Market-Oriented Joint Optimal Dispatch Strategy for Virtual Power Plants Considering Management Costs

Bi-level water and energy nexus of virtual power plant and microgrid systems in distribution systems: A hybrid cooperative & non-cooperative framework

Flexible operation of virtual power plant enabled integrated electricity-heating system under multiple uncertainties via distributionally robust model predictive control

Optimal scheduling of virtual power plant for short term operating reserve considering EV battery swapping

Global collaborations, particularly those between low-income (LIC) and high-income countries (HIC), may inadvertently reproduce the very power differentials they aspire to overcome. The Toronto Addis Ababa Academic Collaboration (TAAAC) is a partnership model deliberately built to follow a relational and invited guest model of collaboration with in-person teaching visits by University of Toronto (UofT) faculty to teach within Addis Ababa University (AAU) programmes. The COVID-19 pandemic required that teaching be conducted virtually, which provided an opportunity to explore our assumptions that an in-person component ensured contextual and relational accountability. This study used a qualitative case study approach that was both descriptive and intrinsic in nature. We sought to examine and describe the adaptations that emerged in response to a shift towards virtual teaching and to understand the experiences of key stakeholders from both AAU and UofT within the specific context of the TAAAC collaboration. Two foundational principles of the TAAAC model were disrupted during the COVID-19 pandemic: its emphasis on local context and its relational component. As virtual teaching replaced the historical on-site teaching of TAAAC programme curricula, these historical structures were unable to mitigate power differentials between AAU and UofT faculty, teachers and leaders. The relational and context-specific aspects of the TAAAC model were undermined with the use of a virtual platform. Virtual teaching reinforced one-sided knowledge exchange and decontextualized teaching, thereby perpetuating epistemic injustice within TAAAC programmes. This injustice was experienced as a loss of accountability to the relationships that had built and sustained a longstanding LIC-HIC partnership. While virtual teaching has an allure of being efficient and accessible, our experience suggests that it may be poorly suited within partnerships where context and relationality are cornerstones of efforts to reshape dimensions of power.

With the increasing penetration of renewable energy and electric vehicles (EVs), virtual power plants (VPPs) have become a key mechanism for coordinating distributed energy resources and flexible loads to participate in electricity markets. However, the uncertainties of renewable generation and EV user behavior pose significant challenges to bidding strategies and real-time execution. This study proposes a two-stage optimal bidding strategy for VPPs by integrating vehicle-to-grid (V2G) technology. An aggregated EV schedulable-capacity model is established to characterize the time-varying charging and discharging capability boundaries of the EV fleet. A unified day-ahead and real-time optimization framework is further developed to ensure coordinated bidding and scheduling. Case studies on a modified IEEE-33 bus system demonstrate that the proposed strategy significantly enhances renewable energy utilization and market revenues, validating the effectiveness of coordinated V2G operation and multi-type flexible load control.

With the increasing penetration of renewable energy, power systems are facing greater uncertainty and volatility, which poses significant challenges for Virtual Power Plant scheduling. Existing research mainly focuses on optimizing economic efficiency but often overlooks system reliability and the impact of forecasting deviations on scheduling, leading to suboptimal performance. Thus, this paper presents a reliability-cost bi-objective cooperative optimization model based on a dual-swarm particle swarm algorithm: it introduces positive and negative imbalance price penalty factors to explicitly describe the economic costs of forecast deviations, constructs a reliability evaluation system covering PV, EVs, air-conditioning loads, electrolytic aluminum loads, and energy storage, and solves the multi-objective model via algorithm design of “sub-swarms specializing in single objectives + periodic information exchange”. Simulation results show that the method ensures stable intraday operation of VPPs, achieving 6.8% total cost reduction, 12.5% system reliability improvement, and 14.8% power deviation reduction, verifying its practical value and application prospects.

A virtual power system stabilizer (PSS) adaptive damping control strategy based on a supercapacitor is used to suppress oscillations in a ship microgrid. The energy transmission path of the proposed strategy is to apply the equivalent damping power to the rotor by varying the electromagnetic power of the generator. Compared with conventional PSSs based on supercapacitors, storage devices not only enhance the capacity of damping power injected into the microgrid but also have more flexible configurations applicable to the size constraints of the ship microgrid. In addition, the adaptive control ensures that the DC bus voltage of the converter of the energy storage device is controlled within the neighborhood of the steady-state operating point, ensuring the asymptotic stability of the damping system. Finally, an experimental platform was built to verify the correctness and validity of the above theory.

