Bid-aggregation based clearing of day-ahead electricity markets

TL;DR

This paper tackles the computational challenge of clearing day-ahead electricity markets under European rules with non-convex bids, notably block orders. It introduces a bid-aggregation based (BA) two-stage clearing method that first aggregates standard bids to form a simpler problem, derives estimated MCP ranges, and then solves the original bid set under these MCP constraints to reduce problem size. The authors analyze potential pitfalls (suboptimality and infeasibility) using illustrative examples and mitigate them by running multiple maximally different aggregations in parallel, achieving substantial speedups in numerical tests. The approach shows promise for time-critical DAM applications, offering a practical path to faster clears while preserving solution quality in most cases, though it may require complementary full-scale clearing or additional aggregations to ensure feasibility and optimality in all scenarios.

Abstract

In this work we propose a heuristic clearing method of day-ahead electricity markets. In the first part of the process, a computationally less demanding problem is solved using an approximation of the cumulative demand and supply curves, which are derived via the aggregation of simple bids. Based on the outcome of this problem, estimated ranges for the clearing prices of individual periods are determined. In the final step, the clearing for the original bid set is solved, taking into account the price ranges determined previously as constraints. Adding such constraints reduces the feasibility region of the clearing problem. By removing simple bids whose acceptance or rejection is already determined by the assumed price range constraints, the size of the problem is also significantly reduced. Via simple examples, we show that due to the possible paradox rejection of block bids the proposed bid-aggregation based approach may result in a suboptimal solution or in an infeasible problem, but we also point out that these pitfalls of the algorithm may be avoided by using different aggregation patterns. We propose to construct multiple different aggregation patterns and to use parallel computing to enhance the performance of the algorithm. We test the proposed approach on setups of various problem sizes, and conclude that in the case of parallel computing with 4 threads a high success rate and a significant gain in computational speed may be achieved.

Figures (9)

• Figure 1: Cumulative demand and supply curves of the original ($\Phi^0$) and of the aggregated ($\Phi^1$) bid set, described in Tables \ref{['tab_bid_data_demo_1']} and \ref{['tab_bid_data_demo_res_1']} and denoted by dashed and continuous lines respectively.
• Figure 2: Example for the derivation of $\overline{MCP}_t$ and $\underline{MCP}_t$
• Figure 3: Cumulative supply and demand curves implied by the bids included in table \ref{['tab_bid_data_ex_subopt']}.
• Figure 4: Cumulative supply and demand curves of $\Phi^1$ of example I.
• Figure 5: Cumulative supply and demand curves implied by the bids included in Table \ref{['tab_bid_data_infeas_ex']}.