基于多因素的改进MSS-PGA及其在水电站负荷优化分配中的应用

机组负荷优化分配是研究水电站经济运行问题的核心部分,长期以来备受研究人员的重视,但目前为止,尚没有找到一个高效的算法来求解这个问题.本文将多因素技术与同步主从式并行GA(MSS-PGA)结合起来,建立了一种基于多因素的同步主从式并行GA,并将其应用于水电站负荷优化分配问题的求解中.该算法与传统GA相比,具有更高的精度和收敛性;与其它并行GA相比,能够很好的节省网络资源和计算代价,更能适用于求解水电站负荷优化分配问题.     

基于免疫克隆选择算法的水电站水库优化调度

在研究了人工免疫系统中的克隆选择学说和克隆选择算法的基础上,研究了1种新的人工免疫算法——免疫克隆选择算法,并将其应用到水库优化调度中,提出了1种基于免疫克隆选择算法的水库优化调度方法.该算法通过在克隆选择算法中引入免疫基因操作,提高了算法的求解精度和求解效率,避免了“维数灾”和早熟问题.实例研究结果表明,相对于动态规划,免疫克隆选择算法计算速度快、收敛性好,提高了计算效率,较好地解决了传统的动态规划方法求解水库(群)优化调度问题存在“维数灾”问题.     

免疫算法与其它模拟进化优化算法的比较研究

免疫算法作为解决优化问题的有力工具，它的有效性已经得到了证明。介绍了免疫算法的数学模型和基本步骤，阐述了它不同于其它优化算法的优点。最后将免疫算法、遗传算法和进化策略同时应用于求解ｓｉｎｃ函数的最优值，以进行比较研究。结果表明，免疫算法在求解某些特定优化问题方面优于其它优化算法，其应用前景非常广阔。     

复杂时段耦合型约束水电站群短期变尺度优化调度方法

复杂时段耦合型约束是制约大规模水电短期优化调度高效求解的主要因素之一。提出了一种水电站群变尺度优化调度方法,旨在通过增大时段步长,以弱化甚至消除时段耦合型约束,进而提高算法搜索效率,改善优化调度质量。在求解过程中,首先将原问题转换为多个具有相同调度周期、相同目标和控制需求但不同步长的水电站群优化调度问题,并按照步长从大到小的顺序依次求解各问题,面临问题的初始解由前一阶段大步长问题的优化出力结果确定,直至完成最小步长问题即原问题的优化求解。所提方法通过云南电网水电站群仿真调度实例得到验证,与单一尺度优化方法相比,结果质量和计算效率均得到不同程度地改善。     

受送电规模限制下水电长期可吸纳电量最大优化调度模型

跨省跨流域水电站群发电优化调度已成为中国大水电系统调度运行的瓶颈问题,以南方电网水电站群为背景,结合其长期优化调度问题,提出水电长期可吸纳电量最大模型。该模型在发电量最大模型基础上,将电网对水电的吸纳能力和受送电量限制作为控制条件,以适应大规模水电站群优化调度的实际需要,结合逐步优化算法、离散微分动态规划算法和逐次逼近算法求解。以南方电网92座水电站组成的水电站群系统为背景的实例研究表明,该模型体现了省级电网间长期受送电需求和限制,能有效解决大规模跨电网受送电模式下的长期水电调度问题。     

乔治亚理工水电站水库调度与发电决策支持软件系统

“乔治亚理工水电站水库调度与发电决策支持软件系统(GTDSS)”是一个将河流中长期径流预报技术与水库群优化调度、水资源管理集于一体的系统。它包括先进的河流中长期水文预报和中长期水库(群)优化调度模块,支持从中长期计划到短期及实时的水库调度与发电调度。此系统的应用可以减少不合理弃水,增加发电量,降低洪涝灾害损失,有效利用水资源。应用该系统后,可相应增加水电站年发电量3%￣10%。该系统已成功应用到北美洲、欧洲、非洲等多个流域,获得了较好的经济效益和社会效益。GTDSS技术具有通用性,可以方便地移植应用到不同流域的水电站水库(群)调度系统中,对当前我国已开发的各流域水电站水库(群)调度管理具有重要的参考价值。     

一种免疫进化算法及其收敛性的研究

基于生物免疫系统的重要原理和机理提出了一种免疫进化算法(IEA),对此算法收敛性进行了理论分析,并将该算法应用到多模态函数优化的求解中.仿真结果表明,该算法用于复杂的函数优化具有较快的收敛速度和有效性,利用此算法研究优化问题具有广阔的前景.     

