Electrochemical energy storage (EES) is a promising kind of energy storage and has developed rapidly in recent years in many countries. EES planning is an important topic that can impact the earnings of EES investors and sustainable industrial development. Current studies only consider the profit or cost of the EES planning program, without considering other economic criteria such as payback period and return on investment (ROI), which are also important for determining an optimal EES planning program. In this paper, a new hybrid multi-criteria decision-making (MCDM) method integrating the Bayesian best-worst method (BBWM), the entropy weighting approach, and grey cumulative prospect theory is proposed for the optimal EES planning program selection with the consideration of multiple economic criteria. The BBWM and entropy weighting approach are jointly employed for determining the weightings of criteria, and the grey cumulative prospect theory was utilized for the performance rankings of different EES planning programs. Five EES planning programs were selected for empirical analysis, including 9MW PbC battery EES, 2MW LiFePO lithium ion battery EES, 3MW LiFePO lithium ion battery EES, 2MW vanadium redox flow battery EES, and 3MW vanadium redox flow battery EES. The empirical results indicate the 2MW LiFePO lithium ion battery EES is the optimal one. The sensitivity analysis related to different risk preferences of decision-makers also shows the 2MW LiFePO lithium ion battery EES is always the optimal EES planning program. The proposed MCDM method for the optimal EES planning program selection in this paper is effective and robust, and can provide certain references for EES investors and decision-makers.

With the grid-connected operation of large-scale wind farms, the contradiction between supply and demand of power systems is becoming more and more prominent. The introduction of multiple types of flexible resources provides a new technical means for improving the supply–demand matching relationship of system flexibility and promoting wind power consumption. In this paper, multi-type flexible resources made up of deep peak regulation of thermal units, demand response, and energy storage were utilized to alleviate the peak regulation pressure caused by large-scale wind power integration. Based on current thermal plant deep peak regulation technology, a three-phase peak regulation cost model of thermal power generation considering the low load fatigue life loss and oil injection cost of the unit was proposed. Additionally, from the perspective of supply–demand balance of power system flexibility, the flexibility margin index of a power system containing source-load-storage flexible resources was put forward to assess the contribution from each flexibility provider to system flexibility. Moreover, an optimal dispatching model of a multi-energy power system with large-scale wind power and multi-flexible resources was constructed, aimed at the lowest total dispatching cost of the whole scheduling period. Finally, the model proposed in this paper was validated by a modified RTS96 system, and the effects of different flexibility resources and wind power capacity on the optimal scheduling results were discussed.

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With the implementation of the “Thirteenth Five-Year Plan”, the scale of my country’s renewable energy installed capacity continues to expand, and the responsibility for consuming renewable energy power is becoming higher and higher. Promoting the reform of the electricity spot market provides a good opportunity to realize the consumption of large-scale renewable energy. This article aims at the northwest region where renewable energy accounts for a relatively high proportion in my country, and clarifies the factors that inhibit the consumption of renewable energy. Based on the investigation of the typical foreign power spot market model, a power spot market model suitable for large-scale renewable energy is proposed. According to the actual data of a province’s power grid in the northwest region, the day-ahead spot market under different models was cleared separately. The results show that the centralized market model is more suitable for large-scale renewable energy grid-connected consumption, and is conducive to promoting energy conservation and emission reduction and reducing power generation costs.