Search Results (297)

The growing deployment of distributed renewable generation, storage, electric vehicles, heat pumps, smart buildings, and controllable demand is expanding the flexibility available to local energy systems. Energy communities provide the governance context for collective participation and value creation, while community virtual power plants provide the operational mechanism for aggregating distributed resources and connecting them to local optimisation, flexibility markets, grid services, and resilience functions. This PRISMA-ScR-informed framework development study synthesises the literature from Scopus, Web of Science, and IEEE Xplore to examine how artificial intelligence supports community VPP operation, where agentic AI adds capabilities beyond established optimisation, reinforcement learning, and multi-agent systems, and which design requirements follow governed orchestration. The synthesis shows that current evidence is strongest for component-level forecasting, scheduling, bidding, adaptive control, distributed coordination, and digital-twin validation, whereas integrated agentic orchestration remains an emerging direction. Classical multi-agent systems already provide decentralised representation, communication, negotiation, and coordinated control; the additional role proposed for agentic AI is therefore narrower and concerns context-aware multi-step workflow orchestration, governed tool use, exception handling, grounded explanation, and bounded delegation across existing analytical and control services. The study introduces operational flexibility intelligence as the capability to transform potential distributed flexibility into deployable, authorised, market-, grid-, resilience-, and community-compatible action. It further develops a conceptual layered reference architecture in which agentic orchestration operates through governed tools and interfaces rather than bypassing validated resource controllers. Fairness, comfort, privacy, cybersecurity, resilience, auditability, and human-in-command authority are treated as cross-cutting operational constraints. The architecture defines a design and validation agenda rather than an empirically validated implementation. Full article

With the global low-carbon transition and increasing wind and photovoltaic penetration, virtual power plant (VPP) scheduling increasingly requires coordinated low-carbon operation, resource management, and long-term stability. Existing studies often treat demand response (DR) resources as passive adjustable capacity, but insufficiently consider how satisfaction variation, fatigue accumulation, and scheduling experience under continuous dispatch feed back into subsequent response behavior. Meanwhile, the coordination among electricity, gas, carbon, and green certificate markets and market incentives reflecting the low-carbon value of power-to-gas (P2G) remain underexplored. To address these issues, this paper introduces a response-willingness feedback mechanism that feeds the satisfaction and fatigue accumulation of the demand response aggregator (DRA) back into subsequent deliverable DR capacity and scheduling behavior. A virtual power plant operator (VPPO)–DRA bi-level optimal scheduling model is then formulated under a Carbon–Green Certificate Coordinated P2G Incentive Mechanism, characterizing the Stackelberg interaction between the VPPO and the DRA. A hierarchical solution framework integrating CMA-ES, dynamic programming, and Gurobi is developed. Deterministic comparisons showed that the model maintained VPP profitability while improving DR sustainability and renewable accommodation. Under ex post stress testing, the fixed nominal day-ahead VPPO strategy maintained feasibility in all 200 cases and positive VPP profit in 94.0%. Full article

To address the voltage violation problem in distribution networks with large-scale distributed generators (DGs), this paper proposes an optimized control strategy for power self-balancing in distribution networks based on virtual power plant (VPP) aggregation. An optimized control architecture for power self-balancing is constructed, comprising a VPP aggregation layer, an independent optimization control layer for individual VPPs, a coordinated optimization control layer for multiple VPPs, and an emerging benefits allocation layer. Then, at the VPP aggregation layer, a VPP aggregation method is proposed considering VPP benefit coupling degree, resource adequacy, and coordination interaction degree. At the independent optimization control layer, an independent optimization control model incorporating active and reactive power regulation of DGs is established for each VPP to achieve power self-balancing for voltage control within the VPP. At the coordinated optimization control layer, a multi-VPP coordination optimization model is constructed based on a linking matrix to achieve coordination among multiple VPPs during the power self-balancing control process and obtain emerging benefits. At the emerging benefits allocation layer, an allocation model based on the contribution degree of each VPP is established to ensure fair distribution of the emerging benefits. Finally, the effectiveness of the proposed method is validated using an actual 10 kV feeder system in Zhejiang Province, China. Full article

