Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility

Non-intrusive energy-management (NIEM) techniques are based on energy signatures. While such approaches lack transient energy signatures, the reliability and accuracy of recognition results cannot be determined. By using neural networks (NNs) in combination with turn-on transient energy analysis, this study attempts to identify load demands and improve recognition accuracy of NIEM results. Case studies are presented that apply various methods to compare training algorithms and classifiers in terms of artificial neural networks (ANN) due to various factors that determine whether a network is being used for pattern recognition. Additionally, in combination with electromagnetic transient program (EMTP) simulations, calculating the turn-on transient energy facilitate load can lead to identification and a significant improvement in the accuracy of NIEM results. Analysis results indicate that an NIEM system can effectively manage energy demands within economic dispatch for a cogeneration system and power utility. Additionally, a new method based on genetic algorithms (GAs) is used to develop a novel operational strategy of economic dispatch for a cogeneration system in a regulated market and approach the global optimum with typical environmental constraints for a cogeneration plant. Economic dispatch results indicate that the NIEM system based on energy demands can estimate accurately the energy contribution from the cogeneration system and power utility, and further reduce air pollution. Moreover, applying the NIEM system for economic dispatch can markedly reduce computational time and power costs.     

Optimal planning and economic evaluation of cogeneration system

Cogeneration plants, which simultaneously produce electricity and heat energy, have been introduced increasingly for commercial and domestic applications in Korea because of their energy efficiency. The optimal plant configuration of a specific commercial building can be determined by selecting the sizes and the number of cogeneration systems and the auxiliary equipment based on the annual demands of electricity, heating and cooling. In this study, a mixed-integer, linear programming, utilizing the branch and bound algorithm was used to obtain the optimal solution. Both the optimal configuration system equipment and the optimal operational mode were determined based on the annual cost method for the installation of a cogeneration system to a hospital and a group of apartments in Seoul, Korea. In addition, the economic evaluation for the optimal cogeneration system depending on the fuel tariff system was calculated. A short payback period and higher internal rate of return on the initial investment were found to be essential for the adoption of cogeneration plants to hospitals and apartments.     

Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey

Energy production is considered as one of the key indicators for economic development. It is vital to improve the renewable energy production for global sustainability, while leveraging the national resources. This study is contributing to the demonstration of using genetic algorithms (GA) in the development of future energy scenarios as well as to the strategic energy studies in Turkey. The forecasting model developed in this study uses forward feeding back-propagation (BP) method improved by GA. The proposed model is applied in the Turkish case. The test errors are shown to emphasize the positive difference between the proposed model and the classical BP model. The results highlight that there is strong evidence indicating that the government should reconsider their current energy strategies.     

考虑供热系统热储能特性的电-热综合系统随机优化调度模型研究

热电联产机组、热泵等装置的应用促进了电-热综合系统间的耦合关系,为风电的消纳提供了新途径。文章考虑了供热系统热储能动态特性,采用多场景法模拟风电出力不确定性,搭建了电-热综合能源系统随机优化调度模型。首先,针对供热管道传输时延动态特性,研究分析了其储热能力;其次,以电-热综合能源系统购能费用最低为目标函数,以热网约束、电网约束为约束条件,提出了综合系统能量最优化调度方案;最后,在IEEE33节点和6节点热网上进行算例分析,验证了模型的有效性。     

中央空调采用合同能源管理节能的应用实例

针对第四军医大学某门诊大楼中央空调系统,分析系统能耗偏大的主要原因,采用合同能源管理新机制,进行相应的节能改造。实践证明,改造后系统总能耗大幅度降低,具有显著的节能效果和经济效益。     

Analysis of total energy system based on solid oxide fuel cell for combined cooling and power applications

