Search Results (121)

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

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

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

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

Real-time energy management for large electric vehicle (EV) clusters requires both fast aggregate flexibility estimation and executable per-vehicle dispatch. Classical LP/MILP/MPC formulations provide strong feasibility and optimality guarantees when the model is fully specified, but their online solve time increases rapidly with cluster size; learning-based methods are fast but often rely on soft constraint penalties or external feasibility repair. We propose the Physics-Constrained Mamba-3 MIMO Aggregator (PC-M3), an amortized, constraint-aware sequence model that integrates a MIMO Mamba backbone, a history-dependent differentiable projection, a sparse routing layer, and an aggregation–disaggregation consistency loop, scaling AI-EMS from a single battery to ten-thousand-vehicle clusters in one forward pass. PC-M3 assigns every EV to one channel of a multi-input multi-output (MIMO) state-space recurrence and embeds the per-vehicle state-of-charge, power and energy constraints as a differentiable in-loop projection, jointly producing the cluster-level flexibility envelope and the per-vehicle charging trajectory. A sparse Routing-Mamba mixture-of-experts layer adaptively allocates capacity to behaviourally distinct sub-populations without supervised labels, and a consistency-trained aggregation–disaggregation loop binds the predicted envelope to the executed dispatch, forming a digital-twin-style predictive EMS pipeline that couples cluster dispatch with per-vehicle SoC evolution. On a single NVIDIA A100, PC-M3 sustains 0.34 s inference for 10,000 EVs over a 24-h horizon, about 18× faster than an Informer baseline and 2.4× faster than PowerMamba. Evaluated on the open ACN-Data and ElaadNL workplace and public charging corpora and on a 10,000-vehicle NREL dsgrid-TEMPO 2030 stress test, PC-M3 reduces the normalised envelope Hausdorff distance from 9.7% (PowerMamba) to 3.4%, cuts closed-loop cluster tracking RMSE from 1.45 MW (model predictive control) to 0.82 MW, and maintains zero observed feasibility violations with respect to the specified or imputed per-vehicle polytopes on every evaluated session. The framework provides a scalable, predictive, constraint-aware AI-EMS for V2G/G2V virtual-power-plant operation of large EV fleets. Full article

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens (technical, economic, environmental, and regulatory) this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

The energy transition requires not only the deployment of low-carbon technologies, but also the organization of dispersed resources into forms of coordination that are operationally effective, institutionally legitimate, and socially durable. The existing transition frameworks explain institutions, niches, and system formation well, yet they are less explicit about how coordination intensifies across physical, digital, and social domains, why technically capable arrangements may remain socially fragile, and how aggregation redistributes authority and visibility. Building on Xue et al.’s Cyber–Physical–Social Systems in Energy (CPSSE) framework, this Perspective develops an interpretive elaboration of CPSSE to address that gap. Its main contribution is a shared analytical vocabulary that links uncertainty, staged coordination, and aggregation, and that recasts virtual power plants as socio-technical accomplishments rather than merely control architectures. Rather than proposing a measurement model, this article uses concepts drawn from information, coordination, and aggregation to examine what conditions render distributed energy governable, whose participation is stabilized or marginalized, and how legitimacy, accountability, and user acceptance become constitutive conditions of coordination. The Perspective contributes to energy social science by clarifying how cyber–physical capability interacts with governance conditions, participation, and institutional durability, while identifying an empirical agenda for studying how coordination is negotiated, stabilized, contested, and unevenly distributed across distributed energy systems. Full article

As the global response to climate change and energy crises accelerates, the large-scale integration of heterogeneous distributed energy resources (DERs) is rapidly transforming traditional passive distribution networks into active distribution networks. However, the massive quantity and high stochasticity of these underlying devices trigger a severe “curse of dimensionality,” creating significant computational and communication bottlenecks for coordinated system dispatch. To overcome these challenges, the “clustering followed by equivalence” aggregation modeling paradigm has emerged as a critical technical pathway. This paper reviews the state-of-the-art clustering and aggregation methodologies for distribution networks with high DER penetration. The review begins by synthesizing multi-dimensional feature extraction techniques and cutting-edge clustering algorithms that establish the foundation for dimensionality reduction. It then delves into refined aggregation models tailored to heterogeneous resources, including dynamic data-driven equivalence for renewable generation, Minkowski sum-based boundary approximations for energy storage, and thermodynamic alongside Markov chain mapping methods for flexible loads. Building upon these models, the paper comprehensively discusses the practical applications of generalized aggregators, such as microgrids and virtual power plants, in feasible region error evaluation, coordinated network control, multi-agent market games, and privacy-preserving architectures. Finally, the review outlines future research trajectories, emphasizing hybrid data-model-driven architectures for real-time dispatch, distributionally robust optimization (DRO) for enhancing grid resilience and self-healing, and decentralized trading ecosystems to ensure equitable system-level surplus allocation. This review aims to provide a systematic theoretical reference for the coordinated management and aggregated trading of flexibility resources in novel power systems. Full article

This study presents a techno-economic analysis of deploying distributed energy resources (DERs), specifically photovoltaic (PV), battery energy storage systems (BESSs) and electric vehicles (EVs), in apartment buildings configured as Virtual Power Plants (VPPs). Utilizing cooperative game theory, the research models strategic collaboration between apartment residents (demand side) and utility operators (plant side) to maximize energy efficiency and economic returns. The VPP structure is analyzed over a 15-year life cycle, incorporating net present value (NPV), payback period (PBP), and government subsidy impacts. A cooperative game framework is applied using the Shapley value to ensure fair profit allocation based on each party’s contribution. Results indicate improved self-sufficiency, peak load reduction, and mutual financial benefits. Scenario analyses show that government subsidies to the plant side significantly increase the likelihood of successful cooperation, while declining DER costs enhance the VPP’s economic viability. The findings demonstrate that apartments configured as VPPs achieve strong economic viability (39% ROI, 10.5-year payback) and operational performance (70% self-sufficiency, 40% peak reduction) when grid arbitrage is enabled and moderate government subsidies (35% PV, 45% BESS) are provided. This research provides a replicable model for urban energy planning and policy development, promoting sustainable energy transitions through shared DER infrastructure and cooperative stakeholder engagement. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article