Robust Transmission Network Expansion Planning With Uncertain Renewable Generation and Loads

Jabr, Rabih A.

doi:10.1109/tpwrs.2013.2267058

Cited by 273 publications

(190 citation statements)

References 18 publications

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“…The robustness of planning schemes R1 is verified by estimating the robustness degree with testing scenarios [3,8]. Two robust planning schemes R1-A and R1-B, which are obtained by the genetic algorithm combined with Taguchi's orthogonal array testing (TOAT) [8] and robust TNEP To further investigate the impact of wind power distribution characteristics on TNEP, four feasible planning schemes, which have been marked in Figure 7, are selected for comparison.…”

Section: Performance Analysis Of the Pareto Optimal Planning Resultsmentioning

confidence: 99%

“…The linearization method of Equation (13) can be found in [3]. The obtained planning result is considered as an individual.…”

Section: The Initialization Of Populationmentioning

confidence: 99%

“…Apart from stochastic optimization, robust optimization has also been widely discussed in TNEP. Since robust optimization only requires the lower and upper bounds of uncertain parameters, uncertainties such as load demand, renewable generation, and transmission investment cost [3,4] can be easily considered in TNEP through a robust model. Nevertheless, the robust TNEP method also has the issue of over-conservatism and may lead to uneconomic solutions.…”

Section: Introductionmentioning

confidence: 99%

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Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Wang

2015

Applied Sciences

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This paper presents a flexible transmission network expansion planning (TNEP) approach considering uncertainty. A novel hybrid clustering technique, which integrates the graph partitioning method and rough fuzzy clustering, is proposed to cope with uncertain renewable generation and load demand. The proposed clustering method is capable of recognizing the actual cluster distribution of complex datasets and providing high-quality clustering results. By clustering the hourly data for renewable generation and load demand, a multi-scenario model is proposed to consider the corresponding uncertainties in TNEP. Furthermore, due to the peak distribution characteristics of renewable generation and heavy investment in transmission, the traditional TNEP, which caters to rated renewable power output, is usually uneconomic. To improve the economic efficiency, the multi-objective optimization is incorporated into the multi-scenario TNEP model, while the curtailment of renewable generation is considered as one of the optimization objectives. The solution framework applies a modified NSGA-II algorithm to obtain a set of Pareto optimal planning schemes with different levels of investment costs and renewable generation curtailments. Numerical results on the IEEE RTS-24 system demonstrated the robustness and effectiveness of the proposed approach.

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Section: Performance Analysis Of the Pareto Optimal Planning Resultsmentioning

confidence: 99%

“…The linearization method of Equation (13) can be found in [3]. The obtained planning result is considered as an individual.…”

Section: The Initialization Of Populationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Wang

2015

Applied Sciences

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“…To encounter the uncertainties brought by the DGs, stochastic optimisation methods such as chance-constrained optimisation [12,13] and robust optimisation techniques [14][15][16][17][18] can be used. Among the robust optimisation method, the uncertainty set is to describe the robust region which the optimal decision should be adaptive to.…”

Section: Introductionmentioning

confidence: 99%

Improved extreme‐scenario extraction method for the economic dispatch of active distribution networks

Zhang

Wen

2017

J. eng.

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Optimisation techniques with good characterisation of the uncertainties in modern power system enable the system operators well trade-off between security and sustainability. This study proposes the extreme-scenario extraction-based robust optimisation method for the economic dispatch of active distribution network with renewables. The extreme scenarios are selected from the historical data using the improved minimum volume enclosing ellipsoid (MVEE) algorithm to guarantee the security of system operation while avoid frequently switching the transformer tap. It is theoretically proved that if the decision can be adaptive to the selected extreme scenarios, it can be robust to all the possible scenarios. Simulation results demonstrate that the proposed improved MVEE algorithm significantly reduces the number of scenarios, so that the computational burden is dramatically cut down. Additionally, compared to the existing robust optimisation approach, the proposed method is less conservative for the smaller size of the uncertainty set.

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“…[3,4]. The charateristics of the uncertain nature of the renewable energy based power plant is used to create the constraints of the optimization using Stochastic programming (SP) [7]- [9] and robust optimization (RO) [10]- [15] is included in the expansion planning problem.…”

Section: Introductionmentioning

confidence: 99%

Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimization and Cuckoo Search Algorithm

Hanumesh¹,

H.R²

2017

IJERA

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This Paper is an attempt to develop the expansion-planning algorithm using meta heuristics algorithms. Expansion Planning is always needed as the power demand is increasing every now and then. Thus for a better expansion planning the meta heuristic methods are needed. The cost efficient Expansion planning is desired in the proposed work. Recently distributed generation is widely researched to implement in future energy needs as it is pollution free and capability of installing it in rural places. In this paper, optimal distributed generation expansion planning with Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) for identifying the location, size and type of distributed generator for future demand is predicted with lowest cost as the constraints. Here the objective function is to minimize the total cost including installation and operating cost of the renewable DGs. MATLAB based `simulation using M-file program is used for the implementation and Indian distribution system is used for testing the results.

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Robust Transmission Network Expansion Planning With Uncertain Renewable Generation and Loads

Cited by 273 publications

References 18 publications

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Flexible Transmission Network Expansion Planning Considering Uncertain Renewable Generation and Load Demand Based on Hybrid Clustering Analysis

Improved extreme‐scenario extraction method for the economic dispatch of active distribution networks

Cost Aware Expansion Planning with Renewable DGs using Particle Swarm Optimization and Cuckoo Search Algorithm

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