Urban Energy Systems Project

Urban Energy Systems Project

Urban Energy Systems – January 20081

Urban Energy Systems Project

The University of Tokyo – Imperial College Joint

Symposium on Innovation in Energy Systems

20 November 2007

Project Objectives

“By 2030 it is estimated that over half the world’s population will be living in cities. So reducing the amount of urban energy wasted is critical in tackling diminishing natural resources and climate change. Our Urban Energy Project at Imperial College London is exploring how cities could be more efficient with their use of power, heating and transport – for example harnessing previously wasted heat from power stations to


transport – for example harnessing previously wasted heat from power stations to heat offices and homes”

bp advertisement, The Times, 8 June 2006

The BP Urban Energy Systems project at Imperial will identify the benefits of a systematic, integrated approach to the design and operation of urban energy systems, in the context of the dynamic evolution of cities.

Project Hypotheses

• Cities are not fully optimised for energy efficiency

• They are suboptimal in primary (conversion) and secondary (end-use service) aspects– Other energy-intensive process systems (e.g. pulp and paper, refineries) have been successively

optimised and integrated with substantial reductions in energy

• Data streams, data mining and optimisation algorithms and computing power are increasingly becoming available to tackle complex problems

• New “hard” and “soft” technologies exist or are emerging that might be relevant to urban


• New “hard” and “soft” technologies exist or are emerging that might be relevant to urban energy systems– The engineering, computing and business skills available at Imperial are ideal to study these

• An integrated, multidisciplinary team will generate new insights• Cities will be amenable to this analytical and business-oriented approach

– They are increasingly believed to have some self-organising characteristics

• But– Cities are evolving, dynamic systems whose behaviour and evolution depend on millions of

autonomous agents; their causalities and links are unclear.

Project Overview and Plans

Data

Metrics

Models

Solutions

Systems

Engineering

Year

Realisation and

business models

BP and its university

Research programmesWider stakeholders

Imperial

Energy

Programme

Data

Metrics

Models

Solutions

Systems

Engineering

Year

Realisation and

business models



Imperial

Energy

Programme

Urban Energy

Systems

Project


Year3-5 6-8 10-12

Potential benefitsPotential effort

Detailed blueprintsQuantified metrics

Realisation strategyPilots

Phase 1 Phase 2 Phase 3Year

3-5 6-8 10-12

Potential benefitsPotential effort

Detailed blueprintsQuantified metrics

Realisation strategyPilots

Phase 1 Phase 2 Phase 3

Analysis Design Implementation

Phase 1

• The overall objectives of phase 1 are:

– Application of quantitative, holistic analysis

– identify achievable benefits of fresh approach to UES

• Economic, energy efficiency, environmental impact, energy security, system resilience and robustness, …

– Identify how benefits might be achieved

Data

Metrics

Models

Solutions

Systems

Engineering

Year3-5 6-8 10-12

Realisation and

business models



Potential benefits

Potential effort

Detailed blueprints

Quantified metrics

Realisation strategy

Pilots

Imperial

Energy

Programme


Data

Metrics

Models

Solutions

Systems

Engineering

Year3-5 6-8 10-12

Realisation and

business models



Potential benefits

Potential effort

Detailed blueprints

Quantified metrics

Realisation strategy

Pilots

Imperial

Energy

Programme



– Identify how benefits might be achieved

– Explore power of modern optimisation techniques in urban context

– Investigate the energy lessons from the differences between cities such as London, Atlanta, and Beijing.

– To identify potential changes in energy market and supply structures and implications for BP

Outcomes

• Scenarios and validated models for energy demand evolution and supply innovation for developed and developing cities– Potential solutions – supply innovation and demand management strategies– Early sight of the local and global benefits of novel approaches– Impacts for BP and the energy supply industry

• Quantitative assessment of current and future alternative technology options in an urban context


• New approaches to optimisation in large self-organising systems

• Innovative engineering possibilities for energy conversion, storage and transport

• Blueprints for new approaches to expanding urban energy systems– New business models– Methodologies and tools– Trained personnel

Phase 1 Activities

1. Understand state of the art in UES analysis and modelling

2. Develop conceptual framework to characterise UES

3. Develop conceptual framework to capture the important interactions between UES, citizens and institutions, understanding consumer demand and supply innovation

4. Apply methodologies to characterise real and representative cities

5. Perform high level urban energy systems optimisation studies


Activity

Key

1

2

3

4

5

Year 1 Year 2 Year 3

today

The team


Project strategy and plan

Indicators

Workstreams

Urban systems modelling

Demand: transport/land use

Ecological/resource models

Demand: consumers

Syntheticcity


Energy supply networks

Supply Innovation

reviews modelsModel

interactionsModel

integration

Demand: consumers

Scenario selection

A city has basic defining characteristics. For example:

shape? diversity?


