At present, the coupling of virtual power plant (VPP) and active distribution network (ADN) has been further deepened. Aiming at the optimal dispatching problem of ADN with VPP as a market participant, it is necessary to study the coordinated interaction between various flexible resources in ADN and uncertainty factors in VPP in different time stages. On this basis, with the goal of minimizing the comprehensive cost of ADN and maximizing the market revenue of VPP, a two-stage dispatching method for ADN considering VPP transaction is proposed. In the day-ahead stage, according to the flexibility boundary of the system, a bi-level optimization model is established considering the dispatching of flexible resources in ADN and day-ahead clearing of the VPP. On this basis, the bidding scheme of VPP and the operation strategy of ADN are adjusted in real time. Finally, an improved IEEE 33-node system is taken as an example for simulation analysis, and the results show that the proposed model can improve the market competitiveness of VPP and the operational flexibility of distribution networks both, which provides a reference for optimal dispatching under the future marketization development of power grid.

As the subsidies of distributed resources decrease, to bid in day-ahead market by forming virtual power plant becomes a new income channel for demand-side entities. This paper designs a complete transaction mechanism from three parts to realize this mode such as bidding strategy, profit distribution and the formation of virtual power plant competing by demand-side entities. The bidding strategy model of price-maker virtual power plant is proposed, and the uncertainty is modeled on the basis of interval optimization. In addition, a generation quantity and uncertainty contribution based allocation mechanism is designed to distribute the profit. Then, this paper analyzes the stability of each coalition status to study the formation of virtual power plant. Finally, an example is given to verify the effectiveness of the proposed method. The results show that the proposed transaction mechanism conforms to the characteristics of the user-side stakeholders, and its allocation mechanism takes into account the stabilizing effect of participants on uncertainty, which can provide auxiliary decision-making for the user side stakeholders.

This paper presented a bottom-up self-organizing aggregation operation scheduling method for virtual power plants, aiming at reducing the amount of regulation in the process of scheduling by dynamic self-organization aggregation among distributed energy resources (DER). Firstly, according to the output characteristics of DER, the consistency between which and the load is evaluated with the tracking coefficient, and the relevant optimal control model is given. As the basis of self-organizing aggregation, the basic intelligence of DER is considered. Secondly, the framework of coalition formation games is introduced, and the aggregating utility function and its corresponding benefit sharing mechanism are proposed, on the basis of which the self-organizing aggregation conditions of DER are established. Finally, according to Pareto rules, the "merge-split" mechanism is proposed, which can promote the orderly evolution of virtual power plants and form a self-organizing aggregation operation scheduling(SAOS) for virtual power plants. The results show that SAOS can be constituted dynamically according to the output characteristics of DER. Its overall scheduling quantity is equivalent to that of centralized optimization, and is significantly reduced compared with that of independent optimization. The calculation amount and calculation time of SAOS are equivalent to those of independent optimization, and significantly lower than centralized optimization.

At present, the operation process of traditional energy system lacks diversified optimization methods for energy supply and demand, lacks flexible peak-regulation strategies, and is prone to pollution. However, the precise supply of virtual power plants provides a development direction for the transformation of energy architecture. This paper proposes a multi-energy collaborative system scheduling optimization model based on virtual power plant. Firstly, by introducing environmental governance subsystem cooperates with the electric-heat-gas integrated energy system to cooperate with electric-thermal energy storage devices, power to gas (P2G) equipment and energy trading markets, giving virtual power plants the more flexible information regulation means. Secondly, the paper established two different system operation modes to compare, and finally using MATLAB+CPLEX to simulate and verify the mathematical model. According to the results, compared with the operation mode of electricity-heat-gas energy system, the multi-energy collaborative control system of virtual power plant under optimal capacity configuration has better economy and environmental protection, and has a significant effect on improving the conversion rate of renewable resources, alleviating the phenomenon of abandoned wind and solar energy and excessive carbon emissions.

Virtual power plants (VPP) can effectively aggregate decentralized demand-side resources. It is an effective way for demand-side resources to participate in electricity market. However, the market behavior of VPP cannot be expressed by the traditional bidding model of the thermal power plant, meanwhile the transmission constraints will also impact the interior resource management strategy of the VPP. Under the aforementioned background, a robust bidding model of multi-type resources in virtual power plant in power market is proposed and it takes into account renewable energy output uncertainty and the adjusting ability of aggregated units. The virtual power plant studied in this paper consists of wind power, energy storage, gas turbine and demand response load. The model takes Karush-Kuhn-Tucker (KKT) equivalent condition of market liquidation model as constraints to represent the relationship between market liquidation process and VPP decision-making. The results show that the model can optimize internal resource combination of VPP according to the market situation, provide an economic and reliable bidding strategy, and effectively improve the economic benefits of VPP.