Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic effici...Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodolo...Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.展开更多
The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant ...The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.展开更多
This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards grea...This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.展开更多
The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challeng...The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.展开更多
An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on...An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.展开更多
The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be furth...The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.展开更多
Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This ...Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.展开更多
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...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.展开更多
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-in...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.展开更多
As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve t...As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.展开更多
A Virtual Power Plant (VPP), aggregating the capacities of distributed energy resources (DER) as a single profile, provides presence of DERs in the electricity market. In this paper, a stochastic bidding model is prop...A Virtual Power Plant (VPP), aggregating the capacities of distributed energy resources (DER) as a single profile, provides presence of DERs in the electricity market. In this paper, a stochastic bidding model is proposed for the VPP to optimise the bids in the day-ahead and balancing market, with the objective to maximise its expected economic profit. The performance of proposed strategy has been assessed in a modified commercial VPP (CVPP) system with wind generation installed, and also the results are compared with the ones achieved from other commonly-used strategies to verify its feasibility.展开更多
RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （v...RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.展开更多
The nature of variable and uncertainty from renewable energy sources (RESs) makes them challenging to be integrated into the main grid separately. A Virtual Power Plant (VPP) is proposed to aggregate the capacities of...The nature of variable and uncertainty from renewable energy sources (RESs) makes them challenging to be integrated into the main grid separately. A Virtual Power Plant (VPP) is proposed to aggregate the capacities of RESs and facilitate the integration and management in a decentralized manner. In this paper, a novel framework for optimal energy management of VPP considering key features such as handling uncertainties with RESs, reducing operating costs and regulating system voltage levels is proposed, and a two-stage stochastic simulation is formulated to address the uncertainties of RESs generation and electricity prices. Simulation result show that the framework can benefit from ensuring the energy balance and system security, as well as reducing the operation costs.展开更多
With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably...With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.展开更多
Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstructio...Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.展开更多
Large-scale renewable energy penetration desires higher flexibility in the power system.Combined heat and power virtual power plants(CUP-VPPs)provide an economic-effective method to improve the power system flexibilit...Large-scale renewable energy penetration desires higher flexibility in the power system.Combined heat and power virtual power plants(CUP-VPPs)provide an economic-effective method to improve the power system flexibility by aggregating the distributed resources of an electric-thermal coupling system.The topology can be optimally reconfigured in a power distribution system by operating tie and segment switches.Similarly,the heat flow profile can be redistributed in the district heating system(DHS)with valve switching and provide notable flexibility for CHP-VPPs self-scheduling.To address this issue,an aggregation model for the CHP-VPP is proposed to trade in typical day-ahead energy and reserve electricity markets,which is formulated as an adjustable robust optimization(ARO)problem to assure the realizability of all dispatch requests.The energy flow model is introduced in DHS formulation to make the model solvable.Due to the binary switching variables in the second stage of the proposed ARO problem,classical Karush-Kuhn-Tucker-based algorithms cannot be adopted directly and a nested column-and-constraint generation solution strategy is proposed.Case studies based on an actual CHP-VPP certify the validity of the proposed model and algorithm.展开更多
Unregulated naked selling of virtual power plants(VPPs)in day-ahead markets poses inherent risks to grid security and market fairness.This paper proposes a joint electricityreserve trading model for VPPs as a strategi...Unregulated naked selling of virtual power plants(VPPs)in day-ahead markets poses inherent risks to grid security and market fairness.This paper proposes a joint electricityreserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling.This model systematically evaluates the economic advantages and risks of naked selling,utilizing metrics such as user comfort and conditional value at risk(CVaR).Furthermore,a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms,including support vector quantile regression(SVQR)and kernel density estimation(KDE),is employed to quantify the uncertainties related to prices and reserve activation precisely.The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling,thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.展开更多
China is adopting virtual power plants to integrate dispersed renewable energy and stabilize the grid.But uncertain profitability,market fragmentation and the absence of a mature spot energy market challenge large-sca...China is adopting virtual power plants to integrate dispersed renewable energy and stabilize the grid.But uncertain profitability,market fragmentation and the absence of a mature spot energy market challenge large-scale commercialization.展开更多
基金funded by the Department of Education of Liaoning Province and was supported by the Basic Scientific Research Project of the Department of Education of Liaoning Province(Grant No.LJ222411632051)and(Grant No.LJKQZ2021085)Natural Science Foundation Project of Liaoning Province(Grant No.2022-BS-222).
