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To promote the consumption and development of new and green energy sources, we propose an economically optimal dispatch model to simulate virtual power plants (VPPs) in parks considering low-carbon objectives based on a dualcarbon background. First, individual models involved in the optimal dispatch of VPPs are introduced. A two-stage operation process of electricity-to-hydrogen transformation and hydrogen methanation of power-to-gas equipment is refined and analysed. Unstable wind power is converted into hydrogen and supplied to the hydrogen load during the electrolysis of the water-tohydrogen stage in conjunction with the development of hydrogen energy demands. Second, to minimise the sum of operating and carbon emission costs, an optimal dispatch model utilised by the virtual power plant considering a low-carbon objective is established. A simulation of the calculation case shows that the proposed model can achieve efficient energy utilisation and improved economic and carbon costs for a VPP in the park. Finally, a sensitivity analysis of the system cost is conducted by adding weighting coefficients; the optimal range of weighting coefficients for the VPP in the park proposed in this paper are derived for reference by considering economic benefits and environmental protection. [ABSTRACT FROM AUTHOR]

With the popularity and promotion of electric vehicles (EVs), virtual power plants (VPPs) provide a new means for the orderly charging management of decentralized EVs. How to set the price of electricity sales for VPP operators to achieve a win–win situation with EV users is a hot topic of current research. Based on this, this paper first proposes a Stackelberg game model in which the VPP participates in the orderly charging management of EVs as a power sales operator, where the operator guides the EV users to charge in an orderly manner by setting a reasonable power sales price and coordinates various distributed resources to jointly participate in the power market. Furthermore, taking into account the impact of wind power output uncertainty on VPP operation, a robust optimization method is used to extend the deterministic Stackelberg game pricing model into a robust optimization model, and a robust adjustment factor is introduced to flexibly adjust the conservativeness of the VPP operator's bidding scheme in the energy market. The model is then transformed into a robust mixed-integer linear programming (RMILP) problem solved by Karush–Kuhn–Tucker (KKT) conditions and strong dyadic theory. Finally, the effectiveness of the solution method is verified in the calculation example, which gives the optimal pricing strategy for the VPP operator, the optimal charging scheme for EV users, and the remaining internal resources' contribution plan, providing an important idea for the VPP to centrally manage the charging behavior of EVs and improve its own operating revenue. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) sources play an important role that not only reduces the pressure on fossil fuels but also produces safe and clean energy by developing the microgrid (MG) technology that fulfills the excess demand for power throughout the world. The technological development and the blessing of information and communication technology converts the MG technology to a smarter one, termed as smart grid (SG) and virtual power plant, by establishing a two‐way communication between the consumers and service provider with the aid of smart metering infrastructure, dynamic pricing scheme, energy management system, smart transmission and distribution system. The transformation of the traditional power system to SG enhances the efficiency, flexibility, resiliency, and robustness of the power system due to the increasing level of cyber security and automation. A directional pathway from conventional to smart power system has been carried out in this paper by addressing the present status of the power system, challenges during the operations, and possible solutions. Initially, the control operation of MG with RE and energy storage system has been investigated which is essential to understand the operation complexity and challenges due to the variable nature of the RE, load dynamics, and uncertainties. But the facilities of end‐user feedback, dynamic pricing scheme, cyber security, and smart metering system are not present in MG technology that hampers the proper regulation of controlling operation, generating and distributing energy as well as energy losses. The development of SG with smart technologies overcomes these problems by proper controlling of power usage. Finally, this paper provides a path to move the MG toward SG by analyzing the technical and critical challenges of smart devices, equipment integration, and their control issues and provides possible solutions to overcome the integration and control challenges for a reliable and sustainable operation. [ABSTRACT FROM AUTHOR]

In order to improve the level of new energy consumption and reduce the dependence of the power system on traditional fossil energy, this paper proposed a bi-level optimization model for virtual power plant member selection by means of coordination and complementarity among different power sources, aiming at optimizing system economy and clean energy consumption capacity and combining it with the time sequence of load power consumption. The method comprises the following steps: (1) The processing load, wind power, and photovoltaic data by using ordered clustering to reflect the time sequence correlation between new energy and load and (2) uses a double-layer optimization model, wherein the upper layer calculates the capacity configuration of thermal power and energy storage units in a virtual power plant and selects the new energy units to participate in dispatching by considering the utility coefficient of the new energy units and the environmental benefit of the thermal power units. The Latin hypercube sampling (LHS) method was used to generate a large number of subsequences and the mixed integer linear programming (MILP) algorithm was used to calculate the optimal operation scheme of the system. The simulation results showed that by reducing the combination of subsequences between units and establishing a reasonable unit capacity allocation model, the average daily VPP revenue increased by RMB 12,806 and the proportion of new energy generation increased by 1.8% on average, which verified the correctness of the proposed method. [ABSTRACT FROM AUTHOR]

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP's optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by modeling different resources—RESs, energy storages, EVs, and distributed generations. In addition, a method based on bi-directional long short-term memory networks is investigated for forecasting various stochastic parameters, wind speed, electricity price, load demand, and EVs' behavior. The results of this study show the superiority of BLSTM methods for modeling these parameters with an error of 1.47% in comparison with real data. Furthermore, to show the performance of BLSTMs, its results are compared with other benchmark methods such as shallow neural networks, support vector machines, and long short-term memory networks. [ABSTRACT FROM AUTHOR]

