A multiobjective approach to the short-term scheduling of ahydroelectric power system

This paper is concerned with the short-term scheduling of the Iguacu river hydroelectric power system, in Southern Brazil. The system comprises four power plants with an unusual high coupling, held by two different utilities. Even though government regulations establishes operation guidelines, electric utilities have their own interests. A multiobjective framework is adopted to study the operation of the power system. Dynamic programming is used to find optimal solutions, with an implementation based on the concept of differential dynamic programming. A case study shows the possibility of obtaining energetic gains with the adoption of nonconventional operation rules and the need of negotiation to avoid wasting energy     

A genetic algorithm solution to the optimal short-term hydrothermal scheduling

This paper presents an algorithm for solving the hydrothermal scheduling through the application of genetic algorithm (GA). The hydro subproblem is solved using GA and the thermal subproblem is solved using lambda iteration technique. Hydro and thermal subproblems are solved alternatively. GA based optimal power flow (OPF) including line losses and line flow constraints are applied for the best hydrothermal schedule obtained from GA. A 9-bus system with four thermal plants and three hydro plants and a 66-bus system with 12 thermal plants and 11 hydro plants are taken for investigation. This proposed GA reduces the complexity, computation time and also gives near global optimum solution.     

A genetic algorithm approach to short‐term scheduling of crude oil operations in refinery

As a type of process plant, a refinery is characterized by the interaction of discrete events and continuous processes. To schedule crude oil operations in a refinery, it is necessary to define and schedule the jobs simultaneously such that heuristics and meta‐heuristics cannot be directly applied. It is very challenging to schedule crude oil operations. To solve this problem, it is decomposed into two subproblems hierarchically. At the upper level, a refining schedule is found, while at the lower level a detailed schedule is obtained to realize the refining schedule. Given a refining schedule at the upper level, this paper studies the detailed scheduling problem at the lower level. Based on a control‐theoretic perspective, the problem is innovatively transformed to a problem of assigning charging tanks to distillers such that meta‐heuristic methods can be applied. Then, a genetic algorithm (GA) approach is developed to solve the problem. In realizing the proposed GA, based on a set of existence conditions of a feasible schedule, methods are presented to guarantee that each chromosome corresponds to a feasible schedule. An industrial case study is used to show the application of the proposed method. It shows that the method works well and is applicable to real‐life problems. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A novel differential evolution application to short-term electrical power generation scheduling

This paper proposes a new way of applying a differential evolution algorithm to short-term electrical power generation scheduling. Traditionally, the problem is divided into two subproblems. An evolutionary algorithm, which works with binary decision variables, is applied to the first subproblem to find a low cost scheduling of power generators, satisfying some operational constraints. Then, the lambda-iteration method, is used to calculate the power generated by the online generators. In this study, the problem is treated as a whole for the first time in literature and an application of a real-valued differential evolution algorithm is proposed. This approach eliminates the use of an iterative local search technique such as lambda-iteration in all solution evaluations. Through comparisons with results from literature, it is shown that the proposed method achieves a similar solution quality to existing methods, without needing the time consuming lambda-iteration step. Finally, the new approach is applied to real-world data from the Turkish interconnected power network.     

A short-term scheduling for a hydropower plant chain

An optimal control of a hydropower plant chain is introduced. The aim is to meet a predefined power demand and in the same time provide sufficient control capabilities. At first a state-space model of the river chain is presented. Then an optimal feedback control method is developed by introducing Hamiltonian for the system. The constraints are considered using Lagrangian multipliers. As an example a river bed with eight hydro plants is studied. The results show the suitability of the method to production planning and to analysis of the hydro system behaviour. It can also be used for on-line scheduling.     

基于遗传算法的水火电混合电力系统短期发电计划优化

在改进传统遗传算法的基础上,提出了水火电混合电力系统短期发电计划优化问题的数学模型和求解方法.模型计及了水电机组的发电流量、净水头和输出功率间的非线性关系,水电系统中多级水库的水流延迟等因素.算例表明本算法能更有效地达到或接近全局最优解,对编制大型水火电混合电力系统的日调度计划有实用价值.     

