Herd Behavior in Decentralized Balancing Models: A Case Study in Belgium

TL;DR

The paper investigates how expanding participation in implicit balancing within Belgium affects grid balancing costs and price formation. It introduces a market simulator that models the feedback loop between real-time TSO price signals and BRP responses, representing BRPs as battery energy storage assets governed by bang-bang control and calibrated risk thresholds. By comparing three imbalance price formulas—Current, Max/min with Smoothed Deadband, and Weighted Average with Dynamic Weights—using 2023 Belgian data, the study finds that implicit balancing can reduce extreme imbalances and costs at moderate capacity but can generate oscillations and higher costs if total implicit capacity becomes too large; BRPs remain profitable across scenarios. The work highlights the importance of the chosen price design in mitigating oscillations and suggests policy directions for the TSO to curb overshoots, as well as avenues to extend the model to additional asset classes and more advanced control strategies.

Abstract

In a decentralized balancing model, Balance Responsible Parties (BRPs) are encouraged by the Transmission System Operator (TSO) to deviate from their schedule to help the system restore balance, also referred to as implicit balancing. This could reduce balancing costs for the grid operator and lower the entry barrier for flexible assets compared to explicit balancing services. However, these implicit reactions may overshoot when their total capacity is high, potentially requiring more explicit activations. This study analyses the effect of increased participation in the decentralized balancing model in Belgium. To this end, we develop a market simulator that produces price signals on minute-level and simulate the implicit reactions for battery assets with different risk profiles. Besides the current price formula, we also study two potential candidates for the near-term presented by the TSO. A simulation study is conducted using Belgian market data for the year 2023. The findings indicate that, while having a significant positive effect on the balancing costs at first, the risk of overshoots can outweigh the potential benefits when the total capacity of the implicit reactions becomes too large. Furthermore, even when the balancing costs start to increase for the TSO, BRPs were still found to benefit from implicit balancing.

Figures (13)

• Figure 1: High-level overview of the simulation environment modelling the feedback loop between the power system and the transmission system operator. Every minute, the TSO observes the actual system imbalance, whereas the power system only receives the imbalance price with a certain delay (e.g., 2 minutes in Belgium).
• Figure 2: Boxplots of the RMSE of the minute-level imbalance price compared to the settlement price, with the diamond marker indicating the mean value. The RMSE is calculated for each imbalance settlement period seperately.
• Figure 3: Distribution of the absolute system imbalance (1-minute and 15-minute level) using the current price formula. The imbalances are binned according to the three regions considered in the price formula: the deadband ($<$$25$ MW), normal system imbalance ($25$$-$$150$ MW) and extreme system imbalance ($>$$150$ MW). The calculations were made based on the simulations for the current price formula.
• Figure 4: Balancing cost per imbalance settlement period for the different price formulas as a function of the total simulated capacity. The balancing costs include the activation costs of reserves (aFRR/mFRR) and the payment from/to BRPs due to their simulated positions during imbalance settlement.
• Figure 5: Profit per imbalance settlement period normalized by the power capacity within each risk group. The calculations were made based on the simulations for the current price formula. The profit does not consider any grid transmission fees.