Most of current prosumer-energy-management approaches are focused on economic optimization by self-consumption maximization. Nevertheless, a lack of energy management strategies (EMS) that tackle different interaction possibilities among community-clustered solar plus battery prosumers has been detected. Furthermore, such active prosumer EMS may include participation in ancillary service markets such as automatic frequency restoration reserves (aFRR) through an optimized battery-energy storage-system (BESS) operation, as well as incorporating community-level energy management. In this study, an optimal EMS that includes aggregated aFRR-market participation of five solar plus battery prosumers participating in an energy community (EC), with the aim of reducing total costs of ownership for each individual prosumer is proposed. For its validation, different scenarios have been analyzed. The results show that the proposed EMS allows a levelized cost of energy (LCOE) reduction for all community members with respect to the base-case scenario. Moreover, the most profitable scenario for all prosumers is still the only PV. [ABSTRACT FROM AUTHOR]

With the widespread development of distributed generation equipment, the stable access of renewable energy and the consequent energy consumption is still a concern. Virtual power plant (VPP) provides a new solution for access and consumption of renewable energy. The distributed wind turbines, photovoltaic generators, fossil power generators and energy storage equipment are taken as the research objective, and the overall economic optimization model of multi generating units in the VPP mode is established. And then Shapley value method (SVM) is used to allocate the total profit of each unit under the cooperative operation of VPP. A security pool mechanism is proposed to improve the stability of VPP, and the ways to evaluate the stability level of VPP and the contribution of VPP members are given. The simulation results show that the information of power grid will be transmitted efficiently under the cooperative operation of VPP, which can improve the utilization efficiency of energy storage equipment and reduce the rate of "abandoning wind and light" to increase the total profit distributed by SVM. The proposed security pool mechanism can effectively strengthen the stability of VPP. [ABSTRACT FROM AUTHOR]

The rapid development of Internet-of-Things in smart grid has enabled millions of grid-connected distributed controllable resources (DCR; e.g., electric vehicles, controllable loads) to provide service to the grid, such as frequency regulation and demand response. The integration of these DCRs may become a large virtual power plant network with various characteristics. This poses great challenges from both control and management perspectives, e.g., computation/communication burden, optimization complexity, scalability limitation, prosumer privacy, etc. In this article, we propose an effective autonomous incentive-based DCR control and management framework to integrate a large amount of DCRs to provide grid services, which simultaneously provides accurate active power adjustment to the grid, optimizes DCR allocations, and maximizes the profits for all prosumers and system operators. A model-free deep deterministic policy gradient-based method is designed to find the optimal incentives in a continuous action space to encourage prosumers to adjust their power consumptions. The method is implemented in a consortium open-source blockchain platform, Hyperledger Fabric, which facilitates controls and transaction management. To demonstrate the effectiveness of the framework, extensive experimental studies are conducted using real-world data. [ABSTRACT FROM AUTHOR]

To optimize the utilization of rural biomass waste resources (e.g., straw and solid waste), biomass waste energy conversion (BWEC) and carbon cycle utilization (CCU) are integrated into a traditional virtual power plant, i.e., a rural BWEC-CCU-based virtual power plant. Furthermore, a fuzzy robust two-stage dispatching optimal model for the BWEC-CCU-based virtual power plant is established considering the non-determinacy from a wind power plant (WPP) and photovoltaic (PV) power. The scheduling model includes the day-ahead deterministic dispatching model and real-time uncertainty dispatching model. Among them, in the day-ahead dispatching phase, the dispatching plan is formulated with minimum operating cost and carbon emission targets. In the real-time dispatching phase, the optimal dispatching strategy is formulated aiming at minimum deviation adjustment cost by applying the Latin hypercube sampling method. The robust stochastic theory is used to describe the uncertainty. Third, in order to achieve optimal distribution of multi-agent cooperation benefits, a benefit distribution strategy based on Nash negotiation is designed considering the three-dimensional interfering factor of the marginal benefit contribution, carbon emission contribution, and deviation risk. Finally, a rural distribution network in Jiangsu province, China, is selected for case analysis, and the results show that 1) the synergistic optimal effect of BWEC and CCU is obvious, and the operation cost and deviation adjustment cost could decrease by 26.21% and 39.78%, respectively. While the capacity ratio of WPP + PV, BWEC, and CCU is 5:3:2, the dispatching scheme is optimum. 2) This scheduling model can be used to formulate the optimal scheduling scheme. Compared with the robust coefficient Γ = 0, when Γ = 1, the WPP and PV output decreased by 15.72% and 15.12%, and the output of BWEC and CCU increased by 30.7% and 188.19%, respectively. When Γ∈ (0.3, 0.9), the growth of Γ has the most direct impact on the dispatching scheme. 3) The proposed benefit equilibrium allocation strategy can formulate the most reasonable benefit allocation plan. Compared with the traditional benefit allocation strategy, when the proposed method is used, the benefit share of the WPP and PV reduces by 5.2%, and the benefit share of a small hydropower station, BWEC, and CCU increases by 1.7%, 9.7%, and 3.8%, respectively. Overall, the proposed optimal dispatching and benefit allocation strategy could improve the aggregated utilization of rural biomass waste resources and distributed energy resources while balancing the benefit appeal of different agents.

Background: The day-ahead power market is an important part of the spot market. In the day-ahead market, participants make short-term forecasts of the load and output to propose the bidding curve more precisely. As energy aggregators that have regulatory resources, virtual power plants (VPPs) need to consider the uncertainty of distributed renewable energy output when participating in power market transactions.Methods: This paper analyzes the uncertainty and built an optimization model for VPP in day-ahead power market considering the uncertainty from both inner parts and the market environment. To verify the model, a simulation study is ran.Results: And the study results show the following: 1) the forecasting model is more efficient than the traditional algorithm in terms of accuracy, and 2) the confidence levels are not fully positive with the benefit of VPPs.Discussion: Improving the confidence level could reduce the uncertainty brought by renewable energy, but could also cause conservative trading behavior and affect the consumption of renewable energy.

