Dynamics of user cooperation and response under electricity price sensitivity: A case study of accurate incentive scheduling

With the rapid growth of new energy capacity, power grid systems are adopting innovative strategies to increase the consumption of clean energy. One such strategy is to designate midday as a low electricity price valley, incentivizing users to shift their consumption to this period, thus optimizing demand distribution and promoting the efficient use of new energy. However, challenges arise when extreme weather events (such as reduced sunlight and wind speed) occur within a quarter, significantly reducing the output of new energy. To mitigate this, the power grid often relies on costly energy storage and incentives to encourage users to adjust their consumption behavior. However, a limited understanding of user willingness to respond has led to increased operational costs and reduced user satisfaction in demand response programs. This paper proposes a user-collaborative, grid-precise dynamic incentive optimization scheduling algorithm based on electricity price sensitivity analysis. The algorithm first analyses users' price sensitivity from two dimensions: adjustment ratio and load quantity, classifying them into low, medium, and high sensitivity groups. It then establishes a Stackelberg game framework integrated with a user collaboration mechanism for grid dynamic incentives. To validate the effectiveness of the proposed algorithm, three comparative algorithms are established. The results show that, compared to other algorithms, the proposed approach significantly reduces both grid and user costs by 20–30 %, while narrowing the cost disparity between the two by nearly 10 %. Furthermore, three scenarios are evaluated: intra-quarter extreme weather, intra-quarter normal weather, and inter-quarter normal weather. The analysis of peak-valley price differences and new energy consumption rates demonstrates that, within a peak-valley price difference range of 30–45 %, the new energy consumption rate exceeds 90 %. These results confirm the robustness and superior performance of the proposed algorithm across various scenarios.