DEVELOPMENT OF INTELLIGENT STRATEGIC BIDDING in AN ELECTRICITY MARKET

Abstract

The power industry has appeared as an intricate interconnected system due to newlinethe competitive business environment and increasing system demand. In an effort to newlineupsurge competition and for providing benefits to consumers, several countries newlinearound the world have improved their energy trading policies from vertically newlineintegrated utility to oligopoly. This change invites private players to trade electricity newlinein power market. A market operation is handled by an Independent System Operator newline(ISO). Precise information of electricity price can be beneficial for Generating newlineCompanies (GenCo) and ISO to find out the profitable solutions and market newlineequilibrium respectively. The decisions regarding generation scheduling, strategic newlinebidding, MCP are dependent on the precise information of electricity price. Power newlineGenCo have the possibility to boost their profit in the Electricity Market (EM) by newlineproviding the power at reasonable prices while operating with inaccurate knowledge newlineabout competitor s behavior. GenCo s have to provide the energy in a day-ahead newlineEM at the optimum bid prices. newlineIn this thesis, firstly a new variant of Whale Optimization Algorithm (WOA) newlinenamed as Amended Whale Optimization Algorithm (AWOA) is developed. The newlineAWOA encompasses Opposition Enabled Learning (OEL) with Cauchy Mutation newline(CM) with the WOA to accelerate the convergence rate and to evade local minima newlinetrap. The efficacy of developed AWOA algorithm is first verified on 23 standard newlinebenchmark functions and then it is applied to solve strategic bidding problem in a newlineday-ahead EM. Sealed auction with uniform MCP have been considered to solve the newlineproblem of bidding with four different Probability Distribution Functions (PDF s) of newlinerivals bid behavior namely normal, lognormal, weibull and gamma. Bidding strategy newlineof a GenCo for every transaction period in a day-ahead EM is expressed as a newlineStochastic Optimization (SO) and the similar is elucidated through Monte Carlo newline(MC) simulations.