Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for ...PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle (
θ
), inspired from phasor theory in the mathematics. This phase angle (
θ
) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at
https://github.com/ebrahimakbary/PPSO
.
Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the ...industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems.
In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools.
The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.
1We present a novel intelligent method based on Type-2 fuzzy neural network (T2FNN) estimation and PSO algorithm for accurate wind power forecasting.2The T2FNN-PSO can efficiently handle the uncertainty with measured parameters by SCADA system and the forecasted parameters from numerical weather prediction (NWP) and measurement tools.3A new training method based on PSO algorithm is applied for tuning parameters of T2FNN
This paper proposes a new, efficient and powerful heuristic-hybrid algorithm using hybrid DE (differential evolution) and PSO (particle swarm optimization) techniques DEPSO (differential evolution ...particle swarm optimization) designed to solve eight optimization problems with benchmark functions and the MAED (multi-area economic dispatch), RCMAED (reserve constrained MAED) and RCMAEED (reserve constrained multi area environmental/economic dispatch) problems with reserve sharing in power system operations. The proposed hybridizing sum-local search optimizer, entitled HSLSO, is a relatively simple but powerful technique. The HSLSO algorithm is used in this study for solving different MAED problems with non-smooth cost function. The effectiveness and efficiency of the HSLSO algorithm is first tested on a number of benchmark test functions. Experimental results showe the HSLSO has a better quality solution with the ability to converge for most of the tested functions.
•Solving multi-area economic dispatch with tie-line constraints.•A comparative study of proposed DEPSOs for MAED problem.•The HSLSO has a better quality solution with the ability to converge.
This article models a hybrid power plant (HPP), including a compressed air energy storage (CAES) aggregator with a wind power aggregator (WPA) considering network constraints. Three objective ...functions are considered including electricity market profit maximization, congestion management, and voltage stability improvement. In order to accurately model the WPA, pitch control curtailment wind power levels are also added to the wind power generator models. To optimize all the mentioned objective functions, a multi‐objective Pareto front solution strategy is used. Finally, a fuzzy method is used to find the best compromise solution. The proposed approach is tested on a realistic case study based on an electricity market and wind farm located in Spain, and IEEE 57‐bus test system is used to evaluate the network constraint effects on the HPP scheduling for different objective functions.
During recent years, with the advent of restructuring in power systems as well as the increase of electricity demand and global fuel energy prices, challenges related to implementing demand response ...programs (DRPs) have gained remarkable attention of independent system operators (ISOs) and customers, aiming at the improvement of attributes of the load curve and reduction of energy consumption as well as benefiting customers.
In this paper, different types of DRPs are modeled based on price elasticity of the demand and the concept of customer benefit. Besides, the impact of implementing DRPs on the operation of grid-connected microgrid (MG) is analyzed. Moreover, several scenarios are presented in order to model uncertainties interfering MG operations including failure of generation units and random outages of transmission lines and upstream line, error in load demand forecasting, uncertainty in production of renewable energies (wind and solar) based distributed generation units, and the possibility that customers do not respond to scheduled interruptions.
Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs on an 11-bus MG over a 24-h period and also a 14-bus MG over a period of 336 h (two weeks). Simulation results indicate the effects of DRPs on total operation costs, customer's benefit, and load curve as well as determining optimal use of energy resources in the MG operation. In this regard, prioritizing of DRPs on the MG operation is required.
•Different types of DR programs are modeled based on price elasticity of the demand and the concept of customer benefit.•Impact of implementing demand response programs on the operation of grid-connected microgrid is analyzed.•Several scenarios are presented in order to model uncertainties interfering MG operations.•Simulations are conducted for two principal categories of DRP including incentive-based programs and time-based programs.
•Using Lévy mutation TLBO (LTLBO) algorithm.•Solving optimal power flow (OPF) problem with the algorithm.•Finding better results compared to the other algorithms.•A comparative study between ...algorithms in literature and the proposed algorithm.
