Location Of Distributed Generation Research Articles

Performance enhancement of a low-voltage microgrid by measuring the optimal size and location of distributed generation

<p class="Abstract">A power system in which the generation units such as renewable energy sources and other types of generation equipment are located near loads, thereby, reducing operation costs and losses and improving voltage is called a distributed generation (DG), and these generation units are called distributed energy resources. However, DGs must be located optimally to improve the power quality and minimize power loss of the system. The objective of this paper is to propose an approach for measuring the optimal size and location of DGs in a low voltage Microgrid using the Autoadd algorithm. The algorithm is validated by testing it on the IEEE 33-bus standard system and compared with previous studies, the algorithm proved its efficiency and superiority on the other techniques. A significant improvement in voltage and reduction in losses were observed when the DGs are placed at the sites decided by the algorithm. Therefore, Autoadd can be used in finding the optimal sizes and locations of DGs in the distribution system, the possibility of isolating the low voltage Microgrid is discussed by integrating distributed generation units and the results showed the possibility of this scenario during faults time and intermittency of energy time.</p>

A Q-learning based electric vehicle scheduling technique in a distribution system for power loss curtailment

Electric vehicles (EVs) are becoming more common in the distribution network (DN), which burdens the system throughout the charging cycle. However, if EVs are used to assist the utility according to its demands, the scenario turns profitable. In this paper, a new methodology to schedule the EVs and distributed generation (DG) according to the DN system demand is proposed. The proposed method is a hybrid of the intelligent Q-learning algorithm, Loss Sensitivity factor (LSF), and Enhanced Grasshopper Optimization (EGOA) techniques. The main objective of this work is to encourage peak shaving, minimize the power loss and improve the voltage profile of overall DN in presence of EVs. The modelling of EVs is done by considering the most significant parameters such as EV State of Charge (EV SoC), travel itinerary, capacity of EV battery, and charging/discharging levels. Initially the location of EV charging stations and DGs are determined using LSF technique. Further peak shaving of DN considering EVs behaviour is implemented using Q-learning based smart charging method. Later the sizes of DGs are determined using EGOA technique. EGOA is an enhancement in the already existing grasshopper optimization technique that improves the algorithm's exploring capability. The hybrid approach helped in identifying the strong buses for EV charging station and DG placement. Further a peak shave of overall DN demand is achieved using Q-learning technique. Later using EGOA the DG sizing along with optimal EV placement helped in reduction of power loss by around 67% compared to base case. The voltage profile of DN is enhanced with the proposed hybrid approach without using compensation devices.

Firefly analytical hierarchy algorithm for optimal allocation and sizing of DG in distribution network

<span lang="EN-MY">Distributed generation (DG) can be beneficially allocated in distribution power systems to improve the power system's efficiency. However, specious DG's allocation and sizing may cause more power loss and voltage profile issues for distribution feeders. Therefore, optimization algorithms are vital for future intelligent power distribution network planning. Hence, this study proposes a multi-objective firefly analytical hierarchy algorithm (FAHA) for determining the optimal allocation and sizing of DG. The multi-objective function formulation is improved further by integrating analytical hierarchy process (AHP) with FA to obtain the weight of the coefficient factor (CF). The performance of the proposed approach is verified on the 118-bus radial distribution network with different bus voltage at DG location (VDG) as regulated PV-bus during load flow calculations. The calculated CF and impact of the unregulated voltage at the PV-bus on the objectives function have been analysed. The findings show that the proposed techniques could allocate the DG at the most voltage deviation while minimizing the power loss and improving the radial distribution’s voltage stability index (VSI). The experimental results indicate that the approach is able to improve the overall voltage profile, especially at PQ-buses, minimize the power loss while improving the network's stability index simultaneously.</span>

Optimal Planning of Power Distribution Network by a Novel Modified Jaya Algorithm in Multiobjective Perspective

This article presents a novel improved Elitism oppositional Jaya algorithm for the optimal power distribution network reconfiguration and optimal sizing, siting of multiple distributed generations (DGs) with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> , PQV buses. Here, PQV bus is a voltage-specified PQ type bus controlled by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> bus. In this proposed planning model, the basic Jaya algorithm is modified by incorporating variable inertia weight, vicinity searching capability, elitism property, and blended with an opposition learning-based strategy. This establishes an effective approach between global search and local search in quest of an optimal solution. A multiobjective framework is developed to optimize technical and economic objectives simultaneously. Optimal weights are allocated to each objective using an improved analytical hierarchy process method. Novel indices are proposed to identify the optimal locations of DGs also considering critical loads. During reconfiguration, the line data sequence is rearranged by a smart graph theory mechanism. Furthermore, different schemes have been studied by the suggested method on IEEE 33 and 69 bus distribution test systems. The findings of the study by using the proposed algorithm in comparison with particle swarm optimization (PSO) and enhanced leader PSO show its effectiveness and validity.

