Dynamic sub-transmission system expansion planning incorporating distributed generation using hybrid DCGA and LP technique

The optimal location and capacity of distributed generation (DG) is essential for electric power systems especially to satisfy the load demand. This paper proposed the hybrid optimization method to obtain optimal location and capacity of DG considering energy storage in dynamic systems. Artificial bee colony (ABC) algorithm and quadratic programming (QP) is applied. The objective functions of the proposed method minimize the cost of distribution line losses and load shedding. ABC algorithm is used to find the optimal location and capacity of DG. Then, objective functions are calculated using QP with dynamic condition of the load profile for 24 hours. Load shedding is generated based on power generation which can’t satisfy the load demand. The proposed method has been implemented in a modified IEEE 30 bus.

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

The continuous growth of power demand poses new challenge to the efficiency of power system. Therefore, distributed generation (DG) is widely concerned because it can provide clean, reliable and economic power. However, the location and sizing of DG connected to the grid shall be properly selected. In this paper, the voltage stability index and the line loss reduction rate are considered synthetically, and the two indexes are transformed into a single index (VSLR) to determine the optimal location of DG. According to this index, the node with high sensitivity and high average power loss reduction rate are selected as the optimum location of DG connected to the power grid, and the optimal access capacity of DG is determined by the algorithm proposed in this paper. The simulation experiments of IEEE30-bus are carried out with MATLAB\\ Matpower toolbox, and the results are compared with the existing research results. The results show that the method of studying the optimal location and sizing of DG in this paper emerges better performance.

Optimal distributed generation placement in distribution system to improve reliability and critical loads pick up after natural disasters

Several extreme weather disasters have demonstrated that grid resilience is a significant aspect of modern power systems to work upon. In event of grid failure, back up power sources must support the network. Thus, optimal planning of distributed generations (DGs) in power distribution networks need to be done so that in addition to grid power support, ancilliary services such as minimized real power losses and voltage deviations are attained. In the present work, combined resilience evaluation and analytic operational enhancement strategy has been studied in a radial power distribution network. To achieve overall minimal power losses and better voltage control, the Marine Predator (MPA) meta-heuristic optimization technique is adopted. The MPA is utilised to discover the best DG location and size at the same time. The proposed method is applicable to both single and multiple DG unit sizing and siting. Furthermore, to calculate the best size of DG unit, the suggested method just requires the results of the base case load flow. The proposed approach is tested on a IEEE 33-bus radial distribution test system.

Due to liberalization in the power market stake of the Distributed Generation (DG) in the power industries has increased radically. Integration of DG will result is the change in the operating conditions of the existing power system network. Due to this DG has drawn attention of utility providers, policy makers and, to effectively use the DG, several researchers also. Inclusion of DG in the existing power system may enhance its power transfer capacity, voltage profile, reliability and it can also reduce the overall system losses if installed in proper capacity and at proper place. Benefits of DG can be efficiently extracted only if an appropriate capacity of DG is introduced in the existing power system at appropriate place. This chapter proposes an analytical and heuristic approach suggesting the optimum size and location of type-1 and type-2 DG. The proposed method is implemented on the IEEE-13 bus radial distribution network (RDN) and result shows the validity of the proposed method.

To minimize power losses, it is important to determine the location and size of local generators to be placed in unbalanced power distribution systems. Because of some inherent features of unbalanced distribution systems, such as radial structure, large number of nodes, and a wide range of X/R ratios, the conventional techniques developed for the transmission systems generally fail to determine optimum size and location for distributed generation (DG). This article presents a simple method for investigating the problem of contemporaneously choosing the best location and capacity of DG in three-phase unbalanced radial distribution systems (URDS) for power loss minimization and to improve the voltage profile of the system. The best DG location is determined by using voltage index analysis, and capacity of DG is computed by a variational technique algorithm according to the available standard capacity of DG. This article presents the results of simulations for 25-bus and IEEE 37-bus unbalanced radial distribution system.

A new emerging trend of distribution networks is to use small generating units, known as distributed generation (DG), operating in parallel with the main grid. This kind of distribution networks has enabled DG to support power systems in fulfilling their requirements to increase power output as well as quality of power supply. In order to maximise benefits from the DG system, proper DG planning is necessary. The main purpose of this research is to maximise voltage support through optimal sizing and location of DG. A new methodology is developed to determine an optimal DG size and DG location on the distribution feeder for optimising system voltages. The developed technique is tested on a long radial feeder of a practical system and results are reported.

