Hybrid Chemical Reaction Optimization Approach for Combined Economic Emission Short-term Hydrothermal Scheduling

—This article presents the hybridization of a newly developed, novel, and efficient chemical reaction optimization technique and differential evolution for solving a short-term hydrothermal scheduling problem. The main objective of the short-term scheduling is to schedule the hydro and thermal plants generation in such a way that minimizes the generation cost. However, due to strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained hydrothermal scheduling formulation is to estimate the optimal generation schedule of hydro and thermal generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. In this context, this article proposes a hybrid chemical reaction optimization and differential evolution approach for solving the multi-objective short-term combined economic emission scheduling problem. The effectiveness of the proposed hybrid chemical reaction optimization and differential evolution method is validated by carrying out extensive tests on two hydrothermal scheduling problems with incremental fuel-cost functions taking into account the valve-point loading effects. The result shows that the proposed algorithm improves the solution accuracy and reliability compared to other techniques.     

计及权系数影响的区域电网时段多目标调度优化策略

为了解决区域电网受风光机组出力的不确定因素影响的调度优化问题,提出了含风光机组在内的多电源多目标调度优化模型,目标函数中考虑了经济运行成本及环境治理成本,同时增加了备用容量和爬坡能力等约束。考虑权系数对多目标优化结果的影响,基于不同的负荷情况,对多目标优化的权系数进行动态选取,得到各阶段负荷下对应的权系数。该文将负荷分为基荷、腰荷及峰荷三种情况,根据模糊综合评价法对各种负荷情况动态确定权系数,以此提出四种优化方案。为了降低综合成本,在考虑了各种约束的前提下,利用改进的粒子群算法求解模型,求得四种方案下的分时段综合成本,并确定调度优化方案,同时得到运行周期内区域电网中机组出力的最佳策略,最后通过实例验证了所提基于动态权系数的多目标调度优化策略的有效性。     

风蓄火联合运行电力系统动态经济优化调度

针对风电的波动性和反调峰特性,为减少火电系统调峰压力,考虑到抽水蓄能机组启停迅速、工况转化灵活等良好的运行特性,建立风电-抽水蓄能电站-火电站联合调度模型。以充分利用清洁能源、降低系统运行成本和火电机组平稳运行为目标建立多目标优化调度模型,充分发挥抽水蓄能机组在电力系统中的调节作用。以避免系统容量冗余、避免火电机组频繁启停和低负荷运行为原则,确定火电机组调度台数。利用引入maxmin函数和ε支配的改进多目标粒子群算法对模型进行求解。通过算例分析,研究抽水蓄能机组的运行效益及联合调度时抽水蓄能机组的调度运行规律和各类机组间的配合运行特点,计算结果表明模型能够为充分利用风电提供良好的调度运行策略,具有一定的实用性。     

梯级水电站多目标模糊优化调度模型及其求解方法

根据梯级水电站不仅具有电力联系而且具有水力联系的运行特点,提出一种以年发电量和一级水电站耗水量为优化目标的梯级水电站多目标长期优化调度模型.通过定义各目标的隶属度函数,将多目标优化问题模糊化;采用最大模糊满意度法将多目标优化问题转化为单目标非线性规划问题;应用协调粒子群算法(CPSO)求解单目标优化问题.仿真验证了模型的正确性和求解方法的可行性,为梯级水电站优化调度提供了一种新颖有效的途径.     

综合能源系统环境下电动汽车分群优化调度

电动汽车(electric vehicles,EV)的大规模接入,给综合能源系统调度带来了机遇和挑战.文章考虑电-热-气混合潮流和电动汽车的调度灵活性,建立了含电动汽车的综合能源系统两层嵌套调度模型,对电动汽车进行分群分层调度,合理制定每辆汽车的充放电策略.调度计划层以调度方案成本最小、能量波动最小和环保性最优为目标函...     