两个并联水电站水库补偿优化调度的一种改进方法

一、问题的提出 随机动态规划是求解水电站水库最优运行策略的有效方法,但是,对于两个以上的水电站水库群优化调度问题的求解,维数灾便成为其难于实际应用的最大障碍。即使是考虑无预报的独立随机径流的两个并联水电站水库的补偿优化调度,主要困难也仍然是对计算机的存储容量和运算速度要求很高。     

变尺度混沌优化算法在梯级水电站水库优化调度中的应用

利用变尺度混沌优化算法(Mutative Scale Chaos Optimization Algorithm,MSCOA)对梯级水电站水库调度问题进行优化调度。主要思想是利用混沌运动的随机性,由Logistic方程随机生成混沌序列;将其载波到包含水电站目标函数可行域S的一个区域;利用随机性、遍历性和规律性,不断缩小优化变量的搜索空间和提高搜索精度进行全局寻优,从中搜索属于可行域S的解;同时在搜索中引入解向量优选,将解向量中那些接近全局最优解的分量找出,构成一个新的向量,代入目标函数中进行计算,从而找出全局最优解,最终求出水电站水库发电调度的最优调度线。实例计算结果表明,算法可以求解具有复杂约束条件的非线性梯级水电站水库优化调度问题。算法求解精度高,具有较大的实用价值,为求解梯级水电站水库优化调度问题提供了一种有效算法。     

基于改进二进制粒子群与动态微增率逐次逼近法混合优化算法的水电站机组组合优化

针对水电站机组组合问题具有高维、非凸、离散、非线性等特点,提出了一种适用于求解大容量、多机组巨型水电站机组组合问题的改进二进制粒子群优化算法,改进了粒子概率变换和位置更新方程,使其具有更强的全局寻优能力和更快的收敛速度。通过将改进二进制粒子群算法与动态微增率逐次逼近法混合嵌套,分别对水电站外层机组组合和内层机组间负荷分配进行交替迭代优化来求解水电站机组组合问题。同时引入启发式机组最短开停机时间修补策略和基于机组启停优先顺序表的系统备用容量修补技术,有效处理了多重约束条件,提高了算法的收敛速度和寻优能力。以三峡水电站为工程应用背景进行了实例研究,并与DP和BPSO算法以及实际耗水量进行了比较分析,结果表明所提算法简单快速,优化效果较好,具有较强的工程实用价值。     

求解大规模水火最优潮流问题的近似牛顿方向解耦算法

分解后计算效率低和解的最优性差一直是困扰大规模水火最优潮流(HTOPF)研究与应用的两个关键问题。针对这些问题,提出了一种求解HTOPF的精确高效的解耦算法。基于近似牛顿方向直接对原问题KKT(Karush-Kuhn-Tucker)条件解耦的思想,将含梯级电厂的HTOPF问题分解为火电问题和水电问题。火电问题分解为单时段最优潮流问题,并进一步划分为多区域子问题;根据水电厂类型的不同将水电问题分解为单个固定水头、单个变化水头水电厂子问题以及梯级水电厂群优化子问题。求解过程中,每个子问题只迭代一次而不用求其最优解,极大地提高了计算效率。仿真计算结果表明:所提算法具有良好的适应性和稳定性,不仅显著减少了内存占用,而且在串行求解时CPU计算时间缩短了3~4倍,在并行计算条件下可获得10~20倍甚至1 000倍以上的加速比,并保证所得最优目标值与准确值之间的误差在10-8以下,确保了分解协调结果的最优性。     