A total energy system (TES) incorporating a solid oxide fuel cell (SOFC) and an exhaust gas driven absorption chiller (AC) is presented to provide power, cooling and/or heating simultaneously. The purpose for using the absorption chiller is to recover the exhaust heat from the SOFC exhaust gas for enhancing the energy utilization efficiency of the TES. A steady-state mathematical model is developed to simulate the effects of different operating conditions of SOFC, such as the fuel utilization factor and average current density, on the performance of the TES by using the MATLAB softpackage. Parametric analysis shows that both electrical efficiency and total efficiency of the TES have maximum values with variation of the fuel utilization factor; while the cooling efficiency increases, the electrical efficiency and total efficiency decrease with increase in the current density of SOFC. The simulated results could provide useful knowledge for the design and optimization of the proposed total energy system.     

SOFC cogeneration system for building applications, part 2: System configuration and operating condition design

An SOFC (Solid Oxide Fuel Cell) cogeneration optimization study was carried out for one small-scale and one large-scale building under both hot and cold weather conditions. Several different configurations of the SOFC system are operated using a defined set of input parameters to meet the actual heating, cooling and electrical demands on those two buildings The results are discussed and compared from four different perspectives: electric-only vs. cogeneration, energetic vs. economic, large-scale vs. small-scale buildings and hot vs. cold weather conditions. The main conclusion of this study is that optimization results vary widely depending on different system configurations and loading conditions and thus SOFC systems should be optimized based on the specific conditions to which they are exposed and not simply on a single operating condition.     

Utilizing coke oven gases as a fuel for a cogeneration system based on high temperature proton exchange membrane fuel cell; energy,exergy and economic assessment

Based on a high temperature proton exchange membrane fuel cell (HT-PEMFC), a cogeneration system is proposed to produce heat and power. The system includes a coke oven gas steam reformer, a water gas shift reactor, and an afterburner. The system is analyzed in detail considering the energy, exergy and economic viewpoints. The analyses reveal the importance of HT-PEMFC in the system and according to the results, 9.03 kW power is generated with energy and exergy efficiencies of 88.2% and 26.2%, respectively and the total product unit cost is calculated as 91.8 $/GJ. Through a parametric study the effects on system performance are studied of such variables as the current density, fuel cell and reformer operating temperatures, and cathode stoichiometric ratio. It is found that an increase in the fuel cell temperature and/or a decrease in the reformer temperature enhance the exergy efficiency. The exergy efficiency is also maximized at the cathode stoichiometric ratio of 2.4. By performing a two-objective optimization using genetic algorithm, the best operating point is determined at which the exergy efficiency is (32.86%) and the total product unit cost is (78.68 $/GJ).     

A review on the economic dispatch and risk management of the large-scale plug-in electric vehicles (PHEVs)-penetrated power systems

Nowadays, the deterioration of ecological environment and the ever rising gas price make green transportation our relentless pursuit. Energy-saving, low-emission even zero-emission electric vehicles (EVs) have been considered as one solution to the problem. With the rapid development of plug-in electric vehicle (PHEV) and forceful support and incentives from the government, PHEV and its supporting facilities are being gradually popularized. When randomly being connected to the power grid in large scale, PHEVs will bring new challenges to power grid in operation and management. This paper presents an overall review on historical research on power system integrated with electric vehicles and especially focuses on economic dispatch of PHEV in the electricity market. The paper also discusses the joint scheduling problem considering other renewable energy resources and risk management of PHEV-penetrated power systems.     

A hierarchical management system for energy storage batteries

现有的储能电池管理系统大都是从电动汽车电池管理系统直接引用过来的,其管理的电池容量小,功能单一,实时性较差.兆瓦级储能系统由大容量电池串联,对电池系统管理效率提出了新要求.为解决这一问题,提出了一种3层分层式储能电池管理系统.对底层BMU,中层BCMS和顶层BAMS从硬件和软件设计两方面做了详细地介绍.分层式储能电池管理系统具有检测与计算,电池单体均衡管理,高压管理,统计存储,充放电管理,报警功能和通信.     

