Multi-objective Optimization of Customers’ Load and Local Energy Resources to Increase Profitability and Decrease Pollution of Smart Microgrid

Authors

Abstract

In this study, a new multi-objective method is proposed to manage optimally local energy resources and customers’ load in a microgrid in the presence of demand-response programs. The wind turbine and photovoltaic panel are the renewable energy sources of the microgrid. The time of use and the critical peak pricing programs are also used to improve the consumption pattern of customers. Moreover, consumers try to use electrical energy when renewable sources are available. Maximizing the profit of microgrid and minimizing the pollutant gases of the microgrid are the objective functions in the demand side of problem management. The multi-objective ant lion optimization algorithm is used to optimize the indices of the microgrid and create the Pareto front. Then, the fuzzy method is utilized to select the best particle equal to the optimal management plan of the microgrid. Ultimately, the proposed method is evaluated in a sample microgrid. The results demonstrate the high efficiency of the proposed method in improving the performance of the microgrid by the optimal managing of the local energy sources and customers’ load. The proposed method of energy management increases the profitability of the distribution company and decreases the environmental pollution of the microgrid.
 

Keywords


[1] Alilou, M., Tousi. B. and Shayeghi, H., "Home energy management in a residential smart microgrid under stochastic penetration of solar panels and electric vehicles", Solar Energy, Vol. 212, pp. 6-18, 2020. [2] Roy, A., Auger, F., Robin, F., Bourguet, S. and Tran, Q., "A multi-level demand-side management algorithm for off-grid multi-source systems", Energy, Vol. 191, pp. 116536, 2020. [3] Zeynali, S., Rostami, N. and Feyzi, M., "Multi-Objective optimal short-term planning of renewable distributed generations and capacitor banks in power system considering different uncertainties including plug-in electric vehicles", International Journal of Electrical Power & Energy Systems, Vol. 119, pp. 105885, 2020. [4] Aznavi, S., Fajri, P., Sabzehgar, R. and Asrari, A., "Optimal management of residential energy storage systems in presence of intermittencies", Journal of Building Engineering, Vol. 29, pp. 101149, 2020. [5] میرزا محمدی، سعید، جبارزاده، آرمین، صالحی شهرابی، مهران، «برنامه‌ریزی تأمین انرژی گلخانه‌ها با محوریت انرژی‌های تجدیدپذیر در حالت ریزشبکه»، مهندسی و مدیریت انرژی، شمارۀ ۱۰، صفحه 56ـ69، 1399. [6] Pallonetto, F., Rosa, M., Ettorre, F. and Finn, D., "On the assessment and control optimization of demand response programs in residential buildings", Renewable and Sustainable Energy Reviews, Vol. 127, pp. 109861, 2020. [7] تقی‌خانی، محمدعلی، ماندگار نیک، محسن، «تأثیر خانه‌های هوشمند در مدیریت و کاهش مصرف انرژی الکتریکی»، مهندسی و مدیریت انرژی، شمارۀ ۹، صفحۀ 74ـ85، 1398. [8] Erdinc, O., Paterakis, N., Pappi, I., Bakirtzis, A. and Catalão, J., "A new perspective for sizing of distributed generation and energy storage for smart households under demand response", Applied Energy, Vol. 143, pp. 26-37, 2015. [9] Sfikas, E., Katsigiannis, Y. and Georgilakis, P., "Simultaneous capacity optimization of distributed generation and storage in medium voltage microgrids", Electrical Power and Energy Systems, Vol. 67, pp. 101-113, 2015. [10] Wang, Y., Li, Y., Cao, Y., Tan, Y., He, L. and Han, J., "Hybrid AC/DC microgrid architecture with comprehensive control strategy for energy management of smart building", Electrical Power and Energy Systems, Vol. 101, pp. 151-161, 2018. [11] Aghajani, G., Shayanfar, H. and Shayeghi, H., "Demand side management in a smart micro-grid in the presence of renewable generation and demand response", Energy, Vol. 126, pp. 622-637, 2017. [12] Amir, V. and Azimian, M., "Dynamic multi-carrier microgrid deployment under