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.

Distribution Network Optimization Model of Industrial Park with Distributed Energy Resources under the Carbon Neutral Targets

Taking an industrial park as an example,this study aims to analyze the characteristics of a distribution network that incorporates distributed energy resources(DERs).The study begins by summarizing the key features of a distribution network with DERs based on recent power usage data.To predict and analyze the load growth of the industrial park,an improved back-propagation algorithm is employed.Furthermore,the study classifies users within the industrial park according to their specific power consumption and supply requirements.This user segmentation allows for the introduction of three constraints:node voltage,wire current,and capacity of DERs.By incorporating these constraints,the study constructs an optimization model for the distribution network in the industrial park,with the objective of minimizing the total operation and maintenance cost.The primary goal of these optimizations is to address the needs of DERs connected to the distribution network,while simultaneously mitigating their potential adverse impact on the network.Additionally,the study aims to enhance the overall energy efficiency of the industrial park through more efficient utilization of resources.

Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods

Statistical distributions are used to model wind speed,and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed.Accurate estimation of Weibull parameters,the scale(c)and shape(k),is crucial in describing the actual wind speed data and evaluating the wind energy potential.Therefore,this study compares the most common conventional numerical(CN)estimation methods and the recent intelligent optimization algorithms(IOA)to show how precise estimation of c and k affects the wind energy resource assessments.In addition,this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia,namely Aljouf,Rafha,Tabuk,Turaif,and Yanbo.Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data.Also,with six wind turbine technologies rating between 1 and 3MW,the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy($/kWh)compared to the assessments by IOAs.The energy cost analyses show that Turaif is the windiest site,with an electricity cost of$0.016906/kWh.The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding$0.02739/kWh.Finally,the outcomes of this study exhibit the potential of wind energy in Saudi Arabia,and its environmental goals can be acquired by harvesting wind energy.

Multi-agent Based Protection on Highly Dominated Distributed Energy Resources

Highly penetration of Distributed Energy Resources (DER) on the grid systems nowadays makes the systems grow dynamically. The system become more complex and the protection system become more complicated. The protection relay should accommodate the system changes according to the system conditions and topologies. As part of developmental aspect of Distributed Artificial Intelligent, Multi Agent System (MAS) is a challenging method for improving the intelligent properties of relay protection. This paper introduces the use of MAS approach on radial distribution system protection dominated with DER using dispersed adaptive rule-based protection supported by distributed database agent. The simulation results confirmed that the proposed algorithm can respond within 15.05 ms.

GRU-integrated constrained soft actor-critic learning enabled fully distributed scheduling strategy for residential virtual power plant

In this study,a novel residential virtual power plant(RVPP)scheduling method that leverages a gate recurrent unit(GRU)-integrated deep reinforcement learning(DRL)algorithm is proposed.In the proposed scheme,the GRU-integrated DRL algorithm guides the RVPP to participate effectively in both the day-ahead and real-time markets,lowering the electricity purchase costs and consumption risks for end-users.The Lagrangian relaxation technique is introduced to transform the constrained Markov decision process(CMDP)into an unconstrained optimization problem,which guarantees that the constraints are strictly satisfied without determining the penalty coefficients.Furthermore,to enhance the scalability of the constrained soft actor-critic(CSAC)-based RVPP scheduling approach,a fully distributed scheduling architecture was designed to enable plug-and-play in the residential distributed energy resources(RDER).Case studies performed on the constructed RVPP scenario validated the performance of the proposed methodology in enhancing the responsiveness of the RDER to power tariffs,balancing the supply and demand of the power grid,and ensuring customer comfort.

Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

In the restructured electricity market,microgrid(MG),with the incorporation of smart grid technologies,distributed energy resources(DERs),a pumped-storage-hydraulic(PSH)unit,and a demand response program(DRP),is a smarter and more reliable electricity provider.DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines.Better bidding strategies,prepared by MG operators,decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources(RES).But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate.To solve these issues,this study suggests non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)for an optimal bidding strategy considering pumped hydroelectric energy storage and DRP based on outage conditions and uncertainties of renewable energy sources.The uncertainty related to solar and wind units is modeled using lognormal and Weibull probability distributions.TOU-based DRP is used,especially considering the time of outages along with the time of peak loads and prices,to enhance the reliability of MG and reduce costs and emissions.

