- The proposed DSM framework comprised with advanced communication mediums and HEMSs is tested on a realistic microgrid environment which consists of solar PV, wind, and back-up diesel generators. The developed DSM strategy can maximize the renewable energy usages, maintain supply and demand balance, while reducing the usage of diesel generators in the microgrid.
- The developed smart HEMS in DSM is a fuzzy, logic-based load controller which optimizes household appliances based on the available renewable generation in the microgrid, local voltage measurements from the smart meter, weather conditions (temperature), and consumer consumption preferences, as well as TOU prices and DLC signals from utility.
- The smart HEMS can significantly reduce the consumer’s energy consumption, standby power loss, and energy costs, while maintaining the consumer’s comfort levels.
- Modeling of Microgrid: A model of a microgrid integrating the solar PV, a wind turbine, and a diesel generator supplying several typical households that participate in DR incorporated with a smart load controller system has been developed.
- Selection of Communication Medium: The most suitable communication infrastructure for DR and HEMS implementation is selected based on the literature review and geographic position of the community.
- Modeling of the Smart HEMS: A smart HEMS is developed based on realistic load profiles and appliances’ consumption data obtained from a smart load monitoring device installed in the end-users’ premises. The developed HEMS provides consumption decisions based on predefined fuzzy logic rules. The fuzzy rules are defined according to consumer consumption priorities, local voltage levels, total renewable generation in the microgrid, DR signals (dynamic pricing and DLC), and weather conditions.
- Case Study Analysis: The effectiveness of the proposed framework is evaluated through a case study analysis. The renewable energy sources and domestic loads have been designed based on realistic data of a region in Western Australia, which helps in determining the potential DR opportunity and standby power consumption of major appliances, including refrigerators, air conditioners (AC), dish washers, washing machines and dryers.
- Microgrid Analysis with HEMS: DR opportunities are investigated and identify energy savings for a microgrid system with HEMSs.
3. Modeling of the Microgrid
3.1. Modeling of Solar PV
3.2. Modeling of Wind Turbine
3.3. Modeling of Diesel Generator
4. Selection of the Communication Medium
5. Modeling of the HEMS
5.1. The Proposed Smart Load Monitoring System
5.2. Intelligent Load Controller
5.3. Fuzzy Load Controller
- Input and Output of the fuzzy load controller
- Fuzzy Membership Functions
- Fuzzy Rules
5.3.1. Input and Output of the Fuzzy Load Controller
5.3.2. Fuzzy Membership Functions
5.3.3. Fuzzy Rules
6. Case Study Analysis
6.1. Load Profiles of a Typical House
6.2. Energy Cost Saving Analysis with HEMS
7. Microgrid Analysis with HEMS
7.1. Residential Loads and Demand Analysis in Microgrid
7.2. Demand Response Opportunity in Microgrid
- Scenario A: only the solar farm and the diesel generator are supplying the residential loads.
- Scenario B: the solar farm and wind farm are active.
- Scenario C: the wind farm and the diesel generator are available, but the diesel generator is partially ON only in the high demand period.
- Scenario D: all the sources are simultaneously active, except for the diesel generator, which only turns ON when it is needed.
Conflicts of Interest
- Yoldaş, Y.; Önen, A.; Muyeen, S.M.; Vasilakos, A.V.; Alan, İ. Enhancing smart grid with microgrids: Challenges and opportunities. Renew. Sustain. Energy Rev. 2017, 72, 205–214. [Google Scholar] [CrossRef]
- Shafiullah, G.M. Hybrid renewable energy integration (HREI) system for subtropical climate in Central Queensland, Australia. Renew. Energy 2016, 96, 1034–1053. [Google Scholar] [CrossRef]
