Multiple Distributed Generation Units Research Articles

Combining two techniques to develop a novel islanding detection method for distributed generation units

Renewable energy is a fast-growing sector given that it does not cause the pollution produced by nuclear and fossil generation systems. The use of renewable energy for distributed generation (DG) is important worldwide not only because it increases profits but also because integrating it into utility systems ensures power supply at points of consumption even when a DG system is disconnected from the main system. Researchers continue to exert efforts in improving islanding detection methods because current detection approaches remain unsatisfactory. Accordingly, this paper presents two new islanding detection methods that are suited for single and multiple DG units. The methods are based on the rate of change in active and reactive power, frequency, and voltage angle. They are tested on single and multiple DG systems with wind turbines and simulated using the Power World Simulator software. The simulation results show that the methods effectively reduce the time spent on islanding detection and efficiently decrease non-detection zones. To verify the accuracy and speed of the proposed methods, the results are compared with those achieved by different islanding detection approaches.

Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm

Abstract Distributed generation (DG) is becoming more important due to the increase in the demands for electrical energy. DG plays a vital role in reducing real power losses, operating cost and enhancing the voltage stability which is the objective function in this problem. This paper proposes a multi-objective technique for optimally determining the location and sizing of multiple distributed generation (DG) units in the distribution network with different load models. The loss sensitivity factor (LSF) determines the optimal placement of DGs. Invasive weed optimization (IWO) is a population based meta-heuristic algorithm based on the behavior of weeds. This algorithm is used to find optimal sizing of the DGs. The proposed method has been tested for different load models on IEEE-33 bus and 69 bus radial distribution systems. This method has been compared with other nature inspired optimization methods. The simulated results illustrate the good applicability and performance of the proposed method.

Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

Dispersed generation generally refers to power generation onthe customer side of a power network. This article demonstrates theimprovement in network parameters which could be achieved by us -ing single and multiple Distributed Generation units in three selectedstandard networks. A reliable multi variable method is used for find -ing optimal installation point and size of distributed generation units.Their sites and sizes are recognized by implementation of a proposedmethod with exhaustive search. Optimization has been applied onnetwork total active and reactive losses together with voltage varia -tion. IEEE 6, 14 and 30 buses standard networks have been selected asstudy cases. The total active and reactive power losses are minimizedwhile voltage profile is improved by installing Distributed Generationunits on recognized optimal points with achieved optimal size.

Optimal Sizing and Siting of Distributed Generators by Exhaustive Search

ABSTRACTDispersed generation generally refers to power generation on the customer side of a power network. This article demonstrates the improvement in network parameters which could be achieved by using single and multiple Distributed Generation units in three selected standard networks. A reliable multi variable method is used for finding optimal installation point and size of distributed generation units. Their sites and sizes are recognized by implementation of a proposed method with exhaustive search. Optimization has been applied on network total active and reactive losses together with voltage variation. IEEE 6, 14 and 30 buses standard networks have been selected as study cases. The total active and reactive power losses are minimized while voltage profile is improved by installing Distributed Generation units on recognized optimal points with achieved optimal size.

Distributed economic dispatch of embedded generation in smart grids

In a smart grid context, the increasing penetration of embedded generation units leads to a greater complexity in the management of production units. In this paper, we focus on the impact of the introduction of decentralized generation for the unit commitment (UC) problem. Unit commitment problems consist in finding the optimal schedules and amounts of power to be generated by a set of generating units in response to an electricity demand forecast. While this problem has received a significant amount of attention, classical approaches assume that these problems are centralized and deterministic. However, these two assumptions are not realistic in a smart grid context. Indeed, finding the optimal schedules and amounts of power to be generated by multiple distributed generator units is not trivial since it requires to deal with distributed computation, privacy, stochastic planning, etc. In this paper, we focus on smart grid scenarios where the main source of complexity comes from the proliferation of distributed generating units. In solving this issue, we consider distributed stochastic unit commitment problems. We introduce a novel distributed gradient descent algorithm which allows us to circumvent classical assumptions. This algorithm is evaluated through a set of experiments on real-time power grid simulator.

Effectiveness of frequency relays on networks with multiple distributed generation

Distributed generation (DG) has gained a vital role in distribution utilities. So, it is important to correctly detect islanding of DG units. Frequency relays are one of the most commonly used loss of mains detection method. However, distribution utilities may be faced by concern related to false operation of these frequency relays. The commercially available frequency relays reported considering standard tight setting. This paper investigates some factors related to relays internal algorithm that contribute to their different operating responses. The factors that will be investigated are frequency measuring techniques, measuring windows, time delays and under voltage interlock function. With the increasing penetration of DG into the network, it is becoming common to have multiple DG units connected at the same network location. Two generators connected at the same location and employing frequency relays with the same setting but different characteristics were simulated. When subjected to the same network disturbances the possible interference between the two relays is analyzed.

