Agent-Based Simulation of Distribution Systems with High Penetration of Photovoltaic Generation

Transcription

1 1 Agent-Based Simulation of Distribution Systems with High Penetration of Photovoltaic Generation Chengrui Cai, Pedram Jahangiri, Auswin George Thomas, Huan Zhao, Dionysios Aliprantis, and Leigh Tesfatsion IEEE PES GM 2011, Detroit, MI 27 June 2011

2 2 Presentation Outline Overview of the Integrated Retail/Wholesale (IRW) project at Iowa State University IRW Test Bed development Solar radiation model PV energy conversion model Experimental setup Conclusion

3 3 Project Directors: Leigh Tesfatsion (Prof. of Econ, Math, & ECpE, ISU) Dionysios Aliprantis (Assistant Prof. of ECpE, ISU) David Chassin (Staff Scientist, PNNL/Department of Energy) IRW Project: Integrated Retail/Wholesale Power System Operation with Smart-Grid Functionality Research Assoc s: Dr. Junjie Sun (Fin. Econ, OCC, U.S. Treasury, Wash, D.C.) Dr. Hongyan Li (Consulting Eng., ABB Inc., Raleigh, NC) Research Assistants: Huan Zhao (Econ PhD student, ISU) Chengrui Cai (ECpE PhD student, ISU) Pedram Jahangiri (ECpE PhD student, ISU) Auswin Thomas (ECpE M.S. student, ISU) Di Wu (ECpE PhD student, ISU) Current Government & Industry Funding Support: PNNL/DOE, the Electric Power Research Center (an industrial consortium), and the National Science Foundation Industry Advisors: Personnel from PNNL/DOE, XM, RTE, MEC, & MISO

4 4 Meaning of Smart Grid Functionality? For our project purposes: Smart-grid functionality = Market design & resource enhancements permitting more responsiveness to the needs, preferences, and decisions of retail energy consumers. Examples: Introduction of advanced metering and other technologies to support flexible dynamic-price contracting between suppliers ( Load-Serving Entities ) and retail energy consumers integration of distributed renewable energy resources, e.g., consumer-owned photovoltaic (PV) panels 4

5 5 Principal IRW Project Research Topics Dynamic retail/wholesale reliability and efficiency implications of integrating demand response resources as realized thru Top-down demand response (e.g., emergency curtailment) Automated demand dispatch (continuous signaling) Price-sensitive demand bidding by demand resources Dynamic retail/wholesale effects of increased penetration of consumer-owned distributed energy resources, such as photovoltaic (PV) generation & plug-in electric vehicles (PEV) Development of agent-based algorithms for smart device implementation (e.g., smart HVAC systems) 5

6 Primary Project Tool: The IRW Power System Test Bed An agent-based computational laboratory Culture dish approach to complex dynamic systems Permits systematic computational experiments Permits sensitivity testing for changes in physical constraints (e.g., grid configuration), market rules of operation, and participant behavioral dispositions Seams empirically grounded test beds (AMES/GridLAB-D) Market rules based on business practices manuals for restructured North American electric power markets Realistically rendered transmission/distribution networks Retail contracting designs based on case studies (e.g., ERCOT) and pilot studies (e.g., Olympic Peninsula 2007) Open source software release planned. 6 6

7 7 IRW Power System Test Bed: AMES & GridLAB-D Bilateral Contracts x x Wholesale AMES ISU Team Seamed Retail GridLAB-D DOE/PNNL Team

8 8 The IRW Power System Test Bed is an Agent-Based Model Agents in the IRW Test Bed include Decision making agents (ISO, GenCos, LSEs) Physical structures (HV transmission grid, distribution grid) Institutions, i.e., systems of rules (day-ahead market, realtime market, ) Each agent is represented as a bundle of data together with methods that act on these data Events in the IRW Test Bed are driven by agent interactions

9 9 IRW Power System Test Bed (Version 1.0) Seams AMES (wholesale) & GridLAB-D (retail) with a focus on net loads from households with HVAC, PV,