In the case of new investment in RES technology, there are two issues related to efficiency assessment. The first one is how to join technical, financial and economic efficiency assessments in one. An investment feasibility study is usually conducted through a financial analysis to obtain the internal rate of return and the net present value. However, a new RES investment is typically financially unprofitable unless the environmental and social aspects are included. The second one is a lack of required financial data. The new RES investment is often innovative and neither the owner nor other entities have financial data on the operating costs and expenditure for the last periods. Therefore, in this paper, we proposed two methods of efficiency assessment. The first one is based on the avoided costs theory. Furthermore, the second one belongs to heuristic methods and is based on the experts’ assessment of different kinds of parameters. The purpose of this study is to assess the efficiency of the pilot project of VPPs using two recommended methods. This paper emphasizes the advantages and disadvantages of each method. The actual technical and financial data for the period of six months from the pilot study were calculated.

This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy.

Distributed energy resources are important measures to increase energy self-sufficiency and overcome the global carbon reduction problem. However, individual planned renewable energy generation poses a significant threat to the power grid. Therefore, virtual power plant (VPP) is attracting considerable attention as a means of aggregating distributed energy in urban areas. This study proposed a VPP model consisting of updating high-efficiency appliances and photovoltaic and energy storage systems. A comprehensive analysis for assessing the technical, economic, and environmental benefits derived from the VPP was presented, indicating the feasibility of a smart community to achieve power self-sufficiency with the support of the VPP. A smart community in Japan was selected as the research object, with a peak power demand of 57,350 kW. The VPP’s load leveling performance, return on investment (ROI), and CO2 emission reduction were analyzed. In addition, external factors, such as electricity price changes and FiT policies, are considered to assess the impact on the economics of the VPP. The results show that the introduction of the VPP system in the community can effectively stabilize the grid load with a peak shaving rate reaching 42.55% and improve the energy self-sufficiency rate of the region reached 100%, besides providing superior economic and environmental benefits (16.26% CO2 emission reduction) on the demand side. Furthermore, the economic performance of VPP shows a good prospect with the fall in equipment prices and the future trend of carbon tax growth. This study provides important insights into the development of VPP in other countries, especially for low-energy self-sufficiency regions.

Traditional centralized transactions require a control center for user demand matching, settlement and other processes. However, with the increase in the penetration rate of distributed energy in the community, the explosive increase in the number of transactions leads to a decrease in efficiency and it is difficult to guarantee user privacy and information security. The smart contract technology based on blockchain technology has the characteristics of decentralization, traceability and tamper resistance, and these key factors show unique advantages in distributed energy transactions. This paper explores Ethereum and smart contract technology, designs a peer-to-peer energy sharing mechanism with reward and punishment incentives and establishes a smart contract trading platform for smart community-based virtual power plant (CVPP). This paper verifies the functionality and effectiveness of smart contract. The results show that when the supply and demand ratio changes, the user can conduct energy transactions according to the contract without a third-party organization, which solves the problem of trust between the two parties and achieves the expected effect and runs successfully. In addition, the simulation results show that the peer-to-peer transaction based on smart contracts reduces the energy cost per household and increases the total benefit of CVPP.

The extensive penetration in the energy mix of variable renewable energy sources, such as wind and solar, guarantees boosting of the transition toward a decarbonized and sustainable energy system as well as tackling of climate targets. However, the instability and unpredictability of such sources predominantly affect their plant production. Thus, utility-scale energy storage is required to aid in balancing supply and demand and, as a result, to prevent unbalances that might cause issues at different grid levels. In the present study, the authors' patented energy storage technology, known as Integrated Energy Storage System (I-ESS), is combined with a 10 MWp solar plant. The PV plant and the I-ESS unit function as a Virtual Power Plant (VPP). The selected VPP management strategy attempts to optimize the daily hours during which the plant supplies steady power output. Numerical simulations show that the VPP plant can effectively smooth the PV peak and manage the power supply. In particular, by the definition of a novel metric expressing the ratio between regular hours of power provided to the grid plus the energy stored in the backup unit and the total number of hours in a year, the results show that the VPP regularity is relatively high in terms of PV output, ranging from a low of 50% in December to a high of 87% in August. Thus, the proposed VPP arrangement seems to be a promising technology for pushing toward the carbon-neutral transition. [ABSTRACT FROM AUTHOR]

A virtual power plant is a new concept. Its central concept is based on distributed generators connected to each other and monitored through a control unit using the structure of information and communication technology. This study aims to identify the components of product innovation for the virtual power plant industry. The data collection tool was an interview, and the data analysis method was thematic analysis (TA). The sample size for the study was selected based on theoretical sampling and saturation, and eventually, it was attained via interviews with 12 experts. Interview analysis uncovered 77 fundamental conceptual codes. These are analyzed in later stages and, by their conceptual abstraction, the primary themes and central themes that make up the components, the research model is formed. The three main themes identified in the data were hub capability, network structure, and network coordination. The hub capability, network coordination, and network structure include four, seven, and four components, respectively. [ABSTRACT FROM AUTHOR]

Virtual Power Plants (VPPs) are efficient structures for attracting private investment, increasing the penetration of renewable energy and reducing the cost of electricity for consumers. It is expected that the number of VPPs will increase rapidly as their financial return is attractive to investors. VPPs will provide added value to consumers, to power systems and to electricity markets by contributing to different services such as the energy and load-following services. One of the capabilities that will become critical in the near future, when large power plants are retired, is grid-forming capability. This review paper introduces the concept of grid-forming VPPs along with their corresponding technologies and their advantages for the new generation of power systems with many connected VPPs. [ABSTRACT FROM AUTHOR]

