Integration Of Distributed Generation Research Articles

AI Powered IoT based Home Energy Management System towards DG Integration

This paper attempts to present an Internet of Things (IoT) based Home Energy Management System (HEMS) to accommodate Artificial Intelligence (AI) based Distributed Generation (DG) Integration in Smart Micro Grid environment. This work presents the feasibility of load and weather Big Data acquisition, online load forecasting /prediction, online load priority assignment, online peak clipping estimation on the cloud (Thingspeak). Load priority and peak clipping Demand Side Management (DSM) Techniques are used to run Load Management Algorithm (LMA) on the cloud which ultimately executes AI-based DG Integration at the load center. Based on the real time load and weather Big Data Load Priority Table (LPT) is generated on the cloud in order to assign the priority to the loads of the load center and online forecasting is also carried out on the cloud to estimate Total Power PT using Artificial Neural Network (ANN) and five different ML algorithms which are later utilized to estimate Threshold /Peak Clipping Power PTh. Using these LPT and PTh values LMA executes AI-based DG Integration. To that end, a novel IoT-based HEMS is designed and fabricated to run cloud-based LMA in order to accommodate proposed DG Integration. Amongst all the prediction algorithms trained on the cloud to estimate PTh, Linear Regression (LR) ML algorithm is witnessed to have lowest RMSE of 4.44e−13, MSE of 1.97e−25 and MAE of 2.11e−13. MATLAB 2018b licensed version is used to run ANN and ML algorithms based forecasting/prediction on the cloud.

Impact Assessment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-control Perspective

The integration of renewable distributed generation (RDG) into distribution networks is promising and increasing nowadays. However, high penetration levels of distributed generation (DG) are often limited as they may have an adverse effect on the operation of distribution networks. One of the operation challenges is the interaction between DG and voltage-control equipment, e. g., an under-load tap changer (ULTC), which is basically designed to compensate for voltage changes caused by slow load variations. The integration of variable DGs leads to rapid voltage fluctuations, which can negatively affect the tap operation of ULTC. This paper investigates the impact of high penetration levels of RDG on the tap operation of ULTC in distribution networks through simulations. Various mitigation techniques that can alleviate this impact are also examined. Among these techniques, constant power-factor mode is regarded as the best trade-off between the simplicity and effectiveness of minimizing the number of tap operations. Simulations are performed on a Canadian benchmark rural distribution feeder using OpenDSS software.

Analysis of Optimal Integration of EVs and DGs Into CIGRE’s MV Benchmark Model

In today’s transportation sector, the growing number of electric vehicles (EVs) is progressively replacing petroleum-fueled vehicles, which are also expected to minimize greenhouse gas emissions. The main problem with EVs is the requirement of charging energy, which is fed through distribution network, while simultaneously feeding already connected load. The EV’s integration with the distribution network will overload the network due to EV’s charging load, which will eventually trip the power system protection. In this context, it is necessary to minimize power losses, improve voltage profile with sustainable power supply network. Therefore, optimal placement of EVs is required in the distribution network. Conventionally, researchers have used DGs to minimize power losses and improve voltage profile. In this paper, authors analyzed the effect of EV’s integration with simultaneous placement of distributed generations (DGs). The integration of EVs with higher penetration of DGs is cumbersome due to higher power losses and voltage variances that are outside allowable boundaries. The optimal placement of EVs into the distribution system with higher penetration of DGs is proposed in this paper using a battle royale optimization (BRO) algorithm. Since it is a new problem on CIGRE network which is not discussed before. Hence, the authors compared results with three most famous algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and accelerated PSO (APSO). The optimization problem is developed as a multi-objective function while decreasing active and reactive power losses, and minimising maximum voltage deviation index. The studied distribution network is the CIGRE 14-bus medium voltage (MV) distribution network. Three case studies are taken in which EVs are integrated in two scenarios with optimally sized and located DGs systems in the CIGRE distribution network using MATLAB. Case-1 includes simple network with DGs integration. Case-2 includes EVs only in the simple network, and case-3 finally includes EVs and DGs for optimal placement with minimum losses. The placement of the EVs results in a decrease in power losses and voltage deviation indices. The bus voltages of case-2, on the other hand, stay unchanged when the EVs are integrated. Case 3 with BRO showed the large reduction in power losses owing to the addition of EVs to the distribution network with DGs (from 19.98 kW, and 19.89 kVar to 2.54 kW, and 3.35 kVar).

