Energy Economic Dispatch for Photovoltaic-Storage via Distributed Event-Triggered Surplus Algorithm

This paper presents a novel approach to economic dispatch in smart grids equipped with diverse energy devices.This method integrates features including photovoltaic(PV)systems,energy storage coupling,varied energy roles,and energy supply and demand dynamics.The systemmodel is developed by considering energy devices as versatile units capable of fulfilling various functionalities and playing multiple roles simultaneously.To strike a balance between optimality and feasibility,renewable energy resources are modeled with considerations for forecasting errors,Gaussian distribution,and penalty factors.Furthermore,this study introduces a distributed event-triggered surplus algorithm designed to address the economic dispatch problem by minimizing production costs.Rooted in surplus theory and finite time projection,the algorithm effectively rectifies network imbalances caused by directed graphs and addresses local inequality constraints.The algorithm greatly reduces the communication burden through event triggering mechanism.Finally,both theoretical proofs and numerical simulations verify the convergence and event-triggered nature of the algorithm.

Quantitative analysis of distributed and centralized development of renewable energy

Global renewable energy has maintained a steady growth in recent years, mainly fostered by national policies and increasing demand. Analyzing the experience of renewable energy development in developed countries can be important to provide reference and guidance for its adoption in other countries. First, we compare and summarize definitions of distributed generation from 18 leading countries and organizations in renewable energy. On this basis, we provide three basic characteristics for successful distributed generation using renewable resources. Then, we empirically analyze the distributed and centralized development of renewable energy in Germany with focus on wind and photovoltaic power. We determined that 95% of the photovoltaic generation and 85% of the wind power generation is distributed in Germany, suggesting that the most suitable generation mode for renewable energy is the distributed approach.

An Optimized Algorithm for Renewable Energy Forecasting Based on Machine Learning

The large-scale application of renewable energy power generation technology brings new challenges to the operation of traditional power grids andenergy management on the load side. Microgrid can effectively solve this problemby using its regulation and flexibility, and is considered to be an ideal platform.The traditional method of computing total transfer capability is difficult due tothe central integration of wind farms. As a result, the differential evolutionextreme learning machine is offered as a data mining approach for extractingoperating rules for the total transfer capability of tie-lines in wind-integratedpower systems. K-medoids clustering under the two-dimensional “wind power-load consumption” feature space is used to define representative operational scenarios initially. Then, using stochastic sampling and repetitive power flow, aknowledge base for total transfer capability operating rule mining is created.Then, a novel method is used to filter redundant characteristics and find featuresthat are closely associated to the total transfer capability in order to decrease theultra-high dimensionality of operational features. Finally, by feeding the trainingdata into the proposed algorithm, the total transfer capability operation rules arederived from the knowledge base. It can be seen that, the proposed algorithmcan optimize the system performance with good accuracy and generality, according to numerical data.

Distributed Renewable Energy under the Guidance of Price Autonomous Operation Technology

Penetration of Distributed Renewable Energy in active distribution network has increased year by year, and the distributed characteristics of active distribution network has become increasingly prominent;it is difficult for traditional centralized energy scheduling to solve the coordination of random output of distributed energy and the communication pressure of a large number of distributed data. In this paper, we propose a Distributed Renewable Energy Coordination Strategy based on price guidance, through hierarchical multi-agent model. The coordination model of each agent is introduced in detail, regional target, price coordination response strategy and regional security constraints, using Agent’s Distributed Autonomy and Global Collaboration to realize the Energy Balance of Active Distribution Network and promote the Storage of Distributed Renewable Energy;the coordination strategy focuses on the impact of price adjustment on energy storage and flexible load response capacity to improve the distributed renewable energy consumption. Finally, through the quantitative analysis of the comprehensive performance of the index, the evaluation results of the traditional sequential simulation method are compared, and the rationality and validity of the proposed method are verified.

An Approach for Cost-Efficient Grid Integration of Distributed Renewable Energy Sources

We describe a specific approach to capacity man a ge ment for distribution grids. Based on simulations, it has been found that by curtailing a maximum of 5% of the yearly energy production on a per-generator basis, distribution grid connection capacity can be doubled. We also present the setting and fi rst results of a fi eld test for validating the approach in a rural distribution grid in northern Germany.

