Optimal Planning of AC-DC Hybrid Transmission and Distributed Energy Resource System: Review and Prospects

With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this ground,An AC-DC hybrid DER system becomes necessary for effective management and control over DER.This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system.The impact of these new features on system configuration planning is analyzed with respect to its flexible networking,rich operation control modes,and tight sourcenetwork-load-storage coupling.Then,based on a review of the existing research,problems and technical difficulties are figured out in terms of converter modeling,steady-state analysis,power flow calculation,operating scenarios management,and optimization model solution.In light of the problems and difficulties,a framework for the configuration optimization of AC-DC hybrid DER systems is proposed.At last,the paper provides a prospect of key technologies from six aspects including morphology forecasting,coupling interaction analysis,uncertainty modeling,operation simulation,optimization model solving algorithm and comprehensive scheme evaluation.

Optimal Design of Distributed Energy Resource Systems under Uncertainties Based on Two-Stage Robust Optimization

Distributed energy resource(DER)systems are widely used owing to their excellent economic and environmental performance.However,uncertainties in the system generate difficulties in the optimal design of DER systems.In practice,the distribution of uncertain parameters is generally unknown.In this work,a two-stage robust optimization(RO)model was proposed for the optimal design of DER systems considering uncertainties in renewable energy intensity,energy prices,and load demands.Three uncertainty sets(i.e.,the box,ellipsoid,and convex-hull uncertainty sets)were adopted to describe the distribution of uncertain parameters,and the proposed two-stage RO problem was solved using affine decision rules.A typical hospital in Lianyungang,Jiangsu Province,China,was selected as the case study object,and the effectiveness of the model was verified.The case study results showed that uncertainties in energy prices and load demands have a significant impact on system configuration and economic performance,and mainly affect the installed capacities of gas boilers,absorption chillers,and storages.Uncertainty set will affect the optimization results and an appropriate uncertainty set should be adopted to describe uncertainties precisely and increase accuracy of results.

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.

Decoding the Einstein-Type Formula for Reducing Energy Conversion to Carbon Emissions in Renewables Systems

This article explores the role of distributed energy resources such as efficient solar cells that drive carbon neutrality within the solar energy. For example, the perovskite solar cells offer high efficiency and potential for low-cost production. A novel theoretical model is discovered in distributed energy resources for power emissions and cost. The smart carbon neutrality approaches are analyzed in both theory and experiments. The advantages, current challenges, and future prospects of the related solutions are discussed methodically. By addressing stability and scalability issues, these approaches can contribute significantly to reducing carbon emissions and promoting sustainable energy solutions.

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.

Blockchain-assisted virtual power plant framework for providing operating reserve with various distributed energy resources

The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid.Distributed energy resources(DERs),which can provide operating reserve to the grid,are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources.Small-scale DERs can be aggregated as a virtual power plant(VPP),which is eligible to bid in the operating reserve market.Since the DERs usually belong to different entities,it is important to investigate the VPP operation framework that coordinates the DERs in a trusted man-ner.In this paper,we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs.Considering the heterogeneity of various DERs,we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs.By considering the mismatch between actual available reserve capacity and the estimated value,the performance of VPP in the operating reserve market is improved.A hardware-based experimental system is developed,and numerical results are presented to demonstrate the effectiveness of the proposed framework.

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.

Review of demand-side energy sharing and collective self-consumption schemes in future power systems

The widespread use of distributed energy sources provides exciting potential for demand-side energy sharing and collective self-consumption schemes.Demand-side energy sharing and collective self-consumption systems are committed to coordinating the operation of distributed generation,energy storage,and load demand.Recently,with the development of Internet technology,sharing economy is rapidly penetrating various fields.The application of sharing economy in the energy sector enables more and more end-users to participate in energy transactions.However,the deployment of energy sharing technologies poses many challenges.This paper comprehensively reviews recent developments in demand-side energy sharing and collective self-consumption schemes.The definition and classification of sharing economy are presented,with a focus on the applications in the energy sector:virtual power plants,peer-to-peer energy trading,shared energy storage,and microgrid energy sharing cloud.Challenges and future research directions are thoroughly discussed.

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.

