Integrated Retail and Wholesale (IRW) Power System Operations with Smart-Grid Functionality

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1 1 Integrated Retail and Wholesale (IRW) Power System Operations with Smart-Grid Functionality Leigh Tesfatsion and Dionysios Aliprantis Last Updated: 21 March 2012

2 2 Presentation Outline Overview of the Integrated Retail/Wholesale (IRW) project at Iowa State University IRW Test Bed development Integration of price-responsive load (e.g., households with intelligent A/C systems) Integration of distributed variable generation (e.g., residential rooftop solar panels) On-line resources

3 3 Project Directors: Leigh Tesfatsion (Prof. of Econ, Math, & ECpE, ISU) Dionysios Aliprantis (Assistant Prof. of ECpE, ISU) Research Assoc s: Dr. Junjie Sun (Fin. Econ, OCC, U.S. Treasury, Wash, D.C.) Dr. Hongyan Li (Consulting Eng., ABB Inc., Raleigh, NC) Dr. Huan Zhao (Market Analyst, ISO-NE) Research Assistants: Wanning Li (ECpE 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 Industry & Government Funding Support: ARPA-E/DOE, PNNL/DOE, Sandia National Lab (SNL), Electric Power Research Center (an industrial consortium), and the National Science Foundation IRW Project: Integrated Retail/Wholesale Power System Operation with Smart-Grid Functionality Industry/Govt Advisors: From SNL, PNNL/DOE, ISO-NE, MISO, XM, RTE, MEC

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 rooftop solar 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 contracting for retail energy demands Dynamic retail/wholesale effects of increased penetration of wind and consumer-owned distributed energy resources such as rooftop solar panels & 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 Communication Seam 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 11 11

12 12 IRW Test Bed Operation (Data Flow) 12

13 Illustrative 5-Bus Test Case with Households Having HVAC, PV Panels, & PEV 13

14 14 IRW Project: Research in Progress Topic 1: Price-Responsive Retail Demand Device Design: Smart air conditioning (A/C) systems able to implement optimal comfort/cost trade-offs for household residents conditional on price and environmental conditions. Incorporation of price-responsive retail energy demand into the IRW test bed in the form of households with smart air-conditioning (A/C) systems responsive to price and environmental conditions. 14

15 15 Intelligent Residential Air-Conditioning System with Smart-Grid Functionality (Original WP November 2011, under revise-and resubmit for journal ) Abstract This paper sets forth a novel intelligent residential air-conditioning (A/C) system controller that has smart grid functionality. The qualifier intelligent means the A/C system has advanced computational capabilities and uses an array of environmental and occupancy parameters in order to provide optimal intertemporal comfort/cost trade-offs for the resident, conditional on anticipated retail energy prices. The term smartgrid functionality means that retail energy prices can depend on wholesale energy prices. Simulation studies are used to demonstrate the capabilities of the proposed A/C system controller.

16 16 Intelligent A/C System Controller Block diagram

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18 18 IRW Project: Research in Progress Topic 2: Incorporation of Variable Generation 1. Develop photovoltaic (PV) and wind generation models 2. Apply practical weather model in analysis 3. Incorporate PV into the IRW Test Bed by permitting households to have PV rooftop panels, and study the impact of PV generation on IRW power system performance 4. Incorporate wind generation into IRW Test Bed, and study the impact of wind penetration on IRW power system performance 18

19 Photovoltaic Generation Modeling Challenges and Approaches C: Solar radiation data with high temporal and spatial resolution 19 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 19

20 Photovoltaic Generation Modeling 20 PV Experimental Setup at ISU LICOR 200 pyranometer RTD sensor Hall effect current sensor Kyocera 135 W PV panel GE temperature and humidity sensor NI ENET 9205 sampling card 20

21 21 PV Work in Progress Apply new weather model in distribution system analysis Develop MPPT tracking surface energy conversion model Use small-scale PV generation and monitoring platform set up at ISU 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. 21

22 22 On-Line Resources Presentation Slides IRW Project Homepage AMES Test Bed Homepage (Code/Manual/Publications) Agent-Based Electricity Market Research Agent-Based Computational Economics Homepage