A Heuristic Algorithm for Combined Heat and Power System Operation Management

Muhammad Faisal Shehzad, Mainak Dan, Valerio Mariani, Seshadhri Srinivasan, Davide Liuzza, Carmine Mongiello, Roberto Saraceno and Luigi Glielmo
Additional contact information
Muhammad Faisal Shehzad: Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy
Mainak Dan: Interdisciplinary Graduate Programme, Nanyang Technological University Computational Intelligence Laboratory, Blk N4, B1a-02, Singapore 639798, Singapore
Valerio Mariani: Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy
Seshadhri Srinivasan: Berkeley Education Alliance for Research in Singapore, Singapore 138602, Singapore
Davide Liuzza: Fusion and Technology for Nuclear Safety and Security Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Rome, Italy
Carmine Mongiello: Energy Technologies and Renewable Sources Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 80055 Portici, Italy
Roberto Saraceno: AtenaTech srl, 00044 Rome, Italy
Luigi Glielmo: Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy

Energies, 2021, vol. 14, issue 6, 1-22

Abstract: This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

Keywords: combined heat and power; co-generation; energy storage system; energy management; heuristics; genetic algorithm; low-cost computing platform (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: 2021
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