Optimal Control of Solar Energy Resources in Loading Margin Enhancement for Peninsular Malaysia Network Using Artificial Neural Network (ANN) Model

摘要: Renewable energy invariably foreseen to be fully spurs by 2030-2050 and Malaysia has been seriously intensifies their capabilities on producing solar for generating amount of power injected into the national grid. The aim this paper is investigate provision photovoltaic generators quantitatively with respect future load generation while considering Loading Margin (LM) capability. resources are known its ambiguity commonly rural electrifications each houses or buildings installed battery storage system which allows unused during daytime stored will utilized at night. This kind would not experience big impact in conjunction intermittent level insolation due small requirement receiving end. Conversely, cities located west region it becomes a issue cope variations demand. In an explicit form voltage deficiency caused irregular operation grid, fragile electrical nodes most likely vulnerable PV inevitably have stay connected point common coupling supporting grid’s voltage. Eventually stage, Grid System Operator (GSO) needs brisk respond these online scenarios but practice could done ease. Thus, intelligent model ANN incorporated tremendous set local data developed regulate loading margin critical node taking account possible wheather condition any given time increments. method employed supervised training successfully yields smart predictive technique LM certain correspond inputs. By having this, GSO can barely make arrangement reactive injection throughout network central coordination control among generators. Simulation results show effectiveness proposed discussed thoroughly.