基于可信性理论的输电网短期线路检修计划

传统方法将短期线路检修计划作为单重不确定性优化问题进行建模和求解。但是，架空线路的可靠性指标难以表达现场运行中线路发生故障的可能性，所以需要在短期线路检修计划中对双重不确定性(随机性和模糊性)同时进行建模和求解。可信性理论是基础数学领域最近完成的数学分支， 它提供了随机性与模糊性综合评估的严格数学基础。基于可信性理论可建立短期线路检修计划的混合整数随机模糊双重不确定性优化模型(原始模型)，其目标函数是检修费用与停电损失费用之和的随机模糊期望值最小。文中利用Benders分解法将原始模型分解为主问题和子问题进行求解：主问题是一个多目标整数规划问题，利用改进Balas算法求解；子问题是一个随机模糊双重不确定性模型，利用可信性理论和直流潮流求解。IEEE-RBTS系统和IEEE-RTS系统的算例表明，文中提出的算法可以综合协调全网的风险和经济目标。同时由于支持原始数据的随机模糊性，使得该算法具有较强的实用性。     

基于可信性理论的水火电机组检修计划

水电机组同时存在着水电电量模糊性和机组强迫停运的随机性这两类不同的不确定性,因此水火电机组检修计划本质上是一个具有随机模糊双重不确定性的混合整数优化问题。但是,因概率论与模糊论在基础数学领域内是相互独立的理论体系,传统的机组检修计划只能对随机性或模糊性这两类不确定性中的一类进行建模,然后采用随机规划或模规划来求解。可信性理论是2004年基础数学领域完成的数学分支,它给出了基于测度论的模糊论公理化体系,并提糊供了随机性与模糊性综合评估的严格数学基础。文中基于可信性理论,建立了水、火电机组检修计划的混合整数随机模糊双重不确定性优化模型(原始模型),并在这一个模型中同时考虑随机性与模糊性2种不确定性。利用Benders分解法,将原始模型分解为多目标整数规划(主问题)和随机模糊规划(运行子问题)2大问题进行分散协调,利用改进Balas算法求解主问题,综合利用可信性理论与半不变量法求解运行子问题。对吉林电力系统中的95台机组安排了全年的检修计划,结果表明:该算法和软件在实际系统中应用是可行、有效的。     

基于可信性理论的输电网规划方法

输电网规划中存在着大量的随机性和模糊性的不确定性因素,传统方法将其分开考虑,作为单重不确定问题进行建模和求解;可信性理论是数学基础领域最近完成的数学分支,提供了随机性和模糊性综合评估的严格数学基础;运用可信性理论建立了双重不确定性的输电网规划模型,模拟了新增发电机节点出力、新增节点负荷变化值和新增线路造价的不确定性因素,结合随机模糊模拟技术和改进量子遗传算法对模型进行了求解,最后用一个18节点系统算例验证了所提模型和方法的有效性。     

计及隐性损失的输电线路检修计划优化方法

建立以经济性为目标函数的检修优化模型,提出了停电损失包含显性损失和隐性损失2个部分,采用可靠性价值指标变化量和风险指标变化量量度隐性损失,利用线路过负荷、节点电压越限和频率稳定等情况描述风险指标变化量,利用期望缺供电量变化描述可靠性价值指标变化量。将该隐性损失模型用于输电网络计划检修优化模型,利用Benders分解法将检修优化模型分解为多目标混合整数规划的主问题和基于可信性理论的子问题进行求解。将该优化模型应用于云南电网输电网络日、周和月计划检修优化,对比粒子群优化算法和遗传算法计算结果,验证了该方法的正确性。     

计及随机模糊双重不确定性的交直流混合微网优化运行

为解决风光出力的不确定性造成微网日前调度偏差大的问题,将随机模糊理论引入到交直流混合微网优化运行中。基于不确定性理论将风速定义为随机模糊变量,用极大似然法提取Weibull分布参数的隶属度函数,建立日前调度经济性最优的随机模糊机会约束优化模型,将随机模糊模拟、神经网络和混沌粒子群算法相结合求解双重不确定性优化模型。通过设置确定性、随机性和随机模糊性3种情景进行仿真,分析3种情景的日前调度出力计划以及不平衡功率调整方案,验证了计及随机模糊双重不确定性的机会约束模型在交直流混合微网优化运行中的正确性和可行性。     

基于可信性理论的电力系统运行风险评估:(二)理论基础

在实际研究中遇到的新问题是;传统架空线路的可靠性统计指标难以表达现场运行中线路故障的可能性,单纯采用可靠性数学理论无法进行全系统的运行风险评估。实际上,电力系统的不确定性同时包含着随机性和模糊性2个方面,但受限于概率论与模糊论在基础数学领域内是相互独立的理论体系,传统上在电力系统只能将它们分开研究。可信性理论是2004年基础数学领域完成的数学分支,它给出了基于测度论的模糊论的公理化体系,并提供了随机性与模糊性综合评估的严格数学基础。文中首先介绍引入可信性理论的必要性,然后较为详尽地介绍了可信性理论中的基本公理、可信性测度、模糊变量期望值、随机模糊变量期望值等基本内容。上述内容是可信性理论在电力系统应用的必要基础。     

