Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV ... · Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV in UK Distribution Networks” UoM_WISE-PV_WP1.1-1.2_FinalReport_v01

Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV in UK Distribution Networks”

UoM_WISE-PV_WP1.1-1.2_FinalReport_v01

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Copyright © 2017 S. Alnaser & L. Ochoa - The University of Manchester

Title: Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV in UK Distribution Networks”

Synopsis: This document presents the methodologies and the key findings corresponding to the work carried out by Work Packages 1.1 and 1.2 of the EPSRC-funded project “Whole system Impacts and Socio-economics of wide scale PV integration (WISE PV)”. This document presents the technical and economic impacts of small-to-medium scale PV systems connected to distribution networks by considering MV/LV integrated models. The benefits of adopting different decentralised and centralised Smart Grid control strategies including residential storage.

Document ID: UoM_WISE-PV_WP1.1-1.2_FinalReport_v01 Date: 29

th Jan 2017

Prepared For: Dr Joseph Mutale Principal Investigator of the EPSRC WISE PV Project School of Electrical and Electronic Engineering The University of Manchester Sackville Street, M13 9PL, UK

Prepared By: Dr Sahban Alnaser The University of Manchester Sackville Street, Manchester M13 9PL, UK

Revised By: Prof Luis(Nando) Ochoa The University of Manchester Sackville Street, Manchester M13 9PL, UK

Contacts: Prof Luis(Nando) Ochoa +44 (0)161 306 4819 [email protected] Dr Sahban Alnaser +44 (0)744 054 7634 [email protected]



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Executive Summary

This report presents the methodologies and the key findings corresponding to the work carried out by Work Packages (WP) 1.1 and 1.2 part of the EPSRC-funded project “Whole system Impacts and Socio-economics of wide scale PV integration (WISE PV)” (grant reference EP/K022229/1 [1]). WP 1.1 and WP 1.2 aim to quantify the technical and economic impacts of small-to-medium scale PV systems connected to distribution networks by considering integrated models of low (400 V line-to-line) and medium (6.6 or 11 kV line-to-line) voltage networks (LV and MV, respectively). Two types of MV/LV networks are investigated: urban and rural. Two demand scenarios are taken into account: the low demand scenario assumes the existing demand level while the high demand scenario also considers that 50% of residential customers have electrical vehicles and electrical heat pumps. For the urban network, PV systems are installed by residential and commercial/industrial customers while the rural network considers ground mounted and residential PV systems. For each network and demand scenario, different PV penetrations are investigated considering five control strategies: no control (business as usual); export limits and PV curtailment; residential storage for the benefit of customers; export limits, residential storage, and PV curtailment; and optimal centralised control (including residential storage, on-load tap changers, and PV curtailment). PV curtailment is used as the last resort to guarantee network voltage and thermal constraints are satisfied. Uncertainties of demand and generation are catered for via Monte Carlo simulations carried out per network, per demand scenario, per PV penetration level, and per control strategy. The key findings of this report can be summarized as follows: PV and low demand without control. In the urban network, high PV penetrations (>40%) will require reinforcing LV feeders (due to voltage rise issues caused by residential PV) but will not affect distribution substations or MV networks. Conversely, in the rural network, residential PV systems are harmless (due to the small numbers). However, ground mounted PV capacity exceeding the peak demand would trigger MV investments. PV and high demand without control. In the urban network, high demand due to electric vehicles and electric heat pumps triggers LV and MV network investments. This, in turn, allows much more PV capacity (beyond 80% of PV penetration LV feeders need to be reinforced). In the rural network, the impacts of high demand are almost negligible; investments are still driven due to ground mounted PV. Export limits and PV curtailment. Although the adequate use of export limits, and hence, PV curtailment, allows keeping voltages and power flows within the limits, thus avoiding network reinforcements, it was found that for high PV penetrations, the rural network will have much larger volumes of energy curtailment than the urban network due to the more severe voltage rise issues. Residential storage benefiting customers. The control adopted by residential storage aims to reduce electricity bills and as such harvests as much as possible excess PV generation to then locally supply the demand at night (no PV curtailment is applied). Considering commercially available storage sizes it was found that customers can indeed significantly benefit (increasing self-sufficiency, i.e., use of local PV generation). However, these devices are likely to be fully charged before critical periods around noon (maximum PV generation and minimum demand). Therefore, impacts from residential PV will still be seen on the networks (particularly urban). However, opportunities exist for decentralised control strategies that benefit customers while reducing network impacts. Optimal centralised control. A DNO-driven optimal centralised Smart Grid scheme able to control PV systems, residential storage facilities, and on-load tap changers was investigated to quantify –in ideal circumstances– how much curtailment could be reduced whilst solving all network issues. While it was possible to reach negligible levels of curtailment, the self-sufficiency of residential customers was reduced as their storage facilities were only absorbing excess generation to support the network. Although this demonstrates the advantages for DNOs when using all the flexible elements in the network, improvements should consider the benefits to customers (self-sufficiency).



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Table of Contents

Executive Summary ............................................................................................................................... 2

1 Introduction ......................................................................................................................... 4

2 Scenarios and Methodologies ........................................................................................... 5

2.1 Scenarios of PV and Demand .............................................................................................. 5

2.2 Probabilistic Impact Assessment .......................................................................................... 5

2.3 Business as Usual Strategy (Network Reinforcements) ....................................................... 6

2.4 Smart Grid Control Strategies ............................................................................................... 7 2.4.1 Urban Networks .................................................................................................................... 7 2.4.2 Rural Networks ................................................................................................................... 11

3 Case Study: Urban MV/LV Distribution Network ........................................................... 12

3.1 Network Description and Modelling .................................................................................... 12

3.2 Low Demand Scenario ....................................................................................................... 13 3.2.1 Business as Usual Strategy (Network Reinforcements) ..................................................... 13 3.2.2 Smart Grid Control Strategies ............................................................................................. 15

3.3 High Demand Scenario ....................................................................................................... 20

4 Case Study: Rural MV/LV Distribution Network ............................................................ 23

4.1 Network Description and Modelling .................................................................................... 23

4.2 Low Demand Scenarios ...................................................................................................... 24 4.2.1 Business as Usual Strategy (Network Reinforcements) ..................................................... 24 4.2.2 Smart Grid Control Strategies ............................................................................................. 25

4.3 High Demand Scenario ....................................................................................................... 26

5 Case Study: Extrapolation of results to the North West of England ........................... 28

5.1 Future PV Uptake ............................................................................................................... 28

5.2 Reinforcement Costs per Network Type ............................................................................. 29

5.3 Extrapolation to the ENWL Area ......................................................................................... 30

6 Conclusions ...................................................................................................................... 33

7 References ......................................................................................................................... 36



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1 Introduction

This report presents the methodologies and the key findings corresponding to the work carried out by the Work Packages 1.1 and 1.2 part of the EPSRC-funded project “Whole system Impacts and Socio-economics of wide scale PV integration (WISE PV)” (grant reference EP/K022229/1 [1]). The WP 1.1 and WP 1.2 aim to quantify the technical and economic impacts of small to medium scale PV systems connected to distribution networks by considering integrated models of low and medium voltage networks. This work also aims to quantify the benefits of adopting different Smart Grid control approaches to increase the hosting capacity of distribution networks without the need for traditional network reinforcements. The adopted control approaches include the use of voltage control devices, storage facilities, and energy curtailment. The results are transferred to the Work Stream 2 (WS2) of the WISE PV project to perform the PV Life Cycle Assessment (LCA) study. The main components of the developed framework to achieve the objectives of WP1.1 and WP1.2 are presented in Figure 1. The PV impacts are found for different penetration levels of residential, commercial/industrial and ground mounted PV farms on real distribution networks. To cater for different network characteristics, the impacts are quantified for both real MV/LV urban and rural distribution networks in terms of network investments, and energy curtailment. Given that the planning horizon of the WISE PV project is for the next twenty years (2015-2035), this work also studies the impact by considering a scenario of high demand that corresponds to a 50% penetration level of electric vehicles (EVs) and electrical heat pumps (i.e., 50% of residential customers have EVs and electrical heat pump). The impacts are first determined for the Business as Usual scenario without any form of control (no control). Then, the benefits and the impacts of different Smart Grid control solutions are explored starting from the use of energy curtailment to manage network constraints to the adoption of a comprehensive optimization-based control approach to control multiple network elements (i.e., PV, storage facilities, and voltage control devices). Furthermore, the benefits of storage facilities are explored under two control strategies; controlling storage for the benefit of customers and controlling storage for the benefit of Distribution Network Operators (DNOs).

Figure 1. Framework of WP 1.1 and WP 1.2

The rest of this report is structured as follows: Chapter 2 presents the methodologies to assess the impacts of PV on urban and rural distribution networks and the benefits from different Smart Grid control strategies. The application of the methodologies to urban and rural distribution networks are presented in Chapters 3 and 4, respectively. Chapter 5 presents the PV impacts on the whole Electricity Northwest License (ENWL) area by extrapolating the results for the urban and rural networks. Finally, the summary and conclusions of this work are presented in Chapter 6.

