“双碳”目标下的核电电力市场差分进化算法竞价策略

“双碳”目标提出后,核电作为清洁能源在电力市场中迎来了提速换挡.核电已经不再是原来填补电源缺口, 可有可无的品种,而将是新型电力市场的主力能源.基于此背景,为实现电力资源合理配置,提高核电公司效益,提出以改进差分进化算法对核电报价.首先,建立核电电力市场竞价模型;然后,介绍差分进化算法并进行改进优化;最后通过 MATLAB 对算例进行仿真,对比其他算法,证明改进后的差分进化算法能有效提高核电公司盈利能力.     

电力人工智能技术理论基础与发展展望(二):自主学习与应用初探EI北大核心CSCD

电力人工智能技术不断取得突破的同时,实际应用中也面临诸多挑战,在电力人工智能的假设分析与应用范式研究基础上,探索电力人工智能自主学习的创新应用模式。首先,针对电力人工智能研究中存在的可信伦理、数据分布与进化迁移等瓶颈,提出并详细阐述数据知识融合、平行互动、模型进化三大机制;进而,基于生物脑认知原理,提出适用于电力领域人工智能应用的意识引导的自主学习技术,通过构建电力领域机器意识引导算法进行模型构建、数据组织、训练调优等自主学习应用,解决规则复杂、数据价值低、场景泛化等情况下的电力人工智能模型训练优化难题;最后,在设备运维检修领域开展应用探索,通过意识引导知识、数据、任务的理解分析,构建面向复杂运维检修任务端到端算法生成的智能应用。     

协同进化算法及其在电力系统中的应用前景

协同进化算法是一种新的进化算法框架，该算法借鉴自然界中的协同进化机制，在传统单物种进化算法的基础上引入生态系统的概念。生态系统中多个物种相互作用，共同进化，从而使整个系统不断演进。将应用领域待求解的问题映射为生态系统，以生态系统的进化来达到问题求解的目的。协同进化算法符合进化博弈论的理论框架，对于各种不同领域问题的求解都取得成功。文中综述了国内外协同进化算法的发展状况，介绍了协同进化算法的基本理论，探讨了该算法在电力系统和电力市场中的可能应用。将该算法引入电力系统和电力市场的研究，可以为电力系统的优化规划和运行、电力市场的竞价机制设计和投标策略优化以及电力市场计算机模拟提供新的方法。     

采用多目标改进差分进化算法的环境经济发电调度

针对差分进化算法在迭代过程中可能出现的早熟收敛问题,在算法中加入早熟判定系数和混沌优化,提出了改进差分进化算法;将改进差分进化算法扩展到多目标规划领域,形成了多目标改进差分进化算法,并应用于多目标环境经济发电调度.根据模糊集方法从帕累托前沿中选择最优折衷解.以6机系统为例进行仿真计算,结果验证了本文所提多目标改进差分进化算法在解决环境经济发电调度中的可行性和有效性.     

协同差分演化算法在梯级水库中长期发电优化调度中的应用研究

本文结合梯级水库中长期发电优化调度问题,针对差分演化算法在进化过程中,其适应度的进化模式未考虑进化的外部环境与进化成分之间的内在联系,借鉴生态学对个体生存环境与种群竞争的关系,提出了协同差分演化算法在梯级水库中长期发电优化调度中的应用。通过实例验证,结果表明该算法具有可靠性与合理性,提高了计算精度和计算效率,为高维、复杂的梯级水库中长期发电优化调度模型提供了一个新的求解途径。     

粒子群优化算法在电力系统中的应用

粒子群优化方法是一种基于群体智能的新型演化计算技术.它在函数优化、神经网络设计、分类、模式识别、信号处理、机器人技术等许多领域已取得了成功应用,但在电力系统中应用的研究起步较晚,关于它实际应用的报道尚不多见.文章较为全面地详述了粒子群优化方法在配电网扩展规划、检修计划、机组组合、负荷经济分配、最优潮流计算与无功优化控制、谐波分析与电容器配置、配电网状态估计、参数辨识、优化设计等方面应用的主要研究成果.随着粒子群优化理论研究的深入,它还将在电力市场竞价交易、投标策略以及电力市场仿真等领域发挥巨大的应用潜力.     

