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.     

An evaluation of the optimal generation mix for controlling carbon dioxide emissions

This paper presents an efficient computational algorithm for selecting the optimal generation mix considering CO₂ emissions. To demonstrate the effectiveness and feasibility of the proposed method, a fundamental study of the evaluation of the optimal generation mix for controlling CO₂ emissions is indicated. Furthermore, by using a parametric analysis which considers load characteristics as parameters, a general trend for the optimal generation mix which is affected by controlling CO₂ can be derived. The proposed method is based on an optimization method known as simulated annealing. In the method, solutions in a generation mix problem are equivalent to state of a physical system, and the cost of a solution is equivalent to the energy of a state. The proposed method can easily accommodate not only CO₂ emissions but also many practical constraints of generation expansion planning, such as integer solutions of unit capacities, condition of existing units, and so on. Case studies with various annual load patterns (combinations of annual load factors and the shapes of annual load duration curve) are presented and discussed. Consequently, a general trend for selecting generation technologies that should be added to a power system is derived, i.e., a useful guideline for studying generation expansion planning under controlling CO₂ emissions can be provided.     

An algorithm for linguistic risk assessment of uncertain factors in generation planning

This paper presents an efficient computational algorithm for evaluating the risks of uncertain factors in generation planning. Uncertainty in a multiobjective risk assessment problem can be divided into (a) uncertainty of the possible conditions to be evaluated; (b) uncertainty in multiobjective decision making; and (c) uncertainty in risk which is obtained using imprecise information. The proposed method basically consists of two phases: (1) the aggregation of risks with respect to all of the objectives for each uncertain factor; and (2) the linguistic expression of the aggregated risk of each uncertain factor. In the first phase, uncertainty in possible conditions is treated as fuzziness in planning parameters, and uncertainty in multiobjective decision making is also treated as fuzziness in decision making. Furthermore, the statistical trend of risk with respect to an objective for each uncertain factor can be extracted from uncertain risks obtained using imprecise information. In this method, both classes of fuzziness and the statistical trend of risk for each uncertain factor can be integrated into the risk of each uncertain factor, and then the risks of each uncertain factor with respect to all the objectives can be aggregated by extension principle in fuzzy sets theory. In the second phase based on the linguistic approximation technique, the aggregated risk of each uncertain factor can be represented by natural language. The proposed method can realize an effective and flexible decision support for evaluating the risks of uncertain factors in generation planning. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.     

A study of robust generation mix under uncertain environments

In recent years, the trend has been toward factors (e.g., demand for load, energy cost) becoming more and more uncertain in power system expansion planning. Thus, it has become necessary to take this uncertainty into account in determining generation plans. In this paper, a robust generation plan is defined explicitly as a plan that ensures fixed performance (economy) despite deviations in these factors. In addition, a new formulation of the robust generation mix problem, whose purpose is to determine the most robust combination of plants in a target year, is proposed. It is generally difficult to solve the formulated problem with parameters. However, by analyzing a proposed formulation from the viewpoint of optimization, we show that the proposed formulation is transformed into the conventional optimization problem without parameters. The simulation results on a typical model show that the generation mix produced by the proposed formulation is more robust than that produced by the conventional formulation. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 141(1): 26–35, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.2009     

Proposal of a simplified method for integrating reduction of effective power of pumped storage into a generation system planning method

Pumped storage hydropower stations are extensively used as peaking and reserve power plants. The current trend in this area is to increase their power output relative to the capacity of reservoirs, which results in a shorter maximum duration of full‐power operation while availability of the energy required for the pumping becomes less evident. This increases the possibility of reducing the effective power of pumped storage units in the case of a tight supply–demand balance in the power system. Although many studies have examined a role of pumped storage in generation systems, few studies have explicitly dealt with the power reduction. This paper proposes a new linear programming model that incorporates the reduction of effective power of pumped storage. The proposed model makes it possible to easily incorporate the power reduction in computations by employing hypothetical daily load curves and dispatching loads to the curves. The model also includes LNG combined cycle power generation plants that are now being intensively implemented. The developed model is applied to a power system model. The sample study reveals that the reduction of effective power considerably varies from season to season and the reduction highly affects the optimal generation mix, that is, the optimal share of pumped storage. © 2001 Scripta Technica, Electr Eng Jpn 134(4): 50–61, 2001     

