Increase Microgrid's Consumer Comfort by Using Fuzzy and Optimization Algorithms
Keywords:
Energy management, intelligent control, Microcontrollers, Microgrids, Multi-agent systemsAbstract
Whereas the most important fundamental factor for today’s human is energy and wasting energy leads to increasing costs and destruction of natural resources, it is attempted through using modern and electronic methods to optimize the energy consumption and preventing of wasting energy. According to technological advancements and level of knowledge of people and having different electronic means, it is applied from several methods including: wireless sensor networks at home automation, energy management system, BEMS system and intelligent electrical keys on building to respond the requirements of users that leads to comfort of users, reducing costs, optimization of energy consumption and prevention of wasting energy. In this article, it is benefit from intelligent control methods by using optimization algorithms (PSO & GA) and fuzzy logic for controlling energy of building in order to obtain the maximum welfare and comfort of inhabitants in a building using from new pneumatic and solar recyclable resources. In order to show this performance, it is benefit from simulation at MATLAB environment.
References
Z. Wang, R. Yang and L. Wang, “Multi-agent intelligent controller design for smart and sustainable buildings,” IEEE International Systems Conference, San Diego, April 2010.
Z. Wang, R. Yang and L. Wang, “Multi-agent control system with intelligent optimization for smart and energyefficient buildings,” the 36th Annual Conference of the IEEE Industrial Electronics Society,Phoenix, AZ, November, 2010.
Erdinc O, Tas¸cıkaraoglu ˘ A, Paterakis N, Eren Y, Catalao ˜ JPS. End-user comfort oriented day-ahead planning for responsive residential HVAC demand aggregation considering weather forecasts. IEEE Transactions on Smart Grid 2017;8:362–72.
A. Nikoobakht, J. Aghaei , M. Shafe-khah , J. P. S. Catal˜ ao, Assessing Increased Flexibility of Energy Storage and Demand Response to Accommodate a High Penetration of Renewable Energy Sources, IEEE Transactions on Sustainable Energy, 2018, Early Access.
J. Prado; W. Qiao, A Stochastic Decision-Making Model for an Electricity Retailer with Intermittent Renewable Energy and Short-term Demand Response, IEEE Transactions on Smart Grid, 2018, Early access.
Leithon J, Sun S, Lim T. Demand Response and Renewable Energy Management Using Continuous-Time Optimization. IEEE Transactions on Sustainable Energy 2018;9:991–1000.
Asensio M, Quevedo P, Delgado G, Contreras J. Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model. IEEE Transactions on Smart grid 2018;9:655–66.
Wang Y, Yang W, Liu T. Appliances considered demand response optimisation for smart grid. IET Generation, Transmission & Distribution 2017;11:856–64.
Park L, Jang Y, Cho S, Kim J. Residential Demand Response for Renewable Energy Resources in Smart Grid Systems. IEEE Transactions on Industrial Informatics 2017;13:3165–73.
Maharjan S, Zhang Y, Gjessing S, Tsang D. User-Centric Demand Response Management in the Smart Grid With Multiple Providers. IEEE Transactions on Emerging Topics in Computing 2017;5:494–505.
Mortaji H, Ow S, Moghavvemi M, Almurib H. Load Shedding and Smart-Direct Load Control Using Internet of Things in Smart Grid Demand Response Management. IEEE Transactions on Industry Applications 2017;53:5155–63.
Hussain M, Gao Y. A review of demand response in an effcient smart grid environment. The Electricity Journal 2018;31:55–63.
Wang Y, Yujing. Huang, Y. Wang, M. Zeng, F. Li, Y. Wang, Y. Zhang. Energy management of smart micro-grid with response loads and distributed generation considering demand response. Journal of Cleaner Production 2018;197:1069–83.
Good N, Ellis K, Mancarella P. Review and classifcation of barriers and enablers of demand response in the smart grid. Renewable and Sustainable Energy Reviews 2018;72:57–72.
Aghajani GR, Shayanfar HA, Shayeghi H. Demand side management in a smart micro-grid in the presence of renewable generation and demand response. Energy 2018;126:622–37.
Shakeri M, Shayestegan M, Abunima H, Salim SM, Akhtaruzzaman M, Alamoudc ARM, et al. An intelligent system architecture in home energy management systems (HEMS) for effcient demand response in smart grid. Energy and Buildings 2018;136:154–64.
Fong KF, Hanby VI, Chow TT. HVAC system optimization for energy management by evolutionary programming. Energ Buildings 2008;38:220–31.
Moon S, Lee J. Multi-Residential Demand Response Scheduling With Multi-Class Appliances in Smart Grid. IEEE Transactions on Smart Grid 2018;9:2518–28.
Yong JY, Ramachandaramurthy VK, Tan KM, Mithulananthan N. A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects. Renew Sust Energ Rev 2015;49:365–85.
Mwasilu F, Justo JJ, Kim EK, Do TD, Jung JW. Electric vehicles and smart grid interaction: a review on vehicle to grid and renewable energy sources integration. Renew Sust Energ Rev 2014;34:501–16.
Román TGS, Momber I, Abbad MR, Miralles ÁS. Regulatory framework and business models for charging plug-in electric vehicles: infrastructure, agents, and commercial relationships. Energ Policy 2011;39:6360–75
Papadimitriou CN, Kleftakis VA, Hatziargyriou ND. A novel islanding detection method for microgrids based on variable impedance insertion. Electr Power Syst Res 2015;121:58–66.
Downloads
Published
How to Cite
Issue
Section
License
Authors who submit papers with this journal agree to the following terms.