Optimal operational strategies for a day-ahead electricity market in the presence of market power using multi-objective evolutionary algorithm
A literature survey revealed that many of the traditional solution algorithms convert multi-objective functions into either a single-objective function using weighting schemas or undertake optimization of one function at a time. Hence, these approaches do not truly optimize the multi-objectives concurrently. Due to these inherent deficiencies of the traditional algorithms, the use of alternative non-traditional solution algorithms for such problems has become popular and widely used. Of these, multi-objective evolutionary algorithms (MOEA) have received wide acceptance due to their solution quality and robustness. In the present research, three distinct algorithms were considered: a non-dominated sorting genetic algorithm II (NSGA II), a multi-objective tabu search algorithm (MOTS) and a hybrid of multi-objective tabu search and genetic algorithm (MOTS/GA). The accuracy and quality of the results from these algorithms for applications similar to the problem investigated here reinforced the selection of these algorithms. The results obtained from each of the three algorithms used in the evaluations are very comparable. Thus one could safely conclude that the results obtained are valid. Three distinct test power systems operating under different conditions were studied for evaluating the suitability of each of these algorithms. The test cases included scenarios in which the power system was unconstrained as well as constrained. Repeated simulations carried out for the same test case with varying starting points provided evidence that the algorithms and the solutions were robust.
Influences of different market concentrations on the optimal economic dispatch are evidenced by the pareto-optimal-fronts obtained for each test case studied. Results obtained from a traditional linear programming (LP) based solution algorithm that is used at present by many market operators are also presented for comparison. Very high market-concentration-indices were found for each solution from the LP algorithm. This suggests the need to use a formal method for mitigating market concentration. Operating the market at industry-recommended threshold levels of market concentration for selecting an optimal operational point is presented for all test cases studied. Given that a solution-set instead of a single operating point is found from the multi-objective optimization methods, additional flexibility to select any operational point based on the preference of those operating the market clearly is an added benefit of using multi-objective optimization methods. However, in order to help the market operator, a more logical fuzzy decision criterion was tested for selecting a suitable operating point. The results show that the optimal operating point chosen using the fuzzy decision criterion provides a higher economic benefit to the market, although at a slightly increased market concentration.
Since the main objective of this research was to simultaneously optimize the economic operation of a day-ahead market while ensuring minimal market power by individual generator owners, the proposed method is much improved from the current industry practice. The current practice of after-the-fact mitigation of market power has created various problems for both the market operator and the market participants, giving rise to a large numbers of disputes and resettlement activities. Hence, an approach that mitigates market power at the time of market dispatch as used in this research would bring about a more efficient market operation.
School:Kansas State University
School Location:USA - Kansas
Source Type:Master's Thesis
Keywords:optimization muli objective engineering electronics and electrical 0544
Date of Publication:01/01/2007