The transition toward deeply decarbonized energy systems requires optimization frameworks that can simultaneously capture long-term dynamics, operational reliability, and contractual stability while managing multiple forms of uncertainty. This paper introduces a comprehensive modeling and solution framework for long-term welfare optimization of virtual power plants, where seasonal, annual, and rolling horizons are jointly considered under constraints of network feasibility, renewable integration, reliability assurance, and carbon accountability. A unified welfare objective is formulated to internalize operating cost, curtailment penalties, reliability risk, and carbon charges, with constraints codifying the technical physics of dispatch, reserve adequacy, and contract coverage. The methodology employs a distributionally robust optimization layer combined with scenario reduction, stability metrics, and fairness tracking to ensure computational tractability and resilience to stochastic variations in renewable output and demand. A case study on a 33-bus system with heterogeneous virtual power plants demonstrates the effectiveness of the approach. Results show that the proposed optimization reduces total seasonal welfare costs by 8-13%, cuts curtailment by up to 45%, and lowers overload probabilities on critical lines by 20-30%. Attribution analysis reveals that 55% of carbon abatement arises from curtailment relief, 25% from redispatch optimization, 12% from loss reduction, and 8% from contract rebalancing, underscoring the multi-mechanistic nature of emission savings. The contributions of this paper are fourfold: the design of a multi-layered welfare optimization model for long-term horizons, the integration of distributionally robust techniques with fairness and stability considerations, the demonstration of quantitative improvements in both welfare and reliability, and the attribution of carbon reduction across complementary drivers. Together, these elements provide a rigorous and adaptable blueprint for optimizing future low-carbon virtual power plant systems under uncertainty.

Under the imperative of achieving dual-carbon goals, the number of distributed energy resources are gradually increasing, thereby amplifying the challenges to grid stability and power balance. Consequently, there is an urgent need to leverage the potential of source-network-load-storage for enhanced power regulation and control. This paper proposes a cloud-edge-end-based multi-time scale economical management of virtual power plant (VPP) source-network-load-storage in the context of electricity-carbon market. In the first layer, a cloud-edge scheduling approach is used to optimize the source-network-load-storage system of the VPP over a long time horizon, aiming to maximize economic benefits. In the second layer, a novel real-time pricing mechanism is employed to effectively manage and regulate the electric vehicle (EV) storage and charging stations. After obtaining the economic management parameters from the previous layer, the second layer employs a real-time scheduling approach based on end-side model predictive control (MPC), to address multi-energy supply-demand fluctuations. To achieve efficient solution, the original two-layer optimization problem is reformulated using mixed-integer linear programming (MILP). Comparative analyses have demonstrated the superior economic and practical performance of the proposed two-layer optimization approach. Simulation results indicate that the total operating cost of the system can be reduced by 2.35%, with a higher flexibility of electricity market operations.

Using cutting-edge simulation tools, this study provides a comprehensive empirical analysis of power system stability and control mechanisms in contemporary electrical grids. The study looks at 33 recent studies on energy storage systems, control strategies, and the integration of renewable energy that were published between 2023 and 2024. Key findings show that when advanced control algorithms are incorporated into hybrid renewable energy systems, stability improvement rates range from 45-78%. Battery systems achieve 85-95% round-trip efficiency, while energy storage systems display capacity factors between 0.35 and 0.82. According to the analysis of control methodologies, H∞ control systems outperform conventional controllers in terms of disturbance rejection by 23–35%. While offshore wind farms with energy storage achieve 89% reliability indices, virtual power plants show a 67% improvement in grid formation capabilities. With implications for sustainable energy transition and grid modernisation strategies, these quantitative results highlight the crucial role that sophisticated simulation tools play in optimising power system performance.

A new decentralized control structure for generation scheduling in a multi-energy virtual power plant considering its interactions with an electric vehicle aggregator and a demand response provider

Contribution-driven cooperative trading strategy for multi-energy virtual power plants in the electricity-carbon coupled markets: An asymmetric Nash bargaining model

Electricity-hydrogen-heat-carbon sharing among virtual power plants considering hydrogen utilization for methane and methanol production

Multi-objective optimization of virtual power plant with mobile storage considering renewable energy uncertainty and multiple flexible loads

Corrigendum to “Prosumer full lifecycle sustainable footprint painting for circular economy and community-based virtual power plant: A multi-objective optimization research” [Process Saf. Environ. Prot. 202 (2025) 107728

A systematic review of Virtual Power Plant configurations and their interaction with electricity, carbon, and flexibility markets

Uncertainty-driven IoT-based management of hydrogen-backed multi-carrier virtual power plants for zero-carbon microgrids

Low-carbon economic scheduling of rural virtual power plants considering carbon-green hydrogen certificates coupling mechanisms and farmers' cognitive preferences

Smart predict-then-optimize-based model predictive control for Virtual Power Plants with battery storage