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.     

Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems

This paper presents differential evolution (DE)-based optimization technique for solving short-term economic generation scheduling of hydrothermal systems. A multi-reservoir cascaded hydrothermal system with non-linear relationship between water discharge rate, power generation and net head is considered here. The water transport delay between the connected reservoirs is also taken into account. Several equality and non-equality constraints on thermal units as well as hydro units and the effect of valve-point loading are also included in the problem formulation. The effectiveness of the proposed method is demonstrated on two test systems comprising of hydro and thermal units. Convergence characteristic of the proposed technique has been found to be quite satisfactory. The results obtained by the proposed technique are compared with other evolutionary methods. It is seen that the proposed technique is capable of producing encouraging solutions.     

水火电力系统短期发电计划优化方法综述

水火电力系统的短期发电计划问题在电力系统的安全可靠和经济运行中发挥着越来越重要的作用，由于其本身的复杂性，很难从理论上找到全局最优解。深入探讨各种优化算法，并加以分类，详细综述各种优化方法在水火电力系统短期发电计划问题中所取得的研究成果和存在的不足之处。     

基于改进PSO算法的短期发电计划研究

介绍了粒子群优化算法PSO(Panicle Swarm Optimization),并针对短期发电计划中的优化问题提出了一种改进PSO算法,将表示机组开停机状态的离散变量转换为0～1范围内的连续变量．与机组出力一起进行PSO优化搜索,然后再利用就近取整函数“mund”将其转换成整数变量。详细描述了应用改进PSO算法求解机组优化启停问题的具体步骤。将该方法应用于10机系统,实验结果表明该改进PSO算法用于短期发电计划是可行的。     

Stochastic mid-term generation scheduling incorporated with wind power

A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m + 1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.     

Optimal power flow using differential evolution algorithm

This paper presents an efficient and reliable evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution algorithm for optimal settings of OPF problem control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objectives that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. The proposed approach results are compared with the results reported in the literature. The results show the effectiveness and robustness of the proposed approach.     

Optimal power flow using differential evolution algorithm

This paper presents an evolutionary-based approach to solve the optimal power flow (OPF) problem. The proposed approach employs differential evolution (DE) algorithm for optimal settings of OPF control variables. The proposed approach is examined and tested on the standard IEEE 30-bus test system with different objective functions that reflect fuel cost minimization, voltage profile improvement, and voltage stability enhancement. In addition, non-smooth piecewise quadratic cost function has been considered. The simulation results of the proposed approach are compared to those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem for the systems considered.     

Optimal VAR control for real power loss minimization using differential evolution algorithm

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.     

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

This paper presents a multi-objective differential evolution (MODE) algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multi-objective problem with competing and non-commensurable objectives of fuel cost, emission and system loss. The proposed MODE approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed approach have been carried out on the IEEE 30- and 118-bus test system. The results demonstrate the capability of the proposed MODE approach to generate well-distributed Pareto optimal non-dominated solutions of multi-objective EED problem. The comparison with reported results of other MOEAs reveals the superiority of the proposed MODE approach and confirms its potential for solving other power systems multi-objective optimization problems.     

基于公平发电的新能源电站协调调度方法研究

我国新能源电站普遍存在限电现象,限电条件下多个新能源电站如何公平的参与调度成为关键问题。文中提出一种考虑新能源发电公平性的新能源电站协调调度方法。考虑技术性、运营特性、经济性和可靠性等多维度提出新能源电站评价指标体系;提出基于灰色关联度和熵权法的新能源电站评价指标体系综合分析方法,以综合评价新能源电站先进性。基于新能源电站先进性综合排名,提出了包含电网对新能源发电的可消纳空间计算和调度计划公平性分配的新能源电站协调调度方法。通过算例结果表明,所提出的评价指标体系及其分析方法可以正确评价新能源电站先进性,所提出的新能源协调调度方法可以有效保障综合排名高的电站优先发电,提升新能源电站参与调度的公平性。     

Quasi-oppositional differential evolution for optimal reactive power dispatch

This paper presents quasi-oppositional differential evolution to solve reactive power dispatch problem of a power system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Quasi-oppositional differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed quasi-oppositional differential evolution (QODE) employs quasi-oppositional based learning (QOBL) for population initialization and also for generation jumping. Reactive power dispatch is an optimization problem that reduces grid congestion with more than one objective. The proposed method is used to find the settings of control variables such as generator terminal voltages, transformer tap settings and reactive power output of shunt VAR compensators in order to achieve minimum active power loss, improved voltage profile and enhanced voltage stability. In this study, QODE has been tested on IEEE 30-bus, 57-bus and 118-bus test systems. Test results of the proposed QODE approach have been compared with those obtained by other evolutionary methods reported in the literature. It is found that the proposed QODE based approach is able to provide better solution.     

