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以含电动汽车、火电、风电和光伏的智能电网为研究对象,综合考虑系统的不确定性、节能减排和电动汽车的智能充放电,建立其多目标节能减排模型。先采用多场景模拟技术将风电场出力、光伏出力和负荷不确定性的随机过程分解为若干典型的离散概率场景,然后将优化问题分解为相互作用的代理优化,控制代理的调度方案由具有自适应交叉变异算子的遗传算法实现,代理间的协同进化过程由自适应协同乘子协调实现。算例表明通过场景缩减的多场景模拟技术可提高计算效率,自适应协同进化实现风电、光伏、火电和电动汽车的有机互补,自适应的协同乘子比传统的次梯度法更新乘子计算效率更高,精度更好。通过电动汽车的智能充放电控制,可以提高系统的旋转备用水平,实现节能减排综合性能好的机组多发电,能耗大或污染气体排放量大的机组少发电;通过权重调节实现节能与减排的折衷,增加系统调度的灵活性。实现最大化利用可再生能源和电动汽车来达到系统的节能减排。
Taking smart grids including electric vehicles, thermal power, wind power and photovoltaic as research objects, the multi-objective energy-saving and emission-reduction model is established by comprehensively considering system uncertainties, energy conservation and emission reduction, and smart charging and discharging of electric vehicles. Firstly, the stochastic process of wind farm output, PV output and load uncertainty is decomposed into several typical discrete probabilistic scenarios using multi-scenario simulation techniques, and then the optimization problem is decomposed into interacting agents. The scheduling scheme of control agents is composed of Genetic algorithm to adapt to cross-mutation operator, inter-agent co-evolution process is coordinated by adaptive cooperative multiplier. The results show that the scene reduction technology can increase the computational efficiency through adaptive scene reduction techniques, and the adaptive co-evolution can realize the organic complementation and self-adaptive collaborative multiplication of wind power, photovoltaic, thermal power and electric vehicles. Compared with the traditional sub-gradient method, Higher, better accuracy. Through the intelligent charge and discharge control of electric vehicles, it can increase the reserve capacity of the system for rotation and achieve energy saving and emission reduction. The unit with good comprehensive performance can generate more power, and the unit with large energy consumption or polluting gas emissions can generate less electricity. Row of compromise, increase the flexibility of system scheduling. Maximize the use of renewable energy and electric vehicles to achieve system energy saving.