Multi-objective planning model for simultaneous reconfiguration of power distribution network and allocation of renewable energy resources and capacitors with considering uncertainties

This research develops a comprehensive method to solve a combinatorial problem consisting of distribution system reconfiguration, capacitor allocation, and renewable energy resources sizing and siting simultaneously and to improve power system's accountability and system performance parameters. Due to finding solution which is closer to realistic characteristics, load forecasting, market price errors and the uncertainties related to the variable output power of wind based DG units are put in consideration. This work employs NSGA-II accompanied by the fuzzy set theory to solve the aforementioned multi-objective problem. The proposed scheme finally leads to a solution with a minimum voltage deviation, a maximum voltage stability, lower amount of pollutant and lower cost. The cost includes the installation costs of new equipment, reconfiguration costs, power loss cost, reliability cost, cost of energy purchased from power market, upgrade costs of lines and operation and maintenance costs of DGs. Therefore, the proposed methodology improves power quality, reliability and security in lower costs besides its preserve, with the operational indices of power distribution networks in acceptable level. To validate the proposed methodology's usefulness, it was applied on the IEEE 33-bus distribution system then the outcomes were compared with initial configuration.

Method for Determination of Optimal Installed Capacity of Renewable Sources of Energy by the Criterion of Minimum Losses of Active Power in Distribution System

New method for determination of optimal placement and value of installed capacity of renewable source of energy (RES) by the criterion of minimum losses of active power, that allows taking into consideration the dependence of RES on natural conditions of region, schedule of energy supply, parameters and configuration of distribution network is suggested in the paper. Results of computations of test scheme confirm the efficiency of the proposed method and its simplicity as compared with the methods considered in literature sources.

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment,unlike non-renewable energy resources.However,they often fail to meet energy requirements in unfavorable weather conditions.The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load.In this paper,an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure.An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system.The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization(PSO)named recurrent PSO(rPSO).Firstly,rPSO algorithm minimizes the installation cost for nanogrid.Thereafter,the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources.State-of-the-art optimization models,including Genetic Algorithm(GA),bat and different Mathematical Programming Language(AMPL)solvers,are used to evaluate the model.The study’s outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.

Energy Efficiency and Renewable Energy Technologies Using Smart Grids: Study Case on NIPE Building at UNICAMP Campus

In its broadest interpretation, the smart grid vision sees the future of power industry transformed by the introduction of intelligent two-way communications, ubiquitous metering and measurement. This enables much finer control of energy flows and the integration and efficient use of renewable forms of energy, energy efficiency methodologies and technologies, as well as many other advanced technologies, techniques and processes that wouldn’t have been practicable until present. The smart grid vision also enables the creation of more reliable, more robust and more secure power supply infrastructure, and helps optimize the enormous investments required to build and operate the physical infrastructure required. The smart grid promises to revolutionize the electric power business that has been in place for the past 75 years. This work discusses the efficiency, targeted at the consumer units of electricity, with a view to sustainability and potential for technological innovation. The issue is addressed from two perspectives: the systems for generation and power distribution, and the design of a building “smart energy”. Because of the novelty of the subject in our country, the concepts presented and treated throughout this work come from material obtained at events and specialized sites on electric power system in Brazil and worldwide, being accompanied by information and data from NIPE’s building at University of Campinas’s campus case study in which it exemplifies the applicability of the techniques and recommended technologies.

Identification of Type of a Fault in Distribution System Using Shallow Neural Network with Distributed Generation

A distributed generation system(DG)has several benefits over a traditional centralized power system.However,the protection area in the case of the distributed generator requires special attention as it encounters stability loss,failure re-closure,fluctuations in voltage,etc.And thereby,it demands immediate attention in identifying the location&type of a fault without delay especially when occurred in a small,distributed generation system,as it would adversely affect the overall system and its operation.In the past,several methods were proposed for classification and localisation of a fault in a distributed generation system.Many of those methods were accurate in identifying location,but the accuracy in identifying the type of fault was not up to the acceptable mark.The proposed work here uses a shallow artificial neural network(sANN)model for identifying a particular type of fault that could happen in a specific distribution network when used in conjunction with distributed generators.Firstly,a distribution network consisting of two similar distributed generators(DG1 and DG2),one grid,and a 100 Km distribution line is modeled.Thereafter,different voltages and currents corresponding to various faults(line to line,line to ground)at different locations are tabulated,resulting in a matrix of 500×18 inputs.Secondly,the sANN is formulated for identifying the types of faults in the system in which the above-obtained data is used to train,validate,and test the neural network.The overall result shows an unprecedented almost zero percent error in identifying the type of the faults.

