Providing an integrated Model for Planning and Scheduling Energy Hubs and preventive maintenance

• Published in: Energy (Oxford) Vol. 163; pp. 1093 - 1114
• Main Authors: Amiri, S., Honarvar, M., sadegheih, A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.11.2018; Elsevier BV
• Subjects: Bi-objective integer linear programming; Constraint modelling; Demand; Energy; Energy conservation; Energy hub; Epsilon constraint; Integer programming; issues and policy; Maintenance management; Mathematical models; Parameter sensitivity; planning; Preventive maintenance; risk; Scheduling; Sensitivity analysis; uncertainty; Epsilon constraint; Energy hub; Bi-objective integer linear programming; Preventive maintenance
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2018.08.046

In this paper, a bi-objective mathematical model is presented for energy hub scheduling with consideration of preventive maintenance policy. In the model, the hub equipment is assumed to be at risk of random failure, and a periodic preventive maintenance action is planned considering the energy hub utilization plan to take the system into an operational state as good as new. The aim of the proposed model is to determine the preventive maintenance cycles and the best strategy to allocate hub energy capacity under different demand scenarios, while the goals are to minimize costs and to maximize the reliability of the system. The novelty of this paper is the integration of preventive maintenance scheduling and energy hub scheduling. Considering the uncertainty of the demand, a scenario-based two-stage stochastic programming approach is used. CPLEX solver of GAMS is used to solve the model based on the Epsilon-constraint method. The sensitivity analysis is provided to define the effect of parameters such as demand and capacity on the model. The performance of the proposed integrated model is compared with the solutions offered by two well-known techniques from the subject literature. The results show that the integrated model possesses outstanding performance. •An integrated energy hub scheduling and preventive maintenance scheduling model is studied.•The risk of random failure for hub equipment is considered.•The scenario-based two-stage stochastic programming for energy hub scheduling is developed.