How to build resilience.io for sustainable urban energy and water systems

How to build resilience.io for sustainable urban energy and water systems

Agent-Based Modelling and Resource Technology Optimization

Xiaonan Wang, Koen H. van Dam, Charalampos Triantafyllidis, Rembrandt Koppelaar and Nilay Shah

Department of Chemical Engineering, Imperial College London, UK

resilience.io platform

Energy Futures Lab Seminar December 2nd, 2016

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Motivation: Sustainable Development Goals

http://www.un.org/sustainabledevelopment/sustainable-development-goals/ By 2030

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Universal and equitable access to safe and affordable drinking water and adequate sanitation and hygiene

•  1.8 billion people globally use a source of drinking water that is fecally contaminated •  2.4 billion people lack access to basic sanitation services, such as toilets or latrines

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Universal access to modern energy services, improve efficiency and increase use of renewable sources

•  One in five people still lacks access to modern electricity •  3 billion people rely on wood, coal, charcoal or animal waste for cooking and heating

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Cities of opportunities for all, with access to basic services, energy, housing, transportation and more

•  More than half of the population (~3.5 billion) live in cities today, and reaching 70 percent by 2050 •  828 million people live in slums today and the number keeps rising

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Motivation: Smart Cities

*Source: UN Habitat (United Nations Human Settlements Program)

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Motivation: resilience.io

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resilience.io

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Motivation: resilience.io

Open access free at source for decision making

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Motivation: Energy-Water-Food Nexus

8%

70% 30%

1%

15%

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Motivation: water and energy systems in sustainable city development

Hydro Plant Labour input Hours/day No. people Job-types

Waste heat

Material inputs Quantity/hour ’’ /day ’’ /week ’’ /month ’’ /year

Goods outputs Quantity/hour ’’ /day ’’ /week ’’ /month ’’ /year

Energy input Electricity Heat Fuels

Liquids Wastes Volume/time

Solid Wastes Mass/time

GHG Emissions

Operational cost Currency/time for labour, energy, materials

Investment cost Currency/facility

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Methodology of modeling and optimization

Local data collection (Satellite, sensor, survey, census etc.)

Data interpretation

Model and Data Initialization

Population and resource parameters adjustment

Option Selection

Technology options

Policy options

Planning options

Scenario construction

Benchmark scenario

Macro Socio-Economic Scenarios

Blueprints Scenario A

Blueprints Scenario Z

Model output

Scenario trajectory

Key performance indicators

Spatial temporal visualization

Individual option assessments

Validation run User defined test Scenario runs and decision making

Model operation

A data-driven open source platform

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Workflow and data structure

Socio-economic Data Demand Simulation Supply Optimization

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Workflow and data structure Spatial-temporal socio-economic dynamics

Java implementation

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Demand-side: Agent-Based Modeling (ABM) Example of Agent Characteristics

Agent

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Demand-side: Agent-Based Modeling (ABM) 1.  Synthetic population generated from a pre-processed master table

that represents the actual population with socio-economic variants. 2.  Demand for water, electricity and other resources estimated based on

agents activities. 3.  Output data visualised and connected to optimisation model.

APi= {(ACTj, MDTj, SDj, PDj)} ACTj : Activity j MDTj : Mean departure time SDj : Standard deviation PDj : Probability of departure

Master Table (part)

Agent Activities Time dependent regressive functions adopted to estimate each agent’s water, electricity

and facilities use based on their characteristics and

activities throughout the day

Time-variant simulation: electricity/ water demand

every 5 minutes Aggregated per region

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Supply-side: Resource Technology Network (RTN)

Data-driven optimization model using mixed-integer linear programming (MILP) Objective function: min L (Demand, Supply, Scenarios) = Σα1(Capital Expenditure) + Σα2(Operating and Maintenance Cost) + Σβ (Environmental Cost) − Σλ (Economic Benefits) Summation over minor time periods t (e.g., peak, normal, off-peak hours) to guarantee supply-demand matching over all major time periods tm (e.g., whole year, multi-year period)

