Electricity markets for future power systems : theoretical analysis and design

Abstract

The Locational marginal price (LMP) is the most important pricing scheme in electricity markets, and the corresponding LMP-based economic dispatch is the most significant operating model in power systems. However, the deepening penetration of renewable supply and distributed energy resources poses fundamental challenges to the current market design. We focus on analyzing and proposing new market designs to accommodate these energy resources in future power systems. First, we deal with the challenge brought by the strategic aggregators of demand-side flexibility. Based on our theoretical analysis of the formulated bi-level program, we find that the strategic aggregators can potentially gain benefits at the cost of other market participants or even system-wide performance, highly depending on their bus locations. We propose to additionally charge the strategic aggregators for the newly introduced congestion to mitigate these negative effects. Second, we deal with the challenge posed by the strategic distribution system operators (DSOs) equipped with renewable generations. We theoretically identify that the LMP-based pricing scheme leaves arbitrage opportunities between day-ahead and real-time markets for DSOs via strategic bidding, especially under high uncertainties. We propose a Nash bargaining solution-based pricing scheme for real-time coordination between operators, to effectively prevent these strategic behaviors and help the system. Finally, we focus on the stochastic economic dispatch, to meet the challenge of system inefficiency under high renewable uncertainties. For individual scenarios, we mathematically address the reason for the potential short-run failure of cost recovery and revenue adequacy, recognizing the unfairness among different market participants. However, we theoretically demonstrate that, in expectation, the stochastic approach could bring more long-run benefits to the system beyond improved cost-efficiency, motivating its promotion regardless of the short-run disadvantages.

Date of Award	2020
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Danny Hin Kwok Tsang (Supervisor)