Multi-objective transmission expansion planning in a smart grid using a decomposition-based evolutionary algorithm

The integration of large-scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security. The conventional TEP models have not fully addressed the cost and the feasibility...

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Vydané v:IET generation, transmission & distribution Ročník 10; číslo 16; s. 4024 - 4031
Hlavní autori: Qiu, Jing, Dong, Zhao Yang, Meng, Ke, Xu, Yan, Zhao, Junhua, Zheng, Yu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 08.12.2016
Predmet:
118‐bus systems
CC risk index
CCRI
corrective control actions
decomposition‐based evolutionary algorithm
demand response resources
DR resources
evolutionary computation
generation rescheduling
large‐scale renewable energy
load curtailment
modified IEEE RTS 24‐bus systems
MOEA‐D
multiobjective evolutionary algorithm
multiobjective transmission expansion planning
Pareto optimal solutions
Pareto optimisation
post‐contingency state
power transmission planning
pre‐contingency state
smart grid
smart power grids
TEP models
corrective control actions
Pareto optimal solutions
Pareto optimisation
smart grid
multiobjective transmission expansion planning
pre-contingency state
demand response resources
118-bus systems
smart power grids
post-contingency state
CCRI
decomposition-based evolutionary algorithm
MOEA-D
multiobjective evolutionary algorithm
large-scale renewable energy
TEP models
evolutionary computation
power transmission planning
CC risk index
modified IEEE RTS 24-bus systems
DR resources
generation rescheduling
load curtailment
ISSN:1751-8687, 1751-8695
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Popis
Shrnutí:The integration of large-scale renewable energy and demand response (DR) resources in smart grids have brought in emerging challenges for transmission expansion planning (TEP), particularly in terms of system security. The conventional TEP models have not fully addressed the cost and the feasibility of corrective control (CC) actions such as generation rescheduling and load curtailment under contingencies. Moreover, the optimality of CC depends on the pre-contingency state, the post-contingency state, as well as the existence and viability of the involved CC actions. In this study, first the authors have given the explicit definition of CC risk index (CCRI), which evaluates the expected system performance under a set of contingencies (i.e. risk of incurring security issues). With the authors’ improvement, the CCRI is now mathematically tractable and may have wide applications to TEP problems. Afterwards, the authors have proposed a multi-objective TEP framework with tradeoffs between cost and risk. A relatively new yet superior multi-objective evolutionary algorithm called the multi-objective evolutionary algorithm (MOEA)/D is introduced and employed to find Pareto optimal solutions. The proposed model is numerically verified on the modified IEEE RTS 24-bus and 118-bus systems. According to the simulation results, the proposed model can provide information regarding variants of risks and coordinate the optimum planning and DR solutions.
ISSN:1751-8687
1751-8695
DOI:10.1049/iet-gtd.2016.0259