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

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 Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective 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.

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Multi-Objective Optimal Power Flow Using Efficient Evolutionary Algorithm

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2016-0233 ◽

2017 ◽

Vol 18 (2) ◽

Cited By ~ 4

Author(s):

S. Surender Reddy ◽

P.R Bijwe

Keyword(s):

Power Flow ◽

Optimal Power Flow ◽

Stability Index ◽

Pareto Optimal ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Fuel Cost ◽

Pareto Optimal Front ◽

Efficient Approach ◽

Optimal Power

Abstract A novel efficient multi-objective optimization (MOO) technique for solving the optimal power flow (OPF) problem has been proposed in this paper. In this efficient approach uses the concept of incremental power flow model based on sensitivities and some heuristics. The proposed approach is designed to overcome the main drawback of conventional MOO approach, i. e., the excess computational time. In the present paper, three objective functions i. e., generation cost, system losses and voltage stability index are considered. In the proposed efficient MOO approach, the first half of the specified number of Pareto optimal solutions are obtained by optimizing the fuel cost objective while considering other objective (i. e., system loss or voltage stability index) as constraint while the second half is obtained in a vice versa manner. After obtaining the total Pareto optimal solutions, they are sorted in the ascending order of fuel cost objective function value obtained for each solution leads to the Pareto optimal front. The proposed efficient approach is implemented using the differential evolution (DE) algorithm. The proposed efficient MOO approach can effectively handle the complex non-linearities, discrete variables, discontinuities and multiple objectives. The effectiveness of the proposed approach is tested on standard IEEE 30 bus test system. The simulation studies show that the Pareto optimal solutions obtained with proposed efficient MOO approach are diverse and well distributed over the entire Pareto optimal front. The simulation results indicate that the execution speed of proposed efficient MOO approach is approximately 10 times faster than the conventional evolutionary based MOO approaches.

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Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) ◽

10.1109/smartgridcomm47815.2020.9303008 ◽

2020 ◽

Author(s):

Ahmed S. Zamzam ◽

Kyri Baker

Keyword(s):

Power Flow ◽

Optimal Power Flow ◽

Optimal Solutions ◽

Optimal Power

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New Dynamic Multi-Objective Constrained Optimization Evolutionary Algorithm

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595915500360 ◽

2015 ◽

Vol 32 (05) ◽

pp. 1550036 ◽

Cited By ~ 1

Author(s):

Chun-An Liu ◽

Yuping Wang ◽

Aihong Ren

Keyword(s):

Constrained Optimization ◽

Evolutionary Algorithm ◽

Optimization Problem ◽

Pareto Optimal ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Constrained Optimization Problem ◽

Multi Objective ◽

Time Period ◽

Pareto Fronts

For dynamic multi-objective constrained optimization problem (DMCOP), it is important to find a sufficient number of uniformly distributed and representative dynamic Pareto optimal solutions. In this paper, the time period of the DMCOP is first divided into several random subperiods. In each random subperiod, the DMCOP is approximately regarded as a static optimization problem by taking the time subperiod fixed. Then, in order to decrease the amount of computation and improve the effectiveness of the algorithm, the dynamic multi-objective constrained optimization problem is further transformed into a dynamic bi-objective constrained optimization problem based on the dynamic mean rank variance and dynamic mean density variance of the evolution population. The evolution operators and a self-check operator which can automatically checkout the change of time parameter are introduced to solve the optimization problem efficiently. And finally, a dynamic multi-objective constrained optimization evolutionary algorithm is proposed. Also, the convergence analysis for the proposed algorithm is given. The computer simulations are made on four dynamic multi-objective optimization test functions and the results demonstrate that the proposed algorithm can effectively track and find the varying Pareto optimal solutions or the varying Pareto fronts with the change of time.

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Evaluating the ε-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions

Evolutionary Computation ◽

10.1162/106365605774666895 ◽

2005 ◽

Vol 13 (4) ◽

pp. 501-525 ◽

Cited By ~ 377

Author(s):

Kalyanmoy Deb ◽

Manikanth Mohan ◽

Shikhar Mishra

Keyword(s):

Steady State ◽

Evolutionary Algorithm ◽

Optimization Problems ◽

Computational Time ◽

Test Problems ◽

Pareto Optimal ◽

Pareto Optimal Solutions ◽

Optimal Solutions ◽

Nsga Ii ◽

Multi Objective

Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the ε-dominance concept introduced earlier (Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the ε-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

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