Optimal Bidding and Coordinated Dispatch of Hybrid Energy Systems in Regulation Markets

TL;DR

This work addresses the challenge of integrating hybrid energy systems into frequency regulation markets by proposing a bi-level framework that couples a chance-constrained bidding problem with a real-time, asset-level dispatch strategy. The outer level uses historical regulation signals to determine a maximum feasible bid that maintains a minimum performance score with high probability, while the inner level disaggregates the regulation signal among a controllable generator, battery, and controllable load to track $Cr[k]$ within operational constraints. Key contributions include a MILP-based offline benchmark, a rule-based real-time control that achieves offline-optimal performance under nonbinding SoC conditions, and a detailed analysis of symmetric versus asymmetric HES configurations, including profitability versus reliability trade-offs in PJM Reg-D data. The results demonstrate that the framework can enhance revenue while maintaining compliance and SOC sustainability, with practical implications for multi-resource, market-participating HES deployments.

Abstract

The increasing integration of renewable energy sources and distributed energy resources (DER) into modern power systems introduces significant uncertainty, posing challenges for maintaining grid flexibility and reliability. Hybrid energy systems (HES), composed of controllable generators, flexible loads, and battery storage, offer a decentralized solution to enhance flexibility compared to single centralized resources. This paper presents a two-level framework to enable HES participation in frequency regulation markets. The upper level performs a chance-constrained optimization to choose capacity bids based on historical regulation signals. At the lower level, a real-time control strategy disaggregates the regulation power among the constituent resources. This real-time control strategy is then benchmarked against an offline optimal dispatch to evaluate flexibility performance. Additionally, the framework evaluates the profitability of overbidding strategies and identifies thresholds beyond which performance degradation may lead to market penalties or disqualification. The proposed framework also compare the impact of imbalance of power capacities on performance and battery state of charge (SoC) through asymmetric HES configurations.

Figures (8)

• Figure 1: Hybrid energy system consisting of a controllable generator, battery storage, and a controllable load connected to an utility grid.
• Figure 2: Illustration of distribution of $x_{\text{p}}$ and $C\mathbb{E}[x_{\text{p}}]$ across various bids $C$ for a given control strategy $g(\cdot)$. The optimal bid $C^{*} = \min\{\hat{C}, C_{\text{max}}\}$ is selected to satisfy the chance constraint $\mathbb{P}(x_{\text{p}} \ge \underline{x}_{\text{p}}) \ge \gamma$ i.e., $z_{\gamma}^{g}(\overline{C}) = \underline{x}_{\text{p}}$, while maximizing expected revenue. In (a), the shaded regions show the support of the distribution of $x_{\text{p}}$, where the darker region shows that $\gamma=90\%$ of the distribution falls above $z_{\gamma}^{g}(C)$.
• Figure 3: Statistical characterization of the PJM Reg-D signal over one year: (a) distribution of hourly energy drift, showing deviations from perfect neutrality; (b) distribution of worst case drift for normalized regulation signal.
• Figure 4: Distribution of $x_{\text{p}}$ across different bid capacities under the real-time control strategy. The shaded areas show $\pm\sigma$, $\pm2\sigma$, and $\pm3\sigma$ standard deviation bands.
• Figure 5: Comparison of real-time dispatch $g(\cdot)$ and optimal offline dispatch (denoted with a $^*$) for one representative hour with $C=12.21$ MW. The plots show the net regulation signal $C\mathbf{r}$, $P_{\text{hes}}$, $P_{\text{gen}}$+ $P_{\text{CL}}$, $P_{\text{batt}}$, and state of charge $E$. The performance $x_{\text{p}}=0.8412$ for both strategies.

Theorems & Definitions (1)

• Theorem 1