(CSIRO)

(Urban Age)

Scenario selection

Cities characteristics/aspirations:

shape

diversity

density

grouping

low medium high

low medium high


grouping

population

growth rate

major focus

climate

…

low medium high

small medium big

low medium high

Hinterland

Linking the city to the hinterland?


City scenario – boundary conditions

Hinterland

Geographical

features


City scenario

features

Hinterland

Airport/transport hub

Geophysical features


City type 459 City scenario


Hinterland


Geo features


City scenario


Regional/national/global roles

Land use

Given the city type scenario, distribute land use:



Land use

Land use

Geo features



Land use

Land use

Land use category

Geo features


Land use category


Land use

Land use

Land use category

Geo features


Land use category


Land use

Housing types …

Agent activities

Given land-use, model agent activities:



Land use

Agent activities

Agent activities

Domestic

characteristics



Land use

Agent activities

Agent activities

Domestic

characteristics

Non-domestic

characteristics



Land use

Agent activities

Agent activities

Non-domestic

characteristics Domestic

characteristics

Transport

infrastructure



Land use

Agent activities

infrastructure

Domestic

characteristics Mode

Agent activities

Non-domestic

characteristics

Transport

infrastructure



Land use

Agent activities

infrastructure

Resource flow

Given activities, model resource flow:

Resource flow



Land use

Agent activities

Resource flow

Resource flow

Resource

demand/output



Land use

Agent activities

Resource flow

Resource flow

Resource

conversion

Resource

demand/output



Land use

Agent activities

Resource flow

Resource flow

Resource

conversion

Resource

demand/outputelectricity

hydrogen

gas

waste

heat

( etc. )



Land use

Agent activities

Service networks

Resource flow

Service networks



Land use

Agent activities

Service networks

Resource flow

Service networks

Type



Land use

Agent activities

Service networks

Resource flow

Service networks

Type



Land use

Agent activities

Workstreams

Resource flow

Service networks

NetworksSystems

Consumer Business



Land use

Agent activities

Consumer behaviour

Business behaviour

LU-T

Workstreams

Resource flow

Service networks

Resource flow



Land use

Agent activities

Resource flow

Ecology

Workstreams

Indicators

Resource flow

Resource flow

Service networks


Indicators

Resource flow

Drivers

Activities

Flows/Stocks

Pressures


Land use

Agent activities

Indicators – our framework

Drivers + headline metrics

Activities + headline metrics

Flows & stocks + headline metrics

Domestic � Total demand � Intensity

System level � Emergy, exergy and thermodynamic metrics (e.g. emergy yield, 1

st & 2

nd order efficiencies)

� Ecological resilience, Fisher index or Shannon entropy measures of system sustainability � Policy oriented (policy-target, trend-target)

Demographics � Population � Education

Energy � Primary demand

(electricity, heat) � Sources

(renewable and

Pressures + headline metrics

Social � Road safety � Fuel poverty


Economy � Employment � Sectoral mix

Local environment & infrastructure � Climate & air

quality � Energy

infrastructure (transport, gas

Transport � Total demand � Intensity

Industrial � Total demand � Intensity

(renewable and non-renewable)

Land � Ecological and

urban demand � Waste

management

Water � Total demand � Waste flows

Economic � Congestion � Economic

sustainability (e.g. ISEW)

Environmental � Air quality � Climate impact � Water quality � Noise pollution

Commercial � Total demand � Intensity

Ecological models: Singapore – London comparison

Singapore Greater London

Population 4,240,000 7,517,700

Area km2 704 1,579

Density hab/km2 6,023 4,761

GDP per capita $ 32,866 53,000

Water use per capita (m3 per year) 125 117


Electricity consumption in 2003 in TWh 35.3 39.4

Electricity consumption in 2003 in MWh per capita

8.3 5.2

CO2 emissions per capita due to electricity production (t/capita)

6.3 2.9

Ecoindicator 99 impacts Gpt per capita for electricity generation ‘2003

0.3 0.1

Activity-based models – complete activity-travel pattern

of a worker

Home-Stay Duration

Work-Stay Duration

Home-Stay Duration

Home-Work

Commute

...