基于卫星图像的城区屋面分布式光伏潜力评估

结合市辖区层面光伏发展情况以及城市中不同用地的特点,利用卫星图片识别长沙市各地区适合装设分布式光伏的建筑区域。基于太阳高度角和方位角,通过山体阴影分析屋顶上的建筑阴影,计算地区屋面光伏可承载容量。对市场侧、制造商及电网部门的分布式光伏规划进行研究。通过识别结果可为光伏建设方及电网提供明确地域列表及可用面积,以及可实现基于实际建设项目的可新增光分布式光伏装机容量的预测,以期为分布式光伏电站用地不足问题提供新的解决思路。

考虑补偿激励的电动汽车多区域优化调度策略

针对充电区域内分布式新能源(DRE)与电动汽车负荷供需不均的问题,提出一种考虑补偿激励的电动汽车多区域优化调度策略,引导电动汽车前往不同充电区域充电,以促进DRE就地消纳.首先,基于需求价格弹性及用户时间焦虑建立电动汽车用户需求电量模型.其次,基于剩余电量可达原则和空闲时间冗余原则将电动汽车划分为可响应和不可响应集群,利用基于后悔理论的充电区域决策模型对可响应集群按区域作进一步划分.最后,建立电动汽车多区域优化调度模型,从最大化充电服务商经济效益及DRE消纳程度两个方面对充电价格进行优化.通过仿真验证证明所提优化策略能够充分考虑时间焦虑和价格弹性对电动汽车用户的影响,充分挖掘用户的响应潜力,在降低DRE消纳偏差量和提高充电服务商经济效益方面具有明显效果.

高比例分布式可再生能源交直流混合配电网运行调控:科学问题与技术框架

随着分布式可再生能源发电与中低压直流组网技术的快速发展,未来配电网必然向复杂的交直流混合形态演进,其运行调控技术面临许多新的挑战。在高比例分布式可再生能源交直流混合配电网(HR-HDN)运行调控技术发展需求分析的基础上,提炼出HR-HDN运行调控技术的3个科学问题,并以多类型可控设备协同、源-网-荷-储高度互动化和市场化需求为研究主线,从稳定控制、故障主动防御与恢复、电力市场机制辅助调控以及“平台+数据”数字化集成调控4个方面,提出了HR-HDN的运行调控技术框架。最后,对HR-HDN运行调控关键技术的未来发展进行了展望。

基于强化学习的含电动汽车虚拟电厂优化调度

大量电动汽车(EV)用户的无序充电可能造成电网负荷剧烈波动,危及电网的安全稳定。随着EV入网(V2G)技术的应用,将EV充电站及其周边的分布式新能源发电聚合为虚拟电厂(VPP)后进行优化调度,有助于改善EV用户充放电的经济性及满意度,同时提高分布式新能源的利用率,平抑电网负荷波动,但EV充电站的整体充放电负荷是大量个体EV用户随机行为的聚合,难以用数学模型精确描述。针对包含EV的VPP,提出一种基于深度强化学习的交互式调度框架,以最大化VPP内EV用户的总效益。VPP控制中心作为智能体决策EV个体的充放电动作,无需掌握个体详细模型,而是通过与区域电网环境的交互,不断学习和更新动作策略,从而克服集中式优化方法的局限性。该优化调度框架采用深度确定性策略梯度(DDPG)算法进行求解。仿真结果表明,与集中式优化方法相比,该优化算法提高了各EV用户的效益,并使EV充放电负荷与分布式新能源发电协调配合实现削峰填谷,改善了VPP的整体运行性能。

新能源电力系统频率时空分布特性分析

随着新能源和电力电子装备在电力系统中的占比快速提升,系统的频率动态特性发生重大变化。为了研究新能源机组并网对新型电力系统频率时空分布特性的影响:首先,建立了新能源电力系统的频率动态响应解析模型,并分析了频率时空分布特性的影响因素及新能源并网对该特性的影响机理;其次,讨论了研究频率时空分布特性的必要性及描述该特性的量化指标;最后,基于仿真结果分析了不同电网形态下的频率时空分布特性,并总结了新能源机组并网对该特性的影响规律。

