Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids
This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users.
In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method.
Postprint accepted for publication in IEEE Transactions on Automation Science and Engineering (T-ASE).
How to cite: R. Carli, G. Cavone, T. Pippia, B. De Schutter, and M. Dotoli, ”Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1338-1351, July 2022.
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ORCID of Submitting Authorhttps://orcid.org/0000-0001-9184-6017
Submitting Author's InstitutionPolytechnic of Bari
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