3 a.m. onday d

Leave home for non-work activities

Arrive back home

Leave for work

Arrive at work

Leave work

Before-Work Tour

Temporalfixity

S1 S2


Leave homefor non-work activities

...

Work-Based Tour

After Work Tour

Arrive back at work

Leave work Arrive back home

Arrive back home

Temporalfixity

Home-Stay Duration

Home-Stay Duration

Work-Stay Duration

Work-Home

Commute

S3 S4 S5 S6

3 a.m. onday d+1

Typically operational land use-transport interaction

models

Demographic &

Economic Changes

Firms & Families by Indust.

Sector/Household Group

Residential &

Employment Locations

LocationalAccessibilities

Travel

Demands

Traffic Volumes & Speeds

Network

Vehicle

Ownership

TransportDemographic &

Economic Changes

Firms & Families by Indust.

Sector/Household Group

Residential &

Employment Locations

LocationalAccessibilities

Travel

Demands

Traffic Volumes & Speeds

Network

Vehicle

Ownership

Transport


Floor-space

Demands

Available Land

Floors-pace supply

Building Allocation

& Change

Housing & Business Rents

Travel Costs

Network

Capacities

Land Use

Links to

capture land

use transport interactions

Floor-space

Demands

Available Land

Floors-pace supply

Building Allocation

& Change

Housing & Business Rents

Travel Costs

Network

Capacities

Land Use

Links to

capture land

use transport interactions

Innovation studies: understanding eco-city design

and operation

• Stage 1: DESIGN (master plan: eco-city system design)

– examines how Arup’s design and management approach was developed and how lessons learnt can be applied to other projects

– studies the innovation and decision-making processes used in the development of the design, including the technical tools employed and why certain options were ruled out

– explores the capabilities needed for this kind of design process


– explores the capabilities needed for this kind of design process

• Stage 2: BUILD & INTEGRATION

– examines how construction and systems integration phase is organised

• Stage 3: OPERATION

– examines performance of the Dongtan as urban operating system

Hierarchical modelling strategy


Synthetic city

Usefulness of synthetic city

• Can look like real cities without being data hungry

• Study extreme cases

• Avoid boundary condition issues

• Can isolate factors

• Can draw insights


• Can draw insights

• Helps to develop algorithms:

– City layout

– LU-T / ABMS models

– Resource flow models

– Service network design models

Model city aspects

• Resource flows to and from hinterland

• Spatially disaggregated (regions – engineering based, administratively/politically based, ….)


• Boundary conditions: where does the city end?

• Discretisation to describe space

– Polygons

– Functional network characteristics

– Admin/political (e.g. wards)

Model city – discretised into “cells”


Primary

Resource flows

Internal resource flows, mobility

Waste

flows

What is to be determined/described?

• Land use plan:

– Where to place housing, and what type

– Where to place other facilities: PE, SE, H, LI, C, L,…

– What transport infrastructure (if any) connects each pair of


– What transport infrastructure (if any) connects each pair of cells?

• What modes of transport are possible?

• What capacities?

Layout model: transport flows a simple function of

layout and infrastructure

What is to be determined?

• Activitities and transport demand

• Flows of people moving from A to B using mode m in season s, day d, hour t to participate in activity f


[introduces stochastic elements: agent based LU-T framework]

ABMS model

What is to be determined

• Resource flows, conversion and integration:

• Resource demands

– Drstg = demand for resource r, season s, hour t, block g

• For all activities other than transport


• For all activities other than transport

– DTrst = transport related demand for r, season s, hour t

– Implied by layout + LU-T model

• Technologies installed

• Resource flow

[so far, can be approximated as deterministic…]

RTN model

What is to be determined?

• Detailed service networks

– Possibly integrated

– Next generation: bidirectional flow, active control, …


Some objective functions

• Minimise total capital cost to establish city

• Minimise lifecycle cost

• Minimise lifecycle fossil energy

• Minimise lifecycle environmental impact (e.g. eco-99)

• Maximise global “utility” (take account of aspirations/preferences, e.g. housing type, car ownership and


aspirations/preferences, e.g. housing type, car ownership and use…)

– May need lower bound on worst case utility

• Design will be re-visited in iterative approach

– First layout is tentative to kick off the iterative algorithm

Themed cities

• Use model to study “themed” cities

– Electric city

– Hydrogen city

– Solar city

– Bioenergy city


• What

– Do they look like

– Is the effect on the hinterland

– Are the technical issues

– Are the key indicators/metrics: cost, efficiency, GHG, other environmental impacts, …

MDH

PE LI C

L

PE L PE

LI HDH SE H

Layouts


L PE SE

H MDH C

Balanced/leisure

city

C LI L

Light industrial

city

Resource technology network

• All resources identified and pooled in aggregate model; disaggregated in spatially disaggregated model– Some resources have external sources (e.g. natural gas, grid electricity)

– Some resources have external sinks (e.g. surplus electricity to grid, waste heat)

– Some resources have demands


– Some resources have demands• Differ by season of year and time of day

– Some resources may be stored• Storage technology and capacity may have significant costs

– Heat qualities discretised in version 1.0

• All technologies identified by capacity(ies) and interactions with resources – Interactions captured by energy balance coefficients (essentially efficiencies, CoP etc)

– Technologies may be “renewable”

Resource technology network: example

gas Gas turbine

electricity

High grade

heat

LP waterpump

Heat exch. Heat pump.

2.5

1.5

1.0

demandCO2


Low grade

heat

Medium grade

heatPRV

turbine

HP water

LP water

Polygen:Variable coefficients

Resource technology network:optimisation

• Given– Spatially and temporally explicity resource demands– Coefficents and metrics (cost, GHG etc) data, economies of scale

• Determine– Network construction

• What technologies?


• What technologies?• What scales?• What interactions?• Which resources are stored

– Network operation• Over seasons• Daily cycle

– Different technologies may be used at different times/seasons

• To optimise some metric of the network– May need multicriteria analysis


Storage

Resource

Store

Stored

Resource

Retrieve


Low-grade

Heat

Electricity

Hold

Example Problem

• City divided into 16 cells • Four resources

– Gas– Electricity– Transport fuel– Waste heat

• Import of gas and transport fuel only• Two types of distributed electricity generation process


• Two types of distributed electricity generation process– Available at 3 scales– Converts gas to electricity– Byproduct: waste heat

Example Problem

• Demands for gas and electricity– Max 3500 each in centre cells

– Max 3000 each in edge cells

– Max 2500 each in corner cells

• Multiplied by dynamic profiles, weighted by season


Electricity

Gas Sp Su Au Wi

Example Results — Gas

Demand

Imports

Nett production

Total in

Total out

Total stored

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000

1 11 21 31

-5000

0

5000

10000

15000

20000

25000

1 11 21 31 -5000

0

5000

10000

15000

1 11 21 31


time

quantity

-8000 -8000

-6000

-10000 -10000

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-5000

0

5000

10000

15000

20000

1 11 21 31

-60000

-40000

-20000

0

20000

40000

60000

80000

100000

120000

140000

1 11 21 31

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

1 11 21 31

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

-10000

-5000

0

5000

10000

15000

1 11 21 31

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

1 11 21 31

-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

1 11 21 31

Example Results — Electricity

Demand

Imports

Nett production

Total in

Total out

Total stored

-500

0

500

1000

1500

2000

2500

3000

1 11 21 31-1000

-500

0

500

1000

1500

2000

2500

3000

3500

1 11 21 310

500

1000

1500

2000

2500

3000

3500

1 11 21 31 -500

0

500

1000

1500

2000

2500

3000

1 11 21 31

-500

0

500

1000

1500

2000

2500

3000

3500

1 11 21 31-500

0

500

1000

1500

2000

2500

3000

3500

4000

1 11 21 310

1000

2000

3000

4000

5000

1 11 21 31 -500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 11 21 31


time

quantity

-1000

-500

-1000

-500 1 11 21 31

-1000

1 11 21 31

-1000

-500 1 11 21 31

0

500

1000

1500

2000

2500

3000

3500

1 11 21 31 -1000

0

1000

2000

3000

4000

5000

1 11 21 31 0

5000

10000

15000

20000

25000

1 11 21 31 -500

0

500

1000

1500

2000

2500

3000

3500

1 11 21 31

-500

0

500

1000

1500

2000

2500

3000

1 11 21 31-1000

-500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 11 21 31

-500

0

500

1000

1500

2000

2500

3000

3500

1 11 21 31-1000

-500

0

500

1000

1500

2000

2500

3000

3500

1 11 21 31

Example Results — Waste Heat

Demand

Imports

Nett production

Total in

Total out

Total stored

0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10 -5000

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 2 3 4 5 6 7 8 9 10 -5000

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

35000

40000

1 2 3 4 5 6 7 8 9 10

5000

10000

15000

20000

25000

30000

4000

6000

8000

10000

12000

14000

16000

50000

100000

150000

200000

250000

300000

10000

15000

20000

25000

30000

35000

40000

45000


time

quantity

0

5000

1 2 3 4 5 6 7 8 9 10 -2000

0

2000

1 2 3 4 5 6 7 8 9 10 -50000

0

50000

1 2 3 4 5 6 7 8 9 10-5000

0

5000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

35000

1 2 3 4 5 6 7 8 9 10 -5000

0

5000

10000

15000

20000

25000

30000

35000

40000

1 2 3 4 5 6 7 8 9 10 -5000

0

5000

10000

15000

20000

25000

1 2 3 4 5 6 7 8 9 10

-5000

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

35000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10

0

5000

10000

15000

20000

25000

30000

1 2 3 4 5 6 7 8 9 10

Example Results — Gas Transport

Transport network Max rate = 10000t = 14690 172

4970

10000 4401

10000 4763

t = 26181 3483

3100

10000 5905

10000 9932

t = 35691 2394

3160

10000 5410

10000 8231

t = 44454

5345

9646 4163

10000 4305

t = 53218

3862

6877 2916

7456 2822

t = 61734

2072

3545 1412

7790 1032

t = 71962

2355

8778 1650

10000 1315

t = 84219

5063

10000 3926

10000 4023

t = 95220

6264

10000 4935

10000 5224

t = 106221

7465

10000 5944

10000 6425


5280 10000 10000

392

4730 2644

6450 10000 10000

1023

6234 6890

6390 10000 10000

493

5739 5680

4555 10000 9646

829

4492 3785

3895 10000 6877

3246 1498

2090 10000 3545

1768

2375 10000 8778

1979 1438

5106 10000 10000

4255 5106

6317 10000 10000

5264 6317

7528 10000 10000

6274 7528

Example Results — Electricity Network

Infrastructure network Max rate = 10000


Example Results — Electricity Network Utilisation

t = 1t = 2t = 3t = 4t = 5t = 6156

t = 7t = 8t = 9t = 10t = 11t = 12t = 13t = 14t = 1550

t = 16t = 17125

t = 18t = 19t = 20t = 21t = 22t = 23t = 24t = 25t = 26156

t = 27t = 28t = 29t = 30t = 31t = 32

697 131

t = 33

349

t = 34t = 35t = 36t = 37t = 38t = 39t = 40

SpringSummerAutumnWinter


351125 11411885227486

4

286125 30602569195451500

3300

40821250

131

1876

6077

508

875

4256

4886

312

125

2437

30312012122418642901125

1043

3479500

3290

4293

Some case study cities

• London, New York, Shanghai

– The “world cities energy partnership”

• Atlanta

– Low density US city; the AtlantIC Alliance

• Dongtan and Chula Vista


• Dongtan and Chula Vista

– Greenfield “eco-cities”

• A Chinese city to be decided

– Collaboration with the BP-Tsinghua clean energy centre

• Singapore

• …

Conclusions

• A high level study of what is possible in cities

– Not diving into too much detail

– Not constrained by implementation issues

– Holistic – looking for integration opportunities

• Based on interlinked models


• Based on interlinked models

– Conceptual and mathematical

• Combines technologies, infrastructure, communities, governance, business, …

• Assess options against a variety of indicators

Documents

Urban Energy Systems Project