基于风-光-蓄-火联合发电系统的多目标优化调度

针对风电、光伏出力的随机性、间歇性和波动性而导致其在大规模接入电网时对电网发电计划制定和调度产生的影响,提出了含风-光-蓄-火联合发电系统的多目标优化调度模型。利用抽水蓄能的抽蓄特性,将风电和光伏出力进行时空平移,使风-光-蓄联合出力转变为稳定可调度电源,具备削峰填谷的功能,与火电机组共同参与系统优化调度。以风-光-蓄联合出力最大、广义负荷波动最小和火电机组运行成本最小作为目标函数,建立多目标优化调度模型,通过多目标处理策略,使目标函数简化为2个,以降低问题维数;在求解阶段,利用分层求解思想,将模型划分为两层,分别采用混合整数规划方法和机组组合优化方法进行求解。10机测试系统仿真结果表明:所建模型可以提高风能和太阳能的利用率,缓解火电机组的调峰压力,大幅降低风电反调峰特性对电网的影响,从而保证电力系统安全、稳定、经济运行。     

基于改进粒子群算法的电力系统有功调度

针对电力系统有功优化调度,提出了一种改进的粒子群算法,该算法考虑了火电厂的煤耗量,污染物排放量,以及线路损耗等,通过分别求解各个单目标优化问题和定义各单项目标的隶属度函数,把多目标优化问题转化为单目标优化问题,从整体上降低电力系统的发电成本。该算法以标准粒子群算法为基础,对其参数进行了改进,并对其搜索速度加以限制。将其应用于电力系统的3机组模型,算例仿真结果表明该算法节省了收敛时间,具有收敛速度快,计算精度高的优点。     

Short-term hydrothermal generation scheduling by a modified dynamic neighborhood learning based particle swarm optimization

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.     

水火电力系统短期节能与经济发电优化调度

选取节能和经济两个决策目标,建立水火电力系统发电多目标优化调度模型,寻求满足决策目标的最优调度方案。根据水力发电和火力发电的能耗特性,引入同等装机容量技术条件下水煤转换系数的概念,建立了水火电力系统联合发电能耗模型以及火电综合成本模型,并选取水火电力系统发电等效总煤耗最小作为节能调度的目标,选取火电厂发电综合成本最小作为经济调度的目标,对含有梯级水电站群和多个火电厂的大区域性电力系统进行多目标优化调度。以一个具有8个梯级水电站和8个火电厂的水火电力系统为例进行仿真,其结果证明所建的节能与经济发电优化调度模型能够在增加发电量的同时,提高水资源利用率,节约煤炭资源,降低火电成本,创造良好的发电效益和经济效益。     

Optimal Spinning Reserve for a Wind-Thermal Power System Using EIPSO

This paper presents an evolutionary iteration particle swarm optimization (EIPSO) algorithm to solve the nonlinear optimal scheduling problem. A new index called iteration best is incorporated into particle swarm optimization (PSO) to improve the solution quality. The new PSO, named iteration PSO (IPSO), is embedded into evolutionary programming (EP) to further improve the computational efficiency. The EIPSO is then applied to solve the optimal spinning reserve for a wind-thermal power system (OSRWT). Results are used to evaluate the effects of wind generation on the spinning reserve selection of a power system. The OSRWT program considers the outage cost as well as the total operation cost of thermal units to evaluate the level of spinning reserve. The up spinning reserve (USR) and down spinning reserve (DSR) are also introduced into the OSRWT problem. The optimal scheduling of spinning reserve was reached while minimizing the sum of total operation cost and outage cost. Two practical power systems are used as numerical examples to test the new algorithm. The feasibility of the new algorithm is demonstrated by the numerical example, and EIPSO solution quality and computational efficiency are compared to those of other algorithms.     

