Fuzzy Mixed Integer Linear Programming for Daily Unit Commitment with Pumped Storage Hydropower, EV Charging Demand, and Solar PV Supply

There is increasing penetration of renewable energy sources such as photovoltaic (PV) systems and the growing popularity of electric vehicles (EVs) in many countries. The uncertainty of PV generation and EV charging demand has become a significant challenge in unit commitment (UC) planning. Pumped S...

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Bibliographic Details
Published in:2023 IEEE PES 15th Asia-Pacific Power and Energy Engineering Conference (APPEEC) pp. 1 - 6
Main Authors: Ny, Dino, Jeenanunta, Chawalit, Takada, Jun-Ichi, Chanyachatchawan, Sapa, Kaewpasuk, Sukita, Intiyot, Boonyarit, Dumrongsiri, Aussadavut
Format: Conference Proceeding
Language:English
Published: IEEE 06.12.2023
Subjects:
Costs
Electric vehicle charging
EV charging
fuzzy mixed integer linear programming
Generators
Hydroelectric power generation
pumped storage hydropower
PV penetration
Schedules
Uncertainty
Upper bound
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Summary:There is increasing penetration of renewable energy sources such as photovoltaic (PV) systems and the growing popularity of electric vehicles (EVs) in many countries. The uncertainty of PV generation and EV charging demand has become a significant challenge in unit commitment (UC) planning. Pumped Storage Hydropower (PSH) is introduced into the power system to respond to these uncertainties. This paper presents a Fuzzy Mixed-integer Linear Programming (FMILP) model for the schedule of the usage of PSHs with the uncertainties of demand and supply. The FMILP model considers uncertainties and imprecisions associated with forecasted demand, EV charging demand, and PV supply in the daily UC problem. The objective is to minimize the total production cost while ensuring the power system's reliable and sustainable operation by determining the optimal schedule for power generators to meet the power demand over 48 periods. A small system of 33 generators, including thermal and hydro generators, and 7 PSHs are studied. Numerical simulations have demonstrated the effectiveness of the FMILP model by identifying the uncertainty range between the lower and upper bounds, including the worst-case scenario. The numerical results show that the FMILP model can provide a more reliable and robust solution than deterministic MILP unit commitment.
DOI:10.1109/APPEEC57400.2023.10562013