Impact of demand response management on improving social welfare of remote communities through integrating renewable energy resources

This paper investigates the impact of demand response (DR) management on improving the social welfare of remote communities by integrating renewable energy (RE) resources into their diesel generator-based isolated microgrids. A multiple-year planning optimization problem is developed to determine the optimal combination of RE resources and energy storage system (ESS) while considering demand response. Demand response is implemented as a means to reschedule a fraction of total daily load to another time in a 24-hour time window. The results confirm that DR helps to improve the total social welfare by maximizing the use of RE resources. It also helps to reduce the need for ESS which is often considered a costly solution for such applications.

[1]  Ines Sansa,et al.  Optimal sizing design of an isolated microgrid based on the compromise between the reliability system and the minimal cost , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[2]  Noboru Yamada,et al.  Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids , 2016, IEEE Transactions on Smart Grid.

[3]  Geza Joos,et al.  Economic assessment of the remote community microgrid: PV-ESS-diesel study case , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[4]  Timothy M. Weis,et al.  Stakeholders’ perspectives on barriers to remote wind–diesel power plants in Canada , 2008 .

[5]  Fangxing Li,et al.  Optimal design of battery energy storage system for a wind–diesel off-grid power system in a remote Canadian community , 2016 .

[6]  Amir H. Hajimiragha,et al.  Research and development of a microgrid control and monitoring system for the remote community of Bella Coola: Challenges, solutions, achievements and lessons learned , 2013, 2013 IEEE International Conference on Smart Energy Grid Engineering (SEGE).

[7]  Rodrigo Palma-Behnke,et al.  A methodology for community engagement in the introduction of renewable based smart microgrid , 2011 .

[8]  M. Kazerani,et al.  Renewable Energy Alternatives for Remote Communities in Northern Ontario, Canada , 2013, IEEE Transactions on Sustainable Energy.

[9]  Li Guo,et al.  Multi-objective stochastic optimal planning method for stand-alone microgrid system , 2014 .

[10]  Kankar Bhattacharya,et al.  Optimal Sizing of Battery Energy Storage Systems for Microgrids , 2014, 2014 IEEE Electrical Power and Energy Conference.

[11]  Claudio A. Cañizares,et al.  A Centralized Energy Management System for Isolated Microgrids , 2014, IEEE Transactions on Smart Grid.

[12]  S. M. Hakimi,et al.  Optimal Planning of a Smart Microgrid Including Demand Response and Intermittent Renewable Energy Resources , 2014, IEEE Transactions on Smart Grid.

[13]  Claudio A. Canizares,et al.  Long-Term Renewable Energy Planning Model for Remote Communities , 2016, IEEE Transactions on Sustainable Energy.

[14]  Kankar Bhattacharya,et al.  Optimal planning and design of a renewable energy based supply system for microgrids , 2012 .

[15]  Majid Ahmadi,et al.  Optimizing Load Control in a Collaborative Residential Microgrid Environment , 2015, IEEE Transactions on Smart Grid.

[16]  Geza Joos,et al.  Generation Dispatch Techniques for Remote Communities With Flexible Demand , 2014, IEEE Transactions on Sustainable Energy.