文摘Virtual power plant(VPP)integrates a variety of distributed renewable energy and energy storage to participate in electricity market transactions,promote the consumption of renewable energy,and improve economic efficiency.In this paper,aiming at the uncertainty of distributed wind power and photovoltaic output,considering the coupling relationship between power,carbon trading,and green cardmarket,the optimal operationmodel and bidding scheme of VPP in spot market,carbon trading market,and green card market are established.On this basis,through the Shapley value and independent risk contribution theory in cooperative game theory,the quantitative analysis of the total income and risk contribution of various distributed resources in the virtual power plant is realized.Moreover,the scheduling strategies of virtual power plants under different risk preferences are systematically compared,and the feasibility and accuracy of the combination of Shapley value and independent risk contribution theory in ensuring fair income distribution and reasonable risk assessment are emphasized.A comprehensive solution for virtual power plants in the multi-market environment is constructed,which integrates operation strategy,income distribution mechanism,and risk control system into a unified analysis framework.Through the simulation of multi-scenario examples,the CPLEXsolver inMATLAB software is used to optimize themodel.The proposed joint optimization scheme can increase the profit of VPP participating in carbon trading and green certificate market by 29%.The total revenue of distributed resources managed by VPP is 9%higher than that of individual participation.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00266141)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).
文摘Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.
基金supported by the National Key Research and Development Program of China,Grant No.2020YFB0905900.
文摘The Virtual Power Plant(VPP),as an innovative power management architecture,achieves flexible dispatch and resource optimization of power systems by integrating distributed energy resources.However,due to significant differences in operational costs and flexibility of various types of generation resources,as well as the volatility and uncertainty of renewable energy sources(such as wind and solar power)and the complex variability of load demand,the scheduling optimization of virtual power plants has become a critical issue that needs to be addressed.To solve this,this paper proposes an intelligent scheduling method for virtual power plants based on Deep Reinforcement Learning(DRL),utilizing Deep Q-Networks(DQN)for real-time optimization scheduling of dynamic peaking unit(DPU)and stable baseload unit(SBU)in the virtual power plant.By modeling the scheduling problem as a Markov Decision Process(MDP)and designing an optimization objective function that integrates both performance and cost,the scheduling efficiency and economic performance of the virtual power plant are significantly improved.Simulation results show that,compared with traditional scheduling methods and other deep reinforcement learning algorithms,the proposed method demonstrates significant advantages in key performance indicators:response time is shortened by up to 34%,task success rate is increased by up to 46%,and costs are reduced by approximately 26%.Experimental results verify the efficiency and scalability of the method under complex load environments and the volatility of renewable energy,providing strong technical support for the intelligent scheduling of virtual power plants.
基金supported by the Science and Technology Project of State Grid Sichuan Electric Power Company Chengdu Power Supply Company under Grant No.521904240005.
文摘This paper presents a novel approach to dynamic pricing and distributed energy management in virtual power plant(VPP)networks using multi-agent reinforcement learning(MARL).As the energy landscape evolves towards greater decentralization and renewable integration,traditional optimization methods struggle to address the inherent complexities and uncertainties.Our proposed MARL framework enables adaptive,decentralized decision-making for both the distribution system operator and individual VPPs,optimizing economic efficiency while maintaining grid stability.We formulate the problem as a Markov decision process and develop a custom MARL algorithm that leverages actor-critic architectures and experience replay.Extensive simulations across diverse scenarios demonstrate that our approach consistently outperforms baseline methods,including Stackelberg game models and model predictive control,achieving an 18.73%reduction in costs and a 22.46%increase in VPP profits.The MARL framework shows particular strength in scenarios with high renewable energy penetration,where it improves system performance by 11.95%compared with traditional methods.Furthermore,our approach demonstrates superior adaptability to unexpected events and mis-predictions,highlighting its potential for real-world implementation.
基金Department of Navy Awards N00014-22-1-2001 and N00014-23-1-2124 issued by the Office of Naval Research。
文摘The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.
基金Department of Science and Technology of Guangdong Province(Project 2019B0909011001).