Low Demand Scenario High Demand Scenario

Residential storage benefiting customers

Export limits and PV curtailment

Optimal control

286

Representative Networks

Network investments

No control

Export limits, residential storage, and PV curtailment

Energy curtailment

Customer benefits

Centralised (Optimisation-based)

Decentralised (Rule-based)



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2 Scenarios and Methodologies

This chapter provides the assumptions and the key aspects of the developed methodologies to achieve the objectives of this work. First, the scenarios of PV and demand used in the impact studies are presented. The adopted methodologies to assess the technical and economic impacts of PV systems are then provided. Finally, this chapter presents the adopted models of Smart Grid solutions to facilitate the connections of PV systems.

2.1 Scenarios of PV and Demand

Scenarios of future PV installations are typically produced for large areas – such as the UK-wide projections from the Department of Energy & Climate Change (DECC) [2]. Many studies in the literature use those scenarios to understand the PV impacts on distribution networks by averaging the future PV installed capacity across customers [3]. This approach, however, results in small, distributed PV installations that are unlikely trigger impacts on the network under study; clusters of relatively large PV installations that normally drive network reinforcements will not be captured. To avoid underestimating PV impacts on distribution networks, this work carries out an assessment for different PV penetration levels by considering representative networks (i.e., rural and urban MV/LV distribution networks). Once the impacts are defined for each penetration level and for each network, they can be extrapolated across the region or DNO license area by matching the total PV installed capacity to that of the PV scenario to be considered for that area. Within the WISE PV project, PV scenarios were produced by Work Stream 2 (WS2) for each of the DNO license areas. The PV impacts are assessed for different penetration levels of residential, commercial/industrial and ground mounted PV farms. The adopted PV technologies and scenarios of PV penetrations are determined according to the network type (urban/rural) and based on the discussions with members of the WS2 of the WISE PV project (responsible also for the LCA study). For the urban network, PV impacts are found due to the connection of both residential and commercial/industrial PV systems (i.e., it is not feasible to connect ground mounted PV farms in urban areas). In this respect, five PV penetration levels are explored. Each of them consists of both residential and commercial/industrial PV. The penetration of residential PV refers to the percentage of residential customers with PV systems (e.g., 60% penetration level means that 60% of the dwellings have PV systems). The penetration level for residential PV (in the adopted five penetration levels) is increased in steps of 20% until each residential customer has a PV system (i.e., 100% penetration). Since commercial/industrial demand include loads from different categories (e.g., school, hospital), it is not accurate to relate the PV penetration to the number of customers likewise the residential PV. Therefore, the penetration for commercial/industrial PV is defined with respect to the peak demand of commercial/industrial loads. The penetration level for commercial/industrial PV (in the adopted five penetration levels) is increased in steps of 20% of the peak demand of commercial/industrial load. For the rural network, PV impacts are found for different penetration levels of both residential PV systems and ground mounted PV farms directly connected to the MV network. In this respect, 15 penetration levels are studied. Three residential PV penetration levels are adopted (i.e., 0 %, 50%, 100% of residential customers with PV systems). Each residential PV penetration is studied in combination with five ground mounted PV penetrations (0%, 50%, 100%, 150%, and 200% of the network peak demand). The impact assessment for both networks is performed for both low and high demand scenarios. At the low demand scenario, the existing demand level in the network is adopted. The high demand scenario corresponds to a 50% penetration level of electrical vehicles and electrical heat pumps for residential customers.

2.2 Probabilistic Impact Assessment

The probabilistic impact assessment methodology presented in [4] for residential low voltage networks is adopted and extended to understand the technical impacts of PV on integrated MV/LV urban and



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rural distribution networks for each scenario of PV penetration and demand. To cater for the uncertainties in the inputs, the probabilistic impact assessment performs multiple simulations each considering different locational and behavioural aspects of PV and demand. To understand the need for network reinforcements, the hourly average loading of network assets and voltage issues are both assessed. Compliance with the BS EN 50160 standard is used to quantify voltage issues (in particular voltage rise issue due to reverse power flows). The voltages limits are +10%/-6% for LV customers (nominal voltage 230 V line-to-neutral) and +/-6% for MV customers (nominal voltage 6.6 or 11kV line-to-line). To assess the performance of different control strategies from the perspective of the customers, two energy related metrics are also used: the volume of curtailed energy and self-sufficiency (local use of the energy produced by the PV system). The self-sufficiency metric will be important when assessing the implications of using residential storage facilities to support the network as the benefits to customers are expected to be reduced. To reduce the computational burden of the stochastic and high-granular (10 min) analyses carried out in this report the number of realistic PV generation profiles had to be limited to just a few. Using actual annual PV production provided by The University of Sheffield (part of the micro-generation dataset [5]) from 204 different locations across the UK (recorded at 10 or 30 min intervals in 2012), it was found that the daily energy production of a PV system ranges from 1 to 7 kWh/kWp (with an average of 3.1 kWh/kWp) and that 70% of the total annual PV energy system is produced from April to October. Based on this, and for simplicity, two main seasons were considered: summer (Apr-Oct) and winter (Nov-Mar). To capture PV variations throughout these seasons, six daily normalised PV profile types were adopted to cover minimum and maximum energy content ([1, 2], [2, 3], …, [6, 7] kWh). The actual data (10 min only) was normalised and each day was allocated to each of the six PV profile types. The resulting frequency of occurrence for each PV profile type is presented in Table 1.

Table 1: Probability of daily energy production throughout the year

PV Profile Type

Daily energy content

(kWh/kWp)

Summer (Apr-Oct)

PV Profile

Type

Daily energy content

(kWh/kWp)

Winter (Nov-Mar)

Type-1 1-2 6% Type-7 1-2 21%

Type-2 2-3 9% Type-8 2-3 13%

Type-3 3-4 10% Type-9 3-4 11%

Type-4 4-5 11% Type-10 4-5 3%

Type-5 5-6 9% Type-11 5-6 1%

Type-6 6-7 5% Type-12 6-7 1%

2.3 Business as Usual Strategy (Network Reinforcements)

This section presents the adopted methodology to determine the reinforcements in terms of MV and LV lines and MV/LV transformers required to solve network issues resulting from the connections of PV without any form of control (Business As Usual). The new network assets are determined for each PV penetration level. To do so, the methodology for network reinforcements used in the Low Carbon Networks Fund Tier 1 project “LV Network Solutions” [6] is adapted for the integrated MV/LV distribution networks. These network reinforcements consider the replacement of conductors with bigger cross sections and transformers with larger capacities. To minimize the investment cost, the replacement is done segment by segment until network issues are solved. Congestion issues are first solved and then reinforcements are used to keep voltages within limits. Since reinforcements of MV lines will affect voltages at LV networks, the additional MV lines are first determined in order to keep voltages at MV within network constraints. Then, new LV lines are defined to ensure that voltages for residential customers are within limits. The main steps of the adopted approach are summarised as follows:

1. Run a power flow and check for thermal overloading at LV, MV/LV transformers and MV lines.



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2. The MV and LV segments with thermal overloading are upgraded with the minimum standard conductor size adequate to meet the required loading level. Likewise, MV/LV transformers are also upgraded (considering standard sizes) to meet the required loading level.

3. To keep voltages at MV within limits, the following process is adopted:

a. Run a power flow and check for voltages at the LV side of MV/LV transformers. b. Determine the worst location with highest voltage rise/drop. c. Identify the path between the primary substation and the worst location. d. Divide the path into equally small segment starting from the primary substation. e. Replace a small segment of the identified path using the next larger conductor size. f. Run a power flow and check for voltages, if voltage problems are not solved and there are

more cable sizes that can be used go to (e). Else, identify the next segments and go to (e). g. Repeat (e) and (f) until voltage issues are solved.

4. Once the new MV lines are determined, the reinforcements for the LV lines can be identified to

keep customer voltages within limits. The process described above (a-g) is repeated. By following the above approach, network reinforcements are found for each simulation within each scenario of PV penetration and demand. To avoid overinvestments, the selected reinforcement for each scenario is the one that addresses the issues of 95% of the corresponding simulations. In the case of PV impacts when considering the high demand scenario, it is important first to understand the network reinforcements required to cope with load growth without PV. To do so, the above approach is adopted to determine network reinforcements due to EVs and electric heat pumps (running multiple simulations to cater for locational and behavioural uncertainties). Once the new network at the high demand scenario is defined, network investments required due to PV deployments could be then determined for different PV scenarios (penetration levels).

2.4 Smart Grid Control Strategies

This section presents the philosophy behind the adopted Smart Grid control strategies to manage network constraints in real time reducing the need for network reinforcements. In this respect, the solutions are classified according to network types (urban/rural) so as to capture their particular characteristics and the corresponding drivers for network investments. In the urban network, with relatively high density of residential customers, PV impacts are expected to occur first closer to those customers. Therefore, adequate control strategies should be in place to solve network issues at the LV level and cater for the exported power from houses with PV. On the other hand, rural networks, with a low density of residential customers, are expected to have mostly ground mounted PV systems. Consequently, the corresponding control strategies should cater for the impacts resulting from larger PV installations connected at higher voltages.

2.4.1 Urban Networks

Export Limits and PV Curtailment This control approach aims to solve network issues locally at the house level by using rules and without having extensive monitoring and communication infrastructures (as shown in Figure 2). Since network issues will occur due to large volumes of export power from houses with PV, this control strategy curtails the exports considering a pre-defined limit. Figure 3 shows an example of the net demand for a house with PV during a high PV generation day. The coincidence of high PV production and minimum demand (particularly during periods around noon) results in power exported to the LV network which may lead to voltage and/or thermal issues. Once the excess generation (generation minus demand) exceeds the pre-defined export power limit, generation is curtailed such that the excess is kept below the limit.



