Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL

Abdelaziz, Morad; Moradzadeh, Majid

doi:10.1016/j.epsr.2019.01.012

Cited by 32 publications

(15 citation statements)

References 41 publications

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“…A comprehensive and meaningful survey of these studies is summarized in Table 1. The objective functions include technical objectives such as system's annual energy losses [5]- [9], renewable generation capacity , voltage stability [10]- [12], voltage profile [11], [12] and reliability [13], [14]; and economic objectives such as deferral of upgrade investments [15], cost of energy losses [15], [16], cost of interruption [15], installation cost of DGs [17], total cost [18]- [20] and investment and operational costs [21].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In some studies, only one objective function is considered [5]- [10], [18], [19], [21], but in some studies, distributed generation planning is defined as a multi-objective problem [12], [15], [17], [20], [22].…”

Section: B Literature Reviewmentioning

confidence: 99%

“…The DG planning has been solved by Mixed Integer Non-Linear Programming (MINLP) [5], [6], [10], Ant Lion Optimization Algorithm (ALOA) [11], Genetic Algorithm (GA) [15], [18], [21], Non-dominated Sorting Genetic Algorithm II (NSGA-II) [17], [20], Particle Swarm Optimization (PSO) [8], [12], [22], Tribe-PSO and Ordinal Optimization (OO) [19] and Tabu Search (TS) [13], [14], [16], [21].…”

Section: B Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Combined Firm and Renewable Distributed Generation and Reactive Power Planning

Niazi

Golshan

Alhelou³

2021

IEEE Access

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The benefits of integrating Distributed Generation (DG) into the distribution networks depend on the characteristics of different types of DG units, loads and Reactive Power Sources (RPS). These benefits can be optimized if the firm DG units such as biomass energy and renewable DG units such as photovoltaic (PV) and wind system are optimally sized, located and coordinated with reactive power sources. In this paper, by assuming that the Distribution System Operator (DSO) has got the ownership and operation of DG units and RPS, a new planning strategy is proposed for determining the optimal placement and rating of DG units and RPS. This strategy overcomes the challenge of intermittency of renewable production in order that this planning will assist the system operators in defining the better integration strategies of firm and intermittent energy systems and reactive power sources in distribution networks. The proposed planning is based on single objective optimization so that it optimizes one of the following objective functions every time: the system energy losses, voltage stability margin, self-adequacy of microgrids defined on the distribution system and exchange of active and reactive powers between the distribution system and upstream network. The proposed technique accounts for the uncertainties associated with solar irradiance, wind speed and demand through a probabilistic optimization. The formulation of each planning problem is presented and applied to the 69-bus distribution system. The results of different planning strategies are compared and analyzed. Furthermore, the impact of the planning with each objective function on other indices is evaluated.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Combined Firm and Renewable Distributed Generation and Reactive Power Planning

Niazi

Golshan

Alhelou³

2021

IEEE Access

View full text Add to dashboard Cite

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“…Different objective functions have been studied, many types of optimization techniques have been used, and various kinds of constraints and limitations have been applied in the optimization of DG connected to the grid. Power losses and cost functions have been simultaneously minimized by finding the optimum size and location of PV-and WT-based DG through applying the non-dominated sorting genetic algorithm II (NSGA-II) [34]. Sizing and allocation of multiple DGs with unity power factor and capacitor bank have been adjusted to optimize different objective functions.…”

Section: Introductionmentioning

confidence: 99%

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

et al. 2020

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Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems.

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“…Zhu et al proposed a mixed-integer particle swarm optimization algorithm on optimal placement of battery energy storage systems to improve power system oscillation damping, in which the New England 39-bus system and the Nordic test system were used as test examples [14]. Abdelaziz and Moradzadeh presented a parallelized implementation of NSGA-II using OpenCL to solve the multi-objective renewable DG planning problem, where the IEEE 32-bus test system and two real distribution test systems were used as test examples [15]. Roberts et al proposed a probabilistic simulation-based multi-objective optimization approach for dimensioning robust renewable based Hybrid Power Systems, where a rural community of the Amazonian region of Brazil was used as a test example [16].…”

Section: Introductionmentioning

confidence: 99%

Application of Ordinal Optimization to Reactive Volt-Ampere Sources Planning Problems

2019

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Reactive volt-ampere sources planning is an effort to determine the most effective investment plan for new reactive sources at given load buses while ensuring appropriate voltage profile and satisfying operational constraints. Optimization of reactive volt-ampere sources planning is not only a difficult problem in power systems, but also a large-dimension constrained optimization problem. In this paper, an ordinal optimization-based approach containing upper and lower level is developed to solve this problem efficiently. In the upper level, an ordinal search (OS) algorithm is utilized to select excellent designs from a candidate-design set according to the system’s structural information exploited from the simulations executed in the lower level. There are five stages in the ordinal search algorithm, which gradually narrow the design space to search for a good capacitor placement pattern. The IEEE 118-bus and IEEE 244-bus systems with four load cases are employed as the test examples. The proposed approach is compared with two competing methods; the genetic algorithm and Tabu search, and a commercial numerical libraries (NL) mixed integer programming tool; IMSL Numerical Libraries. Experimental results illustrate that the proposed approach yields an outstanding design with a higher quality and efficiency for solving reactive volt-ampere sources planning problem.

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Monte-Carlo simulation based multi-objective optimum allocation of renewable distributed generation using OpenCL

Cited by 32 publications

References 41 publications

Combined Firm and Renewable Distributed Generation and Reactive Power Planning

Combined Firm and Renewable Distributed Generation and Reactive Power Planning

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

Application of Ordinal Optimization to Reactive Volt-Ampere Sources Planning Problems

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