计及输电阻塞的帕累托最优多目标电网规划

在解除管制环境下,要求输电扩展规划有效缓解输电网络阻塞,文中分别以年阻塞盈余、线路投资费用和系统缺电成本为规划目标,基于帕累托(Pareto)多目标最优建立综合考虑阻塞问题、经济性和可靠性的多目标电网规划模型,并通过一种改进的强度帕累托进化算法(strength Pareto evolutionary algorithm,SPEA),实现对模型的求解。建立一种基于多维空间欧氏距离的排序方法,实现帕累托最优解集范围内的优化决策。18节点系统算例表明通过改进SPEA可以有效形成分布均匀的帕累托解集,并且所提多目标规划方法能够缓解和控制规划网络的输电阻塞。     

计及过负荷风险的输电网多目标期望值规划

建立以方案总投资费用贴现值和平均网损功率期望值为目标函数的多目标动态电网规划的标准期望值模型。在应用随机变量的标准差对支路潮流过负荷风险进行控制的基础上,进一步提出了计入标准差的期望值规划模型。采用随机直流潮流实现了各随机变量的相关计算。采用SPEA (strength pareto evolutionary algorithm)对所提模型进行了求解,针对SPEA应用于动态电网规划问题中的种群初始化冗余问题和具体的进化操作实现2个环节进行了重点探讨。以18节点系统为算例验证了所提方法对过负荷风险评估和控制的效果。     

Transmission surplus capacity based power transmission expansion planning

A power transmission expansion planning model with consideration of transmission surplus capacity and network load factor is presented. With traditional planning model, some transmission lines will operate on high load factors due to ignorance of the load levels of transmission lines. This may lead to network congestion or degrade the dispatch flexibility of future network. Traditional planning model has put more emphasis on investment cost rather than other aspects such as operation environment, transmission benefit, etc. The transmission expansion planning model in the paper aims to maximize network transmission surplus capacity and optimize network load factor distribution with least investment. Chaos Optimal Algorithm (COA) is introduced to solve this nonlinear integer planning optimization problem for its advantage of stochastic and ergodic searching characteristics. The effectiveness of proposed model and methodology is tested with two typical systems.     

考虑暂态稳定性的网储多目标双层优化

随着风电和负荷的不断发展,输电阻塞、弃风和运行安全等问题对电网规划提出了新的挑战。储能系统具有响应速度快、配置方便灵活、适用范围广等优点,在风电消纳、缓解输电阻塞、系统安全运行中发挥着重要作用。为提高电网输电能力和抗扰动能力,结合系统故障后的暂态运行特性,在规划和运行层面综合考虑电网输电能力、弃风水平和暂态稳定性,以系统建设运行指标和系统稳定指标为多目标建立双层网储联合规划模型。外层模型为考虑储能系统选址定容规划的多目标模型,内层模型为考虑机组组合问题的输电网扩容模型。为提高求解效率,将扩容线路潮流约束、储能系统充放电模型、机组发电成本曲线等进行线性化处理。采用NSGA-Ⅱ算法和GUROBI求解器求解所提模型,并利用改进Garver-6 节点系统进行算例分析,在不同场景下验证了所建模型的有效性和合理性。     

市场环境下基于系统阻塞指标约束的多阶段输电网规划

电力市场环境下的多阶段输电网扩展规划在满足发电公司和用户的需求的同时,对减缓输电系统阻塞,促进市场的公平竞争具有重要作用。在此背景下,利用系统阻塞指标和系统阻塞成本分别作为评估规划网络可靠性指标和运行指标,以改进的小生境遗传算法为研究方法,提出了基于阻塞指标约束下的输电网多阶段规划模型。IEEE-24节点系统上算例分析表明该模型综合考虑了各规划阶段输电系统的经济运行,能更科学地指导输电网规划决策,有效减缓系统阻塞发生,提高规划系统的网络充裕性。     

