Multi-objective dynamic generation and transmission expansion planning considering capacitor bank allocation and demand response program constrained to flexible-securable clean energy

2021 ◽  
Vol 47 ◽  
pp. 101469
Author(s):  
Abdolmohammad Davoodi ◽  
Ali Reza Abbasi ◽  
Samad Nejatian
Keyword(s):  
Demand Response ◽  
Capacitor Bank ◽  
Clean Energy ◽  
Multi Objective ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion ◽  
Demand Response Program ◽  
Dynamic Generation

Transmission expansion planning systems using algorithm genetic with multi-objective criterion

2017 ◽  
Vol 15 (3) ◽  
pp. 563-568 ◽  
Author(s):  
J.T. Jimenez ◽  
J. L. Guardado ◽  
N. G. Cabrera ◽  
J. R. Rodriguez ◽  
F. Figueroa
Keyword(s):  
Planning Systems ◽  
Objective Criterion ◽  
Multi Objective ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

scholarly journals The Effect of Different Non-linear Demand Response Models Considering Incentive and Penalty on Transmission Expansion Planning

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Amir Sadegh Zakeri ◽  
Oveis Asgari Gashteroodkhani ◽  
Iman Niazazari ◽  
Hossein Askarian-Abyaneh
Keyword(s):  
Linear Model ◽  
Demand Response ◽  
Nonlinear Models ◽  
Load Factor ◽  
Peak Reduction ◽  
Electrical Transmission ◽  
Load Curve ◽  
Expansion Planning ◽  
Transmission Expansion Planning ◽  
Transmission Expansion

The Transmission Expansion Planning (TEP) problem involves adding new lines to the existing electrical transmission network in order to meet the electrical demand requirements. Demand Response (DR) plays an important role in solving the TEP problem due to the delay in the investment costs. Researchers usually focus on the linear model of DR, while the focus on nonlinear models including power, exponential and logarithmic of DR is small. In this paper and in order to understand which model gives the realistic results, the linear model of DR is studied simultaneously with nonlinear models including power, exponential and logarithmic of DR. Moreover, the effect of incentive and penalty which has been neglected in the studies, is investigated. The study is investigated based on the viewpoint of different participants of the market including Independent System Operator (ISO), Customers and Utilities. In order to prioritize and select the most effective DR program, five characteristics including Peak Reduction, Energy Consumption, Load Factor, Peak to Valley and Customer’s Total Cost are extracted from the load curve. Then, using the weighting coefficients obtained by Entropy technique and implementing the TOPSIS and AHP technique, different DR programs are prioritized.

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