Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bioinspired optimization algorithms

Purpose:The increase in plug-in electric vehicles(PEVs)is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior.To address this problem,the present work mainly focuses on optimal integration of distributed generators(DG)into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily(24 h)power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approach:To achieve the desired outcomes,an efficient weighted factor multi-objective function is modeled.Particle Swarm Optimization(PSO)and Butterfly Optimization(BO)algorithms are selected and implemented to minimize the objectives of the system.A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system.The simulations are carried out using MATLAB software.Findings:The simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects.Among PSO and BO,BO is comparatively successful in achieving the desired objectives.Originality/value:The main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential,industrial and commercial loads.Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

Distributed Generators Location and Capacity Effect on Voltage Profile Improvement and Power Losses Reduction Using Genetic Algorithm

This paper presents a powerful approach to find the optimal size and location of distributed generation units in a distribution system using GA （Genetic Optimization algorithm）. It is proved that GA method is fast and easy tool to enable the planners to select accurate and the optimum size of generators to improve the system voltage profile in addition to reduce the active and reactive power loss. GA fitness function is introduced including the active power losses, reactive power losses and the cumulative voltage deviation variables with selecting weight of each variable. GA fitness function is subjected to voltage constraints, active and reactive power losses constraints and DG size constraint.

Impact of the penetration of distributed generation on optimal reactive power dispatch

Optimal reactive power dispatch(ORPD)is a complex and non-linear problem,and is one of the sub-problems of optimal power flow(OPF)in a power system.ORPD is formulated as a single-objective problem to minimize the active power loss in a transmission system.In this work,power from distributed generation(DG)is integrated into a conventional power system and the ORPD problem is solved to minimize transmission line power loss.It proves that the application of DG not only contributes to power loss minimization and improvement of system stability but also reduces energy consumption from the conventional sources.A recently proposed meta-heuristic algorithm known as the JAYA algorithm is applied to the standard IEEE 14,30,57 and 118 bus systems to solve the newly developed ORPD problem with the incorporation of DG.The simulation results prove the superiority of the JAYA algorithm over others.The respective optimal values of DG power that should be injected into the four IEEE test systems to obtain the minimum transmission line power losses are also provided.

Hybrid intelligence approach for multi-load level reactive power planning using VAR compensator in power transmission network

This paper formulates and solves a techno-economic planning problem of reactive power (VAR) in power transmission systems under loadings. The objective of the proposed research work is to minimize the combination of installation cost of reactive power sources, power losses and operational cost while satisfying technical constraints. Initially, the positions for the placement of reactive power sources are determined technically. Different cost components such as VAR generation cost, line charging cost etc. are then added in the total operating cost in a most economical way. Finally, the optimal parameter setting subjected to reactive power planning (RPP) is obtained by taking advantages of hybrid soft computing techniques. For the justification of the efficiency and efficacy of the proposed approach the entire work is simulated on two inter-regional transmission networks. To validate the robustness and ease of the soft computing techniques in RPP the responses of benchmark functions and statistical proof are provided simultaneously.

The analysis of distributed photovoltaic access system schemes

As the share of photovoltaic power generation in power system has increased year by year, the optimization choice of access system schemes become one of the first and most important problems in grid before admitting photovoltaic power generation. Therefore, this article takes a proposed distributed photovoltaic as an example to research and analyze two kinds of high density multiple access points distributed photovoltaic access system schemes. The emphasis is making a comprehensive comparison and selection among the aspect of active power loss and economic benefit, etc. In the premise of ensuring the normal power generation of the photovoltaic system, it puts forward the recommended scheme that can help to spontaneous self-consumption, elimination on the spot, effectively decrease network loss and economic benefit.

Optimal Site and Size of Distributed Generation Allocation in Radial Distribution Network Using Multi-objective Optimization

Distributed generation(DG)allocation in the distribution network is generally a multi-objective optimization problem.The maximum benefits of DG injection in the distribution system highly depend on the selection of an appropriate number of DGs and their capacity along with the best location.In this paper,the improved decomposition based evolutionary algorithm(I-DBEA)is used for the selection of optimal number,capacity and site of DG in order to minimize real power losses and voltage deviation,and to maximize the voltage stability index.The proposed I-DBEA technique has the ability to incorporate non-linear,nonconvex and mixed-integer variable problems and it is independent of local extrema trappings.In order to validate the effectiveness of the proposed technique,IEEE 33-bus,69-bus,and 119-bus standard radial distribution networks are considered.Furthermore,the choice of optimal number of DGs in the distribution system is also investigated.The simulation results of the proposed method are compared with the existing methods.The comparison shows that the proposed method has the ability to get the multi-objective optimization of different conflicting objective functions with global optimal values along with the smallest size of DG.