市场环境下计及阻塞集中度指标的输电网扩展规划

与传统的输电网规划相比,电力市场环境下的输电网扩展规划不仅要满足市场用户的需求,还对减缓输电系统阻塞、抑制市场力的滥用和提高社会效益起着重要作用。对于因网络充裕度不足造成的长期输电阻塞,首先参考市场集中度监管指标Lerner指数的定义,提出了新的阻塞集中度指标作为评估输电网络充裕度的指标;然后从市场运营的实际情况出发,以阻塞集中度指标作为约束条件来抑制网络的垂直市场力,构造了将阻塞收益用于回收投资成本的输电网扩展规划模型。IEEE-24节点算例系统分析表明该模型综合考虑了输电投资成本回收以及输电阻塞管理等实际问题,能有效减缓系统阻塞发生,降低投资风险,并为输电网监管提供可靠依据。     

利用强度Pareto进化算法的多目标无功优化

为更好地解决电力系统多目标无功优化问题,分析了当前多目标无功优化算法存在的缺陷,首次将强度Pareto进化算法(SPEA2)应用于多目标无功优化,为真正意义上的多目标无功优化提供了依据。SPEA2是一种新型的多目标进化算法,参数设置少,收敛速度快,寻优能力强,求得的Pareto最优解分布均匀。IEEE30节点测试系统的算例结果表明所提出的算法在多目标无功优化中具有良好的效果,为各目标之间的权衡分析提供了有效工具,是一种求解多目标无功优化问题的有效方法。     

A novel modified GBMO algorithm based static transmission network expansion planning

Due to the growing demand of electricity, transmission sector has become important part of the power sector. The penetration level of renewable energy resources has increased presently, which gives more challenges to the transmission expansion planner. To overcome this problem better transmission expansion planning (TEP) needs to be done. Hence, it is necessary to incorporate the impact of high wind power penetration in TEP problem. In this paper wind farm are considered as an alternative source for supplying the load to the transmission networks. The complex wind energy cost model is incorporated with the traditional transmission network expansion planning (TNEP) problem. Factors accounting for wind power utilization cost, underestimation, and overestimation cost model of wind power are included. Static transmission network expansion planning (STNEP) problem is modeled using the DC power flow model. The main objective function is to minimize the total cost of the system, which consists of transmission line investment cost, fuel cost of generators and wind energy cost. To solve this non-linear, non-convex optimization problem with a novel optimization algorithm i.e. Modified Gases Brownian Motion Optimization (MGBMO) algorithm is applied. To validate the capability of the proposed method is tested with modified Garver’s 6-bus system, IEEE 24-bus system and IEEE 25-bus system.     

市场环境下基于最优潮流的输电网规划

电力市场环境下,扩展或加强输电系统对满足发电公司和用户的需求、减缓输电系统阻塞、促进市场的公平竞争具有重要作用。文中首先以Pool市场模式为背景,基于最优潮流(OPF)的输电网边际定价模型,提出一个计及多场景的综合性系统阻塞指标作为评估规划网络可靠性的经济指标;然后建立了阻塞指标约束下的输电网静态规划模型,并用IEEE-24RTS实验系统进行了验证。与传统的输电网规划相比,该模型由市场条件下的OPF确定系统最优经济运行状况,能更科学地进行输电网规划决策,有效减缓系统阻塞发生,提高规划系统经济性能,易于扩展从而计及电网规划中的各种不确定性因素。     

市场环境下基于Benders分解的输电网规划决策

市场条件下,由于不确定因素对市场经济运行的影响,输电网的扩展规划必须考虑发电公司和用户的需求,减缓输电系统阻塞,促进市场的公平竞争。首先以电力联营市场模式为研究背景,针对不确定因素作用下可能的未来场景,基于最优潮流的输电网边际定价模型,提出了以投资成本和运行成本为优化目标的输电网静态规划模型;然后基于奔德斯(B enders)分解算法先求解出各个典型场景下输电网规划优化方案,再根据决策理论中的最小最大悔则进行多场景规划决策;最后在IEEE-24节点系统上进行了仿真计算。与确定性输电网规划方法相比,该模型计及了电网规划与经济运行中不确定因素的影响,从而能更有效地指导市场环境下输电网规划综合决策,提高规划系统经济性能。     