One of the major tools for power system operators is optimal power flow (OPF) which is an important tool in both planning and operating stages, designed to optimize a certain objective over power network variables under certain constraints. This article investigates the possibility of using recently emerged evolutionary-based approach as a solution for the OPF problems which is based on a new teaching–learning-based optimization (TLBO) algorithm using Lévy mutation strategy for optimal settings of OPF problem control variables. The performance of this approach is studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems with different objective functions and is compared to methods reported in the literature. At the end, the results which are extracted from implemented simulations confirm Lévy mutation TLBO (LTLBO) as an effective solution for the OPF problem.
In this study we present a new and effective grouping optimization algorithm (namely, the Turbulent Flow of Water-based Optimization (TFWO)), inspired from a nature search phenomenon, i.e. whirlpools ...created in turbulent flow of water, for global real-world optimization problems. In the proposed algorithm, the problem of selecting control parameters is eliminated, the convergence power is increased and the algorithm have a fixed structure. The proposed algorithm is used to find the global solutions of real-parameter benchmark functions with different dimensions. Besides, in order to further investigate the effectiveness of TFWO, it was used to solve various types of nonlinear Economic Load Dispatch (ELD) optimization problems in power systems and Reliability–RedundancyAllocation Optimization (RRAO) for the overspeed protection system of a gas turbine, as two real-world engineering optimization problems. The results of TFWO are compared with other algorithms, which provide evidence for efficient performance with superior solution quality of the proposed TFWO algorithm in solving a great range of real-parameter benchmark and real-world engineering problems. Also, the results prove the competitive performance and robustness of TFWO algorithm compared to other state-of-the-art optimization algorithms in this study. The source codes of the TFWO algorithm are publicly available at https://github.com/ebrahimakbary/TFWO.
•A new variant of DE algorithm is proposed based on turbulent flow of water (TFWO).•TFWO is tested on real-parameter benchmark functions with different dimensions.•Also, TFWO is tested on Economic Load Dispatch as a real-world optimization problem.•A Reliability–Redundancy Allocation Optimization was also solved by TFWO.•The results prove superiority and robustness of TFWO compared to other algorithms.
This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching-learning-based optimization ...(TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability-redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series-parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30-100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results.
•A new DE algorithm based on socio-political evolution for ELD problem.•A review and comparative study of proposed methods for ELD problem.•CCDE algorithms were successfully implemented for ELD ...problem.
Differential evolution (DE) algorithm is a population-based algorithm designed for global optimization of the optimization problems. This paper proposes a different DE algorithm based on mathematical modeling of socio-political evolution which is called Colonial Competitive Differential Evolution (CCDE). The two typical CCDE algorithms are benchmarked on three well-known test functions, and the results are verified by a comparative study with two original DE algorithms which include DE/best/1 and DE/rand/2. Also, the effectiveness of CCDE algorithms is tested on Economic Load Dispatch (ELD) problem including 10, 15, 40, and 140-unit test systems. In this study, the constraints and operational limitations, such as valve-point loading, transmission losses, ramp rate limits, and prohibited operating zones are considered. The comparative results show that the CCDE algorithms have good performance and are reliable tools in solving ELD problem.
•Multi-objective optimal electric power planning.•Considered fuel cost, active power loss, gaze emission and voltage deviation.•Proposed MGBICA algorithm was successfully implemented for ...multi-objective OPF.
In this paper, a Gaussian Bare-bones multi-objective Imperialist Competitive Algorithm (GBICA) and its Modified version (MGBICA) are presented for the optimal electric power planning in the electric power system. Two sub-problems of multi-objective optimal electric power planning namely Optimal Power Flow (OPF) and Optimal Reactive Power Dispatch (ORPD) problems are considered. The OPF and ORPD problems are formulated as a nonlinear constrained multi-objective optimization problem with competing objectives. The performance of multi-objective algorithms are studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems. The proposed algorithm provides better results compared with the other algorithms as demonstrated by simulation results.
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