The operation process of a virtual power plant is affected by many uncertainties, and how to ensure its comprehensive operation quality is a pressing challenge. For the virtual power plant incorporating electric vehicles, the interval number is used to describe the stochastic fluctuation of system uncertainties, and the optimization objectives are to (1) improve the operating economy, environmental protection, and grid load smoothing, (2) build a multi-objective interval optimal dispatching model considering the constraints of power balance and equipment operating characteristics, (3) solve the Pareto solution set by adopting the improved NSGA-II algorithm incorporating extreme scenario analysis, and (4) determine the optimal dispatching solution by the hierarchical analysis method. The median values of the determined optimal target intervals are 6456.11 yuan, 9860.01 kg, and 2402.56 kW. The algorithm shows that the proposed optimal dispatching strategy can effectively improve the economy of the virtual power plant and ensure that environmental protection and grid load smoothing requirements are met. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) is a promising paradigm to promote the integration of renewable energy-based microgrid (MG) into the power system. This paper addresses the coordinated operation and energy trading problem for VPP aggregated with multi-stakeholder MGs. A two-stage hierarchical coordinated operation framework for VPP is developed consisting of the day-ahead bidding and intra-day dispatch fully considering the settlement of deregulated electricity market, the profit of the VPP operator and the aggregation interest of MGs. To cope with the intra-day operational uncertainties of individual MGs, receding horizon-based model predictive control (MPC) is adopted and extra power balancing services are provided by the VPP operator. The proposed solution is extensively assessed through simulation experiments in presence of four MGs with different ownerships and penetration levels of RDERs, and the numerical results confirm that the proposed solution outperforms two benchmarks. Further, the comparison with two existing methods demonstrates that the adoption of time-of-use price (ToUP) and extra power balancing services in the proposed solution can benefit both MGs and the VPP operator with enhanced energy management performance. [ABSTRACT FROM AUTHOR]

By coordinating the operation of regionally interconnected virtual power plants (VPPs), the growing penetration problem of renewable energy sources (RESs) into the power system can be addressed. This study presents an interactive trading cooperation model of regionally interconnected VPPs using bilateral contracts. The proposed model maximizes overall electricity market social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). The focus of the proposed approach is to design and develop a parallel energy exchange cooperation model of interconnected VPPs, ensuring the operational efficiency and reliability of interconnected power systems over the planning horizon. Given that adjacent VPPs may have differences in their energy generation and usage patterns, a scenario tree method is used to model the uncertainties associated with solar irradiation and load demand. A case study of two interconnected VPPs is used, the operational scenario is designed, and the corresponding computational model is developed. The results highlight that the proposed approach gives VPPs the option to utilize their internal network's maximum capacity. As a result, there will be less reliance on the main grid for interconnected VPPs, and an improvement in key performance indicators, including the cost of the VPPs systems and renewable power variations. [ABSTRACT FROM AUTHOR]

For a decade, distributed energy resources in Malaysia have growth as one of the paths in battling with sustainable energy crisis and environmental pollution. Several intriguing initiatives and incentives have been established to encourage the use and sales-side of renewable energy at the distribution consumers. However, Malaysia's distributed energy resources penetration is still at its slow pace, with only 7.6% (excluding large hydropower) shared in energy mix generation. Therefore, innovation in power systems is required to drive the uptake of distributed energy resources. This paper reviews the business model innovation that allows distributed energy resources to participate in national grid services and the wholesale electricity market. Different technical and non-technical challenges with high shares of variable renewable energy in power systems are highlighted, and the current update on compensation scheme, Net-Energy-Metering 3.0 is also discussed. Along with these challenges, stance the prospect of adopting distributed energy resources innovation projects such as peer-to-peer energy trading and virtual power plant in the electricity market. It could further furnish the benefits to a better environmental and power system in terms of carbon dioxide avoidance, grid flexibility and increase revenue for distributed energy resources owners respectively. Through the review, it led to observation that policy and regulatory in Malaysia are the main factors in accelerating the distributed energy resources deployment. Therefore, the abilities and roles of Malaysia Energy Commission and Sustainable Energy Development Authority as a regulator and implementing agencies are crucial in determining the present and future distributed energy resources business model. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) has the potential to provide frequency regulation services by aggregating demand-side distributed energy resources (DERs). Cellular communication networks are commonly utilized for connecting DERs. Nevertheless, conventional cellular networks prior to 5G were unreliable, while private cellular networks incur huge investment costs. Radio access network (RAN) slicing in 5G communications can address these issues by providing the VPP with virtual private networks in public cellular networks. The VPP must evaluate the benefits of 5G RAN slicing and determine the size of slices before implementing it. This paper proposes to apply 5G RAN slicing to assist the VPP in participating in frequency regulation services. First, an optimization model is developed for the VPP to maximize the profit of engaging DERs in various frequency regulation services. Its solution determines the VPP’s DER portfolio schedule of regulation services and communication needs. Then, the effects of communication reliability on the regulation performance are quantified. 5G RAN slicing enhances the predictability of communication reliability, and its benefit is explicitly modeled as its contribution to reducing VPP’s risk profit loss. Next, based on the 5G RAN slicing benefit, an optimization model for determining the size of slices is further established, considering the cost of purchasing slices from mobile service providers. Additionally, an online solution method is developed, such that DERs’ regulation service portfolio can be determined in real-time. Numerical case studies are conducted to demonstrate the benefit of 5G RAN slicing for the VPP’s frequency regulation services. [ABSTRACT FROM AUTHOR]