- Lee, E.K.; Shi, W.; Gadh, R.; Kim, W. Design and Implementation of a Microgrid Energy Management System. Sustainability 2016, 8, 1143. [Google Scholar] [CrossRef]
- Elsied, M.; Oukaour, A.; Gualous, H.; Brutto, O.A.L. Optimal economic and environment operation of micro-grid power systems. Energy Convers. Manag. 2016, 122, 182–194. [Google Scholar] [CrossRef]
- Rahman, M.M.; Arefi, A.; Shafiullah, G.M.; Hettiwatte, S. Penetration maximisation of residential rooftop photovoltaic using demand response. In Proceedings of the 2016 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), Bali, Indonesia, 6–8 October 2016; pp. 21–26. [Google Scholar]
- Broeer, T. Analysis of Smart Grid and Demand Response Technologies for Renewable Energy Integration: Operational and Environmental Challenges. Ph.D. Thesis, University of Victoria, Victoria, BC, Canada, 2015. [Google Scholar]
- Rahman, M.M.; Arefi, A.; Shafiullah, G.M.; Hettiwatte, S. A new approach to voltage management in unbalanced low voltage networks using demand response and OLTC considering consumer preference. Int. J. Electr. Power Energy Syst. 2018, 99, 11–27. [Google Scholar] [CrossRef]
- Aghajani, G.R.; Shayanfar, H.A.; Shayeghi, H. Presenting a multi-objective generation scheduling model for pricing demand response rate in micro-grid energy management. Energy Convers. Manag. 2015, 106, 308–321. [Google Scholar] [CrossRef]
- Safamehr, H.; Rahimi-Kian, A. A cost-efficient and reliable energy management of a micro-grid using intelligent demand-response program. Energy 2015, 91, 283–293. [Google Scholar] [CrossRef]
- Bruni, G.; Cordiner, S.; Mulone, V.; Sinisi, V.; Spagnolo, F. Energy management in a domestic microgrid by means of model predictive controllers. Energy 2016, 108, 119–131. [Google Scholar] [CrossRef]
- Tabar, V.S.; Jirdehi, M.A.; Hemmati, R. Energy management in microgrid based on the multi objective stochastic programming incorporating portable renewable energy resource as demand response option. Energy 2017, 118, 827–839. [Google Scholar] [CrossRef]
- Hakimi, S.M.; Moghaddas-Tafreshi, S.M. Optimal planning of a smart microgrid including demand response and intermittent renewable energy resources. IEEE Trans. Smart Grid 2014, 5, 2889–2900. [Google Scholar] [CrossRef]
- Rahman, M.M. Modelling and Analysis of Demand Response Implementation in the Residential Sector; Murdoch University: Murdoch, Australia, 2018. [Google Scholar]
- Zunnurain, I.; Maruf, M.N.I. Automated Demand Response Strategies using Home Energy Management System in a RES-based Smart Grid. In Proceedings of the 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh, 28–30 September 2017. [Google Scholar] [CrossRef]
- Pinson, P.; Madsen, H. Benefits and challenges of electrical demand response: A critical review. Renew. Sustain. Energy Rev. 2014, 39, 686–699. [Google Scholar]
- Rahman, M.M.; Hettiwatte, S.; Shafiullah, G.M.; Arefi, A. An analysis of the time of use electricity price in the residential sector of Bangladesh. Energy Strategy Rev. 2017, 18, 183–198. [Google Scholar] [CrossRef]
- Rahman, M.M.; Hettiwatte, S.; Gyamfi, S. An intelligent approach of achieving demand response by fuzzy logic based domestic load management. In Proceedings of the IEEE Power Engineering Conference (AUPEC), Perth, Australia, 28 September–1 October 2014; pp. 1–6. [Google Scholar]
- Pipattanasomporn, M.; Kuzlu, M.; Rahman, S. An algorithm for intelligent home energy management and demand response analysis. IEEE Trans. Smart Grid 2012, 3, 2166–2173. [Google Scholar] [CrossRef]
- Beaudin, M.; Zareipour, H. Home energy management systems: A review of modelling and complexity. Renew. Sustain. Energy Rev. 2015, 45, 318–335. [Google Scholar] [CrossRef]
- Kailas, A.; Cecchi, V.; Mukherjee, A. A survey of communications and networking technologies for energy management in buildings and home automation. J. Comput. Netw. Commun. 2012, 2012, 932181. [Google Scholar] [CrossRef]
- Zhou, B.; Li, W.; Chan, K.W.; Cao, Y.; Kuang, Y.; Liu, X.; Wang, X. Smart home energy management systems: Concept, configurations, and scheduling strategies. Renew. Sustain. Energy Rev. 2016, 61, 30–40. [Google Scholar] [CrossRef]