Optimal Placement and Sizing of Distributed Generation via an Improved Nondominated Sorting Genetic Algorithm II

An improved nondominated sorting genetic algorithm–II (INSGA-II) has been proposed for optimal planning of multiple distributed generation (DG) units in this paper. First, multiobjective functions that take minimum line loss, minimum voltage deviation, and maximal voltage stability margin into consideration have been formed. Then, using the proposed INSGA-II algorithm to solve the multiobjective planning problem has been described in detail. The improved sorting strategy and the novel truncation strategy based on hierarchical agglomerative clustering are utilized to keep the diversity of population. In order to strengthen the global optimal searching capability, the mutation and recombination strategies in differential evolution are introduced to replace the original one. In addition, a tradeoff method based on fuzzy set theory is used to obtain the best compromise solution from the Pareto-optimal set. Finally, several experiments have been made on the IEEE 33-bus test case and multiple actual test cases with the consideration of multiple DG units. The feasibility and effectiveness of the proposed algorithm for optimal placement and sizing of DG in distribution systems have been proved.

Analysis and Mitigation of Resonance Propagation in Grid-Connected and Islanding Microgrids

The application of underground cables and shunt capacitor banks may introduce power distribution system resonances. In this paper, the impacts of voltage-controlled and current-controlled distributed generation (DG) units to microgrid resonance propagation are compared. It can be seen that a conventional voltage-controlled DG unit with an LC filter has a short-circuit feature at the selected harmonic frequencies, while a current-controlled DG unit presents an open-circuit characteristic. Due to different behaviors at harmonic frequencies, specific harmonic mitigation methods shall be developed for current-controlled and voltage-controlled DG units, respectively. This paper also focuses on developing a voltage-controlled DG unit-based active harmonic damping method for grid-connected and islanding microgrid systems. An improved virtual impedance control method with a virtual damping resistor and a nonlinear virtual capacitor is proposed. The nonlinear virtual capacitor is used to compensate the harmonic voltage drop on the grid-side inductor of a DG unit LCL filter. The virtual resistor is mainly responsible for microgrid resonance damping. The effectiveness of the proposed damping method is examined using both a single DG unit and multiple parallel DG units.

Analytical approach for optimal siting and sizing of distributed generation in radial distribution networks

This study presents an analytical approach for optimal siting and sizing of distributed generation (DG) in radial power distribution networks to minimise real and reactive power losses. For this purpose, suitable analytical expressions have been derived which are based on change in active and reactive components of branch currents cause by the DG placement. This method first determines the DG capacity causing maximum benefit at different buses, and then selects the bus as the best location for DG placement which corresponds to highest benefit. The proposed method is applicable for sizing and siting of single as well as multiple DG units. Moreover, the proposed method requires only the results of base case load flow to determine the optimal size of DG unit(s). The proposed method is tested on 33-bus and 69-bus radial distribution test systems. The results obtained by this proposed method validate the suitability and importance of proposed analytical method to determine the size and site of DG unit(s).

Online Coordinated Voltage Control in Distribution Systems Subjected to Structural Changes and DG Availability

The responses of multiple distributed generation (DG) units and voltage regulating devices, such as tap changers and capacitor banks, for correcting the voltage may lead to operational conflicts and oscillatory transients, where distribution systems are subjected to network reconfiguration and availability of the DG units. Therefore, coordinated voltage control is required to minimize control interactions, while accounting for the impact of structural changes associated with the network. This paper proposes a strategy for coordinating the operation of multiple voltage regulating devices and DG units in medium voltage (MV) distribution systems, under structural changes and DG availability, for effective voltage control. The proposed strategy aids to minimize the operational conflicts by allowing voltage regulating devices to operate in accordance with the priority scheme designed based on the electrical-distance between voltage regulating devices and DG units, while maximizing the voltage support by the DG units. The proposed coordination scheme is designed to enact with an aid of a substation centered distribution management system for online voltage control. The control actions of proposed coordination strategy are tested on a MV distribution system, derived from the state of New South Wales, Australia, through simulations, and results are reported.

