research-article

Energy Management in Microgrids with Renewable Energy Sources and Demand Response

Authors:

Pushpendra Singh,

Praveen Bansal,

Yashwant SawleAuthors Info & Claims

Volume 110, Issue C

https://doi.org/10.1016/j.compeleceng.2023.108848

Published: 01 September 2023 Publication History

Highlights

•

To reduce peak load demands and energy costs, MG uses DGs for a 24-hour period.

•

Minimize operational l.,costs as well as environmental emissions.

•

Equality and inequality constraints are considered.

•

The DE algorithm is simple and performs better than GA & PSO technique.

Abstract

This manuscript presents a unique method for stochastic management of renewable energy sources using Micro Grid (MG). This method is based on the demand reduction function to provide a demand side reserve to supply electricity to consumers. To reduce peak demand and energy costs, a typical MG for distributed generation (DG) is used to schedule 24-hour power. The optimal generation scheduling problem is solved as both single-objective and multi-objective optimization problems. Differential Evolution and Multi-Objective Differential Evolution are used to minimize operating costs and environmental emissions, which consider equality and inequality constraints to find the best way to allocate power among the many units connected to MG. Power allocation using DE is shown to be more cost effective and environmentally friendly compared to Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) based technique.

Graphical abstract

Fig. A.1: Schematic configuration of micro grid with connecting all DG and feeders

Fig. A.1 shows a schematic diagram of the micro grid with connecting all DGs and feeders. The considered system contains several technical power components such as micro turbine, wind turbine, photo-voltaic, phosphoric acid fuel cell and battery for reliable operation of the system.

Display Omitted

References

[1]

C.A. Hernandez-Aramburo, T.C. Green, N. Mugniot, Fuel consumption minimization of a microgrid, IEEE Transactions on Industry Applications 41 (3) (2005) 673–681,.

[2]

G.R. Aghajani, H.A. Shayanfar, H. Shayeghi, Presenting a multi-objective generation scheduling model for pricing demand response rate in MG energy management, Energy Conversion and Management 106 (2015) 308–321,.

[3]

A.A.Z. Diab, H.M. Sultan, I.S. Mohamed, O.N. Kuznetsov, T.D. Do, Application of different optimization algorithms for optimal sizing of PV/wind/diesel/battery storage stand-alone hybrid microgrid, IEEE Access 7 (2019) 119223–119245,.

[4]

S.P. Bihari, P.K. Sadhu, S. Kumari, B. Khan, L.D. Arya, R.K. Saket, D.P. Kothari, A comprehensive review of microgrid control mechanism and impact assessment for hybrid renewable energy integration, IEEE Access 9 (2021) 88942–88958,.

[5]

V. Indragandhi, R. Logesh, V. Subramaniyaswamy, V. Vijayakumar, P. Siarry, L. Uden, Multi-objective optimization and energy management in renewable based AC/DC microgrid, Computers and Electrical Engineering 70 (2018) 179–198,.

[6]

H. Afrakhte, P. Bayat, A self-evolving type-2 fuzzy energy management strategy for multi-microgrid systems, Computers and Electrical Engineering 85 (2020),.

[7]

L. Zhu, Market-based versus price-based optimal trading mechanism design in microgrid, Computers and Electrical Engineering 100 (2022),.

Digital Library

[8]

P.K.C. Wong, R.A. Barr, A. Kalam, Generation modelling of residential rooftop photovoltaic systems and its applications in practical electricity distribution networks, Australian Journal of Electrical and Electronics Engineering 12 (4) (2015) 332–341,.

[9]

Z. Liu, S. Wang, Competitive trading scheduling in smart microgrid market with uncertainty, Computers and Electrical Engineering 100 (2022),.

Digital Library

[10]

T. Hai, J. Zhou, K. Muranaka, Energy management and operational planning of renewable energy resources-based microgrid with energy saving, Electric Power Systems Research 214 (2023),. Part A.

[11]

Y. Zeng, Y. Han, D. Zhang, A deep learning-based microgrid market modeling with planning assumptions, Computers and Electrical Engineering 100 (2022),.

Digital Library

[12]

Y. Ma, M. Zhang, H. Yang, X. Wang, J. Xu, X. Hu, Decentralized and coordinated scheduling model of interconnected multi-microgrid based on virtual energy storage, International Journal of Electrical Power & Energy Systems 148 (2023),.

[13]

Z.P. Yuan, P. Li, Z.L. Li, J. Xia, Data-driven risk-adjusted robust energy management for microgrids integrating demand response aggregator and renewable energies, IEEE Transactions on Smart Grid 14 (1) (2023) 365–377,.

[14]

V. Suresh, P. Janik, Z.M. Jasinski, J.M. Guerrero, Z. Leonowicz, Microgrid energy management using metaheuristic optimization algorithms, Applied Soft Computing 134 (2023) 10998,.

