Improving Electricity Market Economy via Closed-Loop Predict-and-Optimize

The electricity market clearing is usually implemented via an open-loop predict-then-optimize (O-PO) process: it first predicts the available power of renewable energy sources (RES) and the system reserve requirements; then, given the predictions, the markets are cleared via optimization models, i.e., unit commitment (UC) and economic dispatch (ED), to pursue the optimal electricity market economy. However, the market economy could suffer from the open-loop process because its predictions may be overly myopic to the optimizations, i.e., the predictions seek to improve the immediate statistical forecasting errors instead of the ultimate market economy. To this end, this paper proposes a closed-loop predict-and-optimize (C-PO) framework based on the tri-level mixed-integer programming, which trains economy-oriented predictors tailored for the market-clearing optimization to improve the ultimate market economy. Specifically, the upper level trains the economy-oriented RES and reserve predictors according to their induced market economy; the middle and lower levels, with given predictions, mimic the market-clearing process and feed the induced market economy results back to the upper level. The trained economy-oriented predictors are then embedded into the UC model, forming a prescriptive UC model that can simultaneously provide RES-reserve predictions and UC decisions with enhanced market economy. Numerical case studies on an IEEE 118-bus system illustrate potential economic and practical advantages of C-PO over O-PO, robust UC, and stochastic UC.