An elegant emergence of optimal siting and sizing of multiple distributed generators used for transmission congestion relief

After the implementation of deregulation in a power system, an appreciable volume of renewable energy sources is used to generate electric power. Even though they are intended to improve the reliability of the power system, the unpredictable outages of generators or transmission lines, an impulsive increase in demand, and failures of other equipment lead to congestion in one or more transmission lines. There are several ways to alleviate this transmission congestion, such as the installation of new generation facilities in the place where the demand is high, the addition of a new transmission facility, generation rescheduling, and curtailment of load demand processes. Among the above methods generation rescheduling and load shedding are normally preferred, since the other methods require additional investments. However, some critical cases require improved methods to alleviate congestion. With the extensive application of distributed generation (DG), congestion management is also accomplished by the optimal placement of multiple DG units. It is well known that incorrect sizes and improper locations of DG undoubtedly create higher power losses and an undesirable voltage profile. Hence, this research effort employs the line flow sensitivity index to establish the optimal location of DG units and genetic algorithm-based optimization for determining the optimal sizes of DG units. The objective of this research is to minimize the total losses and real power flow performance index and to improve the voltage shape of the modified IEEE 30-bus test system. The results of this proposed approach are encouraging and help in anticipating higher efficiency by satisfying all the objectives.

Optimal Sizing and Siting of Distributed Generators by a Weighted Exhaustive Search

Dispersed generation generally refers to power generation on the customer side of the power network. This article demonstrates the improvement in network parameters that could be achieved by the usage of single and multiple distributed generation (DG) units in three selected standard networks. A reliable multi-variable method is suggested for finding the optimal installation point and size of distributed generation units. Their sites and sizes are recognized by the implementation of the proposed method with an exhaustive search. Optimization is applied on network total active and reactive losses together with voltage variation. IEEE 6-, 14-, and 30-bus standard networks are selected as study cases. The total active and reactive power losses are minimized, while the voltage profile is improved by installing distributed generation units on the recognized optimal points with the achieved optimal size.

A MINLP technique for optimal placement of multiple DG units in distribution systems

Loss reduction is one of the prime objectives for planning of distributed generation (DG). To improve performance of distribution systems, optimal placement of distributed generator is critically important, as DG benefits are site and size specific. Optimal placement of multiple DG units is non-convex and non-linear optimization problem. The methodology proposed in this paper solves Mixed Integer Non-Linear Programming (MINLP) formulation for loss minimization. The proposed methodology simplifies the problem by dividing it in two phases, namely Siting Planning Model (SPM) and Capacity Planning Model (CPM) thereby reducing the search space and computational time. The SPM model selects the candidate buses based on Combined Loss Sensitivity (CLS). In CPM, optimal locations and sizes are obtained by integrating Sequential Quadratic Programming (SQP) and Branch and Bound (BAB) algorithm for MINLP problem. The performance of the proposed method is tested on IEEE 33-bus and IEEE 69-bus distribution system for placement of single and multiple DG units capable of delivering either real or both real and reactive power. The obtained results are then compared with three basic classes of optimization methods, viz., Exhaustive Load Flow (ELF), Improved Analytical (IA) and Particle Swarm Optimization (PSO). The key advantage of the proposed method is that it simultaneously considers placement of multiple DG units giving better optimal solution in reasonably less computational time.

An Approach for Assessing the Effectiveness of Multiple-Feature-Based SVM Method for Islanding Detection of Distributed Generation

Islanding detection is a critical protection issue, as conventional protection schemes such as vector surge (VS) and rate of change of frequency relays do not guarantee islanding detection for all network conditions. Integration of multiple distributed generation (DG) units of different sizes and technologies into distribution grids makes this issue even more critical. This paper presents a comprehensive analysis of the effectiveness of a new method for islanding detection in DG networks. The proposed method, which is based on multiple features and support vector machine (SVM) classification, has the potential to overcome the limitations of conventional protection schemes. The multifeature-based SVM technique utilizes a set of features generated from numerous set of offline dynamic events simulated under different network contingencies, operating conditions, and power imbalance levels. Parameters (such as voltage, frequency, and rotor angle) showing distinguishable variation during the formation of islanding are selected as features for classification of the events. Features associated with different islanding and nonislanding events are used to train the SVM. The trained SVM is tested on a typical distribution network containing multiple DG units. Simulation results indicate that the proposed method can work effectively with high degree of accuracy under different network contingencies and critical levels of power imbalance that may exist during islanding.

An optimal investment planning framework for multiple distributed generation units in industrial distribution systems

This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit–cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery.