Analysis of possible introduction of PV systems considering output power fluctuations and battery technology,employing an optimal power generation mix model

This paper presents an evaluation of the impact of extensive introduction of photovoltaic (PV) systems and stationary battery technology into the optimal power generation mix in the Kanto and Kinki regions. The introduction of solar PV systems is expected to be extensively deployed in the Japanese household sector and utility companies in order to address the concerns of energy security and climate change. Considering this expected large‐scale deployment of PV systems in electric power systems, it is necessary to investigate the optimal power generation mix which is technologically capable of controlling and accommodating the intermittent output‐power fluctuations inherent in PV systems. Against this background, we develop both a solar photovoltaic power generation model and an optimal power generation mix model, including stationary battery technology, which can be used to explicitly analyze the impact of PV output fluctuations at a detailed time interval resolution such as 10 minutes for 365 consecutive days. Simulation results reveal that PV introduction does not necessarily increase battery technology due to the cost competitiveness of thermal power plants in the load‐following requirement caused by PV systems. Additionally, on the basis of sensitivity analysis on PV system cost, dramatic cost reduction proves to be indispensable for PV to supply bulk electricity similarly to thermal and nuclear power plants. © 2012 Wiley Periodicals, Inc. Electr Eng Jpn, 182(2): 9–19, 2013; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.22329     

火电厂厂级负荷优化分配系统研制

提出一种新颖的火电厂厂级负荷优化分配系统的设计与解决方案.采用OPC服务器与IEC608705-104远动通信方式替换了传统负荷分配系统方案中的硬接线,简化了系统硬件结构.同时软件上增加了机组负荷指令偏置调整和手动功能,分配方式更加多样.通过分析基于动态规划的多目标负荷优化分配算法,同时要求兼顾负荷响应快速性和电厂经济...     

发输电系统鲁棒优化规划研究综述与展望

随着电力系统不确定性增加,应用发输电系统鲁棒优化规划研究抵御不确定性极端场景已成为重要研究方法。首先从是否计及不确定因素概率分布特征角度,将鲁棒优化分为经典鲁棒优化和分布鲁棒优化,梳理了这2类鲁棒优化的数学模型和不确定集合特征。然后将现有的发输电系统经典鲁棒优化和分布鲁棒优化研究分为考虑节点注入功率不确定性、电源容量增长及成本不确定性、输电网络状态不确定性3个方面,提炼了发输电系统鲁棒优化规划的研究框架和局限性。最后展望了发输电系统鲁棒优化规划值得深入研究的问题,为发输电系统鲁棒优化规划后续研究提供思路和方向。     

水库发电优化调度模型的快速求解算法及应用

针对水库"以水定电"优化调度数学模型求解方法中,传统动态规划方法易陷入"维数灾",而现代智能算法搜索效率低且易陷入局部最优解等不足。本文基于水库在高水头运行时耗水率小的特点,提出了水电站水库"以水定电"调度模型的快速求解方法——浮子算法。该方法在水电站满足保证出力和最小下泄流量等要求的前提下,尽可能提高水库调度期的运行水位以达到直接寻找优化调度解的目的。通过案例分析并与离散微分动态规划(DDDP)方法求解结果比较,本算法结果与DDDP算法十分接近,计算时间约为后者的50%。与其他算法联合使用,可在提高优化效果的同时缩短模型的求解时间,为水电站水库"以水定电"调度计划的制定和快速决策提供技术支持。     