An IEEE 1547-Based Power Conditioner Test System for Distributed Energy Resources

Power conditioner, that is responsible for electric power conversion, is a critical component used in many renewable energy power generation systems. Most of the electric power produced by distributed energy resources cannot directly import to utility network without power conversion. Meanwhile, power conversion may includes several different types, for example AC/DC, and DC/AC, which is realized by a variety types of power conditioners in the electric power system. Currently, many concerns are focused on the operation of these power conditioners used in distributed energy resources due to the worse designing may cause the terrible influence on safety and performance characteristic of distributed energy resources. The power quality and reliability of interconnected electric power network may be affected as well. In the view of this, IEEE standards board provides a uniform standard for interconnection of distributed resources with electric power systems. It provides requirements relevant to the performance, operation, testing, safety considerations, and maintenance of the interconnection. Based on the IEEE 1547 standard, this paper presents a test system for power conditioners that are used in distributed energy resources or other renewable energy applications. Some of the test items that described in IEEE 1547.1 relevant to interconnection issues can be realized by proposed test system.

Supply modes for renewable-based distributed energy systems and their applications:case studies in China

Distributed energy systems(DES),as an integrated energy system with coupled distributed energy resources,have great potential in reducing carbon dioxide emissions and improving energy efficiencies.Considering the background of urbanization and the energy revolution in China,the study investigates the renewable-based DESs supply modes and their application in China.A new method is proposed to classify DESs supply modes into three categories considering the renewable resource in domination,and their application domains are discussed.A comprehensive model is given for economic and environmental evaluation.Typical case studies show that the renewable-based DES systems can supply the energy in a cost-effective and environment-friendly way.Among them,the biomass waste dominated supply mode can not only achieve"zero"carbon emissions but also"zero"energy consumption,even though not yet economically attractive under the present policy and market conditions.Thus,recommendations are given to promote the further deployment of renewable-based DESs,regarding their supply modes,policy requirements,and issues to be addressed.

Collaborative Multiple Access And Energy-Efficient Resource Allocation in Distributed Maritime Wireless Networks

With the rapid increasing of maritime activities, maritime wireless networks(MWNs) with high reliability, high energy efficiency, and low delay are required. However, the centralized networking with fixed resource scheduling is not suitable for MWNs due to the special environment. In this paper,we introduce the collaborative relay communication in distributed MWNs to improve the link reliability, and propose an orthogonal time-frequency resource block reservation based multiple access(RRMA) scheme for both one-hop direct link and two-hop collaborative relay link to reduce the interference. To further improve the network performance, we formulate an energy efficiency(EE) maximization resource allocation problem and solve it by an iterative algorithm based on the Dinkelbach method. Finally, numerical results are provided to investigate the proposed RRMA scheme and resource allocation algorithm, showing that the low outage probability and transmission delay can be attained by the proposed RRMA scheme. Moreover,the proposed resource allocation algorithm is capable of achieving high EE in distributed MWNs.

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.

Design and Analysis of a 24 Vdc to 48 Vdc Bidirectional DC-DC Converter Specifically for a Distributed Energy Application

The design of a bidirectional dc-dc power converter specifically for a distributed energy application is presented. The existing two different DC voltage battery bank of the distributed generation needs to interlink each other using a bi-directional dc-dc converter in order to minimize the unbalance of the output load currents of the three inverters connected to electric grid system. Through this connection, a current can flow from one system to another or vice versa depending on which systems need the current most. Thus, unbalanced currents of the grid line have been minimized and the reliability and performance of the DER grid connected system has been increased. A detailed mathematical analysis of the converter under steady state and transient condition are presented. Mathematical models for boost and buck modes are being derived and the simulink model is constructed in order to simulate the system. Moreover, the model has been validated on the actual operation of the converter, showing that the simulated results in Matlab Simulink are consistent with the experimental ones.

Distribution,exploitation,and utilization of intermediate-to-deep geothermal resources in eastern China

