A line loss reduction optimization for renewable energy‐based distribution networks using a probabilistic approach

The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration increases, determining the associated impacts on distribution networks is vital. This paper proposes a concept around sphere of influence of single and multiple DG units through line current analysis and integrated Sankey and Geospatial visualisation approaches. With the increasing integration of renewable energy sources and the need for grid stability, it becomes imperative to analyse the sphere of influence of DGs in geographic context. The main contribution of the paper is to present the visualisation techniques to determine the sphere of influence of DGs within power networks by considering single and multiple DGs. The proposed study combines the power of Sankey visualisation, a flow diagram technique, with geospatial data to gain insights into the DG sphere. The advantage of the proposed method is it acts as a visual lens to determine DG clusters, flow of power, coherency analysis, voltage support, resource allocation, operational planning, and grid resilience assessment. The proposed method is tested using 33 bus distribution network and visualisation on a representative geospatial map is carried out using Python library and Tableau.

A major research is going on distributed generation (DG) which utilise renewable energy sources such as solar, wind, fuel cells, etc., for electric power generation. As the renewable energy sources are geographically widely spread and of low capacity, they can be connected to distribution networks. This paper proposes a three-phase power flow algorithm (PFA) for unbalanced radial distribution networks (URDN) with multiple installations of DGs and distribution static synchronous compensator (D-STATCOM). The proposed PFA utilised the special topology of radial distribution networks (RDN) to divide the network into different sections. The bus numbers and branch numbers of these sections are stored in row entries of BUS_NUM and BRANCH_NUM matrices, respectively. These matrices make the implementation of PFA simple. The accuracy of the three-phase PFA without the integration of DGs and D-STATCOM is tested on IEEE-13 bus URDN and the results are found to be very accurate with the literature. Several case studies are carried on IEEE-13 bus URDN with integration of DGs and D-STATCOM. The test results show that the proposed PFA can effectively handle different DG modelling and D-STATCOM and overall energy saving, reduced power loss and improved voltage profile are achieved with these integrations.

This study presents the uncertainty analysis of a distribution network (DNR) caused by unit power output control (UPC) distributed generations (DGs) (solar, wind) and load demand by point estimate method (PEM) based on mixed‐discrete‐based particle swarm optimisation (MDPSO) technique. The uncertainties are taken care by the feeder flow control (FFC) DGs which make the DNR power independent of the main grid, which means the DNR does not exchange any power with the main grid at any load level. To analyse the situation, the load flow technique is modified with introducing and zero bus in the system. and the higher‐order PEM methods are applied in this study for uncertainty analysis. The FFC and the UPC DGs are placed and sized by the MDPSO algorithm. The uncertainty analysis of the system is done based on different objective functions and test cases which are the combinations of active power loss, voltage deviation, and the DG operation cost. The proposed method is applied to the 69‐bus DNR, and the results are compared with teaching learning‐based meta‐heuristic optimisation method. The cumulative distribution function and probability density function of the output random variable are approximate with Gram–Charlier expansion method.

Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

A novel distributed generation planning algorithm via graphically-based network reconfiguration and soft open points placement using Archimedes optimization algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Impact of Distributed Generation on the Electric Protection System

With the setting up of regulatory authorities the cost pressure on the electricity distribution companies will increase further in Germany. Due to political reasons the distributed generation (DG) has increased. Therefore, nowadays many distribution network companies face two important tasks: reduction of network costs to improve the network efficiency and integration of the large-scale DG into existing systems. A long-term planning of the distribution networks can provide an optimal target network for the efficiency analysis of distribution networks and the evaluation of effects, which are caused by the DG. Planning of distribution networks without the DG is known as a non-linear mixed integer problem. Additional technical constraints (such as voltage rising effect, short-circuit current, etc.) and stochastic characteristics of the DG make the planning problem become more complex. In this paper we propose a new efficient two-stage heuristic method, which is based on large neighborhood search and guided local search, for the planning problem of distribution networks with the DG. Technical modeling and stochastic characteristics of the DG are described. Applications of the proposed method for the planning of real German distribution networks with large-scale DG show that distribution network companies can use this method, not only for planning the future optimal network structure but also for analyzing the effects, which are caused by different DG scenarios