The objective of this study is to perform peak load shaving at a virtual power plant (VPP) to maximize the electricity cost-saving for local distribution companies (LDCs) while satisfying the necessary operational constraints. It can be achieved by implementing an efficient algorithm to control the conservation voltage reduction technique (CVR) with embedded energy resources (EERs) to optimize electricity costs during peak hours. EERs consist of distributed energy resources (DERs) such as solar and diesel generators and energy storage systems (ESSs) such as utility-scale and residential batteries. An objective function of mixed integer linear programming is formulated as the electricity cost function. Different operational constraints of EERs are formulated to solve the peak shaving optimization problem. The proposed algorithm is tested using data from a real Australian power distribution network. This paper discusses four cases to demonstrate the performance and economic benefits of the control algorithm. Each of these cases illustrates how EERs contribute differently each year, month, and day. Results showed that the proposed algorithm offers significant cost savings and can shave up to three daily peaks. [ABSTRACT FROM AUTHOR]

The power systems of today seem inseparable from clean energy sources such as wind turbines (WTs) and photovoltaics (PVs). However, due to their uncertain nature, operational challenges are expected when WT and PV energy is added to the electricity network. It is necessary to introduce new technologies to compensate for the intermittent nature of renewable energy sources (RESs). Therefore, rationally implementing a demand response (DR) program with energy storage systems (ESSs) in a virtual power plant (VPP) environment is recommended as a way forward to minimize the volatile nature of RESs and improve power system reliability. Our proposed approach aims to maximize social welfare (SW) (i.e., maximization of consumer benefits while minimizing energy costs). Our method assesses the impact of the DR program on SW maximization. Two scenarios are examined, one with and one without a DR program. Stochastic programming theory is used to address the optimization problem. The uncertain behavior of WTs, PVs, and load demand is modeled using a scenario-based approach. The correctness of the proposed approach is demonstrated on a 16-bus UK generic distribution system. Our results show that SW and active power dispatch capacity of WT, PV, and ESS are fairly increased using the proposed approach. [ABSTRACT FROM AUTHOR]

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Towards the development and demonstration of an innovative business model where the value proposition for consumers/prosumers, aggregators and network operators are well maintained, this study assesses the performance of different aggregation control strategies for a distributed energy storage based residential virtual power plant (VPP). A special focus is given on the social welfare and network strength and their relation to energy storage capacity and power budgets allocation. The study is based on a real-life demonstration project, StoreNet where the basic self-consumption (SB-SC) control strategy has already been deployed. Analysing one-year measured data, it is observed that the implemented SB-SC approach allows 16%−19% electricity cost-saving, whereas the proposed VPP-bill minimisation approach can benefit from 37%−42% cost saving. This is also 7%−8% higher than the single home bill minimisation approach where the community does not participate in the VPP model. In contrast, the peak shaving approach is more favourable for the network operator. It reduces the load peak by 46.5%−64.7% and also drastically reduces the benefits for the customers and aggregator. Based on these studies and learning, some recommendations are made addressing the integration aspect of residential VPP and the future development of this concept for the local and wholesale energy markets. [ABSTRACT FROM AUTHOR]

High penetration of distributed generation and renewable energy sources in power systems has created control challenges in the network, which requires the coordinated management of these resources. Using virtual power plants (VPPs) on a large scale has solved these challenges to a significant extent. VPPs can be considered systems consisting of distributed generations, energy storage, controllable loads, electric vehicles (EVs), and other types of resources to provide energy and ancillary services. VPPs face various challenges such as energy management, operation, resource uncertainty, participation in electricity markets, etc. This paper discusses an overview of the basic challenges of VPPs, including control and communication issues, electricity markets, its different models, and energy management issues. The main purpose is to investigate the performance of VPP in different markets, energy management of VPP in different operating conditions and strategies, and compare different planning methods for VPP. Note that the application of blockchain to control and improve VPP performance has been investigated, taking into account the different layers of this technology. [ABSTRACT FROM AUTHOR]

The capability of shifting the electricity generation or consumption to proper time of the day, also defined as energy shift (ES), is the key factor to ensure the power balance, especially under high penetration of variable renewable energy (VRE). However, the ES is not characterized and traded as an independent product in current market mechanisms. In this letter, the marginal utility of an ES is assessed and leveraged to characterize the effective ES, while a novel market scheme is proposed considering the trading of both ES and energy level (EL). The proposed scheme can well integrate ES producers such as virtual power plants that cannot be rewarded sufficiently to actively participate in the current market because they are principally labeled as EL consumers. Finally, the novel concept and mechanism are illustrated by a numerical study and verified to outperform the existing price schemes on integrating the ES resources and VRE.

Virtual Power Plants (VPPs) allow the aggregated management of diverse power technologies in systems of different size, complexity and connectivity. The main objectives of a VPP are to ensure the correct integration of these technologies into power systems, to optimize the power production and load demand, and to meet the technical and commercial requirements, considering the stakeholders’ needs. This paper provides a literature review of the most influential factors as regards the management of VPPs. This literature review includes aspects like the evolutionary frequency of the concepts/definitions most used, the VPP operation, planning and interaction with the systems, programming methods, math techniques and software tools used to solve the issues proposed and the international VPP projects as reference for the systems development. Therefore, this work aims to provide the reader the information and concepts that can support for a better understanding of the role of VPPs in power systems, the approach to sustainability and the interaction with the power market as the good way to bring new economic opportunities to small producers, consumers and prosumers of renewable energy.