基于量子差分进化算法的电力系统无功优化

以系统有功网损最小为目标,建立了一种电力系统无功优化数学模型,并提出了一种基于量子差分进化算法的电力系统无功优化方法。该算法采用量子计算中的并行、坍缩等特性,增强了对解空间的遍历能力;同时在传统选择策略的基础上加入了量子计算的概率表达特性,有效地避免了算法的早熟现象。对IEEE-30节点测试系统进行仿真分析,并将优化结果与传统差分进化算法和粒子群优化算法进行对比分析,结果表明量子差分进化算法在解决系统无功优化问题上更科学、更有效。     

考虑输电阻塞盈余的输电网双层扩展规划模型

电力市场的建立使发、输、配、售环节分离,电网规划将面临许多不确定的市场因素,电力市场环境下的输电网规划比传统输电网规划面临更多困难和挑战。文章提出了一种考虑电力交易信号的输电系统双层优化配置模型,上层模型主要考虑投资者的投资成本,下层模型则考虑线路阻塞下节点边际电价(locational margin price)数学模型,并以输电阻塞盈余为目标函数,以节点边际电价信号来引导输电系统扩展规划。针对动态差分进化算法收敛速度较慢的缺点,文章综合了不同更新策略的优势,提出了改进动态差分进化算法求解输电网规划模型。最后,以中国西部某 500/220 kV输电网络为例,仿真结果验证了模型和算法的有效性和可行性。     

采用基于分解的多目标进化算法的电力环境经济调度

为了准确、快速地求解电力系统环境经济调度(environmental economic dispatching,EED)问题,将基于分解的多目标进化算法(multi-objective evolutionary algorithm based on decomposition,MOEA/D)应用于电力调度领域,提出了基于MOEA/D的多目标环境经济调度算法。该算法首先采用Tchebycheff法将整个EED Pareto最优前沿的逼近问题分解为一定数量的单目标优化子问题,然后利用差分进化同时求解这些子问题,并在算法中加入约束处理及归一化操作,以获得最优的带约束EED问题的调度方案。最后,应用模糊集理论为决策者提供最优折中解。对IEEE 30节点测试系统进行仿真计算,并与其它智能优化算法的调度方案对比。结果表明,该算法有效可行,且具有很好的收敛速度和求解精度。     

基于差分进化膜计算的MMC-HVDC控制参数优化

基于模块组合多电平换流器MMC的新型高压直流HVDC输电具有广阔的应用前景。针对传统控制器优化方法的不足,提出了一种基于差分进化膜计算的MMC-HVDC控制参数优化方法。基于MMC-HVDC的控制原理,在PSCAD下搭建了MMC-HVDC的仿真模型,利用差分进化膜计算优化控制系统参数。算例分析表明:该控制器能有效改善系统受到短路故障扰动后的动态特性;通过比较原参数、基于差分进化算法的优化结果和基于自适应差分进化算法的优化结果,验证了该方法的有效性。     

A binary-real-coded differential evolution for unit commitment problem

The unit commitment problem (UCP) is a nonlinear mixed-integer optimization problem encountered in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. Due to the inadequacy of deterministic methods in handling large-size instances of the UCP, various metaheuristics are being considered as alternative algorithms to realistic power systems, among which differential evolution (DE) is one of the widely investigated metaheuristics. However, DE is usually applied for solving the integer part of the UCP, along with some other schemes for the real part of the problem. In this paper a binary-real-coded DE is proposed as a complete solution technique of the UCP. Some repairing mechanisms are also incorporated in the DE for speeding up its search process. In the computational experiment carried out with power systems up to 100 units over 24-h time horizon, available in the literature, the performance of the proposed DE is found quite satisfactory in comparison with the previously reported results.     

Optimal location and parameter setting of UPFC for enhancing power system security based on Differential Evolution algorithm

This paper presents a new approach based on Differential Evolution (DE) technique to find out the optimal placement and parameter setting of Unified Power Flow Controller (UPFC) for enhancing power system security under single line contingencies. Firstly, we perform a contingency analysis and ranking process to determine the most severe line outage contingencies considering line overloads and bus voltage limit violations as a Performance Index. Secondly, we apply DE technique to find out the optimal location and parameter setting of UPFC under the determined contingency scenarios. To verify our proposed approach, we perform simulations on an IEEE 14-bus and an IEEE 30-bus power systems. The results we have obtained indicate that installing UPFC in the location optimized by DE can significantly enhance the security of power system by eliminating or minimizing the overloaded lines and the bus voltage limit violations.     

Seeker Optimization Algorithm for Optimal Reactive Power Dispatch

Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.      

Network loss minimization with voltage security using differential evolution

This paper presents a differential evolution (DE) based optimal power flow (OPF) for reactive power dispatch in power system planning studies. DE is a simple population-based search algorithm for global optimization and has a minimum number of control parameters. The problem is formulated as a mixed integer non-linear optimization problem taking into account both continuous and discrete control variables. The proposed method determines control variable settings such as generator voltages (continuous), tap positions (discrete) and the number of shunt reactive compensation devices to be switched (discrete) for real power loss minimization in the transmission system using DE algorithm. Most of the evolutionary algorithm applications to optimization problems apply penalty function approach to handle the inequality constraints, involving penalty coefficients. The correct combination of these coefficients can be determined only by a trial and error basis. In the proposed approach, the inequality constraints are handled by penalty parameterless scheme. Voltage security margin was evaluated using continuation power flow (CPF), to ensure the feasibility of the optimal control variable setting. The suitability of the method was tested on IEEE 14 and IEEE RTS 24-bus systems and results compared with sequential quadratic programming (SQP) method. The DE provides near global solutions comparable to that obtained using SQP.     