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

A novel resilient state of charge balancing method for distributed storage systems based autonomous microgrids

In order to reduce the number of charge/discharge cycles of distributed energy storage systems (DESSs) to extend their lifespan, a State-of-Charge (SoC) balancing control is used. In conventional SoC balancing strategies, a local integral control is generally introduced in each DESS to ensure SoC equalization and Distributed Generators (DGs) frequency restoration. These strategies increase the system order and have a constraining bandwidth that may lead to stability issues. The few methods in which a local integrator is not added generally result in poor performance of the SoC synchronization strategy and frequency restoration, with the inability to eliminate static error. In this proposal, a novel resilient distributed control is proposed to ensure an effective SoC balancing of DESSs and frequency restoration with high performance without increasing the system order with a local integral control. The dynamic average consensus is modified to evaluate the participation level of each DESS in order to determine the active power references to synchronize the SoCs of the DESSs. These active power references are chosen to regulate the DG frequency to the nominal frequency of the MG. Therefore, the proposed method ensures SoC balancing with automatic frequency regulation and accurate output power sharing performance. Only voltage restoration control is added for DGs voltage regulation. In this distributed control architecture, SoC equalization as well as voltage/frequency restoration are performed only with local DG information and information from their neighbors. Simulations and then experimental results are performed to verify the effectiveness of the proposed method in discharging mode, in charging mode, in the presence of intermittent renewable energy sources, in the case of communication failure, in the case of communication delay, in the case of load variation, and in the case of “plug and play”. Comparisons were also made with two SoC balancing strategies from the literature.

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

&lt;span&gt;The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.&lt;/span&gt;

Power loss mitigation and voltage profile improvement by optimizing distributed generation

In a developing country, electricity has become the necessity of the growth industries; thus, the distribution system power quality and reliability are crucial. With low carbon initiatives, renewable energy or distributed generation (DG) is a promising source of electricity and leads the complex distribution system. Vital rises in DGs in power grids will significantly impact the system reliability and security, especially in power losses and voltage profiles parameters. This research focuses on an optimization placement and size of DGs in distribution systems to minimize power loss and improve voltage profile using the Modified Lightning Search Algorithm (MLSA). This research has modelled the practical 69-bus radial distribution system. Then MLSA with a weight summation approach is used to identify the suitable location and size for the DGs in the design proposal stage. The optimization objectives are to reduce power losses and improve the voltage profile, especially at the connection point of DGs. Besides that, load profile, DGs constant load and the solar load in distribution system modelled using MATLAB software. The results of the simulation using MLSA indicated that the optimization allocation and sizes of solar DGs applied with current load and load changes can minimize the power losses and improve voltage profile. These results verify the proposed approach’s effectiveness and success in determining the optimal location and sizing of solar DGs to reduce power losses as well as improve voltage profiles.

Optimal integration of renewable distributed generators and electric vehicles in a radial distributed network

With the rising interest in sustainable transportation efforts, the optimal integration of Electric Vehicles (EVs) in the transportation business has come into existence very drastically. However, because of the increased power consumption, their impact on the electric power market might result in more enormous losses in the active power, a reduced voltage profile, as well as reduced voltage stability limits. In Radial Distribution System (RDS), getting the reduced effect of EVs load is crucial. It is significantly important to build charging stations for EVs and Distributed Generators (DGs) at the best locations. This article presents a combined approach to solve the optimal integration of DGs and EVs problem. First, the desired locations of DGs and EVs are found using the Voltage Stability Index (VSI) method.Further, the resource ratings are obtained by the Firefly algorithm (FA) method. EVs charging dump and smart charging strategies are used, and comparison is done with both systems. Furthermore, the main objective of this work is to reduce the power loss and maintain a good voltage profile at each bus in the RDS. Finally, the developed approach is tested on IEEE 69 bus system.

A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms.

An intelligent optimization technique for performance improvement in radial distribution network

PurposeThe demand for electricity supply increases day by day due to the rapid growth in the number of industries and consumer devices. The electric power supply needs to be improved by properly arranging distributed generators (DGs). The purpose of this paper is to develop a methodology for optimum placement of DGs using novel algorithms that leads to loss minimization.Design/methodology/approachIn this paper, a novel hybrid optimization is proposed to minimize the losses and improve the voltage profile. The hybridization of the optimization is done through the crow search (CS) algorithm and the black widow (BW) algorithm. The CS algorithm is used for finding some tie-line systems, DG locations, and the BW algorithm is used for finding the rest of the tie-line switches, DG sizes, unlike in usual hybrid optimization techniques.FindingsThe proposed technique is tested on two large-scale radial distribution networks (RDNs), like the 119-bus radial distribution system (RDS) and the 135 RDS, and compared with normal hybrid algorithms.Originality/valueThe main novelty of this hybridization is that it shares the parameters of the objective function. The losses of the RDN can be minimized by reconfiguration and incorporating compensating devices like DGs.

Optimal Sizing and Scheduling of Mobile Energy Storage Toward High Penetration Levels of Renewable Energy and Fast Charging Stations

This paper presents a planning model that utilizes mobile energy storage systems (MESSs) for increasing the connectivity of renewable energy sources (RESs) and fast charging stations (FCSs) in distribution systems (DSs). The proposed planning model aims at enabling high penetration levels of green technologies while minimizing the total DS cost that includes investment, operating, and emission costs. The proposed model determines the optimal MESS sizes and transportation schedules as well as the optimal sizes and locations of wind-based distributed generators (DGs), photovoltaic (PV) DGs, and FCSs. The model takes into account techno-economic and environmental factors in addition to the power variations of RESs, FCSs, and load demands. The proposed planning model is applied on two benchmark test systems (i.e., 33-bus and 69-bus DSs). To evaluate the efficacy of the proposed model, the results obtained from the model are compared to those obtained from a traditional planning technique. The comparison of the results demonstrates that the proposed model with MESSs successfully achieves a significant cost saving and increases the penetration levels of RESs and FCSs.

Performance enrichment in optimal location and sizing of wind and solar PV centered distributed generation by communal spider optimization algorithm

PurposeThe distributed generation (DG) proper placement is an extremely rebellious concern for attaining their extreme potential profits. This paper aims to propose the application of the communal spider optimization algorithm (CSOA) to the performance model of the wind turbine unit (WTU) and photovoltaic (PV) array locating method. It also involves the power loss reduction and voltage stability improvement of the ring main distribution system (DS).Design/methodology/approachThis paper replicates the efficiency of WTU and PV array enactment models in the placement of DG. The effectiveness of the voltage stability factor considered in computing the voltage stability levels of buses in the DS is studied.FindingsThe voltage stability levels are augmented, and total losses are diminished for the taken bus system. The accomplished outcomes exposed the number of PV arrays accompanied by the optimal bus location for various penetration situations.Practical implicationsThe optimal placement and sizing of wind- and solar-based DGs are tested on the 15- and 69-test bus system.Originality/valueMoreover, the projected CSOA algorithm outperforms the PSOA, IAPSOA, BBO, ACO and BSO optimization techniques.

Sizing and Allocation of DGs in A Passive Distribution Network Under Various Loading Scenarios

Integration of renewable energies in distribution system networks has grown significantly over the years. Finding the optimal location and precise capacity of distributed generations (DGs) in various load conditions is a profound challenge. This article proposes an analytical approach to find the optimal location and capacity of different characteristic DGs in a passive distribution network (PDN). Real-life scenario of power consumption is considered by using different load scenarios. Key benefits include loss reduction and improved voltage profile of the PDN. Special attention is paid for the role of DGs to provide active/reactive power in different load scenarios. Monetary benefits are calculated for optimized installed generators. Extensive case studies have been performed over IEEE-33 and 69 bus system to verify the benefits of the proposed methodology.

A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

Distributed generation (DG) aims to generate part of the required electrical energy on a small scale closer to the places of consumption. Integration of DG into an existing electric distribution network (EDN) has technical, economic, and environmental benefits. DG placement is typically determined by investors and local conditions such as the availability of energy resources, space, and licenses, among other factors. When the location of DG is not a decision of the distribution network operator (DNO), the simultaneous integration of distribution network reconfiguration (DNR) and DG placement can maximize the benefits of DG and mitigate eventual negative impacts. DNR consists of altering the EDN topology to improve its performance while maintaining the radiality of the network. DNR and optimal placement of DG (OPDG) are challenging optimization problems since they involve integer and continuous variables subject to nonlinear constraints and a nonlinear objective function. Due to their nonlinear and nonconvex nature, most approaches to solve these problems resort to metaheuristic techniques. The main drawbacks of such methodologies lie in the fact that they are not guaranteed to reach an optimal solution, and most of them require the fine-tuning of several parameters. This paper recasts the nonlinear DNR and OPGD problems into linear equivalents to obtain a mixed-integer linear programming (MILP) model that guarantees global optimal solutions. Several tests were carried out on benchmark EDNs evidencing the applicability and effectiveness of the proposed approach. It was found that when no DG units are considered, the proposed model can find the same results reported in the specialized literature but in less computational time; furthermore, the inclusion of DG units along with DNR usually allows the model to find better solutions than those previously reported in the specialized literature.

