An investigation of optimum PV and wind energy system capacities for alternate short and long‐term energy storage sizing methodologies

The goal of this study is to find the optimal sizes of renewable energy systems (RES) based on photovoltaic (PV) and/or wind systems for three energy storage system (ESS) scenarios in a micro‐grid; (1) with pumped hydro storage (PHS) as a long‐term ESS, (2) with batteries as a short‐term ESS, and (3) without ESS. The PV and wind sizes are optimally determined to accomplish the maximum annual RES fraction (F_RES ) with electricity cost lower than or equal to the utility tariff. Furthermore, the effect of the use of battery and PHS on the electricity cost and F_RES are studied. A university campus on a Mediterranean island is selected as a case study. The results show that PV‐wind hybrid system of 8 MW wind and 4.2 MW PV with 89.5 MWh PHS has the highest F_RES of 88.0%, and the highest demand supply fraction as 42.6%. Moreover, the results indicate that the economic and technical parameters of RESs are affected significantly by the use of ESSs depending on the type and the capacity of both the RES and the ESS.     

Modelling electricity storage systems management under the influence of demand‐side management programmes

Electricity storage systems (ESS) for bulk energy storage are principally used for load levelling purposes or for relieving the intermittency of renewables. Another use is electricity arbitrage through the rule of ‘buy low, sell high’. This operation tracks the market‐clearing price (MCP) profiles and produces profit by exploiting the differences between peak and off‐peak prices. The profits made in this way depend on technology characteristics and the market competition level. We investigate the influence of demand‐side management (DSM) on ESS profitability when the only income is from provision of electricity arbitrage services, by optimizing the time allocation of the charge and discharge operations. Two scenarios of DSM in the market have been selected for two management periods (MP): 1 day and 3 days. The longer MP is examined in order to investigate the potential for higher economic value when energy transfer to the next day is permitted. The key finding is that a very small load shifting from peaks to off‐peaks, due to DSM, significantly affects the ESS profit. The significant profit losses the ESS showed are a result of the high capital costs and the small difference of the peak and off‐peak electricity prices in the Greek market. Therefore, under the assumptions we have made for this research, any attempt to use ESS in ‘buy low, sell high’ operation is not profitable. Copyright © 2008 John Wiley & Sons, Ltd.     

Multistage generation expansion planning incorporating large scale energy storage systems and environmental pollution

This paper addresses a multistage electricity generation expansion planning (GEP) incorporating large-scale energy storage systems (ESSs). The proposed coordinated GEP-ESS planning aims at minimizing the planning cost and environmental pollution at the same time, while it considers large-scale ESSs. Problem is expressed as a mixed-integer nonlinear programming and solved using PSO algorithm. Problem is solved subject to practical constraints of the network. ESS capacities are installed to support peak load level and reducing planning cost and environmental pollution. A typical test system including several existing and candidate generating units is considered to evaluate the proposed methodology. ESSs with various capacities are considered as candidate ESSs. Considering a large number of generating units and ESSs capacities increases the flexibility of the planning. Simulation results demonstrate that utilizing ESSs significantly reduces GEP cost as well as decreases the environmental pollution.     

A comprehensive state‐of‐the‐art survey on power generation expansion planning with intermittent renewable energy source and energy storage

Generation expansion planning (GEP) is a power plant mix problem that identifies what, where, when, and how new generating facilities should be installed and when old units be retired over a specific planning horizon. GEP ensures that the quantity of electricity generated matches the electricity demand throughout the planning horizon. This kind of planning is of importance because most production and service delivery is dependent on availability of electricity. Over the years, the traditional GEP approaches have evolved to produce more realistic models and new solution algorithms. For example, with the agitation for green environment, the inclusion of renewable energy plants and energy storage in the traditional GEP model is gradually gaining attention. In this regards, a handful of research has been conducted to identify the optimal expansion plans based on various energy‐related perspectives. The appraisal and classification of studies under these topics are necessary to provide insights for further works in GEP studies. This article therefore presents a comprehensive up‐to‐date review of GEP studies. Result from the survey shows that the integration of demand side management, energy storage systems (ESSs), and short‐term operational characteristics of power plants in GEP models can significantly improve flexibility of power system networks and cause a change in energy production and the optimal capacity mix. Furthermore, this article was able to identify that to effectively integrate ESS into the generation expansion plan, a high temporal resolution dimension is essential. It also provides a policy discussion with regard to the implementation of GEP. This survey provides a broad background to explore new research areas in order to improve the presently available GEP models.     

