电源规划优化软件实用化研究

简述电源规划算法研究现状及软件开发和应用情况后，介绍了新一代图形化电源规划软件POWERPLAN，采用快速电源优化排序法，以电源投资费用等年值最小为目标函数；以及相关技术约束（电力平衡、水火电平衡、电源投产／电厂开工时间）条件、人机交互、报表输出等关键技术。详叙了POWERPLAN软件在满足各种技术经济约束条件下，自动生成电源优化装机方案的设计思想。该软件主要由数据处理、规划计算、报表输出、图形管理等模块组成。最后，利用该软件对某省2006～2020年的电源规划工作进行了分析，结果表明．规划优化效果符合要求。     

计及分布式发电的配电网扩展规划

计及分布式发电(DG)进行配电网扩展规划.从电网年支出费用的角度出发,采用蒙特卡洛方法模拟DG的出力,建立新的数学模型.提出一种基于隐性编码方式的遗传算法,用它对待选新建或升级改造线路、DG的种类、位置和容最进行综合优化规划,并分析DG出力不确定性及其发电成本对规划的影响.经算例验证,对DG进行合理地选择、布点和定容,能够给电网带来可观的经济效益.     

Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation

A new approach to the electricity generation expansion problem is proposed to minimize simultaneously multiple objectives, such as cost and air emissions, including CO₂ and NO_x, over a long term planning horizon. In this problem, system expansion decisions are made to select the type of power generation, such as coal, nuclear, wind, etc., where the new generation asset should be located, and at which time period expansion should take place. We are able to find a Pareto front for the multi-objective generation expansion planning problem that explicitly considers availability of the system components over the planning horizon and operational dispatching decisions. Monte-Carlo simulation is used to generate numerous scenarios based on the component availabilities and anticipated demand for energy. The problem is then formulated as a mixed integer linear program, and optimal solutions are found based on the simulated scenarios with a combined objective function considering the multiple problem objectives. The different objectives are combined using dimensionless weights and a Pareto front can be determined by varying these weights. The mathematical model is demonstrated on an example problem with interesting results indicating how expansion decisions vary depending on whether minimizing cost or minimizing greenhouse gas emissions or pollutants is given higher priority.     

现代启发式算法及其在输电网络扩展规划中的应用

对遗传算法、模拟退火法、Tabu搜索法,蚂蚁算法和粒子群算法等具有代表性的现代启发式算法的发展、特点及其比较和在输电网络扩展规划中的应用进行了总结和综述,提出了对现代启发式算法改进的三种思路,以及一些尚待深入研究的工作。     

基于免疫遗传算法的含分布式电源配网规划

大量分布式电源的接入使配电网规划更加复杂.在限制分布式电源接入总量和考虑多种约束的基础上,建立以配网年费用最小为目标函数的规划模型.针对该模型的特点,采用一种新型免疫遗传算法( IGA)对配电网扩展规划进行优化.该算法综合了免疫系统和遗传算法的优点,可实现群体收敛性和个体多样性间的动态平衡,具有良好的全局收敛能力.分别...     

Practical method for generation expansion planning based on dynamic programming

This paper presents an efficient method which provides the optimal generation mix and the optimal generation construction process. The approximation method in which the dynamic programming technique and gradient method are combined is applied to determine the optimal generation mix with hydropower generation technologies. The successive approximations dynamic programming (SADP) technique, which is very suitable for high-dimensional multistage decision process problems, is used for obtaining the optimal generation construction process. The effectiveness and feasibility of the developed technique are demonstrated on a practical power system model which has five types of generation technologies including a hydropower generation technology.     

NSGA-II algorithm for multi-objective generation expansion planning problem

This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail.     

Integrated generation-transmission expansion planning for offshore oilfield power systems based on genetic Tabu hybrid algorithm

To address the planning issue of offshore oil-field power systems, an integrated generation-transmission expansion planning model is proposed. The outage cost is considered and the genetic Tabu hybrid algorithm（GTHA）is developed to find the optimal solution. With the proposed integrated model, the planning of generators and transmission lines can be worked out simultaneously,which outweighs the disadvantages of separate planning,for instance, unable to consider the influence of power grid during the planning of generation, or insufficient to plan the transmission system without enough information of generation. The integrated planning model takes into account both the outage cost and the shipping cost, which makes the model more practical for offshore oilfield power systems. The planning problem formulated based on the proposed model is a mixed integer nonlinear programming problem of very high computational complexity, which is difficult to solve by regular mathematical methods. A comprehensive optimization method based on GTHA is also developed to search the best solution efficiently.Finally, a case study on the planning of a 50-bus offshore oilfield power system is conducted, and the obtained results fully demonstrate the effectiveness of the presented model and method.     

Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm

This paper introduces a modified shuffled frog leaping algorithm (MSFLA) to solve reliability constrained generation expansion planning (GEP) problem. GEP, as a crucial issue in power systems, is a highly constrained non-linear discrete dynamic optimization problem. To solve this complicated problem by MSFLA, a new frog leaping rule, associated with a new strategy for frog distribution into memeplexes, is proposed to improve the local exploration and performance of the SFLA. Furthermore, integer encoding, mapping procedure and penalty factor approach are implemented to improve the efficiency of the proposed methodology. To show the effectiveness of the method, it is applied to a test system for two planning horizon of 12 and 24 years. For the sake of methodology validation, an ordinary SFLA as well as a Genetic Algorithm (GA) are both applied to solve the same problem. Simulation results show the advantages of the proposed MSFLA over the SFLA and GA.     

Development of an analytical model for generation expansion planning as a tool to provide guidelines for preventing instability in the long‐term electricity market

This paper considers the problem of deciding multiperiod investments for generation expansion planning (GEP) in restructured power systems. This problem has presented a challenge for both market managers and suppliers regarding the stability in the electricity market and minimum income for suppliers over the planning period. In this paper, an analytical model for studying the GEP problem from the viewpoint of a central management entity is presented. The aim of this method is to establish a dynamic balance between energy supply and demand by adjustment of GEP over the horizon of planning so that not only the expected profit is provided for all new generating plants but the long‐term stability in the electricity market is also improved. This analytical model can be utilized by regulatory bodies to obtain some guidelines and thereby to set their policies for improving GEP and preventing instability in the long‐term electricity market. To do so, in this study, the uncertainties of demand and supply have been modeled through two stochastic processes. Furthermore, the market price dynamics and their mutual effects on the GEP's results have been considered. Finally, this nonlinear dynamic optimization problem is solved using a modified genetic algorithm (GA). The efficiency and ability of the proposed method are examined on a test power system. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An approach for transmission expansion planning using neurocomputing hybridized with a genetic algorithm

The aim of transmission expansion planning is to determine which right-of-way to use when constructing new lines in order to meet a forecasted load in the most economical way. This problem has been solved previously by mathematical sensitivity analysis (which finds a single nonoptimal solution). It is difficult to plan for economical and reliable expansion due to its discrete and combinatorial nature. Although another method that has efficiency for combinatorial problems is neurocomputing, this approach saves computational efforts but obtains poor solutions. The most desirable approach for this planning problem is one in which many good solutions are found in reasonable time, because planning experts will then be able to plan economical and reliable expansion according to these solutions. This paper presents an approach for solving transmission expansion planning based on neurocomputing hybridized with a genetic algorithm. This approach generates suitable initial states, which include past information, of neural networks utilizing genetic algorithm. Mingling neurocomputing and a genetic algorithm, the proposed approach can find many good solutions in reasonable time making full use of their merits. Computational examples show the effectiveness of the proposed approach by comparison with conventional approaches.     

电源规划模型及求解方法研究综述

对传统的电源规划模型结合需求侧管理、考虑环境保护和电力市场环境下的电源规划模型进行了详细的研究,总结了实施需求侧管理、环境保护对电源规划的影响和电力市场改革对电源规划的新要求,分析了各种模型中电源规划目标函数和考虑约束条件的不同。然后将电源规划问题所采用的求解方法主要分为数学优化方法和人工智能方法进行归纳,包括动态优化法、混合整数规划法、专家系统、模糊理论、遗传算法和人工神经网络等,对比了各种方法的优缺点。最后阐述了当前电源规划过程中尚待深入研究的问题。     

改进的遗传算法在汕头电网规划中的应用

遗传算法是由自然界生物遗传机理抽象出来而形成的一种新型的优化算法,具有适合于电网规划的独特优点。因此,介绍了遗传算法应用于电网规划的模型和实现步骤,分析了其特点,并提出把改进的自适应代沟遗传算法引入电网规划,经改进的遗传算法在汕头电网规划的网架优化中应用,取得了较好的效果。     

Application of particle swarm optimization technique and its variants to generation expansion planning problem

This paper presents the application of particle swarm optimization (PSO) technique and its variants to least-cost generation expansion planning (GEP) problem. The GEP problem is a highly constrained, combinatorial optimization problem that can be solved by complete enumeration. PSO is one of the swarm intelligence (SI) techniques, which use the group intelligence behavior along with individual intelligence to solve the combinatorial optimization problem. A novel ‘virtual mapping procedure’ (VMP) is introduced to enhance the effectiveness of the PSO approaches. Penalty function approach (PFA) is used to reduce the number of infeasible solutions in the subsequent iterations. In addition to simple PSO, many variants such as constriction factor approach (CFA), Lbest model, hybrid PSO (HPSO), stretched PSO (SPSO) and composite PSO (C-PSO) are also applied to test systems. The differential evolution (DE) technique is used for parameter setting of C-PSO. The PSO and its variants are applied to a synthetic test system of five types of candidate units with 6- and 14-year planning horizon. The results obtained are compared with dynamic programming (DP) in terms of speed and efficiency.     

