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.

The increased penetration of renewable Distributed Generation (DG) creates room for further improvement of the reliability of supply in distribution networks. Several modelling approaches have been proposed to address the variability of renewable sources in reliability assessment. In this paper the renewable DG models used in reliability assessment of distribution networks are discussed and three representative models (capacity probabilistic table, hourly profiles and representative time-segments) are critically compared. The capacity of the methods to model the chronological variability of demand and generation is also addressed. The results of reliability indices and the computational times were used to compare the three modelling approaches when applied to a distribution network. It will be shown that hourly profiles yield the most accurate modelling of variable renewable generation but require intensive computational resources. Probabilistic table is not recommended when accurate results are required, while representative time-segments can be used as an alternative to hourly profiles when computational efficiency is needed.

Different types of distributed generation (DG) are broadly used and optimally placed in a distribution system to improve its performance. Since the network configuration affects the system operational conditions, the network reconfiguration and DG placement should be manipulated simultaneously. Nevertheless, the complexity of the problem may prevent from achieving the optimal solution. This paper presents a novel hybrid method of metaheuristic and heuristic algorithms, in order to boost robustness and shorten the computational runtime to achieve network minimum loss configuration in the presence of DGs. The developed backward/forward power flow is adopted to consider the PV(Q) model of DG. Moreover, different patterns of load types are taken into consideration to perform a practical study. To assess the capabilities of the proposed method, simulations are carried out on IEEE 33-bus and 83-bus practical distribution network of Taiwan Power Company. Furthermore, the proposed method is applied to a 33-bus unbalanced distribution network to verify its applicability in unbalanced distribution systems. The obtained results demonstrate the effectiveness of the proposed method to find optimal status of switches, as well as locations and sizes of DG units, in a rather shorter time than other approaches in the literature.

This paper presents an approach to coordinated planning of distribution network (DN) and distributed generation (DG) considering uncertainties of output of DG and load. To overcome shortage of traditional separated planning of DN with DG and maximize positive influence brought by DGs, the multi-objective integrated planning model of DN and DG is established, in which the types, location, capacity of DGs, lines in need of being upgraded and lines to be built for access of new load point are taken into account as variables, and investment, power purchasing, network loss and power failure are considered as objectives. In face of uncertainties of load and output of DGs, first the corresponding probability models are established, then latin hypercube sampling (LHS) based on those is adopted to get samples and research scenarios are obtained through samples reduction. A multi-objective chaos improved PSO based on theory of pareto optimal solution set is proposed to solve the model. Finally, the method is applied in improved IEEE 33-node distribution system and effectiveness and feasibility of the model and algorithm are verified.

This paper meticulously evaluates the reliability of distribution networks integrated with distributed generation (DG), considering various user classifications. Initially, we delve into the daily load profiles of residential, commercial, and industrial sectors, establishing a nuanced time-series load model that encapsulates the distinct characteristics of these customer segments. Subsequently, we formulate a model for the harmonious operation of energy storage systems, tailored to the interplay between DG output and load requirements. Utilizing the sequential Monte Carlo simulation algorithm, we compute the reliability metrics for the IEEE RBTSBUS6 feeder system, revealing that DG integration significantly enhances distribution network reliability. Notably, identical DG output yields varied impacts on the reliability of distinct customer sectors, underscoring the need for tailored strategies in DG deployment.

Guest Editorial: Situational awareness of integrated energy systems

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)

An improved wild horse optimization algorithm for reliability based optimal DG planning of radial distribution networks

In this paper the impact of both the distributed generation (DG) modeling and the static load response to voltage upon the optimal DG size, the radial distribution real power losses, and on the resulting voltage profiles are investigated. The optimal DG sizing nonlinear programming problem is tackled by the sequential quadratic programming deterministic technique. The DG modeling and the different static load representations were tested on 30-bus radial distribution system. Simulation results indicate that the DG size and network losses as well as voltage profiles are highly dependent on the DG and load representations.

Driven by environmental concerns and fluctuations of electric energy cost, the integration of distributed generators (DGs) into distribution networks close to the loads, has gained its importance and emerged as complementary to the conventional central power plants. However, the proliferation of distributed generators (DGs) has altered a distribution network from a passive system to an active one. Hence, modifications in the existing distribution power flow methods are needed to account for a DG unit, which can operate either as a PV or PQ bus. Among these methods, the Sequential Power Flow (SPF) method can readily accommodate for PV buses as long as the DG models in the sequence-component frame can be formulated. However, the convergence of SPF is heavily dependent on the choice of initial conditions. Moreover, when the resistance-to-reactance ratio (or R/X) is high, SPF will also diverge. This paper presents a new robust three-phase distribution power flow, which incorporates a homotopic method into the SPF method to overcome these problems. An IEEE benchmark system is used to test the validity and robustness of the proposed algorithm

Guest Editorial

This study proposes a probabilistic dynamic model for multi-objective distributed generation (DG) planning, which also considers network reinforcement at presence of uncertainties associated with the load values, generated power of wind turbines and electricity market price. Monte Carlo simulation is used to deal with the mentioned uncertainties. The planning process is considered as a two-objective problem. The first objective is the minimisation of total cost including investment and operating cost of DG units, the cost paid to purchase energy from main grid and the network reinforcement costs. The second objective is defined as the minimisation of technical risk, including the probability of violating the safe operating technical limits. The Pareto optimal set is found using non-dominated sorting genetic algorithm method and the final solution is selected using a max–min method. The model is applied on two distribution networks and compared with other models to demonstrate its effectiveness.

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

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.

With recent development of technology and management in power market and equipment, more and more distributed generation (DG) is embedded in the distribution network. However the approach of connecting DG in most cases is based on a so-called ‘fit and forget’ policy and the capacity of DG is limited rigidly by distribution system operator to avoid the negative effects of high level penetration. New management technologies have been proposed to handle the integration of DGs in the distribution networks. In this review, the micro grid (MG) was treated as the local control method to coordinate DGs within a small area of distribution network. And the active distribution network (AND) was treated as the global control mechanism to actively manage DGs, MGs and other equipment. The operation framework of ADN was firstly introduced. Then based on the static and dynamic models of DGs and MGs, impacts of DGs and MGs on the ADN are surveyed from power quality, stability to the operation. Finally, the conclusion and suggestion is given in this paper.