A study of a unit commitment problem with transmission constraints

Unit commitment problem is an optimization problem to determine the start‐up and shut‐down schedule of thermal units while satisfying various constraints, for example, generation‐demand balance, unit minimum up/down time, system reserve, and so on. Since this problem involves a large number of 0–1 type variables that represent up/down status of the unit and continuous variables expressing generation output, it is a difficult combinatorial optimization problem to solve. The study at present concerns the method for requiring the suboptimum solution efficiently. Unit commitment method widely used solves the problem without consideration of voltage, reactive power, and transmission constraints. In this paper, we will propose a solution of unit commitment with voltage and transmission constraints, based on the unit decommitment procedure (UDP) method, heuristic method, and optimal power flow (OPF). In this method, initial unit status will be determined from random numbers and the feasibility will be checked for minimum start‐up/shut‐down time and demand‐generation balance. If the solution is infeasible, the initial solution will be regenerated until a feasible solution can be found. Next, OPF is applied for each time period with the temporary unit status. Then, the units that have less contribution to the cost are detected and will be shut down based on the unit decommitment rules. This process will be repeated until suboptimal solution is obtained. The proposed method has been applied to the IEEE 118‐bus test system with 36 generating units with successful result. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(3): 36–45, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10187     

Stochastic unit commitment problem considering risk constraints and its improved GA‐based solution method

This paper proposes a model of the stochastic unit commitment (SUC) problem, which takes account of the uncertainty of electric power demand and its resulting risk, and its solution method based on an improved genetic algorithm (IGA). The uncertainty of electric power demand is modeled using a set of scenarios which are introduced by scenario analysis. The variance, which measures the dispersion of generation costs of unit commitment schedule under each scenario around the expected generation cost, is used as a measure of risk. Based on the expected returns–variance of returns (E–V) rule in the theory of portfolio analysis, a utility function is devised by appending the variance of the expected generation cost into the original expected generation cost function, with consideration of the risk attitude of the generation companies and power exchange centers. The objective of this optimization problem is to minimize the utility function. The proposed IGA is used to solve this NP‐hard optimization problem. Based on numerical examples, the superiority of the IGA‐based solution method is verified through comparison with a traditional GA‐based solution method. Optimal schedules of SUC, as well as the expected costs and variances, are compared with/without risk constraints, and with different risk attitudes. Test results show that, in solving the SUC problem, it is necessary to consider the electric power demand uncertainty and its resulting risk, as well as the risk attitude of the decision maker. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

A solution method of unit commitment by artificial neural networks

The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging     

A solution to unit commitment problem using fire works algorithm

This paper presents a new approach using swarm intelligence algorithm called Fireworks Algorithm applied to determine Unit Commitment and generation cost (UC) by considering prohibited operating zones. Inspired by the swarm behaviour of fireworks, an algorithm based on the explosion (search) process and the mechanisms of keeping the diversity of sparks has been developed to minimize the total generation cost over a given scheduled time period and to give the most cost-effective combination of generating units to meet forecasted load and reserve requirements, while adhering to generator and transmission constraints. The primary focus is to achieve better optimization while incorporating a large and often complicated set of constraints like generation limits, meeting the load demand, spinning reserves, minimum up/down time and including more realistic constraints, such as considering the restricted/prohibited operating zones of a generator. The generating units have certain ranges where operation is restricted based upon physical limitations of machine components or instability, e.g., due to steam valve or vibration in shaft bearings. Therefore, prohibited operating zones as a prominent constraint must be considered. In this paper the incorporating of complicated constraints of an optimization problem into the objective function is not considered by neglecting the penalty term. Numerical simulations have been carried out on 10 – unit 24 – hour system.     

A solution to the unit commitment problem considering LNG fuel constraints

In this paper, an algorithm is proposed for finding a quasi-optimal schedule for the short-term thermal unit commitment problem taking LNG fuel constraints into account. In recent years, LNG fuel has been used increasingly. As a result, LNG fuel constraints should be considered in making a unit commitment schedule. Generally, unit commitment is a nonlinear combinatorial problem including discrete variables. To solve the problem, a two-step algorithm is developed using mathematical programming methods. First a linear programming problem is solved to determine the amount of LNG fuel to be consumed by each LNG unit, then a Lagrangian relaxation approach is used to obtain a unit commitment schedule. This two-step algorithm simplifies the problem and thus has good convergence characteristics. To test the effectiveness of the proposed algorithm, a numerical simulation was carried out on a 46-unit thermal system over a 24-hour period. A result with a dual gap of 0.00546 was obtained. © 1998 Scripta Technica, Electr Eng Jpn, 125(3): 22–30, 1998     

