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.

Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture

Based on the energy storage cloud platform architecture,this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction,smooth the peak/valley difference of the load side power,and improve energy efficiency.A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers.Based on the load perception of the power grid,this study aims to investigate the operating state and service life of distributed energy storage devices.By selecting an integrated optimal control scheme,this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side.The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

Comparison of life cycle performance of distributed energy system and conventional energy system for district heating and cooling in China

The distributed energy system has achieved significant attention in respect of its application for singlebuilding cooling and heating.Researching on the life cycle environmental impact of distributed energy systems(DES)is of great significance to encourage and guide the development of DES in China.However,the environmental performance of distributed energy systems in a building cooling and heating has not yet been carefully analyzed.In this study,based on the standards of ISO14040-2006 and ISO14044-2006,a life-cycle assessment(LCA)of a DES was conducted to quantify its environmental impact and a conventional energy system(CES)was used as the benchmark.GaBi 8 software was used for the LCA.And the Centre of Environmental Science(CML)method and Eco-indicator 99(EI 99)method were used for environmental impact assessment of midpoint and endpoint levels respectively.The results indicated that the DES showed a better life-cycle performance in the usage phase compared to the CES.The life-cycle performance of the DES was better than that of the CES both at the midpoint and endpoint levels in view of the whole lifespan.It is because the CES to DES indicator ratios for acidification potential,eutrophication potential,and global warming potential are 1.5,1.5,and 1.6,respectively at the midpoint level.And about the two types of impact indicators of ecosystem quality and human health at the endpoint level,the CES and DES ratios of the other indicators are greater than1 excepting the carcinogenicity and ozone depletion indicators.The human health threat for the DES was mainly caused by energy consumption during the usage phase.A sensitivity analysis showed that the climate change and inhalable inorganic matter varied by 1.3%and 6.1%as the electricity increased by 10%.When the natural gas increased by 10%,the climate change and inhalable inorganic matter increased by 6.3%and 3.4%,respectively.The human health threat and environmental damage caused by the DES could be significantly reduced by the optimization of natural gas and electricity consumption.

Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system

This study aimed to evaluate the components of a fintech ecosystem for distributed energy investments.A new decision-making model was created using multiple stepwise weight assessment ratio analysis and elimination and choice translating reality techniques based on quantum spherical fuzzy sets.First,in this model,the criteria for distributed energy investment necessities were weighted.Second,we ranked the components of the fintech ecosystem for distributed energy investments.The main contribution of this study is that appropriate strategies can be presented to design effective fintech ecosystems to increase distributed energy investments,by considering an original fuzzy decision-making model.Capacity is the most critical issue with respect to distributed energy investment necessities because it has the greatest weight(0.261).Pricing is another significant factor for this condition,with a weight of 0.254.Results of the ranking of the components of the fintech ecosystem indicate that end users are of the greatest importance for the effectiveness of this system.It is necessary to develop new techniques for the energy storage process,especially with technological developments,to prevent disruptions in energy production capacity.In addition,customers’expectations should be considered for the development of effective and user-friendly financial products that are preferred by a wider audience.This would have a positive effect on fintech ecosystem performance.

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.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

Design of the Multi-Energy Complementary Distributed Energy System for Towns

The multi-energy complementary distributed energy system (MCDES) covers a variety of energy forms, involves complex operation modes, and contains a wealth of control equipment and coupling links. It can realize the complementary and efficient use of different types of energy, which is the basic component of the physical layer of the Energy Internet. In this paper, aiming at the demand of the energy application for towns, a distributed energy system based on multi-energy complementary is constructed. Firstly, the supply condition of the distributed energy for the demonstration project is analyzed, and the architecture of the multi-energy complementary distributed energy system is established. Then the regulation strategy of the multi-energy complementary distributed energy system is proposed. Finally, an overall system scheme for the multi-energy complementary distributed energy system suitable for towns is developed, which provides a solid foundation for the development and promotion of the multi-energy complementary distributed energy system.

Risk Assessment of Distributed Energy System Based on Fuzzy Analytic Hierarchy Process

Risk assessment of distributed energy system often has uncertainty and subjective problems. The problems will have some impact on the results. To solve the problems, a method of improved fuzzy analytic hierarchy process is proposed. By using the fuzzy analytic hierarchy process method, a hierarchical analysis model is established. And then according to the given judgment matrix of each index layer, we calculate whether it meets the consistency condition. And then if the judgment matrix does not meet the consistency condition, the problem will be solved by the improving of particle swarm optimization (PSO) with Kalman filter. The practice in the distributed energy system shows that the method can not only fully reflect the fuzziness of assessment elements and process, but also reduce the influence of individual subjective factors and better evaluation results can be achieved.

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.

Overview and Prospect of Distributed Energy P2P Trading

After a century of relative stability in the electricity sector,the widespread adoption of distributed energy resources,along with recent advancements in computing and communication technologies,has fundamentally altered how energy is consumed,traded,and utilized.This change signifies a crucial shift as the power system evolves from its traditional hierarchical organization to a more decentralized approach.At the heart of this transformation are innovative energy distribution models,like peer-to-peer(P2P)sharing,which enable communities to collaboratively manage their energy resources.The effectiveness of P2P sharing not only improves the economic prospects for prosumers,who generate and consume energy,but also enhances energy resilience and sustainability.This allows communities to better leverage local resources while fostering a sense of collective responsibility and collaboration in energy management.However,there is still no extensive implementation of such sharing models in today’s electricitymarkets.Research on distributed energy P2P trading is still in the exploratory stage,and it is particularly important to comprehensively understand and analyze the existing distributed energy P2P trading market.This paper contributes with an overview of the P2P markets that starts with the network framework,market structure,technical approach for trading mechanism,and blockchain technology,moving to the outlook in this field.