The impending deregulated environment facing the electric utilities in the twenty first century is both a challenge and an opportunity for a variety of technologies and operating scenarios. The need to provide acceptable power quality and reliability will create a very favorable climate for the entry of distributed resources and innovative operating practices. Of all the different parts of an electric power system, customers identify closely with the distribution subsystem due to its proximity and visibility on a daily basis. Several recent developments have encouraged the entry of power generation and energy storage at the distribution level. Distributed resources (DR) is a term that encompasses both distributed generation (DG) and distributed energy storage (DS). A distributed utility will use both distributed resources and load management to achieve its goal. In addition, several compact distributed generation technologies are fast becoming economically viable. Integration of DG into an existing utility can result in several benefits. These benefits include line loss reduction, reduced environmental impacts, peak shaving, increased overall energy efficiency, relieved transmission and distribution congestion, voltage support, and deferred investments to upgrade existing generation, transmission, and distribution systems. Benefits are not limited to utility. Customers also benefit from DG in term of better quality of supply at lower cost. Among the many benefits of distributed generation is a reduced line loss. The purpose of this paper is to quantify this benefit for the simple case of a radial distribution feeder with concentrated load and distributed generator. With the introduction of DG, line loss reduction can be expected. The analysis is presented for varying locations of the distributed generator along the feeder and for varying output capacities. The results are presented in graphical form in terms of clearly-defined normalized parameters

In order to find the causes of statistical line loss abnormalities and propose better loss reduction strategies, it is necessary to improve the accuracy of theoretical line loss calculations. Since Distributed Generation (DG) access to the distribution network causes variability of power flow in the distribution network within a short period, the error of the traditional line loss calculation method increases. For the line loss calculation of medium-voltage distribution networks containing DGs with high-density collection data, a continuous line loss calculation method for the distribution network was proposed, aiming at improving the accuracy compared with the traditional line loss calculation method. The proposed method makes full use of the high-density collection data on the dispatch side and DG side, distributing the supply power to each load node by power to calculate real-time power flow, thus obtaining a more credible line loss value. The effectiveness and accuracy of the proposed method were verified in the IEEE 17-node distribution system.

Distributed generations (DGs) are widely used to meet electric energy demand in distribution networks. DGs are advantageous compared to the centralized generating units in many ways including reducing power loss, improving voltage profile, enhancing network stability, decreasing pollution, releasing transmission line capacity, and reducing congestion of distribution lines. The size and site of DGs logically influence power loss in distribution networks. The optimal place and capacity of DGs are determined aiming at minimizing power loss in distribution networks. In this article, firefly algorithm (FA) is used for optimum placement of various DGs in distribution networks. Optimum power factor of DGs along with active and reactive powers are obtained in this work. Various DGs of generating/consuming types are considered in the suggested method. Performance analysis of the proposed method has been done on standard IEEE 33 bus system in MATLAB.

Proliferation of electric demand and congestion in the existing network intensify challenges and problems for researchers and power system planners. Currently, these problems are mostly solved by Distributed Generation (DG). DG is placed near the customer end to meet the load demand economically, hence reducing the existing network burden. This paper attempts to present an overview of technological development carried out in the DG field. Numerous technical constraints must be considered while integrating DG in the system. The improvement in voltage stability, voltage profile, and reduction in power losses are the main benefits posed by DG integration. This paper also highlights the key issues associated with the DG allocation problem, types of DG, and methodologies for optimal DG units allocation.

This paper presents a new framework to analyze the penetration of distributed generation (DG) in distribution systems (DS) based on modified Monte Carlo simulation. The placement and capacity of DG are generated as random variables with probability density functions following the optimality principle of the system. The method of power flows analysis is employed to evaluate the economic and technical aspect of the network both with and without DG sources inside. The results in different scenarios are combined with weighted factors to form the performance indices. The proposed framework is testified in a practical 15 MVA, 22 kV, 48-bus DS in Thai Nguyen, Vietnam. The simulation result shows that many advantages can be achieved with DG such as lower power losses, smaller voltage deviation, over-load reduction, etc. The proposed framework can serve as a platform for DS planning with DG sources in practice.

Distribution network line loss calculation refers to the calculation of the electrical energy loss, which is generated by all components in a distribution network system in a given period of time. Distribution network, as the end of the power network, is directly connected with users; there are many equipment on the network; the system has impedance, electric energy in the process of conversion, transmission and distribution with a lot of loss, so the calculation of distribution network line loss has very important economic significance. In addition, with the development of power network construction, the rationality of distribution network topology design and the comparison of practical effects of various measures need the guidance of line loss calculation. Line loss and line loss rate is one of the main criteria to reflect the operation of distribution system. Reducing line loss of distribution system is very important for the effective use of power and economic operation of distribution system. In order to better find the effective reduction method and lay the foundation for the scientific establishment of linear contraction targets. This study uses a line loss calculation method based on BP neural network. The method uses the super matching property of the network to map the complex nonlinear relationship between the line loss and the characteristic variables and stores the evolution of the line loss changes with the changes of the structural and operational variables of the distribution line. On the basis of analyzing the theoretical methods, management methods, and various loss reduction measures of power loss calculation, this study also analyzes the status quo and existing problems of line loss analysis and calculation and collects data from line loss calculation and theoretical line loss calculation methods. This study discusses in detail the method and idea of the application of the improved neural network in the estimation of distribution network line loss, and it is used to predict the line loss in a certain area, and the prediction result is good.