4.7 Article

Comparative study of optimization method and optimal operation strategy for multi-scenario integrated energy system

Journal

ENERGY
Volume 217, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2020.119311

Keywords

Integrated energy system; Isolated and connected scenarios; Optimization scheme; Minimal annualized cost; Collaborative optimization method

Funding

  1. National Key R&D Program of China [2019YFE0193100]
  2. Natural Science Foundation of Hebei Province [E2018502059]

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This study established the theoretical model and optimization scheme for integrated energy system, and conducted a case study to determine optimal nominal capacity and operation parameters for different scenarios. By adopting self-adaption strategy and optimization scheme, the system cost can be effectively reduced and the economic efficiency can be improved. The applicability of building-scale and district-scale integrated energy systems in terms of economy was compared, and a collaborative optimization method was introduced to further reduce system cost.
Integrated energy system as a welcome multiple-energy system has significant contribution to alleviate the worldwide energy shortage problem. In this paper, the theoretical model of integrated energy system was initially built. Also, a new self-adaption strategy relying on exhaustive search method is proposed for minimal hourly-operation-cost. On the basis, an optimization scheme oriented by minimal annualized system cost is presented for integrated energy system. Based on a case study, the optimal nominal capacity of devices and operation parameters are determined by the optimization scheme, respectively for isolated and connected scenarios with different operation strategies. In isolated scenario, the annualized system cost with self-adaption strategy is the lowest, around $23.60/m(2) for commercial building and around $19.39/m(2) for office building. In connected scenario, the minimal annualized system cost is $693.3 k (about $19.26/m(2)). Moreover, the applicability of building-scale and district-scale integrated energy systems in economy is compared. Finally, a collaborative optimization method that combines genetic algorithm and orthogonal experimental design is introduced to determine multiple decision variables for district-scale integrated energy system. The results show that the district-scale integrated energy system determined by collaborative optimization reduces the annualized cost by 0.67% (about $78.8 k), compared by non-collaborative optimization. These findings could provide the reference about integrated energy system optimization and further explore the development potential of multiple energy system. (C) 2020 Elsevier Ltd. All rights reserved.

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