Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

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.

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.

Peer-to-Peer Energy Trading Method of Multi-Virtual Power Plants Based on Non-Cooperative Game

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.

Electricity-Carbon Interactive Optimal Dispatch of Multi-Virtual Power Plant Considering Integrated Demand Response

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.

Impact of industrial virtual power plant on renewable energy integration

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.

Distributionally Robust Optimal Dispatch of Virtual Power Plant Based on Moment of Renewable Energy Resource

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.

Optimal Control and Bidding Strategy of Virtual Power Plant with Renewable Generation

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.

Optimal Energy Management for Virtual Power Plant with Renewable Generation

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.

Battery Energy Storage System and Demand Response Based Optimal Virtual Power Plant Operation

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.

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.

Allocating Ex-post Deviation Cost of Virtual Power Plants in Distribution Networks

Virtual power plants(VPPs)including distributed generation,energy storage,and elastic load are emerging in distribution networks.Multiple VPPs can participate in electricity market as an aggregated entity and effective cost allocation mechanism among VPPs is a crucial issue.This paper focuses on allocating ex-post cost of VPPs incurred by deviation between actual power and ex-ante schedule in a two-settlement electricity market.We obtain approximate quadratic formulation of ex-post deviation cost considering network loss and develop an analytical cost allocation algorithm based on cooperative game theory.The allocated cost is consistent with cost causation principle and provides VPPs with incentive for aggregation.The proposed allocation method and relevant theoretical result are evaluated and verified by numerical tests.

Model-free Demand Response Scheduling Strategy for Virtual Power Plants Considering Risk Attitude of Consumers

Driven by modern advanced information and communication technologies,distributed energy resources have great potential for energy supply within the framework of the virtual power plant(VPP).Meanwhile,demand response(DR)is becoming increasingly important for enhancing the VPP operation and mitigating the risks associated with the fluctuation of renewable energy resources(RESs).In this paper,we propose an incentivebased DR program for the VPP to minimize the deviation penalty from participating in the power market.The Markov decision process(MDP)with unknown transition probability is constructed from the VPP’s prospective to formulate an incentivebased DR program,in which the randomness of consumer behavior and RES generation are taken into consideration.Furthermore,a value function of prospect theory(PT)is developed to characterize consumer’s risk attitude and describe the psychological factors.A model-free deep reinforcement learning(DRL)-based approach is proposed to deal with the randomness existing in the model and adaptively determine the optimal DR pricing strategy for the VPP,without requiring any system model information.Finally,the results of cases tested demonstrate the effectiveness of the proposed approach.

Comprehensive Optimization-based Techno-economic Assessment of Hybrid Renewable Electricity-hydrogen Virtual Power Plants

Hydrogen is being considered as an important option to contribute to energy system decarbonization.However,currently its production from renewables is expensive compared with the methods that utilize fossil fuels.This paper proposes a comprehensive optimization-based techno-economic assessment of a hybrid renewable electricity-hydrogen virtual power plant(VPP)that boosts its business case by co-optimizing across multiple markets and contractual services to maximize its profits and eventually deliver hydrogen at a lower net cost.Additionally,multiple possible investment options are considered.Case studies of VPP placement in a renewable-rich,congested area of the Australian network and based on real market data and relevant sensitivities show that multi-market participation can significantly boost the business case for cleaner hydrogen.This highlights the importance of value stacking for driving down the cost of cleaner hydrogen.Due to the participation in multiple markets,all VPP configurations considered are found to be economically viable for a hydrogen price of 3 AUD$/kg(2.25 USD$/kg),which has been identified as a threshold value for Australia to export hydrogen at a competitive price.Additionally,if the high price volatility that has been seen in gas prices in 2022(and by extension electricity prices)continues,the flexibility of hybrid VPPs will further improve their business cases.

Optimal Offering Strategy of Virtual Power Plant with Hybrid Renewable Ocean Energy Portfolio

This paper proposes a hybrid ocean energy sys-tem to form a virtual power plant(VPP)for participating in electricity markets in order to promote the renewable ocean energy utilization and accommodation.In the proposed system,solar thermal energy is integrated with the closed-cycle ocean thermal energy conversion(OTEC)to boost the temperature differences between the surface and deep seawater for efficiency and flexibility improvements,and the thermodynamic effects of seawater mass flow rates on the output of solar-boosted OTEC(SOTEC)are exploited for deploying SOTEC as a renewable dispatchable unit.An optimal tidal-storage operation model is also developed to make use of subsea pumped storage(SPS)with hydrostatic pressures at ocean depths for mitigating the intermittent tidal range energy in order to make the arbitrage in the electricity market.Furthermore,a two-stage coordinated scheduling strategy is presented to optimally control seawater mass flow rates of SOTEC and hydraulic reversible pump-turbines of SPS for enhancing the daily VPP profit.Comparative studies have been investigated to confirm the superiority of the developed methodology in various renewable ocean energy and electricity market price scenarios.

