Energy management of a microgrid using demand response strategy including renewable uncertainties
-
Suchitra Dayalan
and
Rajarajeswari Rathinam
Abstract
Microgrid is an effective means of integrating multiple energy sources of distributed energy to improve the economy, stability and security of the energy systems. A typical microgrid consists of Renewable Energy Source (RES), Controllable Thermal Units (CTU), Energy Storage System (ESS), interruptible and uninterruptible loads. From the perspective of the generation, the microgrid should be operated at the minimum operating cost, whereas from the perspective of demand, the energy cost imposed on the consumer should be minimum. The main key in controlling the relationship of microgrid with the utility grid is managing the demand. An Energy Management System (EMS) is required to have real time control over the demand and the Distributed Energy Resources (DER). Demand Side Management (DSM) assesses the actual demand in the microgrid to integrate different energy resources distributed within the grid. With these motivations towards the operation of a microgrid and also to achieve the objective of minimizing the total expected operating cost, the DER schedules are optimized for meeting the loads. Demand Response (DR) a part of DSM is integrated with MG islanded mode operation by using Time of Use (TOU) and Real Time Pricing (RTP) procedures. Both TOU and RTP are used for shifting the controllable loads. RES is used for generator side cost reduction and load shifting using DR performs the load side control by reducing the peak to average ratio. Four different cases with and without the PV, wind uncertainties and ESS are analyzed with Demand Response and Unitcommittment (DRUC) strategy. The Strawberry (SBY) algorithm is used for obtaining the minimum operating cost and to achieve better energy management of the Microgrid.
Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Pascual, J, Barricarte, J, Sanchis, P, Marroyo, L. Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting. Appl Energy 2001;158:12–25.10.1016/j.apenergy.2015.08.040Search in Google Scholar
2. Nikmehr, N, Najafi-Ravadanegh, S. Optimal operation of distributed generations in micro-grids under uncertainties in load and renewable power generation using heuristic algorithm. IET Renew Power Gener 2015;9:982–90.10.1049/iet-rpg.2014.0357Search in Google Scholar
3. Zhang, L, Gari, N, Hmurcik, LV. Energy management in a microgrid with distributed energy resources. Energy Convers Manag 2014;78:297–305.10.1016/j.enconman.2013.10.065Search in Google Scholar
4. Kanchev, H, Lu, D, Colas, F, Lazarov, V, Francois, B. Energy management and operational planning of a microgrid with a PV-based active generator for smart grid applications. IEEE Trans Ind Electron 2011;58:4583–92.10.1109/TIE.2011.2119451Search in Google Scholar
5. Dongmei, Z, Nan, Z, Yanhua, L. Micro-grid connected islanding operation based on wind and PV hybrid power system. In: IEEE PES innovative smart grid technologies. IEEE, Tianjin; 2012.10.1109/ISGT-Asia.2012.6303168Search in Google Scholar
6. Gupta, S, Sharma, N. A literature review of maximum power point tracking from a PV array with high efficiency. IJEDR 2016;4:2321–9939.Search in Google Scholar
7. Liu, X. An improved interpolation method for wind power curves. IEEE Trans Sustain Energy 2012;3:528–34.10.1109/TSTE.2012.2191582Search in Google Scholar
8. Chen, SX, Gooi, HB. Sizing of energy storage system for microgrids. In: IEEE 11th international conference on probabilistic methods applied to power systems. IEEE, Singapore; 2010.10.1109/PMAPS.2010.5528720Search in Google Scholar
9. Moghaddam, AA, Seifi, A, Niknam, T, Pahlavani, MRA. Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source. Energy 2011;36:6490–507.10.1016/j.energy.2011.09.017Search in Google Scholar
10. Micky, RR, Lakshmi, R, Sunitha, R, Ashok, S. Generation adequacy assessment for microgrid with ESS. In: 2016 IEEE 7th power India international conference (PIICON). IEEE, Bikaner; 2016.10.1109/POWERI.2016.8077182Search in Google Scholar
11. Jayadev, V, Shanti Swarup, K. Optimization of microgrid with demand side management using genetic algorithm. In: IET conference on power in unity: a whole system approach. IET, London, UK; 2013.10.1049/ic.2013.0124Search in Google Scholar
12. Palensky, P, Dietrich, D. Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inf 2011;7:381–8.10.1109/TII.2011.2158841Search in Google Scholar
13. Logenthiran, T, Srinivasan, D, Shun, TZ. Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 2012;3:1244–52.10.1109/TSG.2012.2195686Search in Google Scholar
14. Logenthiran, T, Srinivasan, D, Khambadkone, AM. Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system. Elec Power Syst Res 2011;81:138–48.10.1016/j.epsr.2010.07.019Search in Google Scholar
15. Alharbi, W, Raahemifar, K. Probabilistic coordination of microgrid energy resources operation considering uncertainties. Elec Power Syst Res 2015;128:1–10.10.1016/j.epsr.2015.06.010Search in Google Scholar
16. Dietrich, K, Latorre, JM, Olmos, L, Ramos, A. Demand response in an isolated system with high wind integration. IEEE Trans Power Syst 2012;27:20–9.10.1109/TPWRS.2011.2159252Search in Google Scholar
17. Gupta, I, Anandini, GN, Gupta, M. An hour wise device scheduling approach for demand side management in smart grid using particle swarm optimization. In: National power system conference. IEEE, Bhubaneswar, India; 2016.10.1109/NPSC.2016.7858965Search in Google Scholar