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.

Data-driven Predictive Voltage Control for Distributed Energy Storage in Active Distribution Networks

Integration of distributed energy storage(DES)is beneficial for mitigating voltage fluctuations in highly distributed generator(DG)-penetrated active distribution networks(ADNs).Based on an accurate physical model of ADN,conventional model-based methods can realize optimal control of DES.However,absence of network parameters and complex operational states of ADN poses challenges to model-based methods.This paper proposes a data-driven predictive voltage control method for DES.First,considering time-series constraints,a data-driven predictive control model is formulated for DES by using measurement data.Then,a data-driven coordination method is proposed for DES and DGs in each area.Through boundary information interaction,voltage mitigation effects can be improved by interarea coordination control.Finally,control performance is tested on a modified IEEE 33-node test case.Case studies demonstrate that by fully utilizing multi-source data,the proposed predictive control method can effectively regulate DES and DGs to mitigate voltage violations.

Fuzzy Droop Control for SOC Balance and Stability Analysis of DC Microgrid with Distributed Energy Storage Systems

The unbalanced state of charge(SOC)of distributed energy storage systems(DESSs)in autonomous DC microgrid causes energy storage units(ESUs)to terminate operation due to overcharge or overdischarge,which severely affects the power quality.In this paper,a fuzzy droop control for SOC balance and stability analysis of DC microgrid with DESSs is proposed to achieve SOC balance in ESUs while maintaining a stable DC bus voltage.First,the charge and discharge modes of ESUs are determined based on the power supply requirements of the DC microgrid.One-dimensional fuzzy logic is then applied to establish the relationship between SOC and the droop coefficient R,in the aforementioned two modes.In addition,when integrated with voltage-current double closed-loop control,SOC balance in different ESUs is realized.To improve the balance speed and precision,an exponential acceleration factor is added to the input variable of the fuzzy controller.Finally,based on the average model of converter,the system-level stability of microgrid is analyzed.MATLAB/Simulink simulation results verify the effectiveness and rationality of the proposed method.

Power Generation Enhancement in a Solar Energy and Biomass-Based Distributed Energy System using H_(2)O/CO_(2)Hybrid Gasification

A new solar energy and biomass-based distributed energy system using H_(2)O/CO_(2)hybrid gasification is proposed,and their complementarity to enhance the system's energy efficiency is investigated and shown.In the system,concentrated solar energy is used to provide heat for biomass gasification;two gasifying agents(H_(2)O and CO_(2))are adopted to enhance syngas yields,and the produced solar fuel is further burned for power production in a combined cycle plant.Results show that CO share in gasification products is remarkably increased with the increment of CO_(2)/H_(2)O mole ratio caused by the boudouard reaction with the consumption of fixed carbon,while the H_(2)share is decreased;the optimal solar-to-fuel efficiency,27.88%,is achieved when the temperature and CO_(2)/H_(2)O mole ratio are 1050℃and 0.45,respectively.The emission reduction rate of CO_(2)in the system under design conditions is reduced by 2.31%compared with that using only H_(2)O agent.The annual power production of the system is increased by 1.39%,and the thermodynamic and environmental performances are significantly improved.Moreover,an economic assessment is conducted to forecast the technical feasibility of the hybrid gasification technology.This work provides a promising route to improving the thermochemical utilization efficiency of solar energy and solid fuel.

State-of-charge Balance Control and Safe Region Analysis for Distributed Energy Storage Systems with Constant Power Loads

This paper presents a fully distributed state-of-charge balance control (DSBC) strategy for a distributed energy storage system (DESS). In this framework, each energy storage unit (ESU) processes the state-of-charge (SoC) information from its neighbors locally and adjusts the virtual impedance of the droop controller in real-time to change the current sharing. It is shown that the SoC balance of all ESUs can be achieved. Due to virtual impedance, voltage deviation of the bus occurs inevitably and increases with load power. Meanwhile, widespread of the constant power load (CPL) in the power system may cause instability. To ensure reliable operation of DESS under the proposed DSBC, the concept of the safe region is put forward. Within the safe region, DESS is stable and voltage deviation is acceptable. The boundary conditions of the safe region are derived from the equivalent model of DESS, in which stability is analyzed in terms of modified Brayton-Moser's criterion. Both simulations and hardware experiments verify the accuracy of the safe region and effectiveness of the proposed DSBC strategy.

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.

Low-Carbon Dispatching for Virtual Power Plant with Aggregated Distributed Energy Storage Considering Spatiotemporal Distribution of Cleanness Value

The scale of distributed energy resources is increasing,but imperfect business models and value transmission mechanisms lead to low utilization ratio and poor responsiveness.To address this issue,the concept of cleanness value of distributed energy storage(DES)is proposed,and the spatiotemporal distribution mechanism is discussed from the perspectives of electrical energy and cleanness.Based on this,an evaluation system for the environmental benefits of DES is constructed to balance the interests between the aggregator and the power system operator.Then,an optimal low-carbon dispatching for a virtual power plant(VPP)with aggregated DES is constructed,where-in energy value and cleanness value are both considered.To achieve the goal,a green attribute labeling method is used to establish a correlation constraint between the nodal carbon potential of the distribution network(DN)and DES behavior,but as a cost,it brings multiple nonlinear relationships.Subsequently,a solution method based on the convex envelope(CE)linear re-construction method is proposed for the multivariate nonlinear programming problem,thereby improving solution efficiency and feasibility.Finally,the simulation verification based on the IEEE 33-bus DN is conducted.The simulation results show that the multidimensional value recognition of DES motivates the willingness of resource users to respond.Meanwhile,resolving the impact of DES on the nodal carbon potential can effectively alleviate overcompensation of the cleanness value.

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.

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.

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.