<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Environmental and Climate Technologies

This work is licensed under the Creative Commons Attribution 4.0 International License.

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...

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

Published Online: Apr 13, 2020

Page range: 209 - 232

DOI: https://doi.org/10.2478/rtuect-2020-0013

Keywords
Decision-making method, Demand Side Management (DSM), Multi-objectives optimal scheduling, Smart energy Hub system (SEHS), ε-constraint method

© 2020 Heydar Chamandoust et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Published Online: Apr 13, 2020

Page range: 209 - 232

DOI: https://doi.org/10.2478/rtuect-2020-0013

KeywordsDecision-making method, Demand Side Management (DSM), Multi-objectives optimal scheduling, Smart energy Hub system (SEHS), ε-constraint method

© 2020 Heydar Chamandoust et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Decision-making method, Demand Side Management (DSM), Multi-objectives optimal scheduling, Smart energy Hub system (SEHS), ε-constraint method