Multi-objectives Optimal Scheduling in Smart Energy Hub System with Electrical and Thermal Responsive Loads

• Published in: Environmental and Climate Technologies Vol. 24; no. 1; pp. 209 - 232
• Main Authors: Chamandoust, Heydar, Derakhshan, Ghasem, Hakimi, Seyed Mehdi, Bahramara, Salah
• Format: Journal Article
• Language: English
• Published: Riga Sciendo 01-01-2020; Riga Technical University
• Subjects: Air pollution; Case studies; Decision making; Decision-making method; Demand Side Management (DSM); Electric power demand; Energy; Energy management; Energy sources; Hybrid systems; Multi-objectives optimal scheduling; Multiple objective analysis; Natural gas; Optimization; Renewable energy sources; Scheduling; Sensitivity analysis; Smart energy Hub system (SEHS); Stochastic models; Stochasticity; Thermal analysis; ε-constraint method
• ISSN: 2255-8837; 1691-5208; 2255-8837
• DOI: 10.2478/rtuect-2020-0013

In this study, multi-objective optimal scheduling of smart energy Hub system (SEHS) in the day ahead is proposed. A SEHS is comprising of interconnected energy hybrid system infrastructures such as electrical, thermal, wind, solar, natural gas and other fuels to supply many types of electrical and thermal loads in a two-way communication platform. All objectives in this paper, are minimized and consist of 1) operation cost and emission polluting in generation side, 2) loss of energy supply probability (LESP) in demand side, and 3) deviation of electrical and thermal loads with the optimal level of electrical and thermal profile in the day ahead. The third objective to flatten electrical and thermal demand profile using Demand Side Management (DSM) by the optimal shifting of electrical and thermal shiftable loads (SLs) is proposed. Also, stochastic modelling of renewable energy sources (RESs) and electrical and thermal loads by Monte Carlo technique is modelled. Using GAMS optimization software, proposed approach by ε -constraint method for obtaining to non-dominated Pareto solutions of objectives is implemented. Moreover, by the decision-making method, the best solution of non-dominated Pareto solutions is selected. Finally, two case studies and sensitivity analysis in case studies for confirmation of the proposed approach are analysed.