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.     

A novel method for maintenance scheduling of generating units considering the demand side

This paper proposes a novel competitive mechanism for maintenance scheduling of generating units in the deregulated environment. In restructured power systems, the objective function of power producers is to maximize their benefits, and the aim of the Independent System Operator (ISO) is to increase the reliability throughout the year as much as possible. Therefore, there are two objective functions for finding an optimal maintenance schedule in the deregulated environment. The main contribution of this paper is considering the condition of demand side in the maintenance scheduling of generating units. In this scheme, authority of the maintenance scheduling has been granted to the ISO. The proposed method schedules the outage windows of generating units to attain maximum producers’ benefits and maximum annual social welfare. This method is tested in a bilateral energy market, and the IEEE-RTS system is used to demonstrate the effectiveness of the proposed method.     

Reliability based generator maintenance scheduling using hybrid evolutionary approach

With the growth of electrical energy demand, providing reliable energy without interruption has become very important nowadays. Maintenance scheduling of generating units is one of the crucial factors in delivering reliable electrical energy to the vital industrial and urban loads. As number of generating units and constraints over their operation is increasing, there is growing need for developing new methods for planning optimal outage of generating units for maintenance. This paper presents a hybrid evolutionary algorithm to tackle the reliability based generator maintenance scheduling problem. Uncertainties in the generating units and the load variations are included so that a more realistic scheduling is obtained. Maintenance scheduling problem is a large scale constrained optimization problem with a large number of variables which needs novel methods to cope with it. A new local search method which is derived from Extremal Optimization (EO) and Genetic Algorithm (GA) is presented to tackle the problem. The proposed method can be used as a local optimizer to further improve the potential solutions in the GA. The proposed method, Hill Climbing Technique (HCT), GA and their hybrid approaches are applied to the IEEE Reliability Test System (RTS) and the obtained results are discussed.     

基于虚拟控制单元与启发式搜索的线路过载紧急控制策略

为了快速消除由潮流转移引起的线路过载,给出了基于虚拟控制单元与启发式搜索的紧急控制策略。首先,引入虚拟控制单元的概念:节点的出力调整量等于最大线路过载量时,能够完全消除线路过载的控制单元。然后,给出了最优可控单元的双层搜索策略:第一层,通过设置合理的阈值,筛选减载能力较强的控制节点对构成优先控制单元集;第二层,综合考虑控制单元的减载效果和消除过载能力,给出了基于虚拟控制单元的启发式搜索步骤,得到优先控制单元集中的最优可控单元。若所选的阈值范围内无法找到可控单元,则适当扩大第一层搜索范围,依次类推,直至得到满足要求的控制单元。考虑正常线路冗余量约束来计算调整量,避免了潮流校验。经过一次调整后,依据灵敏度关系修正线路潮流与节点的最大可调整出力,提高控制速度。IEEE 30节点系统的仿真验证了所提算法的有效性。     

市场环境下电力系统机组检修规划模型

构建了基于市场竞争机制的机组检修规划模型。首先,发电商根据其效益分析确定其机组在各检修窗口的竞价费用,独立系统运行员（ISO）据此计算各机组在相应时段的满意度水平,两者均用以表征其在各时段检修及运行的意愿程度。基于此,建立了考虑检修竞价费用、满意度水平及系统切负荷水平的检修规划模型及基于满意度的费用结算机制。算例仿真结果表明,该模型可确保系统的可靠性及发电商的经济效益,并体现了公平、公正的市场原则。     

Thermal unit commitment using binary/real coded artificial bee colony algorithm

This paper presents a binary/real coded artificial bee colony (BRABC) algorithm to solve the thermal unit commitment problem (UCP). A novel binary coded ABC with repair strategies is used to obtain a feasible commitment schedule for each generating unit, satisfying spinning reserve and minimum up/down time constraints. Economic dispatch is carried out using real coded ABC for the feasible commitment obtained in each interval. In addition, non-linearities like valve-point effect, prohibited operating zones and multiple fuel options are included in the fuel cost functions. The effectiveness of the proposed algorithm has been tested on a standard ten-unit system, on IEEE 118-bus test system and IEEE RTS 24 bus system. Results obtained show that the proposed binary ABC is efficient in generating feasible schedules.     

