1 Introduction to energy planning in cities and key toolsProf.dr.sc. Neven Duić, IC SDEWES Workshop on Tools and Methodologies for Municipal Sustainable Energy Planning webinar, September 6, 2017

2 Do we need energy planning? YESLong investment cycles Integration of power, heating, cooling, transport, water Solve technical issues particular to your community Build arguments and have answers to all criticism Accounting for SEAPs, strategies, budgets etc.

3 Security of energy supply Employment and regional development policiesEU energy context Security of energy supply Import dependence from 50% to 70% by 2030 Employment and regional development policies Deindustrialization and trade liberalization “Boosting growth and jobs by meeting our climate change commitments” Mitigation of global warming Environmental protection Sustainable development

4 Security of energy supplyEU energy context Security of energy supply EU: Import dependence from 50% to 70% by 2030 Energy dependence is potentially catastrophic, but primarily reduces day to day independence EU has not much fossil fuels left Nuclear is good but quite expensive (110 EUR/MWh), politically difficult in some countries, additionally expensive for small countries, even more for new entrants Therefore renewables are only available to increase security of supply

5 Security of energy supply - renewablesEU energy context Security of energy supply - renewables Hydro – excellent but all viable projects have been made long time ago Biomass – excellent but also very much used in EU, any further increase risks food production and environment Low solar to electricity efficiency (2% * 20% = 0,4%) Only waste biomass and from co-production is sustainable Geothermal – complicated and only available in some places Wind – available everywhere and cheap Solar – available everywhere and now cheap

6 Integral energy planningBUSINESS AS USUAL ENERGY USE EFFICIENCY RESOURCES & REDUCED USE ENERGY REQUIREMENTS UNSUSTAINABLE ENERGY RESOURCES SUSTAINABLE ENERGY RESOURCES TIME Blair Hamilton, Managing energy demand, 2009

7 Integral energy planningSOURCE: International Energy Agency

8 LCOE – various technologies

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10 Barriers to Higher RES PenetrationPUBLIC PERCEPTION Lack of knowledge and awareness / information; Lack of Familiarity with RES potential (Technical and Economic); ECONOMIC/FINACING Investor confidence / perceived risk; Low Conventional Energy Prices; Financing Structures; Taxes; Fossil fuel subsidies; Lack of markets NATURE OF RENEWABLE ENERGY SOURCES Intermittence; Technical limits for grid penetration; Need for back-up or Storage; INERTIA Centralized (existing) Infrastructure; Immaturity of Market and Infrastructure; Distance from main Grid / Infrastructure; Weak lobby; STRUCTURAL BARRIERS Government Policies; Decision Making Structures; Legislation and Regulations, Standards and Codes; Lack of policy harmonization; ENVIRONMENTAL BARRIER Local Environmental Impact (noise, visual, land use…)

11 Electricity supply – old wayVertically integrated system: Base load (NPP + coal PP) Peak load (HPP + gas PP) Transmission and dispatching Distribution and sales Customers – tariff PSO Does not work with high penetration of RES and markets

12 Sustainable CommunitySolar panels COMMODITIES Gasification Heat Electricity Cold Hydrogen Storage Water Wastewater Biogas Solar Wind Sustainable Community RESOURCES TECHNOLOGIES PV panels Electrolysis Fuel cell Trigeneration Desalination Reforming Sea Biomass Hydro Fresh water Reversible hydro p.p. Waste Biofuels Esterification Fermentation Geothermal Biomass p.p. Wind turbines Geothermal p.p. Wastewater treatment Wave & tidal Energy Planning course - MF Niš, 2010

13 RenewIslands/ADEG METHODOLOGYMapping the needs Mapping the resources Devising scenaria with technologies that can use available resources to cover needs Modelling the scenaria

14 How to increase penetration of renewables in energy system?More grid capacity Cycling of thermal power plants Power exchanges Demand response and integration of power, heating, cooling, transport and water systems – smart energy systems Energy storage

15 Electricity production in Germany in week 18 2016

16 Skagen DH – power to heat

17 Demand management Electromobility Only personal cars and short distance utility vehicles, PHEV and BEV sold in (http://www.ev-volumes.com/country/total-world-plug-in-vehicle-volumes/) If RESe 80% reduction of primary energy Fast charging 70 kW – huge problem if left uncontrolled, ex AT, 4 mln cars arrives home, plugs in – 280 GW (14 GW installed cap) Smart charging – market based, smoothing the demand

18 Smart charging

19 Integration of wind + heating + transport

20 Which tools? A review of computer tools for analysing the integration of renewable energy into various energy systems, D.Connolly, H.Lund, B.V.Mathiesen, M.Leahy, https://doi.org/ /j.apenergy ICLEI, LEAP, GIS, EnergyPLAN, Dispaset, Retscreen, Markal/TIMES, PLEXOS, GIS tools, MATLAB, EnergyPRO, H2RES ….

