arXiv subjects

Johannes Knörr

Publications and source records attributed to Johannes Knörr.

4 recordsLinked to original sources

arXiv2025

Zonal vs. Nodal Pricing: An Analysis of Different Pricing Rules in the German Day-Ahead Market

The European electricity market is based on large pricing zones with a uniform day-ahead price. The energy transition leads to changes in supply and demand and increasing redispatch costs. In an attempt to ensure efficient market clearing and congestion management, the EU Commission has mandated the Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. Based on a unique data set published in the context of the BZR for the target year 2025, we analyze the short-run effects of various pricing rules for the German-Luxembourgish bidding zone. We compare market clearing and pricing for different zonal models, including their generation and redispatch costs. In numerical experiments with this dataset, the differences in the average prices in different zones are low. The total costs across different configurations are similar and the reduction of standard deviations in prices is also small. This might be different with other load and generation scenarios, but the BZR data is important as it was created to make a decision about splits of the existing bidding zones. We can replicate several results from the BZR study, except the large cost savings when moving from one to two price zones in Germany and Luxembourg. In addition to the four zonal configurations analyzed in the BZR study, we compare these against a nodal pricing system. While the total cost savings after introducing zonal splits were less than 1%, nodal pricing led to savings of 5-6%. We also evaluate differences of nodal pricing rules with respect to the necessary uplift payments, which is relevant in the context of the discussion on non-uniform pricing in the EU. While the study focuses on Germany, the analysis is relevant beyond and feeds into the broader discussion about pricing rules in non-convex markets.

econ.GN↗

arXiv2025

Challenges in Finding Stable Price Zones in European Electricity Markets: Aiming to Square the Circle?

The European day-ahead electricity market is split into multiple bidding zones with a uniform price. The increase in renewables leads to a growing number of interventions in the generation of energy sources and increasing redispatch costs. To ensure efficient congestion management, the EU Commission mandated a Bidding Zone Review (BZR) to reevaluate the configuration of European bidding zones. An integral part of this process was a locational marginal pricing study. Based on these prices, alternative bidding zone configurations were proposed. These bidding zones shall be stable and robust over time. For Germany, four configurations were suggested. We analyzed the proposed configurations considering different clustering algorithms and periods based on the publicly released data set in the context of the BZR, and found that the configurations do not reduce the price standard deviations within zones much, and the average prices across zones are similar. Other configurations identified based on clustering the prices lead to lower price variance but they are not geographically coherent. Independent of the clustering features and algorithms used, the resulting clusters are not stable over time. Interestingly, the effect of a split on prices would be low based on an analysis of the BZR data set.

econ.GN↗

arXiv2023

Solving Large-Scale Electricity Market Pricing Problems in Polynomial Time

Electricity market operators worldwide use mixed-integer linear programming to solve the allocation problem in wholesale electricity markets. 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 fast 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.

cs.GT↗

arXiv2023

Pricing Optimal Outcomes in Coupled and Non-Convex Markets: Theory and Applications to Electricity Markets

According to the fundamental theorems of welfare economics, any competitive equilibrium is Pareto efficient. Unfortunately, competitive equilibrium prices only exist under strong assumptions such as perfectly divisible goods and convex preferences. In many real-world markets, participants have non-convex preferences and the allocation problem needs to consider complex constraints. Electricity markets are a prime example, but similar problems appear in many real-world markets, which has led to a growing literature in market design. Power markets use heuristic pricing rules based on the dual of a relaxed allocation problem today. With increasing levels of renewables, these rules have come under scrutiny as they lead to high out-of-market side-payments to some participants and to inadequate congestion signals. We show that existing pricing heuristics optimize specific design goals that can be conflicting. The trade-offs can be substantial, and we establish that the design of pricing rules is fundamentally a multi-objective optimization problem addressing different incentives. In addition to traditional multi-objective optimization techniques using weighing of individual objectives, we introduce a novel parameter-free pricing rule that minimizes incentives for market participants to deviate locally. Our theoretical and experimental findings show how the new pricing rule capitalizes on the upsides of existing pricing rules under scrutiny today. It leads to prices that incur low make-whole payments while providing adequate congestion signals and low lost opportunity costs. Our suggested pricing rule does not require weighing of objectives, it is computationally scalable, and balances trade-offs in a principled manner, addressing an important policy issue in electricity markets.

cs.GT↗