Integrated evolutionary neural network approach with distributed computing for congestion management

Electric supply industry is facing deregulation all over the world. Under deregulated power supply scenario, power transmission congestion has become more intensified and recurrent, as compared to conventional regulated power system. Congestion may lead to violation of voltage or transmission capacity limits, thus threatens the power system security and reliability. Also the growing congestion may lead to unanticipated divergent electricity pricing. Owing to these facts congestion management has become a crucial issue in the deregulated power system scenario.Fast and precise prediction of nodal congestion prices in real time deregulated/spot power market may enable market participants and system operators to keep pace with the congestion by taking preventive measures like transaction rescheduling, bids (both for supplying and consuming electricity) modification, regulated dispatch of electric power, etc. This paper proposes an integrated evolutionary neural network (ENN) approach to predict nodal congestion prices (NCPs) for congestion management in spot power market. Distributed computing is employed to tackle the heterogeneity of the data in the prediction of NCP values. Developed ENNs have been trained and tested under distributed computing environment, using a message passing paradigm. Proposed hybrid approach for NCP prediction is demonstrated on a 6-bus test power system with and without distributed computing. The proposed approach not only demonstrated the computing efficiency of the developed ENN model over the conventional optimal power flow method but also shows the time saving aspect of distributed computing.     

Growing RBFNN-based soft computing approach for congestion management

In the emerging restructured power system, the congestion management (CM) has become extremely important in order to ensure the security and reliability of the system. In addition to this, lack of CM can impose a hindrance in electricity trading. This paper presents a novel, growing radial basis function neural network (GRBFNN)-based approach for CM. For achieving CM, Nodal congestion price (NCP) forecasting is performed in real time competitive power market. NCP forecasting is an effective way of price-based preventive CM as it directly indicates the presence as well as the severity of the congestion in the system. In present paper, GRBFNN has been developed for NCP forecasting dividing the whole power system into various congestion zones. An unsupervised learning vector quantization (VQ) clustering algorithm is applied as feature selection technique for the developed GRBFNN and for partitioning the power system into different congestion zones. For each congestion zone a separate neural network has been developed to ensure faster training and accurate forecasting results. The proposed approach of CM is implemented on an RTS 24-bus system. The results obtained are compared with a different constructive algorithm-based RBF network called as general regression neural network (GRNN) and two back-propagation algorithms based ANNs. Comparison results show that proposed GRBFNN is more computationally efficient with better predictive ability.     

可视化基站发电管理系统

针对以前的发电管理系统或只是数据呈现,提供市电及发电相关数据信息,或只是提供出车轨迹,主要实现对出车的管理,不能全面反应发电全过程且不便于对发电事件进行分析的情况,提出可视化发电管理系统的概念。在简要介绍了LBS系统、动力环境监控系统后,讲述了可视化基站发电管理系统的组成以及通过LBS定位管理、短信互动、实时监测数据等进行系统实现。可视化发电管理系统通过可视化的界面帮助发电管理人员进行管理,直观、便捷。     

Congestion management by determining optimal location of series FACTS devices using hybrid bacterial foraging and Nelder–Mead algorithm

In this paper, the Thyristor-Controlled Series-Compensated (TCSC) devices are located for congestion management in the power system by considering the non-smooth fuel cost function and penalty cost of emission. For this purpose, it is considered that the objective function of the proposed optimal power flow (OPF) problem is minimizing fuel and emission penalty cost of generators. A hybrid method that is the combination of the bacterial foraging (BF) algorithm with Nelder–Mead (NM) method (BF-NM) is employed to solve the OPF problems. The optimal location of the TCSC devices are then determined for congestion management. The size of the TCSC is obtained by using of the BF-NM algorithm to minimize the cost of generation, cost of emission, and cost of TCSC. The simulation results on IEEE 30-bus, modified IEEE 30-bus and IEEE 118-bus test system confirm the efficiency of the proposed method for finding the optimal location of the TCSC with non-smooth non-convex cost function and emission for congestion management in the power system. In addition, the results clearly show that a better solution can be achieved by using the proposed OPF problem in comparison with other intelligence methods.     

An approach to on-line power dispatch with ambient air pollution constraints

An on-line optimal environmental/economic dispatch methodology for electric power generation is developed in this paper. Aside from the conventional economic dispatch, constraints on air quality (such as those specified by the U.S. Environmental Protection Agency) are added to the minimum fuel cost problem. Using the integrated Gaussian puff model based on the statistical turbulent theory, rapid dynamic features of pollutant dispersion and its forecast surrounding the plants are emphasized. By applying a convex programming algorithm repeatedly, a set of marginal environmental imposts for the power plants at different times are obtained. Such imposts are incorporated with the fuel cost in the ordinary short-term economic dispatching program to indirectly account for the environmental impact of power generation on the quality of the ambient air. The approach is specifically taken to have little modification for existing economic dispatch programs and be implemented for real power networks. The proposed approach has been simulated in a power system with three plants and three monitoring points.     