The part of China,east of the Hu Huanyong Line,is commonly referred to as eastern China.It is characterized by a high population density and a well-developed economy;it also has huge energy demands.This study assesses and promotes the large-scale development of geothermal resources in eastern China by analyzing deep geological structures,geothermal regimes,and typical geothermal systems.These analyses are based on data collected from geotectology,deep geophysics,geothermics,structural geology,and petrology.Determining the distribution patterns of intermediate-to-deep geothermal resources in the region helps develop prospects for their exploitation and utilization.Eastern China hosts superimposed layers of rocks from three major,global tectonic domainsd namely Paleo-Asian,Circum-Pacific,and Tethyan rocks.The structure of its crust and mantle exhibits a special flyover pattern,with basins and mountains as well as well-spaced uplifts and depressions alternatively on top.The lithosphere in Northeast China and North China is characterized by a thin,low density crust and mantle,whereas the lithosphere in South China has a thin,low density crust and a thick,high density mantle.The middle and upper crust contain geobodies with high conductivity and low velocity,with varying degrees of development that create favorable conditions for the formation and enrichment of geothermal resources.Moderate-to-high temperature geothermal resources are distributed in the MesozoiceCenozoic basins in eastern China,although moderate temperature geothermal resources with low abundance dominate.Porous sandstone reservoirs,karstified fractured-vuggy carbonate reservoirs,and fissured granite reservoirs are the main types of geothermal reservoirs in this region.Under the currently available technical conditions,the exploitation and utilization of geothermal resources in eastern China favor direct utilization over large-scale geothermal power generation.In Northeast China and North China,geothermal resources could be applied for large-scale geothermal heating purposes;geothermal heating could be applied during winter along parts of the Yangtze River while geothermal cooling would be more suitable for summer there;geothermal cooling could also be applied to much of South China.Geothermal resources can also be applied to high value-added industries,to aid agricultural practices,and for tourism.

“双碳”目标下天然气分布式能源发展现状及机遇

为实现碳达峰碳中和(以下简称“双碳”)目标,必须建设清洁低碳、安全高效的新型能源体系。天然气分布式能源具有相对清洁高效、综合能源利用效率高、输出能源类型多等优点,是我国实现“双碳”目标的重要发展路径之一。为此,剖析了我国天然气分布式能源存在的问题和发展机遇,进而对天然气分布式能源项目的发展提出了建议。研究结果表明:(1)中国天然气分布式能源整体发展相对较慢,且不同地区之间发展速度也存在差异,现有项目主要集中在经济较发达的京津冀鲁、长三角、珠三角以及天然气资源丰富的川渝等地区;(2)中国天然气分布式能源发展面临的主要问题包括前期投入成本较高、电网企业与项目用户端存在利益冲突使得项目上网困难、项目用户端对能源消纳程度的不确定性影响其投资回报等;(3)随着政策普及、技术发展、碳交易市场完善及经济性的提高,天然气分布式能源项目发展有望进入快速发展期。结论认为,在“双碳”目标引领下,中国天然气分布式能源产业正处于重要的战略机遇期,建议从政策支持、调峰需求、碳排放收益以及技术突破4个方面加强研究;天然气分布式能源的高质量推进对我国构建清洁低碳、安全高效的新型能源体系,助力实现“双碳”目标具有重要的现实意义。

考虑电动汽车充放电的输配协同能量-灵活性市场出清机制

为应对输电网对灵活性资源的迫切需求,挖掘配电网分布式资源的灵活性支撑潜力,首先,构建市场环境下电动汽车的车-网互动响应模型,分析不同充放电方式下电动汽车的需求响应能力,为电动汽车参与市场环境下电网调度控制提供模型基础;然后,计及配电网运营商对需求侧灵活性资源的聚合作用,以购买电力和灵活性资源总成本最低为目标,构建考虑电动汽车充放电方式的输配协同能量-灵活性市场出清模型,通过协调控制输配电网中各类灵活性资源来满足输电网的灵活性需求;最后,通过修改的IEEE 33节点输电网与2个IEEE 33节点配电网耦合的测试系统对所提输配协同能量-灵活性市场出清方法的有效性进行分析与验证。结果表明,所提方法可提升电力系统的运行经济性和灵活性。

基于机器学习的主动配电网能量管理与运行控制

随着分布式资源和灵活负荷广泛接入,配电网正演变成为主动配电网,其能量管理与运行控制面临着巨大挑战:1)海量分布式资源并网使得调控需求大增,同时引入了大量随机性使得运行风险增加,需要挖掘其主动支撑能力;2)分布式资源量大且异动频繁,难以及时维护,配电网模型精度差,基于精确建模的运行控制和优化调度技术的工程应用困难。为应对上述挑战,文中介绍了基于机器学习的理论和方法,提出了“测-辨-控”一体化的主动配电网能量管理与运行控制技术体系,实现少/免模型维护的运行控制与优化调度。同时,分析了以下核心技术:1)配电网弱/无模型实时调控技术,实现自律优化;2)分布式资源集群自适应动态控制技术,实现对电网的主动支撑;3)风险量化的概率优化调度方法,实现风险与经济的平衡。最后,简要介绍了适应含极高比例分布式资源的主动配电网的能量管理与运行控制系统架构。