10 Typical Day-D Market Operator (ISO) Activities 10

11 IRW Test Bed Development Seaming of AMES (wholesale) & GridLAB-D (retail) implemented via MySQL database server and a data management program

12 12 IRW Test Bed Operation (Data Flow) 12

13 Initial Retail Focus: Household Residents with PV Panels, HVAC, 13

14 14 Photovoltaic Generation Modeling Modeling, Analyzing and Control of Large-Scale Distributed PV Generation 1. Develop PV generation model 2. Apply practical weather model in analysis 3. IRW test bed serves as the experiment platform 4. Utilize controllable load to mitigate intermittency 5. Study the impact of PV generation on wholesale energy and ancillary service markets 14

15 15 Photovoltaic Generation Modeling Solar Energy Conversion Photovoltaic (PV) power plant 14 MW capacity with 22% capacity factor 72,000 solar panels; $100 million; build cost $7.14/Watt 0.57 km 2 land use; 5.2 W/m 2 Nellis SEGS Concentrated Solar Power (CSP) plant World s largest solar energy plant 354 MW capacity with 21% capacity factor 936,384 mirrors; build cost of a CSP $2.5~$4/Watt 6.5 km 2 land use; 11.4 W/m 2 By 2009, U.S. had 1.25 GW PV capacity and MW CSP capacity 15

16 Photovoltaic Generation Modeling Characteristics of Distributed PV Generation 16 Static system without inertia Fast variation of power output with large amplitude High repetitive output pattern due to sun diurnal cycle Large land use Ota-Pal town pilot project in Japan 2.16 MW, 1.65 km 2 land use 16

17 Photovoltaic Generation Modeling Challenges and Approaches C: Solar radiation data with high temporal and spatial resolution 17 A: Generate realistic cloud pattern to obtain the synthesized data C: Energy conversion model to convert the received radiation to electric power, considering environmental factors A: Establish a small-scale PV generation system and validate the MPPT surface model C: Interdependency between feeder geographic topology and distributed PV generation A: Superimpose the moving cloud pattern on the taxonomy feeder topology map 17

18 Photovoltaic Generation Modeling Generate Cumulus Cloud Pattern 18 Cumulus cloud 20% coverage 40% coverage 18

19 Photovoltaic Generation Modeling Generate Solar radiation 19 Components of solar radiation Global radiation on a tilted surface Horizontal beam radiation Conversion factor from horizontal beam to tilted beam radiation Horizontal diffuse radiation Tilted angle in degree 19

20 Photovoltaic Generation Modeling Determine the Radiation Components Theoretically compute the clear sky horizontal beam radiation 20 Read in the hourly horizontal diffuse radiation from the TMY data Synthesize the radiation along with the shading condition determined by the cloud pattern 20

21 Photovoltaic Generation Modeling Simulated Solar Radiation 3.5 km by 3.5km area 21 Define the area installed with PV systems Determine the PV system density Obtain the total available solar radiation in the defined area 21

22 Photovoltaic Generation Modeling Simulated Solar Radiation Single point radiation 22 W/m 2 Average radiation second 22

23 PV Model Photovoltaic Generation Modeling 23 PV panel circuit 23

24 Photovoltaic Generation Modeling Power Curves of PV Panel 24 24

25 Photovoltaic Generation Modeling PV Energy Conversion Model 25 Calculated result for KC200GT module Experiment result for ASP 140 Module 25

26 Photovoltaic Generation Modeling PV Experimental Setup 26 LICOR 200 pyranometer RTD sensor Hall effect current sensor Kyocera 135 W PV panel GE temperature and humidity sensor NI ENET 9205 sampling card 26

27 27 Conclusion and Planned Future Work Apply new weather model in distribution system analysis Develop MPPT tracking surface energy conversion model Set up a small-scale PV generation and monitoring platform for collection of high resolution data Use calibrated PV model to represent household PV panels in the IRW Power System Test Bed Use resulting PV-extended IRW Power System Test Bed to study systematically the effects of PV penetration on retail and wholesale power system operations. 27