SIMWIND: A Geospatial Infrastructure Model for Optimizing Wind Power Generation and Transmission

Transcription

1 Engineering Conferences International ECI Digital Archives Modeling, Simulation, And Optimization for the 21st Century Electric Power Grid Proceedings Fall SIMWIND: A Geospatial Infrastructure Model for Optimizing Wind Power Generation and Transmission Benjamin Phillips DOE Richard Middleton Los Alamos National Laboratory Follow this and additional works at: Part of the Electrical and Computer Engineering Commons Recommended Citation Benjamin Phillips and Richard Middleton, "SIMWIND: A Geospatial Infrastructure Model for Optimizing Wind Power Generation and Transmission" in "Modeling, Simulation, And Optimization for the 21st Century Electric Power Grid", M. Petri, Argonne National Laboratory; P. Myrda, Electric Power Research Institute Eds, ECI Symposium Series, (2013) This Conference Proceeding is brought to you for free and open access by the Proceedings at ECI Digital Archives. It has been accepted for inclusion in Modeling, Simulation, And Optimization for the 21st Century Electric Power Grid by an authorized administrator of ECI Digital Archives. For more information, please contact franco@bepress.com.

2 Overview SimWIND: : A Geospatial Infrastructure Model for Optimizing Wind Power Generation and Transmission LA-UR AP Benjamin R. Phillips SRA International, Inc. / U.S. Department of Energy Richard Middleton Los Alamos National Laboratory, Earth and Environmental Sciences

3 Challenge and Opportunities Targeting 10 times today s wind capacity by 2030 Need to optimally develop and connect resources Overview Major infrastructure improvements needed Regional transmission planning: FERC 1000, etc. wind resource population

4 SimWIND Approach: Generation, Transmission, Delivery + + Unique algorithm to devise a candidate network of all possible least-cost network arcs Simultaneously optimize for a given wind power target: Location and amount of power to generate Location and capacities of transmission lines Quantitative, discrete spatial accounting of: Geographical (land slope, roughness, etc.) and Social (land use, population, politics, etc.) costs Transmission losses

5 SimWIND Mixed integer-linear program Candidate network defined by nodes (i,j) and arcs (ij) with capacities (c) Model builds wind farms (w i, capacity factor β i ) and transmission lines (t ijc, loss α ijc ) to serve loads (l kj ) with a power delivery Target Network and solution are optimized over a weighted cost surface

6 ERCOT Wind Resource Zones ERCOT case study Isolated system Overview CREZ selection process Existing development plan Clear disparity between quality resource and demand locations

7 SimWIND Cost Surface and Candidate Network Geospatial cost accounting ROW and construction costs Overview Weight each attribute (e.g. slope, land use, population) to give each grid cell a relative cost Develop a candidate network of all possible least-cost arcs

8 Wind Resource Curves 12,000 Zone 3 Overview 10,000 Zone 2 Zone 25 Installed ca apacity (MW) 8,000 6,000 4,000 Zone 6 Zone 23 2,000 Zone Capacity factor (%) Adapted from ERCOT, 2006

9 Resistive Losses and Transmission Cost Q (MW) total electricity generated σ (Ω/phase) conductor resistance d (km) V (kv) distance from source to load line voltage IEAGHG R&D Programme, 2002

10 SimWIND Example Solutions

11 Transmission Network Length and Cost

12 Overview Annualized Costs for Generation and Transmission Generation accounts for ~95% of system costs Transmission Generation Cost ($M/M MW/yr) Delivery Target (GW)

13 System-Wide Transmission Losses Overview 12 Transmissio on Loss (%) Delivery Target (GW)

14 Overview SimWIND vs. ERCOT Scenarios: 18,456 MW installed ERCOT Scenario 2 SimWIND Total Length: 3823 km 1952 km Total Cost: 4728 $M 2422 $M Adapted from ERCOT, 2006

15 Overview Conclusions and Future Directions Conclusions SimWINDquantifies potential savings from simultaneous siting of generation and transmission These optimal solutions are often non-intuitive Accounting for transmission losses amplifies economies of scale Offer a flexible platform for translating varied stakeholder interests into costs that are an integral part of the optimization Priorities Coupling with a power-flow model to address system reliability Incorporating existing transmission and reserve capacity Considering other/multiple generation types and storage Incorporating dynamic planning capabilities Phillips, B.R., Middleton, R.S., 2012, SimWIND: A geospatial infrastructure model for optimizing wind power generation and transmission. Energy Policy 43,