A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation

Arabali, A.; Ghofrani, M.; Etezadi-Amoli, M.; Fadali, M. Sami; Moeini‐Aghtaie, Moein

doi:10.1109/tpwrs.2014.2316529

Cited by 109 publications

(78 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first objective comprises investment cost and operation cost (including power generation and DR costs), as seen in (21). In a probabilistic formulation, the second term is the mean (expected) value after MC simulations converge.…”

Section: Objectivesmentioning

confidence: 99%

“…With the obtained PF, a final solution can be selected according to practical needs, e.g., Nash equilibrium [31], minimizing the normalized Euclidian distance [21], individual risk preference or engineering judgments [3,32]. In this paper, a fuzzy satisfaction decision-making approach is adopted, and technical details regarding this approach can be found in [33].…”

Section: Step 4: Solution Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

2017

View full text Add to dashboard Cite

This paper presents a multi-objective transmission expansion planning (TEP) framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR) costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver's six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

show abstract

Section: Objectivesmentioning

confidence: 99%

Section: Step 4: Solution Selectionmentioning

confidence: 99%

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

2017

View full text Add to dashboard Cite

show abstract

“…As the fitness of a population may remain static for a number of generations before a superior individual is found, the application of convergence termination criteria becomes problematic, a common practice is to terminate GA after a specified number of generationsand then test the fitness of best members in the last population. If no acceptable solutions are found, the GA may be restarted or fresh search initiated [5][6][7][8]. 3.7 Selection process depending on the Fitness…”

Section: =1mentioning

confidence: 99%

“…This is done using the Roulette Wheel Selection method where individuals with a higher fitness are more likely to be selected then others [4][5][6][7][8].…”

Section: =1mentioning

confidence: 99%

Multi Fuels Allocation for Power Generation using Genetic Algorithm

Choeichum¹,

Keeratipranon²,

Khemapatapan³

2017

Proceedings of the 2017 International Conference on Economics, Finance and Statistics (ICEFS 2017)

View full text Add to dashboard Cite

Abstract. This paper presents solution of optimal multi fuels allocation for electric power generation planning problem via a genetic algorithm ( GA). The objective is to maximize the electric power energy output and minimize the fuels cost. This is a considerably difficult problem because of its data variation.GA can provide an appropriate heuristic search method and return an actual or near optimal solution. This research used some heuristic in addition during crossover and mutation for tuning the system to obtain a better candidate solution.An experimental resultshowed a significantly improve result compared to other techniques.

show abstract

“…Also, the NSGA II does not use additional parameters for preserving the diversity of the solutions. This algorithm has proven to be effective in tackling the multi-objective TNEP problem as shown in [15], [24] and [25].…”

Section: Introductionmentioning

confidence: 99%

Transmission network expansion planning considering weighted transmission loading relief nodal indexes

Saldarriaga-Zuluaga

López-Lezama

Villada

2018

TecnoL.

View full text Add to dashboard Cite

This paper presents a model and a solution approach for the transmission network expansion planning (TNEP) problem that integrates security constraints given by weighted transmission loading relief (WTLR) indexes. Such indexes integrate shift and power distribution factors and allow to measure the severity of overloads in normal conditions and under any single contingency. Furthermore, the inclusion of small-scale generation was considered as complementary to TNEP solutions. The proposed model was solved by means of the metaheuristic NSGA-II (Non-dominated Sorting Genetic Algorithm II), which enabled to find a set of solutions that represent a trade-off between the cost of the expansion plan and its security level. Several tests were performed on the 6-bus Garver system and the IEEE 24-bus reliability test system, thus showing the applicability of the proposed approach. It was found that the inclusion of small-scale generation in strategic nodes allows to reduce the cost of expansion plans and increases their level of security for single contingencies.

show abstract

A Multi-Objective Transmission Expansion Planning Framework in Deregulated Power Systems With Wind Generation

Cited by 109 publications

References 28 publications

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Multi Fuels Allocation for Power Generation using Genetic Algorithm

Transmission network expansion planning considering weighted transmission loading relief nodal indexes

Contact Info

Product

Resources

About