Under the background of the “carbon peak and carbon neutrality” goals, the contradiction between the high proportion of renewable energy grid connection and the reliance on fossil energy has become increasingly prominent. To coordinate low-carbon constraints with energy security, this paper proposes an optimized scheduling model for virtual power plants (VPPs) based on the collaboration of carbon capture and storage (CCS), power-to-gas (P2G), and electric vehicles (EVs). This model builds an integrated framework of “emission reduction - conversion - benefit” by aggregating distributed resources such as gas turbine units, combined heat and power (CHP), wind power, photovoltaic power and EVs: Firstly, CCS is used to capture CO₂; Secondly, through CCS-P2G, CO₂ is converted into methane by utilizing the abandoned wind and photovoltaic energy, and the captured CO₂ is consumed to form a carbon cycle. Finally, aggregated EVs participate in carbon market transactions and increase their profits by using the China certified emission reductions (CCERs) they generate. The case analysis based on Matlab/Cplex shows that compared with the traditional gas-CHP system model, the model proposed in this paper can reduce carbon emissions by 91.3% (from 2,466.9 tons to 214.34 tons), lower the cost of wind and solar power curtailage by 71,457 yuan, and increase the consumption rate of renewable energy. And by selling CCER, the net cost of VPP was reduced by 8,208 yuan. Ultimately, the overall net cost of VPP was reduced by 96,611 yuan. The research verified the effectiveness of multi-technology collaboration in enhancing the economic and environmental benefits of VPP, providing theoretical support and practical paths for the low-carbon transformation of the new power system.

The injection of a high percentage of renewable energy sources introduces many uncertainties into the virtual power plant. If these uncertainty sources are ignored or inaccurately characterized, it will bring great risks to the operation and scheduling of the virtual power plant. In order to solve this problem, this paper considers the correlation of wind and solar energy sources in the virtual power plant, and utilizes Frank-Copula as a "connectivity function" to solve the joint distribution function of wind and solar power, so as to generate a typical scenario of wind and solar power. In order to minimize the operating cost of the virtual power plant, this paper comprehensively considers the electricity-carbon-green certificate market and the incentive-based demand response mechanism. The interval method, stepped carbon price, and Gounod model are used to describe the uncertainties of demand response, carbon price, and green certificate price, respectively, and an optimization scheduling model for virtual power plants based on interval linear programming is constructed. The case study results show that the model is able to quantify the risk of each component's uncertainty on the virtual power plant dispatch, and ensure the safe and reliable operation of the virtual power plant with a certain degree of economy at the same time.

Integrating demand response into virtual power plants can enhance their flexibility and economic efficiency, but the inherent uncertainty of demand response poses challenges for scheduling and operation. Moreover, research on applying multiple demand response types in virtual power plants remains limited. To address these issues, this paper proposes an incentive-based demand response model incorporating a benefit coefficient that adjusts incentive compensation to reduce payouts under unsatisfactory demand response performance, as well as a replaceable-based demand response model considering customer satisfaction to better reflect its influence on replaceable demand response. Finally, a multi-energy virtual power plants model integrating multiple demand response types is established, considering three demand response strategies to achieve superior optimization. Case studies demonstrate that the incentive-based demand response model considering the benefit-coefficient can improve economic efficiency of the system, and the replaceable-based demand response model considering customer satisfaction can more accurately capture potential load variations to enhance demand response precision, and the coordinated participation of multiple demand response types in virtual power plant scheduling yields optimal overall performance.

With the in-depth promotion of the strategy of "carbon peak and carbon neutrality", virtual power plant (VPP) has shown significant advantages in integrating and dispatching new energy. It is one of the effective means to improve the operation economy of VPP to deeply tap the huge potential of new energy in the field of reactive power support. Firstly, a set of new energy reactive power capacity evaluation system considering active power output and inverter constraints was constructed. Secondly, in order to improve the solving speed of the reactive power optimization model, according to the nonlinear characteristics of active network loss and power flow constraints, an active network loss estimation method based on power flow iteration and a power flow linearization method based on variable space optimization selection were proposed. A VPP linear power flow reactive power optimization model considering the reactive power potential of new energy sources and based on variable space optimization selection was constructed. Finally, the improved IEEE 33-node active distribution system was taken as an example to verify the effectiveness of the proposed model. The results show that when VPP makes full use of the reactive power support ability of new energy, the system voltage deviation is reduced by 0.673 pu, and the operating cost is reduced by 1254.9 yuan.

With the continuous development of the power system and the increasing awareness of environmental protection, the proportion of renewable energy generation in the power system is constantly increasing, and the scheduling of single thermal power generation units has become a coordinated scheduling mode for multi-energy generation. To solve the scheduling optimization problem of multi-energy power systems with energy storage devices, this paper establishes a multi-energy power system scheduling model of wind-solar-thermal-energy storage battery-pumped storage with the goal of minimizing system generation costs and pollution emissions. This paper introduces an adaptive strategy based on the number of iterations to optimize position update factor. Gaussian mutation is used to perturb the algorithm population, and the elite strategy in the ant lion algorithm is combined with the grasshopper algorithm to solve the proposed scheduling model using an improved multi-objective grasshopper algorithm. Real examples are simulated and analyzed on the Matlab platform, and an optimal multi-objective power system scheduling scheme is proposed. Through simulation analysis of test functions and simulation examples, the superiority of the improved algorithm and the rationality of the established model are verified.