A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources

Niknam, Taher; Taheri, Seyed Iman; Aghaei, Jamshid; Tabatabaei, Sajad; Nayeripour, Majid

doi:10.1016/j.apenergy.2011.06.023

Cited by 177 publications

(110 citation statements)

References 30 publications

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“…The latter includes biomass, wind, solar photovoltaic (PV) and ocean-based power plants. From the utilities' perspective, DG units can bring multiple technical benefits to distribution systems such as loss reduction, voltage profile improvement, voltage stability enhancement, network upgrade deferral and reliability while supplying energy sales as a primary purpose [3][4][5][6][7][8][9][10][11][12][13]. In addition, DG units can participate into the competitive market to provide ancillary services such as spinning reserve, voltage regulation, reactive power support and frequency control [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…An immune-GA method was presented for placing and sizing DG units to reduce the total emission while minimizing the total cost as a sum of electricity purchased from the grid, installation, operation and network reinforcement costs [36]. An improved honey bee mating optimization approach was also proposed for locating and sizing DG units to reduce the total emission while minimizing the capital, fuel, operation and maintenance costs, voltage deviation and energy loss [5]. In addition, an planning framework was also developed for PV integration by reducing the installation, operation and maintenance costs and the energy imported from the grid [37].…”

Section: Introductionmentioning

confidence: 99%

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An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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highlights DG allocation for minimizing energy loss and enhancing voltage stability. Expressions to find the optimal power factor of DG with commercial standard size. A methodology for DG planning to recover investment for DG owners.Impact of technical and environmental benefits on DG investment decisions. Benefit-cost analysis to specify the optimal location, size and number of DG units. Keywords:Distributed generation; Emission reduction; Loss reduction; Network upgrade deferral; Optimal power factor; Voltage stability abstract This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit-cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

2014

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“…Their technique is based on a genetic algorithm and an epsilon-constraint method that produces a set of non inferior solutions [9]. Niknam et al used a modified honey bee mating optimization algorithm for multi-objective placement of renewable energy resources [10]. They considered conflicting objectives such as the total cost, deviation of the bus voltage, power losses and emission.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this procedure, a fuzzy membership function is used to specify the best compromise solution. For any particle in the repository, the membership function of each objective function is defined as follows [10]: The normalized membership value is calculated for each particle in the repository as follows [10]:…”

Section: Best Compromise Solutionmentioning

confidence: 99%

Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network

Farjah¹,

Bornapour

Niknam

2012

Energies

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This paper presents a new Fuzzy Adaptive Modified Particle Swarm Optimization algorithm (FAMPSO) for the placement of Fuel Cell Power Plants (FCPPs) in distribution systems. FCPPs, as Distributed Generation (DG) units, can be considered as Combined sources of Heat, Power, and Hydrogen (CHPH). CHPH operation of FCPPs can improve overall system efficiency, as well as produce hydrogen which can be stored for the future use of FCPPs or can be sold for profit. The objective functions investigated are minimizing the operating costs of electrical energy generation of distribution substations and FCPPs, minimizing the voltage deviation and minimizing the total emission. In this regard, this paper just considers the placement of CHPH FCPPs while investment cost of devices is not considered. Considering the fact that the objectives are different, non-commensurable and nonlinear, it is difficult to solve the problem using conventional approaches that may optimize a single objective. Moreover, the placement of FCPPs in distribution systems is a mixed integer problem. Therefore, this paper uses the FAMPSO algorithm to overcome these problems. For solving the proposed multi-objective problem, this paper utilizes the Pareto Optimality idea to obtain a set of solution in the multi-objective problem instead of only one. Also, a fuzzy system is used to tune parameters of FAMPSO algorithm such as inertia weight. The efficacy of the proposed approach is validated on a 69-bus distribution system. OPEN ACCESS

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“…Furthermore, some researchers also considered the cost of operation and its availability in the analysis. Taher et al [22] considered 3 types of RESs in their studies, which were PV, wind, and fuel cells. The authors implemented the modified honey ABC optimization method to allocate 8 fuel cell units, 2 PV units, and 2 wind energy units in the network, where the sizes of the RESs were assumed to be fixed.…”

Section: Related Researchmentioning

confidence: 99%

Comparative learning global particle swarm optimization for optimal distributed generations' output

Jamian¹,

Mokhlis²,

Mustafa³

et al. 2014

Turk J Elec Eng & Comp Sci

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Abstract:The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation of individual cognitive and social acceleration coefficient values for each particle and a new fourth term in the velocity formula make the process of convergence faster. This new algorithm is tested on 6 standard mathematical test functions and a 33-bus distribution system. The performance of the CLGPSO is compared with the inertia weight particle swarm optimization (PSO) and evolutionary PSO methods. Since the CLGPSO requires fewer iterations, less computing time, and a lower standard deviation value, it can be concluded that the CLGPSO is the superior algorithm in solving small-dimension mathematical and simple power system problems.

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A modified honey bee mating optimization algorithm for multiobjective placement of renewable energy resources

Cited by 177 publications

References 30 publications

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

Placement of Combined Heat, Power and Hydrogen Production Fuel Cell Power Plants in a Distribution Network

Comparative learning global particle swarm optimization for optimal distributed generations' output

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