Multi-Objective Optimization for Optimal Allocation and Coordination of Wind and Solar DGs, BESSs and Capacitors in Presence of Demand Response

The renewable energy sources (RES) based distributed generations (DGs) have been proven to be of great technical and economic benefits if optimally allocated in distribution networks. Their proper deployment is usually made in conjunction with demand response (DR) programs and renewables curtailment to match the demand load with the available generated power. Battery energy storage systems (BESSs) and capacitor banks (CBs) are two other tools that can compensate for the shortcomings of the RES. BESSs could complement the renewables generation intermittency and improve the reliability of the system while CBs could indemnify the limited reactive power support of the RES and improve the power quality of the system. Thus, the simultaneous integration of RES-based DGs, DR, BESSs, CBs, and curtailment could have great benefits if optimally planned and coordinated. In this paper, a multi-objective optimization planning model is formulated to determine the optimal locations and capacities of different RES-based DGs, BESSs, and CBs in presence of DR and renewables curtailment to maximize the economic index and the average voltage stability factor, and to minimize the average power losses in distribution networks. The modelling of RES-generated power is considered. The proposed model is implemented and tested on standard IEEE 33-bus radial distribution system, the model is formulated and solved in GAMS environment. Different renewable configurations and test cases are investigated. The obtained results validate the effectiveness of the simultaneous integration of the used tools in optimizing the techno-economic benefits.

Analysis of Distributed Generation Integration Effect on Active Power Losses in Distribution Networks

Distributed Generation (DG) is commonly used to reduce active power losses on a distribution network. The optimal location and size of DG will result in minimum active power losses and voltage profile improvement. This research proposes Novel Voltage Sensitivity Index (NVSI) and Stability Index (SI) methods to determine the optimal location and analytical expression to find optimal size and location of DG in Makassar distribution system, Feeder Kima, 76 buses to minimize active power losses and to improve voltage profile. Therefore, DG interconnection has a significant effect on improving the quality of the distribution network. The results show that the sensitivity method does not lead to the best placement DG in reducing active power losses. However, it is an analytical expression, which is very effective in determining optimal location and size of DG to reduce active power losses and to improve voltage profile in Makassar distribution system, Feeder Kima, 76 buses. The most optimal location for DG placement is on Bus 73 (Mega Sakti Pyramid), with a DG size of 0.8515MW. These combinations reduce 45.77% active power losses and increase 1.7336% voltage profile.

Stochastic multi-objective optimal energy management of grid-connected unbalanced microgrids with renewable energy generation and plug-in electric vehicles

Microgrids (MGs) contribute to the integration of renewable energy-based distributed generation (DG) units and electric vehicles (EVs) in a smart, secure, sustainable, and economic fashion. However, the unbalanced nature of MGs along with the probabilistic nature of renewable energy, electricity prices, and EV demand complicate the energy management process. To overcome that challenge, a stochastic multi-objective optimization model for grid-connected unbalanced MGs is proposed here to minimize the total operational cost and the voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem and to obtain compromise solutions. Uncertainties are considered by employing the roulette wheel mechanism for generating scenarios regarding renewable energy generations, EV charging demands, electric loads, and electricity prices. In addition, to avoid adopting infeasible and impractical solutions, a three-phase power flow is integrated in the proposed model. The proposed method is assessed in a modified IEEE 34-bus test system consisting of EVs, battery systems, wind turbine units, photovoltaic units, and diesel generators. The results show the effectiveness and benefits of the proposed model for handling uncertainties while minimizing both operational cost and voltage deviation index and providing more realistic and reliable solutions that can be applied by MG operators.

Impact analysis of residential induction cooking on medium voltage distribution network system: A case study of Nagarkot feeder, Bhaktapur, Nepal

Electricity is mostly used for cooking purposes in developed countries for a long time. Electricity could be used for cooking in Nepalese residential areas by transfering from Liquifty Petroleum Gas (LPG) to Induction Cooking(IC). Significant use of IC to the distribution feeder can increase the losses of the feeder, reducing the voltage profile at the buses, which in fact increases the current-carrying conductor. So, the grid impact analysis by IC loading to the distribution feeder is necessary. The study is carried out by performing technical analysis by load flow analysis on the feeder by calculating current, voltage profile, and power losses. The load flow has been performed for different loading of IC, and optimized Distributed Generation (DG) size is calculated. The bundling of the conductor is performed to reduce the loss at the feeder. This can be performed by checking the rated current of the conductor used (i.e., the branches where the rated current limit violates then that branches need bundling). The power loss at a different penetration level of IC is calculated. The IC power rating of 1500 Watts at each residential consumer when total 4924 number of the consumer is loading to Nagarkot feeder, active power loss increases to 1887.013 kW from 469.443 kW, and reactive power loss increases to 943.507 kVar from 234.722 kVar. The Minimum voltage is 0.664 Per unit (pu) at bus number 104 (Halede bus) which violates the voltage stability limit. The optimal penetration level of IC can be done up to 25% of total peak load by DG integration of 5965 kVA at 0.8 Power Factor (pf) lagging. This will give an active power loss of 530 kW. The next method, i.e., bundling of the conductor in 9 number of branches (at branch number 1, 2, 31, 34, 36, 38, 78, 79, and 86), should be done to improve the IC loading level. The maximum IC loading can be done up to 40% of the total peak load with a power loss of 477.7 kW by this bundling technique.

