A dynamic model for generation expansion planning

Selection of optimal size and optimal timing of addition of (new) generators are the two basic problems in generation expansion planning. In this paper we have developed a simple mathematical model for generation expansion planning which consists of an economic indicator (cost functional) and a dynamic model relating future expansion with the existing capacity and the retirement characteristics of generating units available on the market. For illustration, the proposed model has been applied to a hypothetical example of a utility and gives encouraging results.     

基于随机期望值规划的输电网规划方法

电力市场环境下输电网规划面临大量的不确定因素的影响,运用不确定规划理论建立了输电网随机期望值规划模型,采用随机数来描述新增发电机节点出力,新增节点负荷变化值,新增线路造价的不确定性因素,利用Monte-Carlo仿真和改进小生境遗传算法求解模型,修改的18和46节点系统验证了所提模型和方法有效.     

Uncertainty based distributed generation expansion planning in electricity markets

Decision making under uncertainty is a difficult task in most areas. The distributed generation expansion planning is certainly one of the problems where it is difficult to find an optimal solution for, due to the existence of various uncertain parameters that affect the optimal solution. This paper presents a distributed generation planning strategy based on the multi-objective decision making (MODM) approach under uncertainties. The proposed strategy achieves a Pareto set of locations, sizes, and typical types, for new DG installations in a case-study power network (as the result of a multi-objective optimization). The technical constraints of DG units are also considered in the MODM process. The effects of the uncertain parameters on choosing the best plan(s) are considered using robustness, flexibility, and risk exposure attributes. This aim is performed based on a composite index which is weighted sum of the three mentioned attributes. This paper considers four categories of uncertain parameters including the peak loading factor, electricity market price, and investment and operating costs for the DG long-term planning.     

A minimum cost assessment method for composite generation andtransmission system expansion planning

Composite generation and transmission system expansion analysis should take into account both economic considerations and adequacy requirements. An optimum expansion plan should achieve the minimum total investment, operation and damage cost. A minimum cost assessment method for composite system expansion planning, which can be used to consider generation expansion and transmission expansion simultaneously, is presented. The minimization model proposed to incorporate both operating and outage costs can recognize different customer damage functions at different load buses and includes the duration of the simulated contingency system states . A computer program based on the presented method has been developed to provide a set of line, load bus, generator bus and system indices which can be used to select optimal expansion plans at different load growth levels. Case studies in which the method is applied to the IEEE Modified Reliability Test System indicate its effectiveness     

基于并行动态规划的水库发电优化

动态规划是水库发电优化调度研究中的经典方法。为了提高动态规划算法计算性能以及适应多核计算技术发展趋势,本文在分析传统串行动态规划算法计算特点的基础上,建立基于主从模式的并行动态规划模型,并将其运用到水布娅水库的发电优化计算中。计算结果表明,在多核运算平台下,并行动态规划算法能够充分利用多核资源,有效提高算法计算效率,缩短计算时间。     

Market-driven dynamic transmission expansion planning

We present a novel transmission expansion planning model that explicitly considers a multi-year planning horizon. The proposed formulation is a mixed-integer linear problem that can be solved using commercially available software. The algorithm determines the best overall transmission expansion plan, reflecting both investment and operational costs. To analyze the results obtained from the model, we use a set of efficiency metrics to appraise the effect of the expansion among generators and demands, as well as congestion metrics to measure changes in nodal prices and line congestions. This paper also provides a case study that considers the expansion plan for a transmission system based on the one of mainland Spain.     

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.     

