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     

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。     

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 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     

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。     

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。     

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.     

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.     

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).     

Active power filter for three-phase four-wire electric systems using neural networks

This paper presents the design of neural networks compared with the conventional technique, a hysteresis controller for active power filter for three-phase four-wire electric system. A particular three-layer neural network structure is studied in some detail. Simulation and experimental results of the active power filter with both controllers are also presented to verify the feasibility of such controller. The simulation and experimental result show that both controller techniques can reduce harmonics in three-phase four-wire electric systems drawn by nonlinear loads and can reduce neutral current. The advantage of the neural network controller technique over hysteresis controller technique are less voltage ripple of d.c. bus, and less switching loss. Furthermore, the neural networks controller has better fault tolerance than the hysteresis controller.     

Learning in dynamic neural networks using signal flow graphs

The paper presents the universal approach to the determination of the sensitivity functions for dynamic neural networks and its application in learning algorithms of adaptive networks. The method is based on the application of signal flow graph and specially defined graph adjoint to it. The method is equally applied to either feed‐forward or recurrent network structures. This paper is mainly concerned with neural network applications of the approach. Different kinds of dynamic neural networks are considered and discussed in the paper: the FIR dynamic multilayer perceptron (MLP), the cascade connection of dynamic MLPs as well as two non‐linear recurrent systems: the dynamic recurrent MLP network and ARMA recurrent network. The rule of sensitivity determination has been applied in practical learning of neural networks. Chosen results of numerical experiments concerning the application of this approach to the learning processes of recurrent neural networks are also given and discussed. Copyright © 1999 John Wiley & Sons, Ltd.     

基于神经网络的非线性最优滑动模态设计

针对具有简约形式的非线性系统,提出了神经网络最优滑动模态控制策略。首先基于最优控制理论设计变结构控制的滑动模态,然后利用多层神经网络给出了变结构控制切换函数的设计方法,并依此设计控制律,使得所设计的变结构控制系统具有最优滑动模态。     

Learning of symbiotic phenomena between agents by using neural networks

Recently, numerous attempts have been made by researchers to understand the essence of complex phenomena (complex systems). In this paper, we consider biological systems in nature as being among the most complex systems. The purpose of this paper is to propose a method of realizing symbiotic phenomena such as mutual benefit, competition, and exploitation more generally than the Lotka–Volterra equation by using neural networks. © 2002 Wiley Periodicals, Inc. Electr Eng Jpn, 140(1): 77–88, 2002; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.1172 Copyright © 2002 Scripta Technica     

人工神经网络在锂离子动力电池管理中的应用

电动汽车的使用对锂离子动力电池组管理系统提出了更高的要求。在详细分析了人工神经网络理论的基础上,介绍了一种锂离子电池组管理系统的实施方案,对现有的电池管理系统在理论上进行了创新设计。人工神经网络技术简单直观,是研究锂离子动力电池的有力工具。     

Transmission lines distance protection using artificial neural networks

This work presents the development and implementation of an artificial neural network based algorithm for transmission lines distance protection. This algorithm was developed to be used in any transmission line regardless of its configuration or voltage level. The described ANN-based algorithm does not need any topology adaptation or ANN parameters adjustment when applied to different electrical systems. This feature makes this solution unique since all ANN-based solutions presented until now were developed for particular transmission lines, which means that those solutions cannot be implemented in commercial relays.     

Transient performance of a single-phase self-regulated self-excited induction generator using a three-phase machine

This paper presents the transient behavior of a three-phase star connected self-excited induction generator (SEIG) using three capacitors connected in series and parallel with a single-phase load. The voltage regulation of this generator is very small due to the effect of three capacitors. The dynamic model of the above generator has been developed based on stationary reference frame d–q-axes theory incorporating the effect of cross-saturation. The steady-state model of the scheme has also been developed. The simulated results of both the transient analysis for the different dynamic conditions, such as initiation of self-excitation, load perturbation and short-circuit and the steady-state analysis are compared with the experimental results. Both the simulated and experimental results are in close agreement with each other.