A Neuro-Fuzzy Controlled SolarPhotovoltaic and PHEV basedFrequency Regulation in a Microgridwithout Storage System

Abstract

This paper proposes a distributed resource management scheme to avoid the need for energy storage and achieve the required frequency regulation. The energy sources considered in the microgrid are Solar PhotoVoltaics (SPV), Diesel Generators (DG), Fuel Cells (FC) and Plug-in Hybrid Electric Vehicles (PHEV). The frequency regulation is done by considering the SPV operation at a Limited-Power-Point (LPP) for spinning reserve using neurofuzzy control in coordination with PHEVs and other sources. In vehicle-to-grid (V2G) mode, State of Charge (SOC) of the battery in a PHEV shall be the deciding factor in allowing it to participate in the regulation. A simple additive-adaptive strategy based algorithm is proposed and tested in the isolated microgrid environment. Satisfactory results of frequency regulation were obtained upon consideration of scenarios, varying the load and insolation in steps.

Integrated Operation of Distributed Resources to Enhance Frequency Regulation in an Isolated Microgrid Environment

“…The solar power generation happens day time and the power generated must be connected to the load at its limited power point tracking position [10]. This was achieved by a Neuro Fuzzy controller used to change the output power.…”

“…There are several modes of operation for the selection of generators [17] like traditional droop control method, adaptive method and the synchronous method. In this paper, the adaptive-additive control method is employed as mentioned in Figure 11 in [10].…”

“…Solar PV generation is based on the concept of Limited Power Point Tracking (LPPT) achieved by means of NFC [10]. The neural network is trained to remember the Maximum power points and the LPPs for a temperature range of [283 333] K and for an insolation range of [0 1300] W/m2 for about 10,000 samples.…”

“…The system is simulated using MATLAB R2015b where the mathematical equations pertaining to the generators SPV, FC, DG and BEVs are modelled as described in Section 2, load is modelled with small step changes along with white noise as described in Section 2 of [10]. In this paper two scenarios are considered for analysing the system's performance.…”

“…This droop control can be modelled as a transfer function [9] and the power output can be controlled. Earlier work [10] has focussed on building the system with PI, Fuzzy and Neural network based controllers to coordinate between the renewable generators. The presence of EVs in the system brings dynamism in the load dispatching and reserve management.…”