A quantile-based simulation optimization model for sizing hybrid renewable energy systems

As the public has gradually realized the adverse impacts brought by global warming, hybrid renewable energy system (HRES) has become increasingly popular because it can reduce dependence on fossil fuels, while maintaining the stability of power supply. While the HRES is an attractive option in many aspects, the fundamentally uncertain nature of renewable energy sources makes the determination of the proper sizing of the HRES a very challenging task. Contrasting with the existing models that are largely focused on expectation-based system performance, this paper provides a quantile-based simulation optimization model, followed by the development of an efficient solution methodology, to enable the control of the upside risk and, as a result, to enhance the decision quality regarding the sizing of HRES. One advantage of the proposed model is that they can be based on any existing deterministic model that carries a cost structure regarding the sizing of the HRES. Moreover, the proposed solution methodology, consisting of a Monte Carlo simulation method, quantile estimation techniques, and an efficient stochastic optimizer, allows for not only accurate estimation of the objective function value, but also quick identification of the optimal solution due to a uniquely-defined neighborhood structure. An extensive numerical experiment is conducted to verify the efficacy and efficiency of the proposed solution methodology. Finally, in collaboration with a partner in industry, the proposed model and the solution methodology are integrated into a decision support system to provide visualized results for sizing HRES in practice.     

A methodology to design a fuzzy logic based supervision of Hybrid Renewable Energy Systems

Hybrid Renewable Energy Systems (HRES) are increasingly used to improve the grid integration of wind power generators. The goal of this work is to propose a methodology to design a fuzzy logic based supervision of this new kind of production unit. A graphical modeling tool is proposed to facilitate the analysis and the determination of fuzzy control algorithms adapted to complex hybrid systems. To explain this methodology, the association of wind generators, decentralized generators and storage systems are considered for the production of electrical power. The methodology is divided in six steps covering the design of a supervisor from the system work specifications to an optimized implementation of the control. The performance of this supervisor is shown with the help of simulations. Finally, the application of this methodology to the supervision of different topologies of HRES is also proposed to bring forward the systematic dimension of the approach.     

Global optimization of energy and production in process industries: a genetic algorithm application

The process industries exhibit an increasing need for efficient management of all the factors that can reduce their operating costs, leading to the necessity for a global multi-objective optimization methodology that will enable the generation of optimum strategies, fulfilling the required restrictions. In this paper, a genetic algorithm is developed and applied for the optimal assignment of all the production sections in a particular mill in the kraft pulp and paper industry, in order to optimize energy the costs and production rate changes. This system is intended to implement all programmed or forced maintenance shutdowns, as well as all the reductions imposed in production rates.     

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem.     

基于网格计算的机构投资者IRMIS研究

在评述集成风险管理理论进展的基础上，指出了机构投资者集成风险管理信息系统(IRMIS)的复杂自适应系统(CAS)特征；引入网格计算思想提出了基于网格计算的机构投资者IRMIS的概念、特点及其系统原型，该原型具有多智能体(Multi-Agent)的良好结构，并基于管理熵对原型各Agent间的耦合关系进行了优化，强调风险管理战略、组织、方法、文化和过程诸要素的集成性。     

Passivity-based control design for a grid-connected hybrid generation system integrated with the energy management strategy

Hybrid generation systems produce electric energy from a wide variety of energy sources, including renewable sources. A hybrid system based on renewable sources usually consists of two or more renewable energy sources with the possibility of including storage units so as to enhance the reliability of the system. The hybrid system requires an energy strategy that determines the operation point of each element of the system depending on multiple variables and subjected to the constraints inherent in this kind of systems. In addition, the system needs controllers to command each of these elements in order to reach the operation point established by the energy strategy. Here, we propose a control design via passivity-based control integrated with an energy management strategy for a hybrid generation system based on solar energy and coupled with the grid. The performance of the control methodology is extensively assessed through computer simulation using a comprehensive nonlinear model of the plant. The results show that the controlled system accomplishes the control tasks with good responses, working under very different atmospheric conditions and required load power.     

