Outline
- I. Introduction
- II. Problem Formulation
- III. Improved Nsga-Ii Algorithm
- IV. Experiments and Results
- V. Conclusion
رئوس مطالب
- چکیده
- کلمات کلیدی
- 1. مقدمه
- 2. فرمول بندی مسئله
- A. توابع هدف
- B. محدودیت ها
- C. مرور فرمول بندی
- D. عملیات قیود برابری و نابرابری
- 3. الگوریتم NSGA-II بهبود یافته
- 4. آزمایش ها و نتایج
- 5. نتیجه گیری
Abstract
An improved nondominated sorting genetic algorithm-II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved.
Keywords: Distributed generation - distribution system planning - multiobjective optimization - nondominated sorting genetic algorithm–II (NSGA-II)Conclusions
To summarize the modeling, optimization algorithm improvement, and comparison study for optimal planning of multiple DG units, the following conclusions can be derived as: 1) three objectives to consider minimum line loss, minimum voltage deviation, and maximal voltage stability margin can correctly formulate optimal planning of multiple DG units; 2) by improving the mutation and crossover procedure, strengthening the nondominated sorting and truncation strategies, and determining the Pareto solution set using the fuzzy membership function method, the proposed INSGA-II can obtain the best compromise solution for all objectives. Taking IEEE 33-, actual 292-, and 588-bus systems as test cases, the comparisons of the proposed INSGA-II with the traditional multiobjective optimization algorithms, such as NSGA-II, DEMO and SPEA2, indicate that the proposed method can achieve better precision and diversity. In practice, the choice of the best site may not always be feasible due to many reality constraints. But the optimization and analysis here suggest that considering multiobjectives helps to decide placement and sizing of DG units for the decision-maker.