Optimal Placement of Distributed Generation Resources

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

With the increase of distributed generation(DG) in distribution networks, the impact of these DGs in the power grid is also increasingly significant. In particular, with the development of intermittent DG which is more difficult to predict power generation, it challenges the reliability of the power grid. According to the characteristics of the grid with DG, this paper simplifies the distribution network to a system which takes autonomous control units(ACU) as the main component. Then the power grid is divided and classified into autonomous control regions(ACR) and calculates the reliability indices of different classes of ACR. And through the reliability index to establish an optimization model, eventually configure the system's DG capacity and improving the system's reliability.

Distributed Generation (DG) offers many benefits to the distribution networks for instance increase reliability, power losses reduction and voltage improvement. Nevertheless, one of the issues still faced by the utilities is uncertainty to determine optimal output power and location of DG. Improper coordination of the DG can deteriorate the distribution performance. Therefore, many researchers have suggested various tools to compute optimally the DG coordination. Usually, calculation of location and size of the DG are determined separately, which means different method is applied for sizing and location. Thus, it may result the solution trap in local optimum. This manuscript provides a solution to the output power and location of multiple DG sources by using modified Artificial Bee Colony. Furthermore, in the analysis, several case studies with different load profile are presented. The solution is simulated by using MATLAB programming and tested on 33-bus distribution system.

Properly sited distributed generation (DG) can increase the feeder capacity limit, but this does not necessarily produce an improvement in system reliability or power quality, as quantified by standard indices. The DG may have a positive impact on reliability through faster system restoration following a fault. The DG can also improve reliability for the owner, and may reduce the severity of voltage sags near the DG. Indices normalized to the number of customers dilute these positive benefits. The DG often has a negative impact on reliability indices through sympathetic tripping, required changes to utility overcurrent device settings, and increased fuse blowing. The utility cannot assume DG automatically improves system reliability, and action may be required to ensure that reliability does not actually degrade for other customers.

Position and capacity of the distributed generations (DG) have a direct impact on static security of the distribution network. A method to calculating maximum allowed output of DG for each node using voltage sensitivity and current sensitivity based on linearized power equations is proposed, and it reduces the computation obviously. The calculation results are analysised. The method of installing DSTATCOM to improve maximum allowed power of DG is analysised. The accuracy and effectiveness of this method is tested by IEEE-33 system.

Although Thailand has significant potential resources for distributed power generation, the development of renewable distributed generation faces several difficulties. This paper examines and discusses conflicts between the development of distributed power and centralized power system, on both structural issues, especially asymmetric power within the Thai power system, and instrumental issues, such as unfavorable pricing system. Therefore, to support distributed power generation and reduce asymmetric power, the clear division of roles and authorities within the Thai power sector, including the establishment of a regulatory body, is highly needed. The internalization of external benefits from distributed generation in pricing mechanism and the comprehensive and interactive R&D programs are also essential to fulfill the target of the Thai government.

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.

Interest in distributed generation (DG) arose in the 1990s with the deregulation of the electricity industry and the development of improved DG technologies. Furthermore, the appearance of high-technology loads requires an increase in the reliability of the power system. In this new scenario, it is important to consider DG in the planning of distribution networks. For example, a utility can install DG to provide additional capacity to feeders or substations. Additionally, DG can be used to improve the restoration capability of the distribution networks, that is, DG can eliminate network constraints (voltage drop or feeder loading) during the restoration process. Consequently, DG can improve the reliability indices associated with the restoration time. In this paper, a methodology to estimate the impact of DG on the reliability indices of distribution networks is proposed. This impact is assessed considering that DG can be connected to an isolated feeder (isolated operation) or to a feeder with tie-lines (interconnected operation). The network constraints model used in this paper is based on a simplified version of the power summation load flow method and compensation techniques. The results with the proposed methodologies, to model the isolated and interconnected operation of DG, demonstrate that DG has a significant impact on the reliability indices of distribution networks. The proposed methodologies have been validated and tested in a large distribution test system. The results show the importance of considering the impact of DG on the reliability indices of distribution networks

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Distributed generation (DG) represents a reliable option for solving current major problems of distribution companies, such as load growth, overloaded lines, quality of supply and reliability. Moreover, it has been proven that the additional benefits brought by DG could be substantial if properly used. This paper addresses the issue of optimizing DG planning in term of DG size and location to reduce the amount of line losses in the distribution networks. The optimization methodology, which is based on the sequential quadratic programming (SQP) algorithm, firstly assesses the compatibility of different generation schemes upon the level of power loss reduction and DG cost. The solutions obtained are finally validated with the constraints of maximum number and size of DG, as well as voltage violation and DG penetration. The proposed method is tested on IEEE 33 bus system, proving that the technique is effective and applicable.

The use of Distributed Generation (DG) throughout the world increasing. DG siting and sizing is an important engineering consideration, which is inherently influenced by the system load and DG power output uncertainties. This paper presents research results of the uncertainty influence on DG allocation problem. This influence is investigated using a constrained optimization problem for power loss reduction. The optimization is performed using Genetic Algorithm. The power system load and DG power output uncertainties are addressed using a possibilistic (α – cut method). The algorithm is applied to realistic distribution system to demonstrate its practical relevance. Results indicate that DG can reduce losses. and that uncertainties play a major role in final optimisation results. This paper contributes to the existing knowledge by applying, to a realistic test power system, a DG allocation method, which considers the influence of load and generation uncertainties on optimization results.

Maximal optimal benefits of distributed generation using genetic algorithms

The Distributed Generators (DGs) are connected in distribution system to reduce the power losses and to improve the reliability of the distribution system. The most important process to decrease the total power loss and to improve the reliability of the system is to identify the proper placement of DG units and also to find the amount of power to be generated by them. A hybrid technique is proposed which includes Particle Swarm Optimization (PSO) and Neural Network. By fixing DGs in suitable optimal locations and by generating power based on the load conditions, the total power loss in the system can be reduced and the system reliability can be improved. The proposed method is tested for different load conditions on IEEE 30 bus system, by connecting one DG, two DGs, three DGs and four DGs in the system. The results obtained show the improved voltage profiles and reliability indices like EENS and ECOST.

This paper first analyses the impact of Distributed Generations (DG) on distribution system loss and voltage profile. Distribution loss depends on various factors such as: the number and locations of DG connections, injected DG powers, distribution system configuration etc. To study the impact of injected DG powers and locations on the distribution loss, a 15-bus radial distribution system is modeled using PSCAD and simulations are carried with varying DG power injections and locations. Next, the optimum sizes of DG are evaluated which gives the minimum loss satisfying various other system constraints. Dynamic Programming approach is used to solve this optimization problem and the subroutine is developed in MATLAB.