Smart residential energy management system for demand response in buildings with energy storage devices

In the present scenario, the utilities are focusing on smart grid technologies to achieve reliable and profitable grid operation. Demand side management (DSM) is one of such smart grid technologies which motivate end users to actively participate in the electricity market by providing incentives. Consumers are expected to respond (demand response (DR)) in various ways to attain these benefits. Nowadays, residential consumers are interested in energy storage devices such as battery to reduce power consumption from the utility during peak intervals. In this paper, the use of a smart residential energy management system (SREMS) is demonstrated at the consumer premises to reduce the total electricity bill by optimally time scheduling the operation of household appliances. Further, the SREMS effectively utilizes the battery by scheduling the mode of operation of the battery (charging/floating/discharging) and the amount of power exchange from the battery while considering the variations in consumer demand and utility parameters such as electricity price and consumer consumption limit (CCL). The SREMS framework is implemented in Matlab and the case study results show significant yields for the end user.     

Battery control for leveling the amount of electricity purchase in smart‐energy houses

An optimal operation method in smart‐energy houses with photovoltaics (PV) and a storage battery was investigated in a trial production system. In this method, the inverse current of the PV output is not conveyed to the commercial electricity system as operation conditions. Instead, the excess of the consumed PV power is applied to leveling the electricity purchase by appropriately charging and discharging the storage battery. To validate the proposed system, a lithium battery (4 kWh) and PV cell (3 kW) used in average individual houses was installed in a smart‐energy house in a local city (Kitami) in Japan. Another example was introduced into a wider area (Hokkaido, Japan). Accounting for the error between the weather forecast and actual solar radiation, the trial production system reduced the range in the electricity purchase amount by 75.0%, 77.0%, and 73.0% on a representative day in January, April, and July, respectively. The accuracy of the reduction effect in the trial production system, obtained in the proposed optimization analysis, ranged from 1.9% to 7.2%. Moreover, the CO₂ emissions were reduced by 1.990 kg‐CO₂/(Day‐House) in January, 2.910 kg‐CO₂/(Day‐House) in April, and 2.210 kg‐CO₂/(Day‐House) in July.     

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.     

智能电网下多时间尺度家庭能量管理优化策略

建立负荷在功率约束与需求响应约束下的激励需求响应模型以及含分布式电源、储能与电动汽车的家庭用电模型,在预测模型多时间尺度能量管理的基础上,以最小化用户自身用电费用与买电功率波动的两层目标函数实时优化调整策略。通过实时调整储电池、电动汽车的充放电,从而保证用户购电满足需求相应的要求。最后采用改进的粒子群算法对多时间尺度目标函数进行求解,并且与原始的粒子群算法进行对比,结果表明所提算法可显著降低用户的用电费用与功率波动。     

Optimal operations for hydrogen-based energy storage systems in wind farms via model predictive control

Efficient energy production and consumption are fundamental points for reducing carbon emissions that influence climate change. Alternative resources, such as renewable energy sources (RESs), used in electricity grids, could reduce the environmental impact. Since RESs are inherently unreliable, during the last decades the scientific community addressed research efforts to their integration with the main grid by means of properly designed energy storage systems (ESSs). In order to highlight the best performance from these hybrid systems, proper design and operations are essential. The purpose of this paper is to present a so-called model predictive controller (MPC) for the optimal operations of grid-connected wind farms with hydrogen-based ESSs and local loads. Such MPC has been designed to take into account the operating and economical costs of the ESS, the local load demand and the participation to the electricity market, and further it enforces the fulfillment of the physical and the system's dynamics constraints. The dynamics of the hydrogen-based ESS have been modeled by means of the mixed-logic dynamic (MLD) framework in order to capture different behaviors according to the possible operating modes. The purpose is to provide a controller able to cope both with all the main physical and operating constraints of a hydrogen-based storage system, including the switching among different modes such as ON, OFF, STAND-BY and, at the same time, reduce the management costs and increase the equipment lifesaving. The case study for this paper is a plant under development in the north Norway. Numerical analysis on the related plant data shows the effectiveness of the proposed strategy, which manages the plant and commits the equipment so as to preserve the given constraints and save them from unnecessary commutation cycles.     