用神经网络对风力发电中电力电子故障分析

随着风能资源的利用水平不断提高,风力发电系统中的电力电子装置使用也越来越多,其工作可靠性要求也越来越高。风力发电装置多位于野外,为减轻检修人员的工作负担,对风机运行中远方检测到的大量数据进行快速而有效的可视化分类及故障状态粗略判断,引入了一种用单层前向神经网络来对数据进行快速分类绘制故障非故障分界线的方法。通过该方法能够很好地根据实时数据判断风机电力电子装置的故障。     

基于神经网络的半直驱永磁同步风力发电机组有功功率控制

针对半直驱风力发电机组,提出了一种基于神经网络的有功功率控制方法,分别对变桨控制器和转速控制器进行设计,实现机组在额定功率控制模式和非额定功率的恒功率控制模式的有功功率控制.着重研究了转速控制算法,采用线性反馈化方法对系统模型进行处理,然后根据动态面控制算法设计转速控制器,并利用RBF神经网络逼近特性避免由于对具有非线性特性风力发电机组线性化而导致的模型不确定性问题.最后基于MATLAB/Simulink平台,将该功率控制方法应用于2 MW半直驱永磁同步风力发电机组,验证了所提方法的有效性.     

基于改进的逆序排序法的机组组合优化算法

文章提出了改进的逆序排序法来求解机组组合优化问题.该算法从可用机组全投入运行这一可行解出发,在每次迭代过程中优化一台机组在整个调度周期内的开停状况,以最小化总生产成本或总购电成本,直到连续两次迭代的目标函数值不再减小为止.该方法的显著优点在于计算不会振荡,迭代不会发散,且每次迭代的结果均为可行解.该算法在单机组优化过程中,以机组的最小启停区间而不是单个时段为研究调度对象,缓解了组合爆炸问题,明显地加快了计算速度.     

A new method for system optimization by neural networks

This paper proposes a new method of optimizing the operation of a power transmission system, based on a structured neural network. Because the structured neural network is built by prewiring, the network construction time is very short. Simulations of transmission loss of reduction have been carried out. It is shown that the optimization performance of the proposed method is satisfactory in practice. © 1997 Scripta Technica, Inc. Electr Eng Jpn. 118 (4): 27–34, 1997     

基于小波网络的水轮发电机组自适应控制研究

由于水轮发电机组的动力学模型比较复杂,其动态模型解析式难以精确得到,因此依据小波的非线性逼近能力和神经网络的自学习特性,提出了一种基于小波神经网络自适应控制算法。文中系统由两个小波网络组成,分别实现水轮发电机组的在线辨识与控制。仿真试验表明,该系统比采用神经网络控制具有更好的控制效果。     

A method for unit commitment with transmission losses and flow constraint

Researches on the unit commitment with transmission network have been reported recently. However, most of these researches mainly discussed the security constrained unit commitment, while the relationship between unit commitment and transmission losses was not considered. However, from the standpoint of operating reserve for ensuring power supply reliability, a unit commitment considering transmission losses is required. Further, under the deregulation and liberalization of the electric power industry, not only the line's security but also transmission losses are expected to play an important role in calculating the network access charge, and unit commitment taking into account transmission losses is also desired from this viewpoint. In this paper, a unit commitment approach with both transmission losses and line flow constraint is presented. Based on a heuristic iterative optimization method, first, an initial schedule is created by using a successively decommitting unit approach that is proposed in this paper. Then, we determine constraints included in the unit commitment schedule by a heuristic iterative optimization approach, in which an algorithm able to get rid of line overload by DC optimal power flow is developed. Through numerical simulations on two test power systems, the effectiveness of the proposed method is shown. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 142(4): 9–19, 2003; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10116     