电力市场发电机组检修计划的快速算法

电力市场下发电机组检修计划的安排不当是造成近几年大停电的原因之一。电力市场机组检修计划需要兼顾可靠性与经济性两个方面。采用Benders分解法可以将这个高维度、非线性、混合整数随机规划问题分解为主问题和子问题求解：主问题是一个多目标整数规划问题,而子问题则是一个非线性随机问题。针对Benders分解法求解效率不高的问题,求解主问题时利用了机组检修连续性的特点,对Balas隐枚举法中的前向搜索部分和回溯部分进行了改进;求解子问题时,利用了半不变量法的偏导数解析表达式来求对偶乘子。对IEEE-RBTS和IEEE-RTS系统中所有发电机组安排了全年的检修计划,测试表明所提出的算法快速、有效。     

发输电设备联合检修安排模型及算法研究

该文提出了一种基于Benders分解法的优化算法，将电力市场下的长期发输电联合检修计划问题分解为多目标整数规划和随机规划两大问题进行分散协调，利用改进的隐枚举法和基于直流潮流的发输电组合系统可靠性评估模型分别求解主问题以及子问题。目标函数中考虑了停电损失费用，从而体现出可靠性与经济性的综合运筹。应用该文提出的方法对IEEE—RBTS系统和：IEEE—RTS系统进行了计算，结果表明该方法是合理、有效的。     

基于模糊机会约束二层规划的配电网检修计划优化

建立了配电网检修计划优化的模糊机会约束二层规划模型。与传统模型相比,该模型考虑了配电网检修计划优化问题(DMSOP)涉及的不确定因素,并引入可信性理论来描述和处理检修计划优化过程中检修资源、负荷预测以及支路潮流上限等模糊性因素。采用模糊函数描述检修总成本,该函数计及了检修费用和各时段负荷转移情况。提出基于模糊模拟的混合智能优化方法求解模型,IEEE-RBTS测试系统的计算结果表明模型和方法是有效的,并可以得到强鲁棒性的配电网检修计划方案。     

水火混合电力系统发输电设备联合检修计划优化模型及其算法实用研究

建立的发输电检修计划模型,以节能调度为原则,目标函数为检修费用、发电能耗费用以及购电费用之和最小.特别考虑了水火电协调、水电发电量约束、水库调节能力、梯级水电约束、关键线路传输稳定限额、机群最小开机等实用约束条件.在遗传算法框架内,将原优化问题分解为离散变量的检修子问题和连续变量的发电运行子问题.对检修子问题,结合问题的特点设计编码方式和遗传算子.并采用了一种快速的启发式方法求解运行子问题.通过个体适合度的评估对两个子问题进行协调.实际系统算例表明所提出的模型和算法的可行性.     

Flexible generator maintenance scheduling based on subjective relaxation of constraints

This paper presents a fuzzy-based method for determining a flexible generator maintenance scheduling by means of subjective relaxation of constraints imposed on the maintenance scheduling problem. The constraints are divided into hard (crisp) constraint set and soft (fuzzy) constraint set according to reflecting conditions which surround power systems. The problem is formulated as a fuzzy mathematical programming problem and solved with the fuzzy branch-and-bound method using Bellman-Zadeh maximizing decision. The proposed approach provides not only a new flexible concept of planning problems in power systems, but also natural expansion of conventional approaches based on crisp set theory. The effectiveness and feasibility of the proposed approach are demonstrated on two typical power system models which consist of 15 generators and 60 generators, respectively.     

A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS–PSO–SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS–PSO–SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO–SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.     

GENCO's Risk-Based Maintenance Outage Scheduling

This paper presents a stochastic model for the optimal risk-based generation maintenance outage scheduling based on hourly price-based unit commitment in a generation company (GENCO). Such maintenance outage schedules will be submitted by GENCOs to the ISO for approval before implementation. The objective of a GENCO is to consider financial risks when scheduling its midterm maintenance outages. The GENCO also coordinates its proposed outage scheduling with short-term unit commitment for maximizing payoffs. The proposed model is a stochastic mixed integer linear program in which random hourly prices of energy, ancillary services, and fuel are modeled as scenarios in the Monte Carlo method. Financial risks associated with price uncertainty are considered by applying expected downside risks which are incorporated explicitly as constraints. This paper shows that GENCOs could decrease financial risks by adjusting expected payoffs. Illustrative examples show the calculation of GENCO's midterm generation maintenance schedule, risk level, hourly unit commitment, and hourly dispatch for bidding into energy and ancillary services markets.     

A probabilistic approach to generation maintenance scheduler with network constraints

Most generating unit maintenance scheduling packages consider the preventive maintenance schedule of generating units over a one or two year operational planning period in order to minimize the total operating cost while satisfying system energy requirements and maintenance constraints. In a global maintenance scheduling problem, we propose to consider network constraints and generating unit outages in generation maintenance scheduling. The inclusion of network constraints in generating unit maintenance will increase the complexity of the problem, so we decompose the global generator scheduling problem into a master problem and sub-problems using Benders decomposition. At the first stage, a master problem is solved to determine a solution for maintenance schedule decision variables. In the second stage, sub-problems are solved to minimize operating costs while satisfying network constraints and generators’ forced outages. Benders cuts based on the solution of the sub-problem are introduced to the master problem for improving the existing solution. The iterative procedure continues until an optimal or near optimal solution is found.