18. Rahmani-Andebili, M. Investigating effects of responsive loads models on unit commitment collaborated with demand-side resources. IET-Gen Transm Distr 2013;7:420–30.10.1049/iet-gtd.2012.0552Search in Google Scholar
19. Rahmani-Andebili, M. Risk-cost-based generation scheduling smartly mixed with reliability-driven and market-driven demand response measures. Int Trans Electr Energy Syst 2014;25:994–1007.10.1002/etep.1884Search in Google Scholar
20. Rahmani-Andebili, M. Nonlinear demand response programs for residential customers with nonlinear behavioral models. Energy Build 2016;119:352–62.10.1016/j.enbuild.2016.03.013Search in Google Scholar
21. Rahmani-Andebili, M, Shen, H. Energy management of end users modeling their reaction from a GENCO’s point of view. In: International conference on computing, networking and communications (ICNC): green computing, networking, and communications. IEEE, Santa Clara, CA, USA; 2017.10.1109/ICCNC.2017.7876193Search in Google Scholar
22. Merrikh Bayat, F. A numerical optimization algorithm inspired by the strawberry plant. arXiv Preprint arXiv 1407.7399 2014.Search in Google Scholar
23. Asif, S, Ambreen, K, Iftikhar, H, Khan, HN, Maroof, R. Energy management in residential area using genetic and strawberry algorithm. In: International conference on network-based information systems. NBiS; 2017:165–76 pp. https://doi.org/10.1007/978-3-319-65521-5_15.Search in Google Scholar
24. Khan, MS, Anwar ul Hassan, CH, Abubakar Sadiq, H, Ali, I, Rauf, A, Javaid, N. A new meta-heuristic algorithm inspired from the strawberry plant for demand side management in smart grid. In: The international conference on intelligent networking and collaborative systems. Springer, Cham; 2017.10.1007/978-3-319-65636-6_13Search in Google Scholar
25. Abubakar Sadiq, H, Muhammad, S, Khan, I, Javaid, N. Demand side management using strawberry and enhanced differential evolution algorithms. In: Conference: W-INWC-2017: the 7-th international workshop on information networking and wireless communications. Springer, Cham; 2017.10.1007/978-3-319-65521-5_90Search in Google Scholar
26. Noor, S, Guo, M, Van Dam, KH, Shah, N, Wang, X. Energy demand side management with supply constraints: game theoretic approach. Energy Procedia 2018;145:368–73.10.1016/j.egypro.2018.04.066Search in Google Scholar
27. Rajamand, S. Cost reduction in microgrid using demand response program of loads and uncertainty modeling with point estimation method. In: International transactions on electrical energy systems. Wiley; 2019:30 p. https://doi.org/10.1002/2050-7038.12299.Search in Google Scholar
28. Li, Y, Zhen, Y, Li, G, Mu, Y, Zhaob, D, Chen, C, et al.. Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing. Appl Energy 2018;232:54–68. doi:https://doi.org/10.1016/j.apenergy.2018.09.211.Search in Google Scholar
29. Li, Y, Yang, Z, Li, G, Zhao, D, Tian, W. Optimal scheduling of an isolated microgrid with battery storage considering load and renewable generation uncertainties. IEEE Trans Ind Electron 2019;66:1565–75. https://doi.org/10.1109/TIE.2018.2840498.Search in Google Scholar
30. Logenthiran, T, Srinivasan, D. Short term generation scheduling of a microgrid. In: IEEE TENCON. IEEE, Singapore; 2009.10.1109/TENCON.2009.5396184Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Research on intelligent substation monitoring by image recognition method
- Design optimization of permanent magnet synchronous motor using Taguchi method and experimental validation
- Performance analysis of shunt active filter for harmonic compensation under various non-linear loads
- A neural network approach to detect winding faults in electrical machine
- Adaptive relay settings for distribution network with distributed generation (DG) using Sugeno fuzzy inference
- Research on fault clearing scheme for half-bridge modular multilevel converters high voltage DC based on overhead transmission lines
- A comparative study of the speed control of an IM–based flywheel energy storage system using PI–DTC and RFOC strategies
- Energy management of a microgrid using demand response strategy including renewable uncertainties
- Solar powered battery charging scheme for light electric vehicles (LEVs)
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Articles in the same Issue
- Frontmatter
- Research Articles
- Research on intelligent substation monitoring by image recognition method
- Design optimization of permanent magnet synchronous motor using Taguchi method and experimental validation
- Performance analysis of shunt active filter for harmonic compensation under various non-linear loads
- A neural network approach to detect winding faults in electrical machine
- Adaptive relay settings for distribution network with distributed generation (DG) using Sugeno fuzzy inference
- Research on fault clearing scheme for half-bridge modular multilevel converters high voltage DC based on overhead transmission lines
- A comparative study of the speed control of an IM–based flywheel energy storage system using PI–DTC and RFOC strategies
- Energy management of a microgrid using demand response strategy including renewable uncertainties
- Solar powered battery charging scheme for light electric vehicles (LEVs)
- Analysing integration issues of the microgrid system with utility grid network
