285 No FGD 770 Wet Other -

Suqian Minor 宿迁 Suqian 13 - 33.785 118.519 MIN_13_13 - 75% - - - Wet Wet 1 - SSub Wet 0.0% 2,791,051 - - - - 0 0 No FGD - Wet Other -

-

-

CGE model 一般均衡模型 Sum Major MW -

Nanjing CGE 南京 Nanjing 1 9,140 31.930 118.842 CGE_01_01 220.51 65% 1,254 Wet Wet 0.415 221 Sub Wet 0.5 221 Super Direct 0.085 221 Super Direct Saline 1,194,445 983,336 175,517 35,592 64,329,525 1,233,072 63,060,861 35,592 10,684,754 - - ###### 392 168 192 33 FGD 1,081 Wet Yangtze -

Wuxi CGE 无锡 Wuxi 2 5,446 31.527 120.076 CGE_02_02 1,962.88 68% 11,727 Wet Wet 0.415 1,963 Sub Wet 0.5 1,963 Super Direct 0.085 1,963 Super Direct Saline 11,172,800 9,198,088 1,641,783 332,930 601,736,107 11,534,110 589,869,067 332,930 99,944,811 - - ###### 3671 1575 1792 305 FGD 10,111 Wet Yangtze -

Xuzhou CGE 徐州 Xuzhou 3 8,550 34.360 117.512 CGE_03_03 3,968.67 64% 22,225 Wet Wet 0.415 3,969 Sub Wet 0.5 3,969 Super Direct 0.085 3,969 Super Direct Saline 21,174,240 17,431,844 3,111,440 630,956 1,140,385,964 21,858,979 1,117,896,029 630,956 189,411,369 - - ###### 6958 2985 3396 577 FGD 19,162 Wet Other -

Changzhou CGE 常州 Changzhou 4 2,300 31.627 119.638 CGE_04_04 369.90 54% 1,766 Wet Wet 0.415 370 Sub Wet 0.5 370 Super Direct 0.085 370 Super Direct Saline 1,682,365 1,385,019 247,214 50,132 90,607,519 1,736,770 88,820,617 50,132 15,049,373 - - ###### 553 237 270 46 FGD 1,522 Wet Other -

Suzhou CGE 苏州 Suzhou 5 13,570 31.376 120.642 CGE_05_05 3,559.50 58% 18,221 Wet Wet 0.415 3,560 Sub Wet 0.5 3,560 Super Direct 0.085 3,560 Super Direct Saline 17,360,320 14,292,007 2,551,005 517,307 934,978,808 17,921,724 916,539,776 517,307 155,294,455 - - ###### 5705 2447 2784 473 FGD 15,710 Wet Yangtze -

Nantong CGE 南通 Nantong 6 6,700 32.190 120.994 CGE_06_06 699.60 67% 4,107 Wet Wet 0.415 700 Sub Wet 0.5 700 Super Direct 0.085 700 Super Direct Saline 3,912,571 3,221,052 574,931 116,588 210,720,264 4,039,097 206,564,579 116,588 34,999,391 - - ###### 1286 551 627 107 FGD 3,541 Wet Sea -

Lianyungang CGE 连云港 Lianyungang 7 2,660 34.534 119.131 CGE_07_07 1,478.08- 80% 10,391- Wet Wet 0.415 1,478- Sub Wet 0.5 1,478- Super Direct 0.085 1,478- Super Direct Saline 9,900,260- 8,150,459- 1,454,790- 295,011- 533,200,582- 10,220,417- 522,685,154- 295,011- 88,561,466- - - ####### -3253 -1395 -1588 -270 FGD 8,959- Wet Sea -

Huaian CGE 淮安 Huaian 8 2,820 33.357 118.963 CGE_08_08 493.09 63% 2,735 Wet Wet 0.415 493 Sub Wet 0.5 493 Super Direct 0.085 493 Super Direct Saline 2,606,112 2,145,500 382,954 77,658 140,357,972 2,690,389 137,589,925 77,658 23,312,630 - - ###### 856 367 418 71 FGD 2,358 Wet Other -

Yancheng CGE 盐城 Yancheng 9 3,410 33.518 120.173 CGE_09_09 629.44- 75% 4,111- Wet Wet 0.415 629- Sub Wet 0.5 629- Super Direct 0.085 629- Super Direct Saline 3,916,852- 3,224,576- 575,560- 116,715- 210,950,793- 4,043,516- 206,790,561- 116,715- 35,037,680- - - ####### -1287 -552 -628 -107 FGD 3,545- Wet Sea -

Yangzhou CGE 扬州 Yangzhou 10 3,720 32.739 119.471 CGE_10_10 572.34 62% 3,090 Wet Wet 0.415 572 Sub Wet 0.5 572 Super Direct 0.085 572 Super Direct Saline 2,944,146 2,423,789 432,626 87,730 158,563,571 3,039,355 155,436,485 87,730 26,336,472 - - ###### 967 415 472 80 FGD 2,664 Wet Other -

Zhenjiang CGE 镇江 Zhenjiang 11 8,150 32.018 119.455 CGE_11_11 1,859.93- 68% 11,128- Wet Wet 0.415 1,860- Sub Wet 0.5 1,860- Super Direct 0.085 1,860- Super Direct Saline 10,601,721- 8,727,943- 1,557,866- 315,913- 570,979,363- 10,944,563- 559,718,887- 315,913- 94,836,298- - - ####### -3484 -1494 -1700 -289 FGD 9,594- Wet Yangtze -

Taizhou CGE 泰州 Taizhou 12 3,320 32.571 120.055 CGE_12_12 210.50- 69% 1,277- Wet Wet 0.415 211- Sub Wet 0.5 211- Super Direct 0.085 211- Super Direct Saline 1,216,566- 1,001,547- 178,768- 36,252- 65,520,901- 1,255,908- 64,228,741- 36,252- 10,882,634- - - ####### -400 -171 -195 -33 FGD 1,101- Wet Yangtze -

Suqian CGE 宿迁 Suqian 13 - 33.785 118.519 CGE_13_13 119.78 75% 789 Wet Wet 0.415 120 Sub Wet 0.5 120 Super Direct 0.085 120 Super Direct Saline 751,521 618,695 110,432 22,394 40,474,817 775,823 39,676,599 22,394 6,722,628 - - ###### 247 106 121 20 FGD 680 Wet Other -

Major Stations Individually

Minor Stations by Municipality

CGE by Municipality

Names, location

Capacity , Technology

Phases with capacity & Tech Water Consumption(by phase)

Water Withdrawal(by phase)

Seawater Use

Coal Use

GHGEmmision

Water source and reference



Figure 3-5 PowerWEN Schematic GIS system



3.2. Data Preparation for PowerWEN Model

3.2.1. Sources of Data A list of power stations in Jiangsu is available on Wikipedia (https://en.wikipedia.org/wiki/List_of_major_power_stations_in_Jiangsu_province), this then references two main data sources:

http://www.industcards.com/st-coal-china-jiangsu.htm

http://www.sourcewatch.org/index.php

These sources list the major thermal power stations with their different units and phases and in some cases with detailed dates of commissioning and decommissioning. These also include information on planned and under construction units. Other information in this includes the type of boiler in the station – Subcritical, Super or ultra-supercritical. And some information on the cooling type, also grid locations (latitude and longitude).

In the Model spreadsheet the base data were loaded into the Nodes sheet, separating out the information into columns and cross referencing to other sources to build a model. The different sources were cross checked and where not completely consistent, the best estimate of data was made.

The locations were checked on mapping systems with satellite photos – variously Baidu, Google Earth, google maps etc. This was helpful in confirming the locations, the number of units in operation, construction or decommissioned and the cooling type – where wet cooled the cooling towers are very prominent.

In March 2016 the MEP permit approvals data base for 2015 was checked and some additional stations and construction or commissioning dates on power plants in Jiangsu added to the database. Though there were 21 new stations approved in 2015 most of these were small CHP or Co-generation stations, only 5 were for major power units (Coal and some Gas) – though these constitute some 5.5 GW of new capacity expected to be in service by 2018. (data in “2015 Permits” sheet). The Tianwan Nuclear power station was also added to the model.

Cross referencing locations.

GIS

To establish the municipality and the county of the stations publically available GIS mapping systems of China were used and also the WRI Aqueduct system. A GIS system was set up in QGIS, an open source GIS system.

Administrative boundaries (Province, Municipality, County) road, rail and water features were derived from the GADM database (www.gadm.org), version 2.5, July 2015 uploaded in September 2015. Roads, Rail, waterways etc data were also obtained from Openstreetmap.org on 31 August 2015.

The Aqueduct GIS data were downloaded from the WRI website and were extracted for the China boundary area.

The Jiangsu Power spreadsheet was linked to the QGIS system

Union queries were used to cross reference the data and determine the Municipality and county in which was located each power station. The outputs of these queries are seen in sheets “Power_Mun” and “Power_County”. From these the Municipality and County are added to the Jiangsu Power sheet using a Vlookup function searching on the English version of the power station name. For this reason care must be taken if changing the name of any station to ensure that the same change is effected through all of the sheets otherwise such links will break.

The WRI Aqueduct data was also referenced in this way to derive the basin resources and scarcity for the WRI Basin in which each power station is located. Note that to bring this data into a municipality level for

http://www.industcards.com/st-coal-china-jiangsu.htm

http://www.sourcewatch.org/index.php



comparison would require that further union functions were applied to allocate proportions of each basin to the areas covered by municipal boundaries.

In the GIS the power stations are projected via a CSV file export of Jiangsu Power sheet, generated by a linked spreadsheet. The stations are then thematically sized by Capacity in GW and coloured by cooling type.

Water and Coal Consumption

The amount of water withdrawn and consumed by each power station and the amount of coal used can be calculated by the application of standard coefficients for water consumption and coal use that have been derived by various researchers globally and specifically for China.

The consumption varies from site to site depending on the level of the technology and the local geographic setting and also the state of maintenance and operations al rules. However there are significant differences based on the scale of the plant, the type of boiler technology and the cooling system. Data for these have been derived from Qing 2015 who presents typical global figures and then updated with Zhang et al 2016 for China specific data and Max, mean and min ranges.

The classifications selected are

Ultra-supercritical, Supercritical, Subcritical >300 MW, Subcritical<300 MW and Gas plants.

Then on Direct (Once through cooling), Wet Cooled (evaporative Cooling Towers), Dry cooled

(radiative cooling towers) and Direct Saline (once through cooling with sea water)

For saline cooling the amount of seawater used is not considered but there would still be subsidiary fresh water use for boiler makeup, flue gas treatment and ash cooling. Likewise for dry cooling (though not currently applied in Jiangsu).

These coefficients are stored on the “Consumption” sheet

The amount of water use is calculated based on the capacity and processes of each phase of a power station, dates are checked to establish if the phase is in operation at the time of the reference year, then the use for each phase is summed to give a total.

For each phase (selected by reference to an OFFSET equation) the appropriate coefficient is selected using and index lookup on the station type (super, sub, gas etc) and the cooling type (Direct, wet etc).

The control panel (“Control” sheet) can be used to select the range of cooling water coefficients to be used from Mean, Low or High. There is also an option for % of mean. The control panel also has the option to select from the current cooling system or to switch all to wet cooling. This can be used for sensitivity testing and exploring the impact of different cooling technologies.

3.2.2. Comparison to Statistical Yearbook Data 2012 Thermal power stations

Data are available on the thermal power plants in Jiangsu and their water use for 2012 from the Jiangsu statistical Yearbook. This lists 189 power stations and gives there main outputs – electricity, heat, chemicals etc and their water use as total and amount recycled. This was supplied by NJU. The units vary for each station but are stated so with some processing it was possible to calculate the total electric power (in MWh) produced by each station and given the reported operational hours, the estimated capacity in GW.

There are some inconsistencies in data – for example Taizhou Jingjiang station is listed with outputs in this 2012 table, despite sourcewatch data reporting that did not enter service until 2013 to 2014.

As far as possible the list of major power stations was compared to the 2012 Thermal Stations list by matching on the Chinese station name. Most of them could be matched, though there were a few in each case that were different. The rest of the stations are mostly small co-gen or industrial process stations of a few MW. These constitute about 10 to 20% of the total based on capacity in each municipality.



The 2012 Data includes water use as Industrial water, Total water and recycled water. The last 2 of these sum to the first, thus it is assumed that the total water figure is the amount used after recycling of water in the plant. However, comparing these data with the model estimates of water use shows almost no correlation. It is thought that there are major variations in the units and figures related to these numbers and at present no conclusions can be drawn from them.

2013 Yearbook summary

The other data that was made available was the Statistical year book summary of data for 2013 for the province and each municipality. This just gives, for each municipality the total capacity and the total power produced. By a comparison of these (the actual power produced / the theoretical power produced if run at 100% capacity for 8760 hours per year) the utilisation of the power station can be calculated for each municipality. The average was 65% for Jiangsu with significant variation by municipality. This utilisation by municipality is fed back to the main Jiangsu Power sheet and applied for each power station to estimate the total power produced and also the water and coal consumptions.

Data Comparisons

The 2012 and 2013 data were compared with the power stations model data for comparable years and found to be broadly similar. However the addition of minor power stations to the model causes it to overestimate the capacity in 2013 compared to the statistical yearbook. The minor stations were estimated by municipality.

Minor Power Stations

There are many minor power stations listed in the 2012 Statistics data. These are mostly cogeneration, CHP and MSW or chemical plant installations. These are analysed in the comparison of 2012, 2013 and Model data, by extracting the identified major stations to see for each municipality what is the difference. In the Jiangsu Power sheet This percentage is applied to the model list of capacity in each municipality to estimate the amount of minor stations capacity in GW. When calculating the actual power production in GWh the capacity is multiplied by 8760 hours then by the utilisation in the municipality and then by the co-gen utilisation worked out from the operating hours in the 2012 data sheet. This is on the basis that these minor stations will have lower utilisation than the major ones. The minor stations are treated as a single phase of construction, assumed to be wet cooled and then the coefficients for water consumption and withdrawal are calculated using the figures for MSW (Municipal Solid Waste) cogeneration data from the consumption coefficients lookup table.

Water resources

From the 2013 Statistical Yearbook of Jiangsu, NJU provided information of the breakdown of water resources in each municipality (though with some censored) and also the 3 red lines water allocations for each Municipality for 2015 to 2020 (13 FYP period) and for the post 2020 period.

Also provided by NJU in March 2016, from the 2013 Jiangsu water resources bulletin were data on the total water resource in each Municipality and the water pressure from the Power sector. This allows estimates of the total water use by the power sector in each municipality expected in 2020 and 2030.

In combination with the results of the model for estimation of water use these can be used to put the water withdrawal and consumption of the power sector into context of the overall resources of the Province. These data are analysed in “Water Resources” sheet and various graphics produced.



3.3. Summary of Data Used in the model The different data sources used in the model are summarised in Figure 3-6.

Figure 3-6 Summary of data used in Jiangsu Water Energy Model

3.4. Model Files and Copyright The model is provided in the accompanying spreadsheet “Jiangsu PowerWEN Model-V16 issued

160422.xlsm” The model is issued under a commons license as such copyright remains with Atkins Ltd but

the model data and code may be copied, modified and shared as long as Atkins are cited as the copyright

owners and the originators of the model and incorporated software.

