MAKING ENERGY MODELING SPATIALLY EXPLICIT TO ENABLE A COST- OPTIMAL RESOURCE USE

Transcription

1 MAKING ENERGY MODELING SPATIALLY EXPLICIT TO ENABLE A COST- OPTIMAL RESOURCE USE Marianne Zeyringer Marianne.zeyringer@ec.europa.eu Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences (BOKU), Austria European Commission, Directorate- General Joint Research Centre, Institute for Energy and Transport, Energy Systems Evaluation Unit, The Netherlands

2 OVERVIEW I. Background information - Spatially explicit characteristics of the energy system II. III. IV. Research Questions Modeling the maximum integration of PV into the distribution grid Conclusions and future work / expected results

3 I. BACKGROUND INFORMATION - SPATIALLY EXPLICIT CHARACTERISTICS OF THE ENERGY SYSTEM

4 I. BACKGROUND INFORMATION EC target: Increase share of RES in the gross final energy consumption from 8.5% in 2005 to 20% in 2020 Existing energy system: spatially explicit characteristics less important Energy system with high integration of RES is constrained by geographic characteristics: - Demand - RES potential - Infrastructure

5 I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE ENERGY SYSTEM Demand - Location specific - Building specific - Industrial profiles: autoproduction? - Importance of load profiles Infrastructure Electricity grid - New building: network optimization problem - Integration of RES - Location of necessary upgrades CO2 District Heating Hydrogen

6 I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE ENERGY SYSTEM Renewable energy sources Biomass: Trade off between transportation distance, plant location and plant size Hydropower: - reservoir and pumped: topography, falling height - run of river: slope, velocity, water depth, flowlines Geothermal: - High temperature: highly location specific transmission line upgrades - Low temperature: conductivity, thermal capacity, temperature, topography

7 I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE ENERGY SYSTEM Spatial diversification decreases variability Solar energy: irradiation, temperature, shadowing, turbidity, cloudiness, rooftops, grid upgrades Wind: Resource potential, grid upgrades

8 I. SPATIALLY EXPLICIT CHARACTERISTICS OF THE ENERGY SYSTEM: MODELS - Mixed integer programming - Geographic Information Systems - Simulation Studies - Grid Studies But currently energy system models have relevant spatial limitations - models do not consider the real cost limitations due to geographical distribution of resources and of demand - new capacity can be added at a fixed generic cost which does not consider specific real terrain constraints

9 I. CONCLUSIONS Spatial disaggregation of the generation potentials and demand patterns estimate and optimize the required investments in RES by taking into account the necessary infrastructure reinforcements Importance of studying the interaction of several parts of the energy system in one model - example: Lack in methodologies to determine the maximum integration of small RES such as PV - impact depends on: local PV potential, installed infrastructure, regional composition of consumers Data limits on the spatially explicitly resource supply potential and the demand side, e.g. spatially disaggregated load data need of methodologies capable of simulating the data required

10 I Conclusions of spatially explicit energy systems: MODELS Spatially explicit energy models: most common for forest energy Extension of BeWhere (Leduc et al, 2008), Schmidt et al MIP model: whole supply chain from biomass production to delivery of the final product, optimal locations of biofuel plants, biomass CHP minimizes production costs with regard to distances to biomass supply and fuel demands bottom up optimization model

11 II. RESEARCH QUESTIONS

12 II. RESEARCH QUESTIONS How to model geographically explicit electricity load profiles with limited data? What is the maximum PV integration in the distribution grid? What are the associated costs for a higher integration? What is the cheapest option to manage high integration rates? (upgrade, integration of electric vehicles ) and what are the associated opportunity costs

13 III. MODELING THE MAXIMUM INTEGRATION OF PV INTO THE DISTRIBUTION GRID

14 III. DATA AND METHODOLOGY TO MODEL THE MAXIMUM INTEGRATION OF PV IN THE DISTRIBUTION GRID Number of households and employees per sector per km² Measured Industry load profiles Standardized load profiles PV: high spatial and temporal resolution Address points Exact location of transformers Rooftop areas I. Modeling of demand data II. Simulation of distribution grid using minimum spanning tree algorithm III. Net flow calculations in order to determine the maximum PV integration IV. Step cost curve for additional integration V. Costs for other options (opportunity costs)

15 IV. SIMULATION OF DISAGGREGATED LOAD PROFILES FOR MODELING PURPOSES

16 III. LITERATURE ON SPATIALLY EXPLICIT MODELING OF DEMAND Importance of electric load modeling for low aggregation levels: - integrating renewable energy supply into distribution grids [1] - cogeneration [2] and micro CHP [1], [3] - mixed energy distribution systems i.e. incorporating more than one carrier [4] Randomness of consumption - loads at a low aggregation are difficult to model [5] - need of large number of diverse residential load profiles [1] Load profiles are modeled from - appliance profiles [6] solely or adding human behavior [7], and information on the buildings [8] - statistical analyses of electrical data from apartments [9] - using a time use survey [15] - using billing data [16]

17 III. DATA TO MODEL SPATIALLY EXPLICIT DEMAND Industry load profiles collected for the analysis VDEW standardized load profiles: - for 3 days: Weekday, Saturday, Sunday - for 3 seasons: winter, summer, interim period - for different load types - normalized for a consumption of 1000kWh Workplace assessment (Statistics Austria, 2001) - Number of employees and - Type of sector per km² Buildings- and dwellings census (Statistics Austria, 2001) - Number of building and - Heating types per km²

18 III. METHODOLOGY TO MODEL SPATIALLY EXPLICIT DEMAND

19 III. METHODOLOGY TO MODEL SPATIALLY EXPLICIT DEMAND Grid cells of 1km² - More than 150 households: usage of standardized load profiles depicts reality [23]. - Less than 150 households: need to simulate stochastic load profiles along a Gaussian distribution for each consumer unit connected to the distribution grid. Simulation of load profiles in every grid cell based on the composition and number of consumers. Aggregation of generated load profiles of all households and sectors total load profile in each 1km².

20 III. RESULTS: VALIDATION ON A PER COUNTRY LEVEL Simulated load profiles for an average summer, winter, and interim period day - Average consumption is highest during weekdays MW Winter_Saturday Winter_Sunday Winter_Weekday Summer_Saturday Summer Sunday Summer Weekday Interim_Saturday Interim_Sunday Interim_Weekday - Three peaks time

21 III. RESULTS: VALIDATION ON A PER COUNTRY LEVEL Aggregated load for one day over the year (2010). GW e-control modelled time Aggregated load for one day over the interim period (2010). MW e-control modelled Aggregated load for one day for Vorarlberg e-control (2010). modelled MW time time

22 IV. CONCLUSIONS AND FUTURE WORK / EXPECTED RESULTS

23 IV CONCLUSION: SPATIALLY EXPLICIT DEMAND SIMULATION Conclusion Methodology on simulating demand allows to: - improve spatially explicit energy system models - load modeling to be coupled with the renewable energy potential in a geographically explicit context realistic optimization of supply and demand - application to other regions Future work Validation with measured household load profiles

24 IV OUTLOOK Maximum integration of rooftop PV in the distribution grid More realistic integration of RES in cost-minimisation energy system models via a spatially specific step cost curve Estimation of Option value/ Opportunity costs Usage of Data in Spatially Explicit Optimization Model for Austria