Recently the extreme weather caused by El Niño-Southern Oscillation (ENSO) events has had a significant impact on the power system with high proportion of renewable energy, resulting in a seasonal electricity disequilibrium between source and load. Therefore, a novel model of optimal capacity allocation of seasonal energy storage (SES) for the High-Proportion Renewable Energy System (HP-RES) considering ENSO events is proposed. Firstly, the assessment model of source-load mismatch considering ENSO events is carried out. Specifically, the Spearman correlation coefficient is used to present the correlation between the ENSO events index and source/load data. And the modified Random Forest Regression Algorithm (RFRA) is applied to build a prediction model with the ENSO events index and related meteorological indicators (RMIs) as input under three Shared Socioeconomic Pathways (SSPs). The source-load mismatch of each SSP is calculated using predicted data by the modified RFRA. Secondly, the optimal capacity allocation model is proposed, the objective function of which is to minimize the comprehensive cost. Finally, the validity of the proposed model is verified by comparing the power gap penalty cost in different cases under three SSPs, based on the practical data of a certain region.

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

To address the issues of the current nodal pricing mechanism, which does not incorporate fixed transmission costs and leads to unfair transmission cost allocation, this paper proposes a full-cost electricity pricing model based on power flow tracing technology. By defining full-cost electricity pricing and integrating generation marginal costs with effective transmission costs, the proposed model adheres to the principle of "costs borne by beneficiaries." The effectiveness of the proposed model is validated through case studies on IEEE 30-bus and IEEE 118-bus systems. The results demonstrate that the proposed method can optimize power grid resource allocation and enhance investment efficiency while considering power grid congestion.

This study proposes a full-cost electricity pricing model (M3) based on power flow tracing, addressing limitations in traditional nodal pricing and postage stamp methods. M3 dynamically allocates fixed transmission costs based on actual grid utilization, improving fairness, price signal accuracy, and congestion management. The model achieves fast convergence within 20 iterations across tested networks. Sensitivity analysis confirms that fuel costs and load variations significantly impact pricing, making M3 more adaptive and responsive. A regression-based forecasting model further enhances price predictability. The dual IEEE 118-bus case study validates M3’s feasibility in inter-provincial electricity markets, demonstrating its effectiveness for real-time pricing and investment planning.