文摘An industrial park is one of the typical en ergy con sumption schemes in power systems owing to the heavy in dustrial loads and their abilities to resp ond to electricity price cha nges.Therefore,en ergy in tegrati on in the industrial sector is significant.Accordingly,the concept of industrial virtual power plant(IVPP)has been proposed to deal with such problems.This study demonstrates an IVPP model to man age resources in an eco-i ndustrial park,including en ergy storage systems,dema nd resp onse(DR)resources,and distributed energies.In addition,fuzzy theory is used to cha nge the deterministic system constraints to fuzzy parameters,considering the uncertainty of renewable energy,and fuzzy chance constraints are then set based on the credibility theory.By maximizi ng the daily ben efits of the IVPP owners in day-ahead markets,DR and energy storage systems can be scheduled economically.Therefore,the energy between the grid and IVPP can flow in both directions:the surplus renewable electricity of IVPP can be sold in the market;when the electricity gen erated in side IVPP is not enough for its use,IVPP can also purchase power through the market.Case studies based on three win d-level scenarios dem on strate the efficie nt syn ergies betwee n IVPP resources.The validatio n results indicate that IVPP can optimize the supply and demand resources in in dustrial parks,thereby decarbonizing the power systems.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China,under Grant 2021200.
文摘The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China,under Grant J2020090.
文摘Virtual power plants can effectively integrate different types of distributed energy resources,which have become a new operation mode with substantial advantages such as high flexibility,adaptability,and economy.This paper proposes a distributionally robust optimal dispatch approach for virtual power plants to determine an optimal day-ahead dispatch under uncertainties of renewable energy sources.The proposed distributionally robust approach characterizes probability distributions of renewable power output by moments.In this regard,the faults of stochastic optimization and traditional robust optimization can be overcome.Firstly,a second-order cone-based ambiguity set that incorporates the first and second moments of renewable power output is constructed,and a day-ahead two-stage distributionally robust optimization model is proposed for virtual power plants participating in day-ahead electricity markets.Then,an effective solution method based on the affine policy and second-order cone duality theory is employed to reformulate the proposed model into a deterministic mixed-integer second-order cone programming problem,which improves the computational efficiency of the model.Finally,the numerical results demonstrate that the proposed method achieves a better balance between robustness and economy.They also validate that the dispatch strategy of virtual power plants can be adjusted to reduce costs according to the moment information of renewable power output.
基金The Science and Technology Development Fund,Macao SAR(File No.0011/2022/AGJFile No.SKL-IOTSC(UM)-2021-2023).
文摘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.
基金supported by the Sichuan Science and Technology Program(grant number 2022YFG0123).
文摘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.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.52107107).
文摘As new power systems and dual carbon policies develop,virtual power plant cluster(VPPC)provides another reliable way to promote the efficient utilization of energy and solve environmental pollution problems.To solve the coordinated optimal operation and low-carbon economic operation problem in multi-virtual power plant,a multi-virtual power plant(VPP)electricity-carbon interaction optimal scheduling model considering integrated demand response(IDR)is proposed.Firstly,a multi-VPP electricity-carbon interaction framework is established.The interaction of electric energy and carbon quotas can realize energy complementarity,reduce energy waste and promote low-carbon operation.Secondly,in order to coordinate the multiple types of energy and load in VPPC to further achieve low-carbon operation,the IDR mechanism based on the user comprehensive satisfaction(UCS)of electricity,heat as well as hydrogen is designed,which can effectively maintain the UCS in the cluster within a relatively high range.Finally,the unit output scheme is formulated to minimize the total cost of VPPC and the model is solved using theCPLEX solver.The simulation results showthat the proposed method effectively promotes the coordinated operation among multi-VPP,increases the consumption rate of renewable energy sources and the economics of VPPC and reduces carbon emissions.
文摘A Virtual Power Plant (VPP), aggregating the capacities of distributed energy resources (DER) as a single profile, provides presence of DERs in the electricity market. In this paper, a stochastic bidding model is proposed for the VPP to optimise the bids in the day-ahead and balancing market, with the objective to maximise its expected economic profit. The performance of proposed strategy has been assessed in a modified commercial VPP (CVPP) system with wind generation installed, and also the results are compared with the ones achieved from other commonly-used strategies to verify its feasibility.
文摘RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.
文摘The nature of variable and uncertainty from renewable energy sources (RESs) makes them challenging to be integrated into the main grid separately. A Virtual Power Plant (VPP) is proposed to aggregate the capacities of RESs and facilitate the integration and management in a decentralized manner. In this paper, a novel framework for optimal energy management of VPP considering key features such as handling uncertainties with RESs, reducing operating costs and regulating system voltage levels is proposed, and a two-stage stochastic simulation is formulated to address the uncertainties of RESs generation and electricity prices. Simulation result show that the framework can benefit from ensuring the energy balance and system security, as well as reducing the operation costs.