Managing a hybrid energy smart grid with a renewable energy source

The aim of this work is to develop tools for optimal power flow management control in a micro grid (MG). The latter consists of a wind turbine, energy storage system, two gas turbines (GTs), and the main grid. Unlike the traditional approach, which is limited to the distribution of active power, this paper models an electrical system to coordinate and optimize the flow of both active and reactive power using discrete controls. The proposed optimal power distribution strategy has two objectives. First, it aims at forecasting over a time horizon of 24 hours the optimal distribution of the active and reactive power required for each power source connected to the MG. The proposed management incorporates the forecasts of consumption, weather, and tariffs. Second, it aims at reducing the CO₂ emissions rate by optimizing both the operating point of the two GTs and the usage of the storage unit. The proposed optimization problem for the energy management system is solved using the Bellman algorithm through dynamic programming. Its effectiveness is illustrated with various simulations carried out in the Matlab environment. The supremacy of the proposed management algorithm is highlighted by comparing its performance with conventional (restricted) management.     

Power flow Petri Net modelling for building integrated multi-source power system with smart grid interaction

This paper presents an energy management modelling of a multi-source power system composed of photovoltaic (PV) array, storage and power grid connection, and taking into account messages from smart grid. The designed system can supply a tertiary building at the same time as PV may produce energy. The control strategy aims to manage the power flow through the load with respect to its power demand and public grid constraints. The proposed energy management modelling is based on interpreted Petri Nets formalism. The system is tested using a simulation Stateflow model and responds within certain limits. The results show that this approach is valid and can be a solution for the future smart grid communication between buildings and public grid and may contribute to a better balance between production and consumption and future energy management.     

高能物理网格数据管理关键技术研究

首先概要介绍高能物理网格的需求和发展,然后对其中数据管理的关键技术进行深入分析和探讨,包括名字服务、数据复制管理、数据传输、海量存储系统、用户访问接口等.最后,介绍一个用于高能物理网格数据管理的文件系统原型设计.     

Optimal scheduling of battery storage with grid tied PV systems for trade-off between consumer energy cost and storage health

With the advent of renewable energy in India has initiated consumers to get energy storage systems to mange solar power variation. To solve intermittency issues from weather related events that occur with residential photovoltaic generation, intelligent power management strategies have been carried outto tune efficacy of the consumer's renewable energy system while reducing cost. The proposed method decides the state of charge schedule for the battery storage based on a dynamic programming algorithm that minimizes consumer energy cost and maximizes energy storage state of health. The battery state of health was introduced into the model as an ageing coefficient that forces conservative battery behaviour to preserve its lifetime with continued use.Simulation results show a high potential to increase the profitability of a grid connected PV- BESS system using time of use (TOU) tariff.     

零能耗小屋能源管理与智能监控系统的设计与实现

针对2013中国国际太阳能十项全能竞赛比赛规则及零能耗建筑的特点,结合比赛地点山西大同的气候数据,给出了厦门大学参赛作品,零能耗小屋Sunny Inside的能源管理与智能监控系统设计方案.以光伏建筑一体化理论为基础,设计15.25kW光伏并网发电系统,并用相关软件对能耗平衡进行了模拟验证.以三菱PLC为核心,构建了能源管理与智能监控系统,分析了不同条件下空调、相变及遮阳系统的控制策略,并给出具体实现方法.     

On making energy demand and network constraints compatible in the last mile of the power grid

In the classical electricity grid power demand is nearly instantaneously matched by power supply. In this paradigm, the changes in power demand in a low voltage distribution grid are essentially nothing but a disturbance that is compensated for by control at the generators. The disadvantage of this methodology is that it necessarily leads to a transmission and distribution network that must cater for peak demand. So-called smart meters and smart grid technologies provide an opportunity to change this paradigm by using demand side energy storage to moderate instantaneous power demand so as to facilitate the supply-demand match within network limitations. A receding horizon model predictive control method can be used to implement this idea. In this paradigm demand is matched with supply, such that the required customer energy needs are met but power demand is moderated, while ensuring that power flow in the grid is maintained within the safe operating region, and in particular peak demand is limited. This enables a much higher utilisation of the available grid infrastructure, as it reduces the peak-to-base demand ratio as compared to the classical control methodology of power supply following power demand. This paper investigates this approach for matching energy demand to generation in the last mile of the power grid while maintaining all network constraints through a number of case studies involving the charging of electric vehicles in a typical suburban low voltage distribution network in Melbourne, Australia.     

