基于模拟退火?逐步优化算法的多形态高载能负荷协调降损二层优化模型

A Bi-Level Coordinative Optimization Model of Polymorphic High Energy Load for Grid Loss Reduction Based on Simulated Annealing-Progressive Optimality Algorithm

大规模风电远距离输送及其出力的随机性和波动性对电网损耗产生了显著影响。提出利用容量大、调节能力强的高载能负荷对风电进行响应控制,从而有效降低网损。在此基础上,针对不同形态的高载能负荷在调节周期与调节特性上的差异,以及它们之间的协调配合关系,建立两者协调降损二层优化模型,在上、下层中分别进行不同形态的高载能负荷的优化,两层优化相互制约,交替迭代至最优解。求解二层优化模型时,提出模拟退火?逐步优化算法,克服了现有算法不能同时兼顾全局搜索能力和求解速度的矛盾。甘肃西部电网的实际算例验证了协调降损二层优化模型及模拟退火?逐步优化求解算法的有效性和优越性。

Long-distance transmission of large-scale wind power, stochastic and fluctuant, has significant impact on grid losses. In this paper, usage of chartered high energy load of easy regulation and large capacity to respond to wind power, thus effectively reducing grid losses, was proposed. On this basis, regarding to differences of regulation period, features of various high-energy loads and interactions between them, a coordinative bi-level optimization model for grid loss reduction was built. The upper level dealt with discrete load optimization while the lower level dealt with continuous load optimization. These two levels in the model were restrained with each other, alternatively iterating till optimal solution reached. To solve the proposed bi-level model, a simulated annealing-progressive optimality algorithm was proposed to overcome deficiency of the algorithm now available, not giving simultaneous consideration to both global search capability and speed. Practical examples of Western Gansu Power Grid validated effectiveness and superiority of the bi-level coordinative loss-reduction optimization model and simulated annealing-progressive optimality algorithm.