An Optimal PMU Placement Method Against Measurement Loss and Branch Outage

This paper presents a new method for an optimal measurement placement of phasor measurement units (PMUs) for power system state estimation. The proposed method considers two types of contingency conditions (i.e., single measurement loss and single-branch outage) in order to obtain a reliable measurement system. First, the minimum condition number of the normalized measurement matrix is used as the criteria in conjunction with the sequential elimination approach to obtain a completely determined condition. Next, a sequential addition approach is used to search for necessary candidates for single measurement loss and single-branch outage conditions. These redundant measurements are optimized by binary integer programming. Finally, in order to minimize the number of PMU placement sites, a heuristic technique to rearrange measurement positions is also proposed. Numerical results on the IEEE test systems are demonstrated     

Coordinated preventive maintenance scheduling of GENCO and TRANSCO in restructured power systems

Preventive maintenance of generating units and lines, in a competitive electric energy environment is proposed. Inclusion of transmission constraints and forced outage rates, over a specified operational period is considered. For generator maintenance the objective of the ISO is to maintain adequate level of reliability throughout the operational period, for which Bender’s decomposition technique is used. The objective of the GENCO is to maximize profit or to minimize loss in profit, for which transmission constrained price based unit commitment (TCPBUC), based on Lagrangian relaxation method is used. Bender’s decomposition technique is used for line maintenance, with adequate level of reliability. A coordinating technique using penalty factors is incorporated to obtain convergence of the conflicting objectives. The transmission constraints are modeled using dc sensitivity factors. Detailed case studies of six-bus systems and IEEE RTS system are presented and discussed.     

含风电机组的电力系统动态经济排放调度研究

为了解决风电接入后的电力系统动态经济排放调度问题,首先提出了风电机组的出力模型,并结合风电前期投资和后期维护成本折算的风电发电成本,风电的波动引起的系统的备用容量补偿成本,火电机组的发电成本和排污导致的环境补偿成本模型,建立了满足各种约束条件下的动态经济排放调度模型,然后针对标准差分算法易陷入局部最优解的问题,利用了一种约束满足度来进行解的比较的水平比较差分进化算法来求取系统最小运行费用,最后通过了IEEE 39节点算例验证所提出模型和算法的正确性。     

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.     

基于博弈论的发电公司检修决策

电力市场环境下,发电公司在制定机组检修计划时,必须考虑竞争对手检修策略的影响,以追求收益损失最小化。根据预测电价和系统发电容量充裕度,构造了发电公司机组检修非合作博弈模型。当系统允许检修容量不太充裕时,考虑了可中断负荷的合理利用。局中人的策略包括检修起始时间和检修报价策略,收益函数由检修损失和风险损失构成。博弈过程考虑了多均衡点的协调,并采用随机求解方法。仿真算例说明了该方法的合理性和有效性。     

考虑源荷协调的风电并网系统旋转备用容量优化

风电的接入给电力系统带来更大不确定性,要求电网公司购买更多的旋转备用以维持电力系统的功率平衡和稳定,兼顾系统运行可靠性与经济性的旋转备用优化配置具有重要意义。考虑风电、需求侧互动资源,提出一种基于多场景的概率性旋转备用优化方法。该方法综合考虑风电预测误差、负荷波动及发电机非计划停运不确定性因素对旋转备用的需求,将弃风、可中断负荷分别作为部分负、正旋转备用融入发电日前调度计划,以购电总费用最低为目标函数建立日前机组组合优化模型,获得各时段旋转备用优化配置量。通过对IEEE 30节点、IEEE 118节点系统进行算例分析,验证了所提方法的正确性和有效性。     

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.     

