Hybrid Methodology for Optimal Allocation of Synchronous Generator-Based DG

In modern power system, distributed generators (DGs) have become an effective method to reduce active power loss, improve voltage stability, etc.. In this paper, a new Improved Firefly Algorithm (IFA) is proposed to obtain optimal placement and capacity of DGs to improve power flow distribution and voltage stability, reduce active power loss in distribution system. Compared with the standard Firefly Algorithm (FA), the proposed IFA has better convergence speed and can obtain a smaller active power loss. In addition, the power loss sensitivity factors are introduced to steer the algorithm to seek the best position about the DGs. In order to verify the performance of IFA, IFA and FA are tested on the standard IEEE-33 and IEEE-69 systems to solve the problem of joint configuration optimization of multiple generators. The results of simulation show the active power loss of the distribution system decreases and the voltage stability of the system node improves. By comparing the results of the IFA and FA, a conclusion can be reached, the IFA is more effective and more suitable to reduce active power loss by reasonably allocating the capacity and placement of DGs than FA.

Distributed generation (DG), including photovoltaics (PVs), wind turbines (WTs) are becoming vital for Active Distribution Networks (ADN). Therefore, optimal sizing and allocation of these units can improve voltage profiles and reduce active power losses. This study concentrates on optimal allocation and sizing of two kinds of DG units (PVs and WTs) with 1 MW maximum size limits, due to the regulations in Turkey. The Whale optimization algorithm (WOA) and Grey wolf optimization algorithm (GWO) are used as optimization tools to minimize active power losses of 33 and 69 Bus Test Systems. The performance analysis of the methods are performed through simulations and the numerical results are compared in terms of optimal OF values and convergence characteristics.

This paper addresses a new methodology for the simultaneous optimal allocation of DSTATCOM and DG in radial distribution systems to maximize power loss reduction while maintaining better node voltage profiles under multilevel load profile. Cat Swarm Optimization (CSO) is one of the recently developed powerful swarm intelligence-based optimization techniques that mimics the natural behavior of cats but usually suffers from poor convergence and accuracy while subjected to large dimension problem. Therefore, an Improved CSO (ICSO) technique is proposed to efficiently solve the problem where the seeking mode of CSO is modified to enhance its exploitation potential. In addition, the problem search space is virtually squeezed by suggesting an intelligent search approach which smartly scans the problem search space. Further, the effect of network reconfiguration has also been investigated after optimally placing DSTATCOMs and DGs in the distribution network. The suggested measures enhance the convergence and accuracy of the algorithm without loss of diversity. The proposed method is investigated on 69-bus test distribution system and the application results are very promising for the operation of smart distribution systems.

Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DNs.

Distributed Generation (DG) generally means power generation in consumption locality. But sometimes, it Told technologies that use renewable energies for power generation. This paper presents a method for optimal sitting and sizing of DG in distribution systems. In this paper, our aim would be optimal distributed generation allocation and sizing for several parameters, include: voltage profile improvement, loss reduction, and THD (Total Harmonic Distortion) reduction in distribution networks. Particle Swarm Optimization (PSO) is used as the solving tool, which referring determined aim, the problem is defined and the objective function is introduced. Considering the fitness values sensitivity in PSO algorithm process, it is needed to apply load flow and harmonic calculations for decision-making. Finally, the feasibility of the proposed method is demonstrated for typical distribution network, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method is indeed capable of obtaining higher quality solutions efficiently.

Concept of renewable hybrid energy systems have attracted many utilities and implemented by them too. With such energy systems, customers are not only supplied more economically but also more reliably. Moreover, optimal placements of energy storage systems (ESSs) increase the reliability of such hybrid renewable system up to a great extent. Therefore, this paper presents a new approach for optimal placement and sizing of energy storage systems (ESSs) in hybrid renewable radial distribution system to improve the reliability of such system without violating the system constraints. The cost of energy not supplied (CENS) associated with power service interruption and power shortage is also been considered in the objective function during placement of ESSs. Hence, the proposed optimal placement planning of ESSs is presented with an aim of minimizing the objective function includes cost of energy not supplied (CENS), investment cost and operational cost of ESSs, and power loss in distribution system. It is to be noted that the Particle swarm optimization (PSO) technique is adopted to minimize the objective function. The presented methodology is demonstrated by considering several case studies on 11 kV, 30 bus radial distribution system. Further, a rigorous sensitivity analysis is performed by limiting the number of applied ESSs and varying the maximum capacity of participating ESSs.

Now a days the usage of electrical power is increasing enormously and it is also becoming highly impossible in imagining the world without the electricity. So here is one of the techniques for maintaining the continuous supply i.e., by generating the electrical power in the distribution premises. Distribution Generation has been growing rapidly in power systems due to their capable solution for the issues like power system deregulation, to meet the demand of electric power and shortages in the electric power transmission capacities. Concept of Distributed generation (DG) plays an effective key role in the distribution networks. Among many of their merits, real power loss reduction, THD reduction voltage sag and voltage swell reduction and voltage profile improvement can be the salient merits of DG. Improper location of DG sources in to the electrical power system would lead to the increase energy or power loss, and also jeopardize the system operation. In this paper, determination of DG location is done by using Sensitivity Analysis and corresponding DG size is found by using Particle Swarm Optimization (PSO) to mitigate the real power losses in the distribution networks. Standard IEEE 15 bus test system is chosen to test the proposed PSO — Sensitivity Analysis approach. The results so obtained show the enhancement of voltage profile, reduction of real power loss using MATLAB.

