International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 06 | June -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 5639 Gravitational Search Algorithm for Bidding Strategy in Uniform Price Spot Market Ajay Bhardwaj 1 ,Dr. Tanuj Manglani 2 1 M. Tech. Scholar, YIT, Jaipur 2 Professor, YIT, Jaipur ----------------------------------------------------------------------------***-------------------------------------------------------------------------- Abstract - In a deregulated market environment, Generation Companies (GENCOs) develop bidding strategies to increase their benefits. Electricity Markets (EMs) are not perfectly competitive due to limited number of power producers, large investment size and various transmission constraints. In this oligopolistic market environment, it is of great interest for generation companies to develop bidding strategies to share maximum profit. In this work, an optimal bidding strategy has been developed for a GENCO whose profit is to be maximized by using Gravitational Search Algorithm (GSA). The approach has been applied on four generator test system and compared with the results obtained from Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Key Words: Electricity Markets, Gravitational Search Algorithm, Genetic Algorithm, Particle Swarm Optimization, System Operator 1. INTRODUCTION Restructuring of the power system means eradicating the monopoly in the generation and transmission trading sectors thereby introducing competition at various levels. Economic power market in which the participants separately submit their favored schedules, this market is called pool market. The system operator (SO) allot the dispatches of generators using an optimal power flow (OPF) which admits bid submitted by the participants as their input. Participants in competitive electricity pool market (EPM) develop strategic bidding in order to maximize their own profits. This problem is known as ǮPower transaction gameǯ, which can be modeled as static non co-operative incomplete game with perfect information [1]. The game is static due to the fact that the process of decision making is applied for all the players involved. Non-cooperative means that each individual player is pursuing for his own interest and the incomplete information means players lack full information on the mathematical structure of the game. Perfect information stands for the fact that all players have full information of all strategies in primary stages. Electricity generators (sellers) and electricity dealers/customers (buyers) have to introduce a transmission network for rolling the power from the generation point to the consumption point. Thus, unified transmission system is considered to be a natural monopoly so as to avoid the duplicity, huge investment for beginning and to take the advantage of the unified network viz. reduced installed capacity, increased system reliability and improved system performance. For SO, it is necessary to explore strategic bidding behavior of participants in order to recognize probable power market abuse and limit it by presenting EPM rules and regulation. In past years, considerable amount of research papers has been presented on optimal bidding strategies for number of generators for exploring the market power in EPM. There are number of simulation methods proposed by researchers to form bidding strategy such as dynamic programming [2], stochastic optimization [3]-[6], two level optimization [7]-[9], lagrangian relaxation [10]-[11], genetic algorithm [12]-[13], fuzzy approach [14], game theory [15]-[16]. Supply side bidding strategies are classified in pool markets [17]-[18] and bilateral markets [19]-[22]. Dynamic programming approach was presented in [2]. The approach was applied on England- Wales type electricity markets. In [2], Probability distribution function (PDF) was used to predict rivalǯs behavior and supplierǯs bid was calculated by stochastic optimization technique. Song et al. [4] proposed Markov Decision Process (MDP) to estimate optimal bids of suppliers. Monte Carlo method is used in [5] to model supply function. In [6], Zhejiang provincial model was taken as pilot market and step wise bidding technique was applied for bidding problem. In [7], a two level optimization procedure was proposed to solve strategic bidding problem. The market operator decide the optimal bid to be selected while taking social welfare into account. A centralized economic dispatch is used to determine MCP and output of generators for a profitable bid in [8]. In [9], each suppliers bids a linear supply function based on probabilistic estimation of demand and rivalǯs behavior. Langrangian relaxation based approach [10] is adopted to form bidding curve for England-Wales type electricity markets. The MCP is assumed to be known which is not practical case in real electricity markets. Zhang et al. [11] applied same approach in New England market in which rivalǯs bids are assumed to be in discrete distributions. Gentic algorithm approach [12] is used to develop the bidding strategy in day ahead electricity market. Same methodology is adopted for spinning reserve