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Figure 2. Architecture of the decentralised control approach

Figure 3. Example of net demand for a residential house with PV (high PV generation)

To ensure fairness among houses with PV, this limit must be defined as a percentage of the PV rating so that all PV exports are curtailed in the same proportion. Furthermore, the limit should be determined for each PV penetration level (in this case, percentage of houses with PV) to avoid the adoption of unnecessary conservative limits when network issues are not severe (low PV penetrations). In addition, given the decentralised nature of this control strategy, the export power limit should also ensure that network constraints are effectively managed for different PV locations and ratings at each penetration level without the need of updating the defined limit. Here, the export power limit is determined ‘off-line’ for each PV penetration level considering multiple simulations with different PV locations and ratings (maximum generation and after diversity minimum demand) and using a three-phase unbalanced AC Optimal Power Flow (OPF). To avoid an over conservative limit, the selected value for each PV penetration is the one that addresses the issues of 95% of the corresponding simulations. The approach proposed in [7] is extended to a three-phase AC OPF. To determine the optimal export

power limit 𝑆𝑝𝑙𝑖𝑚𝑖𝑡 for houses with PV (set H indexed by h), the objective function of the AC OPF is

formulated to maximize export power such that network constraints are managed within limits as given

in (1).

𝒎𝒂𝒙: ∑ 𝑺𝒑𝒍𝒊𝒎𝒊𝒕 × 𝒑𝒉𝒓𝒂𝒕𝒊𝒏𝒈

𝒉∈𝑯

(1)

where 𝑝ℎ𝑟𝑎𝑡𝑖𝑛𝑔

is the rating of PV system and 𝑆𝑝𝑙𝑖𝑚𝑖𝑡 is within zero and one.

This objective is subject to the typical AC OPF constraints (i.e., Kirchhoff’s voltage and current laws)

as well as voltage and thermal limits. For simplicity, the implemented AC OPF neglects the mutual

effects between phases. The problem is formulated in the modelling language AIMMS [8] and solved

using the non-linear programming solver CONOPT 3.14V.

It is important to highlight that the adoption of export power limits has taken place in different countries

HC HC

HC

HC

HC: House Controller PV system

HC

Export power limit Generation curtailment

Demand Demand



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around the world. For instance, in Germany, export power from customers with installed PV capacities

smaller than 30 kWp is limited to 70% of their installed capacity [9]. The adopted approach in this work

to define the limit is innovative compared to previous studies in the literature that define the limits

according to the impacts on customers (energy curtailment) neglecting the impacts on distribution

networks [10-12]. Although an optimization-based methodology was presented in [13] to define the

export limits able to effectively solving network issues, the problem formulation neglects the thermal

constraints of network assets that may be an issue at high PV penetration.

Residential Storage Benefiting Customers This control strategy aims to understand the extent to which the adoption of residential storage facilities in combination with PV systems reduces voltage and thermal issues. The residential storage control approaches adopted by manufacturers aim to maximise residential customers’ benefits (i.e., reducing electricity bills) by harvesting as much as possible excess PV generation to then locally supply the demand at night (reducing imported energy from the grid). However, manufacturers do not provide a detailed control approach (charging and discharging operations). This work adopts the following decentralised time-based control approach (using predefined periods of daily charging and discharging operations) for each residential storage facility:

1. Charging periods: Charging occurs during the daylight, as soon PV production is higher than demand (excess generation). Charging stops when the PV production drops below the demand level. The charging continues until the stored energy reaches the energy capacity of the storage facility. At no point the battery is discharged in this period.

2. Discharging periods: Discharging occurs during evening and night periods. The stored energy throughout the day is used to supply demand. The discharging rates are determined based on the demand level. To preserve the lifespan of the battery, the stored energy is not allowed to fall below 20% percentage of the energy capacity. At no point the battery is charged in this period.

It is important to highlight that PV subsidies around the world are expected to be reduced significantly or even disappear due to the rapid cost reduction in solar PV systems (i.e., about 70% between 2011-2015) [14]. The drop in PV cost will also make energy produced from PV cheaper than retail prices in many countries by 2020 (i.e., grid parity) [15]. In this context, residential customers are likely to explore options to make the most of their PV systems so that their electricity bills could be reduced even further [16]. One potential solution is the use of battery storage systems [17]. Export Limits, Residential Storage and PV Curtailment Residential storage installations will be sized in a cost-effective way, i.e., trying to make the most of the corresponding PV installation whilst not oversizing the storage capacity. To achieve this, customers are likely to determine their storage needs based on their average daily PV generation, the PV rated capacity, and the commercially available options. However, such a storage facility may not be large enough to store excess energy during a day with high PV generation and, therefore, network issues could still be seen [18, 19]. This is demonstrated in Figure 4. If the average excess generation (red area) is absorbed by the storage facility, network issues will not occur. Nonetheless, during high PV generation (blue area), the storage facility may become fully charged earlier in the day, even before noon. This means that any excess generation after this time will be exported to the grid which may result in voltage rise and/or congestion issues. To avoid network issues, the use of export limits (as defined for the control strategy “Export Limits and PV Curtailment”) will be explored in combination with residential storage and PV curtailment. A new decentralised control strategy is adopted in which the storage facility only charges excess generation when PV exports exceed the pre-defined limit. If the storage system reaches its maximum energy capacity, or the power required to solve congestion (or voltage rise) is larger than the power rating of storage, generation curtailment is applied as last resort.



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Although this control strategy helps mitigating PV impacts, it will affect customers as it will reduce self-sufficiency. Therefore, it is important to quantify this metric.

Figure 4. Residential storage benefiting customers

Optimal Centralised Control A DNO-driven optimal centralised Smart Grid scheme able to control PV systems, residential storage facilities, and on-load tap changers was investigated to quantify –in ideal circumstances– how much curtailment could be reduced whilst solving all network issues. The Network Management System (NMS) proposed in [20] is extended and adapted to actively manage network constraints. This involves the expansion of the AC OPF into three-phase unbalanced AC OPF so as to simultaneously model medium and low voltage (MV-LV) networks. The full AC unbalanced OPF can produce set points for PV systems, storage facilities and voltage control devices in distribution substations as illustrated in Figure 5. The adopted AC OPF is a mono-period bi-level AC OPF. It is mono-period because it only deals with a single network state (obtained from the distribution network analysis software package OpenDSS [21]). It is bi-level because the optimisation is carried out in two levels (Level 1 and Level 2) as given in (2) and (3). The optimization carried out in Level 1 minimises the total volume of curtailed energy while keeping network constraints within limits. To guarantee that the storage devices only store the excess of PV generation determined in Level 1 (set N indexed by n), Level 2 is introduced to minimise the

energy to be stored by each residential facility 𝐸𝑠𝑡𝑠𝑡𝑜𝑟𝑒 (set ST indexed by st).

𝑚𝑖𝑛: ∑(1 − 𝑆𝑃𝑛)𝑝𝑛𝑃𝑉

𝑛∈𝑁

(2)

𝒎𝒊𝒏: ∑ 𝑬𝒔𝒕𝒔𝒕𝒐𝒓𝒆

𝒔𝒕∈𝑺𝑻

(3)

where 𝑆𝑃𝑛 and 𝑝𝑛𝑃𝑉 represent each house PV set point and the PV available resource, respectively.

High PV generation

Demand

Average PV generation

Storage is full before noon

Demand



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Figure 5. Optimal centralised control strategy for MV-LV networks

2.4.2 Rural Networks

Due to the small number of residential customers in the adopted rural network, the LV networks are not modelled and, therefore, the residential customers are aggregated at the secondary side of the distribution transformers. Although the decentralised approaches are not investigated (as the LV networks are not modelled), two optimal centralised control strategies are implemented to cater for the impacts at MV level and distribution transformers whilst reducing energy curtailment. Optimal Centralised Curtailment This control strategy solves network issues by the optimal management of both ground-mounted (directly connected to the MV network) and residential PV systems. A single-phase AC OPF is adopted since the LV networks are not modelled. The AC OPF is formulated to minimise PV curtailment from ground-mounted and residential PV systems, as given in (4). Network problems will be first solved by reducing power injections from ground-mounted PV (main cause of voltage rise and/or congestion issues). If network constraints are not effectively managed, then residential PV will be subject to curtailment.

𝒎𝒊𝒏: ∑(𝟏 − 𝑺𝑷𝒈)𝒑𝒈𝑷𝑽

𝒈∈𝑮

+ 𝝆 ∑(𝟏 − 𝑺𝑷𝒏)𝒑𝒏𝑷𝑽

𝒏∈𝑵

(4)

where 𝑆𝑃𝑔 and 𝑝𝑔𝑃𝑉 represent each ground-mounted PV set point (set G indexed by g) and the PV

available resource, respectively. The weighting coefficient 𝜌 is a constant selected to allow curtailing first the PV power output from ground-mounted PV (𝜌>1). Optimal Centralised OLTC and Curtailment To further reduce the impacts on voltages, and, hence, the volume of curtailment, the tap ratio, τl, for

OLTC-fitted transformers (lines and transformers are all represented by the set 𝐿 indexed by 𝑙) are

also controlled (in addition to PV systems) considering its capabilities [τl−, τl

+].