基于组态软件的电力负荷监控系统的设计

为做好电力负荷管理,实现科学用电,本文提出一种在组态软件开发平台下配电监控系统,实现电力负荷参数实时采集及控制系统。利用工控机、电能仪表、485总线等设备构成硬件系统,利用工业组态软件平台开发监控系统软件,设计了配电所微机监控集散控制系统功能,实现了电力负荷参数记录、实时监控、超限报警、负荷历史曲线、报表、事件查询等功能,对用电单位做好负荷控制,提高电力运行的经济性、电力的综合管理水平等方面发挥了重要的作用。     

A novel symbiotic organisms search algorithm for congestion management in deregulated environment

Abstract

In today’s competitive electricity market, managing transmission congestion in deregulated power system has created challenges for independent system operators to operate the transmission lines reliably within the limits. This paper proposes a new meta-heuristic algorithm, called as symbiotic organisms search (SOS) algorithm, for congestion management (CM) problem in pool based electricity market by real power rescheduling of generators. Inspired by interactions among organisms in ecosystem, SOS algorithm is a recent population based algorithm which does not require any algorithm specific control parameters unlike other algorithms. Various security constraints such as load bus voltage and line loading are taken into account while dealing with the CM problem. In this paper, the proposed SOS algorithm is applied on modified IEEE 30- and 57-bus test power system for the solution of CM problem. The results, thus, obtained are compared to those reported in the recent state-of-the-art literature. The efficacy of the proposed SOS algorithm for obtaining the higher quality solution is also established.     

自然能供电的照明系统优化设计

针对在自然能发电系统的设计中合理设计能源组合形式,并进行容量计算,以及合理配置机组容量的问题,进行了研究和探讨。系统利用风力发电和太阳能发电两种方式获得电能,采取蓄电池储存富余电能,通过逆变器实现输出供电,以解决风电、光电资源较丰富区的商业楼宇照明供电。为得到合理的风/光发电设计比例,使系统成本最小,发电效率最高,在设计中根据系统所在地理位置及技术要求进行了发电系统的优化设计。实验证明,该系统组合方案较经济、合理。     

基于OPC和Winsock API的远程数据采集系统设计

在现代化区域发电企业经营中,将分布在不同地理位置、不同发电机组的DCS的实时数据上传到区域发电企业的生产调度管理系统中,为企业提供发电机组的实时信息监视,及时掌握发电机组的运行状态,这对提高企业管理水平十分重要。本文简介了OPC和Winsock API技术标准,并在满足《电力二次系统安全防护规定》原则下,设计了基于OPC和Winsock API技术的通用远程数据采集系统,并在实际中得到了应用。     

电力通信中的信息安全研究

随着电力自动化通信技术的快速发展,对网络信息安全的要求也不断提高。首先对DES和RSA两类典型加密算法进行了分析,然后设计了电力通信系统的密匙生成和管理方案,并对加密方案的性能进行了分析,其结果表明该方案能够有效地提高系统的信息安全性能。     

基于BMS与云平台的动力电池健康管理体系

为解决电动汽车现有BMS系统对锂离子动力电池SOH评估与预测难以满足多种工况条件、各种类动力电池,且难同时兼顾预测精度与反馈速度等应用缺陷,提出了一套全新的EVs电池健康管理系统设计思路,采用了结合云计算与存储平台,融入BMS评估体系等关键方法；通过BMS增加5G通讯模块,利用5G/4G信号实时上传电芯数据,经过云平台搭载的多种SOH评估模型与算法,多线程在线计算得到预测结果,及时反馈至用户端和BMS,实现电池健康管理；该体系的设计案例展示出较好的未来应用价值,为电动汽车电池管理设计提供了新方向。     

Unit commitment considering generator outages through a mixed-integer particle swarm optimization algorithm

The optimum economic operation and planning of electric power generation systems occupies a crucial position in the electric power industry. Unit commitment (UC) is an important function in generation resource management. Moreover, it is nowadays critical to incorporate reliability analysis of the power system into its design of operation strategy. For this purpose, equipment malfunction or failure should be considered in unit commitment. In this paper, first the model for UC considering generator outages is formulated, where the reliability requirement is incorporated into the spinning reserve constraint in the optimization design. Then, a mixed binary- and real-coded particle swarm optimization (PSO) is developed to locate the optimum generation combination. A 10-generator test power system is used to verify the effectiveness of the proposed approach along the 24-h planning horizon. A comparative study is conducted to examine the impact of reliability constraint on the optimal solution obtained. Furthermore, comparison is made between the proposed method and other methods including both analytical and meta-heuristic algorithms.     

供电关口电能量采集与计费自动化管理系统的设计与实现

以供电关口电能量采集与计费自动化管理系统为对象,详细论述了研发系统利用的技术:面向对象编程技术,OLE技术,预测技术,数据库技术和Web技术.本系统是完成电费核算,负荷预测,电量统计分析的电力管理系统,着重研究了基于历史数据预测未来用电量的一元线性回归模型与算法及付之实现使用,实现了各种电量数据的Web查询浏览并完成线损电量、网损电量等的统计分析与计算.     