Methodologies for supervision of Hybrid Energy Sources based on Storage Systems – A survey

Hybrid Energy Sources based on Storage Systems (HES) are increasingly used to improve the grid integration of renewable energy generators, or to improve the energy efficiency and the reliability of transport systems. This paper proposes a survey on the methodologies to design fuzzy logic based supervision strategies of this new kind of energy generating systems. Different ways to manage energy storage system are particularly discussed.A graphical modeling tool is used to facilitate the analysis and the determination of fuzzy control algorithms adapted to complex hybrid systems. The methodology is divided in different steps covering the design of a supervisor from the system work specifications to an optimized implementation of the control.An Experimental Design Methodology (EDM) combined with optimization algorithms is used to tune a set of parameters suitable for a fuzzy supervisor in order to optimize power, energy, efficiency, voltage quality, economic or environmental indicators.The application of this methodology to the supervision of different topologies of HES, based on renewable energy in a grid or in building applications or dedicated to transport systems, illustrates the performance and the systematic dimension of the approach.     

绿色能源与生态环境控制

控制技术的发展和应用紧密联系着社会的进步和需求 ,不可再生的化石能源的日益短缺和环境污染 ,使人们越来越重视开发利用各类可再生的绿色能源和生态环境保护建设 .文章研究分析了能源状况与可持续发展问题 ,通过研究风力发电、太阳能光伏发电系统 ,提出若干关键控制问题与对策 .作为应用实例 ,文中给出一个利用CAN总线对风力-太阳能混合电站、温室进行监控与能量管理 ,从而构成一个环保、节能、和谐生态环境的混合电站     

以智慧能源服务平台为载体打造能源服务产业链

我国提出“双碳”、“产业生态化”与“数字化发展”的政策方向。南方电网提出向“三商转型”与打造平台型企业的战略目标。广东电网提出以客户代维为切入口构建能源服务生态圈的规划。广东电网能源投资有限公司践行南方电网战略与广东电网规划,以云计算、大数据、物联网、移动互联网、人工智能、区块链等新一代数字技术为核心驱动力搭建“粤能投”智慧能源服务平台,以数据为关键生产要素推动客户管理、营销推广、业务应用、创新孵化、决策管控等多项专业高效协同,以产业链上下游合作伙伴、代维服务商、设备供应商、综合能源服务商的聚合打造一个共享、开放、共赢的能源服务生态,从而实现数据为公司的业务赋能与管理赋能,并促进公司向能源价值链整合商转型。     

Smart energy pricing for demand‐side management in renewable energy smart grids

Smart grids are expected to provide various benefits to society by integrating advances in power engineering with recent developments in the field of information and communications technology. One of the advantages is the support to efficient demand‐side management (DSM), for example, changes in consumer demands for energy based on using incentives. Indeed, DSM is expected to help grid operators balance time‐varying generation by wind and solar units, and the optimization of their usage. This paper focuses on DSM considering renewable energy generation and proposes an auction, in which consumers submit bids to renewable energy usage plans. An additional model is introduced to allow consumers to compute their bid for a given usage plan. Both models have been extended to include energy storage devices. The proposed model is compared to a system with time‐varying pricing for energy, where it is shown to allow consumers to use more appliances, to lead to a larger profit, and to reduce the peak‐to‐average ratio of energy consumption. Finally, the impact of the use of energy storage in households and in the energy provider is also considered.     

