Security-constrained optimal generation scheduling for GENCOs

This paper presents an approach for maximizing a GENCO's profit in a constrained power market. The proposed approach considers the Interior Point Method (IPM) and Benders decomposition for solving the security-constrained optimal generation scheduling (SC-GS) problem. The master problem represents the economic dispatch problem for a GENCO which intends to optimize its profit. The formulation of the master problem does not bear any transmission network constraints. The subproblem will be used by the same GENCO to check the viability of its proposed bidding strategy in the presence of transmission network constraints. In this case if the subproblem does not yield a certain level of financial return for the GENCO or if the subproblem results in an infeasible solution of the GENCO's proposed bidding strategy, the GENCO will modify its proposed solution according to the Benders cuts that stem out of the subproblem. The study shows a more flexible scheduling paradigm for a GENCO in a competitive arena. The proposed approach proves practical for modeling the impact of transmission congestion on a GENCO's expected profit in a competitive environment.     

A new approach to security-constrained generation scheduling of large-scale power systems with a piecewise linear ramping model

This paper presents a new and efficient approach to determine security-constrained generation scheduling (SCGS) in large-scale power systems, taking into account dispatch, network, and security constraints in pre and post-contingency states. A novel ramp rate limit is also modeled as a piecewise linear function in the SCGS problem to reflect more practical characteristics of the generating units. Benders decomposition is applied to this constrained solution process to obtain an optimal SCGS problem based on mixed-integer nonlinear programming (MINLP). The formulation can be embedded in two stages. First, a MIP is formulated in the master problem to solve a unit commitment (UC) problem. This stage determines appropriate on/off states of the units. The second stage, the subproblem, is formulated as a NLP to solve a security-constrained economic dispatch (SCED) problem. This stage is used to determine the feasibility of the master problem solution. It provides information to formulate the benders cuts that connect both problems. The proposed approach is tested in the IEEE 118-bus system to show its effectiveness. The simulation results are more realistic and feasible, whilst assuring an acceptable computation time.     

应用粒子群优化算法求解市场环境下的电力系统动态经济调度问题

在竞争性的电力市场环境下,为了获得最大化的社会利润,提出了基于竞价机制的动态经济调度模型,该模型综合考虑了发电机组的爬坡约束、输电线路的容量约束和污染气体排放量的约束。针对该模型,提出了一种新的求解方法:粒子群优化算法(PSO)。算例的结果表明,PSO算法能够有效地得到一个高性能的优化调度结果     

Multi-area generation scheduling algorithm with regionally distributed optimal power flow using alternating direction method

This paper calls attention to the core issue as to the multi-area generation scheduling algorithm in interconnected electric power systems. This algorithm consists in deciding upon on/off states of generating units and their power outputs to meet the demands of customers under the consideration of operational technical constraints and transmission networks while keeping the generation cost to a minimum. In treating the mixed integer nonlinear programming (MINLP) problem, the generalized Benders decomposition (GBD) is applied to simply decouple a primal problem into a unit commitment (UC) master problem and inter-temporal optimal power flow (OPF) sub-problems. Most prominent in this work is that the alternating direction method (ADM) is introduced to accomplish the regional decomposition that allows efficient distributed solutions of OPF. Especially, the proposed distributed scheme whose effectiveness is clearly illustrated on a numerical example can find the most economic dispatch schedule incorporated with power transactions on a short-term basis where utilities are less inclined to pool knowledge about their systems or to telemeter measured system and cost data to the common system operator and nevertheless the gains from trade such as economy interchange are vital as well.     

基于两阶段鲁棒优化的西南地区微电网经济调度

针对西南地区可再生能源储备丰富的特点,考虑微电网中可再生分布式能源出力和负荷的不确定性对微电网运行调度的影响,搭建了两阶段鲁棒优化经济调度模型,求解系统在极端运行场景下的经济性最优解。模型考虑系统功率平衡和输出功率约束、需求响应负荷约束以及微电网与配电网的交互约束,通过不确定性调节参数处理微电网中的不确定性,调节模型的保守度。随后通过Benders分解算法将模型分为主问题和子问题求解,并采用对偶理论对内层模型进行解耦。最终得出的结果验证了所搭建模型的有效性,为微电网接入西南电网后的调度策略提供参考。     

Optimal power dispatch in multinode electricity market using genetic algorithm

The application of the genetic algorithm to solve the optimal power dispatch problem for a multi-node auction market is proposed in this paper. The optimal power dispatch problem is a non-linear optimisation problem with several constraints. The objective of the proposed genetic algorithm is to maximise the total participants’ benefit at all nodes in the system. The proposed algorithm is simple to implement and can easily incorporate additional constraints. The algorithm was tested on a 17-node, 26-line system. The results have shown that the proposed algorithm yields good results that are consistent with typical market behaviour.     