𝝉𝒍− ≤ 𝝉𝒍 ≤ 𝝉𝒍

+; ∀ 𝒍 (5)

Centralised controller



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3 Case Study: Urban MV/LV Distribution Network

This chapter presents the impacts of PV systems on a real MV/LV network. The impacts are found due to the connection of both residential and commercial/industrial PV systems. Five PV penetration levels and two demand scenarios (low and high) are taken into account. The low demand scenario considers the existing demand level whilst the high demand scenario considers that 50% of residential customers have electrical vehicles and electrical heat pumps. The analysis is carried out with each of five control strategies presented in Chapter 2: no control (business as usual); export limits and PV curtailment; residential storage for the benefit of customers; export limits, residential storage, and PV curtailment; and optimal centralised control (including residential storage, on-load tap changers, and PV curtailment).

3.1 Network Description and Modelling

To understand the impacts of PV on an integrated MV/LV urban network, models for both MV and LV networks are required. For this purpose, a real MV (6.6 kV) urban network from the North West of England, made available by the Low Carbon Networks Fund project “Smart Street” run by Electricity North West Limited, was modelled [22]. The corresponding single line representation is given in Figure 6. The network is supplied by two 23 MVA, 33/6.6 kV transformers. The fourteen 6.6 kV feeders supply power to 12,000 and 1,175 residential and commercial/industrial customers, respectively, through 63 MV/LV transformers with capacities ranging between 315 and 1,500 kVA. The lengths of the 6.6 kV feeders and the corresponding number of distribution transformers are given in Figure 7. It is worth highlighting that all the distribution transformers are within a distance of 5 km from the primary substation.

Figure 6. Tree representation of a real UK MV/LV urban network

(a) (b)

Figure 7. (a) Length of each 6.6 kV feeder and (b) number of MV/LV transformers.

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Dis

tance (

km

)

Feeder

0

2

4

6

8

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14

No

. tr

ansfo

rme

rs

Feeder



29th Jan 2017

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To reduce the computational burden of the stochastic and highly-granular (10 min) analyses carried out in this report, the MV network model is limited to a single 6.6 kV feeder. The feeder with more distribution transformers (i.e., more customers) is selected. The main characteristics of this feeder are presented in Table 2. To model the LV networks, 10 real underground residential LV feeders from the North West of England, made available by the Low Carbon Networks Fund project “Low Voltage Network Solutions” run by Electricity North West Limited [4], are integrated to the MV feeder. These LV feeders are connected to each of the 9 MV/LV transformers such that the corresponding number of residential customers is approximately matched (e.g., a distribution transformer with 100 customers will have a mix of LV feeders that add up to a similar value). Since the available LV feeders are purely residential, it is still needed to model commercial/industrial customers. For simplicity, the aggregated commercial/industrial load at each distribution transformer is connected to the LV busbar and defined based on the number of customers. To model residential load profiles, the tool provided by the Centre of Renewable Energy System Technology (CREST) [23] is adopted. The load profiles for Industrial/commercial customers are obtained from ELEXON-based profiles [24].

Table 2: Characteristics of the integrated MV/LV urban network

Peak demand (winter) 3 MVA

Minimum demand (summer) 1.2 MVA

No. of distribution transformers 9

Distribution transformer capacities 315 to 1000 kVA

Total capacity of distribution transformers 5.5 MVA

No. of residential customers 1,373

No. of residential customers per distribution transformer 70 to 250

No. of commercial/industrial customers 212

No. of commercial/industrial customer per distribution transformer 12 to 39

Total length of MV lines 5 km

Total length of LV lines 19 km

Length of LV lines per distribution transformer 1 to 3.4 km

3.2 Low Demand Scenario

3.2.1 Business as Usual Strategy (Network Reinforcements)

This section presents the PV impacts on the urban MV/LV distribution network. The impacts are assessed for five PV penetration levels each with both residential and commercial/industrial PV. The penetration of residential PV refers to the percentage of residential customers with PV systems (e.g., 60% penetration level means that 60% of the dwellings install PV systems). The penetration level for residential PV (in the adopted five penetration levels) is increased in steps of 20% until each residential customer has a PV system (i.e., 100% penetration). Since commercial/industrial demand include loads from different categories (e.g., schools, hospitals), it is not accurate to relate the PV penetration to the number of customers as in the residential PV case. Therefore, the penetration for commercial/industrial PV is defined with respect to the peak demand of commercial/industrial loads. The penetration level for commercial/industrial PV (in the adopted five penetration levels) is increased in steps of 20% of the peak demand of commercial/industrial load.



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To cater for the uncertainties related to demand and PV generation behaviour, capacity and location of residential PV systems, a Monte Carlo-based quantification of impacts is carried out. In this respect, 100 simulations are considered for each penetration level. For simplicity, the industrial/commercial load profiles and the sizes of corresponding PV systems are fixed throughout the Monte Carlo analysis (PV profiles still vary). For illustration purposes, the total PV capacity at each of the five PV penetration levels is presented in Table 3 in MW and as percentage of the overall peak demand (3 MW). It considers an average residential PV installation of 3.06 kW (based on UK statistical data of PV installations [25]) as well as industrial/commercial PV installations (based on the individual peak demand). The resulting total PV installed capacity ranges from a relatively small 1.2 MW (41% of the total peak demand) to a value more than 5 times higher, 6.2 MW (207% of the total peak demand).

Table 3: Illustration of PV installed capacity for the MV/LV urban feeder per penetration level

Penetration Level

Residential PV Commercial/Industrial PV Total PV Capacity

% of customers with PV

MW % of individual peak demand

MW MW % of peak demand

1 20 0.8 20 0.4 1.2 41

2 40 1.7 40 0.8 2.5 83

3 60 2.5 60 1.2 3.7 124

4 80 3.4 80 1.6 5.0 165

5 100 4.2 100 2 6.2 207

(a) (b)

Figure 8. No-control strategy: Impact assessment of PV on (a) voltage (b) loading of LV lines. The bottom of the box is the 25

th percentile of the simulations. The top of the box is the 75

th

percentile of the simulations. The horizontal bold line inside the box is the median (50th

percentile of the simulations).

To understand the need for network reinforcements, voltage issues and the hourly average loading of network assets (LV lines, distribution transformers and MV lines) are assessed also using the Monte Carlo analysis. Since network reinforcements would be triggered by any voltage exceeding 1.10 pu (statutory upper limit), it is important to quantify the maximum voltage across the residential customers. The results for all the simulations are presented using boxplots in Figure 8 (a). PV penetration level 3 (60% residential PV and 60% commercial/industrial PV - as in Table 3) was found to be the first with many simulations exceeding the upper limit (with a median close to 1.1 p.u.). The frequency and magnitude

1.02

1.06

1.10

1.14

1.18

1 2 3 4 5

PV Penetration

Max Voltage (p.u.)

0.40

0.60

0.80

1.00

1.20

1 2 3 4 5

PV Penetration

Max LV Loading (p.u.)



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of voltage issues increase significantly at higher PV penetrations. For example, the median of the maximum voltage at PV penetration level 5 is 1.14 p.u. The maximum loading level across the LV lines was also found per simulation and the results are presented using boxplots in Figure 8 (b). It can be seen that median of the loading level for all the PV penetrations is below 100% (i.e., in most cases PV does not present congestion issues in LV). However, it is important to highlight that the 100% loading level is reached for about 30% of the simulations at PV penetration level 5. It was also found that PV does not present congestion issues in distribution transformers and MV lines. The loading levels of MV/LV transformers and MV lines are within their thermal limits for all the simulations at each penetration level. To solve LV issues resulting from the connections of PV without any form of control (Business As Usual), the methodology presented in Section 2.3 is used to determine the required reinforced LV lines. The reinforced LV lines are determined for each PV penetration level. To allow extrapolating the results to larger areas, the reinforced LV lines are expressed as percentage of the total LV network lengths. It is worth highlighting that the selected reinforcement is the one that addresses the issues of 95% of the corresponding simulations (so as to avoid outliers). Figure 9 shows that LV network reinforcements are needed at PV penetration level 3 to address voltage rise issues of 95% of the simulations (Figure 8 (a)). The required reinforced LV lines are increased at higher PV penetrations as voltage issues become more severe. For example, 53% of the LV lines should be reinforced at PV penetration level 5 (100% of residential PV and 100% of commercial/industrial PV - as in Table 3). Since PV does not present congestion issues in distribution transformers and MV lines for this urban network, there is no need to upgrade the MV networks and distribution transformers.

Figure 9. Business As Usual: LV network reinforcements (no control) that address 95% of the simulations at each PV penetration level

3.2.2 Smart Grid Control Strategies

Export limits and PV curtailment To avoid the expensive and time-consuming network reinforcements (“Business as Usual”), this control strategy “export limits and PV curtailment” aims to solve LV network issues by defining a limit on the export power from houses with PV. The export limit (percentage of the PV installed capacity) is determined ‘off-line’ for each PV penetration level considering a Monte Carlo analysis with 100 simulations with different PV locations and ratings (but limited, for simplicity, to maximum generation and an after diversity minimum demand of 0.4 kW, i.e., no time-series), and using a three-phase unbalanced AC Optimal Power Flow (OPF). The export limit is defined for each simulation and the results are presented using boxplots in Figure 10.