Integrating distributed energy resources (DERs) via Virtual Power Plants (VPPs) faces challenges like renewable intermittency, communication scheduling uncertainties, and high data collection costs. While existing studies often overlook practical implementation efficiency, this paper proposes a VPP scheduling framework integrated with communication optimization. First, a communication-scheduling model is established to quantify the impact of network uncertainties on revenue. Second, an equipment pre-allocation strategy based on historical data clustering is presented to lower trial-and-error costs and algorithm complexity. Finally, a global optimization algorithm achieves time-segmented collaborative optimization of equipment access, reducing network switching frequency while balancing packet loss, transmission delay, and operational revenue. Simulation results demonstrate that the proposed strategy reduces VPP scheduling revenue loss by approximately 23.6% compared with the traditional greedy algorithm. Furthermore, when evaluated against classic metaheuristic baseline algorithms such as PSO under identical forecasting conditions, Network-Aware FA (NAFA) effectively escapes local optima and achieves the lowest revenue loss, strongly validating the economic efficiency, algorithmic superiority and scheduling reliability of the proposed framework. Full article

Multi-energy virtual power plants (VPPs) aggregate electricity, heating, and natural gas resources to provide flexible regulation services to the external power grid. Their operational flexibility, however, cannot be accurately characterized using equipment capacities or single-period energy balances alone, because district heating and natural gas networks introduce heat transport delays, pipeline thermal storage, pressure dynamics, and linepack effects. This paper proposes an operational flexibility boundary assessment method for electricity–heating–gas VPPs based on a dynamic energy circuit model (ECM). The frequency-domain ECM converts heating-network temperature dynamics and gas-network pressure dynamics into algebraic constraints, which are integrated with electric-network and multi-energy coupling-device constraints. The net exchange power at the point of common coupling (PCC) is used as the external flexibility interface, and the period-wise upper and lower boundaries are determined subject to network and device constraints, terminal-state recovery requirements, and an economic feasibility limit. Case studies on an electricity–heating–gas VPP demonstrate that the dynamic ECM captures the intertemporal regulation capability provided by pipeline thermal storage and gas-network linepack. Compared with the static model, the dynamic ECM exhibits consistently greater downward flexibility and comparable or lower upward flexibility in several periods, thereby correcting the underestimation of electrical absorption capability and the optimistic estimation of power-export capability caused by the static approximation. The economic feasibility constraint further excludes high-cost boundary schedules, yielding a technically feasible and economically acceptable flexibility range. Full article

The rapid growth of distributed renewable generation and electric vehicles has increased the demand for flexible peak-shaving resources. Battery swapping stations (BSSs), which centrally manage standardized batteries under the battery-as-a-service model, can provide station-to-grid (S2G) services when aggregated by virtual power plants (VPPs). However, S2G adoption is influenced by contract design, market returns, subsidies, battery degradation, and heterogeneous consumer attitudes. This study develops a complex-network evolutionary diffusion model for VPP–BSS cooperation. The framework integrates a VPP profit-accounting module, a segmented Hotelling demand model, and an evolutionary game on a Newman–Watts small-world network. BSS strategies are updated through a partial asynchronous Fermi rule to reflect bounded rationality and investment inertia. Numerical simulations examine contract parameters, subsidy policies, consumer structures, exogenous variables, and network characteristics. The results show that S2G adoption follows an S-shaped trajectory but does not automatically reach full penetration. Successful diffusion requires a feasible combination of electricity prices, revenue sharing, settlement mechanisms, subsidies, consumer acceptance, and available battery capacity. The findings also reveal a trade-off between promoting BSS participation and maintaining VPP profitability, while robustness tests confirm the stability of the main conclusions. Full article

The increasing integration of distributed energy resources, renewable generation, and flexible loads has made Virtual Power Plant (VPP) market participation highly exposed to price volatility and operational uncertainty. This paper proposes Graph-X, a unified graph-structured deep learning and stochastic optimization framework for day-ahead electricity price forecasting and risk-aware VPP bidding. Unlike conventional temporal forecasting models, Graph-X captures structural market-clearing behavior by converting raw bids into continuous differentiable curves represented on a discrete price–quantity graph. The proposed architecture combines sparse graph convolutions, recurrent temporal learning, dilated temporal convolutions, and cyclical calendar encodings to model spatial, temporal, and operational market dependencies. Forecasts are further integrated with a stochastic bidding model governed by a coherent spectral risk measure to align prediction accuracy with financial performance. The framework is validated using historical hourly data from the ISO New England day-ahead electricity market. Results show that Graph-X achieves an MAE of 1.81 $/MWh and an R² score of 0.944, outperforming GNN, LSTM, Transformer, and ARIMA baselines. In VPP bidding, Graph-X delivers an average daily profit of 52.3 k$, improving profitability by 18.1% (equivalent to $2.92 annually) over the GNN baseline, with an average inference time of 12.5 ms per forecast. Full article

