Coordinated control of conventional power sources and plug-in hybrid electric vehicles for a hybrid power system

Abstract

Globally, the requirement for renewable and clean energy technologies is becoming vastly popular. With the high implementation of solar and wind energy systems, together with plugin hybrid electric vehicle (PHEV) aggregators, energy costs can be minimised, greenhouse gas emissions decrease, and overall maintenance becomes reduced. The constant increase of load demand is becoming a challenge for the current power systems, with difficulties including stability concerns and excessive regulations by the government. Due to irradiance and wind speed fluctuations, the solar and wind energy system’s non-linearity affects the existing power system stability. The growth of the electric vehicle industry has also shed new light on potential auxiliary services that can be provided, as and when required, to the power system. Hence, this research examines the potential control strategies that are required to maintain the system in steady-state conditions after disturbances that occur with higher penetration of renewable energy systems (RESs) and PHEVs. The case study models a isolated two-area thermal type power system that is interconnected through an AC tie-line. Three scenarios are modelled, simulated and analysed. The first scenario models a isolated thermal power system with PHEVs with two areas which utilises a fractional order proportional integral derivative (FOPID) controller in each area. The resulting model is analysed to see the effects of PHEVs coupled with FOPID on the power system. The second scenario models a isolated two-area thermal power system with RES and utilises a fuzzy type-2 (FT2) FOPID controller in each area. The RES penetration istested for its non-linearity effect on the isolated power system, and the error is reduced by an advanced controller that uses artificial intelligence techniques. The third scenario is modelled as an isolated two-area thermal power system with PHEVs and RES coupled with neural network predictive controller (NNPC) in each area. The three scenarios are simulated in MATLAB/Simulink with results displayed graphically and numerically. The results show that the integration of PHEVs for load and/or storage in the multi-area power system, and the proposed control methods for each scenario, have the best dynamic response with the least error, no oscillations and the fastest response to steady state condition.