混沌正余弦算法在含风能电力系统经济排放调度中的应用

Application of an improved chaotic sine cosine algorithm in the economic emission scheduling of a power system with wind energy

由于风能的随机性,含风能的经济排放调度问题变得更加复杂。因此,为了解决含风能的经济排放调度问题,将该问题描述为一个机会约束问题来处理风力发电的随机特性。基于此,提出了一种混沌正余弦算法来提供最优发电计划,以同时最小化发电成本和碳排放量。该算法利用混沌序列替代正余弦算法的随机数,避免了局部最优点的早熟收敛,提高了解的精度。然后,对比实数编码化学反应优化算法和基于二次逼近的混合人工协同搜索算法,通过2个10-unit案例仿真,验证了所提方法的有效性和准确性。最后研究了约束条件阀值水平σ对优化结果的影响,当σ增加时,风电渗透率增加,从而导致总生产成本和排放值的降低。

Because of the random availability of wind energy, the economic emission scheduling(EED) problem with wind energy becomes more complex. Therefore, in order to solve this problem, this paper describes the problem as opportunity constrained to deal with the stochastic characteristics of wind power generation. A chaotic sine cosine algorithm(CSCA) is proposed to provide the optimal generation plan to minimize the generation cost and emission at the same time. The algorithm uses a chaotic sequence to replace the random number of an SCA, avoids premature convergence into a local optimum and improves the accuracy of solution. Finally, by comparing the real coded chemical reaction optimization algorithm with the hybrid artificial collaborative search algorithm based on quadratic approximation,the effectiveness and accuracy of the proposed method are demonstrated by two 10-unit case simulations. Finally, the impact of threshold level of constraints σ on optimization results is studied. When σ increases, the wind power permeability increases, resulting in the reduction of total production cost and emission value.