基于混合粒子群算法的源-荷-储协调优化调度

为了提高新型电力系统中对风电和光伏的消纳能力,降低电力系统运行成本,将火电机组、光伏、风电、需求响应负荷和储能系统作为调度资源建立了基于源-荷-储协调的优化调度模型。以火电机组运行成本、弃风弃光成本和需求响应负荷调度成本最小为目标,提出了一种两阶段优化方法。第一阶段优化采用离散二进制粒子群优化算法,使火电机组启动成本和弃风弃光成本之和最小。在第一阶段优化结果的基础上,第二阶段的优化采用双层连续粒子群优化算法使基于电价的需求响应负荷调度成本和燃料成本之和最小。算例结果验证了该优化调度模型的可行性和有效性。     

Short-term hydrothermal scheduling using clonal selection algorithm

An efficient optimization procedure based on the clonal selection algorithm (CSA) is proposed for the solution of short-term hydrothermal scheduling problem. CSA, a new algorithm from the family of evolutionary computation, is simple, fast and a robust optimization tool for real complex hydrothermal scheduling problems. Hydrothermal scheduling involves the optimization of non-linear objective function with set of operational and physical constraints. The cascading nature of hydro-plants, water transport delay and scheduling time linkage, power balance constraints, variable hourly water discharge limits, reservoir storage limits, operation limits of thermal and hydro units, hydraulic continuity constraint and initial and final reservoir storage limits are fully taken into account. The results of the proposed approach are compared with those of gradient search (GS), simulated annealing (SA), evolutionary programming (EP), dynamic programming (DP), non-linear programming (NLP), genetic algorithm (GA), improved fast EP (IFEP), differential evolution (DE) and improved particle swarm optimization (IPSO) approaches. From the numerical results, it is found that the CSA-based approach is able to provide better solution at lesser computational effort.     

计及电转气的区域综合能源系统日前优化调度

为了提高区域综合能源系统的经济性以及可再生能源的消纳能力,提出含电转气（power-to-gas,P2G）的调度优化模型。首先将电转气分为2个阶段运行,在电解水产生氢气环节加入储氢罐作为氢燃料电池的燃料来源,通过氢燃料电池实现氢能向电能、热能的转化,之后将剩余氢气输入到甲烷反应器中,减少将氢气全部直接甲烷化所产生的能量损耗。其次燃气轮机采用变效率运行模式,通过灵活调节燃气轮机的供电、供热效率,使热电出力更为经济合理。基于此,以由系统购电成本、购气成本、弃风成本以及环境成本构成的日运行成本最小为经济目标,构建含电转气的区域综合能源系统日前优化调度模型。最后利用基于空间距离的混沌粒子群算法求解,并通过算例仿真表明所提调度模型可有效促进多级能源合理高效利用,提高可再生能源消纳能力与系统运行经济性。     

Hydrothermal scheduling of a multireservoir power system withstochastic inflows

A suboptimal method for solving the annual hydrothermal scheduling problem for a multireservoir hydrothermal system is presented. Hydrothermal scheduling is performed in order to find the optimum allocation of hydro energy so that the annual operating cost of a mixed hydrothermal system is minimized. In the present work, the hydrothermal scheduling problem is formulated as a constrained optimization problem which is decomposed into three subproblems; maintenance scheduling of thermal units, dispatch of thermal units, and dispatch of hydroplants. The algorithm takes into consideration the stochastic nature of the water inflows to the reservoirs and the forced outage rates of the thermal units. Results obtained by the application of the algorithm to a simplified version of the Hellenic Hydrothermal Power System, showing the effect of the hydroplants, are reported     

考虑环境约束的电力系统动态经济平稳调度

针对目前减排指标具有阶段性的特点,污染物排放的控制只需满足一定的减排要求,不必达到最小值,将污染物排放作为环境约束,同时考虑到机组的运行效率问题,建立电力系统动态经济平稳调度模型。通过选取不同的波动系数和波动函数,实现不同容量机组承担不同波动值的目的。利用引入maxmin函数和ε支配的改进多目标粒子群算法对模型进行求解。通过算例表明,模型能够在满足给定环境约束的条件下,得到考虑机组经济性和平稳性的出力方案,保证大型机组出力稳定性的同时改善小型机组的出力曲线。     