12

30

53

1 2 3 4 5

PV pentration

(%) LV Network Length



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It can be seen in Figure 10 that the median of the export limit becomes, as expected, smaller at higher PV penetration levels so as to ensure voltages are kept within limits. For instance, at penetration level 5, the export limit is below 40% for most of the simulations. The export limit for each PV penetration level is then selected as the value that addresses the issues of 95% of the corresponding simulations (to avoid outliers). The adopted export limits are presented in Figure 11.

Figure 10. Export limits (% residential PV capacity) for 100 simulations with different PV locations and ratings. The bottom of the box is the 25

th percentile of the simulations. The top of

the box is the 75th

percentile of the simulations. The horizontal bold line inside the box is the median (50

th percentile of the simulations).

Figure 11. Export limits (% of PV installed capacity) that address 95% of the simulations at each PV penetration level

To understand the impact on customers due to the adoption of the defined export limits, the annual energy curtailment is assessed for the five PV penetration levels considering a Monte Carlo analysis and daily time-series PV and demand profiles. The average annual energy curtailment of the simulations is calculated for each PV penetration level and the results are presented in Figure 12. It can be seen that the volume of curtailment required to solve network issues increases significantly to 13% of the potential available resource at PV penetration level 5 (100% residential PV and 100% commercial/industrial PV). Although generation curtailment avoids network reinforcements, the volume of curtailment might reduce significantly the economic benefits brought by PV to householders.

20%

40%

60%

80%

100%

1 2 3 4 5

PV Penetration

% Export limit

100%

72%

51%

41%

33%

1 2 3 4 5

PV Penetration

% Export limit (95th percentile)



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Figure 12. Annual energy curtailment: Export limit and PV curtailment control strategy

Residential storage benefiting customers This control strategy aims to understand the extent to which the adoption of residential storage facilities in combination with PV systems reduces voltage and thermal issues. As explained in Section 2.4.1, a decentralised time-based control approach (using predefined periods of daily charging and discharging operations) is adopted to maximise the benefits to residential customers (i.e., reducing electricity bills) by harvesting as much as possible excess PV generation to then locally supply the demand at night (reducing imported energy from the grid). Based on the discussion with the members of WS2 of the WISE PV project, the adopted energy capacity of storage facilities for all the customers is 5 kWh. The power rating is defined as the same as the rating of the corresponding PV system. To understand the benefits of storage to customers in terms of increasing the local use of energy produced by PV system, the average self-sufficiency metric of all the residential customers (with PV systems) and simulations are calculated for each PV penetration level. The results are presented in Figure 13. It can be seen that storage can indeed significantly increasing self-sufficiency. For example, at penetration level 5, 60% of the annual energy consumption will be supplied from self-produced energy (instead of importing it from the grid). This means that residential customers become more grid independent. Although residential storage facilities provide benefits for customers, these devices are likely to be fully charged under this control strategy before critical periods around noon (maximum PV generation and minimum demand). Therefore, impacts from residential PV will still be seen on the networks. This can be seen in Figure 14. The required network investments in this control strategy are similar to the one without any form of control (Business As Usual, see Figure 9).

Figure 13. Self-sufficiency: Residential storage benefitting customers

4

9

13

1 2 3 4 5

PV Penetration

(% ) Annual Energy Curtailment

18%

33%

44%

53%

59%

1 2 3 4 5

PV pentration

(%)Self -Sufficiency



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Figure 14. LV network investments: Residential storage benefitting customers

Export Limits, Residential Storage and PV Curtailment To avoid network issues when storage is controlled to benefit customers, the use of export limits (as defined for the control strategy “Export Limits and PV Curtailment”) will be explored in combination with residential storage and PV curtailment. To ensure that there is headroom in the store able to charge excess generation during critical periods, the storage facility in this control strategy only charges excess generation when PV exports exceed the pre-defined limit. Once the stored energy reaches its energy capacity and the exported power is larger than the defined limit, PV curtailment is applied. Figure 15 presents the average annual energy curtailment for all the simulations at each PV penetration level. It can be seen that significantly smaller curtailment can be achieved by using storage facilities when compared to the “export limit and PV curtailment” control approach (i.e., curtailment is only used to solve network issues). For example, at PV penetration level 5, the annual energy curtailment is reduced from 13 to 3% with storage. At PV penetration level 3, the energy curtailment is almost negligible. To assess the implications on customers from using residential storage facilities to support the network, the average self-sufficiency of customers (with PV systems) is calculated for each PV penetration level. The results are presented in Figure 16. It can be seen that the sufficiency of customers is reduced by approximately 10% for PV penetration levels 3 to 5 when compared to controlling storage only for the benefit of the customer. Although this reduction might be considered significant, given that voltage and congestion issues are solved, the savings from not having network reinforcements should also be taken into account in any cost-benefit analysis.

Figure 15. Annual energy curtailment: Export Limits and PV curtailment control strategy

9%

30%

52%

1 2 3 4 5

PV pentration


4

9

13

1

3

1 2 3 4 5

PV pentration

(% ) Annual Energy Curtailment

Export limit + PV curtailment

Export limit + storage + PV curtailment



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Figure 16 Self-sufficiency: Storage benefitting customers and Export Limits, Storage and PV Curtailment

Optimal Centralised Control The adoption of a conservative export limit allows the decentralised control strategies to locally solve network issues with limited observability (only at the customer connection point). This, however, results in a relatively high volume of energy curtailment. To understand the benefits of having a more complex Smart Grid scheme, an optimal centralised control strategy able to control PV systems, residential storage facilities and on-load tap changers is investigated to quantify (in ideal circumstances) how much curtailment could be reduced whilst solving all network issues. After calculating the average annual energy curtailment of customers for all the simulations at each PV penetration level it was found that network issues can be solved using negligible levels of curtailment (less than 0.5%). Although this demonstrates the advantages for DNOs when using all the flexible elements in the network, a cost-benefit analysis is needed to determine the suitability of the potential solutions. In particular, the optimal centralised control strategy requires replacing the existing off-load tap changers distribution transformers with OLTC-fitted distribution transformers. In addition, this control strategy requires extensive observability (information and communication infrastructure).

To assess the implications on customers from using residential storage facilities to support the network, Figure 17 presents the average self-sufficiency of customers (with PV systems) for each PV penetration level. It can be seen that the sufficiency of customers is reduced by approximately 17% for PV penetrations 4 and 5 when compared to controlling storage only for the benefit of the customer. Since network issues (voltage rise) could also be solved using the OLTC (priority is given to the OLTC, see equations 2 and 3 in Section 2.4.1), the volume of charged energy by the storage facilities is smaller compared to only using storage to support network at the “Export Limits, Residential Storage and PV Curtailment” control strategy. Therefore, the self-sufficiency of customers is also smaller (i.e., approximately 7% reduction for PV penetration 4 to 5). This shows that improvements to the control strategy should consider the benefits to customers by, for instance, reserving only part of the energy capacity to support the network.

18%

33%

44%

53%

59%

34%

42%

49%

1 2 3 4 5

PV pentration

% Customers’ self-sufficiency

Storage benefitting customers

Export limit, storage and PV curtailment



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Figure 17. Self-sufficiency: Storage benefitting customers and Export Limits, Storage and PV Curtailment, Optimal centralised control

3.3 High Demand Scenario

Given that the planning horizon of the WISE PV project is for the next twenty years, this work also studies the impact by considering a scenario of high demand that corresponds to a 50% penetration level of electric vehicles (EVs) and electrical heat pumps (i.e., 50% of residential customers have EVs and electrical heat pumps). To do so, it is first important to understand the network reinforcements required to cope with the high demand due to electrical vehicles and electrical heat pumps without PV. To do so, load profiles of EVs and electrical heat pump are both needed. To produce the charging profiles of EVs, the statistical data (start charging time and energy demanded during a connection) in [26] from a one-year EV trial in Dublin is adopted. It is also considered that EVs are connected once a day at home. All EVs are assumed to have a battery capacity of 24 kWh and a charging rate of 3.3 kW (slow charging). The electrical heat pump profiles made available by the Low Carbon Networks Fund project “Low Voltage Network Solutions” [4] are adopted. The methodology presented in Section 2.3 is used to determine the reinforcements (in terms of MV and LV lines and MV/LV transformers) required to solve network issues due to the connection of EVs and electrical heat pumps (without PV installations). Once the new network at the high demand scenario is defined, network reinforcements required due to PV deployments could be then determined for different PV penetration levels. It was found that the high demand due to EVs and electric heat pumps (without PV installations) requires 30 and 40% of the LV and MV lines to be reinforced, respectively. All MV/LV distribution transformers also need to be replaced by larger ones (20% more capacity). This reinforcement, in turn, allows having a PV installed capacity up to PV penetration level 3 without creating any technical impact on the network and only minor further reinforcements are needed for PV penetration level 4. This can be seen in Figure 18 where LV reinforcements due to PV are still needed at penetration level 5 but are much smaller than with the low demand scenario (18% compared to 53%, respectively). To reduce PV-driven LV network reinforcements, the Smart Grid control strategies were investigated considering the high demand scenario and the corresponding demand-driven reinforcements. It is worth highlighting that it is considered that EVs and electrical heat pump are not controllable.