基于分层深度强化学习的分布式能源系统多能协同优化方法

分布式能源系统的多能协同运行对于促进新能源的消纳具有重要意义。然而,分布式能源系统中源荷的不确定性以及异质能源网络的时空差异性,给多能协同优化问题带来巨大挑战。针对这一问题,提出了一种面向分布式能源系统的两阶段多能协同优化模型,通过采用长时间尺度控制和短时间尺度控制两阶段解耦决策方式,实现了对不同时间响应特性的复合空间进行序贯决策。然后,面对高维复合搜索空间和源荷不确定性因素,采用了深度强化学习无模型解决方案,并提出一种全新的分层深度强化学习算法进行求解。通过算例仿真验证了所提模型和求解方法的有效性和优越性。

考虑分级重构的配电网安全低碳两阶段运行优化方法

随着配电网拓扑结构的复杂程度提高,针对高比例可再生能源出力预测误差会导致配电网优化调度决策的准确性下降的问题,提出考虑分级重构方法的配电网安全低碳两阶段优化方法。首先,分析了配电网两类分级开关的影响程度和两阶段运行优化框架。其次,提出了配电网日前-日内两阶段优化方法,通过协调分级开关动作、储能系统、工业负荷需求响应等手段实现配电网最优化运行。然后,考虑到光伏出力在不同时间尺度上预测误差,分别构建了以低碳性为目标的日前全局低碳经济调度模型及以安全性为目标的日内滚动安全经济调度模型。最后,算例验证了所提模型能够在光伏高比例渗透背景下,有效支撑复杂配电网安全低碳运行。

面向规模化分布式可再生能源并网的群网协同互动优化方法

实现规模化分布式可再生能源接入下电网的稳定运行,是低碳化转型新形势下电网优化运行的重要研究方向。针对分布式可再生能源聚合形成集群参与电网优化运行带来的多主体性特点以及随机性问题,提出面向规模化分布式可再生能源并网的群网协同互动优化方法。首先,通过在灵活性资源的约束空间中构建高维内接长方体,形成表征灵活调节范围的可调功率域,通过构造可调功率域对可再生能源集群波动范围全覆盖的灵活性约束,使优化结果在可再生能源集群随机波动范围内全场景可行。然后,基于电网运行多主体的自利自主特性,构建满足同质个体并行优化、非同质个体串行优化要求的势博弈群网协同互动优化模型,提出与模型相适应的分布式优化方法与流程。最后,仿真算例表明,所提方法有效实现了多主体协同互动优化,并保障了优化结果的全场景可行。

新型电力系统多阶段输-储协同分布鲁棒规划

在电力系统中长期规划中,可再生能源发展受到政策变迁、关键技术发展和气候变化等因素的影响,具有不确定性。这一不确定性将对短期规划决策产生不可忽视的影响。针对以上问题,在可再生能源发展场景树之间嵌套距离的基础上引入鲁棒因子,构建概率分布模糊集。随后,综合考察整个规划周期规划方案的经济性与鲁棒性,建立了考虑可再生能源出力不确定性与发展不确定性的电力系统多阶段输-储协同分布鲁棒规划模型,并采用列与约束生成算法对模型进行求解。最后,采用Garver-6节点系统和HRP-38标准测试系统进行算例分析,得到多阶段规划方案。通过调节鲁棒因子可以平衡模型的经济性与鲁棒性,证明了所提方法的有效性。

储能/发电机级联式供电系统功率传输控制策略

随着可再生能源渗透率的不断提升,区域电网惯性逐步降低,暂态支撑能力严重不足,给电网带来了一系列安全问题。文中提出一种储能与开绕组永磁同步发电机(open-winding permanent magnet synchronous generator,OW-PMSG)级联供电的新型分布式并网发电拓扑,在保证可再生能源消纳的前提下,重新利用原有柴油机,为电网提供功率后备以及惯量支撑。通过对OW-PMSG进行建模以及系统功率流矢量分析,设计基于源网相位差闭环控制的功率传输策略,实现发电机的稳定同步运行。基于该策略,拓扑中的储能变流器可具备同步发电机特性,实现了电网惯性和暂态支撑能力的进一步提升。仿真结果验证了所提控制策略的有效性。