鼠笼异步式风机的新型控制方法研究

针对目前鼠笼式异步风力发电机控制系统的缺点,提出了一种用于鼠笼式异步风力发电机组的新型控制方法,目的在于实现鼠笼式风机发电效率的最大化和系统成本的最小化。该控制方法的拓扑由电压源变流器(voltage source converter, VSC)、功率因数转换器(power factor correction converter, PFCC)和电容器组成,通过VSC为鼠笼式发电机提供励磁以实现最大功率跟踪控制,同时通过并联PFCC将发电机输出的全部有功功率传输至直流侧,使得VSC仅为发电机提供无功功率。并通过并联电容器补偿发电机所需无功功率以减小系统所需VSC的容量,从而最大程度地降低系统成本。同时,以提高机组效率为目的,通过分析确定了该机组的转差率和最佳电容器容量。在理论分析的基础上,基于Matlab软件对设计的控制系统进行仿真验证,仿真结果证明了该方法的可行性和控制方法的有效性。     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

内点-分支定界法在最优机组投入中的应用

机组投入是现代电力系统编制发电计划的重要优化任务,具有显著的经济效益。从数学上讲,机组投入问题是一个多约束的NP难组合优化问题,很难得到理论上的最优解。提出运用内点-分支定界法求解最优机组投入问题。该方法将机组投入的离散变量松弛为[0,1]区间上的连续变量,结合有功出力,进行优化。原始-对偶内点法收敛迅速、对初值不敏感,用来求解松弛问题,分支定界法用来处理离散变量。通过对2个算例的计算及与其它算法结果的比较,验证了该算法能得到更好的全局最优解。     

基于神经网络的电力电能自适应测量方法

详细介绍了基于神经网络的电力电能自适应测量方法，给出系统原理图及电力电能测量误差自动校正方法；首次提出了LEA判别法，从而实现了梯度牛顿法的有效结合神经网络学习方法；采用DSP技术实现了电能实时快速测量，给出了实验结果。     

A new artificial neural network based method for islanding detection of distributed generators

This paper presents an artificial neural network (ANN) based method for islanding detection of distributed synchronous generators. The proposed method takes advantage of ANN as pattern classifiers. It is capable of identifying the islanding condition based on samples of the voltage waveform measured at the distributed generator terminals only, which is an important advantage over other ANN-based anti-islanding methods. Moreover, the proposed method is robust against false operation. In order to create a training data set for the ANN, a data selection procedure has been proposed, so that the ANN could be trained more effectively, which has contributed positively to the good performance of the method. The concept of the time-performance region has been introduced to assess the method performance, as well as the non-detection zones. A detailed discussion about the data sampling rate to feed the proposed method has also been conducted, so that the computational burden can be faced as an important factor to assess its performance.     

Damping enhancement of multimachine power system using adaptive generator control system with neural networks

The authors proposed a nonlinear adaptive generator control system with neutral networks for improving damping of power systems, and showed its effectiveness in a one-machine infinite bus test power system in a previous paper. The proposed neurocontrol system adaptively generates appropriate supplementary control signals to the conventional controllers such as the automatic voltage regulator and speed governor so as to enhance transient stability and damping of the power system. In this paper, the applicability of the proposed neurocontrol system to multimachine power systems is discussed. Digital time simulations are carried out for a 4-machine test power system, where one or several synchronous generators is equipped with the neurocontrol system. As a result, also in the multimachine power system, the proposed adaptive neurocontrol systems improve the system damping effectively and they work adaptively against the wide changes of the operating conditions and the network configuration.     

规模化电动汽车和风电协同调度的机组组合问题研究

基于电动汽车通过集中控制器与电网交互的模式,考虑集中控制器所辖区域电动汽车负荷每个调度时段的可控特性,提出将集中控制器充电负荷作为机组组合模型的控制变量。通过蒙特卡洛抽样模拟电动汽车并网场景,计算集中控制器的可调度上限值和下限值,建立了规模化电动汽车与风电协同调度的机组组合模型。算例分析结果表明了应用提出的机组组合模型提高风电消纳能力和降低系统运行成本的有效性。     

适合于机组组合问题的扩展优先顺序法

针对传统PL（Priority List）方法采用单一排序指标,即平均满负荷费用AFLC（Average Full-Load Cost）不能全面反映机组优先顺序的不足,提出一种扩展优先顺序法EPL（Extended Priority List）解决机组组合问题。在分析PL方法特点的基础上,定义μ-Load Cost反映机组在不同出力范围内的经济指标,形成不同μ值的机组组合的邻域,而后定义机组的效用系数UUR（Unit Utilization Ratio）优化机组的优先顺序。此外,引入参数控制机组组合邻域的规模并采取策略对机组组合进行调整使其满足所有约束,从而提高计算效率。最后采用26机组、38机组以及45机组24时段等3个系统的测试结果来验证该方法的有效性。     

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg–Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)–(d).