18%

33%

44%

53%

59%

34%

42%

49%

16%

25%

30%

36%

42%

1 2 3 4 5

PV pentration

% Customers’ self-sufficiency

Storage benefitting customers

Export limit, storage and PV curtailment

Optimal centralised control



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Figure 18. Network reinforcements at high demand scenario

Figure 19 presents the export power limits found in the “export limits and PV curtailment” control strategy for each PV penetration level. The export limits determined at the high demand scenario are larger than the ones found at the low demand scenario (since the network has already been reinforced to cope with demand) and, therefore, result in smaller volumes of curtailment. As it can be seen in Figure 20, the annual volume of curtailment is reduced to 4% at PV penetration level 5 with the high demand scenario compared to 13% curtailment with the low demand scenario. This shows that the reinforcement to cope with the high demand in turn allows much more PV capacity. All network issues were solved when controlling the residential; storage to support the network in the “export limits, residential storage and PV curtailment” control strategy – as expected. However, due to the much higher export limits, curtailment was negligible (less than 0.1%). The “optimal centralised” control strategy also solved network issues with negligible levels of curtailment. Therefore, in this particular high demand scenario (where EVs and electric heat pumps are not controlled), it can be argued that a decentralised approach can be as effective as a centralised one. From the perspective of residential customers, in both control strategies, the self-sufficiency was reduced as their storage facilities were only absorbing excess generation to support the network.

Figure 19. Export limits at high demand scenario

1 2 3 4 5

PV pentration


demand-driven PV-driven

28%

45%

27% 27% 27%

100% 100% 100%

60%

50%

1 2 3 4 5

PV Penetration

(% ) Exported power limit (High demand)



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Figure 20. Annual energy curtailment at the high demand scenario: Export limit and PV curtailment control strategy

4%

9%

13%

1%

4%

1 2 3 4 5

PV pentration

% Annual Energy Curtailment

Low demand High demand



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4 Case Study: Rural MV/LV Distribution Network

This chapter presents the impacts of PV systems on a real rural MV network. The impacts are found due to the connection of both ground mounted and residential PV systems. Fifteen penetration levels and two demand scenarios (low and high) are taken into account. The analysis is carried out with the no control (business as usual) strategy to understand the required network reinforcements at each combination of ground-mounted and residential PV systems. Due to the small number of residential customers in the adopted rural network, the LV networks are not modelled and, therefore, the optimal centralised control strategy is the only investigated strategy from the Smart Grid Control Strategies.

4.1 Network Description and Modelling

To understand the impacts of PV on an rural network, a real MV (11 kV) rural network from the North West of England, made available by the Low Carbon Networks Fund project “Assessing the Benefits of Reactive Power Compensation in HV Networks” [27] run by Electricity North West Limited, was modelled. The corresponding single line representation is given in Figure 21. The network is supplied by two 3.5 MVA, 33/11 kV transformers. The MV feeder supply power to 619 and 150 residential and commercial/industrial customers, respectively, through 127 distribution transformers with capacities ranging between 5 and 300 kVA (much smaller than the capacities in the urban feeder, i.e., 315 kVA - 1000kVA). It is worth highlighting that all the distribution transformers are within a distance of 15 km from the primary substation (much longer than the urban feeder, i.e., 5 km). Due to the low density of residential customers, it is expected to have PV impacts due to ground-mounted PV installations rather than residential PV systems. Therefore, it is considered to model only the MV network, while the residential and commercial/industrial load at each distribution transformer is connected to the LV busbar. The main characteristics of this feeder are presented in Table 4. The annual real-power measurements (provided by ENWL) at the primary substation are used to produce load profiles at the distribution transformers (i.e., load allocation) based on the number and type of customers.

Figure 21. Tree representation of a real UK MV/LV rural network

286



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Table 4: Characteristics of the rural network

Peak demand (winter) 1.5 MVA

Minimum demand (summer) 0.8 MVA

No. of distribution transformers 127

Distribution transformer capacities 5 to 300 kVA

Total capacity of distribution transformers 4.5 MVA

No. of residential customers 619

No. of residential customers per distribution transformer

1 to 80

No. of commercial/industrial customers 150

No. of commercial/industrial customer per distribution transformer

0 to 20

Total length of MV lines 70 km

4.2 Low Demand Scenarios

4.2.1 Business as Usual Strategy (Network Reinforcements)

This section presents the PV impacts on the rural distribution network. The impacts are assessed for different penetration levels of both residential PV systems and ground-mounted PV farms directly connected to the MV network. In this respect, 15 penetration levels are studied. Three residential PV penetration levels are adopted (i.e., 0 %, 50%, 100% of residential customers with PV systems). Each residential PV penetration is studied in combination with five ground-mounted PV penetrations (0%, 50%, 100%, 150%, and 200% of the network peak demand). A Monte Carlo-based quantification of impacts is carried out in which 100 simulations are considered for each penetration level. For simplicity, the uncertainties considered throughout the Monte Carlo analysis is related to the generation behaviours of ground mounted PV systems. The residential and industrial/commercial load profiles are fixed throughout the Monte Carlo analysis. In addition, the uncertainties related to the locations and the sizes of residential PV systems are not considered in the simulations (PV profiles still vary). It is considered that each residential PV installation has a capacity of 3.06 kW that corresponds to the average UK residential PV installation [25]. For illustration purposes, the total PV capacity at each of the fifteen PV penetration levels is presented in Table 5 in MW and as percentage of the overall peak demand (1.5 MVA). It considers an average residential PV installation of 3.06 kW (based on UK statistical data of PV installations) as well as ground-mounted PV installations (based on the individual peak demand). The resulting total PV installed capacity ranges from a relatively small 0.75 MW (50% of the total peak demand) to a value about 10 times higher, 4.9 MW (327% of the total peak demand). The methodology presented in Section 2.3 is used to determine the required reinforced network to solve network issues resulting from the connections of PV without any form of control (Business As Usual). Differently from the urban network were no congestion issues were found for distribution transformers, for the rural network congestion did occur when 100% of residential customers are with PV systems (PV penetration levels 11, 12, 13, 14 and 15) – reverse power flows exceeded approximately 7% of the aggregated rated capacity of the distribution transformers. The reinforced MV lines for each combination of ground-mounted and residential PV systems are given in Table 6. The reinforced MV lines are expressed as percentage of the total MV network lengths.



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Table 5: Illustration of PV installed capacity for the rural feeder per penetration level

Penetration level

Residential PV Ground-mounted PV Total PV capacity (MW)

% of customers with PV

(MW)

% of individual peak demand

(MW) (MW) % of peak demand

1

0% 0

0% 0 0 0%

2 50% 0.75 0.75 50%

3 100% 1.5 1.5 100%

4 150% 2.25 2.25 150%

5 200% 3 3 200%

6

50% 0.95

0% 0 0.95 63%

7 50% 0.75 1.7 113%

8 100% 1.5 2.45 163%

9 150% 2.25 3.2 213%

10 200% 3 3.95 263%

11

100% 1.9

0% 0 1.9 126%

12 50% 0.75 2.65 177%

13 100% 1.5 3.4 227%

14 150% 2.25 4.15 277%

15 200% 3 4.9 327%

The results show that residential PV systems are harmless due to the small numbers (no MV network reinforcements with only residential PV systems, PV penetration levels 6 and 11). However, ground-mounted PV capacity exceeding the peak demand (PV penetration levels 3) would trigger MV investments. It is also worth highlighting the importance of considering the growth of residential PV into ground-mounted impact studies. To connect, for example, a capacity of ground-mounted PV equivalent to 100% of peak demand, the reinforced MV lines increases significantly from 8% at PV penetration level 3 (0% residential PV) to 14% at level 13 (100% residential PV).

4.2.2 Smart Grid Control Strategies

Optimal Centralised Curtailment To understand the impact on PV systems due to the adoption of the generation curtailment, the annual energy curtailment is assessed for each combination of ground-mounted and residential PV systems. The results corresponding to the average annual energy curtailment of the customers for all the simulations and PV penetration levels are presented in Table 7. It can be seen that the volume of curtailment is relatively small (less than 5%) when the capacity of ground-mounted PV is smaller than 50% of the peak demand (PV penetration levels 2, 7, and 12). However, it will increase significantly with more ground-mounted PV capacity. Although generation curtailment avoids network reinforcements, the volume of curtailment might reduce significantly the economic benefits brought by ground PV owners. Therefore, it is important to explore other sources of flexibility to solve network issues rather than resorting to generation curtailment only.



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Table 6: Business As Usual: MV network reinforcements at each PV penetration level

Penetration level Residential PV Ground-mounted PV MV reinforcements

% of customers with PV % of individual peak demand (% MV length)

1

0%

0% 0%

2 50% 0%

3 100% 8%

4 150% 14%

5 200% 16%

6

50%

0% 0%

7 50% 9%

8 100% 11%

9 150% 15%

10 200% 17%

11

100%

0% 0%

12 50% 12%

13 100% 14%

14 150% 16%

15 200% 18%

Optimal Centralised OLTC and Curtailment Table 8 presents the annual energy curtailment when the existing off-load tap changers distribution transformers are replaced with OLTC-fitted distribution transformers. It can be seen that the energy curtailment is reduced significantly. For example, the volume of curtailment is reduced significantly from 23% to 3.5% at PV penetration level 15 (100% residential PV and 200% ground-mounted PV). Although this demonstrates the advantages for DNOs when using more flexible elements in the network, a cost-benefit analysis is needed to determine the suitability of the potential solutions.