Station Name

45 Plants• Lat, Lon

Linked through GIS to• Municipality• County• WRI Basin ID

Raw Data

• Total Capacity• Phase 1 to 3• Under

Construction• Planned• Decommissio

ned• WRI Basin ID

Sources• https://en.wikipedia.org/wiki/List_of_major_power_stations_in_Jiangsu_province• http://www.industcards.com/st-coal-china-jiangsu.htm• http://www.sourcewatch.org/index.php

Processed Datawith dates and filters• Total Capacity (MW)• Total Energy Production• Cooling Type • Utilisation (derived from

Yearbook 2013 data in Water resources sheet)

For each Phase:• Capacity• Critical / Sub• Date of commissioning• Under Construction• Planned

Water and Coal useFor Each Phase with Filters• Water Consumption• Water Withdrawal• Coal use

Linked to “Consumption” sheet. This has Standard Water and coal use for Super and Sub Critical Direct, Wet and Dry cooled Power stations. From Zhang et al 2016

2013 Year book Data

Water Allocations by Municipality

Water use by Municipality and sector

Calculations and Graphics for scenarios and results outputs2014 consumption data

Jiangsu Coal Water Resource

2012 Data from NJU

187 Power stations in Jiangsu• Name• Municipality• County• Capacity (Approx)• Electric Power Output• Heat output• Units• Water withdrawal• Water recycled

Thermal power

Also Sheets for Nuclear, Hydro Pumped Storage

电站名称

45 个电站•经纬度

通过GIS连接到•各市•各镇• WRI 流域ID

原始数据

•总装机容量阶段1-3

•在建•规划建设•废弃• WRI流域ID

来源• https://en.wikipedia.org/wiki/List_of_major_power_stations_in_Jiangsu_province• http://www.industcards.com/st-coal-china-jiangsu.htm• http://www.sourcewatch.org/index.php

按照时序分类的已处理数据

•装机总量 (百万瓦特)•总发电量•冷却技术•利用率针对每个阶段:•装机容量•临界 / 亚临界•启用时间•在建•规划建设

各阶段煤炭与水资源消耗

•耗水量•取水量•煤耗

Linked to “Consumption” sheet. This has Standard Water and coal use for Super and Sub Critical Direct, Wet and Dry cooled Power stations. From Zhang et al 2016

2013年年鉴数据

各市水资源分配

各市及各行业耗水量

模型呈现的不同情境下的计算及图表都基于2014年耗水量数据

江苏省火电生产水资源

2012年南大数据

187个江苏发电站•名字•所属市•所属镇•装机容量•发电量•产热•机组•取水量•回水量

火力

同时包含了核能，水力发电储量

Appendices



Appendix A. Power Sector Permit Approvals 2015

A.1. Permit approvals Jiangsu provincial government approved 21 power sector projects for construction in 2015.

Figure A-1 Permits Approved 2015

名称 name 文件 file 地点 location MW 建设内容 Construction Content

江苏英奇热电有限公司热电联

产项目一期工程 Jiang Suying

Qi Cogeneration Co., Ltd. cogeneration project a project

苏环审﹝2015﹞155号

Jiangsu trial number

﹝ 2015 ﹞ 155

盐城市响水经济开发区

Yancheng Xiangshui Economic Development Zone

12 co-gen

新建2×45吨/小时高温高压循环流化床锅炉+1×12兆瓦高

温高压背压式汽轮发电 New 2 × 45 tons / hour high

temperature high pressure circulating fluidized bed boiler + 1 × 12 MW high temperature and pressure back-pressure turbo-generator

江苏通达热电有限公司热电联

产项目 Cogeneration Co., Ltd.,

Jiangsu Tongda Cogeneration Project

苏环审﹝2015﹞148号


﹝ 2015 ﹞ 148

徐州市新沂市经济开发区

Xuzhou Xinyi City Economic Development Zone

12 co-gen

新建3×75吨/小时循环流化床锅炉（两用一备）+2×CB6

兆瓦供热机组，并配套建设相关公辅工程。 New 3 × 75 t

/ h circulating fluidized bed boiler (Uses a) + 2 × CB6 MW heating units, and related public auxiliary supporting construction projects.