一种求解电力经济负荷分配问题的改进微分进化算法

针对电力系统经济负荷分配(economic dispatch,ED)这一典型的非凸、非线性、组合优化问题,提出一种改进的微分进化(improved differential evolution,IDE)算法。微分进化(differential evolution,DE)算法虽有简单、搜索效率高的优点,但是仍然有局部最优的问题。该文在对DE算法搜索机理进行分析的基础上,针对DE算法参数难于动态调整的问题,提出不依赖于优化问题的控制参数自适应调整机制,并根据动态监视群体适应度方差的变化,增加个体迁移策略,进一步提高DE算法的全局寻优能力和鲁棒性。运用该算法对IEEE3机、40机及69机300节点标准测试用例进行计算,并考虑机组的爬坡约束、出力限制区约束、非光滑费用函数曲线等非线性特性,将其计算结果与遗传算法(genetic algorithm,GA)及粒子群算法(particle swarm optimization,PSO)进行比较,分析表明该方法是可行的、有效的。     

Reactive power dispatch considering voltage stability with seeker optimization algorithm

Optimal reactive power dispatch (ORPD) has a growing impact on secure and economical operation of power systems. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In the last decades, computation intelligence-based techniques such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA) based method is proposed for ORPD considering static voltage stability and voltage deviation. The SOA is based on the concept of simulating the act of human searching where search direction is based on the empirical gradient by evaluating the response to the position changes and step length is based on uncertainty reasoning by using a simple Fuzzy rule. The algorithm's performance is studied with comparisons of two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms on the IEEE 57 and 118-bus power systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the ORPD problem.     

智能控制与电力系统

本文简要介绍了控制科学的发展进程及智能控制的主要研究内容,综述了电力系统应用智能控制的现状。分析指出,应根据自然需要和解决问题的实际能力来确定是否采用智能控制,而不要对智能控制期望过高。     

南方电网竞争情报系统建设研究

在分析国内外企业以及电力行业竞争情报系统发展现状基础上,提出了南方电网竞争情报系统“一级部署、二级运维、四级应用”的建设方案,对建设方案的必要性、基础条件、目标、主要内容、关键点以及预期效果进行了较详细的分析和论述。该系统的建设对南方电网的科学管理,提高企业竞争力等方面起到促进和支撑作用。     

Modelling and simulation of a high penetration wind diesel system with battery energy storage

Wind Diesel Hybrid Systems (WDHS) are isolated power systems which combine Diesel Generators (DG) with Wind Turbine Generators (WTG). Depending on the generators which are supplying, high penetration (HP) WDHS have three operation modes: Diesel Only (DO), Wind Diesel (WD) and Wind Only (WO). The HP-WDHS presented in this article consists of a Diesel Engine (DE), a Synchronous Machine (SM), a Wind Turbine Generator, the consumer load, a Ni-Cd Battery based Energy Storage System (BESS) and a Dump Load. The DE can be engaged (DO and WD modes) or disengaged (WO mode) from the SM by means of a clutch. All the models of the previously mentioned components are presented and the performance of the WDHS has been tested through dynamic simulation. Simulation results with graphs for the frequency and voltage of the isolated power system, active powers generated/absorbed by the different elements and the battery voltage/current/state of charge are presented for a load change in WO mode and for the transition from WO to WD mode in order to substitute a supplying BESS for the DE as the active power source.     

Solving non-smooth economic dispatch by a new combination of continuous GRASP algorithm and differential evolution

The power economic dispatch is one of the most important problems in power system operation. Its objective is to minimize the total generation cost of the generating units while satisfying various constraints of the units and system. In past decades, modern metaheuristics have been applied in solving the economic dispatch problem due to their ability to find an almost global optimal solution. Recently, inspired in classical GRASP (Greedy Randomized Adaptive Search Procedure) algorithm, Hirsch et al. (2006) proposed a novel variant of the GRASP for continuous domain, called continuous GRASP (C-GRASP) algorithm. This paper proposes a combination of C-GRASP and differential evolution (DE) algorithm (C-GRASP–DE) in order to improve the global searching capability and prevent the convergence to local minima. The feasibility and performance of proposed C-GRASP and DE is demonstrated by power systems consisting of 13, 40 and 140 generators whose fuel cost function is calculated taking into account the effect of valve-point loading. Simulation results demonstrate the superiority of the proposed C-GRASP in combination with a self-adaptive DE approach (C-GRASP–SaDE) compared to the classical C-GRASP. Furthermore, some obtained optimization results for the three case studies by the proposed C-GRASP–SaDE are competitive with the best results presented in recent literature.