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.

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.

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.

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.

Fuzzy stochastic long-term model with consideration of uncertainties for deployment of distributed energy resources using interactive honey bee mating optimization

This paper presents a novel modified inter- active honey bee mating optimization （IHBMO） base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources （DERs）. The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting, A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduc- tion in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power orDERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.

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-driven local transactive energy market fordistributed energy resources

Local energy markets are emerging as a tool for coordinating generation, storage, and consumption of energyfrom distributed resources. In combination with automation, they promise to provide an effective energymanagement framework that is fair and brings system-level savings. The cooperative–competitive natureof energy markets calls for multi-agent based automation with learning energy trading agents. However,depending on the dynamics of the agent–environment interaction, this approach may yield unintended behaviorof market participants. Thus, the design of market mechanisms suitable for reinforcement learning agentsmust take into account this interplay. This article introduces autonomous local energy exchange (ALEX) asan experimental framework that combines multi-agent learning and double auction mechanism. Participantsdetermine their internal price signals and make energy management decisions through market interactions,rather than relying on predetermined external price signals. The main contribution of this article is examinationof compatibility between specific market elements and independent learning agents. Effects of different marketproperties are evaluated through simulation experiments, and the results are used for determine a suitablemarket design. The results show that market truthfulness maintains demand-response functionality, while weakbudget balancing provides a strong reinforcement signal for the learning agents. The resulting agent behavioris compared with two baselines: net billing and time-of-use rates. The ALEX-based pricing is more responsiveto fluctuations in the community net load compared to the time-of-use. The more accurate accounting ofrenewable energy usage reduced bills by a median 38.8% compared to net billing, confirming the ability tobetter facilitate demand response.

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.

Coalition Formation of Microgrids with Distributed Energy Resources and Energy Storage in Energy Market

Power grids include entities such as home-microgrids(H-MGs),consumers,and retailers,each of which has a unique and sometimes contradictory objective compared with others while exchanging electricity and heat with other H-MGs.Therefore,there is the need for a smart structure to handle the new situation.This paper proposes a bilevel hierarchical structure for designing and planning distributed energy resources(DERs)and energy storage in H-MGs by considering the demand response(DR).In general,the upper-level structure is based on H-MG generation competition to maximize their individual and/or group income in the process of forming a coalition with other H-MGs.The upper-level problem is decomposed into a set of low-level market clearing problems.Both electricity and heat markets are simultaneously modeled in this paper.DERs,including wind turbines(WTs),combined heat and power(CHP)systems,electric boilers(EBs),electric heat pumps(EHPs),and electric energy storage systems,participate in the electricity markets.In addition,CHP systems,gas boilers(GBs),EBs,EHPs,solar thermal panels,and thermal energy storage systems participate in the heat market.Results show that the formation of a coalition among H-MGs present in one grid will not only have a significant effect on programming and regulating the value of the power generated by the generation resources,but also impact the demand consumption and behavior of consumers participating in the DR program with a cheaper market clearing price.

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.

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.

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.

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.

Design of a Peer-to-Peer Energy Trading Platform Using Multilayered Semi-Permissioned Blockchain

A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.

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.

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.

Control and Stability of Large-scale Power System with Highly Distributed Renewable Energy Generation:Viewpoints from Six Aspects

Power systems are moving toward a low-carbon or carbon-neutral future where high penetration of renewables is expected.With conventional fossil-fueled synchronous generators in the transmission network being replaced by renewable energy generation which is highly distributed across the entire grid,new challenges are emerging to the control and stability of large-scale power systems.New analysis and control methods are needed for power systems to cope with the ongoing transformation.In the CSEE JPES forum,six leading experts were invited to deliver keynote speeches,and the participating researchers and professionals had extensive exchanges and discussions on the control and stability of power systems.Specifically,potential changes and challenges of power systems with high penetration of renewable energy generation were introduced and explained,and advanced control methods were proposed and analyzed for the transient stability enhancement of power grids.

Modeling and management performances of distributed energy resource for demand flexibility in Japanese zero energy house

Zero-energy buildings(ZEBs)can contribute to decarbonizing building energy systems,while the energy mismatch between energy demand and on-site stochastic generation in ZEBs increases the need for energy flexibility.This study proposed mixed-integer linear programming energy management schemes for optimizing the flexible scheduling of distributed energy resources,including battery energy storage,heat pump,and building thermal mass as a passive thermal energy storage system.With optimally designed objectives,this study used case studies to evaluate the flexibility potential provided by the demand-side management,considering dynamic characteristics of the process.The results showed that the proposed demand-side management for battery storage offers significant potential in increasing photovoltaic(PV)self-consumption and reducing operational costs.Cost reduction ratios of flexible dispatch of combined PV and battery storage systems exceed 15%.Flexible coupling of PV and heat pump systems for meeting hot water demand can reduce energy cost by more than 20%.The flexible coupling of the heat pump and PV system also had a significant impact on the power consumption pattern of domestic heat pumps,the load-shifting potential highly depends on the available PV generation and hot water demand.The optimal trade-off between thermal energy use and thermal comfort violation may not reduce the total energy used for space heating,the increased PV consumption helped reduce grid imports.The study provides insights into the energy flexibility behavior and efficiency of the proposed demand-side management for ZEBs,which is expected to provide guidelines for exploring demand-side flexibility.

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.