Virtual power plants (VPPs) are emerging as flexible resources for automatic generation control (AGC) frequency regulation by coordinating geographically dispersed distributed energy resources. However, the timely execution of AGC commands is highly sensitive to communication latency and reliability, and conventional cellular networks may fail to provide stable service guarantees under high concurrency regulation scenarios. To address these issues, this paper proposes 5G radio access network (RAN) slicing technology to provide dedicated communication resources for VPP frequency regulation command transmission. First, the resulting communication performance is further embedded into the AGC performance score, establishing an explicit mapping from network slicing resources to VPP regulation revenue. Next, a Stackelberg game model between the telecom operator and the VPP is constructed to achieve coordinated optimization of network slice resource pricing and allocation. Simulation results show that the proposed method can significantly improve AGC command transmission reliability and frequency regulation tracking performance, while achieving a coordinated enhancement of both VPP regulation profit and telecom operator revenue. Full article

With the increasing variety of electric vehicles (EVs) joining virtual power plants (VPPs), VPP operators increasingly require precise and tailored models for schedulable EV energy. Based on a publicly available anonymous EV charging power dataset, EV users are clustered through a weighted K-means++ algorithm. Secondly, based on the results of clustering, we analyzed the daily traveling patterns of various types of EVs, including commuting EVs, electric light-duty trucks (ELDTs) and electric tractors (ETs), and then customized the all-day schedulable energy domain model (SEDM) for each category. Subsequently, an optimal bidding strategy for a VPP consisting of diversified-member EVs, air conditionings (ACs), energy storage (ES) and distributed energy resources (DERs) is constructed. By modifying the levels of participation in supplementation and absorption of DERs among VPP members, while integrating considerations such as user comfort, EV defying rate, and seasonal variability, diverse VPP operational frameworks are established. Finally, using the Gurobi solver, the optimal bidding strategies and profit results under different scenarios are derived. The results indicate that (1) increasing the VPP members’ participation in the supplementation and absorption of DERs will bring higher benefits to both the VPP and its members; (2) with the increased sensitivity of users to room temperature and range anxiety, the demand response capacity of AC clusters decreases, reducing EV clusters’ market participation and VPP profits; and (3) among various types of EVs, ELDTs and ETs have a larger battery energy adjustment range, which can fully supplement the output shortfalls of DERs. Therefore, these EVs prove to be a good supplement for the improvement of VPP’s schedule capability and profitability. Full article

In Virtual Power Plants (VPPs), computing the Optimal Energy Flow (OEF) for integrated electricity–heat systems is essential but challenged by highly asymmetric topologies and multi-timescale characteristics. Traditional data-driven models often fail to extract cross-domain coupling features and violate physical laws, yielding infeasible solutions. To address these challenges, this paper proposes an efficient OEF framework based on a Hierarchical Physics-Informed Heterogeneous Graph Neural Network (HPI-HGNN). First, a lossless mapping mechanism transforms physical networks into a heterogeneous graph, utilizing generalized edge attributes and feature projection to resolve dimensional discrepancies. Second, an attention-driven feature fusion algorithm is developed to adaptively evaluate path sensitivities and deeply extract cross-domain features. Finally, a hierarchical constraint mechanism embeds polar-coordinate power flow and thermo-hydraulic balance equations directly into hidden layers. This approach enables mechanism-guided optimization through layer-wise cross-gradient feedback. Simulations on a coupled IEEE 33-bus power and 32-node thermal system show that HPI-HGNN improves prediction accuracy for key variables (voltage magnitude, phase angle, and temperature) by 42.58–78.51% compared to a baseline PINN. Furthermore, it effectively suppresses voltage and temperature limit violations to a near-zero level, ensuring a highly accurate and physically reliable solver for secure online VPP scheduling. Full article

The deterministic evaluation framework for traditional virtual power plants (VPP) overlooks the capacity squeeze caused by day-ahead forecasting errors in real-time settlement, resulting in a significant overestimation of the adjustable capacity committed to external parties; simultaneously, conventional distributed PV forecasting methods struggle to capture the dynamic spatiotemporal coupling characteristics arising from meteorological time-varying factors. To address these issues, this paper proposes a comprehensive evaluation method for VPP adjustable capacity that integrates front-end dynamic spatiotemporal forecasting with back-end risk-averse scheduling. Firstly, a two-step dynamic sub-region partitioning strategy based on a sliding time window is proposed. By combining the Spacetimeformer self-attention model with K-means error mining, high-precision scenario-based available power boundaries are generated. Secondly, conditional value at risk (CVaR) is introduced to construct a two-stage risk-averse stochastic programming (RA-SP) model that accounts for expected physical default penalties. Finally, a parallel control evaluation system is established based on the physical baseline anchoring without response of internal flexible resources and real-time post-event verification. Simulations using real operational data demonstrate that the proposed dynamic spatiotemporal forecasting architecture significantly improves regional power forecasting accuracy; compared with traditional deterministic models, the RA-SP model effectively eliminates artificially inflated committed capacity exceeding physical limits (reducing the assessed tracking root-mean-square error by 32.42%), and by proactively setting aside a physical risk-resistance margin, the cumulative physical default electricity consumption throughout the day was reduced by 99.99%. This not only significantly enhanced the reliability of physical delivery but also achieved a flexible and reliable settlement rate with high confidence. Full article