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Supply-side: Resource Technology Network (RTN) Data-driven optimization model using mixed-integer linear programming (MILP) Constraints: Technology and Investment balance/ limits, N(j, i, tm) = N(j, i, tm-1) + INV(j, i, tm). Resource balance and capacity limits- mass and energy balance, D (r, i, t, tm) = MU * P (j, i, t, tm) + Q (r, i’, i, t, tm) – Q (r, i, i’, t, tm) + IM (r, i, t, tm) Production limits based on capacities, P(j, i, t, tm) ≤ N(j, i, tm) CF(j) CAP(j). Flow limits based on pipe and grid connections and capacities- geometric distance related. Other realistic factors, e.g., -  pipe leakage/transmission loss; -  existing infrastructure: set as pre-allocation matrix.

j – Technologies: electricity generation plants, water treatment facilities … i – Districts: spatial zones/ cells r – Resource: raw water, wastewater, process chemicals, solid waste, electricity, labor…

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Supply-side: Resource Technology Network (RTN) Data-driven optimization model using mixed-integer linear programming (MILP)

Objective function: Z = ΣWT(m, tm) VM(m, tm), where WT(m, tm): weighting factors for metrics including CAPEX, OPEX, GHG VM(m, tm) = ∑ VIJ (j, i, m) INV (j, i, tm) + ∑ VPJ (j, i, t, m) P(j, i, t, tm) + ∑ VQ (r, t, m) dist (i, i′) Q(r, i, i′, t, tm)

+ ∑ VI (r, t, m) IM(r, i, t, tm) + ∑ VY (r, m) dist (i, i′) Y(r, i, i′, tm) Decision variables: INVj, i, tm (investment of technology j in district i during time period tm) Pj, i, t, tm (production rate of technology j in district i during time period t, tm) Qr, i, i’, t, tm (flow of resource r from district i to i’ during time period t, tm) IMr, i, t, tm (import of resource r to district i during time period t, tm) Yr, i, i’, tm (distance based connection expansion [binary], e.g., water pipeline, electrical grid, for resource r in district i during time period t, tm)

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Case study (WASH and power sectors in GAMA)

Example optimization model output:

§  Resource supply & demand matching §  Investment suggestions in energy and water sectors (i.e., infrastructure expansion, operational strategies).

Central source watertreatment plantBorehole with pipes

Protected wells and spring Sachet drinking water plant

Bottled water plantCentral waste water treatment plant

Waste stabilization pondAerated lagoon

Decentralized activated sludge systemDecentralized anaerobic bio-gas treatment plant

Decentralized aerobic treatment plantDesalination plant

Chlor-alkali plant (example industrial plant) Fossil fuel electricity station (e.g. coal, nuclear)

Hydro electricity stationPV solar electricity station

Bioethanol from sugar cane, cassava

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Case study (demand simulation)

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Residential electricity demand profile per district over 24 hour period

n  Total residential water demand per district over 24 hour period in year 2030

n  Projection of demands (m3) for 2010-2030 socio-demographic scenario

Sub-results for residential water use

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Sub-results for sanitation

n  Total toilet use profile per MMDA per MMDA over 24 hour period

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Use Times

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Optimal clean water flows (thousands of m3) among districts

Optimal un-treated waste water flows (m3) among districts

Water sector (investment strategies)

GAMA WASH SDG - 100% water supply and wastewater treatment by 2030

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Additional treatment capacity needs by 2025: 200,000 m3/day

Water sector (What technologies and capacity can meet future needs?)

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Water sector (How will the proposed system(s) affect other sectors?)

New desalination plant substantially increases electricity needs:

350,000 kWh /day

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Additional treatment needs capacity needs by 2025 : 200,000 m3/day

Water sector (What will be the cost and is it affordable?)

Population and Demands 2015 2025 Population 4.39 million 5.68 million

Faecal Sludge Generation 6,651 m3/day 8,708 m3/day Waste-Water Treatment Needs

243 thousand m3/day 423 thousand m3/day

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Water sector (What will be the cost and is it affordable?)




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Water sector (What will be the total cost?)




Public Decentralised (million USD) 2010-2015 2015-2025 Expenditure for treatment capacity 90 260 Expenditure for public toilets 42 192

Total Capital Costs 132 352

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Water sector (Will it be affordable?)

