Binary fireworks algorithm for profit based unit commitment (PBUC) problem

The deregulation of electricity markets has transformed the unit commitment and economic dispatch problem in power systems from cost minimization approach to profit maximization approach in which generation company (GENCO)/independent power producer (IPP) would schedule the available generators to maximize the profit for the forecasted prices in day ahead market (DAM). The PBUC is a highly complex optimization problem with equal, in equal and bound constraints which allocates scheduling of thermal generators in energy and reserve markets with no obligation to load and reserve satisfaction. The quality of the solution is important in deciding the commitment status and there by affecting profit incurred by GENCO/IPPs. This paper proposes a binary coded fireworks algorithm through mimicking spectacular display of glorious fireworks explosion in sky. In deregulated market GENCO/IPP has the freedom to schedule its generators in one or more market(s) based on the profit. The proposed algorithm is tested on thermal unit system for different participation scenarios namely with and without reserve market participation. Results demonstrate the superiority of the proposed algorithm in solving PBUC compared to some existing benchmark algorithms in terms of profit and number of iterations.     

Ramp rate constrained unit commitment by improved priority list and augmented Lagrange Hopfield network

This paper proposes an improved priority list (IPL) and augmented Hopfield Lagrange neural network (ALH) for solving ramp rate constrained unit commitment (RUC) problem. The proposed IPL-ALH minimizes the total production cost subject to the power balance, 15 min spinning reserve response time constraint, generation ramp limit constraints, and minimum up and down time constraints. The IPL is a priority list enhanced by a heuristic search algorithm based on the average production cost of units, and the ALH is a continuous Hopfield network whose energy function is based on augmented Lagrangian relaxation. The IPL is used to solve unit scheduling problem satisfying spinning reserve, minimum up and down time constraints, and the ALH is used to solve ramp rate constrained economic dispatch (RED) problem by minimizing the operation cost subject to the power balance and new generator operating frame limits. For hours with insufficient power due to ramp rate or 15 min spinning reserve response time constraints, repairing strategy based on heuristic search is used to satisfy the constraints. The proposed IPL-ALH is tested on the 26-unit IEEE reliability test system, 38-unit and 45-unit practical systems and compared to combined artificial neural network with heuristics and dynamic programming (ANN-DP), improved adaptive Lagrangian relaxation (ILR), constraint logic programming (CLP), fuzzy optimization (FO), matrix real coded genetic algorithm (MRCGA), absolutely stochastic simulated annealing (ASSA), and hybrid parallel repair genetic algorithm (HPRGA). The test results indicate that the IPL-ALH obtain less total costs and faster computational times than some other methods.     

复合能源分布式发电系统的机组组合问题

针对分布式发电的特点,提出了一种新的含多种复合能源的分布式发电系统发电成本最低的机组组合模型,并提出了一种针对分布式发电系统的调度策略,用改进的遗传算法求取在此策略下机组组合的发电费用,约束条件中不仅包含了常规机组组合问题的约束,还考虑了分布式发电系统的特殊约束,算例证明此模型和算法是可行的。     

基于电力系统日发电计划的混合智能messy遗传算法

机组组合是电力系统日发电计划中主要的优化任务,在满足各种约束条件下求得全局最优解是一个比较困难的问题.传统遗传算法的二进制编码和随机遗传操作不适合于求解大规模机组组合问题.针对电力系统日发电计划的特点,提出了一种混合智能messy遗传算法(HIMGA),该算法实现简单,大大减小了求解问题的规模,保证了群体的多样性,提高了算法的搜索效率,改善了算法的收敛性.仿真计算结果表明了该算法的有效性和实用性.     

Hydro-thermal unit commitment problem using simulated annealing embedded evolutionary programming approach

This paper presents a new approach to solve the hydro-thermal unit commitment problem using Simulated Annealing embedded Evolutionary Programming approach. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. A utility power system with 11 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consist of 25, 44 and 65 units. Numerical results are shown comparing the cost solutions and computation time obtained by conventional methods.     

A Joint Smart Generation Scheduling Approach for Wind Thermal Pumped Storage Systems

Abstract

Energy storage plays a crucial role in the development of smart grids with high wind power penetration. Pumped storage is an effective solution for smoothing wind power fluctuation and reducing the operating cost for a wind thermal power system. The joint generation scheduling of power systems with mixed wind power, pumped storage, and thermal power is a challenging problem. This article proposes a novel two-stage generation scheduling approach for this problem in the contexts of smart grids. Through optimization, a day-ahead thermal unit commitment and pumped storage schedule are provided; then, in real time, the pumped storage schedule is updated to mitigate the wind power forecasting error and hence avoid the curtailment of wind power generation. The proposed model aims to reduce the total operating cost, accommodate uncertain wind power as much as possible, and smooth the output fluctuation faced by thermal units, while making the system operate in a relatively reliable way. A binary particle swarm optimization algorithm for solving the proposed model and the pumped storage schedule update algorithm are also presented. The model and algorithm are tested on a ten-generator test system.     