4.3 High Demand Scenario

This section explores the PV impacts by considering a scenario of high demand that corresponds to a 50% penetration level of EVs and electrical heat pumps (i.e., 50% of residential customers have EV and electrical heat pump). However, due to the small number of residential customers, network reinforcements due to the high demand scenario are almost negligible. Consequently, the low demand scenario results are also valid for this case.



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Table 7: Annual Energy curtailment: Generation curtailment

Penetration Level

Residential PV (% of customers with PV)

Ground-mounted PV (% of individual peak demand)

Annual Energy Curtailment (%)

1

0%

0% 0%

2 50% 0%

3 100% 4%

4 150% 14%

5 200% 18%

6

50%

0% 0%

7 50% 2%

8 100% 8%

9 150% 18%

10 200% 21%

11

100%

0% 0.4%

12 50% 5%

13 100% 12%

14 150% 20%

15 200% 23%

Table 8: Annual Energy curtailment: Generation curtailment and OLTCs

Penetration level

Residential PV Ground-mounted PV Annual Energy Curtailment

% of customers with PV % of individual peak demand (%)

1

0%

0%

0%

2 50%

3 100%

4 150%

5 200%

6

50%

0%

0%

7 50%

8 100%

9 150%

10 200%

11

100%

0% 0.4%

12 50% 0.4%

13 100% 0.6%

14 150% 1.3%

15 200% 3.5%



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5 Case Study: Extrapolation of results to the North West of England

The results corresponding to the rural and urban distribution networks are extrapolated to the North West of England considering the 2035 User-Led scenario produced by Work Stream 2 which considers most PV installations are residential and include storage facilities. Within this geographical area (3.5 GW+ of peak demand), operated by Electricity North West Limited (ENWL), approximately 60% of the MV/LV networks (1500+ HV feeders) are urban and the rest (40%) are rural. It is important to highlight that this extrapolation provides a ballpark figure rather than an actual quantification. The latter would require a set of actual (or representative) MV/LV network models to cater for the differences in network topology and conditions across the studied area. This chapter will first introduce the adopted methodology to extrapolate the results of the previously presented urban and rural networks. Finally, the results across the ENWL area are provided for the low demand scenario.

5.1 Future PV Uptake

The Work Stream 2 (WS2) team in the WISE PV project has provided estimates of the PV uptake during the upcoming 20 years (2015-2035). The volume of PV capacity has been distributed among the UK licence areas. In the ENWL licence area, 2.6 GW have been estimated to be connected by 2035 (more than 22 times the PV installed capacity in 2015). The share of PV capacity to be connected in urban areas makes about 60% of the total estimated capacity in 2035 (40% in rural areas), as given in Table 9. The PV capacity has been broken into residential, commercial/industrial, and ground-mounted. Residential PV installed capacity (1.4 GW) makes about 54% of the total cumulative PV capacity by 2035 (2.6 GW). The PV uptake scenarios also show that 80% of the residential PV installations will be connected in urban areas.

Table 9: Cumulative PV Installed Capacity (GW) in the ENWL area in 2015 and 2035

Network Type (Urban/Rural) Total PV capacity (GW)

2015 2035

Urban

Residential 0.10 1.20

Commercial/Industrial 0.02 0.30

Urban Total 0.12 1.50

Rural

Residential 0 0.20

Ground-mounted 0 0.90

Rural Total 0 1.10

To determine the reinforcement cost to the ENWL area in 2035 due to the connection of the estimated PV installed capacity (2.6 GW), the reinforcement cost for each of the 1500+ HV feeders should be determined. Since the reinforcement cost of a feeder depends on the impacts from PV, the PV penetration level of each HV feeder must be defined first – which is a challenge in itself given the different uncertainties surrounding the future uptake of PV. To understand the range of reinforcement costs, each of the PV penetration levels investigated for the urban (five) and rural (fifteen) networks are considered independently. The reinforcement costs associated with each of the PV penetrations for a single urban or rural HV feeder are then extrapolated to the number of HV feeders in the ENWL area needed to achieve the overall installed capacity given in Table 9. The following section presents the reinforcement costs for a single HV feeder for each of the corresponding urban or rural PV penetrations previously investigated. The extrapolation results are presented in the sequence.



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5.2 Reinforcement Costs per Network Type

The PV impact analysis for the urban and rural networks presented in Section 3.2.1 and Section 4.2.1, respectively, determines the reinforcements in terms of MV and LV lines and distribution transformers at each PV penetration level. To determine the corresponding cost, the unit costs for lines and substations for urban and rural networks are adopted from the connection charges document provided by ENWL [28]. Table 10 and Table 11 show the adopted unit costs for urban and rural networks, respectively.

Table 10: Unit costs for lines and substations in urban networks

Item Unit cost (£)

LV underground cables 140,000 £/km

MV underground cables 85,000 £/km

Ground mounted distribution substation up to 315 kVA 36,000 £/unit

MV overhead line 35,000 £/km

Pole mounted distribution substation up to 100 kVA 21,000 £/unit

Table 11: Unit costs for lines and substations in rural networks

Item Unit cost (£)

MV overhead line 35,000 £/km

Pole mounted distribution substation up to 100 kVA 21,000 £/unit

With the unit costs and the required reinforcements (from Section 3.2.1 and Section 4.2.1) for each of the investigated PV penetration levels, the total reinforcement costs can be calculated per HV feeder. Table 12 shows the results for the urban network. It can be seen that the resulting reinforcement costs are increased at higher PV penetrations. It ranges from 0 m£ (penetration level 1 and 2, no reinforcements needed) to a value of 1.4 m£ (penetration level 5). The difference in the reinforcement cost among the penetration levels shows the importance of having an extrapolation methodology able to capture the effect of clusters of relatively large PV installation (e.g., PV penetration 5 in the urban network).

Table 12: Reinforcement cost per penetration level (m£) for the urban network

Penetration Level

Residential PV Commercial/Industrial PV Reinforcement

cost

% of customers with PV % of individual peak demand (m£)

1 20 20 0

2 40 40 0

3 60 60 0.3

4 80 80 0.8

5 100 100 1.4

The reinforcement cost in the rural network for each combination of ground mounted and residential PV systems are given in Table 13. The results show that the reinforcement cost triggered at 100% residential PV penetration (PV penetration 11) is relatively small (0.08 m£). However, ground-mounted PV capacity exceeding the peak demand results in a reinforcement cost between 0.2 m£ - 0.4 m£ (penetration levels 3, 4 and 5). The results also show that the reinforcement cost ranges from 0 m£ (penetration level 1 and 2, no reinforcement needed) to a value of 0.54 m£ (penetration level 15).



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Table 13: Reinforcement cost per penetration level (m£) for the rural network

Penetration level

Residential PV Ground-mounted PV Reinforcement cost

% of customers with PV % of individual peak demand (m£)

1

0%

0% 0.00

2 50% 0.00

3 100% 0.20

4 150% 0.35

5 200% 0.40

6

50%

0% 0.00

7 50% 0.23

8 100% 0.28

9 150% 0.38

10 200% 0.43

11

100%

0% 0.08

12 50% 0.39

13 100% 0.44

14 150% 0.49

15 200% 0.54

5.3 Extrapolation to the ENWL Area

This section extrapolates the reinforcement costs found for the urban and the rural network to the ENWL area assuming a 2035 PV penetration. For each type of HV feeder (urban or rural) and each of the corresponding PV penetration levels, the number of ‘actual’ HV feeders within the ENWL area that would need to have the same penetration so as to match the cumulative PV installed capacity in 2035 (Table 9) is calculated. The reinforcement costs for each PV penetration level are presented for urban and rural areas, in Table 14 and Table 15, respectively.

