Junjie Sun

An agent-based computational laboratory for testing the economic reliability of wholesale power market designs

In April 2003 the U.S. Federal Energy Regulatory Commission proposed the wholesale power market platform (WPMP) for adoption by all U.S. wholesale power markets. The WPMP market design envisions day-ahead, real-time, and ancillary service markets maintained and operated by an independent system operator or regional transmission organization. Previous work reports on the development of an agent-based model for testing the economic reliability of the WPMP market design. This paper reports on the implementation of this model as an agent-based computational laboratory. Initial experiments focusing on optimal power flow solution methods for the day-ahead and real-time markets are also discussed.

Dynamic LMP response under alternative price-cap and price-sensitive demand scenarios

This study investigates the complicated nonlinear effects of demand-bid price sensitivity and supply-offer price caps on locational marginal prices (LMPs) for bulk electric power when profit-seeking generators can learn over time how to strategize their supply offers. Systematic computational experiments are conducted using AMES, an open-source agent-based test bed developed by the authors. AMES models a restructured wholesale power market operating through time over an AC transmission grid subject to line constraints, generation capacity constraints, and strategic trader behaviors.

Open-Source Software for Power Industry Research, Teaching, and Training: A DC-OPF Illustration

Software currently available for power industry studies is largely proprietary. Lack of open-source access prevents users from gaining a complete and accurate understanding of what has been implemented, restricts the ability of users to experiment with new software features, and hinders users from tailoring software to specific training scenarios. This study reports on the development of a stand-alone open-source Java solver for DC optimal power flow (DC-OPF) problems suitable for research, teaching, and training purposes. The DC-OPF solver is shown to match or exceed the accuracy of BPMPD, a proprietary third-party QP solver highly recommended by MatPower, when tested on a public repository of small to medium-sized QP problems. The capabilities of the DC-OPF solver are illustrated for a 5-node DC-OPF test case commonly used for training purposes.

Market design test environments

Power industry restructuring continues to evolve at multiple levels of system operations. At the bulk electricity level, several organizations charged with regional system operation are implementing versions of a wholesale power market platform (WPMP) in response to U.S. Federal Energy Regulatory Commission initiatives. Recently the Energy Policy Act of 2005 and several regional initiatives have been pressing the integration of demand response as a resource for system operations. These policy and regulatory pressures are driving the exploration of new market designs at the wholesale and retail levels. The complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance demand a flexible computational environment where designs can be tested and sensitivities to power system and market rule changes can be explored. This paper discusses the use of agent-based computational methods for the study of electricity markets at the wholesale and retail levels, and explores distinctions in problem formulation between these levels

Testing Institutional Arrangements via Agent-Based Modeling: A U.S. Electricity Market Application

Many critical goods and services in modern-day economies are produced and distributed through complex institutional arrangements. Agent-based computational economics (ACE) modeling tools are capable of handling this degree of complexity. In concrete support of this claim, this study presents an ACE test bed designed to permit the exploratory study of restructured U.S. wholesale power markets with transmission grid congestion managed by locational marginal prices (LMPs). Illustrative findings are presented showing how spatial LMP cross-correlation patterns vary systematically in response to changes in the price responsiveness of wholesale power demand when wholesale power sellers have learning capabilities. These findings highlight several distinctive features of ACE modeling: namely, an emphasis on process rather than on equilibrium; an ability to capture complicated structural, institutional, and behavioral real-world aspects (micro-validation); and an ability to study the effects of changes in these aspects on spatial and temporal outcome distributions.