电网自动化中的高可靠分布式任务调度算法

电网自动化系统实现了电网变电站的现代化管理，是确保电力系统安全、稳定、可靠运行的重要手段，本文提出了针对电网自动化调度监控中心的分布式结构模型，并在此基础上设计和实现了两种高可靠的分布式任务调度算法－－ADS算法和LFS算法，对系统性能的测试结果表明，采用本文的分布式结构和算法可保证电网自动化系统的实时性和高可靠性。     

智能马达控制器在济钢余热发电的应用

发电机组的高效运行由其配套电动机稳定运行所决定,电动机运行状态的实时监控以及主回路电气元件的预知预修和周期性管理是电动机稳定运行的保障,济钢烧结机余热发电系统电动机采用MACH智能马达控制器进行控制、保护,对发电系统的高效运行起到了十分重要的作用.     

一种新型的船舶能量管理系统设计研究

随着综合电力系统技术及全电力船舶的发展,船舶能量管理系统的应用逐渐成为未来船舶发展的必然趋势。本文针对一类新型船舶能量管理系统研制过程中主要技术问题进行研究。能量管理系统信息网络采用硬线直连、双冗余现场总线和双冗余以太网混合的网络构架,以保证控制的可靠性;软件系统基于松散耦合的分层架构构建,利用动态耦合组件技术实现软件接口,建立船舶能量管理系统公用信息模型,利用实时数据缓存技术来构建能量管理实时数据库。基于这些关键技术研发的能量管理系统保证了全电力船舶网络化监控的实时性、有效性和可靠性。     

Modified neo-fuzzy neuron-based approach for economic and environmental optimal power dispatch

At the central energy management center in a power system, the real time controls continuously track the load changes and endeavor to match the total power demand with total generation in such a manner that the operating cost is minimized while all the operating constraints are satisfied. However, due to the strict government regulations on environmental protection, operation at minimum cost is no longer the only criterion for dispatching electrical power. The idea behind the environmentally constrained economic dispatch formulation is to estimate the optimal generation schedule of generating units in such a manner that fuel cost and harmful emission levels are both simultaneously minimized for a given load demand. Conventional optimization techniques become very time consuming and computationally extensive for such complex optimization tasks. These methods are hence not suitable for on-line use. Neural networks and fuzzy systems can be trained to generate accurate relations among variables in complex non-linear dynamical environment, as both are model-free estimators. The existing synergy between these two fields has been exploited in this paper for solving the economic and environmental dispatch problem on-line. A multi-output modified neo-fuzzy neuron (NFN), capable of real time training is proposed for economic and environmental power generation allocation.This model is found to achieve accurate results and the training is observed to be faster than other popular neural networks. The proposed method has been tested on medium-sized sample power systems with three and six generating units and found to be suitable for on-line combined environmental economic dispatch (CEED).     

东北电网调度自动化系统的研究

东北电网调度自动化系统包括CC 2 0 0 0实时调度自动化系统、D2 0 0 0电能量自动采集系统、信息管理系统和建立在这三套系统之上的联络线关口调度技术支持系统     

风力发电与电动汽车充放电协同优化调度策略

大规模电动汽车无序充电以及风力发电在电网中的渗透率不断提高,给电力系统带来安全经济运行问题。在考虑电动汽车电池容量约束、充放电功率约束以及24 h的电动汽车运行行为特性基础上,建立了风力发电及电动汽车负荷平抑、降低电动汽车充放电费用和负荷峰谷差率的多目标协调优化调度模型;采用传统遗传算法和自适应非线性遗传算法对所建模型进行求解。仿真结果验证了模型的合理性以及算法的正确性。     

电动汽车复合储能系统能量管理策略及其 快速控制原型验证

纯电动汽车储能系统需同时满足高功率密度与高能量密度的要求,但现阶段单一储能单元往 往难以同时具备这两种特点。将高能量密度的锂电池与高功率密度的超级电容进行合理搭配,形成复 合储能系统,是解决以上问题的一个有效方案。该文以宝马 I3 纯电动汽车作为目标车型,设计了锂电 池/超级电容复合储能系统,并制定了一种基于规则的能量管理策略,综合考虑了外部工况要求、锂电 池与超级电容的荷电状态,自动规划工作模式,充分发挥各储能单元自身优势,在极端状况下可自动 启动保护模式;同时,基于快速控制原型的思想,设计搭建了以 dSPACE 为控制中心的复合储能系统 能量管理策略快速控制验证平台,搭配可编辑电力参数的外部电子负载设备,完成了能量管理策略的 半实物实验验证。实验结果表明,电动汽车锂电池/超级电容复合储能系统搭配合理的能量管理策略, 能够充分发挥锂电池的能量特性与超级电容的功率特性,更好地满足了现代纯电动汽车对续航里程与 动力性能的要求,同时可节约能源,在一定程度上起到延长储能系统使用周期的作用。