An improved genetic algorithm for generation expansion planning

This paper presents a development of an improved genetic algorithm (IGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. In this paper, an improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the “curse of dimensionality” and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA approach is applied to two test systems, one with 15 existing power plants, 5 types of candidate plants and a 14-year planning period, and the other, a practical long-term system with a 24-year planning period     

A new decomposition based method for optimal expansion planning oflarge transmission networks

A new method is presented for long-range transmission network expansion planning, based on the decomposition principle. The overall transmission expansion planning task is divided into two problems, the first one dealing with investments, and the second with operations. The investment problem is specified as the minimum cost problem of network programming, leading to the decomposition into the model of minimum load curtailment and the proposed model of the (local) marginal network. The operation problem is solved by applying the Monte Carlo simulation, with suitable control strategies and additional reliability constraints. The final result is the software package, verified on test examples, as well as on the real transmission network of the power pool of eastern Yugoslavia     

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.     

大规模风电并网的电源规划研究

随着风电的大规模并网,电网的调峰能力将成为电源发展的重要约束。所以,为了寻求一个最经济可行的电源规划,笔者提出通过建立数学模型来获取。在建立模型时,首次考虑了风电机组的规划问题,把电网对风电的接纳能力作为重要的约束条件进行研究。同时,在使用进化规划方法求解电源规划优化问题的过程中,针对常规电源与风电机组在单台容量上的差异,设计了新的初始种群形成方法与进化策略,改进了原有算法的优化精度和收敛特性,并基于完备的数学模型和改进的优化算法,在最后对实际算例进行了计算求解。     

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.     

Generation expansion planning in electricity markets: A novel framework based on dynamic stochastic MPEC

This paper presents a novel framework for generation expansion planning (GEP) of restructured power systems under uncertainty in a multi-period horizon, which includes generation investment from a price maker perspective. The investment problem is modeled as a bi-level optimization problem. The first level problem includes decisions related to investment in order to maximize total profit in the planning horizon. The second level problem consists of maximizing social welfare where the power market is cleared. Rival uncertainties on offering and investment are modeled using sets of scenarios. The bi-level optimization problem is then converted to a dynamic stochastic MPEC and represented as a mixed integer linear program (MILP) after linearization. The proposed framework is examined on a typical six-bus power network, MAZANDARAN regional electric company (MREC) transmission network as an area of IRAN interconnected power system and IEEE RTS 24-bus network. Simulation results confirm that the proposed framework can be a useful tool for analyzing the behavior of investments in electricity markets.     

基于改进型动态规划算法的厂级负荷优化分配

在详细介绍了实行厂级负荷优化分配的重要性后,着重论述了用于火电厂厂级负荷优化分配的一般动态规划算法并指出其不足,提出了一种改进的动态规划算法,它根据经验所形成的策略来变换步长,再对不同步长下的所求解进行二次寻优,基本上克服了因步长选取不当而带来的非最优化问题.案例表明：改进型动态规划算法效果良好,具有不错的工程应用价值.     

应用GASP软件进行广西中长期电源规划优化

在分析广西能源资源条件、电力市场需求及各类电源技术经济特性的基础上,应用基于遗传算法的电源规划优化软件(GASP),并通过对多方案的优化计算和不同边界条件的敏感性分析,从广西自建电源的结构与布局、接受区外电力的合理规模与特性及各种电源扩展方案的经济性等方面,对广西各类电源(包括区外送电)的特点和竞争优势进行研究,提出广西未来应继续开发水电、优化建设煤电、高度重视核电、适度建设抽水蓄电站、积极接受西电的建议,研究成果为电源结构和布局调整及项目决策提供参考.     

Practical reactive power allocation/operation planning usingsuccessive linear programming

A method for reactive power planning is presented that it finds an optimal solution for both allocation and operation planning in large systems using linear programming (LP). The method utilizes calculated linear sensitivities including active power and voltage phase angle in the formulation. Although the overall method includes these relations, the number of constraints and variables are not augmented in its first procedure, APPROACH-1. Its second procedure, APPROACH-2, overcomes numerical problems caused by a dense constraint matrix. This is achieved by retaining untouched sparse sensitivities in the constraint matrix and by eliminating any calculations related to the inverse matrix. The results of applying this method to a practical 224-bus system and the IEEE-30 bus system verify its robustness and fast convergence     

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.