Energy Loss Minimization in Distribution Systems Utilizing an Enhanced Reconfiguration Method Integrating Distributed Generation

Actual operating conditions of Secondary Radial Distribution System are unbalanced in nature and for stable operation of the system, the distribution system engineer should account for operational imbalances within the system. With the continuing increase in load demand, future expansion of the network is to be done basing on the load flow study of the distribution system network and thus, it is one of the most important research fields for electrical engineering. Optimally placed Distributed Generation (DG) units reduce system losses and lead an improvement in the voltage profile, system reliability, load ability, voltage stability, voltage security and power quality. There are several techniques available for the optimum size and placement of DGs in the unbalanced radial secondary distribution system (URSDS). In this paper, in URSDS, optimal placement of DGs is determined by using modified sensitivity factor method and optimal size is determined by using Adaptive Particle Swarm Optimization (APSO) technique. The proposed technique is tested on 25 & 37 bus URSDS. Results show that network power losses are reduced and voltage profiles are improved in each phase with optimal allocation.

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.

Increase in demand of electricity has resulted in inclusion of many new ways of generation, known as Distributed Generation (DG) sources. But, the interconnection of these units is a major issue. In this paper, distribution system load flow is performed using the various models of DG units for getting more accurate results. Differential evolution algorithm is used for finding the optimal site & size of DG units. Here, the 69-bus radial distribution test system is considered for analysis using MATLAB.

The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods.

Optimal selection of distributed generating units and its placement for voltage stability enhancement and energy loss minimization

Large-scale integration of Wind Generators (WGs) with distribution systems is underway right across the globe in a drive to harness green energy. The Doubly Fed Induction Generator (DFIG) is an important type of WG due to its robustness and versatility. Its accurate and efficient modeling is very important in distribution systems planning and analysis studies, as the older approximate representation method (the constant PQ model) is no longer sufficient given the scale of integration of WGs. This thesis proposes a new three-phase model for the DFIG, compatible with unbalanced three-phase distribution systems, by deriving an analytical representation of its three major components, namely the wind turbine, the voltage source converter, and the wound-rotor induction machine. The proposed model has a set of nonlinear equations that yields the total three-phase active and reactive powers injected into the grid by the DFIG as a function of the grid voltage and wind turbine parameters. This proposed model is integrated with a three-phased unbalanced power flow method and reported in this thesis. The proposed method opens up a new way to conduct power flow studies on unbalanced distribution systems with WGs. The proposed DFIG model is verified using Matlab-Simulink. IEEE 37-bus test system data from the IEEE Distribution System sub-committee is used to benchmark the results of the power flow method.

Variations of load demands and generations bring multiple uncertainties to power system operation. Under this situation, power flows become increasingly uncertain, especially when significant distributed generations (DGs), such as wind and solar, are integrated into power systems. In this paper, a Complex Affine arithmetic based unbalanced Three-phase Forward-Backward Sweep power flow model (CATFBS) is proposed to study the impacts of uncertainties in unbalanced three-phase distribution systems. An index of Relative Influence of Uncertain Variables on Outcomes (RIUVO) is proposed for quantifying the impacts of individual uncertain factors on power flows and bus voltages. The CATFBS method is tested on the modified IEEE 13-bus system and a modified 292-bus system. Numerical results show that the proposed method outperforms the Monte Carlo method for exploring the impacts of uncertainties on the operation of distribution systems. The proposed CATFBS method can be used by power system operators and planners to effectively monitor and control unbalanced distribution systems under various uncertainties.

The unbalanced situation in power distribution systems is affected to distribution system operation considering efficiency and quality. This paper aims to provide an insight into the power loss reduction in unbalanced distribution systems through phase balancing using a genetic algorithm (GA) and an expansion of lines. The three different scenarios of phase balancing are proposed based on the possibilities of incoming lines (i.e., single-phase, bi-phase, or three-phase) for each MV/LV distribution transformer. To validate the proposed method, this concept is to be performed with the standard IEEE unbalanced distribution systems such as IEEE 13 and IEEE 37 respectively. The simulation results of these cases are compared considering minimum power losses and energy losses.