Planning Active Distribution Networks Considering Multi-DG Configurations

Planning distribution systems without considering the operation status of multiple distributed generation (DG) units could result in constraining the network, lowering the utilization of its assets and minimizing the total DG capacity that can be accommodated. In this paper, the impact of multiple DG configurations on the potential of active network management (ANM) schemes is firstly investigated. Secondly, the paper proposes a multi-configuration multi-period optimal power flow (OPF)-based technique (MMOPF) for assessing the maximum DG capacity under ANM schemes considering 1) variability of demand and generation profiles (multi-period scenarios), and 2) different operational status of DG units (multi-configurations). The results show that the availability of DGs at certain locations could critically impact the amount of DG capacity at other locations. If DGs are properly allocated and sized at certain locations up to the optimal limits, even with a “fit-and-forget” approach, the total connected DG capacity can be maximized, with minimum utilization of ANM schemes. However, exceeding these optimal limits may lead to minimizing the total DG penetration in the long term, impacting the system reliability due to the operational status of multiple DG units, and consequently, imposing more investments on ANM schemes to increase the amount of connected DG capacity.

Optimal Placement and Sizing of Distributed Generation in Smart Distribution System

This paper solves the distributed generation (DG) planning problem. Firstly, multiple-objective functions have been formed with the consideration of minimum line loss, minimum voltage deviation and maximal voltage stability margin. Secondly, the proposed improved NSGA-II algorithm has been described in detail to solve the multi-objective planning problem. An improved Non-dominated Sorting Genetic Algorithm II has been proposed for optimal planning of multiple DG units in this paper. Experiment has been made on the IEEE 33-bus test case with the consideration of multiple DG units. The computational result and comparison indicate the proposed algorithm for optimal placement and sizing of DG in distribution system is effective.

Reliability-constrained Based Optimal Placement and Sizing of Multiple Distributed Generators in Power Distribution Network Using Cat Swarm Optimization

This article presents optimal placement and sizing of multiple distributed generators to achieve higher overall system reliability in large-scale primary distribution networks using a novel random search algorithm known as cat swarm optimization. A composite reliability index is used as the objective function in the optimization process. Furthermore, the effect of multiple distributed generator units on power transfer capacity and power loss reduction has been observed. Extensive simulations are carried out based on three practical distribution systems to demonstrate the effectiveness of the proposed method. Further, qualitative comparisons are made with adaptive weight particle swarm optimization, particle swarm optimization with constriction factor, and binary-coded genetic algorithm to show the efficacy of the proposed method for optimal placement and sizing of distributed generators in power distribution networks.

Three-Phase Primary Control for Unbalance Sharing between Distributed Generation Units in a Microgrid

For islanded microgrids, droop-based control concepts have been developed both in single and three-phase variants. The three-phase controllers often assume a balanced network; hence, unbalance sharing and/or mitigation remains a challenging issue. Therefore, in this paper, unbalance is considered in a three-phase islanded microgrid in which the distributed generation (DG) units are operated by the voltage-based droop (VBD) control. For this purpose, the VBD control, which has been developed for single-phase systems, is extended for a three-phase application and an additional control loop is added for unbalance mitigation and sharing. The method is based on an unbalance mitigation scheme by DG units in grid-connected systems, which is altered for usage in grid-forming DG units with droop control. The reaction of the DG units to unbalance is determined by the main parameter of the additional control loop, viz., the distortion damping resistance, Rd. The effect of Rd on the unbalance mitigation is studied in this paper, i.e., dependent on Rd, the DG units can be resistive for unbalance (RU) or they can contribute in the weakest phase (CW). The paper shows that the RU method decreases the line losses in the system and achieves better power equalization between the DG unit’s phases. However, it leads to a larger voltage unbalance near the loads. The CW method leads to a more uneven power between the DG unit’s phases and larger line losses, but a better voltage quality near the load. However, it can negatively affect the stability of the system. In microgrids with multiple DG units, the distortion damping resistance is set such that the unbalanced load can be shared between multiple DG units in an actively controlled manner rather than being determined by the microgrid configuration solely. The unit with the lowest distortion damping resistance provides relatively more of the unbalanced currents.

A new algorithm for allocating multiple distributed generation units based on load centroid concept

Allocation of distributed generation (DG) units is commonly formulated as a constrained nonlinear optimization problem solved by complex iterative mathematical or heuristic techniques. Heavy computational burden, very long solution time, probable divergence and possibility of getting only a sub-optimal solution are some serious drawbacks. In this paper, a systematic simple approach to allocate multiple DG units in radial/meshed distribution network is proposed. The concept of equivalent load is introduced and extended to identify the load centroid precisely with two methods. A performance index that combines the power system real power loss and average node voltage is defined. Based on load centroid and performance index, a straightforward algorithm for sizing and locating multiple DG units is developed. The proposed technique is applied to radial and meshed test systems. Results confirm stability, integrity and efficacy of the proposed approach.