Abstract
The primary objective of this research article is to introduce Differential Evolution (DE) algorithm for solving bidding strategy in deregulated power market. Suppliers (GENCOs) and consumers (DISCOs) participate in the bidding process in order to maximize the profit of suppliers and benefits of the consumers. Each supplier bids strategically by choosing the bidding coefficients to counter the competitors bidding strategy. Electricity or electric power is traded through bidding in the power exchange. GENCOs sell energy to power exchange and in turn ancillary services to Independent System Operator (ISO). In this paper, Differential Evolution algorithm is proposed for solving bidding strategy problem in operation of power system under deregulated environment. An IEEE 30 bus system with six generators and two large consumers is employed to demonstrate the proposed technique. The results show the adaptability of the proposed method compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Monte Carlo simulation in terms of Market Clearing Price (MCP).
Highlights
The electric power industry worldwide is experiencing restructuring and deregulation of power market
The main objective of this paper is to suggest a new technique for solving bidding strategy problem
Differential evolution for solving bidding strategy in deregulated power market is presented in this paper
Summary
The electric power industry worldwide is experiencing restructuring and deregulation of power market. Each GENCO reasonably builds strategic bid to maximize its own profit [1] [2]. A brief literature survey on bidding strategy is presented in [7] Several classical techniques such as Markov decision process [8] [9], Lagrangian Relaxation [10], Monte Carlo based approach [11] have been proposed by various scholars in the past decade. Apart from these methods, few other techniques [12]-[21] are suggested to solve the bidding strategy. Modern heuristic methods [22]-[32] like particle swarm optimization, gravitational search algorithm and hybrid algorithms have been proposed to solve the bidding strategy problem with wide variety
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