Anhui Grid Study: How Economic Willingness Reshapes Market Clearing in 2026

Hefei, China, Source:

Renewable energy penetration is reaching unprecedented levels, yet power grids face a critical paradox. Physical infrastructure suggests ample flexibility, but actual market resources often fail to deliver on that promise. A groundbreaking study published in September 2026 by researchers from the Economic and Technology Research Institute of State Grid Anhui Electric Power Co., Ltd., and Hefei University of Technology introduces a novel solution to this challenge. The research proposes a Multi-Time Scale Collaborative Clearing Model based on the Willingness Feasible Region (WFR). This approach fundamentally shifts how grid operators evaluate and utilize centralized control resources.

Traditional clearing models rely heavily on Physical Feasible Regions (PFRs). These regions define the maximum technical limits of assets like thermal generators, wind farms, and energy storage systems. However, these models assume that resource owners will unconditionally follow dispatch instructions. This assumption ignores the economic realities of electricity markets. Participants are driven by profit motives, risk aversion, and opportunity costs. For instance, a wind farm may physically be able to provide upward reserve capacity. If the potential loss from deviating from its forecasted output outweighs the reserve payment, it will simply refuse to participate. This reluctance to sell leads to overestimated regulation capabilities and increased risks of supply-demand imbalances during extreme events.

The proposed WFR model addresses this gap by mapping physical boundaries into economic decision spaces. By integrating cross-market opportunity costs and Conditional Value at Risk (CVaR) metrics, the model quantifies how price signals and uncertainty shrink the effective available capacity of each resource type. Thermal units exhibit asymmetric boundary contractions depending on peak or off-peak pricing. Wind farms show significant reductions in usable capacity due to prediction errors and penalty risks. Energy storage systems display strong inter-temporal coupling where state-of-charge constraints limit immediate availability. Demand response resources activate only when prices exceed specific compensation thresholds.

Implemented within a Model Predictive Control (MPC) framework, the study demonstrates a sequential day-ahead and intraday clearing process. Day-ahead markets establish baseline schedules using static willingness boundaries. Intraday rolling optimizations dynamically adjust for real-time deviations. Case studies on an improved IEEE 24-bus system reveal that incorporating WFR constraints reduces supply-demand imbalance risks and enhances system resilience against renewable fluctuations. During extreme scenarios, such as sudden drops in wind output, the model successfully coordinates heterogeneous resources across different timescales. It shifts reserve provision from conventional thermal units toward faster-responding energy storage and demand-side flexibility.

This approach not only improves the accuracy of reserve allocation but also fosters a more efficient and resilient electricity market. By aligning dispatch instructions with the true economic willingness of market participants, grid operators can access latent flexibility. This ensures stable operations even as the energy transition accelerates. The findings underscore the necessity of moving beyond purely physical constraints to embrace behavioral and economic dimensions in modern power system planning.