电力市场环境下解决机组组合问题的新方法

机组组合问题是电力市场环境下编制短期发电计划所面临的主要问题,在满足各种约束条件的情况下,如何合理地开、停机组、以及负荷如何在运行的发电机组之间经济地分配是一个比较困难的问题,特别是由于发电机组出力上升、下降速度的限制,使这个问题一直没有很好的解决方法。提出一种组合优化方法解决这一问题,即用启发式方法确定机组组合,用分段线性规划算法分配功率,并满足各种约束条件,特别是可以处理发电机组出力上升、下降速度约束、经实际系统检验是一种非常有效的算法。     

A new approach to generation scheduling in interconnected power systems using predictor-corrector proximal multiplier method

The purpose of this paper is to elaborate a new generation scheduling algorithm in the interconnected power systems. Typically, the generation scheduling problem as a mixed integer non-linear programming can be effectively solved by the generalized Benders decomposition technique which decouples an original problem into the master problem and subproblems to tremendously allow fast and accurate solutions of large-scale problems. In order to formulate efficient inter-temporal optimal power flow (OPF) subproblems, we will explore a regional decomposition framework based on predictor-corrector proximal multiplier method. In fact, this scheme can find the most economic generation schedules under the power transactions for a multi-utility system without the exchange of each utility’s own private information and major disruption to existing economic dispatch or OPF adopted by individual utilities.     

电力市场下发电机组计划检修模型

发电机组的计划检修作为防止机组老化失效的主要方法,在确保电力系统可靠运行方面起着重要作用。文中对市场环境下机组检修策略模型及算法进行了综述,并提出了进一步研究的问题。在市场环境下,制定机组检修计划时应当考虑系统运行的可靠性和发电公司的经济利益2个方面。通常,首先由发电公司确定本公司机组的计划检修时段,然后由独立系统运营商（ISO）考虑系统可靠性充裕度后按一定的原则调整机组检修计划;前者确保了发电公司的经济利益,体现了其独立的经济实体地位,后者保证了系统的安全可靠运行。对于该优化问题,一般采用遗传算法、模拟退火算法等启发式方法或者整数规划法、Benders解耦法等数学规划方法加以求解。     

市场环境下满足机组成本回收约束的电源规划方法EI北大核心CSCD

为保证规划期内系统的发电容量充裕度,提出市场环境下满足机组成本回收约束的电源规划方法。基于市场价格信号和容量补偿机制推导机组成本回收的约束条件;将该约束嵌入电源规划模型中,构建电源规划与电力市场耦合的双层模型;利用Benders分解算法对模型进行求解,解决了传统上下层迭代求解方法不能保证得到全局最优解的问题,实现了全局优化求解。IEEE RTS-96系统算例结果验证了所提模型与方法的有效性。     

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.     

电力市场的日有功优化调度

在电力市场条件下,电力系统日有功调度已由单纯的电能供需平衡分配过渡到追求全网总共电成本最小、寻求用户电价最低为目标。针对这一特点,提出了在多约束的条件下,寻求全网经济效益最优的两步简单求解法。首先应用优先级法以求得机组经济组合,然后再采用改进型优先顺序法进行经济负荷分配。该方法还可应用于水火电混合电力系统和互联电力系统的日有功优化调度。该研究成果已被开发成软件系统,投入省级电网的调度运营,效益十分显著。     

基于粒子群算法的火电厂机组负荷优化分配研究

负荷优化分配是火电厂运行优化的一个重要研究领域,在机组之间合理地优化分配负荷能够提高整个火电厂运行的经济性。针对火电厂实际的运行情况,考虑多个实际约束条件,建立了并行火电机组间连续多时段动态负荷优化分配的数学模型;提出运用新近发展起来的智能算法-粒子群算法来解决动态负荷优化分配问题,详细介绍和研究了该算法的基本原理以及在负荷优化分配问题上的实现过程,并针对原算法的不足,对算法进行了改进;根据负荷分配和算法的特性,对初始种群的生成方法进行了改进,同时对约束条件进行了有效处理。仿真实例表明,该方法收敛性好,收敛速度快,能够有效地达到或接近全局最优,从而为火电厂机组负荷优化分配的求解提供了新的有效算法。     

Application of model predictive control to optimal dynamic dispatch of generation with emission limitations

Reducing emission from fossil-fueled electric power generating plants has received considerable attention in recent years in both regulated and deregulated power markets. Under regulated power systems, utilities solve the dynamic economic dispatch problem to determine the optimal scheduling of the committed unit's output at minimum fuel cost while satisfying a set of constraints. In this paper, we introduce the following problems where the emission effects are included in the mathematical model: (1) dynamic economic emission dispatch and (2) emission constrained dynamic economic dispatch. Under deregulated markets, the generation company can determine the optimal amounts of energy to be sold in the market by running profit-based dynamic economic dispatch problem to maximize its own profit. To take into account the emission limitations we introduced two problems: (1) profit-based dynamic economic emission dispatch problem and (2) profit-based emission constrained dynamic economic dispatch problem. In this paper we applied the model predictive control (MPC) approach proposed recently to the dynamic dispatch problems in both regulated and deregulated systems. The convergence and robustness of the MPC algorithms are demonstrated through the application of MPC to these problems with a six-unit system.     