苏州淞港热能有限公司热电联

产项目 Song Hong Kong heat

Ltd. Suzhou Cogeneration Project

苏环审﹝2015﹞144号

Jiangsu trial ﹝ 2015 ﹞

144 Number

苏州市吴中区甪直镇淞港

村西北部 Lu Song village

northwest of the port of the town of Wuzhong District, Suzhou City straight

20 co-gen

新建3×90吨/小时高温高压循环流化床锅炉（两用一备）

+2×B10兆瓦背压式汽轮发电机组及相关公辅工程。 New

3 × 90 t / h high temperature and high pressure circulating fluidized bed boiler (Uses a) + 2 × B10 MW back pressure turbine generator and related public auxiliary works.

盐城经济技术开发区热电联产

项目 Yancheng Economic

Development Zone Thermoelectric Technology Cogeneration Project

苏环审﹝2015﹞138号


138 Number

盐城市经济技术开发区

Yancheng Economic and Technological Development Zone

21 co-gen

新建1×130t/h高温高压锅炉和1×15MW+1×9MW高温高压

背压式汽轮发电机组，并对已有2×75t/h锅炉的除尘、脱

硫和脱硝系统进行改造，最终形成3炉2机规模;其他公辅

工程作配套改扩建。 New 1 × 130t / h high temperature

and high pressure boiler and 1 × 15MW + 1 × 9MW high temperature and pressure back pressure turbine generator, and there are 2 × 75t / h boiler dust removal, desulfurization and denitrification system transformation, forming 3 scale furnace 2 machine; other auxiliary works well for supporting expansion.



江苏新海发电有限公司“上大压

小”扩建工程第二台百万机组

Jiangsu Xinhai Power "great pressure on small" second million-unit expansion project

苏环审﹝2015﹞137号


﹝ 2015 ﹞ 137

连云港市海州区西北边缘

地带 Haizhou District

Lianyungang northwest fringe

1030 Ultra 新建1X1030兆瓦超超临界燃煤发电机组 New 1X1030

MW ultra-supercritical coal-fired generating units

睢宁宝源新能源发电有限公司

热电联产项目 Sui Ning

Baoyuan New Energy Co., Ltd. Cogeneration Project

苏环审﹝2015﹞126号


126 Number

徐州市睢宁县桃园镇朱官

路 Xuzhou Suining Road,

Taoyuan Town official Zhu

21 co-gen

新建3×75吨/小时高温高压循环流化床锅炉+1×B9兆瓦

+1×B12兆瓦背压式汽轮发电机组及相关公辅工程。 New

3 × 75 tons / hour high temperature and high pressure circulating fluidized bed boiler + 1 × B9 MW + 1 × B12 MW back pressure turbine generator and related public auxiliary works.

连云港板桥工业园热电联产项

目 Lianyungang Banqiao

Industrial Park Cogeneration Project

苏环审﹝2015﹞112号


﹝ 2015 ﹞ 112

连云港市板桥工业园供热

工程厂区 Lianyungang

plant Banqiao Industrial Park Heating Project

50 co-gen

新建1台240吨/小时锅炉和2台25兆瓦抽背式汽轮发电机

组，对已投运的2台130吨/小时供热锅炉进行环保措施改

造。 New Taiwan 240 t / h boiler and two 25 MW turbine

generator for pumping back, have been put into operation 2 130 t / h boiler heating transformation of environmental protection measures.

江苏银珠化工集团有限公司洪

泽县西顺河片区热电联产项目

Jiangsu Hongze Yinzhu Chemical Group Co., Ltd. Shunhe West Area Cogeneration Project

苏环审﹝2015﹞96号


﹝ 2015 ﹞ 96

淮安市洪泽县西顺河镇

Hongze County, Huai'an City West-Town

30 co-gen

对现有2台90吨/小时高温高压循环流化床锅炉进行扩容改

造，使其单台容量达到130吨/小时，新增1台130吨/小时

高温高压循环流化床锅炉作为备用锅炉，新增2台15兆瓦

背压式汽轮发电机组，并对现有公辅工程进行增加和扩

建。 There are 2 sets of 90 tons / hour high-temperature

high-pressure circulating fluidized bed boiler expansion transformation to a single capacity of 130 t / h, the new station 1 130 t / h high temperature and high pressure circulating fluidized bed boiler as a backup boiler, Add two 15 MW backpressure steam turbine generator set, and existing public and auxiliary works to increase and expansion.