Optimal generation dispatch with renewable energy embedded using multiple objectives

Traditional economic dispatch focuses mainly on minimizing the total operation cost of the power system. With the appearance of energy crisis and environmental pollution becoming a public issue, environmental effect of generator should be taken into consideration through the dispatch process. In this paper a multi-objective dispatch problem considering the integration of wind power is solved whose objectives include the generation cost, the reserve capacity and the environmental emission. To compromise different objectives, a coordination degree combined with a satisfaction degree are introduced in order to transform the multi-objective dispatch problem into a single-objective one, i.e., the optimal generation dispatch (OGD) model. Then the OGD is solved by a particle swarm optimization algorithm on an IEEE 30-bus system, with wind power generation and coal-fired generation embedded. The simulation results show that better results in terms of all the three objectives can be obtained from the OGD model, in comparison with two other multi-objective dispatch models. The simulation results also show that the integration of wind power will cause the increase of both the generation cost and the reserve capacity but will decrease the environmental emission.     

基于总体协调满意度的水火电力系统多目标短期优化调度

水火电力系统多目标优化调度符合国家节能减排的政策导向,在考虑整个系统梯级水电站发电效益最大化、火电机组发电成本最小化和梯级水电站发电用水最小化的目标同时,考虑了火电机组二氧化碳排放最小化的优化目标。针对水火电力系统短期优化运行调度中目标函数的权重处理困难的因素,采用一种基于目标函数总体协调满意度的处理方法,通过采用自然选择机制策略和异步学习因子策略提高算法的性能。以5个梯级水电站和4台火电机组组成的水火电力系统为实例进行计算．优化仿真计算证明了所提出的优化调度模型和求解算法具有可行性和适用性。     

基于源荷状态的跨区互联系统协调优化调度

随着跨区互联结构的日益完善及分布式电源的大规模接入电网,为充分利用区域间现有调度资源的互补效益,研究了基于源荷状态的跨区互联系统协调优化调度。以送、受端电网总运行成本及网损为目标函数,首先根据送、受端源荷状态,判断送端储能设备充放电状态及功率跨区流动状态。其次,考虑实际系统运行的各项约束,利用改进的基于粒子群优化算法的多目标搜索算法寻优,优化送端储能充放电功率、跨区联络计划及受端调峰火电机组出力。最后,以中国新疆地区实际电网为算例进行仿真计算。仿真结果表明,所提跨区优化调度策略可显著提升送、受端电网运行效益,充分发挥互联区域的互济合作。     

基于改进多目标粒子群算法的微电网并网优化调度

微电网能够协调分布式电源,从而充分发挥分布式发电技术在经济、能源和环境中的优势。针对微电网并网时的优化调度问题,建立了考虑发电成本、污染物排放的微电网系统的环保优化模型,并利用改进的多目标粒子群算法,在这两个目标之间进行协调权衡和折中处理,使所有目标函数尽量达到最优。选取微电网案例的日负荷数据进行了优化调度计算,仿真结果表明了所提模型和算法的有效性。     

基于协调负荷调度模型的蓄热采暖解耦方法

中国“三北”地区冬季受煤电机组供热期、水电机组枯水期、风电机组大发期的相互叠加影响,出现巨额弃风和环境污染问题。将具有可控特性的蓄热电采暖作为调峰调度资源,在调峰困难时段降低CHP强迫出力并实现移峰填谷,增加风电冬季上网空间,形成源荷互动的负荷调度模式。首先研究CHP机组电-热耦合特性,然后构建风电-蓄热补偿的热电解耦方案,以系统运营成本最小和系统污染排放量最小为目标建立多目标负荷调度模型,最后采用修正多目标粒子群算法对模型进行求解,通过小生境技术保持Pareto解集的多样性。算例结果表明所构建的电锅炉蓄热式调度方案可以有效消纳风电,达到环境和经济指标的综合最优,具有较好的应用前景。