Optimal Sitting and Sizing of Distributed Generators using Pareto-Based Multi-Objective Particle Swarm Optimization for Improving Power System Operation

Utilization of distributed generation (DG) in the distribution network has become trending ever since it has been introduced with proven benefits. DG plays a significant role in improving the quality and quantity as well as the efficiency of the power transmission and distribution system. By allowing smaller generating units to operate in parallel with the main grid, a continuous reliable power supply with lower power loss and higher power output can be achieved. However, improper placement and inappropriate sizing of DG leads to a moderate level performance in terms of power loss and voltage profile. Limited studies have been conducted on mitigating these problems in order to maximize the benefits from DG’s application. To solve this problem, a research is proposed which mainly aims in determining the location and size of DG as well as improving the voltage profile and efficiency of the distribution system significantly. A metaheuristic algorithm called Multi-Objective Particle Swarm Optimization (MOPSO) method is used to simultaneously determine the optimal size and location of DG. To assist the proposed method, Pareto analysis is incorporated to handle conflicting objectives. This method is then tested on the IEEE 14-bus and 33-bus distribution systems under two different conditions which is before and after optimization. The percentage of power loss reduction is calculated and the voltage profile is drawn to compare the output of both conditions. Evaluations from the tests have proven that by using the Pareto-Based MOPSO method, the most optimal size and location of DG in producing an improved voltage profile with lower power loss is identified.

Optimal Planning of Electric Vehicle Charging Station along with Multiple Distributed Generator Units

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

Optimal Allocation Method of Distributed Generation in Active Distribution Network Based on Bi-level Programming

Abstract The optimal allocation method to distributed generation(DG) of active distribution network was studied. Firstly, a bi-level programming model of DG in active distribution network is established. For the cause of determining the optimal installation location and capacity of DG, the upper layer model considers the minimum cost of DG investors as the objective, and the decision variables are the installation location and capacity of DG. The lower layer model considers the system operation with the objective of minimizing the network loss. The decision variables include the voltage amplitude and phase angle of each node and the consumption of distributed generation. Secondly, an modified grey wolf algorithm is proposed to solve the lower layer model, and the lower layer model is turned to a second-order cone programming problem by convex relaxation and linearization technology. Finally, the bi-level programming model developed in this paper is verified by taking IEEE33 bus system as an example, and the simulation results verify the effectiveness of the proposed method.

Optimal distributed generation penetration considering relay coordination and power quality requirements

Although the electric energy supply from distributed generation (DG) resources is increasing, these sources also face limitations. Protection and power quality constraints are the most important issues that limit the maximum DG penetration in power systems. Changing the short circuit level due to DGs' presence—especially synchronous-based (SDG) type—may lead to miscoordination of the protective relays. Also, standard harmonic limits may be violated due to harmonic injection of inverter-based DGs and non-linear loads. A protection coordination problem is solved for the basic configuration, and the optimal settings are applied. Then, using a proposed approach and a new objective function, the location, size, and type of DGs are optimized to maximize the DG penetration and the speed of protection system, while reducing power losses. Finally, the protection setting of SDGs is optimized and applied. In these optimizations, the protection coordination constraints and the power quality requirements are considered and met. IEEE 14-bus system and a real sub-transmission system are simulated to evaluate the proposed approach and the new objective function. The obtained results show significant power loss reduction and protection speed improvement for different scenarios and case studies.

Optimal analysis of time varying load radial distribution system with photovoltaic and wind generating system using novel hybrid optimization technique

The paper proposes the optimal placement of distributed generators (DGs) in radial distribution system along with reconfiguration. The DGs which are considered in this paper are designed with photo voltaic (PV) cells and wind power generation. The paper studies on the PV and wind generation penetration in radial distribution system as per the irradiation and wind velocity throughout the day. The irradiation levels are considered throughout the day. Unlike in former hybrid optimization techniques, the placement of DGs and reconfiguration is shared between two optimization techniques. The paper also details the reliability improvement of radial distribution system with reconfiguration and DGs placement. Some part of reconfiguration and DG locations are selected with one optimization technique and rest of reconfiguration and DG size are selected with another optimization technique. The combination of two optimization techniques properties leads towards the novel hybrid optimization. The methodology is formulated with the black widow algorithm and crow search algorithm. The performance of the method is tested on two test systems which are 16 bus RDS and 33 bus RDS.