Understanding Sichuan's Power Shortage and the Role of New Electricity Market Clearing Systems

In August 2022, Sichuan Province suffered a large‑scale power shortage that forced factories to halt production and even affected residential electricity supply.

Data from the National Energy Administration show that in 2021 China’s installed generation capacity was about 2.38 billion kW, enough for a full‑load generation of 20.85 trillion kWh, yet actual generation was only 8.11 trillion kWh, indicating a utilization rate of less than 50% and an overall surplus of generation capacity.

Electricity cannot be stored in large quantities; generation must match consumption in real time, so excess capacity is often wasted.

Sichuan’s renewable energy share is as high as 84%, but renewable sources are intermittent, volatile and weather‑dependent, making power supply vulnerable during extreme conditions such as the severe drought that reduced river flows and sharply cut hydro generation while demand rose due to high temperatures.

Because China has not yet formed a unified electricity market, cross‑regional power trading is limited. Although temporary measures like the Shaanxi‑Sichuan DC transmission project have supplied power to Sichuan, the “West‑to‑East Power Transfer” agreements still require Sichuan to export electricity to eastern provinces.

China’s electricity spot market consists of inter‑province and intra‑province markets. The inter‑province market organizes cross‑regional power transactions, utilizes surplus transmission capacity, and aims to optimize resource allocation.

Building a new market‑clearing system is essential. The system relies on four key models: Unit Commitment (SCUC), Security‑Constrained Economic Dispatch (SCED), AC/DC constraint iteration, and node pricing.

SCUC determines how much power each generator should produce, while SCED decides how to dispatch that power to meet demand under network and security constraints. Both models become increasingly complex with higher renewable penetration and larger unit mixes.

Node pricing requires calculating shadow prices for each network node to maximize market clearing efficiency, which involves handling numerous nonlinear constraints.

These challenges are addressed by formulating comprehensive mathematical models, applying operations‑research optimization techniques, and using advanced solvers.

Shanshu (杉数) implemented a demonstration clearing system for a power‑grid enterprise using its proprietary COPT solver, integrating day‑ahead secure unit commitment, real‑time secure economic dispatch, and node pricing models. The system incorporates AC/DC iterative constraints and cascade hydro constraints, achieving a reduction of clearing computation time from the order of ten‑thousands of seconds to thousands of seconds while reliably satisfying all constraints.

The new clearing system improves dispatch decision efficiency, balances supply and demand, prevents future power shortages like Sichuan’s, enhances grid safety and stability, and supports the integration of generation, transmission, load, and storage into a unified, economical, and secure power system.