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Smart Energy Systems: Guidelines for Modelling and Optimizing a Fleet of Units of Different Configurations

Veil Energy Srl–San Pietro in Gu, 35010 Padova, Italy
Energies 2019, 12(7), 1320; https://doi.org/10.3390/en12071320
Received: 7 February 2019 / Revised: 2 April 2019 / Accepted: 4 April 2019 / Published: 6 April 2019
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Abstract

The need to reduce fossil fuels consumption and polluting emissions pushes towards the search of systems that combine traditional and renewable energy conversion units efficiently. The design and management of such systems are not easy tasks because of the high level of integration between energy conversion units of different types and the need of storage units to match the availability of renewables with users’ requirements properly. This paper summarizes the basic theoretical and practical concepts that are required to simulate and optimize the design and operation of fleet of energy units of different configurations. In particular, the paper presents variables and equations that are required to simulate the dynamic behavior of the system, the operational constraints that allow each unit to operate correctly, and a suitable objective function based on economic profit. A general Combined Heat-and-Power (CHP) fleet of units is taken as an example to show how to build the dynamic model and formulate the optimization problem. The goal is to provide a “recipe” to choose the number, type, and interconnection of energy conversion and storage units that are able to exploit the available sources to fulfill the users’ demands in an optimal, and therefore “smart”, way. View Full-Text
Keywords: smart energy systems; Mixed-Integer NonLinear/Linear Programming (MINLP/MILP); dynamic modelling; design and operation optimization; fleet of energy conversion and storage units smart energy systems; Mixed-Integer NonLinear/Linear Programming (MINLP/MILP); dynamic modelling; design and operation optimization; fleet of energy conversion and storage units
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Rech, S. Smart Energy Systems: Guidelines for Modelling and Optimizing a Fleet of Units of Different Configurations. Energies 2019, 12, 1320.

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