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.

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.

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.

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.

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.

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.

Blockchain-assisted virtual power plant framework for providing operating reserve with various distributed energy resources

The paradigm shift from a coal-based power system to a renewable-energy-based power system brings more challenges to the supply-demand balance of the grid.Distributed energy resources(DERs),which can provide operating reserve to the grid,are regarded as a promising solution to compensate for the power fluctuation of the renewable energy resources.Small-scale DERs can be aggregated as a virtual power plant(VPP),which is eligible to bid in the operating reserve market.Since the DERs usually belong to different entities,it is important to investigate the VPP operation framework that coordinates the DERs in a trusted man-ner.In this paper,we propose a blockchain-assisted operating reserve framework for VPPs that aggregates various DERs.Considering the heterogeneity of various DERs,we propose a unified reserve capacity evaluation method to facilitate the aggregation of DERs.By considering the mismatch between actual available reserve capacity and the estimated value,the performance of VPP in the operating reserve market is improved.A hardware-based experimental system is developed,and numerical results are presented to demonstrate the effectiveness of the proposed framework.

Modeling curve dynamics and spatial geometry characteristics of rice leaves

The objective of this work was to develop a dynamic model for describing leaf curves and a the rice leaf （including sub-models for unexpanded leaf blades, expanded leaf blades, and dimensional （3D） dynamic visualization of rice leaves by combining relevant models detailed spatial geometry model of leaf sheaths）, and to realize three- Based on the experimental data of different cultivars and nitrogen （N） rates, the time-course spatial data of leaf curves on the main stem were collected during the rice development stage, then a dynamic model of the rice leaf curve was developed using quantitative modeling technology. Further, a detailed 3D geometric model of rice leaves was built based on the spatial geometry technique and the non-uniform rational B-spline （NURBS） method. Validating the rice leaf curve model with independent field experiment data showed that the average distances between observed and predicted curves were less than 0.89 and 1.20 cm at the tilling and jointing stages, respectively. The proposed leaf curve model and leaf spatial geometry model together with the relevant previous models were used to simulate the spatial morphology and the color dynamics of a single leaf and of leaves on the rice plant after different growing days by 3D visualization technology. The validation of the leaf curve model and the results of leaf 3D visualization indicated that our leaf curve model and leaf spatial geometry model could efficiently predict the dynamics of rice leaf spatial morphology during leaf development stages. These results provide a technical support for related research on virtual rice.

Optimal scheduling strategy for virtual power plants based on credibility theory

The virtual power plant(VPP)is a new and efficient solution to manage the integration of distributed energy resources(DERs)into the power system.Considering the unpredictable output of stochastic DERs,conventional scheduling strategies always set plenty of reserve aside in order to guarantee the reliability of operation,which is too conservative to gain more benefits.Thus,it is significant to research the scheduling strategies of VPPs,which can coordinate the risks and benefits of VPP operation.This paper presents a fuzzy chance-constrained scheduling model which utilizes fuzzy variables to describe uncertain features of distributed generators(DGs).Based on credibility theory,the concept of the confidence level is introduced to quantify the feasibility of the conditions,which reflects the risk tolerance of VPP operation.By transforming the fuzzy chance constraints into their equivalent forms,traditional optimization algorithms can be used to solve the optimal scheduling problem.An IEEE 6-node system is employed to prove the feasibility of the proposed scheduling model.Case studies demonstrate that the fuzzy chance strategy is superior to conservative scheduling strategies in realizing the right balance between risks and benefits.

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.