Cogeneration systems planning using structured genetic algorithms

This paper proposes a new approach to plan cogeneration systems, that of distributed energy systems. The proposed approach uses structured genetic algorithms. Cogeneration systems planning provides optimal allocation of cogeneration systems, a layout of the pipeline network structure for distributing heat energy between cogeneration systems and demand areas, and optimal heat and electric energy supply to meet the energy demands. The planning is formalized as a combinatorial optimization problem with minimizing cost of energy supply as its objectives. The traditional solution method is based on mathematical programming methods. But it is difficult to get an optimal solution as the number of areas increases because of combinatorial explosion and nonlinearity. This paper describes a new method to solve the cogeneration systems planning based on genetic algorithms. The solution of the cogeneration systems planning problem has a network structure. The proposed method applies structured genetic algorithms whose genotype has a tree structure to represent a network structure. The characteristics of the proposed method are analyzed by applying the new method to empirical data of the area around station K. © 1997 Scripta Technica, Inc. Electr Eng Jpn 119(2): 26–35, 1997     

A decision support system for generation expansion planning in competitive electricity markets

This paper describes an approach to address the generation expansion-planning problem in order to help generation companies to decide whether to invest on new assets. This approach was developed in the scope of the implementation of electricity markets that eliminated the traditional centralized planning and lead to the creation of several generation companies competing for the delivery of power. As a result, this activity is more risky than in the past and so it is important to develop decision support tools to help generation companies to adequately analyse the available investment options in view of the possible behavior of other competitors. The developed model aims at maximizing the expected revenues of a generation company while ensuring the safe operation of the power system and incorporating uncertainties related with price volatility, with the reliability of generation units, with the demand evolution and with investment and operation costs. These uncertainties are modeled by pdf functions and the solution approach is based on Genetic Algorithms. Finally, the paper includes a Case Study to illustrate the application and interest of the developed approach.     

Optimal corrective actions for power systems using multi-objective genetic algorithms

In this paper, optimal corrective control actions are presented to restore the secure operation of power system for different operating conditions. Genetic algorithm (GA) is one of the modern optimization techniques, which has been successfully applied in various areas in power systems. Most of the corrective control actions involve simultaneous optimization of several objective functions, which are competing and conflicting each other. The multi-objective genetic algorithm (MOGA) is used to optimize the corrective control actions. Three different procedures based on GA and MOGA are proposed to alleviate the violations of the overloaded lines and minimize the transmission line losses for different operation conditions. The first procedure is based on corrective switching of the transmission lines and generation re-dispatch. The second procedure is carried out to determine the optimal siting and sizing of distributed generation (DG). While, the third procedure is concerned into solving the generation-load imbalance problem using load shedding. Numerical simulations are carried out on two test systems in order to examine the validity of the proposed procedures.     

DG integrated multistage distribution system expansion planning

In this paper, a framework is presented to solve the problem of multistage distribution system expansion planning in which installation and/or reinforcement of substations, feeders and distributed generation units are taken into consideration as possible solutions for system capacity expansion. The proposed formulation considers investment, operation, and outage costs of the system. The expansion methodology is based on pseudo-dynamic procedure. A combined genetic algorithm (GA) and optimal power flow (OPF) is developed as an optimization tool to solve the problem. The performance of the proposed approach is assessed and illustrated by numerical studies on a typical distribution system.     

Security-constrained expansion planning of fast-response units for wind integration

This paper proposes a stochastic expansion planning of fast-response thermal units for the large-scale integration of wind generation (WG). The paper assumes that the WG integration level is given and considers the short-term thermal constraints and the volatility of wind units in the planning of fast-response thermal units. The new fast-response units are proposed by market participants. The security-constrained expansion planning approach will be used by an ISO or a regulatory body to secure the optimal planning of the participants’ proposed fast-response units with the WG integration. Random outages of generating units and transmission lines as well as hourly load and wind speed forecast errors are modeled in Monte Carlo scenarios. The Monte Carlo simplification methods are introduced to handle large-scale stochastic expansion planning as a tradeoff between the solution accuracy and the calculation time. The effectiveness of the proposed approach is demonstrated through numerical simulations.     

分位数拟合的点估计法随机潮流在输电网规划中的应用

为了更全面地描述输电网规划中面临的负荷波动、风电出力这些不确定因素,以点估计法随机潮流为基础、年综合费用最小为目标函数,并计及网络损耗,建立输电网优化规划模型。引入多项式正态变换技术对非正态变量相关性进行处理。采用基于最小二乘的分位数拟合法求取多项式正态变换的系数,有效地避免了积分运算。以总体样本均值和样本方差均值为指标,量化相关性对风电出力的影响。采用改进的粒子群优化算法对修改的IEEE-RTS 24节点系统进行算例分析,结果表明,随着相关性的增强,负荷、风速出现极值的概率增加,电网需要投建更多线路以应对系统中的不确定因素。