Reinforcement Learning with Enhanced Safety for Optimal Dispatch of Distributed Energy Resources in Active Distribution Networks

As numerous distributed energy resources(DERs)are integrated into the distribution networks,the optimal dispatch of DERs is more and more imperative to achieve transition to active distribution networks(ADNs).Since accurate models are usually unavailable in ADNs,an increasing number of reinforcement learning(RL)based methods have been proposed for the optimal dispatch problem.However,these RL based methods are typically formulated without safety guarantees,which hinders their application in real world.In this paper,we propose an RL based method called supervisor-projector-enhanced safe soft actor-critic(S3AC)for the optimal dispatch of DERs in ADNs,which not only minimizes the operational cost but also satisfies safety constraints during online execution.In the proposed S3AC,the data-driven supervisor and projector are pre-trained based on the historical data from supervisory control and data acquisition(SCADA)system,effectively providing enhanced safety for executed actions.Numerical studies on several IEEE test systems demonstrate the effectiveness and safety of the proposed S3AC.

Decentralized coordination of distributed energy resources through local energy markets and deep reinforcement learning

As the energy landscape evolves towards sustainability,the accelerating integration of distributed energy resources poses challenges to the operability and reliability of the electricity grid.One significant aspect of this issue is the notable increase in net load variability at the grid edge.Transactive energy,implemented through local energy markets,has recently garnered attention as a promising solution to address the grid challenges in the form of decentralized,indirect demand response on a community level.Model-free control approaches,such as deep reinforcement learning(DRL),show promise for the decentralized automation of participation within this context.Existing studies at the intersection of transactive energy and model-free control primarily focus on socioeconomic and self-consumption metrics,overlooking the crucial goal of reducing community-level net load variability.This study addresses this gap by training a set of deep reinforcement learning agents to automate end-user participation in an economy-driven,autonomous local energy market(ALEX).In this setting,agents do not share information and only prioritize individual bill optimization.The study unveils a clear correlation between bill reduction and reduced net load variability.The impact on net load variability is assessed over various time horizons using metrics such as ramping rate,daily and monthly load factor,as well as daily average and total peak export and import on an open-source dataset.To examine the performance of the proposed DRL method,its agents are benchmarked against a optimal near-dynamic programming method,using a no-control scenario as the baseline.The dynamic programming benchmark reduces average daily import,export,and peak demand by 22.05%,83.92%,and 24.09%,respectively.The RL agents demonstrate comparable or superior performance,with improvements of 21.93%,84.46%,and 27.02%on these metrics.This demonstrates that DRL can be effectively employed for such tasks,as they are inherently scalable with near-optimal performance in decentralized grid management.

Transactive control:a framework for operating power systems characterized by high penetration of distributed energy resources

The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.

State-of-the-Art Analysis and Perspectives for Peer-to-Peer Energy Trading

As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with distributed energy resources(DERs)are able to directly trade and share energy with each other.This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers,research projects,and industrial practice.Key aspects in P2P energy trading are identified and discussed,including market design,trading platforms,physical infrastructure and information and communication technology(ICT)infrastructure,social science perspectives,and policy.For each key aspect,existing research and practice are critically reviewed and insights for future development are presented.Comprehensive concluding remarks are provided at the end,summarizing the major findings and perspectives of this paper.P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.

Complete active-reactive power resource scheduling of smart distribution system with high penetration of distributed energy resources

In traditional power systems,besides the conventional power plants that provide the necessary reactive power in transmission system,the shunt capacitors along with the tap changers of transformers are also employed in distribution networks.In future years,because of the high number of distributed resources integrated into the distribution networks,it will be essential to schedule complete active-reactive power at distribution level.In this research work,an economic framework based on the active-reactive power bids has been developed for complete active-reactive power dispatch scheduling of smart distribution networks.The economical complete active-reactive power scheduling approach suggested in this study motivates distributed energy resources(DERs)to cooperate in both active power markets and the Volt/Var control scheme.To this end,using DER’s reactive power capability,a generic framework of reactive power offers for DERs is extracted.A 22-bus distribution test system is implemented to verify the impressiveness of the suggested active-reactive power scheduling approach.

Integration of Utility Distributed Energy Resource Management System and Aggregators for Evolving Distribution System Operators

With the rapid integration of distributed energy resources(DERs),distribution utilities are faced with new and unprecedented issues.New challenges introduced by high penetra-tion of DERs range from poor observability to overload and reverse power flow problems,under-/over-voltages,maloperation of legacy protection systems,and requirements for new planning procedures.Distribution utility personnel are not adequately trained,and legacy control centers are not properly equipped to cope with these issues.Fortunately,distribution energy resource management systems(DERMSs)are emerging software technologies aimed to provide distribution system operators(DSOs)with a specialized set of tools to enable them to overcome the issues caused by DERs and to maximize the benefits of the presence of high penetration of these novel resources.However,as DERMS technology is still emerging,its definition is vague and can refer to very different levels of software hierarchies,spanning from decentralized virtual power plants to DER aggregators and fully centralized enterprise systems(called utility DERMS).Although they are all frequently simply called DERIMS,these software technologies have different sets of tools and aim to provide different services to different stakeholders.This paper explores how these different software technologies can complement each other,and how they can provide significant benefits to DSOs in enabling them to successfully manage evolving distribution networks with high penetration of DERs when they are integrated together into the control centers of distribution utilities.

Protection coordination in distribution systems with and without distributed energy resources-a review

Emission of greenhouse gases and depletion of fossil fuel reserves are two key drivers,which are forcing the mankind to generate the future energy demand from the renewable energy resources.These resources are generally distributed in nature and are directly integrated at distribution levels.Increasing penetration of the distributed energy resources in distribution power networks creates additional operational and control issues.These are mostly regulatory,economical load dispatching,power quality and protection issues.Generally power distribution systems are protected with the help of dedicated over current based protection schemes.But increasing share of distributed energy resources penetration in electric utilities poses a serious threat to the existing protection coordination schemes of the distribution systems.Distributed energy resources connected distribution networks become interconnected in nature and protection coordination schemes,which are designed for unidirectional flow of fault currents become ineffective/non-functional.Therefore,new protection coordination schemes are required for providing the adequate protection coordination for distributed energy resources connected electric power networks.In the available literature,the protection coordination schemes for radial distribution systems and developments in the area of protection coordination are discussed in detail.A thorough review for all these protection coordination schemes for distribution systems with and without distributed energy resources is done in this review article.It includes the analytical and artificial intelligence based techniques application for coordination of protective relays in the distribution systems.The limitations and research gaps in the area of protection coordination schemes are also presented in this review article.The aim of this research paper is to bring all the available research in the area of relay coordination on one platform,so that it will help the emerging researcher to identify the future scope of relay coordination application for distributed energy resources connected distribution systems.

Trading platform for cooperation and sharing based on blockchain within multi-agent energy internet

With the release of the electricity sales side,large-scale small-capacity distributed power generation units are connected to the distribution side,forming multi-type market entities such as microgrids,integrated energy systems,and virtual power plants.With the large-scale integration of distributed energy,the energy market under the energy internet is different from a traditional transmission grid.It is currently developing in the direction of diversified entities and commodities,a flat structure,and a flexible and competitive multi-agent market mechanism.In this context,this study analyzes the value of combining blockchain and the electricity market presents the design of a blockchain trading framework for multi-agent cooperation and sharing of the energy internet.The nodes in market transactions are modeled through power system modeling in the physical layer and the transaction consensus strategy in the cyber layer;moreover,the nodes are verified in a modified IEEE 13 testing feeder of a distribution network.A transaction example is demonstrated using the multi-agent cooperation and sharing transaction platform based on the Ethereum private blockchain.

Microgrids for power system resilience enhancement

Power system resilience is defined as the ability of power grids to anticipate,withstand,adapt and recover from high-impact low-probability(HILP)events.There are both long-term and short-term measures that system operators can employ for resilience rein-forcement.Longer-term measures include infrastructure hardening and resilient planning,while short-term operational measures are applied in the pre-event,during-event and post-event phases.Microgrids(MGs)can effectively enhance resilience for both transmission and distribution systems,due to their ability to operate in a controlled,coordinated way,when connected to the main power grid and in islanded mode.In this paper,MG-based strategies for resilience enhancement are presented,including MG-based resilient planning and MG-based operational measures,consisting of preventive MG scheduling and emergency measures and MG-based system restoration.Classification of literature is made by considering whether the transmission system,distribution system or individual MG resilience is targeted.The way uncertainties are handled by various methods is also outlined.Finally,challenges and future research requirements for improving MG-based power system resilience are highlighted.

Impact of Cascade Disconnection of Distributed Energy Resources on Bulk Power System Stability:Modeling and Mitigation Requirements

This work presents a new approach to establishing the minimum requirements for anti-islanding protection of distributed energy resources(DERs)with focus on bulk power system stability.The proposed approach aims to avoid cascade disconnection of DERs during major disturbances in the transmission network and to compromise as little as possible the detection of real islanding situations.The proposed approach concentrates on the rate-of-change of frequency(RoCoF)protection function and it is based on the assessment of dynamic security regions with the incorporation of a new and straightforward approach to represent the disconnection of DERs when analyzing the bulk power system stability.Initially,the impact of disconnection of DERs on the Brazilian Interconnected Power System(BIPS)stability is analyzed,highlighting the importance of modeling such disconnection in electromechanical stability studies,even considering low penetration levels of DERs.Then,the proposed approach is applied to the BIPS,evidencing its benefits when specifying the minimum requirements of anti-islanding protection,without overestimating them.