The concept of Virtual Power Plant (VPP) has arisen over a decade ago from the relatively low competitiveness of the back then emerging non-dispatchable RES. A set of smaller generators imitates the behavior of large synchronous generators. So far, static aspects such as generation or slow dynamics have been of interest, as it is the case for the zonal secondary frequency control scheme in Spain, which can be viewed as a VPP. However, considering dynamic aspects is of high importance, especially to further increase the current penetration level of Renewable Energy Sources (RES). Indeed, one should deal with the full participation of RES in grid ancillary services. This means not only to get some positive impact on grid voltage and frequency dynamics but to bring concepts which allow integrating RES to existing secondary regulation schemes on the same level as classic synchronous generators. For that, we propose here a new concept called Dynamic VPP (DVPP) which fully integrates the dynamic aspects at all levels: locally (for each RES generator), globally (for grid ancillary services and interaction with other close-by elements of the grid) and economically (for internal optimal dispatch and participation in electricity markets). A DVPP is a set of dispatchable and non-dispatchable RES along with a set of common control and operation procedures. The latter procedures include the choice of dispatchable and non-dispatchable RES constituting the DVPP, the control of DVPP generators for local objectives and participation of the DVPP as a single unit in ancillary services (especially in case of loss of natural resources - e.g., wind, sun - on a part of the DVPP), the limitation of the risk of adversely interaction with close-by elements and the feasibility in both current power systems scenarios and future ones with large share of RES. This new DVPP framework and approaches developed for its implementation allows ensuring optimal operation of a mixed portfolio of dispatchable and non-dispatchable RES generators for planning, participation to the markets and real-time control. For the control, all time-scale dynamics are considered to improve RES management (internal re-dispatch inside the DVPP to take advantage of dispatchable/non-dispatchable nature of each RES and to optimally manage the lack of natural resources in some regions of the DVPP) and their participation to grid ancillary services. Concrete structures of DVPP as well as ways to address the other control and economical aspects will be shown. This new DVPP concept is now under development in the H2020 POSYTYF project (https://posytyf-h2020.eu/).

The high penetration of variable renewable energies requires the flexibility from both the generation and demand sides. This raises the necessity of modeling stochastic and flexible energy resources in power system operation. However, some distributed energy resources have both stochasticity and flexibility, e.g., prosumers with distributed photovoltaics and energy storage, and plug-in electric vehicles with stochastic charging behavior and demand response capability. Such partly controllable participants pose challenges to modeling the aggregate behavior of large numbers of entities in power system operation. This paper proposes a new perspective on the aggregate modeling of such energy resources in power system operation. Specifically, a unified controllability-uncontrollability-decomposed model for various energy resources is established by modeling the controllable and uncontrollable parts of energy resources separately. Such decomposition enables the straightforward aggregate modeling of massive energy resources with different controllabilities by integrating their controllable components with linking constraints and uncontrollable components with dependent discrete convolution. Furthermore, a two-stage stochastic unit commitment model based on the proposed model for power system operation is established. The proposed model is tested using a three-bus system and real Qinghai provincial power grid of China. The result shows that this model is able to characterize at high accuracy the aggregate behavior of massive energy resources with different levels of controllability so that their flexibility can be fully explored.

Abstract Aiming at the data authenticity and storage problems in the current coordinated scheduling of virtual power plants, as well as the opaque information and high transaction costs, a dual blockchains security mechanism is proposed to solve above problems. In the process of security scheduling, a hybrid attribute proxy re-encryption algorithm based on ciphertext strategy is designed. The algorithm is composed of an identity encryption algorithm and an attribute proxy re-encryption algorithm with ciphertext strategy. Combining blockchain, tamper-proof smart metering equipment can effectively solve data authenticity and confidentiality in the information transmission process of distributed energy. In the research process of the trading mechanism, a continuous double auction mechanism based on reputation is proposed. In order to create a favorable trading atmosphere, reputation-based market segmentation mechanisms are integrated, and participants are divided according to reputation value. Depending on the properties of the stored information, they are divided into the private blockchain (with coordination scheduling information) and the consortium blockchain (with transaction information). The system analysis shows the reliability of the dual blockchains architecture. The communication and calculation costs of the proxy re-encryption algorithm verify the practicability of the proposed scheme. The case analysis of the auction mechanism declares that the mechanism can operate effective in the electricity trading market.

As an emerging and active entity in China’s electricity market, electricity selling companies call for a more reliable operational mechanism and new consumption mode to broaden their profit margins. Aiming at distributed power generation-based sales companies and by considering the participation of virtual power plants (VPPs), this paper presents the relevant operating systems in the Chinese power market. Then the paper proposes a new platform for power transactions and optimal dispatch based on a master-slave game optimization model. The model is built so that the main gamer, power sales company, can achieve maximum profit while at the same time the secondary gamer, represented by the VPP attains the lowest internal dispatching cost. The energy of both parties is linked together, and the two parties continuously exchange their own strategies and optimize the operational decisions and scheduling plans iteratively. The results of the investigated case study reveal the benefits to retail electricity companies from adopting the proposed model to aggregate and manage decentralized resources, and optimize decision-making. The platform facilitates the use of controllable loads and distributed energy sources to participate in market transactions on a large scale, and optimize the operational strategies of electric utilities.

The importance of resources for balancing electricity supply and demand is increasing with the decentralization of power systems; the role of demand response is being emphasized to effectively deal with the volatility of renewable energy. In this study, a model to participate in the electricity market, especially energy and demand response, utilizing demand response resources by compensating for the uncertainty of renewable energy products is proposed. The probabilistic distribution of photovoltaic generation, derived based on stochastic programming, was used as a framework for solving the optimization problem by reflecting the uncertainty of photovoltaic generation. Moreover, a demand response modelling approach to enhance flexibility was developed by estimating the maximum potential demand response capacity in an industrial load. The output of these models was used to derive an optimal operational strategy to participate in the day-ahead market. The simulated Korean electricity market results confirmed that the virtual power plant aggregator’s profits increased when using the suggested strategy to utilizing demand response resources.

