With the integration of large-scale， distributed， and diverse distributed resources， virtual power plant （VPP） technology has become a vital tool for effectively managing and optimizing demand-side resources. To better align VPPs with the development needs of China’s new-type power system， this paper proposes an optimal scheduling model for VPPs participating in a green certificate-carbon joint trading mechanism， taking into account uncertainty risks. First， an optimal operation model for a VPP is constructed， consisting of wind turbines， photovoltaic units， gas turbine units， energy storage systems， and flexible load resources on the user side. The objective is to minimize the VPP’s operating cost， considering electricity markets， the green certificate-carbon joint trading mechanism， and incentive-based demand response. Second， multiple uncertainty factors within the VPP， such as generation sources， loads， and demand response， are comprehensively considered， and the conditional value-at-risk （CVaR） theory is applied to quantify the risks associated with these uncertainties. Finally， a case study is introduced to verify the economic and environmental benefits of the proposed model. The inclusion of CVaR also provides a robust decision-making basis for balancing VPP profits and risks.

With the rapid development of new energy generation technology， renewable energy sources such as wind energy and photovoltaic not only serve as important active power sources， but their reactive power regulation potentials are also receiving increasing attention. In this paper， an innovative optimization strategy based on the improved Genghis Khan shark optimization （GKSO） algorithm is proposed to address the shortage of virtual power plant （VPP） reactive power sources and the model solving difficulties under high percentage of new energy access. First， a reactive power co-regulation model containing multiple distributed power sources such as wind power， photovoltaic， energy storage and gas turbine is constructed， and the key influencing factors of the uncertainty of new energy reactive power output are revealed through parameter sensitivity analysis. In order to accurately characterize the uncertainty， Latin hypercube sampling （LHS） combined with the scenario generation and reduction technique of Kantorovich distance is innovatively adopted to establish a typical set of scenarios of wind and solar power output. On this basis， a multi-objective optimization model of VPP considering the uncertainty of new energy reactive power is established and efficiently solved using the improved GKSO algorithm. The simulation results show that compared with the particle swarm optimization （PSO） algorithm and seagull optimization algorithm （SOA）， the optimized GKSO algorithm has a significant advantage in solving the VPP reactive power optimization problem， and it is necessary to take the new energy reactive power uncertainty into account in order to reduce the operational risk for large new energy stations with large installed capacity.

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 1 254.9 yuan.

As a new type of power system scheduling mode，virtual power plant （VPP） can realize the efficient utilization of new energy power by aggregating distributed energy resources. However，the traditional scheduling strategy aiming at economy has been unable to meet the needs of current low-carbon development. Based on this，this paper proposes a multi-objective optimization scheduling strategy for VPP considering both economy and carbon emissions. Firstly，a post-combustion carbon capture device was introduced into the VPP system，and combined with a flexible carbon trading strategy，a multi-objective optimization scheduling model considering economic cost and carbon emissions was constructed. Secondly，to obtain the optimal solution of the model，the augmented ε-constraint method was used to solve the Pareto solution set，and the entropy weight - technique for order preference by similarity to ideal solution （TOPSIS） method was used to evaluate the solution set. Finally，multi-case simulation experiments were carried out around different carbon capture and carbon trading strategies to compare and analyze the differences in scheduling results between the single-objective model only considering economy or low-carbon characteristics and the multi-objective model considering both economy and carbon emissions. The experimental results show that when the ladder carbon trading mechanism and the corresponding carbon capture operation mode are adopted，the carbon emissions of VPP reach the lowest level. In addition，compared with the single objective model，the multi-objective optimization strategy considering both economy and carbon emissions can effectively reduce carbon emissions and improve economic benefits.

Under the goal of “Dual Carbon” strategy，how to realize the flexible interaction between virtual power plants and promote the low-carbon operation of virtual power plants through carbon price is a problem worthy of study. Therefore，the peer to peer （P2P） trading model of virtual power plants is studied based on the carbon flow theory. Firstly，based on the carbon emission flow theory，the distribution characteristics of carbon flow in the network are analyzed，and natural gas is introduced to form a multi-energy network，and a low-carbon economic scheduling model is established. Secondly，considering the privacy of each virtual power plant participating in the trading，a quantitative index method including the trading information of the bid volume and quotation is proposed，and a P2P trading model based on comprehensive priority is established. At the same time，combined with the carbon emission responsibility of virtual power plants in the network，carbon pricing method is introduced into the P2P trading mechanism，and a comprehensive price model of “energy-carbon” based on carbon tax is established. Finally，an example is given to verify that the proposed method can not only reduce the operating cost of the virtual power plant，but also effectively reduce the carbon emission.

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.

In order to deeply explore the role of virtual power plant in carbon emission reduction and realize the effective operation of low carbon economy， a low carbon economic dispatch model of virtual power plants considering carbon trading and demand response is proposed. Firstly， a model of the virtual power plant participating in the carbon trading market is constructed to restrict its carbon emissions. Secondly， according to the characteristics of load demand response， price demand response model and alternative demand response model are established respectively. Finally， a low carbon economic dispatch model is designed to minimize the total operating cost of the virtual power plant. Through the comparative analysis of the results of the four scenarios， the effectiveness of the model is verified. In addition， the influence of carbon trading price and demand response parameters on system operation is investigated. The results show that considering carbon trading and demand response simultaneously can not only significantly reduce the total operating cost of the system， but also reduce the actual carbon emissions. The total operating cost of the system is positively correlated with the carbon trading price， while the actual carbon emissions are negatively correlated with it. At the same time， the change of demand response parameters will also have a certain impact on the operating cost and carbon emissions. In the process of virtual power plant scheduling， the model takes into account the economy and low carbon of system operation， realizes the effect of “peak clipping and valley filling”， and improves the flexibility of system operation.

Against the backdrop of the "dual carbon" target, the power sector has become an important part of carbon reduction. Virtual power plants (VPP) can further improve their overall efficiency by integrating and aggregating distributed resources to participate in the carbon market. However, the uncertainty of distributed new energy output poses many challenges for their operation and management. Therefore, on the basis of using the scenario generation and scenario reduction method based on Latin hypercubic sampling to deal with the uncertainty problem of wind power and photovoltaic output of distributed energy, the VPP, which aggregates multiple units and takes into account the user-side demand response, participates in electric energy market as well as the carbon market as a whole, and the optimal scheduling model with the minimum total cost of the VPP is constructed, which is finally solved by using the improved gray wolf optimization algorithm. Through comparative analysis of different scenarios, it can be concluded that the existence of carbon market and demand response enhances the consumption of clean energy such as wind power and photovoltaic, and reduces greenhouse gas emissions, and reduces the operating cost of the VPPs, and takes into account its economy and environmental protection.

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.

As a key technology for low carbon transformation of power system, carbon capture technology applied to combined heat and power system can reduce carbon emissions of virtual power plants. To this end, a technical route that applies carbon capture technology to the combined heat and power optimization of virtual power plants is proposed to promote their low carbon economic operation. On the one hand, it relies on the ladder carbon trading mechanism, demand response and power-heat interconversion to reduce the carbon emissions of combined heat and power operation of the virtual power plant; On the other hand, it aims to minimize the operating cost of the virtual power plant, taking into account the internal constraints of the virtual power plant such as load regulation potential and the upper limit of new energy output, as well as the physical constraints of power-heat-carbon interconversion to improve the operating economy of the virtual power plant. Based on the actual operating data of a park, a control scenario is designed around whether to configure carbon capture technology and demand response, and the proposed virtual power plant combined heat and power optimization model is verified to have certain realistic availability and robustness.