A generic demand‐side management model for smart grid

The demand‐side management (DSM) is one of the most important aspects in future smart grids: towards electricity generation cost by minimizing the expensive thermal peak power plants. The DSM greatly affects the individual users' cost and per unit cost. The main objective of this research article is to develop a generic demand‐side management (G‐DSM) model for residential users to reduce peak‐to‐average ratio (PAR), total energy cost, and waiting time of appliances (WTA) along with fast execution of the proposed algorithm. We propose a system architecture and mathematical formulation for total energy cost minimization, PAR reduction, and WTA. The G‐DSM model is based on genetic algorithm (GA) for appliances scheduling and considers 20 users having a combination of appliances with different operational characteristics. Simulation results show the effectiveness of G‐DSM model for both single and multiple user scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

Avoided demand charges and behind‐the‐meter wind: insights from an application at the University of Minnesota

One common ownership structure for community‐scale wind development in the USA is a behind‐the‐meter installation. In addition to allowing the displacement of retail energy, such installations may also affect peak demand, which is frequently an important component of electricity tariffs (via ‘capacity’ or ‘demand’ charges). This paper uses Monte Carlo simulation techniques on original wind and load data for the University of Minnesota at Morris in order to estimate the savings associated with lower peak demand, as a result of the installation of a 1.65‐MW turbine in 2005. Results represent the first (to our knowledge) quantitative effort to estimate this aspect of the economics of wind power projects, and they suggest these previously ignored savings comprise nearly 10% of this project's gross projected revenue stream, even though the local utility's demand charge in this case is only 63% of the industry average. Copyright © 2008 John Wiley & Sons, Ltd.     

A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems

Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances.     

A price‐regulated electric vehicle charge‐discharge strategy for G2V,V2H,and V2G

Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future.     

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 centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

In order to accommodate additional plug‐in electric vehicle (PEV) charging loads for existing distribution power grids, the vehicle‐to‐grid (V2G) technology has been regarded as a cost‐effective solution. Nevertheless, it can hardly scale up to large PEVs fleet coordination due to the computational complexity issue. In this paper, a centralized V2G scheme with distributed computing capability engaging internet of smart charging points (ISCP) is proposed. Within ISCP, each smart charging point equips a computing unit and does not upload PEV sensitive information to the energy coordinator, to protect PEV users’ privacy. Particularly, the computational complexity can be decreased dramatically by employing distributed computing, viz., by decomposing the overall scheduling problem into many manageable sub‐problems. Moreover, six typical V2G scenarios are analyzed deliberately, and based on that, a load peak‐shaving and valley‐filling scheduling algorithm is built up. The proposed algorithm can be conducted in real‐time to mitigate the uncertainties in arrival time, departure time, and energy demand. Finally, the proposed scheme and its algorithm are verified under the distribution grid of the SUSTech campus (China). Compared with uncoordinated charging, the proposed scheme realizes load peak‐shaving and valley‐filling by 11.98% and 12.68%, respectively. The voltage values are ensured within the limitation range by engaging power flow calculation, in which the minimum voltage values are increasing and the maximum voltage values are decreasing with the expansion of PEV penetration. What is more, the computational complexity of peak‐shaving and valley‐filling strategy is near‐linear, which verifies the proposed scheme can be carried out very efficiently.     

智能电网技术在智能小区中的应用研究

智能小区体现了智能用电技术先进性、经济性、友好开放的特征,能够实现用户与电力部门之间实时交互响应,是未来发展的重要方向。根据《智能用电小区建设导则》的技术要求,充分考虑智能小区的技术试点、前瞻探索、窗口展示、产业对接等需求的前提下,提出了一种智能小区设计方案。详细分析了应用在智能小区的用电信息采集、配电自动化、分布式电源运行控制、智能用电服务互动、智能家居、统一展示平台等智能电网技术。     