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.     

风-火-水-储-气联合优化调度策略

针对电力系统因调节能力不足而造成弃风等问题,构建多源并存的电力系统内风-火-水-储-气联合优化调度模型,并给出模型的求解方法。该模型将常规机组中的灵活电源包括燃气、水电与储能系统进行联合建模,考虑联合运行后不同机组间的相互影响,对联合运行时系统消纳清洁能源的能力提升的内在机理进行分析。为求解提出的联合调度模型,对模型线性化之后采用CPLEX调用内点法进行计算。最后用改进的7机39节点系统进行仿真,结果验证了所提模型对于提高系统调节能力、促进风电消纳、优化系统工况的作用。联合调度策略结合实际电力系统中多源并存的现状为应对风电接入问题提供了一种实际可行的调度方案。     

Optimal power scheduling of thermal units considering emission constraint for GENCOs’ profit maximization

In this paper, authors propose a novel method to determine an optimal solution for profit based unit commitment (PBUC) problem considering emission constraint, under a deregulated environment. In a restructured power system, generation companies (GENCOs) schedule their units with the aim of maximizing their own profit by relaxing demand fulfillment constraints without any regard to social benefits. In the new structure, due to strict reflection of power price in market data, this factor should be considered as an important ingredient in decision-making process. In this paper a social-political based optimization algorithm called imperialist competitive algorithm (ICA) in combination with a novel meta-heuristic constraint handling technique is proposed. This method utilizes operation features of PBUC problem and a penalty factor approach to solve an emission constrained PBUC problem in order to maximize GENCOs profit. Effectiveness of presented method for solving non-convex optimization problem of thermal generators scheduling in a day-ahead deregulated electricity market is validated using several test systems consisting 10, 40 and 100 generation units.     

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。     

基于SCUC的可入网混合电动汽车优化调度方法

在可入网混合电动汽车(PHEV)有望规模化应用的背景下,以传统的计及安全约束的机组最优组合(SCUC)问题为基础,发展了能够容纳PHEV的电力系统优化调度数学模型。所发展的模型以保证系统安全运行为前提,兼顾了PHEV车主的经济效益与发电的碳排放成本。利用PHEV作为可移动电量储存单元的特性,将模型解耦为机组最优组合与计及交流潮流约束的充/放电计划优化2个子模型。应用混合整数规划方法和牛顿—拉夫逊潮流算法迭代求解优化问题,可以同时获取日前机组调度计划和各时段的PHEV最优接纳容量及充/放电计划等结果。最后,以6节点和IEEE 118节点2个系统为例,验证了所构建模型的正确性和有效性。     

基于自适应混合差分进化算法的水火电力系统短期发电计划优化

针对水火电力系统发电计划优化问题,引入差分进化 (differential evolution,DE)算法,提出了一种自适应混合差分进化算法(adaptive hybrid differential evolution,AHDE)。该算法在计算过程中自适应调整交叉算子,保持了种群的多样性,提高了算法的全局搜索能力,克服了DE在寻优过程中容易早熟收敛的缺点。同时针对AHDE难以处理实际工程中复杂约束条件问题,提出了一种适合AHDE的约束处理方法。将AHDE应用于求解某水火电力系统的发电计划优化问题,与其他求解方法的对比表明,AHDE求解精度高、计算速度快。     

A solution to the ramp rate and prohibited operating zone constrained unit commitment by GHS-JGT evolutionary algorithm

Unit commitment (UC) problem on a large scale with the ramp rate and prohibited zone constraints is a very complicated nonlinear optimization problem with huge number of constraints. This paper presents a new hybrid approach of ’Gaussian Harmony Search’ (GHS) and ’Jumping Gene Transposition’ (JGT) algorithm (GHS-JGT) for UC problem. In this proposed hybrid GHS-JGT for UC problem, scheduling variables are handled in binary form and other constants directly through optimum conditions in decimal form. The efficiency of this method is tested on ten units, forty units and hundred units test system. Simulation results obtained by GHS-JGT algorithm for each case show a better generation cost in less time interval, in comparison to the other existing results.     

基于HSS算法的多区域虚拟电厂综合能源调度

综合能源的提出实现了不同能源之间的相互耦合,实现了能源高效梯级利用,文章在此基础上建立了计及系统网损的多区域虚拟电厂综合能源日前一日内调度模型.建立了采用日前一日内多时间尺度调度方法,在满足热电联产机组及储能出力约束前提下的以成本最小为目标的系统模型.考虑模型为非线性优化问题,采用超球面搜索算法寻找系统最优解.将某园区作为仿真对象,对比分析虚拟电厂间协同调度和单独运行两者不同情况下的出力及运行费用,研究电网及热网的重要节点或联络线端口的电压,并将超球面搜索算法同传统粒子群算法运行结果进行对比,最终验证所提模型及算法的可行性.     

