Performance assessment of multiple-types co-located storage for uncertainty mitigation in integrated electric-gas system using generalized polynomial chaos

Houhe Chen, Junyan Shao, Tao Jiang, Xue Li and Rufeng Zhang

Applied Energy, 2024, vol. 374, issue C, No S0306261924013138

Abstract: Prudent and sensible deployment of local storage may unlock the potential of mitigating the adverse impact, especially addressing the challenge of uncertainty from independent renewable energy deployment. This paper designs a photovoltaic battery system (PVBS) and a gas turbine hydrogen tank system (GTTS) as the local energy communities to mitigate the uncertainty in the integrated electric-gas systems (IEGS). Firstly, this paper quantified the contribution of the co-located energy storage and remodeled the output of PVBS and GTTS as approximate beta distributions, assessing the local effects of uncertainty mitigation. Subsequently, the steady-state energy flow models incorporated with uncertainty variables of IEGS are reformulated into the chaotic mathematical counterpart via the generalized polynomial chaos method (GPC), highlighting the global performance of novel designs. In the subsequent step, the independent random variables are factorized from chaotic equations leveraged by the improved Galerkin method and remodeled into a high-dimensional deterministic equation. Based on this, the probabilistic distributions of state variables with higher order uncertainty parameters for IEGS, such as bus voltage, natural gas node pressure, etc., are sequentially computed via the Newton method. Furthermore, this paper performed a comparative simulation in the IEGS 4–12 system and IEGS 118–96 system and empirically tested the feasibility and validity of the novel design in uncertainty mitigation. Through the quantitative instantiation, enhancing the co-located energy storage capacity, increasing the outlet pressure of compressors, and decreasing the coupling level between subsystems may mitigate the uncertainty within IEGS. Derived GPC algorithms improve the computational speed and accuracy than other methods for the computation of multiple uncertainty variables in IEGS. This exploration delves into the local energy communities in various operating conditions and offers valuable suggestions for sizing and control strategies for the novel design in IEGS.

Keywords: Integrated energy system; Galerkin method; Probabilistic energy flow; Polynomial chaos; Colocated energy storage (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123930

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