Power arrival evaluation of bulk system including networkconstraints based on linear programming approach

Several useful support tools of power system scheduling and planning are provided. A fast method is proposed and used to evaluate the generating units and transmission capability of a power system and calculate the optimal reserve of each demand side, based on a two-step linear programming technique. A new support tool for power system planning is described by using the parametric linear programming. This support tool quickly gives an optimal reserve curve of the system and also provides some diagnosis information of the systems weak location for the expansion planning and maintenance scheduling of transmission lines or generating units. The outages of generators and transmission lines are considered, and a method of estimating the reliability (LOLP) of a multiarea power system by making use of linear programming is presented. Several examples are used to verify the proposed methodology and illustrate the application. The results show that the approaches are simple, fast, and efficient support tools for generation and transmission capability evaluation or for the contingency testing of a power system     

Short-term hydrothermal scheduling through evolutionary programming technique

An efficient short-term hydrothermal scheduling algorithm based on the evolutionary programming (EP) technique is proposed. In the algorithm, the thermal generating units in the system are represented by an equivalent unit. The power balance constraints, total water discharge constraint, reservoir volume constraints and the constraints on the operation limits of the equivalent thermal and hydro units are fully taken into account. The effectiveness of the proposed algorithm is demonstrated through an example system and the results are compared with those obtained by the classical gradient search and simulated annealing (SA) approaches. Numerical results show that the proposed EP approach provides a cheaper schedule even than the SA approach and hence, has more powerful ability to achieve the global optimum solution than the SA approach.     

Cost/environmentally compromised dispatch for cascaded hydrothermal system using artificial bee colony algorithm

This paper evaluates the robustness of the artificial bee colony (ABC) algorithm while allocating optimal power generation in a hydrothermal power system at the level of minimum fuel cost and minimum pollutant emission impacts on the environment subjected to physical and technical constraints. The hydrothermal scheduling (HTS) is devised in a bi‐objective framework so as to optimize both objectives of fuel cost and emission release, individually and simultaneously subjected to a verity of intricate equality and inequality constraints. Initially, all feasible solutions are obtained through random search, and then the ABC algorithm is used for the exploration and exploitation processes together in the search space, thereby discovering the optimal hourly schedule of power generation in the hydrothermal system. Meanwhile, a dependent hydro‐discharge computation handles the equality constraints; especially, the reservoir end volume and slack thermal generating unit for each sub‐interval handle the power balance equality constraint. The performance of the proposed approach is illustrated on a multi‐chain interconnected hydrothermal power system with due consideration of the water transport delay between connected reservoirs and transmission loss of system load. The results obtained from the proposed technique are compared with those of other techniques. The results demonstrate that the ABC algorithm is feasible and efficient for solving the HTS problem. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

防灾应急电源优化调度的机会约束规划方法

在电力系统防灾应急体系中,需要合理调度防灾应急电源,以最大限度地降低停电损失。在实际停电事故发生时,调度环境非常复杂且具有很多不确定性因素,而现有防灾应急电源优化调度方面的研究大多未计及多重不确定性因素的影响。在此背景下,假定应急电源的行驶时间近似服从正态分布,失电用户的缺电功率近似服从区间均匀分布,在此基础上建立基于机会约束规划的防灾应急电源优化调度模型。该模型以重要失电用户的总停电损失最小为优化目标,采用置信水平处理模型中的不确定参数,并在量子进化算法中嵌入蒙特卡洛仿真来求解。算例分析结果表明所提方法可以处理电力应急过程中的多重不确定性因素,合理分配防灾应急电源,进而降低停电损失。     

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.     

Hybrid: Particle Swarm Optimization–Genetic Algorithm and Particle Swarm Optimization–Shuffled Frog Leaping Algorithm for long-term generator maintenance scheduling

This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.