Solving large-scale electricity market pricing problems in polynomial time

In centralized wholesale electricity markets worldwide, market operators use mixed-integer linear programming to solve the allocation problem. Prices are typically determined based on the duals of relaxed versions of this optimization problem. The resulting outcomes are efficient, but market operators must pay out-of-market uplifts to some market participants and incur a considerable budget deficit that was criticized by regulators. As the share of renewables increases, the number of market participants will grow, leading to larger optimization problems and runtime issues. At the same time, non-convexities will continue to matter, e.g., due to ramping constraints of the generators required to address the variability of renewables or non-convex curtailment costs. We draw on recent theoretical advances in the approximation of competitive equilibrium to compute allocations and prices in electricity markets using convex optimization. The proposed mechanism promises approximate efficiency, no budget deficit, and computational tractability. We present experimental results for this new mechanism in the context of electricity markets, and compare the runtimes, the average efficiency loss of the method, and the uplifts paid with standard pricing rules. We find that the computations with the new algorithm are considerably faster for relevant problem sizes. In general, the computational advantages come at the cost of efficiency losses and a price markup for the demand side. Interestingly, both are small with realistic problem instances. Importantly, the market operator does not incur a budget deficit and the uplifts paid to market participants are significantly lower compared to standard pricing rules.