Maximizing Power Flexibility of Hybrid Energy Systems for Capacity Market

Abstract

Hybrid Energy Systems (HES), integrating generation sources, energy storage, and controllable loads, are well-positioned to provide real-time grid flexibility. However, quantifying this maximum flexibility is challenging due to renewable generation uncertainty and the complexity of power allocation across multiple assets in real time. This paper presents a rule-based framework for characterizing HES flexibility and systematically allocating power among its constituent assets. The flexibility envelope defines the dynamic power boundary within which the HES can inject or absorb power without violating operational constraints. Shaped in real time by capacity bids, available solar generation, and power allocation protocol, it enables reliable and predictable HES participation in regulation markets. Depending on the operational objective, the framework supports both symmetric and asymmetric flexibility cases. Further, the proposed power-allocation rule is benchmarked against an optimal dispatch, providing a performance reference under realistic conditions. Finally, state of charge drift correction control is presented to ensure sustained battery operation and system reliability. This work, therefore, offers a rigorous and practical framework for integrating HES into capacity markets through effective flexibility characterization.

Figures (7)

• Figure 1: Schematic diagram of a hybrid energy system interfacing PV, battery and a controllable load to the grid through inverters.
• Figure 2: Controllable load power allocation for green hydrogen production, assuming $\overline{P}_{\text{CL}}\ge P_{\text{PV}}[k]$ with the maximum flexibility of $\pm \overline{P}_{\text{batt}}\pm P_{\text{PV}}[k]$ and $P_0[k] = \frac{1}{2}P_{\text{PV}}[k]$.
• Figure 3: Flexibility tube defined around the nominal power trajectory $P_{0}[k]$ using the bounds $\pm \Delta P_{\text{hes}}[k]$ over a one-hour horizon for different scenarios as given in Table \ref{['tab:comparison']}. The HES is expected to track any power trajectory within this tube by ramping up or down from $P_{0}[k]$. In (c), $P_{0}[k] =P_{\text{PV}}[k] - \frac{1}{2}\overline{P}_{\text{CL}}$ for scenario $S_1$.
• Figure 4: Comparison of real-time power allocation rule based dispatch and optimal offline dispatch for one representative hour with $C=\Delta P_{\text{hes}}$ = 6.5 MW. The plots show the net regulation signal $C\mathbf{r}$, $P_{\text{hes}}$, $P_{\text{gen}}$+ $P_{\text{CL}}$, $P_{\text{batt}}$,and battery SoC $E$. The performance $x_{\text{p}}=1$ for both strategies.
• Figure 5: Hourly Solar Irradiance Profiles: seasonal variation in mean, percentiles, extremes, and standard deviation for fall, winter, spring and summer for one year.

Theorems & Definitions (9)

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