Secondary Frequency Control Considering Optimized Power Support From Virtual Power Plant Containing Aluminum Smelter Loads Through VSC-HVDC Link

The growing number of renewable energy replacing conventional generators results in a loss of the reserve for frequency control in power systems,while many industrial power grids often have excess energy supply due to abundant wind and solar energy resources.This paper proposes a secondary frequency control(SFC)strategy that allows industrial power grids to provide emergency high-voltage direct current(HVDC)power support(EDCPS)for emergency to a system requiring power support through a voltage source converter(VSC)HVDC link.An architecture including multiple model predictive control(MPC)controllers with periodic communication is designed to simultaneously obtain optimized EDCPS capacity and minimize adverse effects on the providing power support(PPS)system.Moreover,a model of a virtual power plant(VPP)containing aluminum smelter loads(ASLs)and a high penetration of wind power is established for the PPS system.The flexibility and controllability of the VPP are improved by the demand response of the ASLs.The uncertainty associated with wind power is considered by chance constraints.The effectiveness of the proposed strategy is verified by simulation results using the data of an actual industrial power grid in Inner Mongolia,China.The DC voltage of the VSCs and the DC in the potlines of the ASLs are also investigated in the simulation.

Evaluation of greenhouse gas emission reduction potential of a demand–response solution:a carbon handprint case study of a virtual power plant

The transition towards zero-carbon energy production is necessary to limit global warming.Smart energy systems have facilitated the control of demand-side resources to maintain the stability of the power grid and to provide balancing power for increasing renewable energy production.Virtual power plants are examples of demand–response solutions,which may also enable greenhouse gas(GHG)emission reductions due to the lower need for fossil-based balancing energy in the grid and the increased share of renewables.The aim of this study is to show how potential GHG emission reductions can be assessed through the carbon handprint approach for a virtual power plant(VPP)in a grid balancing market in Finland.According to our results,VPP can reduce the hourly based GHG emissions in the studied Finnish grid systems compared with the balancing power without the VPP.Typical energy sources used for the balance power are hydropower and fossil fuels.The reduction potential of GHG emissions varies from 68%to 98%depending on the share of the used energy source for the power balancing,thus VPPs have the potential to significantly reduce GHG emissions of electricity production and hence help mitigate climate change.

Bidding Strategy of a Virtual Power Plant Considering Carbon-electricity Trading

Virtual power plant(VPP)aggregates large amounts of distributed energy and controllable loads.The comprehensive consideration of carbon emissions and electricity transactions has great significance in improving the VPP operation’s economic efficiency.In this paper,the bidding strategy of the VPP by considering the carbon-electricity integration trading in an auxiliary service(AS)market is studied.First of all,the basic structure and operating features of the VPP are briefly introduced.Then,the bidding strategy model of carbon-electricity integration trading in an auxiliary service market is proposed and the corresponding objective function and the constraint conditions are also analyzed.Furthermore,the GAMS solver is utilized to give the optimal solution of the bidding strategy model.Finally,the effectiveness of the bidding strategy of a VPP based on the consideration of carbon-electricity integration trading is verified through simulation cases.

Economic Dispatch of Power Systems with Virtual Power Plant Based Interval Optimization Method

Load prediction and power prediction uncertainties are inevitable aspects of a virtual power plant(VPP).In power system economic dispatch(ED)modeling,the interval is used to describe prediction uncertainties.An ED model with interval uncertainty is established in this paper.The probability degree definition is adopted to convert the interval-based economic dispatch model into a deterministic model for the purposes of solving the modeling problem.Simulation tests are performed on a 10-machine system using professional optimization software(LINGO).The simulation results verify the validity of the proposed interval-based scheme for the economic dispatch of a power system with VPP.

Active Dynamic Aggregation Model for Distributed Integrated Energy System as Virtual Power Plant

Distributed integrated multi-energy systems(DIMSs)can be regarded as virtual power plants to provide additional flexibility to the power system.This paper proposes a robust active dynamic aggregation model for the DIMSs to describe the maximum feasible region.The aggregation model includes the power constraints,energy constraints,and ramping constraints to aggregate different types of resources in the DIMSs.The proposed generator-like and storage-like model does not depend on the ancillary service market and can be directly incorporated into the economic dispatch model of the power system.A novel algorithm based on the column-and-constraint generation algorithm and convex-concave procedure is proposed to solve the two-stage robust optimization problem,which is more efficient than the KKT-based algorithms.Finally,a case study of an actual DIMS is developed to demonstrate the effectiveness of the proposed model.