School of Energy and Power, Jiangsu University of Science and Technology, Zhenjiang, 212100, Jiangsu, China.
State Grid Jiangsu Electric Power Co., Ltd Lianyungang Power Supply Branch, No. 1, Xingfu Road, Haizhou District, Lianyungang City, 222000, Jiangsu Province, China.
Sci Rep. 2022 May 2;12(1):7095. doi: 10.1038/s41598-022-10958-7.
The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of optimization, resulting in a low-quality scheduling scheme. Aiming at the dispatchability of electric and heat loads, this paper proposes an electric and heat comprehensive demand response model considering the participation of dispatchers. On the basis of incentive demand response, the group aggregation model of electrical load is constructed, and the electric load response model is constructed with the goal of minimizing the deviation between the dispatch signal and the load group aggregation characteristic model. Then, a heat load scheduling model is constructed according to the ambiguity of the human body's perception of temperature. On the basis of traditional GWO, the Fuzzy C-means (FCM) clustering algorithm is used to group wolves, which increases the diversity of the population, uses the Harris Hawk Optimizer (HHO) to design the prey to search for the best escape position, and reduces the local The optimal probability, and the use of Particle Swarm Optimizer (PSO) and Bat Optimizer (BO) to design the moving modes of different positions, increase the ability to find the global optimum, so as to obtain an Improved Gray Wolf Optimizer (IGWO), and then efficiently solve the model. IGWO can improve the defect of insufficient population diversity in the later stage of evolution, so that the population diversity can be better maintained during the entire evolution process. While taking into account the speed of optimization, it improves the algorithm's ability to jump out of the local optimum and realizes continuous deep search. Compared with the traditional intelligent Optimizer, IGWO has obvious improvement and achieved better results. At the same time, the comprehensive demand response that considers the dispatcher's desired signal improves the accommodation of new energy and reduces the operating cost of the system, and promotes the benign interaction between the source and the load.
综合能源系统（IES）的优化调度问题具有高度非线性的特点。使用传统的灰狼优化器（GWO）来解决这个问题，在优化过程中很容易陷入局部最优，导致调度方案质量较低。针对电、热负荷的可调度性，本文提出了一种考虑调度员参与的电、热综合需求响应模型。在激励型需求响应的基础上，构建了电气负荷的群体聚合模型，以调度信号与负荷群体聚合特征模型之间的偏差最小化为目标，构建了电气负荷响应模型。然后，根据人体对温度感知的模糊性，构建了热负荷调度模型。在传统 GWO 的基础上，使用模糊 C 均值（FCM）聚类算法对狼群进行分组，增加了种群的多样性，利用哈里斯鹰优化器（HHO）设计捕食者寻找最佳逃逸位置，降低了局部最优概率，并使用粒子群优化器（PSO）和蝙蝠优化器（BO）设计不同位置的移动模式，增强了寻找全局最优的能力，从而得到改进的灰狼优化器（IGWO），然后有效地求解模型。IGWO 可以改善进化后期种群多样性不足的缺陷，使种群多样性在整个进化过程中得到更好的维持。同时，考虑到优化的速度，提高了算法跳出局部最优的能力，实现了持续的深度搜索。与传统的智能优化器相比，IGWO 有明显的改进，取得了更好的效果。同时，考虑到调度员期望信号的综合需求响应提高了新能源的适应性，降低了系统的运行成本，促进了源荷良性互动。