21 Which tools? Long term accounting, penetration of new technologies and phase out of old: LEAP easy, good for communities Markal/TIMES hard Technical simulation, how does one technology work with the system: EnergyPLAN easy Distributed planning, for example heating system, roof PV, grids: GIS family of tools

22 Next step: Harmonisation of SECAPs & SUMPs = SMART ENERGY CITIESDevelopment of Covenant of Mayors Initiative Next step: Harmonisation of SECAPs & SUMPs = SMART ENERGY CITIES The harmonisation should focus on the following aspects: Creation of a joint database gathering data on energy, environment, climate and mobility Harmonisation of the plans’ timeframes, namely their reference years and the timing of their monitoring Definition of common indicators Joint management of participatory processes (i.e. the involvement of stakeholders) Electric mobility, which is one of the main joining links between SEAPs and SUMPs and therefore the area that should be developed the most, together with the production of electricity from renewable energy sources Sources: 10/12/2017

23 Assessment of the current situationICLEI Europe's Basic Greenhouse Gas Inventory Quantification Tool Energy consumption and Baseline emissions inventory Excel sheets which are used to calculate overall energy consumption and quantify greenhouse gas emissions Divided into government and community sectors Government sectors: Buildings, Vehicles, Public Lighting, Water and Sewage, Waste management Community sectors: Residential, Commercial, Industry, Transportation, Waste, Agriculture, Local Energy Production Tool automatically calculates energy consumption in MWh and emissions in tCO2e Available for free to: Local government  Regional government  Local / regional energy agency  Local government association / networks Source: 10/12/2017

24 Assessment of the current situationSource: 10/12/2017

25 Assessment of key factors for the implementation of SEAP measuresUsed in BEAST project for the assessment of SEAP implementation Adapted CAF/EFQM model (Common Assessment Framework/European Foundation for Quality Management) Based on the grade system from 1 to 5 Everything below 3 needs to be improved Source:

26 Self assessment results for the city of Velika GoricaSource:

27 Self assessment results for the city of Velika GoricaSource:

28 Vision and Action Plan developmentSetting up goals and targets: Specific Measurable Achievable Realistic Time-Bounded Choice of measures and scenarios based on Best Practice Examples Development of scenarios using energy planning tools: EnergyPLAN Homer H2RES LEAP, … Prioritisation of measures implementation: Decision makers choice Stakeholders decision Analysis using: Marginal Cost Abatement Curve Pareto line Multi criteria analysis Source: F. Levihn, “On the problem of optimizing through least cost per unit, when costs are negative: Implications for cost curves and the definition of economic efficiency,” Energy, vol. 114, pp. 1155–1163, 2016. 10/12/2017

29 Integral energy planningModeling long term energy demand is the first step towards advanced energy systems and their analysis  since its results presents one of the key input data used for energy systems optimization It is crucial to analyze and quantify all influencing mechanisms (demography, economy, policy, learning curves etc. ) Transition to low carbon society will mean life in energy neutral buildings, integrated electric vehicles etc.

30 Integral energy planning2020, 2030, 2040, 2050…….

31 Long term energy demand modellingClassical energy demand planning is usually focused on establishing relationship between economic variables energy consumption This is usually done based on analyzing different historical data and processing them in a relatively simple way It is argued that this approach has become inefficient in the case of EU countries which strive towards decoupling their economic growth and energy consumption.

32 Long term energy demand modellingCroatia Denmark

33 Long term energy demand modelling approachesEnergy demand modeling and forecasting can be divided into several approaches and philosophies: Simple approach (trend line analysis) Sophisticated approach Econometric End-use Input-output Accounting frameworks Hybrid

34 Long term energy demand modelling approachesIt is often difficult to tell the difference between approaches and some of the models in the literature can easily be classified under one and the other approach. IPCC AR4 report uses top down terminology for all models that have the integrated approach while the bottom-up terminology is used for all the models that focus on individual technology. Based on the literature, bottom-up models generally predict a lower energy demand, with a better description of energy efficiency measures, as opposed to the top down model. The second level of the bottom-up modelling refers to the phase-out or phase-in of certain technologies which can’t be modelled by energy efficiency increase factors

35 Long term energy demand modelling approaches – accounting frameworksRather than simulating decisions of energy consumers and producers, modeler explicitly accounts for outcomes of decisions  Accounting Frameworks simply examine the implications of a scenario that achieves a certain market share. Example: “What will be the costs, emissions reductions and fuel savings if we invest in more energy efficiency & renewables vs. investing in new power plants?” Examples: LEAP

36 Long term energy demand modelling approaches – accounting frameworksPros: Simple, transparent & flexible, lower data requirements Does not assume perfect competition. Especially useful in capacity building applications. Cons: Does not automatically identify least-cost systems: less suitable where systems are complex and a least cost solution is needed. Does not automatically yield price-consistent solutions

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38 Long term energy demand modelling approaches – LEAPLong range Energy Alternatives Planning System Accounting framework, user-friendly, scenario-based, integrated energy-environment model-building tool. Scope: energy demand, energy supply, resources, environmental loadings, cost-benefit analysis, non-energy sector emissions. Most aspects optional. Time: medium to long-term, annual time-step, unlimited number of years.

