Enhanced Transmission Expansion Planning with a High Confidence Strategy Considering Fluctuations in Solar PV Generation and Electricity Demand

Transmission Expansion Planning (TEP) is a popular approach to support the electricity demand increase and accommodate the solar photovoltaic systems increase in the transmission system. In state-of-the-art, maximum and minimum demand situations are often used in the TEP problem which is defined as...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE access Ročník 13; s. 1
Hlavní autori: Somchit, Siripat, Kaewdornhan, Niphon, Chatthaworn, Rongrit
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Binary metaheuristic algorithm
Computer simulation
Computing time
Confidence intervals
Costs
Electric power demand
Electricity
Evolutionary algorithms
Evolutionary computation
Heuristic methods
Investment
Load flow
logistic map
Mathematical models
Metaheuristics
Monte Carlo simulation
Photovoltaic cells
photovoltaic systems
Planning
point estimation method
Power flow
Power transmission lines
Probabilistic logic
probabilistic power flow
Statistical analysis
transmission expansion planning
Uncertainty
ISSN:2169-3536, 2169-3536
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Transmission Expansion Planning (TEP) is a popular approach to support the electricity demand increase and accommodate the solar photovoltaic systems increase in the transmission system. In state-of-the-art, maximum and minimum demand situations are often used in the TEP problem which is defined as a conventional TEP. However, modern TEP typically considers several uncertain scenarios, especially through Monte Carlo Simulation (MCS). Moreover, the metaheuristic algorithm is mostly applied to solve the TEP problem. These lead to an exponential increase in computational time as additional situations are included. To address this issue, this work proposes an enhanced TEP-based 24-hour situation optimized by an improved Binary Differential Evolution (BDE) algorithm assisted by the logistic map for adjusting the mutation factor and crossover rate. Zhao's Point Estimation Method (PEM) combined with the Nataf Transformation (NT) is used instead of MCS to generate effective uncertain scenarios. Moreover, power system operation constraints are calculated through a probabilistic power flow with a 95% confidence level. The modified IEEE 24-bus system is used to test the proposed method. The simulation results show that Zhao's PEM combined with NT can provide the mean and standard deviation of the desired parameters close to those from the MCS, while reducing computational time by 99.91%. Additionally, the improved BDE outperforms the conventional BDE by providing faster convergence in both conventional and proposed TEP problems. Furthermore, the proposed TEP solved by the improved BDE can achieve an optimal solution without violations for all scenarios, whereas the conventional TEP fails to support all scenarios.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3553979