Optimizing Power System Reliability and Carbon Emissions With a Fuzzy Unit Commitment Model Incorporating Renewable Energy, Load Forecast Errors, EV Charging, and Energy Storage System
Optimizing Power System Reliability and Carbon Emissions With a Fuzzy Unit Commitment Model Incorporating Renewable Energy, Load Forecast Errors, EV Charging, and Energy Storage System
Blog Article
The increasing use of Renewable Energy (RE), the influence of forecasted load error, and Electric Vehicles (EVs) add significant uncertainty and variables to power system Unit Commitment (UC) planning.For addressing this challenge, this paper proposes a Fuzzy Mathematical Mixed-Integer Linear Programming (FMMILP) approach for daily UC that incorporates the scheduling of Pumped Storage Hydropower (PSH) and Battery Energy Storage (BES) to manage supply custom congratulations banner and demand fluctuations.The FMMILP model explicitly considers these uncertainties and aims to minimize total production cost and carbon emissions while ensuring reliable and sustainable power system operation.The effective solution method for solving FMMILP is explained.The FMMILP model is applied to Thailand’s national power system, characterized by its diverse generation mix, substantial RES penetration, and growing EV adoption.
A comprehensive stochastic reliability analysis using Monte Carlo Simulation is performed, incorporating a wide range of supply and demand scenarios, including extreme worst-case scenarios, to evaluate the effectiveness of the proposed models.These scenarios incorporate optimal solutions from UC and RES dispatch patterns.Three distinct FMMILP models are compared against a deterministic UC benchmark in the rainy, summer, and winter seasons.Numerical simulations demonstrate that the FMMILP model achieves optimal solutions within acceptable click here computational time and significantly enhances system reliability compared to the deterministic model, with reliability improvements of 15.99%, 26.
14%, and 51.44% in the rainy season, summer season, and winter season, respectively.This is evidenced by the reduction in the Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS) in power system operation.