Abstract
The proposed research work focused on energy management strategy (EMS) in a grid connected system working in islanding mode with the connected renewable energy resources and battery storage system. The energy management strategy developed provides a balancing operation at its output by utilizing perfect load sharing strategy. The EMS technique using smart superficial neural network (SSNN) is simulated, and numerical analyses are presented to validate the effectiveness of the centralized energy management strategy in a grid connected islanded system. A SSNN prediction model is unified to forecast the associated household load demand, PV generation system under various time horizons (including the disaster condition), EV availability, and status on EV section and distance. SSNN is one the most reliable forecasting methods in many of the applications. The developed system is also accounted for degradation battery model and its associated cost. The incorporation of energy management strategy (EMS) reduces the amount of energy drawn from the grid connected system when compared with the other optimized systems.
Highlights
Energy storage and management play a vital role in the installation of the smart grid system as they increase the stability, resiliency, and efficiency of the grid connected system
E power conversion system plays a prominent role in the grid connected renewable energy resource system. e most challenging phenomenon in the power distribution system is the loss occurring in the conversion system. e power conversion system is utilized when the power is required to transfer it from source to load; else the process manages to store the energy in the connected storage system [1]
Based on the specification according to equation (4), the modeled system considers its upper and lower limits of battery state of charge (SOC) and its distance travelled when EV is away from the substation so as to improve the effectiveness of battery state of health (SOH)
Summary
Energy storage and management play a vital role in the installation of the smart grid system as they increase the stability, resiliency, and efficiency of the grid connected system. Due to continuous change in atmosphere, the uncertainties occurring in RES are handled by the two-level control strategy and provide perfect energy management, power regulation, and load scheduling operation and control. E developed fishbone model for CCS reduces the electric tariff for an individual customer and shared stakeholders and prepares an augmented schedule for charging/discharging of EV and connected stationary battery based on the most prominent time of power generation by the PV system. Based on this historical time series datasets, the aggregator is having the access to towards the connected loads, EV charging and discharging station, connected PV source, grid connected power station, and the battery storage system. Based on the mixed-integer linear programming (MILP) model subjected to grid constraints. e load predictions for the day are predicted and given as input to optimization model in order to formulate the optimum schedule of charging/discharging required for the EV station and other connected battery storage systems
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