如东洋口环保热电有限公司二

期扩建工程项目 Yangkou

Environmental Thermal Power Co., Ltd. Phase II expansion project

苏环审﹝2015﹞91号


﹝ 2015 ﹞ 91

南通市如东县 Nantong

Rudong 50 co-gen

建设2×220吨/小时高温高压循环流化床燃煤锅炉和2×25

兆瓦背压式汽轮发电机组及对现有公辅工程进行增加和扩

建。 Construction of 2 × 220 t / h high temperature and

high pressure circulating fluidized bed coal-fired boilers and 2 × 25 MW back pressure turbine generator and auxiliary to the existing public works increase and expansion.

江苏滨海生活垃圾焚烧发电项

目 Jiangsu coastal garbage

incineration power generation projects

苏环审﹝2015﹞81号


﹝ 2015 ﹞ 81

盐城市滨海县天场镇海关

村 Yancheng Binhai

County day field Zhenhai Guan Cun

15 co-gen

建设2台400吨/天机械炉排焚烧炉，2台33.6吨/小时余热

锅炉，1台装机容量为15兆瓦的凝汽式汽轮发电机组主体

工程及垃圾接收、贮存与厂内输送系统、焚烧系统、烟气

处理系统、垃圾热能利用系统等公辅工程。 Construction

of two 400 tons / day of mechanical grate incinerator, two 33.6 t / h waste heat boiler, 1 installed capacity of 15 MW condensing steam turbine generator main project and garbage reception, storage and delivery system plant incineration systems, flue gas treatment system, waste heat utilization systems and other auxiliary works well.

江苏镇江燃气热电有限公司燃

机热电联产项目 Zhenjiang,

Jiangsu Gas Cogeneration Co., Ltd. Gas Turbine Cogeneration Project

苏环审﹝2015﹞80号


80 No.

镇江市丹徒工业园区

Zhenjiang Dantu Industrial Park

800 gas

新建2×400MW 级燃气-蒸汽联合循环热电联产机组及相

关配套辅助工程。 New 2 × 400MW class gas - steam

combined cycle cogeneration units and related ancillary works.

国电宿迁2×660MW机组工程

Guodian Suqian 2 × 660MW unit project

苏环审﹝2015﹞73号


73 No.

宿迁市宿城区洋北镇

Suqian City urban places Yang North Town

1320 Ultra

新建2×660MW燃煤发电机组和现有码头西侧扩建2000吨

级泊位4个及相关公辅工程。 New 2 × 660MW coal-fired

generating units and the expansion of the existing marina on the west side four 2000-ton berths and related public auxiliary works.

江苏华电如皋热电联产项目

Rugao, Jiangsu Huadian Cogeneration Project

苏环审﹝2015﹞63号


63 No.

如皋市如皋港港区内

Rugao Rugao Harbour Port

38 co-gen

新建3台220t/h高温高压煤粉锅炉（2用1备），配套1台

35MW抽背机组和1台3MW背压机组及相关公辅工程。 3

new sets of 220t / h pulverized coal boiler temperature and high pressure (2 with a prepared), supporting



Taiwan 35MW pumped back 3MW units and one back-pressure units and related public auxiliary works.

连云港亚邦供热有限公司热电

联产项目 Heating Co., Ltd.

Lianyungang Yabang Cogeneration Project

苏环审﹝2015﹞62号


62 No.

连云港市灌南县堆沟港化

工园区 Lianyungang

Guannan heap ditch Hong Kong Chemical Industry Park

30 co-gen

新建2×B15MW+3×130t/h燃煤热电联产机组及相关公辅

工程。 New 2 × B15MW + 3 × 130t / h coal-fired

cogeneration units and related public auxiliary works.