Operational concerns and solutions in smart electricity distribution systems

Interfacing high penetration of Renewable Energy Sources (RES) with energy storage and microgrid control systems is an essential feature of future distribution grids for optimal utilization and management of Distributed Energy Resources (DERs). This feature allows distributed system operators to follow the right path to transform their traditional grids into smart grids. With the continued expansion of DERs, the current distribution grid is moving toward a carbon-free electricity system and greener energy-transportation applications. However, there are some practical concerns as well as technical and operational challenges in an interconnected power network. Therefore, this paper reviews the most prominent issues associated with smart grid, such as large-scale distributed generation integration, energy storage, energy trading, operational planning, service reliability, and cyber resiliency, in light of recent technological advancements. Through reviewing several emerging technologies, this study shows the necessary methodologies for establishing smart energy distribution. Finally, this study highlights future research prospects for a fully functional smart grid system.

HybridGA‐MCSbased optimization for distributed generation with dynamic voltage restorer planning in distribution networks

The distributed generations (DGs) and dynamic voltage restorer (DVR) play an important role in the context of environmental problems and sustainable development throughout the world. The traditional DG and DVR locating and sizing method is based on a fixed network structure, which is assumed to be unchangeable during the planning period. The research presented here discusses a hybrid approach working on Monte Carlo simulation (MCS) and genetic algorithm (GA), which can be used for enhancement of system power factor (SPF) and voltage profile (VP) by the integration of DGs with DVR planning in distribution networks with ZIP load models from the minimization of total real power loss of the system viewpoint. The proposed methodology was applied to a 75-bus distribution network. This article is useful for researchers as well as practitioners who are working in the fields of integration of DG with DVR for improvement of system performances such as power factors and voltage profile from minimizing the losses.

HybridGA‐MCSbased optimization for integration of DG, DVR, and DSTATCOM planning in distribution networks

The integration of distributed generations (DGs), dynamic voltage restorer (DVR), and distribution static compensator (DSC) plays an important role in the context of environmental problems and sustainable development throughout the world. The traditional DG, DVR, and DSC locating and sizing methods are based on a fixed network structure, which is assumed to be unchangeable during the planning period. The research presented here discusses a hybrid approach working on genetic algorithm (GA) and Monte Carlo simulation (MCS) which can be used for enhancement of system power factor (SPF) and voltage profile (VP) by the integration of DG, DVR, and DSC planning in distribution networks with constant impedance (Z), constant current (I), constant power (P) load models (ZIP-LMs) from the minimization of total real power loss of the system viewpoint. The proposed methodology is applied to a 75-bus distribution network. This article is useful for researchers as well as practitioners who are working in the fields of integration of DG, DVR, and DSC for the improvement of system performances such as SPF and voltage profile from the minimization of total real power loss of the system viewpoint.

A survey on cooperative control strategies for DC microgrids

Distributed generators have been widely used in modern power systems, and microgrids are becoming a key concept for the integration of multiple distributed generators and clusters of loads with great efficiency and high system reliability. In a microgrid, the control strategy is one of the most important issues. Various control schemes have been proposed to achieve the cooperation of distributed generators in a microgrid. This paper surveys cooperative control strategies for multiple distributed generators in DC microgrids. The three popular coordinated control strategies are presented: decentralized control, centralized control, and distributed control. The advantages and drawbacks of the control strategies are compared. Some intelligent control methods such as fuzzy logic control and neural network approaches are also briefly introduced in the context of microgrids. Further research trends and challenges in this area are also discussed.