This paper investigates the impacts of circuit breaker performance on voltage instability corrective actions for power systems with distributed generations (DGs) under contingencies, and proposes a new computer network-aided strategy for stability improvement. This strategy aims to save a distribution system from imminent voltage collapse due to contingencies occurring in subtransmission system, distribution system, or DG systems from renewable energy resources. The proposed strategy is based on an instability corrective action using an optimal load shedding operation and a stability enhancive action using available capacity of DGs. This paper shows the use of state-of-the-art digital signal processing technology for determination of correct stability controls, and the application of modern computer networking technology for monitoring of distribution system operating states and transmitting of data and stability control commands. This paper presents a new two-layer fault-tolerant architecture designed for network-aided voltage instability corrective actions.

The use of Distributed Generators (DGs) in distribution system has increased at fast pace in recent times. These DGs can work both as grid connected or standalone mode. Thus when it comes to DG placement, getting the optimal location for its placement and the optimal sizing becomes very important. In this paper, finding the optimal location and optimal size of DGs is dealt keeping active power loss as the objective, considering different load models. A novel method called Backtracking Search Optimization Algorithm is used to find the most significant positions for DG placement. DGs supplying both active and reactive power have been studied. Case studies have been done about the effect of 1, 2 or 3 DGs on the IEEE 69 bus test system for DGs operating at 0.85 pf. on different load models.

Rapid depletion of fossil based oil, coal and gas reserves and its greater demand day by day necessitates the search for other alternatives. Severe environmental impacts caused by the fossil fuel based power plants and the escalating fuel costs are the major challenges faced by the electricity supply industry. Integration of Distributed Generators (DG) especially, wind and solar systems to the grid can offer viable solutions to address these challenges. This paper focuses on a new simple and fast load flow algorithm for finding the voltage profile and power losses in a radial distribution system (RDS). Backward Forward Sweep Algorithm (BFSA) has been practiced to find the voltage profile and power losses with the integration of various size of DG at different locations. Genetic Algorithm (GA) based BFSA is adopted in finding the optimal location and sizing of DG to attain an improved voltage profile and considerable reduced power loss. Simulation results show that the proposed algorithm is more efficient in finding the optimal location and sizing of DG in 15-bus radial distribution system. The obtained results are compared with the system modeled in Simulink.

The Distributed Generator (DG) unit nowadays have become an integral part of any power system network. For the effective management of power flowing in a network, the size and position of the DG unit play the important role, but DG integration on the other side, also affects the operating characteristics of the system such as losses may be increased or decreased, variation in voltage and increases the cost for consumers or generators. To eliminate such effects, it is proposed that DG units can be integrated with FACTS devices, as these devices provide more reliability to the network. On the other side, load demand is also increasing day by day therefore, to fulfil the demand it is required to make use of these DG units at various locations. In this paper, an analytical approach with optimal power flow (OPF) method is used in MATLAB environment to identify the optimal location of DG and FACTS devices in maximum loading condition.

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.

Technical advances and institutional changes in the electric power industry have resulted in a constantly increasing penetration of distributed generation (DG) integrated with distribution networks. For the connection of new DG installations to the networks a variety of factors have to be taken into account, including technical requirements imposed by utilities to ensure that the DG doesn't adversely affect the operation and power quality of the networks. The use of power electronics at the front end of many DG types poses harmonic control requirements for their insertion into the grid. The attention is focused on the harmonic emissions. This paper introduces a harmonic based strategy for the best location and size of DG sources integrated with distribution network. Also, this paper introduces a new index called unified harmonic index (UHI) that is based on the values of voltage harmonic distortion, current harmonic distortion, and hosting capacity. The procedure is used to rank the available locations of DG and then examined optimum capacities of DGs in the ranked sites based on both the UHI and harmonic limit consideration in IEEE-519. The strategy is applied to an actual Egyptian feeder to indicate the best location and size of DG connected to the feeder as a preliminary study for the distribution network in Egypt for the near future as planned to allow the DG to be inserted.

To manage the microgrid effectively, it is important to get the power information among multiple distributed generation (DG) units. However, when the droop control method is adopted, as the corresponding DG units cannot be simply modeled as VF or PQ buses, the conventional power flow algorithm may become inapplicable. To solve this issue, a time domain iteration (TDI) based power flow algorithm is hereby proposed. Firstly, a microgrid model is prepared, containing a network model and several DG unit models. Then, the proposed TDI is executed for power flow calculation, which mimics the real-time operation of microgrids. In each iteration, the DG unit models input voltages and currents to the network model; then the network model changes its state accordingly and feeds related parameters back to those DG unit models. As the DG unit models simulate the behavior of actual DG units, the proposed algorithm is not limited to the droop control governed microgrids. Moreover, as the TDI has definite physical meaning, convergence problems can be avoided. Finally, the validity of the proposed power flow algorithm is verified through the Matlab simulation results from an 8-bus microgrid system.