Multiobjective Stochastic Power System Expansion Planning Considering Wind Farms and Demand Response

In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in t...

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Bibliographic Details
Published in:International journal of energy research Vol. 2024; no. 1
Main Authors: Ghadimi, Ali Asghar, Ahmadi, Abdollah, Miveh, Mohammad Reza
Format: Journal Article
Language:English
Published: Bognor Regis Hindawi 2024
John Wiley & Sons, Inc
Subjects:
Electric power demand
Electric power systems
Electricity consumption
Energy resources
Flexibility
Integer programming
Linear programming
Market prices
Mixed integer
New technology
Nonlinear programming
Objectives
Optimization techniques
Planning
Power
Renewable energy
Renewable energy sources
Stochastic models
System effectiveness
Time of use electricity pricing
Uncertainty
Wind
Wind farms
Wind power
ISSN:0363-907X, 1099-114X
Online Access:Get full text
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Summary:In recent years, due to the increase in electricity consumption and environmental problems, power system expansion planning requires new technologies. In this regard, the incorporation of renewable energy sources (RESs) and utilization of demand response (DR) programs need disruptive variations in the present power system configurations. This paper proposes a mixed-integer linear robust multiobjective model for generation and transmission expansion planning (GEP-TEP) taking into account wind farms (WFs) and a DR program based on time-of-use pricing. The suggested model is presented via mixed-integer nonlinear programming (MINLP) at the first stage and then transformed into mixed-integer linear programming (MILP) using the Big M linearization technique. Moreover, long- and short-term uncertainties of load demand and WFs are incorporated into the recommended model to achieve more accurate results. The interval-based method is applied for taking into account long-term uncertainties while the scenario-based stochastic model is applied for modeling short-term uncertainties in the recommended GEP–TEP model. Lastly, the suggested model is investigated on various standard test systems to evaluate the effectiveness of the GEP-TEP model.
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ISSN:0363-907X
1099-114X
DOI:10.1155/2024/9962745