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。     

广东电网节能发电调度模型与应用

为了应对日益严峻的能源与环境形势,中国颁布并实施了电网节能发电调度办法。由于该办法仅仅提供了基本的调度原则,广东电网在节能发电调度的推行过程中,缺少能满足政策的节能发电调度模型与算法。为解决这一实际问题,在分析现有节能发电调度方案不足的基础上,提出了考虑节能发电调度规则的混合整数线性规划机组组合与发电计划模型,以实现广东电网的节能发电调度。由于广东电网的抽水蓄能装机容量比重较高,在模型中考虑了抽水蓄能的作用。所提模型的有效性在IEEE10机39节点系统上得到验证。将该模型用于广东实际电网,分别给出了以节煤和降低购电成本为目标的广东电网节能发电调度模式下的调度策略及分析。在此基础上,探讨广东电网抽水蓄能机组在不同调度模式下的一般调度原则。     

Stochastic unit commitment problem considering risk constraints and its improved GA‐based solution method

This paper proposes a model of the stochastic unit commitment (SUC) problem, which takes account of the uncertainty of electric power demand and its resulting risk, and its solution method based on an improved genetic algorithm (IGA). The uncertainty of electric power demand is modeled using a set of scenarios which are introduced by scenario analysis. The variance, which measures the dispersion of generation costs of unit commitment schedule under each scenario around the expected generation cost, is used as a measure of risk. Based on the expected returns–variance of returns (E–V) rule in the theory of portfolio analysis, a utility function is devised by appending the variance of the expected generation cost into the original expected generation cost function, with consideration of the risk attitude of the generation companies and power exchange centers. The objective of this optimization problem is to minimize the utility function. The proposed IGA is used to solve this NP‐hard optimization problem. Based on numerical examples, the superiority of the IGA‐based solution method is verified through comparison with a traditional GA‐based solution method. Optimal schedules of SUC, as well as the expected costs and variances, are compared with/without risk constraints, and with different risk attitudes. Test results show that, in solving the SUC problem, it is necessary to consider the electric power demand uncertainty and its resulting risk, as well as the risk attitude of the decision maker. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

含混合移动储能的灾后配电网电冷负荷保障策略

现有的灾后配电网夏季应急调度策略普遍从最大化保电角度降低经济损失,未直接度量供冷价值,导致负荷保障成本过高。为此,提出了一种含混合移动储能的灾后配电网电冷负荷保障策略。构建了新型的负荷保障架构,旨在利用移动电储能异地充放电和移动冰储能转移冰蓄冷空调间的储冰,协同保障负荷用能。量化分析了移动电储能和移动冰储能的时空转移、能量变化特性,构建了简化的混合移动储能协同运行模型。在此基础上,以电负荷、冷负荷切除惩罚成本之和为目标,提出了计及潮流约束的配电网负荷保障模型。将优化模型转化为经典的混合整数二阶锥规划问题进行高效求解。算例仿真结果验证了所提策略的合理性与可行性。     

考虑风-储参与直接交易的中长期-现货市场物理交割建模及快速求解算法

文章对带储能的风电参与中长期电能量市场化交易机制进行研究。基于市场建设的现实国情,在考虑了风-储系统的技术特性以及运行约束的基础上,针对带储能的风电场参与直接交易的模式,融合中长期-现货市场物理交割原理,建立了相应的中长期-现货市场联合出清的带安全约束机组组合模型,该模型本质上为一含随机变量的NP-hard复杂优化问题。为提高求解效率,文章进一步提出了基于典型日序列机组组合的快速简化算法,不仅有效解决了中长期电力市场运行中不同负荷曲线日期的机组爬坡衔接问题,而且大力提高了计算效率。基于IEEE 39节点系统算例仿真,本文所提的考虑风-储参与直接交易的中长期-现货市场联合出清模型及快速求解算法的有效性得到验证,且计算效率和相对误差均得到满足。     

计及需求响应的含风电场电力系统发电与碳排放权联合优化调度

提出了一种节能减排形势下的优化调度模型和解法。研究对象上,文中研究了风电、火电、需求响应3种发电(虚拟发电)形式,需求响应可为系统提供零碳发电资源和旋转备用。模型建立上,基于多目标安全约束机组组合模型,采用发电和碳排放联合调度的方式,寻求经济目标与排放目标的折中协调解;约束条件综合考虑了需求响应、安全约束和碳排放等多种约束。解法上,采用分段线性化的方式,利用混合整数线性规划进行求解,解法适用于大规模系统,使模型更具实用价值。算例分析表明,决策结果能够兼顾排放与成本,并且在调度中引入需求响应可提高系统的风电消纳能力,具有显著的节能减排效益。