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

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.

💡 Research Summary

This paper investigates the systemic effects of increasing participation in a decentralized, implicit balancing scheme in the Belgian power market. In such a scheme, the Transmission System Operator (TSO) sends minute‑resolution price signals to Balance Responsible Parties (BRPs), encouraging them to deviate from their scheduled dispatch in order to help the system restore balance. The authors develop a high‑fidelity market simulator that reproduces the 2023 Belgian market data, including real‑time price formation, demand‑supply imbalances, and the scheduling of conventional generators. Within the simulator, they model a fleet of battery storage assets representing BRPs, assigning each a distinct risk profile—conservative, neutral, or aggressive—based on how strongly it reacts to price volatility, its state‑of‑charge limits, and its willingness to incur losses.

Three pricing formulas are examined: the current TSO formula (a weighted average of price, deviation, and capacity), a candidate that scales directly with the real‑time energy price, and a second candidate that dynamically weights the signal by the system’s surplus power. For each formula, the simulator computes the minute‑by‑minute implicit balancing response of each battery, aggregates the total implicit capacity, and evaluates the resulting balancing cost for the TSO as well as the revenue earned by the BRPs.

The simulation results reveal a clear non‑linear relationship between total implicit capacity and system cost. When the aggregate implicit capacity is modest (up to roughly 20 % of total system capacity), implicit balancing delivers substantial savings, reducing the TSO’s balancing cost by 12–18 % relative to a baseline with no implicit participation. This benefit arises because batteries can quickly absorb or inject power in response to price spikes, smoothing short‑term mismatches without invoking costly explicit balancing actions.

However, as participation grows beyond about 35 % of system capacity, a “herd behavior” effect emerges. Many BRPs simultaneously follow the same price cue—e.g., all discharge when the price rises—causing an overshoot that depletes system surplus power. The overshoot triggers the need for explicit balancing interventions, eroding the earlier savings and eventually raising the TSO’s total balancing cost above the baseline. The magnitude of this overshoot depends on the pricing formula. The current TSO formula, being highly sensitive to price changes, amplifies herd behavior. The first candidate (price‑proportional weighting) dampens the response, reducing overshoot but also delivering smaller cost reductions. The second candidate (dynamic surplus‑based weighting) strikes a better balance: it curtails excessive collective actions while preserving most of the cost‑saving potential.

Risk‑profile analysis shows that aggressive BRPs generate the largest individual cost reductions but also contribute most to the overshoot risk. Conservative BRPs provide modest savings but act as a stabilizing force, limiting the magnitude of collective swings. This suggests that the TSO could shape system outcomes by adjusting incentive structures or by imposing participation caps based on risk appetite.

A further key insight is the divergence between TSO and BRP perspectives. Even when the TSO’s balancing cost begins to rise due to overshoot, BRPs continue to profit from implicit balancing because they capture price differentials that exceed the marginal cost of battery cycling. Consequently, a policy that solely focuses on TSO cost minimization may inadvertently encourage BRPs to maintain or increase participation, perpetuating the herd effect.

The authors conclude that implicit balancing is a powerful tool for cost reduction and flexibility integration, but its benefits are not monotonic with participation level. Careful design of price signals—particularly those that incorporate system‑wide surplus information—and targeted risk‑based participation rules are essential to prevent harmful herd behavior. Future work should validate the findings with real‑world battery operation data, explore the impact of other flexible resources (e.g., demand‑response), and assess the transferability of the results to other market designs.