39 Long term energy demand modelling approaches – LEAPFlexible Approach to Modeling: basic relationships are all based on non-controversial physical accounting. Data requirements: flexible, low initial data requirements. Includes TED database, with technical characteristics, costs and emission factors of ~ 1000 energy technologies. Geographic Applicability: local, national, regional.

40 Long term energy demand modelling approaches – LEAPTool for Strategic Integrated Energy-Environment Scenario Studies: Energy Outlooks (forecasting) Integrated Resource Planning. Greenhouse gas mitigation analysis. Energy balances and environmental inventories.

41 Long term energy demand modelling approaches – LEAPHierarchical accounting of energy Choice of methodologies. Optional modeling of stock turnover. Energy Resources: Tracks requirements, production, sufficiency, imports and exports. Optional land-area based accounting for biomass and renewable resources.

42 Long term energy demand modelling approaches – LEAPEnergy Conversion Simulation of any energy conversion sector Electric system dispatch based on electric load-duration curves. Costs: All system costs: capital, O&M, fuel, costs of saving energy, environmental externalities. Environment All emissions and direct impacts of energy system. Non-energy sector sources and sinks.

43 Long term energy demand modelling approaches – LEAP-2 000 -1 500 -1 000 - 500 500 1 000 billion dollars (2000) Difference Additional demand-side investment Efficiency measures Avoided supply-side Generation Transmission Distribution Source WEO 2004 Difference in Electricity Investment in the Alternative vs. Reference Scenario

44 The EnergyPLAN Model: Energy System Analysis Model with the focus on comparing different regulation systems ability to integrate variable renewable energy sources Simplified modelling of energy system: Inputs from demand (hourly electricity, heat, cooling demands, aggregated yearly demands in various sectors) and supply side (installed capacities of production units, hourly distributions for RES) Yearly, monthly and hourly values of electicity production, import/export balances, critical excess production, share of RES, CO2 emissions

45 Source: B. V. Mathiesen, Aalborg, Denmark

46 Source: H. Lund, Aalborg, Denmark

47 Integrating solar production – limiting CEEPSolar and wind production – variable sources Increasing the solar PV integration in various scenarios – different demand response technologies Up to 2000 MW of Solar PV can be integrated with limited CEEP! S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 Minimum CHP [MW] 150 Minimum PP [MW] 200 PTH Storage [GWh] 2,25 4,5 10 HP COP 2,5 HP [MW] 90 180 100 EV cons. [TWh] 0,4 0,72 DEMAND RESPONSE: Electric vehicles in vehicle-to-grid concept Flexible power plants operation Power-to-heat

48 The Dispa-SET model Execution flowchart Conversion tool: Python GAMSSim template: 34 Excel sheets (Ignacio’s model) Raw data: csv sheets from the TSO Data Template: Mysql database Results template: 1 excel sheet with BOTH the inputs and the results Sim template: GDX file Sim template: Python Format Raw data processing: Read csv sheets, assemble data Convert to the right format (timestep, units, etc). Define proper time index (duplicates not allowed) Connect to database Check if data is present & write it down Write metadata Conversion tool: Python GAMS Dos/Batch

49 Financing EE Projects in Bulgarian SchoolsGIS tools for use by municipalities Commercial tools ArcGIS Free tools qGIS Custom made solutions Geoportal, GIS based building census…

50 Examples of GIS development in CroatiaExample, city of Velika Gorica in Croatia GIS map of all individual buildings classified based on their heating demand Additional info including height, type and surface area

51 Examples of GIS development in CroatiaExample, city of Velika Gorica in Croatia GIS map of the DH and natural gas infrastructure Useful analysis and future planning DH and natural gas overlap!!!

52 Examples of GIS development in CroatiaExample, city of Velika Gorica in Croatia Heat demand GIS map calculated using the building data (100X100 meters) Demonstration of high centres of heat demand suitable for DH expansion

53 Examples of GIS development in CroatiaExample, city of Velika Gorica in Croatia GIS analysis of suitable location for DH based on cost and price of heat and existing infrastructure It is possible to group centres of high demand and exclude outliers

54 Examples of GIS development in CroatiaExample, city of Zagreb in Croatia Capital city, large DH grid GIS map of cooling demand with highlighted potential consumers and existing as well as planned infrastructure Developed with DC in mind

55 GIS tools for use by municipalitiesThe use of GIS allows for precise and integrated city planning Sinergy between city offices and departments Easy and quick access to vital information in one place Can be used for the planning and optimisation of some activities such as waste collection From an energy perspective – better spatial matching of supply and demand Quality is highly dependent on the availability of data