Leveraging Demand Flexibility by Exploiting Prosumer Response to Price Signals in Microgrids

Francesco Simmini, Marco Agostini, Massimiliano Coppo, Tommaso Caldognetto, Andrea Cervi, Fabio Lain, Ruggero Carli, Roberto Turri and Paolo Tenti
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Francesco Simmini: Interdepartmental Centre Giorgio Levi Cases, University of Padova, 35131 Padova, Italy
Marco Agostini: Interdepartmental Centre Giorgio Levi Cases, University of Padova, 35131 Padova, Italy
Massimiliano Coppo: Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Tommaso Caldognetto: Department of Management and Engineering, University of Padova, 36100 Vicenza, Italy
Andrea Cervi: Interdepartmental Centre Giorgio Levi Cases, University of Padova, 35131 Padova, Italy
Fabio Lain: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Ruggero Carli: Department of Information Engineering, University of Padova, 35131 Padova, Italy
Roberto Turri: Department of Industrial Engineering, University of Padova, 35131 Padova, Italy
Paolo Tenti: Department of Information Engineering, University of Padova, 35131 Padova, Italy

Energies, 2020, vol. 13, issue 12, 1-19

Abstract: The diffusion of distributed energy resources in distribution networks requires new approaches to exploit the users’ capabilities of providing ancillary services. Of particular interest will be the coordination of microgrids operating as an aggregate of demand and supply units. This work reports a model predictive control (MPC) application in microgrids for the efficient energy management of energy storage systems and photovoltaic units. The MPC minimizes the economic cost of aggregate prosumers into a prediction horizon by forecasting generation and absorption profiles. The MPC is compared in realistic conditions with a heuristic strategy that acts in a instant manner, without taking into account signals prediction. The work aims at investigating the effect that different types of energy tariffs have in enhancing the end-users’ flexibility, based on three examples of currently applied tariffs, comparing the two storage control modes. The MPC always achieves a better solution than the heuristic approach in all considered scenarios from the cost minimization point of view, with an improvement that is amplified by increasing the energy price variations between peak and off-peak periods. Furthermore, the MPC approach provides a cost saving when compared to the case considering a microgrid endowed with only photovoltaic units, in which no storage is installed. Findings in this work confirm that storage units better perform when some knowledge of future demand and supply trends is provided, ensuring an economic cost saving and an important service for the overall community.

Keywords: heuristic approach; energy storage systems; flexible demand; microgrids; model predictive control; renewable energy resources; retail tariffs (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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