The effects of vehicle‐to‐grid systems on wind power integration

Renewable energy portfolio standards have created a large increase in the amount of renewable electricity production, and one technology that has benefited greatly from these standards is wind power. The uncertainty inherent in wind electricity production dictates that additional amounts of conventional generation resources be kept in reserve, should wind electricity output suddenly dip. The introduction of plug‐in hybrid electric vehicles into the transportation fleet presents an possible solution to this problem through the concept of vehicle‐to‐grid power. The ability of vehicle‐to‐grid power systems to help solve the variability and uncertainty issuess in systems with large amounts of wind power capacity is examined through a multiparadigm simulation model. The problem is examined from the perspectives of three different stakeholders: policy makers, the electricity system operator and plug‐in hybrid electric vehicle owners. Additionally, a preliminary economic analysis of the technology is performed, and a comparison made with generation technologies that perform similar functions. Copyright © 2011 John Wiley & Sons, Ltd.     

从标准看智能电网的发展

目前世界各国在什么是智能电网、如何推进智能电网的发展等方面并没有达成一致意见。美国设想的未来电力系统是一个完全自动化的电力传输网络,能够保证从电厂到终端用户整个输配电过程中所有节点之间的信息和电能的双向流动。欧洲智能电网技术平台的目标是提高输配电系统的效率、安全性和可靠性,消除大规模集成配网与可再生能源的障碍。日本将主要以大规模开发太阳能等新能源、确保电网系统稳定作为智能电网建设的主要思路。结合中国的实际情况,我国智能电网业务框架应包含发电、输变电、配电、用户、运行、服务提供者、电力市场及统一信息平台等8个领域。现有标准与智能电网之间存在差距,主要体现在需求响应和电力市场、广域状态测量、电力存储、电力传输、AMI系统、配网管理等6个方面。其中,一部分差距已经有了清晰的发展方向和解决思路;而另一部分的具体发展方向和解决思路尚不明确。智能电网相关标准的开发需要以具体项目实施为载体,标准体系是否完备需要通过具体项目检验和修订,同时具体项目的顺利实施也有赖于标准体系的约束和规范。针对中国某省级电网的特殊性和典型性,建议其智能电网的发展,一是应重点关注标准研究方向,二是由此引出的示范工程项目。     

A new ship energy management algorithm to the smart electricity grid system

In this paper, a new energy management algorithm has been suggested for the ships connected with alternative energies considering the smart electricity grid features. The algorithm focuses on the use of optimum energy source on the shipboard based on the priorities of authorities such as the most economic, environmental, or both criteria at the ports. The algorithm is performed in MATLAB, and several case studies are simulated to validate the results. The 5 maritime nations, which are at different regions: United States, Belgium, Turkey, China, and Australia, are taken into account in the case studies. The actual data of a bulk carrier ship have been used to achieve tangible results in the simulations. The results of the case studies are compared to determine the changes of energy cost and released emission to meet demand of electricity on the ships. Capital cost of the proposed concept is also given to make an economic evaluation. The results show that the ship energy management algorithm provides the significant economic and environmental advantages. This paper aims to contribute to the importance of the ships in the smart electricity grid concept for the maritime and electricity sector representatives.     

A cryogen‐based peak‐shaving technology: systematic approach and techno‐economic analysis

A peak‐shaving technology is recently proposed, which integrates peak‐electricity generation, cryogenic energy storage and CO₂ capture. In such a technology, off‐peak electricity is used to produce liquid nitrogen and oxygen in an air separation and liquefaction unit. At peak hours, natural gas (or alternative gases, e.g. from gasification of coal) is burned by oxygen from the air separation unit (oxy‐fuel combustion) to generate electricity. CO₂ produced is captured in the form of dry ice. Liquid nitrogen produced in the air separation plant not only serves as an energy storage medium but also supplies the low‐grade cold energy for CO₂ separation. In addition, waste heat from the tail gas can be used to superheat nitrogen in the expansion process to further increase the system efficiency. This article reports a systematic approach, with an aim to provide technical information for the system design. Three potential blending gases (helium, oxygen and CO₂) are considered not only for assessing thermodynamic performance but also for techno‐economic analysis. The peak‐shaving systems are also compared with natural gas combined cycle and an oxy–natural gas combined cycle in terms of capital cost and peak electricity production cost. Copyright © 2011 John Wiley & Sons, Ltd.