Optimal dispatch of multi energy system using power-to-gas technology considering flexible load on user side

The relation between power-to-gas technology (P2G) and energy interconnection becomes increasingly close. Meanwhile, the participation of flexible load on user side in system optimization has attracted much attention as an efficient approach to relieve the contradiction between energy supply and energy demand. Based on the concept of energy hub, according to its series characteristic, this paper established a generic multi-energy system model using the P2G technology. The characteristic of flexible load on user side was considered and optimal dispatch analysis was made, so as to reduce the cost, to reasonably dispatch the flexible load, to reduce the discharge, to enhance the new energy output, and to increase the power-to-gas conversion efficiency. Finally, a concrete analysis was made on the optimal dispatch result of the multi-energy system using the P2G technology considering flexible load on user side in the calculating example, and optimal dispatch of the system was verified via four different scenarios. The results indicate that cooperative dispatch of multi-energy system using the P2G technology considering flexible load on user side is the most economic, and can make a contribution to absorption of new energy and P2G conversion. In this way, environmental effects and safe and stable operation of the system can be guaranteed.     

Predictive energy management for a wind turbine with hybrid energy storage system

Hybrid energy storage systems (HESSs) help mitigating the fluctuations and variable availability of certain renewable sources, such as wind power, as they can provide support in different time scales. Therefore, regulating their state-of-charge (SOC) becomes crucial to ensure that the hybrid system complies with generation commitments agreed in time-ahead markets despite subsequent unexpected wind speed variations. So far, research has been mainly targeted at avoiding extreme SOC situations in the storage devices, whereas the regulation of this parameter to specific values has often been disregarded. A novel approach is proposed in this work, where model predictive control (MPC) is used to regulate the SOC of a HESS under variable wind and grid demand scenarios. The MPC-based supervisory controller developed for the hybrid system has been implemented and simulated under different situations. This controller monitors the future variation of the SOC with the aim of having the HESS available to develop its assigned functions successfully. The results show that a proper regulation of the SOC in the HESS increases the capacity to manage the active power supplied to the grid by the hybrid system based on wind power, as well as the level of compliance with generation commitments established time ahead.     

A novel evolutionary algorithm for dynamic economic dispatch with energy saving and emission reduction in power system integrated wind power

An optimization algorithm is proposed in this paper to solve the economic dispatch problem that includes wind farm using the Chaotic Quantum Genetic Algorithm (CQGA). In addition to the detailed models of economic dispatch introduction and their associated constraints, the wind power effect is also included in this paper. The chaotic quantum genetic algorithm used to solve the economic dispatch process and discussed with real scenarios used for the simulation tests. After comparing the proposed algorithm with several other algorithms commonly used to solve optimization problems, the results show that the proposed algorithm is able to find the optimal solution quickly and accurately (i.e. to obtain the minimum cost for power generation in the shortest time). At the end, the impact to the total cost savings for power generation after adding (or not adding) wind power generation is also discussed. The actual implementation results prove that the proposed algorithm is economical, fast and practical. They are quite valuable for further research.     

工业企业能源管理信息系统研究

针对工业企业能源管理信息系统(EEMIS)建设过程中缺乏能效诊断技术的问题,在传统信息系统和自动化系统的基础上,通过机理建模和机器学习相结合的方法,研究了重点用能设备、耗能工序和能量系统的能效分析模型,提出了一种新型的EEMIS的结构和功能,并开发了软件,弥补了EEMIS在能效分析诊断方面的不足,增强了系统的实用性。     

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency‐domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportional‐integral‐derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta‐heuristic algorithms.     

Squirrel search algorithm for economic dispatch with valve-point effects and multiple fuels

ABSTRACT

This paper bestows a new swarm intelligence approach, Squirrel Search Algorithm (SSA) to solve Economic Load Dispatch (ELD) of the thermal unit by addressing the valve point loading effects and multiple fuel options. SSA inspires the foraging behavior of squirrels which is based on dynamic jumping and gliding strategies. The main intention of the ELD problem is to minimize the total generation cost of units while assuring various system constraints. Renovate strategy and selection rules are used in the SSA algorithm to handle the constraints appropriately. The practicability of the proposed algorithm is tested on six different power test systems having different sizes and intricacies. Simulation results ascertain that the proposed SSA approach outperforms the other existing heuristic optimization techniques in terms of solution quality, robustness, and computational efficiency. Consequently, the proposed SSA can be an efficient approach for solving the ELD problems with valve point loading impacts and multi-fuel options.     

Internet of things and cloud computing‐based energy management system for demand side management in smart grid

A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal.     

A Li-ion battery management system for large-capacity energy storage

应用锂离子电池进行储能已成为大容量储能技术研究的重点,但为保证电池组的可靠性、安全性、一致性及使用寿命,必须设计电池管理系统来对锂离子电池进行有效管理。本文提出了一种适用于大容量储能技术的锂离子电池管理系统,该管理系统采用分层采集和管理的方法,分别对单体电池、电池组和储能子系统进行管理。文章详述了分层管理系统的结构、功能和管理策略,其中着重介绍了单体电池数据采集功能、电池状态估计功能和均衡管理功能,并进行了实验验证,给出了实验结果分析。实验结果证明了该管理系统可以满足实际的大容量储能应用需求,可以实现锂离子电池的高精度状态估计功能和高效均衡控制策略,具有很好的应用前景,为后续产业化发展提供了一种技术和思路。     

Energy management strategy and capacity optimization for CCHP system integrated with electric-thermal hybrid energy storage system

In order to improve the comprehensive energy utilization rate of combined cooling, heating, and power (CCHP) system, a hybrid energy storage system (HESS) is proposed in this paper consisting of electric and thermal energy storage systems. And the overall optimization design and operation of CCHP system with HESS are the main problems to be solved in application. Therefore, the topology and the energy flow model of CCHP system with HESS are established and analyzed according to the energy conversion characteristics of the component equipment. Moreover, combined with five evaluative restrictions for HESS system, a rule-based energy management strategy is designed to realize the decoupling regulation of electric energy and thermal energy in CCHP system. On this basis, a multi-objective optimization model is studied by taking the indicators of annual cost ratio, the primary energy consumption ratio, and loss energy ratio, and then the capacity parameters are optimized by particle swarm optimization algorithm (PSOA). Finally, a case is carried out to compare the energy allocation situations and capacity optimization results between CCHP system with HESS and CCHP system with single thermal energy storage system (ST). Results show that the capacity of ICE is reduced by 34%, and the annual cost and the primary energy consumption are saved about 7.69% and 18.47%, respectively, demonstrating that HESS has better optimization effect and applicable for small-scale CCHP system.     

A comprehensive review on energy management strategies of hybrid energy storage system for electric vehicles

The attention on green and clean technology innovations is highly demanded of a modern era. Transportation has seen a high rate of growth in today's cities. The conventional internal combustion engine‐operated vehicle liberates gasses like carbon dioxide, carbon monoxide, nitrogen oxides, hydrocarbons, and water, which result in the increased surface temperature of the earth. One of the optimum solutions to overcome fossil fuel degrading and global warming is electric vehicle. The challenging aspect in electric vehicle is its energy storage system. Many of the researchers mainly concentrate on the field of storage device cost reduction, its age increment, and energy densities' improvement. This paper explores an overview of an electric propulsion system composed of energy storage devices, power electronic converters, and electronic control unit. The battery with high‐energy density and ultracapacitor with high‐power density combination paves a way to overcome the challenges in energy storage system. This study aims at highlighting the various hybrid energy storage system configurations such as parallel passive, active, battery–UC, and UC–battery topologies. Finally, energy management control strategies, which are categorized in global optimization, are reviewed. Copyright © 2017 John Wiley & Sons, Ltd.