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...     

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.     

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.     

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.     

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。     

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.     

Efficient method for AC transmission network expansion planning

A combinatorial mathematical model in tandem with a metaheuristic technique for solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP) is presented in this paper. AC-TNEP is handled through a prior DC model while additional lines as well as VAr-plants are used as reinforcements to cope with real network requirements. The solution of the reinforcement stage can be obtained by assuming all reactive demands are supplied locally to achieve a solution for AC-TNEP and by neglecting the local reactive sources, a reactive power planning (RPP) will be managed to find the minimum required reactive power sources. Binary GA as well as a real genetic algorithm (RGA) are employed as metaheuristic optimization techniques for solving this combinatorial TNEP as well as the RPP problem. High quality results related with lower investment costs through case studies on test systems show the usefulness of the proposal when working directly with the AC model in transmission network expansion planning, instead of relaxed models.     

雷达吸波结构的FGA优化设计

本文采用浮点数编码遗传算法对多层涂敷型的吸波结构进行优化设计时,引入分段变参数遗传算法和权值调整技术来解决算法的早熟和吸波结构性能的宽角适应性问题.文中首先建立了适用于吸波结构优化设计的理论模型,然后设计算法使吸波结构在任意给定的频段和入射角度范围,通过对吸波结构的每层材料电磁参数及其厚度的优选使其吸波性能达到最佳,最后给出了吸波结构的点频和宽带的数值仿真结果.     

用多种群并行自适应遗传算法解混合FloWshop调度问题

混合流水车间（Flowshop）调度问题是一个NP完全问题,很难用一般的方法解决。构造并采用多种群并行自适应遗传算法求解该问题。仿真结果表明,此算法不仅具有较强的全局收敛性,而且有更快的寻优速度,是求解复杂调度问题的有效算法。     

Impact of load management and joint ownership of generation of two interconnected systems utilizing joint cumulants and the bivariate Gram-Charlier expansion

As oil and gas prices continue to increase, many electric utilities are turning to load management as a means of reducing their capital and operating costs. Different load management approaches are considered, and their effect on the global production costs of two interconnected systems is studied. A probabilistic technique for the calculation of production cost of two interconnected systems with joint ownership of generation is developed. A comparative study on production cost is made with and without a jointly owned generating unit. The method is based on the notion of statistical cumulants and the application of the bivariate Gram-Charlier expansion. Joint cumulants are used to represent the joint probability density function of the two systems' loads, and also to represent the probability density function of the outage of the jointly owned generating unit. Demand correlation is considered.     

Maximal optimal benefits of distributed generation using genetic algorithms

Recently, the distributed power generation (DG) takes more attention, because of the constraints on the traditional power generation besides the great development in the DG technologies. To accommodate this new type of generation, the existing network should be utilized and developed in an optimal manner. This paper presents an optimal proposed approach (OPA) to determine the optimal sitting and sizing of DG with multi-system constraints to achieve a single or multi-objectives using genetic algorithm (GA). The linear programming (LP) is used not only to confirm the optimization results obtained by GA but also to investigate the influences of varying ratings and locations of DG on the objective functions. A real section of the West Delta sub-transmission network, as a part of Egypt network, is used to test the capability of the OPA. The results demonstrate that the proper sitting and sizing of DG are important to improve the voltage profile, increase the spinning reserve, reduce the power flows in critical lines and reduce the system power losses.     

基于遗传算法与蚁群算法的电网规划

针对遗传算法求解到一定范围容易产生大量冗余迭代、求解精度低.蚁群算法初期信息素匮乏、求解速度慢的缺陷,在电网规划算法中,将遗传算法与蚁群算法融合,在网架规划初期采用遗传算法求解出最优解,通过最优解生成蚁群算法的初期信息素,确定吸引强度的初始值,建立强度更新的模型,从而得到满足电网规划的最优方案.最后通过18节点的算例证明,融合算法在收敛性与寻优性上均得到提高.