含共享储能的数据中心微网群分布式优化调度

多个数据中心微网可联合成微网群以实现不同数据中心微网的资源联动和能量互补。首先,建立了含共享储能的数据中心微网群运行模型,考虑了不同数据中心微网间的功率交互、数据负载交互、碳配额交互及共享储能容量使用权分配等耦合约束。其次,提出了考虑时变容量使用权和旋转备用服务的共享储能容量分配与运行策略,以进一步提高共享储能的效能。然后,采用高斯混合模型对数据中心微网内可再生发电的预测误差概率分布进行精确拟合,并结合机会约束处理预测误差的不确定性。最后,通过交替方向乘子法实现各个数据中心微网的独立求解,并引入动态乘子更新策略和预测-矫正因子以提高算法的收敛性。所提模型与方法的有效性在仿真中得到验证。

电力-绿证点对点交易下基于多产消者博弈均衡的分布式资源扩展规划

点对点(peer-to-peer,P2P)交易是近年来快速发展建立的配网内新型市场机制。由于投资建设周期较短,分布式资源的扩展规划将受P2P交易的显著影响。考虑多产消者间的博弈互动,该文建立基于多产消者博弈均衡的分布式资源扩展规划模型,并结合模型特征与求解需求构建基于Lp-BoxADMM的分布式求解算法。进一步通过基于IEEE标准测试系统的算例分析,验证所提模型及算法的有效性。分析P2P交易与多产消者间博弈互动行为对分布式资源扩展规划、新能源消纳、产消者成本收益的影响。

计及P2P市场产消者灵活性的配电网阻塞管理

多点接入的柔性负荷和可再生分布式电源会使配网潮流大小和方向频繁变动,而配电侧产消者通常仅考虑自身效益最大为目标进行端对端(peer-to-peer,P2P)电能交易,忽略了不同设备能量流动对系统安全运行的影响,使得交易结果极易导致网络阻塞。针对采用连续双向拍卖机制和零智商增强型投标策略的分散式P2P交易模式所引起的配网阻塞,兼顾产消者数据隐私管理和消费心理学影响,提出一种两级市场动态协调阻塞管理方法。该方法以产消者综合效益最大和解决网络阻塞调节量最小为目标,利用Distflow潮流模型,构建基于节点边际电价的节点灵活性辅助服务价格计算模型。进而通过分布式优化算法求解得到阻塞成本,将该成本按照P2P交易时序和线路阻塞贡献度分摊至有责任的产消者,实现阻塞消除。算例采用IEEE 33节点改进的11节点配网系统,结果表明该方法可在设定的时间尺度和计算精度条件下有效解决网络阻塞,降低阻塞成本和分散式P2P电能交易的效益损失。

基于强化学习的含分布式风-光-储虚拟电厂优化调度

将大量小规模的分布式风电、光伏发电和储能等单元并网的一种有效手段是将它们通过能源聚合商聚合之后参与虚拟电厂(VPP)的优化调度,然而VPP调度中心常难以对能源聚合商的总体出力特性进行精确的建模及预测,给传统的集中式VPP优化调度模式带来挑战。针对含分布式风-光-储单元的VPP,提出一种基于强化学习的交互式优化调度模型。VPP调度中心通过与各聚合商的在线信息交互,逐步学习得到VPP中各类单元的聚合出力及VPP对大电网的购售电量决策。该优化模型采用深度确定性策略梯度(DDPG)算法求解。基于真实新能源出力数据的仿真算例验证了该方法的有效性。与线性规划求解器得到的传统集中式调度结果对比,表明本所提法有助于改善VPP运行总效益,尤其是提高新能源利用率。

含分布式新能源和需求响应负荷的虚拟电厂定价机制及优化调度

面对电网中分布式风电、光伏发电以及柔性负荷的广泛接入,将这些资源通过虚拟电厂(VPP)进行聚合,采用合理的电价机制来引导用户参与需求响应,可以有效提升新能源的消纳能力并降低整体运营成本。传统的分时电价机制往往难以实现需求响应负荷与新能源出力的良好匹配,可能会导致需求响应的不合理或过度反应。为此,针对包括分布式风电、分布式光伏发电和柔性负荷的VPP,提出了一种基于新能源出力的VPP内部定价机制,并设置电力交易优先级来引导VPP内部各资源优化运行,以VPP的整体运营成本最小化为目标,构建了一个VPP优化调度的混合整数线性规划模型。基于内蒙古某地区的实际数据进行仿真计算,结果表明,与基于传统分时电价的优化结果相比,该方法能显著提高新能源的利用率并降低VPP的运行费用。