Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II

Ye, Chengjin; Min-xiang, Huang

doi:10.1109/tpwrs.2014.2339352

Cited by 90 publications

(41 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple OPF model is employed in this paper, and traditional constraints are used. A more practical OPF model will be explored in the future to consider real-world demands, such as dynamic security [41,59], and the reactive power and voltage magnitude [60]. Aimed at security problems in the power system, two safety-related functions are contained in the OPF model.…”

Section: Discussionmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

Processes

View full text Add to dashboard Cite

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Paretooptimal solutions. The primary contribution of the proposal is the innovative application of manyobjective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Keywords: optimal power flow; optimal operation; power systems; multi-objective optimization; knee point-driven evolutionary algorithm; decision analysis; best compromise solutions Recent research suggests that multi-objective evolutionary algorithms (MOEAs) are promising tools for addressing various challenging optimization tasks in engineering fields [29][30][31][32]. In order to optimal distributed generation planning, a MOEA is employed in [29]. Reference [30] reviews the most representative MOEAs that have been reported, and MOEA has developed as an effective method to solve such an optimization problem. In [31], MOEA is employed for planning overtime of software engineers. The layout of wind farms is optimized via MOEA in [32]. In particular, MOEAs can be also applied to solve the OPF issue [23][24][25][26][27][28]. Unfortunately, the MOPF can only cope with the optimization issue with two to three objectives, which, to a certain extent, limits the practicality of this type of methods. In addition, many-objective optimization problems (MaOPs), considering four or more objective functions in the OPF problem [33][34][35][36], are commonly existed phenomenon in the practice of real-world power system operation [33]. In [34], a specially tailored MOEA is presented for tackling the current large-scale MaOPs. Another MOEA based on adaptive search strategy is presented for coping with MaOPs in [35]. In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that MOEAs exhibit their own capabilities in dealing with different MaOPs. For this reason, MaOPs have recently gained a great deal of attention as most existing MOEAs are inadequate for solving OPF problems with four or more objectives, and it has become a hotspot to enhance the ability of MOEAs for addressing MaOPs issues [37][38][39]. However, manyobjective OPF (MaOPF) is quite challenging for solving sinc...

show abstract

Section: Discussionmentioning

confidence: 99%

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

2018

Processes

View full text Add to dashboard Cite

show abstract

“…In the area of energy, the NSGA-II algorithm has been widely used in the reactive power planning problem [15,16,17,18,19], the optimal power flow problem [20,21,22] and the GEP problem [6,23].…”

Section: Methodsmentioning

confidence: 99%

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Palácios¹,

Valdés²,

Batres³

2018

SGCE

View full text Add to dashboard Cite

Planning in the energy sector implies multiple and conflicting objectives. Multi-objective models allow the analysis of the inter-relationships and trade-off solutions to be obtained. This paper presents a mixed integer linear model for multi-step multi-objective generation expansion planning (MMGEP). The MMGEP problem is defined as the problem of determining the answers to the following questions: What types of generation technologies are to be added to the grid? What is the capacity of each new generation plant? Where will the plant be located? When will the plant be located? The MMGEP objectives are to minimize the global cost of the system, minimize the environmental impact and maximize the social profits. The proposed model is based on a real power system in Mexico for the planning period between 2017 and 2037. The problem was solved using the NSGA-II algorithm.

show abstract

“…In fact, several parallel implementations of metaheuristics have been proposed for multicore systems [20], distributed systems [21], supercomputers [22] and, more recently, GPUs [23]. Affordable and installed in most desktop computers, GPUs are a very promising platform for parallel implementations of metaheuristics due to their massively parallel architecture which integrates hundreds to thousands of processing cores on a single chip.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, the network total excess, the network normalized violation factor and the network fitness at Eqs. (19)- (21) are computed with another parallel map function with one thread per network. Once the fitness of every candidate solution is calculated, the PSO algorithm continues its execution.…”

Section: Parallel Evaluation Of the Fitness Functionmentioning

confidence: 99%

Optimal power flow based on parallel metaheuristics for graphics processing units

Roberge

Tarbouchi

Okou

2016

Electric Power Systems Research

View full text Add to dashboard Cite

Multi-Objective Optimal Power Flow Considering Transient Stability Based on Parallel NSGA-II

Cited by 90 publications

References 23 publications

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

A multi-step multi-objective generation expansion planning model-A case study in Mexico

Optimal power flow based on parallel metaheuristics for graphics processing units

Contact Info

Product

Resources

About