Influence of Bidding Mechanism and Spot Market Characteristics on Market Power of a Large Genco Using Hybrid DE/BBO

Abstract

In Day-Ahead (DA) electricity markets, Generating Companies (Gencos) aim to maximize their profit by bidding optimally, under incomplete information of the competitors. This paper develops an optimal bidding strategy for 24 hourly markets over a day, for a multi-unit thermal Genco. Different fuel type units are considered and the problem has been developed for maximization of cumulative profit. Uncertain rivals' bidding behavior is modeled using normal distribution function, and the bidding strategy is formulated as a stochastic optimization problem. Monte Carlo method with a novel hybrid of Differential Evolution (DE) and Biogeography Based Optimization (BBO) (DE/BBO) is proposed as solution approach. The simulation results present the effect of operating constraints and fuel price on the bidding nature of different fuel units. The performance analysis of DE/BBO with GA and its constituents, DE and BBO, proves it to be an efficient tool for this complex problem.

How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach

“…To study this related issue, many scholars focus on modeling and analyzing generators' behavior patterns in a competitive electricity market since 2000 [8][9][10][11][12][13][14][15][16][17][18][19]. Due to the difficulty in obtaining the analytical market equilibrium, the approximate market equilibriums concerning generators' behaviors are sought by market simulations, such as agent-based simulation [9,10], evolutionary simulation [11], and hybrid iterative simulation [12,13]. For instance, Wang et al [11] proposed an evolutionary game approach to analyze the bidding process based on price-responsive demand.…”

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

“…The hybridization between the BBO and Differential Evolution Algorithm (DE) has achieved many great results [24][25][26]. However, they mostly incorporate DE into the migration procedure.…”

“…When the value of fitness function is better than the average, we will reduce the probability of modification and mutation operations, or else we will increase the probability of modification and mutation operations. In other terms, in migration operations, when the value of fitness function is larger than the average, we set constant factor k 1 to smaller value, and the probability of modification operations is decided by Formula (26). When the value of fitness function is less than the average, we set constant factor k 2 to larger value, so as to search extensively in the solution space.…”