Several studies of distribution network enhancement focused only on the optimization of either the integration of distributed generations (DGs) or system reconfiguration (SRC). However, very few researches have been exerted for incorporating both where this merge represents further complexities in searching for maximizing their benefits and minimizing the investments. This paper sheds light on the simultaneous allocation of DGs with SRC in distribution systems. It suggests a multi-objective formulation considering multiple objectives: active power loss minimization, total cost reduction, and voltage profile improvement. For solving this problem, an improved spotted hyena optimizer (ISHO) is developed. The SHO demonstrates a good feature in finding the optimal or nearly optimal subset with simple structure to minimize the given fitness function. To support the exploration characteristics of the proposed ISHO, it augments a switch strategy in the updating mechanism of the spotted hyena’s positions and random positions are generated instead of the violated spotted hyenas. The simulations are investigated for the integration of a single and multiple DGs with SRC for two standard systems, which are IEEE 33 and 69-node test systems. The obtained results prove the efficiency of the suggested method for the network manager to find the optimal DGs allocation with SRC considering multiple criteria. These results are compared with previous literatures to demonstrate the effectiveness of the proposed algorithm.

In order to evaluate the influence factors of power loss in distribution system, an useful software is designed to analyze the power loss problems in this paper. First of all, the influence factors in distribution system are classified into device and operation aspects according to the relationship with the status of the distribution system. Then, simplified power loss models of differentiated distribution network are established based on economic radius of power supply area. Next, influence mechanism of each main influence factor is analyzed. On the basis of sensitivity algorithm, the evaluation software is designed and applied to a simulation example of type E distribution network. This designed software provides a powerful tool for power loss analysis in the differentiated and diversified distribution system.

Guest Editorial

Distributed Generations (DGs) are most commonly used in Advanced Power Distribution System (APDS) for improving the voltage level and minimizing the power loss in distribution system. This paper represents a modified Teaching Learning Based Optimization Technique (TLBO) to reconfigure the distribution system and find the optimal placement and sizing of DGs to minimize the total system loss simultaneously. In modified TLBO radial trees are consider as a initial population and possible paths between nodes are considered as design variables for finding the optimal reconfigured route. A shift operator is used to change the switch position in order to find the desired switch combination. A simultaneous reconfiguration with DG placement is done by the proposed method with different sizes of DGs in search of optimal solution. Proposed approach is illustrated on the standard IEEE-33 bus test system considering real-time design practices.

A novel stochastic fractal search algorithm for optimal allocation of distributed generators in radial distribution systems

High current amounts, low voltage level and radial structure assign high percentage of active power loss in distribution system. There are many means in reducing the active power loss of distribution networks; this paper introduces an ant colony algorithm to solve the optimal network reconfiguration and capacitor placement problem for power loss reduction and voltage profile enhancement in distribution networks. The ant colony algorithm was inspired from natural behavior of the ant colonies on how they find the food source and bring them back to their nest by building the unique trial formation. Ants of artificial colony are able to search for the successively shorter feasible routes by using information accumulated in the form of a pheromone trial deposited on the edge of their traveling path. The proposed approach is demonstrated employing one distribution network. Computational results show that by taking into account feeder reconfiguration and capacitor placement simultaneously, one can be minimize losses more efficiently by considering them separately.

This paper presents a study of control techniques required for microgrid operation using Automated Demand Side Management (ADSM) control strategy. The control strategies are very useful for smooth operation of microgrid in different modes highlighting basic control issues to maintain power quality and the pre-set conditions which will determine the connectivity of microgrid with the load and main utility grid. Two control strategies are proposed using Simulink-Matlab based on automated demand side control. The first one is useful to prevent abnormal load growth which considers capacity of real power generation as reference for further addition of load. The second strategy presented for automated demand side control which decides connection of additional load considering total active power loss in distribution system as reference.

ABSTRACTMeta heuristic algorithms have been introduced as a powerful method to solve the nonlinear optimization problems. These algorithms have been employed in many complex engineering problems due to their high capability in finding the solutions and reaching the optimal results within a short period of time. Optimization of distributed generation units in distribution systems, which have profoundly impacted on the system losses and voltage profile, is one of these nonlinear problems. In this study, a novel objective function was proposed for optimization procedure by meta-heuristic algorithms. The related objective function consists of the total cost of distributed generation units, cost of the purchased natural gas, cost of distribution system power losses, and penalty for greenhouse gas emissions. The electrical, cooling, and heating loads were considered in this study. In the distribution system, the waste and fuel cell were used to supply the required heating and cooling loads. The meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) were employed to find the optimal location and size of distributed generation units in a distribution system. A detailed performance analysis was done on 13 bus radial distribution system. The performances of three algorithms were compared with each other and results showed that the PSO was the fastest; and had the best solution and optimum results. Furthermore, the PSO reached the optimum solution in a fewer number of iterations than the GA and ICA algorithms.