The tremendous engagement of electric energy consumers and the advent of smart grids have left more complexity and challenges in terms of energy management system. Recently, microgrid is a preferable choice to cope with these challenges as small-scale power system and so close to consumers. However, there is a crucial need to find more compatible solutions to achieve economic, environmental, and reliable objectives of energy management system, since most current solutions are based on optimal scheduling of generating units at the supply side. Demand side management develops more opportunities to achieve these objectives by efficiency programs and demand response programs (pricing techniques). Therefore, this article reviews and assesses demand side management, particularly pricing techniques, in the light of energy management system as a part of control system of microgrid. Furthermore, the aspects of control schemes are discussed including centralized and distributed controls. Finally, this article identifies a number of shortcomings in the current research that concern demand side management and highlights future horizons of research.
Purpose This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location. Design/methodology/approach Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance. Findings The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG. Originality/value The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.
This article investigates a day ahead optimal power flow considering the intermittent nature of renewable energy sources that involved with weather conditions. The article integrates the machine learning into power system operation to predict precisely day ahead meteorological data (wind speed, temperature and solar irradiance) that influence directly on the calculations of generated power of wind turbines and solar photovoltaic generators. Consequently, the power generation schedulers can make appropriate decisions for the next 24 hours. The proposed research uses conventional IEEE -30-bus as a test system running in Johor province that selected as a test location. algorithm designed in Matlab is utilized to accomplish the day ahead optimal power flow. The obtained results show that the true and predicted values of meteorological data are similar significantly and thus, these predicted values demonstrate the feasibility of the presented prediction in performing the day ahead optimal power flow. Economically, the obtained results reveal that the predicted fuel cost considering wind turbines and solar photovoltaic generators is reduced to 645.34 USD/h as compared to 802.28 USD/h of the fuel cost without considering renewable energy sources. Environmentally, CO2 emission is reduced to 340.9 kg/h as compared to 419.37 kg/h of the conventional system. To validate the competency of the whale optimization, the OPF for the conventional system is investigated by other 2 metaheuristic optimization techniques to attain statistical metrics for comparative analysis.
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