Abstract

At present, renewable energy is commonly expected to be the main source to lead the green energy transition with the target of carbon neutrality. Despite the crucial importance of renewable energy sources, several issues still require the integration with traditional technologies.

Therefore, this paper investigates the design and operation of a Hybrid Energy Plant (HEP) in which several energy systems (renewable energy systems: photovoltaic panels, solar thermal collectors, heat pumps; fossil fuel energy systems: combined heat and power systems, gas boilers, absorption and compression chillers; thermal storage systems) interact in order to identify the best combination and management strategy in terms of fossil cumulative energy consumption and costs.

The concurrent optimization of both design and operation is identified by employing two algorithms, i.e., surrogate modeling optimization algorithm and mixed-linear-programming algorithm. Fossil cumulative energy demand (FCED) or total cost (TC) are separately set as the minimization target, by taking into account the variability of natural gas cost, CO2 emission cost, and electricity prices. Four different economic scenarios, which also consider future trends of costs and energy prices, are analyzed. In addition, a clustering procedure is employed to partition the data of energy demand into clusters in such a manner that their representative objects simulate the entire dataset and the computational time is significantly reduced. The Campus of the University of Parma (Italy) is selected as the case study to validate the methodology.

The proposed approach is demonstrated to be a powerful tool, since it identifies the optimal design and operation of the considered HEP in different economic scenarios. In fact, it allows to save up to 53% of FCED and up to 41% of TC. In addition, the leading technologies towards the 2030 scenario are identified, i.e., combined heat and power plant systems, heat pumps and absorption chillers.

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