-This paper presents the application of Differential Evolution (DE) algorithm to obtain a solution for Bid Based Dynamic Economic Dispatch (BBDED) problem including the transmission losses and to maximize the social profit in a deregulated power system. The IEEE-30 bus test system with six generators, two customers and two trading periods are considered under various bidding strategies in a day-ahead electricity market. By matching the bids received from supplying and distributing entities, the Independent System Operator (ISO) maximize the social profit, (with the choices available). The simulation results of DE are compared with the results of Particle swarm optimization (PSO). The results demonstrate the potential of DE algorithm and show its effectiveness to solve BBDED.
This paper presents the advancement in power system engineering education and research with power industry moving towards deregulation. Deregulation is a relatively recent concept, whose economic, regulatory and implementation structure continues to be adopted to the specific needs of each nation. For example, price based unit commitment in the present scenario is totally different compared to a regulated set up. Hence adequate exposes towards power engineering curriculum and new software tools are needed to support new activities in the modern power pools. This methodology performed in this will be a great challenges for the power industry and thus an individual human can take their own decision of choosing the reliable continuous supply of power from the electricity markets at an affordable price. Under this restructured system, generation companies (GENCOs) schedule their generators with the objective of maximizing their profit. The profit based unit commitment (PBUC) is performed by considering both the power and reserve generations. The quoting of power and reserve prices in spot markets and reserve markets are the important decision process. This proposed algorithm is tested for a small unit test system with 3 unit 12 hour data and the simulations are carried out to show the performance of proposed methodology using MATLAB.
This paper presents the solution for the suppliers' aims to achieve profit more than the rivals participating in the competition. The supplier (decision maker) optimization problem is formulated and their bid quantities are optimized using Self adaptive Differential Evolution (SaDE). A six unit system is used to illustrate the methodology for a single trading period with both elastic and inelastic loads. The performance of the test systems under perfect and oligopoly market situations are compared to show the importance of optimization of bidding parameters in deregulated markets. Numerical results illustrate the effectiveness of the method in solving the supplier profit maximization problem.
<p>This paper presents the solution for the supplier’s profit maximization problem with unit commitment decisions participating in single side auction markets of a deregulated power system. The bids from market participants are received by a central pool mechanism and the Market Clearing Price (MCP) for energy and spinning reserve is fixed. The bid quantities are optimized using Differential Evolution (DE) algorithm. The supplier aims to achieve (more) profit than that of the rival’s participating in the competition. A GENCO with 6-unit participating in 24-hour day ahead energy and spinning reserve market is used to illustrate the methodology. The bidding parameters of rival’s participating in the competition are calculated by multi-variant Probability Density Functions (PDF). The results of the proposed methodology are compared with Refined Genetic Algorithm (RGA). Numerical results illustrate the effectiveness of the method in solving the supplier profit maximization problem.</p>
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