计及市场化电价信号导向的含高比例水电输电网扩展规划

传统电网扩展规划模型难以反映未来一段时期竞争市场模式下全局供需态势对电网投资收益、市场主体意愿、社会效益的量化影响,且在含高比例水电输电网中水电外送断面容量与丰枯潮流差异的不匹配导致网络阻塞及交易盈余问题愈加严重.为此,提出高比例水电场景下计及市场化电价信号导向的输电网扩展规划模型.上层模型以最大化线路年投资收益为目标函数,形成规划决策线路;下层模型采用改进的k-means聚类算法得到含高比例水电输电网全年典型运行方式,基于市场交易出清结果与区域负荷响应特征,量化线路规划后的负荷增量及过网利用率,以市场化电价信号引导电网扩展规划,促进区域水电消纳.采用KKT最优条件实现上、下层模型的耦合,将双层规划问题转化为混合整数规划问题进行求解.以我国西南部某含高比例水电的实际电网为例,通过综合比较不同规划方法的阻塞盈余、投资收益及水电消纳比例验证了高比例水电接入下市场信号对扩展规划结果的影响.     

基于离散多目标蜻蜓算法和改进FCM的风电协调输电网扩展规划研究

针对风电并网协调输电网扩展规划问题,提出一种基于离散多目标蜻蜓算法和改进FCM的风电协调输电网扩展规划方案。首先以投资建设成本、网损成本和弃风量为指标构建多目标风电协调输电网扩展规划模型。设计加权多核改进FCM算法,有效模拟风电空间分布多维运行时空特性。其次,提出柔性N-1安全校验策略,筛选出隶属度较低、出力和负荷较大的极端场景。设计离散多目标蜻蜓算法,提高算法多目标优化问题求解精度。最后,采用离散多目标蜻蜓算法对风电协调输电网扩展规划模型进行求解,提出最优规划方案确定策略。仿真实验证明了所提方法的有效性。     

电力市场环境下基于多智能体的多目标电网规划方法

网络阻塞费用是表征电力市场竞争程度的指标之一。提出了一种以网络阻塞费用、线路投资和由于传输容量不足引起负荷缺失最少为目标的多目标网络规划模型,依据分层求解的思想设计了基于多智能体的网络规划体系结构并用其对模型进行求解。初始规划层的母线智能体以降低阻塞为目标相互协商形成初始规划集并将其传送给决策规划层;决策规划层采用遗传算法在初始规划集中搜索规划方案,并进行N-1安全校验和阻塞校验,得到优化可行解。该网络规划体系是开放式结构,可以在知识库中考虑各种不确定因素。通过IEEE RTS-14节点算例仿真表明,考虑阻塞的网络规划能够以最少的投资费用为市场参与者提供公平的电能输送平台,最大限度地满足电力传送。     

Security-constrained transmission expansion planning: A stochastic multi-objective approach

This article presents a stochastic multi-objective optimization framework for transmission expansion planning (TEP) with steady state voltage security management, using AC optimal power flow (AC-OPF). The objectives are to minimize the sum of transmission investment costs (ICs), minimize the Expected Operation Cost (EOC), minimize the Expected Load Shedding Cost (ELSC) and maximize the Expected Loading Factor (ELF). The system load uncertainty has been considered and the corresponding scenarios are generated employing the Monte Carlo (MC) simulations. A scenario reduction technique is applied to reduce the number of scenarios. A multi-objective mathematical programming (MMP) is formulated and the ε-constraint method is used to solve the formulated problem. The N − 1 contingency analysis is also considered for the proposed TEP problem.The proposed TEP model has been applied to the well-known IEEE 24-bus Reliability Test System. The detailed results of the case study are presented and thoroughly analyzed. The obtained TEP results show the efficiency of the proposed algorithm.     

