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

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Dr Xiaonan Wang presents the How to build resilience.io for sustainable urban energy and water systems, Energy seminar for The Energy Futures Lab at Imperial College, London on 2nd December 2016

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

  1. 1. 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 1
  2. 2. 2 Motivation: Sustainable Development Goals http://www.un.org/sustainabledevelopment/sustainable-development-goals/By 2030
  3. 3. 3 Motivation: Sustainable Development Goals 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
  4. 4. 4 Motivation: Sustainable Development Goals 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
  5. 5. 5 Motivation: Sustainable Development Goals 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
  6. 6. 6 Motivation: Smart Cities *Source: UN Habitat (United Nations Human Settlements Program)
  7. 7. 7 Motivation: resilience.io
  8. 8. 8 resilience.io
  9. 9. 9 Motivation: resilience.io Open access free at source for decision making
  10. 10. 10 Motivation: Energy-Water-Food Nexus 8% 70%30% 1% 15%
  11. 11. 11 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
  12. 12. 12
  13. 13. 13 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
  14. 14. 14 Workflow and data structure Socio-economic Data Demand Simulation Supply Optimization
  15. 15. 15 Workflow and data structure Spatial-temporal socio-economic dynamics Java implementation
  16. 16. 16 Demand-side: Agent-Based Modeling (ABM) Example of Agent Characteristics Agent
  17. 17. 17 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
  18. 18. 18 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)
  19. 19. 19 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…
  20. 20. 20 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)
  21. 21. 21 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 plant Borehole with pipes Protected wells and spring Sachet drinking water plant Bottled water plant Central waste water treatment plant Waste stabilization pond Aerated lagoon Decentralized activated sludge system Decentralized anaerobic bio-gas treatment plant Decentralized aerobic treatment plant Desalination plant Chlor-alkali plant (example industrial plant) Fossil fuel electricity station (e.g. coal, nuclear) Hydro electricity station PV solar electricity station Bioethanol from sugar cane, cassava
  22. 22. 22 Case study (demand simulation)
  23. 23. 23 Case study (demand simulation)
  24. 24. 24 Case study (demand simulation)
  25. 25. 25 Case study (demand simulation) Residential electricity demand profile per district over 24 hour period
  26. 26. 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 26
  27. 27. Sub-results for sanitation n  Total toilet use profile per MMDA per MMDA over 24 hour period 27 Use Times
  28. 28. 28 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
  29. 29. 29 Additional treatment capacity needs by 2025: 200,000 m3/day Water sector (What technologies and capacity can meet future needs?)
  30. 30. 30 Water sector (How will the proposed system(s) affect other sectors?) New desalination plant substantially increases electricity needs: 350,000 kWh /day
  31. 31. 31 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
  32. 32. 32 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
  33. 33. 33 Additional treatment needs capacity needs by 2025 : 200,000 m3/day Water sector (What will be the total cost?) 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 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
  34. 34. 34 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
  35. 35. 35 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.
  36. 36. 36 Water-Energy (supply optimization)
  37. 37. 37 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 = ( ) 2 t p wP C Avλ ρ
  38. 38. 38 Power generation technology mix in GAMA from model suggested investment strategies (Land use is considered) Energy sector: baseline scenario Energy sector (supply optimization)
  39. 39. 39 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).
  40. 40. 40 Energy sector (scenario tree analysis)
  41. 41. 41 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
  42. 42. 42 Roadmap (using resilience.io) Ø  Workshop on model use and project development in Ghana
  43. 43. 43 Roadmap (using resilience.io) Ø  Workshop on model use and project development in Ghana Slum neighbourhood in Accra, Ghana
  44. 44. 44 Next steps (developing resilience.io) Ø  Design your city in the most efficient way using games Data driven technical innovations
  45. 45. 45 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.
  46. 46. 46 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).
  47. 47. Thank you! Any questions? Xiaonan Wang xiaonan.wang@imperial.ac.uk tel (UK): +44 (0) 7874 349693 http://www.imperial.ac.uk/people/xiaonan.wang 47

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