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A stochastic multi-objective planning framework for distributed energy resources as an alternative to transmission expansion

Saber, H ; Sharif University of technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1109/TPEC60005.2024.10472236
  3. Publisher: 2024
  4. Abstract:
  5. Building new transmission lines has grown increasingly difficult in recent years due to ever-increasing environmental concerns and rising construction costs. An alternative would involve investing in distributed energy resources (DERs) which can reduce the need for new transmission lines by reducing the load. This study presents a new stochastic multi-objective planning model for DERs as a replacement for transmission network reinforcement. The proposed model aims to minimize the investment costs of DERs and the congestion costs of transmission networks while maximizing the absorption of private investments. The uncertainties in the future electric loads and generators' offer prices are captured by scenarios. The non-dominated sorting genetic algorithm II (NSGA-II) is utilized for solving the multi-objective optimization problem. Subsequently, the fuzzy decision-making method is applied to non-dominated solutions to obtain the final optimal expansion plan. Finally, the effectiveness and applicability of the proposed planning model are investigated using the IEEE 24-bus test system, offering insights from both the perspectives of system operators and private investors. © 2024 IEEE
  6. Keywords:
  7. Distributed energy resources ; Multi-objective optimization ; Planning studies ; Private invest-ment ; Uncertainty modeling ; Costs ; Decision making ; Electric loads ; Electric power transmission ; Electric power transmission networks ; Energy resources ; Genetic algorithms ; Investments ; Multiobjective optimization ; Stochastic models ; Stochastic systems ; Traffic congestion ; Uncertainty analysis ; Distributed Energy Resources ; Multi-objective planning ; Multi-objectives optimization ; Planning framework ; Planning studies ; Private invest-ment ; Stochastics ; Transmission expansion ; Transmission-line ; Uncertainty models ; Electric lines
  8. Source: 2024 IEEE Texas Power and Energy Conference, TPEC 2024 ; 2024 ; 979-835033120-2 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10472236