碳排放限制的混杂最优闭环微电网调度算法

针对微电网经济调度效率低下等问题,提出了一种碳排放限制的混杂最优闭环微电网调度算法。算法在考虑火力发电机组和多分布式发电聚合体组合调度的同时,以电网实际需求响应和碳排放限制作为约束条件,建立微电网经济调度模型;并将经济调度问题转化为混杂系统的最优控制,从而建立微电网经济调度与混杂系统最优控制序列的等价一致性;推导混杂系统获取满足贝尔曼方程的最优代价函数,借助神经网络逼近最优值获得最优闭环调度序列。仿真实验验证了本调度算法的闭环有效性,能在不同初始条件和外部扰动下依然获得最优调度序列,同时仿真结果证实降低碳排放限额或提高碳排放交易价格都有助于减少电网系统的总碳排放量。     

多预想事故静态电压稳定预防控制阻塞管理方法

首先建立了考虑多预想事故静态电压稳定约束的阻塞管理模型。对实际系统而言,该模型是一个含大量预想故障电压稳定约束的最优潮流问题。针对该问题采用Benders分解算法进行分解,并提出了一种结合连续潮流方法的串行求解策略。以阻塞费用最小为目标函数,并以系统正常状态的运行约束构造主问题;以节点不平衡功率最小为目标函数,并以故障状态静态电压稳定约束构造子问题。迭代过程中应用连续潮流法对预想故障进行扫描,选择一个最严重故障构造子问题,因此每轮迭代只需求解一个子问题,向主问题返回一个Benders割,提高了Benders分解算法的求解效率。该方法可用于大规模系统求解含多预想故障电压稳定约束的阻塞管理问题。改进IEEE30节点系统的仿真结果验证了所提方法的有效性。     

计及校正控制的安全约束最优潮流的奔德斯分解算法

针对电力系统调度、规划、检修及风险评估中反复涉及的校正控制安全约束最优潮流的求解问题,在深入机制分析基础上,提出一种基于奔德斯(Benders)分解与协调的模型和算法,使复杂大电网的求解得以进行。该模型和算法的主要贡献体现在:对正常状态下的最优潮流问题(也称主问题),基于电网关键元件规律特性的变动形成有效约束,以提高其求解效率;对预想事件(也称子问题)集,基于处于主导地位预想事件概念的有效筛选方法,使子问题数显著减少。由此,借用奔德斯分解的处理思想,实现主问题与子问题间的协调机制。最后以IEEE 30节点标准系统以及山东电网445节点实际系统作为算例,对该算法进行可行性、有效性等验证。     

发电市场预调度计划模型及算法的研究

结合中国发电市场的实际情况,根据最优潮流原理,以整个预调度周期内的市场购电费用（MPC）最小为目标,建立了预调度计划模型,并根据预调度计划的特点,将模型目标简化为各时段的市场清算电价（MCP0最小,同时设计了三段式预调度算法：用静态规划法求解整个顶调度计划周期内的优化问题,用优先级求解机组组合问题,用改进的Powell法求解最优潮流问题。算例结果表明,预调度模型和算法下的市场清算电价能跟踪系统负荷变化,且该模型算法具有复杂度低、结果最佳性较好的特点。     

考虑规模化电动汽车与风电接入的随机解耦协同调度

大规模电动汽车并网和风电出力的随机性增加了电力系统安全经济运行的难度。针对风电出力难以预测的特点,同时兼顾电网侧和电动汽车聚合商侧的运行效益,建立了风电、火电以及电动汽车鲁棒双层随机优化调度模型。为了提高求解效率,基于近似Benders算法构建了电动汽车与电网之间的互动关系。此外,针对规模化电动汽车之间难以协同优化的问题,运用辅助问题原理对电动汽车个体之间的耦合关系进行了解耦,将电动汽车群的联合优化转化成为单辆电动汽车调度的并行计算,实现了对电动汽车个体的调度。最后,针对改进的IEEE 39节点算例进行了仿真分析,验证了所提模型和算法的有效性。     

电网安全节能发电日前调度优化模型及算法

华北电网安全节能发电调度辅助决策系统中的日前调度模块实现了机组组合和安全约束经济调度两大功能,制定日发电计划既符合各种安全约束又满足各类辅助服务需求。采用同一个优化模型,通过修改输入信息实现节能发电调度、电量进度发电调度、成本调度、市场竞争等多种调度模式。引入violation和violation penalty weights两类参数,确保优化模型始终有解,满足工程实际应用的要求。采用同时可行性测试与优化计算相迭代的方法缩短了计算时间。日前调度模型为混合整数规划模型,采用分支定界法进行求解。为了精确校核日前调度结果,该系统开发了研究态全网络多时段安全校核功能,选择多个时间节点对日前调度结果进行精确的交流潮流校核,确保日前调度结果满足安全运行要求。