Digital Library

[15]

H.U.R. Habib, A. Waqar, M.G. Hussien, M.G. Hussien, A.K. Junejo, M. Jahangiri, R.M. Imran, Y.S. Kim, J.H. Kim, Analysis of microgrid's operation integrated to renewable energy and electric vehicles in view of multiple demand response programs, IEEE Access 10 (2022) 7598–7638,.

[16]

M.F. Roslan, M.A. Hannan, P. Jern Ker, R.A. Begum, T.I. Mahlia, Z.Y. Dong, Scheduling controller for microgrids energy management system using optimization algorithm in achieving cost saving and emission reduction, Applied Energy 292 (2021),.

[17]

M.K.K. Darabi, H.G.G. Ganjehlou, A. Jafari, M. Nazari-Heris, G.B.B. Gharehpetian, M. Abedi, Evaluating the effect of demand response programs (DRPs) on robust optimal sizing of islanded micro-grids, Energies 14 (18) (2021) 5750,.

[18]

F.H. Aghdam, N.T. Kalantari, B. Mohammadi-Ivatloo, A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements, Journal of Energy Storage 29 (2020),.

[19]

L. Luo, S.S. Abdulkareem, A. Rezvani, M.R. Miveh, S. Samad, N. Aljojo, M. Pazhoohesh, Optimal scheduling of a renewable based microgrid considering photovoltaic system and battery energy storage under uncertainty, Journal of Energy Storage 28 (2020),.

[20]

M. Manbachi, M. Ordonez, AMI-based energy management for islanded AC/DC microgrids utilizing energy conservation and optimization, IEEE Transactions on Smart Grid 10 (1) (2019) 293–304,.

[21]

K. Sarita, S. Kumar, A.S. S.Vardhan, R. M.Elavarasan, R. K.Saket, G.M. Shafiullah, Power enhancement with grid stabilization of renewable energy-based generation system using UPQC-FLC-EVA technique, IEEE Access 8 (2020) 207443–207464,.

[22]

Braithwait, S. D., and K. Eakin. 2002. The role of demand response in electric power market design. Laurits R. Christensen Associates, Prepared for Edison Electric Institute, Madison 1-57.

[23]

A.A. Moghaddam, T.Niknam A.Seifi, M.R.A. Pahlavani, Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source, Energy 36 (11) (2011) 6490–6507,.

[24]

R. Storn, K. Price, Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces, Journal of Global Optimization 23 (1) (1995) 1–12.

[25]

Arya, L. D., P. Singh, and L. S. Titare. 2012. Optimum load shedding based on sensitivity to enhance static voltage stability using DE. Swarm and Evolutionary Computation, 6: 25-38, https://doi.org/10.1016/j.swevo.2012.06.002.

Cited By

Chakraborty ARay S(2024)Optimal allocation of distribution generation sources with sustainable energy management in radial distribution networks using metaheuristic algorithmComputers and Electrical Engineering10.1016/j.compeleceng.2024.109142116:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109142

Recommendations

Enhanced emperor penguin optimization algorithm for dynamic economic dispatch with renewable energy sources and microgrid

In this paper, an enhanced version of the emperor penguin optimization algorithm is proposed for solving dynamic economic dispatch (DED) problem incorporating renewable energy sources and microgrid. Dynamic economic load dispatch optimally shares the ...
The role of risk-based demand response in resource management of a grid-connected renewable-based large-scale microgrid with stationary and mobile energy storage systems and emission tax
Highlights
- Stationary and mobile battery storage systems integrated with microgrids.
- ...
Abstract
Integration of energy storage systems with local electricity systems, like microgrids, is emerging due to their economical, reliability, and environmental benefits. These advantages can be achieved by load management subject to the ...
A multi-objective energy scheduling of the reconfigurable off-grid microgrid with electric vehicles using demand response program
Abstract
Microgrids are essential in ensuring responsible energy consumption and production patterns to support and advance the United Nations’ sustainable development goals. However, due to the limited supply in the off-grid microgrids, an increase in ...
Graphical abstract

Display Omitted
Highlights
- Priority-ordered incentive-based DR program for the EV and non-EV loads is proposed.
- Fast dynamic reconfiguration of the off-grid microgrid is presented.
- Multi-objective energy scheduling problem for the off-grid microgrid is ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computers and Electrical Engineering

Computers and Electrical Engineering Volume 110, Issue C

Sep 2023

1432 pages

ISSN:0045-7906

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 September 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Chakraborty ARay S(2024)Optimal allocation of distribution generation sources with sustainable energy management in radial distribution networks using metaheuristic algorithmComputers and Electrical Engineering10.1016/j.compeleceng.2024.109142116:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.compeleceng.2024.109142

View Options

View options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents