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China's “14th Five‐Year Plan” proposes that “carbon peak” is the basic prerequisite for achieving “carbon neutrality.” To achieve the “carbon neutrality” goal, the key is promoting the use of clean, low‐carbon energy and building a diversified clean energy supply system on the energy supply side. Wind energy, photovoltaics, and other clean energy sources are increasingly relying on electrification (P2G) technology to achieve utilization, and this phenomenon can help China achieve carbon neutrality goals as soon as possible and realize the carbon cycle. This article first aggregates combined heat and power (CHP), wind power, photovoltaics, P2G equipment, and gas storage devices into virtual power plants (VPPs), and the natural gas and electricity trading market are linked. We then take the virtual power plant operating profit maximization as the objective function considering the P2G device operating costs, carbon capture and storage costs. The optimization model is solved by particle swarm algorithm. Finally, the specific operating data of a gas‐thermal power virtual power plant system were selected to verify the feasibility of the proposed optimization model. We got some relevant conclusions. (1) The optimal scheduling of gas‐thermal power virtual power plant considering multiple energy markets can smooth the power load curve and improve the overall system benefit. (2) There is a negative correlation between the income of gas‐thermal power virtual power plant and the confidence level effectively. (3) The optimization model effectively increases the amount of carbon capture, which proves that the gas‐thermal power virtual power plant is effective for achieving China's carbon neutrality goal, it can be developed on a larger scale in future. The above results verify that the optimal operation mode of the gas‐thermal power virtual power plant is effective for achieving carbon neutrality. China's “14th Five‐Year Plan” proposes that “carbon peak” is the basic prerequisite for achieving “carbon neutrality.” It is necessary to achieve the peak of energy consumption, especially fossil energy consumption, and accelerate the replacement of fossil energy with clean energy.

As an essential platform for aggregating and coordinating distributed energy resources (DERs), the virtual power plant (VPP) has attracted widespread attention in recent years. With the increasing scale of VPPs, energy interaction and sharing among VPP clusters (VPPCs) have become key approaches to improving energy utilization efficiency and reducing operational costs. Therefore, studying the coordinated operation mechanism of VPPCs is of great significance. This paper proposes a two-stage coordinated operation model for VPPCs based on energy interaction to enhance the overall economic performance and coordination of the cluster. In the day-ahead stage, a cooperative operation model based on Nash bargaining theory is constructed. The inherently non-convex and nonlinear problem is decomposed into a cluster-level benefit maximization subproblem and a benefit allocation subproblem. The Alternating Direction Method of Multipliers (ADMM) is employed to achieve distributed optimization, ensuring both the efficiency of coordination and the privacy and decision independence of each VPP. In the intra-day stage, to address the uncertainty in renewable generation and load demand, a real-time pricing mechanism based on the supply–demand ratio is designed. Each VPP performs short-term energy forecasting and submits real-time supply–demand information to the coordination center, which dynamically determines the price for the next trading interval according to the reported imbalance. This pricing mechanism facilitates real-time electricity sharing among VPPs. Finally, numerical case studies validate the effectiveness and practical value of the proposed model in improving both operational efficiency and fairness.

Energy storage and virtual power plant technologies have been developed and become important technical means to enhance power system stability and reduce real‐time dispatching costs. In this study, the dispatching capability and dispatching cost characteristics of the virtual power plants are analysed firstly in detail, as well as the dispatching difficulties under the traditional market modes. Hence, virtual power plant real‐time bidding package model and virtual auction‐based real‐time power market mechanism are proposed. Data‐driven virtual power plant real‐time packing method and bidding package model integrated virtual Vickrey–Clarke–Groves auction model are put forward. Finally, the feasibility and validity of the proposed mechanism and method are verified by case studies and result in analyses of the IEEE‐30 bus test system with multiple virtual power plants, providing a scientific foundation and a practical solution to the real‐time power market.

A virtual power plant (VPP) aggregates various dispersed resources in the distribution network into a unified whole, enhancing the global responsiveness of the aggregated resources. Additionally, it can participate in power market transactions as a whole, improving the dispatchability of resources and operational economy. In this paper, we develop an evaluation model for VPP responsiveness that leverages the absolute response volume of the VPP. Four key indicators are introduced—response volume, response accuracy, load tracking, and power stability—to provide a comprehensive assessment of the system’s performance. Building upon these indicators, we propose an overall responsiveness evaluation method and employ an entropy-weighting approach to calculate a final responsiveness score. Finally, through a 24-hour case study, we derive the VPP’s response capability curve and corresponding scores.

This study uses an artificial neural network (ANN) as an intelligent controller for the management and scheduling of a number of microgrids (MGs) in virtual power plants (VPP). Two ANN-based scheduling control approaches are presented: the ANN-based backtracking search algorithm (ANN-BBSA) and ANN-based binary practical swarm optimization (ANN-BPSO) algorithm. Both algorithms provide the optimal schedule for every distribution generation (DG) to limit fuel consumption, reduce CO2 emission, and increase the system efficiency towards smart and economic VPP operation as well as grid decarbonization. Different test scenarios are executed to evaluate the controllers’ robustness and performance under changing system conditions. The test cases are different load curves to evaluate the ANN’s performance on untrained data. The untrained and trained load models used are real-load parameter data recorders in northern parts of Malaysia. The test results are analyzed to investigate the performance of these controllers under varying power system conditions. Additionally, a comparative study is performed to compare their performances with other solutions available in the literature based on several parameters. Results show the superiority of the ANN-based controllers in terms of cost reduction and efficiency.

To improve the stability and economy of power system operation under the deep penetration of renewable energy, a trilevel adaptive robust optimization model is proposed for the self-scheduling of the multi-energy virtual power plant, which is solved by the column-and-constraint generation algorithm. The uncertainties of the available photovoltaic capacities are addressed by the data-driven scenario-based uncertainty sets, which are developed based on the historical operational scenarios of the power system with the help of data-driven technology. Using the actual operational data from a chosen campus, the results show that the proposed method can achieve a synergistic improvement of operational stability and economy of the multi-energy virtual power plant. It can also improve the accuracy of uncertainty modeling while reducing the conservatism of robust optimization, saving by about 7.3% of the operating costs.

The placement and scale of virtual power plants (VPPs) in distribution networks are the only topics covered in this article that pertain to the resilience of the grid to severe weather. This problem is framed as a two-objective optimization, where the expected energy that the network would not deliver in the case of an earthquake or flood (expected energy not-supplied), and the annual planning cost of the VPP, are the two objective functions to be minimized. Noted that the expected energy not-supplied in the earthquake or flood condition is considered as the resiliency index. The constraints include the formula for VPP planning, limitations on network operation and resilience, and equations for AC power flow. Uncertainties about demand, renewable power, energy prices, and the supply of network hardware and VPP components are all taken into account in stochastic programming. The proposed technique achieves a single-objective formulation in the subsequent stage by the use of a Pareto optimization strategy based on the ε-constraint method. This article uses a solver based on a hybrid of Crow search algorithm (CSA) and sine cosine algorithm (SCA) to achieve the trustworthy optimal solution with lowest dispersion in the final response. In order to tackle the problem, the proposed system looks at how the VPP affects network resilience, scales it, and combines it with the hybrid evolutionary algorithm. In the end, with the implementation of the proposed design on the distribution network of 69 buses, the obtained numerical results confirm the ability of optimal placement and dimensions of VPPs in improving the economic status, utilization and resilience of the distribution network.

To incorporate the operating constraints of a virtual power plant (VPP) in transmission-level operation and market clearing, the concept of the VPP capability curve (VPP-CC) is proposed which explicitly characterizes the allowable range of active and reactive power outputs of a VPP. A two-step projection-based calculation framework is proposed to approximate the VPP-CC by the convex hull of critical points on its perimeter. The output of the proposed algorithm is concise and can be easily incorporated in the existing system operation and market clearing. Case studies based on the IEEE 33 and 123 test feeders show the computational efficiency of the proposed method outperforms existing methods by 4~7 times. Additionally, many fewer inequalities are needed to depict the VPP-CC while achieving the comparative approximation accuracy compared to sampling-based methods, which will relieve the communication and computation burden.

The penetration of renewable energies in the energy market has increased significantly over the last two decades due to environmental concerns and clean energy requirements. The principal advantage of renewable energy resources (RESs) over non‐RESs is that it has no direct carbonisation impact on the environment and that it has none of the global warming effects which are caused by carbon emissions. Furthermore, the liberalisation of the energy market has led to the realisation of the virtual power plant (VPP) concept. A VPP is a unified platform for distributed energy resources that integrates the capacities of various renewable energies together for the purpose of improving power generation and management as well as catering for the buying and selling of energy in wholesale energy markets. This review study presents a comprehensive review of existing approaches to planning, operation and scheduling of the VPP system. The methodologies that were adopted, their advantages and disadvantages are assessed in detail in order to benefit new entrants in the power system and provide them with comprehensive knowledge, techniques and understanding of the VPP concept.

Ensuring reliable power supply in urban distribution networks is a complex and critical task. To address the increased demand during extreme scenarios, this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants (VPPs). The proposed strategy improves system flexibility and responsiveness by optimizing the power adjustment of flexible resources. In the proposed strategy, the Gaussian Process Regression (GPR) is firstly employed to determine the adjustable range of aggregated power within the VPP, facilitating an assessment of its potential contribution to power supply support. Then, an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time. To solve the proposed optimal dispatch model efficiently, the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker (KKT) optimality conditions and linear programming duality theorem. The effectiveness of the strategy is illustrated through a detailed case study.