Improving the energy efficiency of transportation systems is essential for accelerating decarbonization. Integrating regenerative braking energy (RBE) in subway stations is challenging for power systems. The existing multimodal transport of electric bicycles and subways lends subway station energy storage resources to manage the RBE. In this article, we proposed a virtual power plant (VPP) scheme comprising subway stations, electric bicycles, and photovoltaic systems. We developed an optimization model to manage the charging of distributed electric bicycles to integrate the RBE and reduce the total electricity bill of the VPP. We applied the proposed model to Beijing Subway Lines 5 and 13 and investigated the benefits of the VPP scheme and multimodal transport. The results indicate that the VPP can integrate 42% of the RBE and generate a high profit rate. • A VPP consisting of E-bike and subway is proposed. • The E-bike battery capacities along two Beijing subway lines are estimated. • An optimization model to dispatch and plan the VPP is developed. • The E-bike batteries can store a great proportion of the regenerative braking energy. • The economic potential of the VPP for two Beijing subway lines is remarkable. [ABSTRACT FROM AUTHOR]

Prosumers with flexible distributed energy resources (DERs) can be aggregated as a virtual power plant (VPP) to participate in the electricity market. However, the VPP's energy management requires the prosumers to share individual data, which poses privacy concerns. This paper proposes a distributed differentially private energy management strategy for the VPP to maximize its profit by coordinating the prosumers and at the same time mitigate the privacy risks of local prosumers. Specifically, the coordination of prosumers is first formulated as an optimization problem, which includes the operation constraints of each individual prosumer and the VPP coordination process. On this basis, this paper proposes to solve the optimization problem with a two-level privacy protection strategy. For the individual level, a distributed solution framework is proposed to keep the private information of each prosumer preserved locally. For the communication level, a differential privacy mechanism is integrated into the information exchange process thus reducing the privacy leakage risk. Both theoretical and practical results are provided to verify the performance of the proposed method in terms of optimality and privacy protection levels. • A two-level privacy-preserving framework for the energy management of VPPs. • A distributed differentially private solution algorithm implemented in parallel. • Both theoretical and practical results for the performance of privacy and accuracy. [ABSTRACT FROM AUTHOR]

The virtual power plant (VPP) can aggregate massive distributed resources to participate in power system operation. Different types of resources have been investigated to construct VPPs. Internet data centers (IDCs) are regarded as flexible power consumers that can potentially cooperate with power systems. This paper exhibits the insight for constructing VPPs based on IDCs while comprehensively considering the operational flexibility of the workloads and power supply devices. On the workloads, this paper models the temporal and spatial flexibility of workloads in detail. On the power supply devices, this paper explores their spare capacity with respect to the power supply reliability requirement. Then, a day-ahead scheduling method of the IDC-based VPPs is established based on the operational flexibility modeling. Case studies validate the proposed approach. Moreover, because of the coupling of workload dispatch and power supply device operation, the synergy effect does not equal the sum of its parts. • Exploring the operational flexibility of IDCs to construct VPPs.. • Quantifying the potential of backup power under the reliability requirement. • Realizing the coordination of the backup power and computation workloads in IDCS. • Proposing a day-ahead scheduling framework for IDC-based VPPs. [ABSTRACT FROM AUTHOR]

• A scenario clustering and reduction strategy based on the nodal voltage fluctuation is proposed to obtain the input stochastic parameters of the optimization model. • Considering multiple scenarios of renewable energy generation power, a large-scale stochastic optimization model is developed for optimal scheduling of the virtual power plant. • Based on multi-temporal power flow decoupling, a distributed optimization algorithm is proposed to solve the optimization problem in rolling time domain. The virtual power plants are aggregations of distributed generators, grid-connected devices in user side. The operation of virtual power plants affects the economic benefits and environmental benefits. However, due to the stochastic and fluctuating nature of power generation from renewable energy sources, the optimal scheduling problem for the virtual power plant containing high-dimensional random variables is difficult to solve. The conic power flow constraints are considered in the virtual power plant day-ahead optimal scheduling models to maximize the revenue by virtue of optimizing the flexible resources such as energy storage system and interruptible load. To quantify the uncertainties in the optimization problem, this paper proposes a network state-based power scenario reduction strategy for renewable energy generation, where typical scenarios are selected by the state of the grid voltage. The proposed day-ahead scheduling model is a mixed-integer, nonlinear, large-scale, stochastic optimization problem with high dimensional random variables, which is difficult to be solved directly by traditional centralized method. By temporally decoupling the charging and discharging model of energy storage systems, a distributed optimization algorithm is proposed based on multi-temporal power flow decoupling optimization model. The simulation results show that the proposed distributed optimization algorithm has high accuracy and good convergence, the virtual power plant can achieve day-ahead optimal scheduling and effectively promote renewable energy accommodation under the security and reliability of the grid. [ABSTRACT FROM AUTHOR]

• Based on the local market, a VPP-DSO coordination strategy is proposed. • Providing access to local markets improves the profits of both the VPP and the participants. • The behavior of DERs in the local flexibility market is compensated by a price base on loss-sensitivity. • A VPP operation framework for uncertainty is proposed by applying robust optimization. As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants. [ABSTRACT FROM AUTHOR]