基于遗传算法的水火电混合电力系统短期发电计划优化

在改进传统遗传算法的基础上 ,提出了水火电混合电力系统短期发电计划优化问题的数学模型和求解方法。模型计及了水电机组的发电流量、净水头和输出功率间的非线性关系 ,水电系统中多级水库的水流延迟等因素。算例表明本算法能更有效地达到或接近全局最优解 ,对编制大型水火电混合电力系统的日调度计划有实用价值。     

A method to eliminate solution trapping in applying progressiveoptimality principle to short-term hydrothermal scheduling

A method is described to eliminate solution trapping for the POP (progressive-optimality-principle) based short-term hydrothermal scheduling algorithm. In a POP-based scheduling algorithm, the trapping phenomenon can severely hinder solution optimality and thus limits the algorithm's applicability. In order to ensure path continuity, the proposed method identifies and bypasses the potential weak links, so that global optimality can be achieved progressively by successively solving a sequence of two-hour subproblems. Examples are given to illustrate the trapping phenomenon and the proposed method     

考虑短期互补的水光发电系统中长期优化调度北大核心CSCD

为解决水光发电系统在送电通道中的电量消纳与功率波动问题,从保障电网稳定的角度出发,提出水光沉浮互补法对光电随机波动性进行逐次补偿,同时整合电量与出力互补信息,进而通过电量信息识别短期损失电量与波动补偿电量,根据出力信息拟定短期互补过程,以此为基础建立中长期消纳电量最大,考虑短期弃电与波动风险的分级调度模型。实例研究表明:相对于仅短期互补,中长期互补能够进一步调配水电的时空特性,提升长期消纳电量并兼顾短期风险,其中消纳电量相对增益与径流保证率呈单峰曲线分布关系,平水年增益最为显著,而送电稳定性相对增益则与径流保证率呈正相关性,枯水年最为明显,综合两者效果偏好,能够完善调度决策面对不同来水场景的适用性。     

基于改进DFNN的短期电价预测新方法

提出了一种改进的动态模糊神经网络DFNN(Dynam ic Fuzzy Neural Network)的短期电价预测方法。首先对采集到的信息进行特征提取,然后利用模糊粗糙集理论中的信息熵进行属性简化、去掉冗余信息,最后用得到的属性作为动态模糊神经网络(DFNN)的输入进行训练预测。在模糊神经网络内部引入递归环节,构成了动态模糊神经网络,并采用具有全局寻优能力的遗传算法来训练网络,克服了单纯BP算法易陷入局部最优解的困境。最后以美国加州电力市场公布的2000年数据进行了模型训练和预测,结果表明该方法所建立的预测模型具有较高的预测精度。     

基于改进DFNN的短期电价预测新方法

提出了一种改进的动态模糊神经网络DFNN(Dynamic Fuzzy Neural Network)的短期电价预测方法.首先对采集到的信息进行特征提取,然后利用模糊粗糙集理论中的信息熵进行属性简化、去掉冗余信息,最后用得到的属性作为动态模糊神经网络(DFNN)的输入进行训练预测.在模糊神经网络内部引入递归环节,构成了动态模糊神经网络,并采用具有全局寻优能力的遗传算法来训练网络,克服了单纯BP算法易陷入局部最优解的困境.最后以美国加州电力市场公布的2000年数据进行了模型训练和预测,结果表明该方法所建立的预测模型具有较高的预测精度.     

A modified chaotic differential evolution algorithm for short-term optimal hydrothermal scheduling

The short-term optimal hydrothermal scheduling (STOHS) plays one of the most important roles in power systems operation. The STOHS problem involves the solution of difficult constrained optimization problems that require good computational techniques. This paper proposes a modified chaotic differential evolution (MCDE) approach for the solution of this difficult optimization problem. A repair strategy and a novel selection operation are simultaneously introduced into the MCDE approach for handling constraints of the problem. The repair strategy preserves the feasibility of solutions generated and avoids the use of penalty factors as much as possible. The introduced selection operation makes a not clearly distinction between feasible solutions and infeasible ones at early stage of the algorithm and makes a clearly distinction at the later stage. Additionally, an adaptive regeneration operation is proposed to enhance population diversity and to avoid local optimums. Moreover, a chaotic local search technique is introduced also to accelerate the searching process of the algorithm. The proposed MCDE approach is applied to three well-known hydrothermal test systems in order to verify its feasibility and efficiency. The obtained results are compared with those obtained by other population-based heuristic approaches reported in literature. It is observed from the comparisons that the proposed MCDE approach performs effectively and can yield competitive solutions.     