基于太阳能发电的光伏阵列电气系统设计

清洁能源的使用是未来社会发展的趋势。以光伏发电为研究模型,对实现太阳能的高效利用展开研究,提出了一种高效的光伏阵列,使太阳能发电系统的能源利用率得到了显著的提升。通过对太阳能发电的电气系统进行一次设计,提出了35 kV侧无功补偿装置,使得系统更加合理、有效。     

免疫算法在水电站日前现货市场优化调度中的应用

免疫起源于抗感染的研究,是一种机体识别和排斥抗原性异物的生理功能。现代仿生技术将免疫应用到解决非线性优化问题中,用抗体代表问题的可行解,抗原代表问题的约束条件和目标函数,采用期望繁殖率来定义父体选择的概率,形成了独特的免疫算法。本文将其应用到水电站日前现货市场优化调度的研究中,经过对紫坪铺水电站的优化计算,认为其结果是合理和可实际应用的。     

基于分时电价的家庭智能用电设备的运行优化

未来家庭用电作为智能电网的一部分,研究家庭智能用电设备的优化运行具有重要的意义。首先建立了未来典型家庭智能用电设备的数学模型。然后基于分时电价,提出一种以经济性和用户舒适度为目标的未来家庭中智能用电设备的优化运行模型,便于用户制定出满足自身需要的用电计划。最后以某典型家庭用户为例,通过建立典型日仿真场景,采用遗传算法对家庭智能用电设备的运行进行优化,仿真结果表明建立的优化模型的有效性。     

Interval type-2 fuzzy modeling of wind power generation in Genco's generation scheduling

Wind power is a promising source of electric power generation since it has tremendous environmental and social benefits. The generation scheduling (GS) problem encounters several uncertainties in terms of the system's parameters such as load, reserve and available wind power generation. The modeling of those uncertainties is an important issue in power system scheduling. A fuzzy based modeling approach can be used to develop the generation schedule under an uncertain environment.In this paper, the type-2 fuzzy membership function (MF) is implemented to model the linguistic uncertainty of type-1 MF of available wind power generation which stems from opinions of different experts. The proposed approach is applied to two test systems (six and twenty-six conventional generating units both with two wind farms) and the results of generation scheduling using both fuzzy modeling type-1, and type-2 are presented. These results demonstrate that the advantage of using the type-2 fuzzy to model the linguistic uncertainty of the type-1 MF. This paper demonstrates how unit scheduling in an uncertain environment of type-1 fuzzy MF modeling can be performed just by using a single type-2 fuzzy MF when all type-1 MF were in the footprint of uncertainty (FOU) of type-2 MF.     

差分进化算法在电力系统中的应用研究进展

差分进化算法是一种基于群体智能的新型演化计算技术,近年来已在多种领域成功应用,但电力系统中实际应用尚不多见。评述了差分进化算法在电网规划、负荷经济分配、最优潮流计算与无功优化调度、谐波分析与电容器配置、发电计划、优化设计等方面应用的研究成果和进展情况,还将在电力市场竞价交易、投标策略以及电力市场仿真等领域发挥应用潜力。     

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.     

基于免疫遗传算法的超短期发电计划优化

超短期发电计划优化在电力系统调度运行中发挥着越来越重要的作用,但由于其是一个非线性整数约束优化问题,数学模型复杂,很难从理论上找到全局最优解。针对电力系统发电计划优化问题,引入免疫遗传算法,很好地解决了遗传算法局部收敛的问题,实现了群体收敛性和个体多样性间的动态平衡调整,能快速准确求解,及时调整超短期发电计划方案,从而达到安全经济环保调度,优化资源配置的效果。     

考虑火电机组深度调峰的实时发电计划模型及应用

伴随我国大规模新能源接入和用电峰谷差日趋增大,调峰需求逐步提升,而由于我国电源结构不尽合理,调峰电源紧缺,造成电网调峰困难。深度挖掘火电机组深度调峰能力,分析其运行特性,在常规优化模型的基础上,引入深度调峰出力平稳段运行时间、最小深度调峰时间等约束,并对机组最大最小出力、爬坡等常规约束进行改进,建立考虑火电机组深度调峰的实时发电计划优化模型。采用广东电网实际运行数据进行算例分析,结果满足电网实际运行需求。