With the development of multi-energy systems, Multi-Virtual Power Plants (MVPPs) and Load Aggregators (LAs) are playing an increasingly important role in energy trading. However, existing research still has shortcomings in the comprehensive modeling of multi-VPP competition and multi-energy coupling transactions. Therefore, this paper develops an MVPP-LA non-cooperative game optimization model that accounts for hydrogen blending and low-carbon operation. The model incorporates a multi-energy coupling system comprising electricity, heat, gas, and hydrogen, and introduces a staged carbon trading mechanism. In terms of model solving, A C-ADMM-based distributed equilibrium-seeking algorithm is developed to coordinate local optimization and bilateral trading consensus among VPPs and the LA. Case studies show that the proposed model can effectively stimulate trading activity in the MVPP, improve energy efficiency, and reduce carbon emissions. Full article

To address the effective dispatch and coordinated control of ESS, EV, and HVAC resources in building parks participating in grid frequency regulation under the virtual power plant (VPP) architecture, this paper proposes a multi-time scale frequency regulation strategy based on state priority and the Stackelberg game. First, a state-based upward and downward frequency regulation priority model is established to dynamically dispatch ESS, EVs, and HVAC systems according to their operating states. Second, a multi-time-scale frequency regulation Stackelberg game model considering priority incentives is constructed with the goal of economic optimality. A comprehensive utility function integrating the startup threshold and regulation rigidity is designed to achieve a balanced optimization that considers both the operational economy of the VPP and the user comfort of the underlying devices. Finally, considering the frequency regulation response characteristics of the resources, a multi-time-scale dynamic weight optimization and reconstruction method is proposed. Case study results show that the proposed strategy can efficiently dispatch park resources, track automatic generation control (AGC) commands with high precision, and significantly reduce system frequency fluctuations. It ensures the safe operation of the system and user comfort while achieving economic optimality. Full article

Under the background of large-scale renewable energy integration, virtual power plants (VPPs) realize unified coordination and information interaction between generation resources, user loads and power grids, which serves as a critical technical means for efficient operation in the new energy scenario. To improve energy utilization efficiency of VPPs, this paper proposes a data-driven optimal dispatch framework. First, cluster analysis is performed to clean massive operation data and remove redundant information. Then, an improved long short-term memory (LSTM) method is adopted to extract time-series features of wind–solar output and user load. On this basis, a resource aggregation and optimal control model is constructed using real-time demand response and consensus algorithm. Case studies demonstrate that all clusters reach consistent convergence after 30 iterations with a cost increase rate of 0.28 Yuan/kW. The optimal regulation powers of clusters 4, 6, 9, 10 and 13 are 7.2, 3, 1.67, 0.43 and 0.58 kW, respectively. When cluster 14 participates in regulation temporarily, convergence steps rise but the minimum-cost power optimization remains valid. Simulation of whole-day dispatch shows that demand response effectively promotes renewable energy consumption and reduces the comprehensive operating cost by 4.86%. The proposed strategy can strengthen the renewable energy accommodation capability and reduce operation costs of VPPs. Full article

The increasing integration of distributed electricity–heat adjustable resources into distribution networks poses significant challenges for virtual power plant (VPP) dispatch, as conventional aggregation models often neglect network constraints, leading to infeasible or unsafe operation plans. To address this issue, this paper proposes a source-grid-load-storage aggregation optimization method that explicitly incorporates both distribution network power flow constraints and district heating network hydraulic–thermal coupling constraints. The network constraints are integrated into the optimization objective as penalty terms, and the dispatch problem is formulated as a Markov decision process. A deep reinforcement learning framework, combining twin delayed deep deterministic policy gradient (TD3) and deep deterministic policy gradient (DDPG) algorithms, is employed to solve the sequential decision-making problem. Simulation results demonstrate that the proposed method effectively ensures distribution network security and heating quality while maintaining economic efficiency, providing a feasible and safe dispatch strategy for VPPs in coupled electricity–heat systems. Full article