uncertainty", Applied Energy, Vol. 260, pp. 114293, 2020. [13] امیر، وحید، عظیمیان، مهدی، حدادی‌پور، شاپور، «بهره‌برداری چند ریزشبکه با حامل‌های مختلف انرژی با در نظر گرفتن عدم قطعیت»، مجلۀ هوش محاسباتی در مهندسی برق، شمارۀ ۱۰، صفحۀ 69ـ86، 1398. [14] Amir, V., Azimian, M. and Razavizadeh, A., "Reliability-constrained optimal design of multicarrier microgrid", International Transmission on Electrical Energy System, Vol. 29, pp. 12131, 2019. [15] Allahnoori, M., Kazemi, S., Abdi, H. and Keyhani, R., "Reliability assessment of distribution systems in presence of microgrids considering uncertainty in generation and load demand", Journal of Operation and Automation in Power Engineering, Vol. 2, pp. 113-120, 2014. [16] Xu, G., Cheng, H., Fang, S., Ma, Z., Zeng, P. and Yao, L., "Optimal size and location of battery energy storage systems for reducing the wind power curtailments", Electric Power Components and Systems, Vol. 46, pp. 342-352, 2018. [17] Wu, Z., Tazvinga, H. and Xia, X., "Demand side management of photovoltaic-battery hybrid system", Applied Energy, Vol. 148, pp. 294-304, 2015. [18] Santo, K., Santo, S., Monaro, R. and Saidel, M., "Active DSM for households in sg using optimization and artificial intelligence", Measurement, Vol. 115, pp. 152-161, 2018. [19] Rastegar, M., Fotuhi, M. and Aminifar, F., "Load commitment in a smart home", Applied Energy, Vol. 96, pp. 45-54, 2012. [20] Avril, S., Arnaud, G., Florentin, A. and Vinard, M., "Multi-Objective optimization of batteries and hydrogen storage technologies for remote photovoltaic systems", Energy, Vol. 35, pp. 5300-5308, 2010. [21] Gelazanskas, L. and Gamage, K., "Demand side management in smart grid: a review and proposals for future direction", Sustainable Cities and Society, Vol. 11, pp. 22-30, 2014. [22] Shakouri, H. and Kazemi, A., "Multi-Objective cost-load optimization for demand side management of a residential area in smart grids", Sustainable Cities and Society, Vol. 32, pp. 171-180, 2017. [23] Barbato, B., Capone, A., Chen, L., Martignon, F. and Paris, S., "A distributed demand-side management framework for the smart grid", Computer communications, Vol. 57, pp. 13-24, 2015. [24] Rastegar, M., Fotuhi, M. and MoeiniAghtaie, M., "Developing a two-level framework for residential energy management", IEEE Transactions on Smart Grid, Vol. 9, pp. 1707-1717, 2018. [25] Sadati, S., Moshtagh, J., Shafie-khah, M. and Catalão, J., "Smart distribution system operational scheduling considering electric vehicle parking lot and demand response programs", Electrical Power system Research, Vol. 160, pp. 404-418, 2018. [26] Erdinc, O., Paterakis, N., Pappi, I., Bakirtzis, A. and Catalão, J., "A new perspective for sizing of distributed generation and energy storage for smart households under demand response", Applied Energy, Vol. 143, pp. 26-37, 2015. [27] Alilou, M., Nazarpour, D. and Shayeghi, H., "Multi-Objective optimization of demand side management and multi dg in the distribution system with demand response", Journal of Operation and Automation in Power Engineering, Vol. 6, pp. 230-242, 2018. [28] Mirjalili, S., Jangir, P. and Saremi, S., "Multi-Objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems", Applied Intelligence, Vol. 46, PP. 79-95, 2017. [29] Alilou, M., Talavat, V. and Shayeghi, H., "Simultaneous placement of renewable dgs and protective devices for improving the loss, reliability and economic indices of distribution system with nonlinear load model", International Journal of Ambient Energy, Vol. 41, pp. 871-881, 2020. [30] Shuaib, Y., Kalavathi, M. and Rajan, C., "Optimal capacitor placement in radial distribution system using gravitational search algorithm", Electrical Power and Energy Systems, Vol. 64, pp. 384-397, 2015. [31] Alilou, M., Tousi, B. andShayeghi, H., "Multi-Objective energy management of smart homes considering uncertainty in wind power forecasting", Electrical Engineering, In press, 2021.