考虑多源供能波动性的企业生产运作与能源计划耦合优化

多源供能是提高清洁能源占比,助力制造企业绿色低碳转型的有效方式。然而受季节、天气等因素影响,可再生能源出力存在波动性,影响能源系统供应的稳定性。针对该问题,构建了企业生产运作与能源计划耦合优化的不确定整数规划模型,利用区间数描述能源出力的不确定信息。同时通过设计多种群融合策略、外部档案更新策略,提出了多目标混合鲸鱼群算法,有效地利用各个算法的寻优特性,提升整体性能,获得更优的Pareto解集。最后通过算法性能和能源策略对比实验,证明了所构建模型及求解方法的可行性和有效性。同时验证了所设计算法对求解不确定优化模型的优势和竞争力,以及多源供能模式能够有效帮助企业实现节能减排、可持续生产目标。     

Joint optimisation of application QoS and energy conservation in grid environment

Mobile and wireless devices with limited energy are now of interest for the grid community. Reducing the related energy consumption is very important for wireless devices. Compared to conventional grid systems, energy aware grids need to support application quality of service (QoS) with limited energy. There is an inherent conflict in the design goals for high grid application QoS and low energy consumption. To address this challenging problem, this article considers joint optimisation of application QoS and energy conservation in grid environment. Joint optimisation of application QoS and energy conservation is targeted to maximise the system utility without exceeding the deadline and the total energy available, which can be provided by an exhaustible source such as a battery. This article formulates joint optimisation of application QoS and energy conservation as constrained maximisation problem; the constraints include energy budget and application QoS requirements. This article also presents a QoS and energy aware scheduling algorithm (QESA) which balances application QoS and energy conservation to improve system performance. In the simulation, the performance evaluation of QESA is conducted.     

充分发挥电网企业优势 加快拓展基于多能互补的综合能源服务业务

在当前能源转型和电力体制改革的新形势下,电网企业转型开展综合能源服务是必然选择。同时,随着能源互联网技术发展,使得能源之间互联、能源和信息融合进一步加强,为发展综合能源服务提供了可能。电网企业开展综合能源服务顺应客户需求,市场渣力巨大,是未来重要的利润増长点。如何充分发挥电网企业的优势,运用互联网思维,促进多种新能源的模化利用,将多种能源的源、网、荷、储深度融合,拓展出种类丰富的新型商业模式,为用户量身定制能源产品和服务搭配方案,是当下电网企业转型综合能源服务商考虑的首要问题。本文提出了一种适用于IES的扩展ewton－Raphson综合能源系统稳态多能流计算方法,构建综合能源系统多异质能流统一调度模型,进一岁构建智慧能源综合管理平台。将当下电力市场改革环境下实施的较为成功的商业模式与综合能源平台有机结合,可拓展出种类丰高的新型商业模式,进而组成一个从生产到消费再到增值服务的整个综合能源产业链。     

Optimization strategies for Microgrid energy management systems by Genetic Algorithms

Grid-connected Microgrids (MGs) have a key role for bottom-up modernization of the electric distribution network forward next generation Smart Grids, allowing the application of Demand Response (DR) services, as well as the active participation of prosumers into the energy market. To this aim, MGs must be equipped with suitable Energy Management Systems (EMSs) in charge to efficiently manage in real time internal energy flows and the connection with the grid. Several decision making EMSs are proposed in literature mainly based on soft computing techniques and stochastic models. The adoption of Fuzzy Inference Systems (FISs) has proved to be very successful due to their ease of implementation, low computational run time cost, and the high level of interpretability with respect to more conventional models. In this work we investigate different strategies for the synthesis of a FIS (i.e. rule based) EMS by means of a hierarchical Genetic Algorithm (GA) with the aim to maximize the profit generated by the energy exchange with the grid, assuming a Time Of Use (TOU) energy price policy, and at the same time to reduce the EMS rule base system complexity. Results show that the performances are just 10% below to the ideal (optimal) reference solution, even when the rule base system is reduced to less than 30 rules.