No abstract

Pumped-storage plants are the most significant electrical storage component in new power systems and show great potential for scaling up. In this paper, economic costs and benefits have been investigated. Both the costs and benefits can be divided into transmission and distribution tariffs; however, various factors need to be considered to reduce costs in transmission and distribution tariffs. The cost characterization methodology for pumped-storage power plants has been developed. A mathematical model for dispersal through the medium and long-term electricity market, the electricity spot market, the ancillary services market, and the leasing of capacity to scenic power stations has been proposed. A case study has been discussed for a regional grid company and a provincial grid company; also, the effectiveness of dispersal has been analyzed based on the case study. The results indicate that when the spot market and the capacity leasing of new energy sites play the largest role in the diversion, the transmission and distribution prices in Province A can be controlled within 1 cent/kWh by applying various diversion methods.

To accurately improve the balanced new energy distribution network capacity assessment, this paper proposes a combined data-driven and model-driven renewable energy capacity assessment method for distribution networks. Firstly, the distribution generations (DG) output data are pre-processed to construct a scenario training sample set and extract seven types of DG daily output characteristics indicators. Secondly, a scenario generation model based on a conditional generation adversarial network is proposed to realize the coupling of scenario reduction and scenario generation based on K-means++ through daily output characteristics indicators. Finally, the optimization model based on hybrid positive linear programming is proposed for distributed power supply acceptance capacity evaluation of the distribution network with the objective of maximum DG access capacity and full consideration of voltage deviation, branch current, tidal current return, and other operational constraints. It is validated by IEEE 33-bus simulation. The simulation analysis shows that the proposed method can effectively quantify the DG capacity uncertainty compared with the traditional method, and the proposed method has higher computational accuracy and efficiency than the traditional method.

The access of distributed generator (DG) to the distribution network becomes a voltage support point, which is conducive to optimizing the power flow and reducing the losses in the distribution network. The volatility of DG increases the uncertainty of grid operation and increases the difficulty of distribution grid operation control. To address the above contradictions, this paper researches the optimal allocation of distributed power sources. A bi-level optimal allocation model that integrates planning and operation is established. The upper takes the economic optimization of the distribution company as the target, while the lower layer optimizes the DG allocation scheme with minimum network loss and passes the target value to the upper layer to make the distribution network economically optimal. The DG output scenario generation method based on kernel density estimation and k-means clustering addresses the uncertainty of DG active power output. A computational analysis is carried out based on a typical 10kV distribution network with PV as an example to verify the validity of the model.