- Anvari-Moghaddam, A.; Guerrero, J.M.; Vasquez, J.C.; Monsef, H.; Rahimi-Kian, A. Efficient energy management for a grid-tied residential microgrid. IET Gener. Transm. Distrib. 2017, 11, 2752–2761. [Google Scholar] [CrossRef]
- Anvari-Moghaddam, A.; Monsef, H.; Rahimi-Kian, A.; Guerrero, J.M.; Vasquez, J.C. Optimized Energy Management of a Single-House Residential Micro-Grid with Automated Demand Response. In Proceedings of the IEEE Conference Eindhoven PowerTech, Eindhoven, The Netherlands, 29 June 29–2 July 2015. [Google Scholar] [CrossRef]
- Anvari-Moghaddam, A.; Mokhtari, G.; Guerrero, J.M. Coordinated Demand Response and Distributed Generation Management in Residential Smart Microgrids. Energy Manag. Distrib. Gener. Syst. 2016, 1–32. [Google Scholar] [CrossRef]
- Rodriguez-Diaz, E.; Anvari-Moghaddam, A.V.; Vasquez, J.C.; Guerrero, J.M. Multi-Level Energy Management and Optimal Control of a Residential DC Microgrid. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 31 July 2017. [Google Scholar] [CrossRef]
- Rodriguez-Diaz, E.; Palacios-Garcia, E.J.; Anvari-Moghaddam, A.V.; Vasquez, J.C.; Guerrero, J.M. Real-Time Energy Management System for a Hybrid AC/DC Residential Microgrid. In Proceedings of the IEEE Second International Conference on DC Microgrids (ICDCM), Nuremburg, Germany, 27–29 June 2017. [Google Scholar] [CrossRef]
- Arcos-Aviles, D.; Pascual, J.; Guinjoan, F.; Marroyo, L.; Sanchis Guinjoan, F. Fuzzy Logic-Based Energy Management System Design for Residential Grid-Connected Microgrids. IEEE Trans. Smart Grid 2016, 1–7. [Google Scholar] [CrossRef]
- Chehri, A.; Mouftah, H.T. FEMAN: Fuzzy-based energy management system for green houses using hybrid grid solar power. J. Renew. Energy 2013, 2013, 785636. [Google Scholar] [CrossRef]
- Collotta, M.; Pau, G. A solution based on bluetooth low energy for smart home energy management. Energies 2015, 8, 11916–11938. [Google Scholar] [CrossRef]
- Arcos-Aviles, D.; Pascual, J.; Guinjoan, F.; Marroyo, L.; Sanchis, P.; Marietta, M.P. Low complexity energy management strategy for grid profile smoothing of a residential grid-connected microgrid using generation and demand forecasting. Appl. Energy 2017, 205, 69–84. [Google Scholar] [CrossRef]
- Pasetti, M.; Rinaldi, S.; Manerba, D. A Virtual Power Plant Architecture for the Demand-Side Management of Smart Prosumers. Appl. Sci. 2018, 8, 432. [Google Scholar] [CrossRef]
- Kim, T.T.; Poor, H.V. Scheduling power consumption with price uncertainty. IEEE Trans. Smart Grid 2011, 2, 519–527. [Google Scholar] [CrossRef]
- Zhao, Z.; Lee, W.C.; Shin, Y.; Song, K.B. An optimal power scheduling method for demand response in home energy management system. IEEE Trans. Smart Grid 2013, 4, 1391–1400. [Google Scholar] [CrossRef]
- Sharifi, R.; Anvari-Moghaddam, A.; Fathi, S.H.; Guerrero, J.M.; Vahidinasab, V. An optimal market-oriented demand response model for price-responsive residential consumers. Energy Effic. 2018, 1–13. [Google Scholar] [CrossRef]
- Anvari-Moghaddam, A.; Ashkan, H.M.; Rahmi-Kian, A. Cost-Effective and Comfort-Aware Residential Energy Management under Different Pricing Schemes and Weather Conditions. Energy Build. 2014, 86, 782–793. [Google Scholar] [CrossRef]
- Braun, M.; Stetz, T.; Bründlinger, R.; Mayr, C.; Ogimoto, K.; Hatta, H.; Kobayashi, H.; Kroposki, B.; Mather, B.; Coddington, M.; et al. Is the distribution grid ready to accept large-scale photovoltaic deployment? State of the art, progress, and future prospects. Prog. Photovolt. Res. Appl. 2012, 20, 681–697. [Google Scholar] [CrossRef]
- De Brito, M.A.G.; Galotto, L.; Sampaio, L.P.; e Melo, G.D.A.; Canesin, C.A. Evaluation of the main MPPT techniques for photovoltaic applications. IEEE Trans. Ind. Electron. 2013, 60, 1156–1167. [Google Scholar] [CrossRef]
- BoM, Bureau of Meteorology, Australian Government. Available online: http://reg.bom.gov.au/ (accessed on 7 September 2017).