文摘With certain controllability of various distribution energy resources (DERs) such as battery energy storage system (BESS), demand response (DR) and distributed generations (DGs), virtual power plant (VPP) can suitably regulate the powers access to the distribution network. In this paper, an optimal VPP operating problem is used to optimize the charging/discharging schedule of each BESS and the DR scheme with the objective to maximize the benefit by regulating the supplied powers over daily 24 hours. The proposed solution method is composed of an iterative dynamic programming optimal BESS schedule approach and a particle swarm optimization based (PSO-based) DR scheme approach. The two approaches are executed alternatively until the minimum elec-tricity cost of the whole day is obtained. The validity of the proposed method was confirmed with the obviously decreased supplied powers in the peak-load hours and the largely reduced electricity cost.
基金supported by the National Natural Science Foundation of China(no.72471087)Beijing Nova Program(no.20250484853)+1 种基金Beijing Natural Science Foundation(no.9242015)National Social Science Foundation of China(no.24&ZD111).
文摘Given the power system balancing challenges induced by high-penetration renewable energy integration,this study systematically reviews international balancing mechanism practices and conducts an in-depth deconstruction of Germany’s balancing group mechanism(BGM).Building on this foundation,this research pioneers the integration of virtual power plants(VPPs)with the BGM in the Chinese context to overcome the limitations of traditional single-entity regulation models in flexibility provision and economic efficiency.A balancing responsibility framework centered on VPPs is innovatively proposed and a regional multi-entity collaboration and bi-level responsibility transfer architecture is constructed.This architecture enables cross-layer coordinated optimization of regional system costs and VPP revenues.The upper layer minimizes regional operational costs,whereas the lower layer enhances the operational revenues of VPPs through dynamic gaming between deviation regulation service income and penalty costs.Compared with traditional centralized regulation models,the proposed method reduces system operational costs by 29.1%in typical regional cases and increases VPP revenues by 24.9%.These results validate its dual optimization of system economics and participant incentives through market mechanisms,providing a replicable theoretical paradigm and practical pathway for designing balancing mechanisms in new power systems.
基金supported by the National Natural Science Foundation of China(U22A6007)the National Natural Science Foundation of China(52307131).
文摘Large-scale renewable energy penetration desires higher flexibility in the power system.Combined heat and power virtual power plants(CUP-VPPs)provide an economic-effective method to improve the power system flexibility by aggregating the distributed resources of an electric-thermal coupling system.The topology can be optimally reconfigured in a power distribution system by operating tie and segment switches.Similarly,the heat flow profile can be redistributed in the district heating system(DHS)with valve switching and provide notable flexibility for CHP-VPPs self-scheduling.To address this issue,an aggregation model for the CHP-VPP is proposed to trade in typical day-ahead energy and reserve electricity markets,which is formulated as an adjustable robust optimization(ARO)problem to assure the realizability of all dispatch requests.The energy flow model is introduced in DHS formulation to make the model solvable.Due to the binary switching variables in the second stage of the proposed ARO problem,classical Karush-Kuhn-Tucker-based algorithms cannot be adopted directly and a nested column-and-constraint generation solution strategy is proposed.Case studies based on an actual CHP-VPP certify the validity of the proposed model and algorithm.
基金supported by the National Natural Science Foundation of China(No.52477087)Fundamental Research Funds for the Central Universities(No.B230201048)。
文摘Unregulated naked selling of virtual power plants(VPPs)in day-ahead markets poses inherent risks to grid security and market fairness.This paper proposes a joint electricityreserve trading model for VPPs as a strategic measure to mitigate the negative impacts of naked selling.This model systematically evaluates the economic advantages and risks of naked selling,utilizing metrics such as user comfort and conditional value at risk(CVaR).Furthermore,a sophisticated combination of a data-driven level-set fuzzy approach and advanced algorithms,including support vector quantile regression(SVQR)and kernel density estimation(KDE),is employed to quantify the uncertainties related to prices and reserve activation precisely.The results of case studies demonstrate that integrating default penalties within the proposed trading model diminishes the overall revenue of VPPs engaging in naked selling,thereby serving as a robust decision for mitigating the adverse effects of the naked selling of VPPs.
文摘China is adopting virtual power plants to integrate dispersed renewable energy and stabilize the grid.But uncertain profitability,market fragmentation and the absence of a mature spot energy market challenge large-scale commercialization.
