4.7 Article

Robust multi-objective thermal and electrical energy hub management integrating hybrid battery-compressed air energy storage systems and plug-in-electric-vehicle-based demand response

Journal

JOURNAL OF ENERGY STORAGE
Volume 35, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2021.102265

Keywords

Energy hub; Robust multi-objective optimization; Compressed air energy storage; Demand response program; Battery degradation; Plug-in electric vehicles

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This study investigates the cooperation between a compressed air energy storage (CAES) and a battery energy storage system (BESS) in energy hubs to optimize residential thermal and electrical systems for cost and emission reduction. The proposed formulation includes various renewable energy units and demand response programs, with uncertainties handled by a computationally effective robust method.
A compressed air energy storage (CAES) can operate together with a battery energy storage system (BESS) to enhance the economic and environmental features of the energy hubs (EH). In this regard, this paper investigates their mutual cooperation in a multi-objective thermal and electrical residential EH optimization problem, which aims to diminish the total operational cost and emission. The proposed formulation also includes a PEV-based demand response program (DRP), renewable energy production units, solar heat collectors (SHE), thermal energy storage (TES), and hot water storage. Additionally, the CAES is operated as a combined heat and power unit in discharging and simple cycle modes. A linearized battery degradation cost model is integrated into the cost objective function, which ensures global optimization. Moreover, the inherent uncertainties of wind speed, solar irradiation, residential loads, electricity market price and PEVs' load demand are handled by the computationally effective robust mixed-integer linear programming (RMILP) method. The PEVs' uncertain load demand is obtained from their corresponding arrival-departure time, daily traveled miles, and vehicle type. The optimal Pareto front of the multi-objective problem is obtained by epsilon-constrained method for different robustness adjustments. Different deterministic and robust case studies are designed to evaluate the functionality of the proposed formulation.

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