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.

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.

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.

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.

Design Mode for Component-based Virtual Plant Software

According to the morphological structure characteristics of plants, the de- velopment mode for component-based virtual plants software was put forward, and the internal structure of plant organs component under this mode were analyzed, thereby, the overall design mode for virtual plants software was given out, and its characteristics were estimated. Compared with traditional development modes of virtual plants software, component-based virtual plants software had significant advantages in code reusing, development efficiency and expansion of software functions.

Virtual Plant Modeling Based on Mutual Feedback of Function-structure

[Objective] To study virtual plant modeling based on mutual feedback of function-structure.[Method] With the analysis of the shortcomings of current virtual plant modeling method,the modeling with the idea of function-structure mutual feedback was put forward,and the steps of this modeling were elaborated,including the determination of morphological structure model,biomass production model,biomass allocation model,organ reconstruction model,and the integration method of function model and morphological structure model.[Results] The breakthrough of function-structure mutual feedback based mechanism from the boundaries of physiological ecology model and morphological structure model can solve the difficulty of data transmission between the two models and build an integrated model from the two,which can effectively reflect the incidence relation between plant morphology and function,and more suitable for the growth mechanisms of plants.This modeling approach has significant advantages in the dynamic simulation of plant growth.[Conclusion] The virtual plant modeling based on function-structure mutual feedback provides basis for the simulation of plant growth status in the next stage,and has important significance for the accurate simulation of the dynamic growth process of plant.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Stochastic Flexibility Evaluation for Virtual Power Plants by Aggregating Distributed Energy Resources

To manage a large amount of flexible distributed energy resources(DERs)in the distribution networks,the virtual power plant(VPP)is introduced into the industry.The VPP can optimally dispatch these resources in a cluster manner and provide flexibility for the power system operation as a whole.Most existing studies formulate the equivalent power flexibility of the aggregating DERs as deterministic optimization models without considering their uncertainties.In this paper,we introduce the stochastic power flexibility range(PFR)and timecoupling flexibility(TCF)to describe the power flexibility of VPP.In this model,both operational constraints and the randomness of the DERs’output are incorporated,and a combined model and data-driven solution is proposed to obtain the stochastic PFR,TCF,and cost function of VPP.The aggregating model can be easily incorporated into the optimization model for the power system operator or market bidding,considering uncertainties.Finally,a numerical test is performed.The results show that the proposed model not only has higher computational efficiency than the scenario-based methods but also achieves more economic benefits.

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.

Collision detection of virtual plant based on bounding volume hierarchy: A case study on virtual wheat

Visualization of simulated crop growth and development is of significant interest to crop research and production. This study aims to address the phenomenon of organs cross-drawing by developing a method of collision detection for improving vivid 3D visualizations of virtual wheat crops. First, the triangular data of leaves are generated with the tessellation of non-uniform rational B-splines surfaces. Second, the bounding volumes（BVs） and bounding volume hierarchies（BVHs） of leaves are constructed based on the leaf morphological characteristics and the collision detection of two leaves are performed using the Separating Axis Theorem. Third, the detecting effect of the above method is compared with the methods of traditional BVHs, Axis-Aligned Bounding Box（AABB） tree, and Oriented Bounding Box（OBB） tree. Finally, the BVs of other organs（ear, stem, and leaf sheath） in virtual wheat plant are constructed based on their geometric morphology, and the collision detections are conducted at the organ, individual and population scales. The results indicate that the collision detection method developed in this study can accurately detect collisions between organs, especially at the plant canopy level with high collision frequency. This collision detection-based virtual crop visualization method could reduce the phenomenon of organs cross-drawing effectively and enhance the reality of visualizations.

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.

One Step on the Smart Grid Path： Load and Generation Coordination in a Virtual Power System

Worldwide the introduction of dispersed generators （DG） in the distribution network is assuming a significant importance. There is an increasing relevance of the energy process efficiency improvement; as for electric power systems, the most interesting perspective concerns the capability of the system to increase the exploitation of the renewable resources. The integration of DGs in the electric distribution network requires a revision of this infrastructure, so far designed and developed assuming that power flows in one direction： from the high voltage transmission network to the medium voltage distribution, to reach final customers on the low voltage network. The attention to an efficient operation of distribution networks is increasing all over the world; this interest is becoming higher and higher also in Italy, where the high energy prices push in the direction of fostering efficiency as much as possible. This work describes a study developed in the AlpEnergy project framework： an International Cooperation Program aimed at introducing an efficient operational model for the distributed production and consumption. In particular it is proposed a new model for the integration and the management of the DG in the distribution network. The new model （defined VPS： Virtual Power System） is based on a communication channel between the active users （generators）, the loads and, eventually, the Distribution System Operators （DSOs）.

Approach to Modeling and Virtual-reality-based Simulation for Plant Canopy Lighting

Over the past 20 years, significant progress has been made in virtual plant modeling corresponding to the rapid advances in information technology. Virtual plant research has broad applications in agronomy, forestry, ecol- ogy and remote sensing. As many biological processes are driven by light, it is the key for virtual plant to estimate the light absorbed by each organ. This paper presents the radiance equation suitable for calculating sun and sky light intercepted by plant organs based on the principles of the interaction between light and plant canopy firstly; analyzes the process principles of plant canopy primary lighting based on ray casting and projection secondly; describes the multiple scattering of plant lighting based on Monte Carlo ray tracing method and on the radiosity method thirdly; and confirms the research with 3D visualization based on Virtual Reality Modeling Language (VRML) finally. The research is the primary work of digital agriculture, and important for monitoring and estimating corn growth in Northeast China.

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.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

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.

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.

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.

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.

Energy Management System with Power Offering Strategy for a Microgrid Integrated VPP

In the context of both the Virtual Power Plant (VPP) and microgrid(MG), the Energy Management System (EMS) is a key decision-maker forintegrating Distributed renewable Energy Resources (DERs) efficiently. TheEMS is regarded as a strong enabler of providing the optimized schedulingcontrol in operation and management of usage of disperse DERs and RenewableEnergy reSources (RES) such as a small-size wind-turbine (WT) andphotovoltaic (PV) energies. The main objective to be pursued by the EMSis the minimization of the overall operating cost of the MG integrated VPPnetwork. However, the minimization of the power peaks is a new objective andopen issue to a well-functional EMS, along with the maximization of profitin the energy market. Thus, both objectives have to be taken into accountat the same time. Thus, this paper proposes the EMS application incorporatingpower offering strategy applying a nature-inspired algorithm such asParticle Swarm Optimization (PSO) algorithm, in order to find the optimalsolution of the objective function in the context of the overall operating cost,the coordination of DERs, and the energy losses in a MG integrated VPPnetwork. For a fair DERs coordination with minimized power fluctuationsin the power flow, the power offering strategies with an active power controland re-distribution are proposed. Simulation results show that the proposedMG integrated VPP model with PSO-based EMS employing EgalitarianreDistribution (ED) power offering strategy is most feasible option for theoverall operating cost of VPP revenue. The total operating cost of the proposedEMS with ED strategy is 40.98$ compared to 432.8$ of MGs only withoutEMS. It is concluded that each MGs in the proposed VPP model intelligentlyparticipates in energy trading market compliant with the objective function,to minimize the overall cost and the power fluctuation.

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.