基于改进型遗传算法的企业配电网检修计划系统

本文从企业配电网实际情况出发,以经济性最好为目标,提出企业配电网检修计划数学模型,根据线路因数与设备参数制定多种约束条件,采用改进型遗传算法进行优化,最终以线路电量损失最小为最优检修计划方案,并提出了企业配电网检修计划系统模型,大大提高了企业配电网的能量管理水平。     

An aggregate Weibull approach for modeling short-term system generating capacity

Deregulation of electricity markets is occurring all over the world. This trend introduces new risks and uncertainties into the electricity industry, the most significant being price risk. The spot price of electricity is highly volatile, and the ability to price risk management contracts on this commodity is contingent on a robust and realistic model of the underlying price process. One key driver of electricity spot price is the forced outages of generating plants in the system. The current paper describes a system aggregate model of short-term generating capacity that can be adapted to any generating system of interest. After describing the model, we test it using the IEEE Reliability Test System (RTS).     

Combined genetic algorithm/simulated annealing/fuzzy set approachto short-term generation scheduling with take-or-pay fuel contract

This paper first develops a new formulation for short-term generation scheduling with take-or-pay fuel contract. In the formulation, a fuzzy set approach is developed to assist the solution process to find schedules which meet as closely as possible the take-or-pay fuel consumption. The formulation is then extended to also cover the economic dispatch problem when the fuel consumption is higher than the agreed amount in the take-or-pay contract. The extended formulation is combined with the genetic algorithms and simulated-annealing optimization methods for the establishment of new algorithms for the present problem. The new algorithms are demonstrated through a test example, in which the generation loadings of 13 generators in a practical power system are scheduled in a 24-hour schedule horizon     

A Simulated Annealing based approach to solve the generator maintenance scheduling problem

The scheduling of maintenance actions of generators is not a new problem but gained in recent years a new interest with the advent of electricity markets because inadequate schedules can have a significative impact on the revenues of generation companies. In this paper we report the research on this topic developed during the preparation of the MSc Thesis of the second author. The scheduling problem of generator maintenance actions is formulated as a mixed integer optimization problem in which we aim at minimizing the operation cost along the scheduling period plus a penalty on energy not supplied. This objective function is subjected to a number of constraints detailed in the paper and it includes binary variables to indicate that a generator is in maintenance in a given week. This optimisation problem was solved using Simulated Annealing. Simulated Annealing is a very appealing metaheuristic easily implemented and providing good results in numerous optimization problems. The paper includes results obtained for a Case Study based on a realistic generation system that includes 29 generation groups. This research work was proposed and developed with the collaboration of the third and fourth authors, from EDP Produção, Portugal.     

三角旋回算法及其在短期水火协调优化中的应用

提出一种新的全局优化方法--三角旋回算法(Triangle Gyration Algorithm,简称TGA),认为该算法具有结构简单、鲁棒性强和快速收敛的特点.算法的寻优过程采用历史最优目标函数值进行指导,利用三角变换进行迭代使其能够快速收敛到全局最优.用一个典型的算例对三角旋回算法进行了性能分析,且将该算法应用在水火电力系统短期(日前现货市场)协调优化调度中,得出的结论是该算法综合性能优越,在优化方法领域具有广阔的应用前景.     

A genetic algorithm solution approach to the hydrothermal coordination problem

In this paper, a genetic algorithm solution to the hydrothermal coordination problem is presented. The generation scheduling of the hydro production system is formulated as a mixed-integer, nonlinear optimization problem and solved with an enhanced genetic algorithm featuring a set of problem-specific genetic operators. The thermal subproblem is solved by means of a priority list method, incorporating the majority of thermal unit constraints. The results of the application of the proposed solution approach to the operation scheduling of the Greek Power System, comprising 13 hydroplants and 28 thermal units, demonstrate the effectiveness of the proposed algorithm.     

A multiagent approach to distribution system restoration

This paper proposes a multiagent approach to decentralized power system restoration for a distribution system network. The proposed method consists of several feeder agents (FAGs) and load agents (LAGs). LAG corresponds to the customer load, while a FAG is developed to act as a manager for the decision process. From the simulation results, the proposed multiagent system was able to reach the right solution by making use of only local information. This means that the proposed multiagent restoration system is a promising approach to more large‐scale distribution networks. © 2005 Wiley Periodicals, Inc. Electr Eng Jpn, 152(3): 21–28, 2005; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20065