3,973 research outputs found
A simplified analytical approach for optimal planning of distributed generation in electrical distribution networks
DG-integrated distribution system planning is an imperative issue since the installing of distributed generations (DGs) has many effects on the network operation characteristics, which might cause significant impacts on the system performance. One of the most important characteristics that mostly varies because of the installation of DG units is the power losses. The parameters affecting the value of the power losses are number, location, capacity, and power factor of the DG units. In this paper, a new analytical approach is proposed for optimally installing DGs to minimize power loss in distribution networks. Different parameters of DG are considered and evaluated in order to achieve a high loss reduction in the electrical distribution networks. The algorithm of the proposed approach has been implemented using MATLAB software and has been tested and investigated on 12-bus, 33-bus, and 69-bus IEEE distribution test systems. The results show that the proposed approach can provide an accurate solution via simple algorithm without using exhaustive process of power flow computations
Optimal planning of RDGs in electrical distribution networks using hybrid SAPSO algorithm
The impact of the renewable distributed generations (RDGs), such as photovoltaic (PV) and wind turbine (WT) systems can be positive or negative on the system, based on the location and size of the DG. So, the correct location and size of DG in the distribution network remain an obstacle to achieving their full possible benefits. Therefore, the future distribution networks with the high penetration of DG power must be planned and operated to improve their efficiency. Thus, this paper presents a new methodology for integrated of renewable energy-based DG units with electrical distribution network. Since the main objective of the proposed methodology is to reduce the power losses and improve the voltage profile of the radial distribution system (RDS). In this regard, the optimization problem was formulated using loss sensitivity factor (LSF), simulated annealing (SA), particle swarm optimization (PSO) and a combination of loss sensitivity index (LSI) with SA & PSO (LSISA, LSIPSO) respectively. This paper contributes a new methodology SAPSO, which prevents the defects of SA & PSO. Optimal placement and sizing of renewable energy-based DG tested on 33-bus system. The results demonstrate the reliability and robustness of the proposed SAPSO algorithm to find the near-optimal position and size of the DG units to mitigate the power losses and improve the radial distribution system's voltage profile
A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks
The optimal placement and sizing of distributed generators is a classical problem in power distribution networks that is usually solved using heuristic algorithms due to its high complexity. This paper proposes a different approach based on a mixed-integer second-order cone programming (MI-SOCP) model that ensures the global optimum of the relaxed optimization model. Second-order cone programming (SOCP) has demonstrated to be an efficient alternative to cope with the non-convexity of the power flow equations in power distribution networks. Of relatively new interest to the power systems community is the extension to MI-SOCP models. The proposed model is an approximation. However, numerical validations in the IEEE 33-bus and IEEE 69-bus test systems for unity and variable power factor confirm that the proposed MI-SOCP finds the best solutions reported in the literature. Being an exact technique, the proposed model allows minimum processing times and zero standard deviation, i.e., the same optimum is guaranteed at each time that the MI-SOCP model is solved (a significant advantage in comparison to metaheuristics). Additionally, load and photovoltaic generation curves for the IEEE 69-node test system are included to demonstrate the applicability of the proposed MI-SOCP to solve the problem of the optimal location and sizing of renewable generators using the multi-period optimal power flow formulation. Therefore, the proposed MI-SOCP also guarantees the global optimum finding, in contrast to local solutions achieved with mixed-integer nonlinear programming solvers available in the GAMS optimization software. All the simulations were carried out via MATLAB software with the CVX package and Gurobi solver
Optimal Number, Location, and Size of Distributed Generators in Distribution Systems by Symbiotic Organism Search Based Method
This paper proposes an approach based on
the Symbiotic Organism Search (SOS) for optimal determining
sizing, siting, and number of Distributed
Generations (DG) in distribution systems. The objective
of the problem is to minimize the power loss of the
system subject to the equality and inequality constraints
such as power balance, bus voltage limits, DG capacity
limits, and DG penetration limit. The SOS approach is
defined as the symbiotic relationship observed between
two organisms in an ecosystem, which does not need the
control parameters like other meta-heuristic algorithms
in the literature. For the implementation of the proposed
method to the problem, an integrated approach of
Loss Sensitivity Factor (LSF) is used to determine the
optimal location for installation of DG units, and SOS
is used to find the optimal size of DG units. The proposed
method has been tested on IEEE 33-bus, 69-bus,
and 118-bus radial distribution systems. The obtained
results from the SOS algorithm have been compared to
those of other methods in the literature. The simulated
results have demonstrated that the proposed SOS
method has a very good performance and effectiveness
for the problem of optimal placement of DG units in
distribution systems
MULTI-OBJECTIVE OPTIMAL CAPACITY AND PLACEMENT OF DISTRIBUTED GENERATORS IN THE POWER SYSTEM NETWORKS USING ATOM SEARCH OPTIMIZATION METHOD
Nowadays, renewable energy sources become a significant source of energy in the new millennium. The continuous penetration of dispersed resources of the reactive power into power systems is predicted to introduce new challenges. Power loss mitigation and voltage profile development are the major investigation challenges
that recently attracted the attention of researchers in the field of power systems. Distributed generation (DG) is widely preferred because it is a highly effective solution that strengthens the performance of power system networks. This multiobjective function study aims to minimise power losses in the feeders, sustain voltage levels and reduce the application cost of DGs by adapting the atom search optimisation simulated on MATLAB software. Two different IEEE power test systems, namely, a 33 bus radial distribution system (RDS) and a 14-bus power system that hosts 1, 2 and 3 DGs in both systems, are demonstrated in this research. Correspondingly, backward–forward sweep and Newton–Raphson power flow methods are used for each system. The proposed technique is compared with the genetic algorithm particle swarm optimisation (GA-PSO) method. Results depict the effectiveness of the proposed method in minimising system power losses and in regulating the voltage profile where the power loss reduction is 25.38% in the 33 bus RDS using 2 DGs. By contrast, the power loss reduction percentages in the 14 bus system are 0.316% and 0.169% in systems with 1 and 2 DGs, respectively. The voltage profile has been enhanced compared with those in the original case and the results obtained from the GA-PSO method
Optimal DG Location and Sizing for Minimum Active Power Loss in Radial Distribution System using Firefly Algorithm
In this paper, a novel optimization algorithm is presented for optimal location and sizing of Distributed Generation (DG) units on distribution systems. For this purpose, a recently based meta-heuristic called Firefly Algorithm (FA) has been employed to minimize the total active power losses. The results show a considerable improved in voltage profiles of all the buses and enhance the voltage stability index. The investigations were tested on IEEE 33 bus radial distribution system. Simulation results demonstrate the effectiveness of firefly algorithm. Comparison with another method is also given
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