差分進(jìn)化鳥群算法的微電網(wǎng)多目標(biāo)優(yōu)化運(yùn)行
薛陽1，李蕊1，張寧1，王琳2
（1 上海電力大學(xué) 自動(dòng)化工程學(xué)院，上海 200090；2 國網(wǎng)上海市電力公司，上海 200122）
摘 要：為提高微電網(wǎng)在安全可靠前提下調(diào)度運(yùn)行的經(jīng)濟(jì)性和環(huán)保性，提出了一種基于差分進(jìn)化鳥群算法的微電網(wǎng)多目標(biāo)優(yōu)化運(yùn)行策略。建立了考慮經(jīng)濟(jì)性、環(huán)保性及供電可靠性等因素的微電網(wǎng)多目標(biāo)模型，并給出了滿足微電網(wǎng)安全穩(wěn)定運(yùn)行所需的約束條件；將多目標(biāo)函數(shù)轉(zhuǎn)換為單目標(biāo)函數(shù)，應(yīng)用差分進(jìn)化鳥群算法對(duì)其進(jìn)行求解；將所得結(jié)果分別與各單目標(biāo)下求解結(jié)果進(jìn)行對(duì)比。實(shí)驗(yàn)結(jié)果表明，所提方法在經(jīng)濟(jì)性和環(huán)保性上較傳統(tǒng)模型均有所提高，更充分利用可再生能源，降低系統(tǒng)運(yùn)行成本，并且在負(fù)荷變動(dòng)明顯的情況下，系統(tǒng)波動(dòng)性較小，一定程度上提高系統(tǒng)穩(wěn)定性；同時(shí)該組合算法增加了種群的多樣性，防止訓(xùn)練過程陷入局部最優(yōu)解，具有效率高、魯棒性好的優(yōu)點(diǎn)。
關(guān)鍵詞：微電網(wǎng)；多目標(biāo)；優(yōu)化運(yùn)行；差分進(jìn)化算法；鳥群算法
中圖分類號(hào)：TM711 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：1007-3175(2020)08-0001-06
Multi-Objective Optimal Operation of Micro-Grid Based on Differential Evolutionary Bird Swarm Algorithm
XUE Yang1, LI Rui1, ZHANG Ning 1, WANG Lin2
（1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China;
2 State Grid Shanghai Electric Power Company, Shanghai 200122, China）
Abstract: In order to improve the economics and environment friendly of micro-grid dispatching operation under the premise of safety and reliability, a multi-objective optimization operation strategy for micro-grid based on differential evolution bird swarm algorithm is proposed. In this paper, it established a micro-grid multi-objective model that considers factors such as economy, environment friendly, and power supply reliability, and gave the constraints required to meet the safe and stable operation of the micro-grid, the multi-objective function is converted into a single-objective function, and the differential evolution bird swarm algorithm is used to solve it; the obtained results are compared with the solution results under each single-objective. The experimental results show that the proposed method is improved in economy and environmental protection compared with the traditional model, making full use of renewable energy, reducing system operating costs, and under the condition of obvious load fluctuations, the system volatility is small, and to a certain extent, the stability of the system is improved. At the same time, the combined algorithm increases the diversity of the population and prevents the training process from falling into the local optimal solution. It has the advantages of high efficiency and good robustness.
Key words: micro-grid; multi-objective; optimal operation; differential evolutionary algorithm; bird swarm algorithm
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