The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

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Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm-Random Forest Regression

This paper proposes an implementation procedure and performance testing of an algorithm comprise of Cumulative sum algorithm and power flow methods for fault detection in micro-grid systems. The Cumulative sum algorithm is found to be better than the traditional methods in the presence of noise, system frequency deviation, and other uncertainties for radial system. By monitoring the power Flows between buses along with cumulative sum algorithm, detection of faults in micro-grid system is carried out. The proposed digital Fault detection is implemented experimentally for performance testing on a test micro-grid system. Several transient disturbances viz. grid connection, single-phase to ground fault, 2 phase fault, 3 phase to ground faults, etc. occurring in different parts of the test micro-grid system are investigated experimentally.

Majarikar Vikrant Jiwanrao

Papers

An Efficient Fault Detection Algorithm for Micro-Grid