期刊
IET GENERATION TRANSMISSION & DISTRIBUTION
卷 10, 期 16, 页码 4024-4031出版社
INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-gtd.2016.0259
关键词
power transmission planning; smart power grids; evolutionary computation; Pareto optimisation; multiobjective transmission expansion planning; smart grid; decomposition-based evolutionary algorithm; large-scale renewable energy; demand response resources; DR resources; TEP models; corrective control actions; generation rescheduling; load curtailment; post-contingency state; pre-contingency state; CC risk index; CCRI; multiobjective evolutionary algorithm; MOEA-D; Pareto optimal solutions; modified IEEE RTS 24-bus systems; 118-bus systems
资金
- Science and Technology projects of China Southern Power Grid [WYKJ00000027]
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.
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