Optimal Long-Range Generation Expansion Planning for Hydro-Thermal System Based on Analytical Production Costing Model

This paper presents a new algorithm for the optimal long-range generation planning for hydro-thermal system. The algorithm is based upon the analytical production costing model developed under the assumption of Gaussian probabilistic distribution of random load fluctuations and plant outages. The optimization problem consists of the master problem to determine the annual investment, and the hydro subproblem to determine the optimal hydro operation. The master problem is formulated as a Hamiltonian minimization problem, and the hydro subproblem is solved using the concept of peak-shaving operation on the original load curve.     

A complete decomposition and coordination algorithm for large‐scale hydrothermal optimal power flow problems

This paper presents a complete decomposition and coordination algorithm to solve large‐scale hydrothermal optimal power flow (HTOPF) problems. Based on the approximate Newton directions method, which decouples the first‐order Karush–Kuhn–Tucker conditions of the original problem, an HTOPF problem with cascaded hydro plants is decomposed into a thermal plant subproblem with independent optimal power flow solutions for each time period and a hydro plant subproblem combined with fixed and variable heads and cascaded plants issues. In order to verify the effectiveness of the proposed algorithm, numerical tests are performed on three large‐scale test systems with up to 3120 buses and 7 531 915 primal–dual variables over 168 time periods. Test results show that the proposed algorithm gives excellent performances in convergence and stability. It not only reduces memory usage significantly but also decreases CPU time by about 65–75%. With parallel computing, it is capable of achieving 10–20 times or even 1000 times speed without loss of optimality. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Optimal short-term scheduling for a large-scale cascaded hydrosystem

This paper describes a short term hydro generation optimization program that has been developed by the Hydro Electric Commission (HEC) to determine optimal generation schedules and to investigate export and import capabilities of the Tasmanian system under a proposed DC interconnection with mainland Australia. The optimal hydro scheduling problem is formulated as a large scale linear programming algorithm and is solved using a commercially-available linear programming package. The selected objective function requires minimization of the value of energy used by turbines and spilled during the study period. Alternative formulations of the objective function are also discussed. The system model incorporates the following elements: hydro station (turbine efficiency, turbine flow limits, penstock head losses, tailrace elevation and generator losses), hydro system (reservoirs and hydro network: active volume, spillway flow, flow between reservoirs and travel time), and other models including thermal plant and DC link. A valuable by-product of the linear programming solution is system and unit incremental costs which may be used for interchange scheduling and short-term generation dispatch     

电力市场下水电厂竞价综述

针对水电机组的特点,对联营市场模式下的水电机组竞价策略和水电市场势力进行了总结和评述。在过渡时期,水电机组继承垂直一体化管理模式,配合火电运行报价,不能突出水电的效益。采用完全竞争方式则需要建立现货交易市场下的水库优化运行模型,但改进后的模型可以考虑传输网络约束、机组组合以及系统中的不确定因素。为了协调利益关系,对同属一个业主的梯级电厂,可以聚合为一个报价单元,而建立水批发市场共同调节不同业主的收入分配。对市场势力的研究发现,水电厂可以通过研究不同时段间的需求弹性差来行使市场势力,在需求的低弹性阶段不发电而在高弹性时发足够的电。纵观国内外由市场实现水电资源优化配置的研究,今后还需要在水电的市场地位、区域水电厂的竞争模式、综合利用、梯级水电竞价、弃水电量、电价预测以及电力市场环境下的流域梯级开发调度模型和算法上进行更加广泛的研究。     

USE OF PUMPED-HYDRO AS PEAK-LOAD MANAGEMENT PLANT IN OPTIMAL SCHEDULING OF POWER SYSTEMS

ABSTRACT

The system demand, during some intervals of a day, may exceed the available system generation (excluding the pumped-hydro generation). A pumped-hydro plant may then, be used as a peak-load management unit to safeguard the power system by minimizing the power blackout in order to avoid large deviation in system frequency. In this paper, an algorithm is presented to obtain the optimal schedule for hydro, thermal plants including the pumped-hydro unit by proper selection of initial feasible trajectory for the pumped-hydro plant. An additional constraint is introduced to ensure the power balance in each time interval during the perturbation of water storage trajectory. The proposed method decomposes the problem into hydro and thermal subproblems and solves them alternately. The hydro subproblem is solved using a search procedure namely, the local variation method and the thermal subproblem is solved using a judicious combination of participation factors/linear programming method. Optimal scheduling was conducted on a 9-bus system and a 66-bus Utility system. The results obtained for the above two systems demonstrate the effectiveness of the algorithm proposed.     

Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using Flexible Alternating Current Transmission System Devices

This article describes the load frequency control of a multi-area system. Each control area contains both a hydro and thermal power plant to form a multi-source multi-area hydro thermal system. The secondary proportional-integral controller has been tuned using Ziegler–Nichols, genetic algorithm, and fuzzy gain scheduling methods. On comparing the controller performance based on various performance indices, it is found that a fuzzy gain scheduling tuned proportional-integral controller is suitable for a multi-source multi-area hydro thermal system. Further improvement on the load frequency dynamics has been achieved by connecting superconducting magnetic energy storage unit in each control area and a static synchronous series compensator unit on a tie-line.     

A hybrid artificial neural network—differential dynamic programming approach for short-term hydro scheduling

In this paper, a hybrid artificial neural network-differential dynamic programming (ANN-DDP) method for the scheduling of short-term hydro generation is developed. The purpose of short-term hydro scheduling is to find the optimal amounts of generated powers for the hydro units in the system for the next N (N= 24 in this work) hours in the future. In the proposed method, the DDP procedures are performed offline on historical load data. The results are compiled and valuable information is obtained by using ANN algorithms. The DDP algorithm is then performed online according to the obtained information to give the hydro generation schedule for the forecasted load. Two types of ANN algorithm, the supervised learning neural network by Rumelhart et al. and the unsupervised learning neural network by Kohonen, are employed and compared in this paper. The effectiveness of the proposed approach is demonstrated by the short-term hydro scheduling of Taiwan power system which consists of ten hydro plants. It is concluded from the results that the proposed approach can significantly reduce the execution time of the conventional differential dynamic programming algorithm which is required to reach proper hydro generation schedules.     

Optimal operation of variable head hydro-thermal systems using the Glimn-Kirchmayer model and the Newton-Raphson method

Optimal economic power scheduling of an integrated electric power system with variable head hydro plants is treated in this paper. The coordination equations developed by Ricard and Kron provide the general optimality conditions. There are many reported models to represent the performance of a hydro plant. In this paper the classical Glimn-Kirchmayer model is used to derive specific optimality conditions. An algorithm based on the Newton-Raphson iterative method is developed for solving the resulting scheduling problem. The iterative method is implemented using a specially designed initial guess estimator based on the physical properties of the problem to generate a set of feasible initial estimates of the unknowns to speed up the iterative process.Several example systems are used to test the performance of the algorithm. Successful convergence of the algorithm is reported in this paper and results for one sample test system are given.     

OPTIMAL ECONOMIC OPERATION OF THERMAL AND VARIABLE HEAD HYDRO SYSTEMS USING HAMILTON-LAMONT'S MODEL AND NEWTON-RAPHSON METHOD

ABSTRACT

In this paper, optimum scheduling of an integrated hydro-thermal electric power systems with variable head hydro plants, is treated. The scheduling problem is solved by the use of coordination equations developed by Ricard and Kron. There are many reported models to represent the performance of a hydro plant, and here Hamilton-Lamont's model is used to derive specific optimality conditions for the problem. Using Newton-Raphson method, an algorithm is developed for solving iteratively the problem of optimal power scheduling. The iterative method is implemented using a specially designed initial guess estimator based on the physical properties of the problem to generate a set of feasible initial estimates of the unknowns to speed up the process. A number of example systems are used to test the performance of the algorithm. Successful convergence of the algorithm is reported in this paper and results for one sample test system are given.