Table 14: Reinforcement costs (m£) in ENWL urban area in 2035

Urban network ENWL urban area (900 HV feeders)

Penetration level

Total PV capacity (MW)

Reinforcement cost (m£)

No. feeders with PV to achieve 1.5 GW PV installed capacity


1 1.2 0 1250 (not feasible) 0

2 2.5 0 600 0

3 3.7 0.3 405 122

4 5.0 0.8 300 240

5 6.2 1.4 242 339



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Table 15: Reinforcement costs (m£) in ENWL rural area in 2035

Rural network ENWL rural area (600 HV feeders)

Penetration level

Total PV capacity (MW)


No. feeders with PV to achieve 1.1 GW PV installed capacity

Reinforcement cost ( m£)

1 0 0.00 N/A N/A

2 0.75 0.00 1467 (not feasible) 0

3 1.5 0.20 733 (not feasible) 147

4 2.25 0.35 489 171

5 3 0.40 367 147

6 0.95 0.00 N/A N/A

7 1.7 0.23 647 (not feasible) 149

8 2.45 0.28 449 126

9 3.2 0.38 344 131

10 3.95 0.43 278 120

11 1.9 0.08 N/A N/A

12 2.65 0.39 415 162

13 3.4 0.44 324 142

14 4.15 0.49 265 130

15 4.9 0.54 224 121

Table 14 shows that the required reinforcement costs due to the connection of the estimated PV installed capacity in 2035 in urban areas could be between 0 m£ (no reinforcement) and 339 m£. PV penetration level 1 is not feasible as it would require more urban HV feeders than those existing in the ENWL area (900 HV urban feeders). However, it can be seen that with PV penetration level 2 (relatively low) across the HV urban feeders, it is possible to achieve the estimated cumulative PV capacity without the need to distribution network reinforcements. However, such a scenario may underestimate the PV impact as it neglects the effect of clusters. In reality, there will be combinations of PV penetration levels across the feeders and therefore network reinforcements could be triggered. Table 14 also shows that when the accumulative PV capacity are distributed across a relatively smaller number of feeders (e.g., PV penetration level 5), a reinforcement cost of 339 m£ is needed. The latter represents a conservative assessment of PV impacts to cope with the worst-case distribution of PV systems. Different from the previous studies in the literature, this extrapolation approach provides a range of network reinforcements. For example the study provided by EA technology [3] gives a single number of reinforcement cost obtained by averaging the estimated cumulative installed capacity across customers and feeders, which will in turn underestimate the PV impacts. Table 15 shows the reinforcement costs for the rural HV feeders across the ENWL area. A few PV penetration levels (2, 3, and 7) were found to be unfeasible as they would require more rural HV feeders than the existing 600 HV rural feeders. The reinforcement costs in 2035 for the rural HV feeders could vary between 120 m£ and 171 m£. It was also found that the maximum network reinforcements in urban areas was almost twice higher than in rural areas. However, the range of reinforcements cost in rural areas is narrower and, therefore, the reinforcement cost can be considered less uncertain compared to the urban areas. To provide a metric adequate to be used by energy policy makers and to be considered in the PV Life Cycle Assessment (LCA) study, the reinforcement costs found for urban and rural areas are used to



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determine the annuitized reinforcement costs per each kW of PV installed capacity (£/kW.year). To do so, it is considered an annual discount rate of 3.5%, and the PV growth between 2015-2035 is assumed to be uniform (to achieve the PV cumulative capacity in 2035). Table 16 shows that the annuitized reinforcement costs per each kW of PV installed capacity in the ENWL area could be between 33 to 140 £/kW.year. The spread of this range, particularly in urban areas, shows that methodologies that average PV capacity across consumers and feeders underestimates significantly the PV impacts on distribution networks.

Table 16: Total Investments (m£) and average distribution cost (£/kW.year) in ENWL area

ENWL area Total PV capacity

(GW)

Reinforcement costs in 2035

(m£)

Annuitized reinforcement cost

(m£/year)

Annuitized reinforcement cost per kW (£/kW.year)

Urban 1.5 0 - 339 0-12 0 - 160

Rural 1.1 120 - 171 4-6 77 - 110

Total 2.6 120 - 510 4-18 33 - 140



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6 Conclusions

This chapter presents the main aspects and conclusions from the work carried out by Work Packages (WP) 1.1 and 1.2 part of the EPSRC-funded project “Whole system Impacts and Socio-economics of wide scale PV integration (WISE PV)” (grant reference EP/K022229/1 [1]). The aim of the WISE PV project is to provide comprehensive technical, environmental, economic, and social impact assessments of wide scale integration of photovoltaic systems (PV) into electric energy systems. In particular, the project aims to determine the distribution and transmission network infrastructure required to achieve future PV scenarios up to 2035. The WP 1.1 and WP 1.2 aim to quantify the technical and economic impacts of small-to-medium scale PV systems connected to distribution networks by considering integrated models of low and medium voltage networks. Key aspects of the developed methodologies in this report are described as follows:

Networks, Demand and PV: Two types of MV/LV networks are investigated: urban and rural. Two demand scenarios are taken into account: the low demand scenario assumes the existing demand level while the high demand scenario also considers that 50% of residential customers have electrical vehicles and electrical heat pumps. For the urban network (3 MW of peak demand and 1,300+ customers), it is assumed that PV systems are installed by residential and commercial/industrial customers while the rural network (1.5 MW of peak demand and 600+ customers) considers ground-mounted and residential PV systems. For each network and demand scenario different PV penetrations are investigated.

Network Investments (Business as Usual Strategy): As a benchmark, traditional network reinforcements (lines and/or transformers) required to cope with each demand scenario and PV penetration level are quantified.

Smart Grid Control Strategies: Different decentralised (rule-based) and centralised (Optimal Power Flow-based) network management strategies are explored by controlling PV systems, residential storage facilities, and On-load Tap Changers (OLTCs). PV curtailment is used as the last resort to solve network issues and avoid the need for network reinforcements. The volume of energy curtailment is quantified for each demand scenario and PV penetration level.

Probabilistic Impact Assessment: To cater for the uncertainties related to demand and PV generation behaviour and location, a Monte Carlo-based quantification of impacts is carried out per network, per demand scenario, per PV penetration level, and per Smart Grid control solution.

Extrapolation for the North West of England: The results corresponding to the rural and urban distribution networks are extrapolated to the North West of England also considering the 2035 User-Led scenario produced by Work Stream 2, which considers most PV installations are residential and include storage facilities. Within this geographical area (3.5 GW+ of peak demand), operated by Electricity North West Limited (ENWL), approximately 60 and 40% of the MV/LV networks are urban and rural, respectively.

The main conclusions for the real MV/LV urban and rural distribution networks as well as the extrapolation of results for the North West of England can be summarized as follows: Urban Network

For the low demand scenario, voltage rise issues will occur before congestion issues. The uptake of residential PV at penetration levels above 40% will require reinforcing LV networks, while there is no need to upgrade MV networks and distribution substations. At 100% penetration level, for example, 50% of the LV lines should be reinforced.



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The high demand due to electric vehicles and electric heat pumps –without PV installations– requires 30 and 40% of the LV and MV lines to be reinforced, respectively. All MV/LV distribution transformers also need to be replaced by larger ones (20% more capacity). PV related investments are only needed for PV penetrations above 80% (LV networks only).

For any PV penetration, it is possible to define a conservative limit on the export power from houses with PV (expressed as percentage of PV rating) so as to solve network issues. However, this approach, although implementable without the need of further network infrastructure, can lead to relatively high volumes of energy curtailment, particularly at high PV penetrations.

The use of residential storage brings significant benefits to customers as it makes it possible to use excess PV generation later at night (increasing self-sufficiency) so that electricity bills of residential customers can be reduced. However, it was found that these devices (sized to increase self-sufficiency throughout the year) are likely to be fully charged before critical periods around noon (maximum PV generation and minimum demand). Therefore, impacts from residential PV will still be seen on the networks.

The use of an optimal centralised Smart Grid scheme able to control PV systems, residential storage facilities, and on-load tap changers shows that network issues can be solved with negligible levels of curtailment. However, since the storage facilities are only absorbing excess generation to support the network (i.e., ensuring a headroom in the store able to charge excess generation during critical periods), the self-sufficiency of residential customers was reduced compared to only controlling storage for the benefit of customers.

The control of storage facilities to support the network (voltage and congestion management) facilitates the connection of significant volumes of PV capacity to distribution networks. However, the relatively high cost of storage facilities can be seen as a barrier for these devices. Therefore, adequate incentives should be in place. The provision of ancillary services (e.g., frequency regulation) should be also properly explored to increase the value of storage.

Smart Grid control strategies significantly increase the deployment of solar PV on distribution networks. However, a cost-benefit analysis is needed to determine the suitability of the potential strategy as opposed to traditional reinforcements (or a combination of both).

Rural Network

Residential PV systems are harmless due to the small numbers of residential customers. However, ground-mounted PV capacity exceeding the peak demand would trigger MV reinforcements.

Network reinforcements due to the high demand scenario are almost negligible.

The use of OLTC-fitted distribution transformers solves voltage rise issues resulting in smaller volumes of energy curtailment. Although this demonstrates the advantages for DNOs when using more flexible elements in the network, a cost-benefit analysis is needed to determine the suitability of the potential solutions.

Extrapolation for the North West of England

Averaging the future PV installed capacity across customers, results in small, distributed PV installations that are unlikely to trigger impacts on the networks under study; clusters of PV systems that normally drive network reinforcements will not be captured.

The required reinforcement costs due to the connection of the estimated PV installed capacity in 2035 (2.6 GW) could be between 120 m£ and 510 m£. The spread of this range shows the importance of having an extrapolation methodology able to capture the effects of clusters.



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The maximum network reinforcements in urban areas were almost twice higher than in rural areas. However, the range of reinforcements cost in rural areas is narrower and, therefore, the reinforcement cost can be considered less uncertain compared to the urban areas.

The annuitized reinforcement costs per each kW of PV installed capacity in the ENWL area could be between 33 up to 140 (£/kW.year). This figure should be considered in the PV Life Cycle Assessment (LCA) study and added to the cost of transmission investments so that the PV impacts across the UK electricity system can be adequately understood.



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[26] P. Richardson, M. Moran, A. Maitra, J. Taylor, and a. A. Keane, "Impact of electric vehicle charging on residential distribution networks: An Irish demonstration initiative," in CIRED. Stockholm, 2013.

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Documents

Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV ... · Final Report for WP 1.1 and 1.2 “Wide-Scale Adoption of PV in UK Distribution Networks” UoM_WISE-PV_WP1.1-1.2_FinalReport_v01