江苏华电句容二期

（2×1000MW）高效洁净超超

临界“上大压小”扩建工程

Jiangsu Huadian Jurong two (2 × 1000MW) clean and efficient ultra-supercritical "great pressure on small" expansion project

苏环审﹝2015﹞58号


58 No.

镇江市句容市下蜀镇桥头

临港工业集中区 Jurong

Town Bridge Xiashu Lingang Industrial Zone of Zhenjiang City

2000 Ultra 扩建2×1000MW燃煤发电机组及相关公辅工程。

Expansion 2 × 1000MW coal-fired generating units and related public auxiliary works.

中电投协鑫滨海新建（2×100

万千瓦）项目 China Power

Investment Corporation GCL Binhai New (2 × 100 MW) project

苏环审﹝2015﹞57号


57 No.

盐城市滨海县滨海港区

Yancheng Binhai County Binhai Port

0.2 co-gen

新建2×100万千瓦燃煤发电机组、大件码头一座及相关公

辅工程。 New 2 × 100 kilowatts coal-fired generating

units, a large marina and related public auxiliary works.

泗洪县生活垃圾焚烧发电项目

Sihong county solid waste incineration power generation projects

苏环审﹝2015﹞49号


﹝ 2015 ﹞ 49

宿迁市泗洪县青阳镇

Suqian Sihong qingyangzhen

7.5 co-gen

建设1台300t/d机械炉排焚烧炉、1台27t/h余热锅炉、1台

装机容量为7.5MW的纯凝发电机组主体工程及垃圾接

收、贮存与厂内输送系统、焚烧系统、烟气处理系统、垃

圾热能利用系统等公辅工程。 Construction of Taiwan

300t / d mechanical grate incinerator, 1 27t / h waste heat boiler, 1 station installed capacity of 7.5MW pure condensate turbine main works and garbage reception, storage and delivery system plant, incineration systems, smoke treatment system, waste heat utilization systems and other auxiliary works well.



国电电力高淳燃机热电联产项

目 GD Gaochun gas turbine

cogeneration project

苏环审﹝2015﹞29号


29 Hao

南京市高淳区淳溪镇

Gaochun Chun Town area

200 gas

新建2×100MW级6FA燃气-蒸汽联合循环热电联产机组及

相关配套辅助工程；配套建设“西线”和“环线”两条供热主

管道，西线全长约7.93km，环线全长约14.80km。 New

2 × 100MW class 6FA gas - steam combined cycle cogeneration units and related ancillary works; supporting the construction of "Western" and "loop" heating two main conduits, the west line is about 7.93km, total length of loop 14.80km.

睢宁宝源新能源发电有限公司

垃圾焚烧发电项目 Sui Ning

Baoyuan New Energy Co., garbage incineration power generation projects

苏环审﹝2015﹞27号


﹝ 2015 ﹞ 27

徐州市睢宁县桃园镇

Suining County, Xuzhou City, Taoyuan Town

12 co-gen

建设2台350t/d机械炉排焚烧炉、2台29.2t/h余热锅炉、1

台12MW汽轮发电机组主体工程及垃圾接收、贮存与厂内

输送系统、焚烧系统、烟气处理系统、垃圾热能利用系统

等公辅工程。 Construction of 2 350t / d mechanical

grate incinerator, 2 29.2t / h waste heat boiler, 1 × 12MW turbine generator main project and garbage reception, storage and delivery system plant, incineration system, flue gas treatment system, waste heat utilization system, public auxiliary projects.

灌云县临港产业区燃煤热电联

产项目 Guanyun Lingang

Industrial Zone coal-fired cogeneration project

苏环审﹝2015﹞13号


13 Hao

连云港市灌云县临港产业

区 Guanyun Lingang

Industrial Zone of Lianyungang City

50 co-gen

建设2×B25MW+3×220t/h燃煤热电联产机组及相关公辅

工程。 Construction of 2 × B25MW + 3 × 220t / h coal-

fired cogeneration units and related public auxiliary works.

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Documents

Water Energy Nexus of China’s Recent Energy Development … · Case study of Jiangsu UK Foreign and Commonwealth Office, China 21 April 2016 Draft report on Jiangsu Coal Power Water