Transmission Congestion and Competition on Power Generation Expansion

Generation expansion planning involves decisions on location and capacity of new generation, which may lead to adding or relieving congestion in transmission lines and to increasing or reducing competition in deregulated markets. Generation expansion may encounter congestion in the transmission network by constrained single-line flows as well as flowgate transfer capabilities. In this paper, a model to study the interaction between competition and transmission congestion on power generation expansion is proposed. The generation expansion problem is modeled as a Cournot competition game. Network transmission constraints are included in the optimal generation expansion problem to comply with power flow limits. Results from a five-bus power network and the IEEE 24-bus system are presented and discussed     

Transmission expansion planning based on Locational Marginal Prices and ellipsoidal approximation of uncertainties

This paper proposes an algorithm for transmission expansion planning (TEP) which minimizes the congestion surplus calculated from optimized nonlinear (AC) Optimal Power Flow (OPF) and Locational Marginal Prices (LMPs). Uncorrelated and correlated uncertainties related to operating conditions of the future transmission network and expected costs of the submitted energy bids to the energy market are constrained by bounding hyper-ellipsoid around base case AC OPF solution, with assumption of additive uncertainties. Perturbed uncertain points inside a hyper-ellipsoid are selected by proposed quasi-random sampling algorithm. For these points, the linearized OPF around base case AC OPF solution is proposed. The Genetic Algorithm (GA) does selection of lines and years for transmission expansion, where the increments of the fitness function are calculated by proposed linearized AC OPF model. The results and practical aspects of the proposed methodology are illustrated on 12- and 118-bus test power system examples.     

Application of Multi-Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms (EAs). Specifically, strength Pareto EA (SPEA) and multi-objective particle swarm optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost of the VAr planning problem and maximizing the amount of available transfer capability (ATC) are defined as the main objective functions. The aim is to find the optimal allocation of VAr devices in such a way that investment and operating costs are minimized and at the same time the amount of ATC is maximized. The proposed approaches have been successfully tested on IEEE 14 buses system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large-scale multi-objective optimization problem. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A dynamic model for coordination of generation and transmission expansion planning in power systems

This paper presents a new dynamic approach on the expansion planning problem in power systems. First, the coordination between generation system expansion and transmission system expansion has been formulated as a mixed integer nonlinear programming (MINLP) problem. Then, it has been shown that this MINLP model cannot be efficiently solved by the traditional MINLP solvers. Since the nonlinear term comes from the multiplication of a binary variable by a continuous one, a Benders decomposition approach has been employed to convert the MINLP formulation into a mixed integer linear programming (MILP) master problem, and a linear programming (LP) sub-problem. Besides, different times of construction have been considered for different transmission and generation facilities. In addition, a clustering based algorithm has been proposed to evaluate the reliability of the system at hierarchical level II (HLII). Since this dynamic planning method is an upgraded version of a recent developed static model, the result from both methods have been also compared. A simple 6-bus test system and IEEE 30-bus system have been selected to confirm the effectiveness of the introduced method.     

基于潮流越限量的阻塞费用分摊方案研究

提出一种基于潮流跟踪原理,在双边交易模式下按阻塞线路上各交易导致线路阻塞的潮流越限量占线路潮流总越限量的比例分摊阻塞费用。由于线路潮流在达到线路容量上限之前,不存在阻塞,各交易成交量的变化不对阻塞承担责任。在达到线路容量上限后,交易量的增加使得线路发生阻塞,则这部分潮流增量就应承担阻塞的全部责任。采用该方法分摊阻塞成本,能准确地反映引起阻塞的责任,并公平地对待各市场参与者,消除阻塞对市场的不利影响。4节点系统算例表明该算法可行。     

Multi-objective optimal reactive power dispatch using multi-objective differential evolution

This paper presents multi-objective differential evolution (MODE) to solve multi-objective optimal reactive power dispatch (MORPD) problem by minimizing active power transmission loss and voltage deviation and maximizing voltage stability while varying control variables such as generator terminal voltages, transformer taps and reactive power output of shunt VAR compensators. MODE has been tested on IEEE 30-bus, 57-bus and 118-bus systems. Numerical results for these three test systems have been compared with those acquired from strength pareto evolutionary algorithm 2 (SPEA 2).