Optimum placement of biomass DG considering hourly load demand

This paper investigates the loss reduction potential of primary distribution systems with distributed generation (DG) units, capacitors and network reconfiguration. A methodology is proposed to combine different types of DG units and capacitors for minimising losses. A combination of reconfiguration, DG and capacitor placement to obtain high loss reduction is also presented. The results obtained on a 33-bus test distribution system with constant load condition and a 687-bus real distribution system with varying load conditions show significant loss reduction using the proposed approach. The combination of type 1 DG (i.e. DG capable of delivering real power only) and capacitor placement or reconfiguration with type 1 DG and capacitor placement can be viable for minimising losses.

Proliferation of Distributed Energy Resources (DERs) and Phasor Measurement Units (PMUs) into electric power distribution grids presents new opportunities for utility operators to manage distribution systems more effectively. In this work we formulate an Optimal Power Flow (OPF) approach that manages DER power injections to minimize the voltage phasor difference between two nodes on a distribution network with the goal of enabling efficient network reconfiguration. To accomplish this, we develop a linear model that relates voltage phase angles to active and reactive power flows in unbalanced distribution systems. Used in conjunction with existing linearizations relating voltage magnitudes to power flows, we formulate an OPF capable of minimizing the voltage phasor difference between two points in a network. In simulations, we explore the use of the developed approach to minimize the phasor difference across switches to be closed, thereby providing an opportunity to automate and increase the speed of reconfiguration in unbalanced distribution grids.

Recently the number of distributed generators (DG) being integrated in to the distribution system has increased. The distribution system is no longer a single source system. Thus there has to be a change in strategy for distribution analysis. This would require a fast power flow solution with DG. This paper presents the developed three phase unbalanced power flow algorithm with the choice of modeling DG as PQ or PV node. This gives flexibility in modeling various types of DGs. The distribution system model includes modeling of lines, cables, transformers, switches, capacitors, loads and DGs. IEEE test cases are designed for testing the three phase unbalanced algorithm for single source systems. A DG was introduced in the IEEE 13-node test case and the results demonstrate the effect the DG has on voltage profile and currents. Some of the results were verified using radial distribution analysis package (RDAP)

This work proposed a method for the best placement and sizing of the Distributed Generations (DGs) in the distribution systems network with aim to minimize the the total system losses. The total system losses includes the resultant of real power loss and reactive power loss. The main aim of presented work is to minimize the system losses and also to enhance the system's voltage profile. This paper is made from the fact that DGs are more advantageous to achieve the demand of power that are close to the load centers compared to those at the centralized power generation. This work considers 85-bus radial distribution network (RDN) for DG planning based on objective to minimize overall MVA losses of system. The desired objective is carried out with the help of MATPOWER/MATLAB and the comparison of obtained results with DG using cuckoo search algorithm is presented related to the base case results. At last, it can be concluded that cuckoo search is an efficient methodology for optimal size and placement of DGs.

Reliability Assessment of Distribution Networks with Distributed Generations using Monte Carlo Method

Rescent years, some DG (distributed generation) have been introduced in distribution systems, and the voltage violation problem has been at stake for the distribution system. The impact of DG integration on distribution system is studied in this paper, and the transient stability mechanism of DG is analyzed theoretically. This paper investigates the dynamic analysis of a DVR when applied to correct the voltage distortion in distribution system. Then the model of DG is built by Matlab/Simulink. And it is used to study the impact of DG integration on the distribution system. Then the voltage regulation-dynamic voltage restorer (DVR) is introduced to distribution network with distributed generation. A calculation method for DVR in distribution system with distributed generation is proposed, which is based on a semi-period algorithm to extract fundamental component from fault signal then extract the fundamental positive sequence voltage by the decomposition instantaneous link, and then the quantity of voltage compensation can be solved by direct detection algorithm. Simulation results show that the introduction of DVR (dynamic voltage restorer) to the distribution system with distributed generation is good for voltage regulation and making a good guarantee of the voltage quality of sensitive loads; and the algorithm for DVR is simple and effective.