傅氏分析反步法直驱型海浪发电系统功率优化控制

为研究波浪发电系统最大波浪能捕获问题,根据浮子的水动力方程和永磁同步直线电机的状态方程,建立了振荡浮子直驱式波浪发电系统的数学模型。提出等效电路图法模拟浮子的水动力模型。针对随机海浪波,采用傅里叶分析法将波浪发电系统分解为多个频率的正弦子系统,分别构造最大功率传输条件实现最大波浪能捕获,优化了传统的控制方法。采用反步法设计控制律,构造Lyapunov函数跟踪发电机状态。仿真结果表明,采用反步法可实现发电机状态的紧密跟踪。在随机波的条件下,所提傅氏分析法对传统算法的优化效果明显,提高了波能转换效率,结合反步法控制发电机可实现最大波浪能捕获,具有一定的实用性。     

电力稳定控制系统低频低压减载的最优策略实现

对电力稳定控制系统中母线低频低压减载这一运行故障处理的控制策略进行了分析。以18条运行负荷线路为例,列出其最优策略数学模型。分别用穷举法、过切法、动态规划法加以实现,比较各自运算过程、计算分析次数以及特点和不足。结果显示:动态规划法相比于穷举法计算量少、计算结果丰富;相比于过切法,计算量没有大的增加且计算结果准确。     

动态规划和粒子群算法在水电站厂内经济运行中的应用比较研究

随着电站装机容量和机组台数的不断增加,利用动态规划求解水电站厂内经济运行问题,将面临"维数灾"和实效性问题.近些年,粒子群算法作为一种新型的群体智能优化方法,由于能够弥补动态规划计算时间长、内存占用量大等诸多不足,在水电站厂内经济运行等方面得到了广泛重视.现有文献,大多数从方法的应用角度探讨较多,但从替代动态规划的必然性和潜力方面探讨较少,鲜有实例分析.本文以百万级装机千瓦的乌江渡水电站为实例,深入分析与比较了粒子群算法与动态规划的优劣,认为粒子群算法是代替动态规划、求解装机规模庞大的巨型水电站厂内经济运行的有效方法.     

An optimal allocation of distributed generation and voltage control devices for voltage regulation considering renewable energy uncertainty

Distributed generation (DG) may result in voltage fluctuation by changing line flow and reactive power injection, especially DG that generates power from renewable energy resources. To cope with this problem, this paper proposes an optimization process to optimally regulate the system voltage profile to lie close to the desired values by using the adaptive Tabu search (ATS) algorithm. The system voltages will be regulated by using dispatchable DG and voltage control devices, i.e. voltage regulator and capacitor. Moreover, probabilistic load flow calculation by using Monte Carlo simulation is chosen to evaluate the uncertainty of DG powered by renewable energy resources. The number of switching operations of the voltage regulator and capacitor are also accounted for in the optimization constraints, as excessive frequent switching operations can damage these devices. The optimal sizes and locations of dispatchable DGs and capacitors are considered as the optimization variables. The proposed method is demonstrated in an IEEE 34‐bus distribution test system and a modified 21‐bus Provincial Electricity Authority (PEA) system (Thailand). © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

An efficient hybrid method for solving the optimal sitting and sizing problem of DG and shunt capacitor banks simultaneously based on imperialist competitive algorithm and genetic algorithm

Nowadays due to development of distribution systems and increase in electricity demand, the use of distributed generation (DG) sources and capacitors banks in parallel are increased. Determining the installation location and capacity are two significant factors affecting network loss reduction and improving network performance. This paper, proposes an efficient hybrid method based on Imperialist Competitive Algorithm (ICA) and genetic algorithm (GA) which can greatly envisaged with problems for optimal placement and sizing of DG sources and capacitor banks simultaneously. The objective function is power loss reduction, improving system voltage profile, increasing voltage stability index, load balancing and transmission and distribution relief capacity for both utilities and the customers.The proposed method is implemented on IEEE 33 bus and 69 bus radial distribution systems and the results are compared with GA/Particle swarm optimization (PSO) method. Test results show that the proposed method is more effective and has higher capability in finding optimum solutions.