- Herbert-Acero, J.F.; Probst, O.; Réthoré, P.E.; Larsen, G.C.; Castillo-Villar, K.K. A review of methodological approaches for the design and optimization of wind farms. Energies 2014, 7, 6930–7016. [Google Scholar] [CrossRef][Green Version]
- Jaganathan, S.; Palaniswami, S.; Kumaar, R.A.M.N. Synchronous Generator Modelling and Analysis for a Microgrid in Autonomous and Grid Connected Mode. Int. J. Comput. Appl. 2011, 13, 3–7. [Google Scholar] [CrossRef]
- Deng, R.; Yang, Z.; Hou, F.; Chow, M.Y.; Chen, J. Distributed real-time demand response in multiseller–multibuyer smart distribution grid. IEEE Trans. Power Syst. 2015, 30, 2364–2374. [Google Scholar] [CrossRef]
- Shafiullah, G.M.; Oo, A.M.; Ali, A.S.; Wolfs, P. Smart grid for a sustainable future. Smart Grid Renew. Energy 2013, 4, 23–34. [Google Scholar] [CrossRef]
- Luan, W.; Sharp, D.; Lancashire, S. Smart grid communication network capacity planning for power utilities. In Proceedings of the IEEE PES Transmission and Distribution Conference and Exposition, New Orleans, LA, USA, 19–22 April 2010; pp. 19–22. [Google Scholar]
- Fang, M.; Wan, J.; Xu, X.; Wu, G. System for Temperature Monitor in Substation with ZigBee Connectivity. In Proceedings of the IEEE International Conference on Communication Technology, Hangzhou, China, 10–12 November 2008; pp. 25–28. [Google Scholar]
- Aggarwal, A.; Kunta, S.; Verma, P.K. A proposed communication infrastructure for the smart grid. In Proceedings of the Innovative Smart Grid Technologies (ISGT), Gaithersburg, MD, USA, 19–21 January 2010; pp. 1–5. [Google Scholar]
- Masera, M.; Stefanini, A.; Dondossola, G. The Security of Information and Communication Systems and the E+I Paradigm, Critical Infrastructures at Risk; Springer: Dordrecht, The Netherlands, 2006; Volume 9, pp. 85–116. [Google Scholar]
- Gungor, V.C.; Lu, B.; Hancke, G.P. Opportunities and Challenges of Wireless Sensor Networks in Smart Grid. IEEE Trans. Ind. Electron. 2010, 57, 3557–3564. [Google Scholar] [CrossRef][Green Version]
- Lu, B.; Gungor, V.C. Online and Remote Energy Monitoring and Fault Diagnostics for Industrial Motor Systems using Wireless Sensor Networks. IEEE Trans. Ind. Electron. 2009, 56, 4651–4659. [Google Scholar]
- Paruchuri, V.; Durresi, A.; Ramesh, M. Securing powerline communications. In Proceedings of the IEEE International Symposium on Power Line Communications Applications, (ISPLC), Jeju Island, Korea, 2–4 April 2008; pp. 64–69. [Google Scholar]
- Doe, U. Communications Requirements of Smart Grid Technologies; Technical Report; US Department of Energy: Washington, DC, USA, 2010; pp. 1–69. [Google Scholar]
- Gungor, V.C.; Sahin, D.; Kocak, T.; Ergüt, S. Smart Grid Communications and Networking; Technical Report; Türk Telekom: Altındağ, Turkey, 2011; p. 11316–01. [Google Scholar]
- Gungor, V.C.; Sahin, D.; Kocak, T.; Ergut, S.; Buccella, C.; Cecati, C.; Hancke, G. A survey on smart grid potential applications and communication requirements. IEEE Trans. Ind. Inform. 2013, 9, 28–42. [Google Scholar] [CrossRef]
- The Australian Team IEA DSM Task XIII (DRR). Roadmap for Demand Response in the Australian National Electricity Market. Available online: www.ieadsm.org/publication/australia-roadmap-for-demand-response-in-the-australian-national-electricity-market (accessed on 24 September 2016).
|Base Loads||Schedulable/Non-Priority Loads|
|Input Conditions||Optimized Outputs|
|Fuzzy Rules||Time||Comfort Preference (°C)||Temperature Deviation (°C)||Forecasted Load (KW)||Consumption Time (mints)||Local Voltage Level (%)||Network RES Penetration (%)||Load Control (kW)||Perform Load Schedule (KW)||Allow Load Consumption (KW)|
|5||peak (pm)||16.5||14.5||5.4||16>||−7%||50%||Dec. = 4.0||4.86||1.11|
|Load Type||Peak Shaving Capacity (kW)||Shiftable Energy (kWh)||Standby Energy Loss (kWh)/day||Total Energy Cost without HEMS ($/day)||Total Energy Cost with HEMS ($/day)||Cost Saving (%/day)|
|Base loads||N/A||N/A||0.02||$3.49 |
(total daily energy 9.40 kWh)
(total daily energy 9.16 kWh)
(total daily energy saving 3%)
|Grid Condition (Scenario)||Total RES Penetration (kW)||Duration of Demand Surpassing (h)||Average Load Consumption Allowance||Load Shifting Frequency||Consumers’ Comfort Level||Diesel Gen. Fuel Consumption|
|Scenario A||150~300||>4.5||Very Low||Very High||Interrupted |
|Scenario B||200~350||>4||Low||High||Interrupted |
|Scenario C||200~350||>3||Medium||Medium||Not Interrupted||Medium|
|Scenario D (All sources available)||350~500||>2.5||More||Low||Not Interrupted||Low|
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).