GAMA – 15 MMDA values 2015 (million USD) 2025 (million USD) Total operational costs per year 55.6 80.5 Revenues from public toilet use 33.0 82.0 Costs per Citizen per year (USD)

12.7 11.6

GAMA – 15 MMDA values 2015 2025 Greenhouse emissions in tonnes per year

2011 7516

Total jobs for sewerage system 82 625

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Case study (supply technologies)

Key Metrics Units

Nameplate capacity m3 of drinking water output per

year

Capacity factor of

operation

0-100%

Resources, especially

raw source water,

electricity, and labour

hours used

m3 of raw water, kWh of

electricity, hours of labour to

produce 1 m3 potable water

Side products or wastes e.g., tonnes of CO2 emissions

Capital investment CAPEX in USD

Operating cost OPEX in USD

Technology process block example: Water purification by sun http://desolenator.com/

Potential to be more cost-effective and selected as a promising alternative for drinking water supply when its capacity can be scaled up to 10 times.

Demand of drinking water ~4.7 million m3 per year estimated by ABM for the whole region.

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Water-Energy (supply optimization)

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Energy sector (supply technologies)

Technology Sub-Type People fed Typical capacity (MJ) Photovoltaic P-Si, ground 4334 630,720,000 CSP Trough 9865 1,576,800,000 Hydropower Small 1 63,072,000 Hydropower large 1561 37,212,480,000 Coal IGCC 101104 15,768,000,000 Gas CCGT 4 7,884,000,000 Coal IGCC + CCS 101104 15,768,000,000 Gas CCGT + CCS 4 7,884,000,000 Nuclear PWR 62 9,460,800,000 Wind Onshore 63 94,608,000 Wind Offshore 3 113,529,600 Biofuel Cassava - 109618 2,743,632,000 Biofuel Sugar Cane - 191434 2,743,632,000

31= ( )2t p wP C Avλ ρ

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Power generation technology mix in GAMA from model suggested investment strategies

(Land use is considered)

Energy sector: baseline scenario

Energy sector (supply optimization)

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Optimal electrical power grid extension suggested for the electricity network (CAPEX of around 73.39 million USD).

Energy sector (investment strategies)

Optimal transmission schedule among districts (MW of power).

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Energy sector (scenario tree analysis)

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Case study (supply technologies)

Electricity generated from renewable

technology/ source

FIT (USD/

kWh)

Wind with grid stability systems 0.139

Solar PV with grid stability/storage

systems

0.161

Hydro (≤ 10 MW) 0.134

Hydro (10 - 100 MW) 0.135

Landfill Gas, Sewage Gas and Biomass 0.140

Biomass (plantation as feed stock) 0.158

Technology process block example: Biogas process

Energy-Water-Food Nexus

Decentralized anaerobic biogas treatment plant Combing wastewater and food scraps treatment, heat and power generation, and fertilizer by-product

Capital investment: 3,092 USD Operational costs: 0.07 USD per m3 of sewage waste treated Revenues from selling co-generated electricity: 480 USD per year Total value of 0.6 million USD in 2030, with 724 job opportunities

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Roadmap (using resilience.io) Ø  Workshop on model use and project development in Ghana

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Roadmap (using resilience.io) Ø  Workshop on model use and project development in Ghana

Slum neighbourhood in Accra, Ghana

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Next steps (developing resilience.io)

Ø  Design your city in the most efficient way using games Data driven technical innovations

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Next steps (developing resilience.io) Ø  Game theory into agent based models

Arneth, A., Brown, C. and Rounsevell, M.D.A., 2014. Global models of human decision-making for land-based mitigation and adaptation assessment. Nature Climate Change, 4, pp.550-557.

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Acknowledgements

Wonderful and diversified team:

Dr. Koen H. van Dam

Dr. Charalampos Triantafyllidis

Dr. Rembrandt H.E.M. Koppelaar

Dr. Kamal Kuriyan

Dr. Wentao Yang

Prof. Nilay Shah

Jen Ho Ker

Niclas Bieber

Stephen Passmore

Andre Head

Dr. Miao Guo

Prof Peter Head

Great and supportive agencies:

Department of Chemical Engineering, Imperial College London, UK

The Ecological Sequestration Trust

DFID UK Future Cities Africa

Ghana technical group

IIER – Institute for Integrated Economic Research

Energy Futures Lab

The analysis was carried out as part of the DFID funded Future Proofing African Cities for Sustainable Growth project. The authors are grateful to DFID for financial support (grant number 203830).

Thank you! Any questions?

Xiaonan Wang [email protected] tel (UK): +44 (0) 7874 349693 http://www.imperial.ac.uk/people/xiaonan.wang

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Science

How to build resilience.io for sustainable urban energy and water systems