In the pursuit of a near‑carbon-emission electric sector, concentrated solar power plants (CSP) and wind generators have gained prominence, promising dispatchable electricity for renewable-dominated grids. However, the existing studies focus on the coordinated scheduling of CSP and wind energy, overlooking the critical issue of energy pricing and trading. Moreover, a decentralized model for multiple networks that incorporate both CSP and wind generators, remains under-investigated. Accordingly, this paper proposes a fully decentralized distributionally robust transactive energy management (DRTM) framework for the energy trading, pricing and scheduling across multiple integrated wind-concentrated solar virtual power plants (IWC-VPP), using the alternating direction method of multipliers (ADMM). This model allows each IWC-VPP operator to make independent decisions and share minimal information, ensuring privacy encryption. Based on the distributionally robust optimization (DRO), the DRTM framework can balance robustness and cost-effectiveness in making decisions under uncertainties. For efficient resolution, an adaptive buffer-column and constraint generation (AB-C&CG) algorithm is introduced, which reduces the complexity of the master problem compared to the traditional C&CG. Additionally, a varying penalty factor technique is integrated into ADMM to accelerate computation, and a two-block process is implemented to ensure finite convergence of the entire decentralized framework. Numerical studies on the three-VPP 25-Bus system and four-VPP 156-Bus system validate the effectiveness of the proposed DRTM framework. The simulation results demonstrate the varying penalty factor technique bolsters computational efficiency by up to 46.51% for standard ADMM. Compared with the conventional C&CG, the AB-C&CG significantly reduces the computational consumption by 50.98%, and with the error <0.46%. • Co-optimization of energy trading, pricing and scheduling decisions based on DRO for IWC-VPP is presented. • A fully decentralized framework based on ADMM is proposed for multiple IWC-VPPs to encrypt information. • Varying penalty factor technique is integrated to ADMM to accelerate decentralized computation. • Novel AB-C&CG algorithm is employed to efficiently solve the DRO model in each IWC-VPP. • Two-block process is designed to guarantee the finite convergence of the whole decentralized framework. [ABSTRACT FROM AUTHOR]

With the continuous expansion of distributed energy resources (DERs), virtual power plants (VPPs) have emerged as an efficient solution for their aggregation and management. This study proposes a dynamic reconfiguration method for VPPs, addressing the problem of optimally aggregating DERs within a VPP. To enhance DER participation, an innovative membership rewards mechanism is introduced. Furthermore, we develop a method for DER feature extraction and classification to deeply understand their performance. We also assess DERs' willingness to participate, which is the precondition for effective aggregation. The core of our method is an optimized strategy for multi-stage dynamic planning of VPPs, designed to accommodate the dynamic evolution of DERs and electricity markets conditions, and formulated as a two-layer model to offer optimal solutions for DERs aggregation. Numerical simulations validate the superiority of our method in economic benefits and operational efficiency of VPP, highlighting our contributions to enhance the feasibility and efficiency of VPP aggregation through multi-stage planning, dynamic adaptability, and strategic incentives. [ABSTRACT FROM AUTHOR]

• A novel FL-based VPP modeling approach. • A novel multi-task FL method improvement for non-IID data sets such as data sets in VPP. • The proposed algorithm's superiority is its accuracy and calculation velocity. • Presenting the performance analysis of the optimization process of the proposed method. The Virtual Power Plant (VPP) is a promising solution to power systems' challenges. However, its energy management system (EMS) faces challenges due to centralized Big-Data analysis, i.e., complexity and high computational cost. Decentralized strategies, e.g., federated learning (FL), have been proposed to mitigate these challenges. Although conventional FL can be implemented in VPPs, its effectiveness is limited by its typical structure, which relies on single-pattern modeling. Indeed, this particular structure leads to delays and inaccuracies in optimizing the heterogeneous generator dispatch patterns, particularly renewables. Hence, addressing this critical hindrance is essential for FL's efficient and effective deployment in VPPs. To overcome this drawback, this paper proposes novel VPP modeling utilizing multi-task FL modernized by multi-pattern modeling to decentralize the EMS processing. The proposed FL defines electricity generators' agents as clients collaborating to train the decentralized local data coordinated by a central server. The innovative idea is to create a novel multi-pattern-multi-task FL for electricity generation prediction so that the generator's agents learn to provide an optimized dispatch. Thus, the conventional multi-task FL is improved by proposing an optimized neural-network clustering technique. Simulation results demonstrate that multi-pattern-multi-task FL is 39%-41% faster than the best-reported methods, i.e., teaching–learning-based and honey-bee-mating optimization algorithms. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) such as wind, solar, batteries, etc. into the grid presents opportunities as well as challenges with regard to efficient coordination and trading. Virtual power plants (VPPs) have emerged as an important paradigm for aggregating and managing numerous DERs. This paper proposes a decentralized blockchain-based peer-to-peer (P2P) multilayer energy trading framework to facilitate optimized coordination of DERs within a VPP context. A control center, prosumers, and service operators constitute the model's multi-layered architecture. Profits from feed-in tariffs, ancillary services, and P2P sales are optimized for revenue while expenses are minimized using a mixed-integer linear programming formulation. Smart contracts enable prosumers to trade energy units in the Ethereum blockchain with ease via a participatory web platform. The effectiveness of the framework in synchronizing power supply and demand within the VPP by coordinating the charging and discharging of storage systems in accordance with consumption patterns is demonstrated through performance evaluation. In contrast to conventional centralized architectures, the proposed framework utilized decentralized architecture efficiently consolidates and optimizes distributed energy resources (DERs) to improve grid stability and renewable energy utilization. [Display omitted] • A novel multi-layer framework for peer-to-peer (P2P) energy trading, within VPP. • An interactive web-based trading platform using the Ethereum blockchain to facilitate exchange of energy units. • Implementation of MILP-based DER management model for optimal trading. [ABSTRACT FROM AUTHOR]