A Systematic Tri-level Demand-driven Supply-side Management Approach on Enhancing Building Energy Performance in an Educational Institution

Abstract

The concept of demand-side management (DSM) is a ceaseless research topic craving an advanced, superior, and qualitative strategy for improving customers’ level of satisfaction with energy utilization. It encourages the active participation of prosumers and paves the way for sufficient energy proficiency. However, less attention is paid to accounting for consumers’ comfort and priorities. This paper presents a tri-level systematic approach for implementing demand-side and supply-side management at the consumer end. The proposed Tri-level Demand-Driven Supply-Side Management (TDD-SSM) framework commences with gathering knowledge on available loads in the department building block of a higher educational institution situated in the Madurai district of Tamil Nadu, India. The incorporated DSM technique follows strategic load categorization and load scheduling. Following that, a feasibility study on optimal sources is accomplished with Hybrid Optimization of Multiple Energy Resources software. The simulation is done to have a clear comparison in rendering a techno-enviro-economically feasible configuration of energy source to loads (with and without DSM). Ultimately, the framework concludes by picking the most promising energy configuration through the ideal computational multi-criteria decision-making–Preference Ranking Organization Method for Enrichment Evaluation II approach, which highlights the energy configuration of photovoltaic solar generation supply along with grid feeding loads after DSM incorporation and is found to be the best energy configuration under techno-enviro-economic criteria.

Keywords:

• building energy management
• load categorization and scheduling
• decision tree algorithm
• HOMER techno-enviro-economic analysis
• multi-criteria decision-making analysis
• PROMETHEE-II

ACKNOWLEDGMENTS

The authors extend their due thanks to the TCE management, in Madurai, India, for their extensive facilities and the financial backing from the research excellence strand of the Savitha Project, Thiagarajar Research Fellowship (TRF) scheme (File No: TRF/Jul 2022/02) is gratefully acknowledged.

DISCLOSURE STATEMENT

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This study was financially supported by the Savitha Project, Thiagarajar Research Fellowship (TRF) scheme (File No.: TRF/Jul2022/02).

Notes on contributors

A. C. Vishnu Dharssini

A. C. Vishnu Dharssini received her Bachelor’s Degree in Electrical and Electronics Engineering, and Masters in Power Systems Engineering with distinction from Anna University, Chennai in 2019, and 2021 respectively. She is the recipient of the “Best Outgoing Student” Award during her Master’s degree in Power Systems Engineering from Thiagarajar College of Engineering in 2021. She is currently pursuing her Ph.D. at the Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, India. Her topics of interest include power system analysis, smart grid and Machine learning.

S. Charles Raja

S. Charles Raja, Senior Member IEEE, is received the Young Scientist Fellowship- 2009–2010 from the TNSCST, Career Award for Young Teachers 2014-18 from AICTE, and Early Career Research Award 2017-20 from DST-SERB. He is a co-author of a book entitled ‘Electrical Power Systems: Analysis, Security and Deregulation’, PHI Publication, 2017. He has successfully completed a sponsored research project from DST - SERB with the cost of Rs. 30 Lakh. He has published 35 papers in International/National conferences and 45 research papers in International/National Journals. He is a member of IEEE, PES Society, IEEE Young Professorial and IE (I), and a Life member of ISTE. He is presently working as an Associate Professor in the Department of EEE, Thiagarajar College of Engineering, Madurai, Tamilnadu, India. His topics of interest include smart grid, IoT, Machine learning, and power system restructuring.

P. S. Manoharan

P. S. Manoharan is working as Professor in Thiagarajar College of Engineering, Madurai, India. He has completed his UG in EEE and PG in Power System Engineering from Thiagarajar College of Engineering, Madurai and Received Ph.D degree from Anna University, Chennai, India. He has published more than 150 papers in International Journals and Conferences. His research interests include Solar energy, Power system management and Evolutionary computation.