Power Quality and Performance Assessment of Grid-connected Photovoltaic Distributed Generation with Compliance to Stipulated Grid Integration Requirements

Recently, the demand for electrical energy has increased more than energy production due to the growing population and industrialization. Therefore, the distributed generators integration (DGs) into the distribution system has been widely adopted. This work examines the effect of photovoltaic-based distributed generator (PV-DG) integration on power quality effect of a radial distribution system. Firstly, the capacity and optimum placement of the PV-DG units in the distribution network are determined by employing the particle swarm optimization (PSO) algorithm. Then, the impact of PV-DG integration on voltage harmonic distortion is analyzed by performing harmonic load flow analysis. Also, the P-V curve method is used to evaluate the effects of higher PV-DG penetration levels on loading margin and voltage magnitude. The simulation results show that as the PV-DG units’ penetration level increases, a greater level of harmonic distortion is injected, implying that the PV-DG units should only be integrated up to the network’s maximum capacity. Therefore, high harmonic distortion is produced when the PV-DG units are penetrated beyond this maximum penetration level, which has a negative impact on the system’s performance. The total voltage harmonic distortion is 4.17 % and 4.24 % at PCC1 and PCC2 at the highest penetration level, allowing the acceptable harmonic distortion limit. Also, grid-connected PV-DG units improve loading margin and voltage magnitude, according to the P-V curve results. The standard IEEE-33 bus distribution system is modelled in ETAP software and is used as a test system for this study.

Power Quality and Performance Assessment of Grid-connected Photovoltaic Distributed Generation with Compliance to Stipulated Grid Integration Requirements

Recently, the demand for electrical energy has increased more than energy production due to the growing population and industrialization. Therefore, the distributed generators integration (DGs) into the distribution system has been widely adopted. This work examines the effect of photovoltaic-based distributed generator (PV-DG) integration on power quality effect of a radial distribution system. Firstly, the capacity and optimum placement of the PV-DG units in the distribution network are determined by employing the particle swarm optimization (PSO) algorithm. Then, the impact of PV-DG integration on voltage harmonic distortion is analyzed by performing harmonic load flow analysis. Also, the P-V curve method is used to evaluate the effects of higher PV-DG penetration levels on loading margin and voltage magnitude. The simulation results show that as the PV-DG units’ penetration level increases, a greater level of harmonic distortion is injected, implying that the PV-DG units should only be integrated up to the network’s maximum capacity. Therefore, high harmonic distortion is produced when the PV-DG units are penetrated beyond this maximum penetration level, which has a negative impact on the system’s performance. The total voltage harmonic distortion is 4.17 % and 4.24 % at PCC1 and PCC2 at the highest penetration level, allowing the acceptable harmonic distortion limit. Also, grid-connected PV-DG units improve loading margin and voltage magnitude, according to the P-V curve results. The standard IEEE-33 bus distribution system is modelled in ETAP software and is used as a test system for this study.

SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

Renewable energy-based distributed generators (DGs) are gaining more penetration in modern grids to meet the growing demand for electrical energy. The anticipated techno-economic benefits of these eco-friendly resources require their judicious and properly sized allocation in distribution networks (DNs). The preeminent objective of this research is to determine the sizing and optimal placing of DGs in the condensed DN of a smart city. The placing and sizing problem is modeled as an optimization problem to reduce the distribution loss without violating the technical constraints. The formulated model is solved for a radial distribution system with a non-uniformly distributed load utilizing the selective particle swarm optimization (SPSO) algorithm. The intended technique decreases the power loss and perfects the voltage profile at the system’s nodes. MATLAB is used for the simulation, and the obtained results are also validated by the Electrical Transient Analysis Program (ETAP). Results show that placing optimally sized DGs at optimal system nodes offers a considerable decline in power loss with an improved voltage profile at the network’s nodes. Distribution system operators can utilize the proposed technique to realize the reliable operation of overloaded urban networks.

Optimal Planning of a Distribution Network in Egypt

Distribution networks planning is a very complex task as it involves the consideration of various important issues. A model for optimal planning of a distribution network is presented in detail, using Distributed Generation (DG). This paper presented a detailed analysis of the West Delta Network (WDN) as a part of the Egyptian distribution network with a 30% increase in system loading. An Oscillatory Particle Swarm Optimization (OPSO) technique was used to find the optimal locations and sizes of one DG or multiple DGs for achieving the objectives of minimizing total active power losses and system voltage regulation as a single-and-multi-objective optimization problem in a distribution network. Several penetration scenarios have done to evaluate concentrated (at one location) and distributed DGs (at different locations). The results are presented in a comparative form and thoroughly discussed. The obtained results will help in system operation with DG integration and identifying the critical penetration levels for the studied system.

Optimal integration of photovoltaic distributed generation in electrical distribution network using hybrid modified PSO algorithms

The satisfaction of electricity customers and environmental constraints imposed have made the trend towards renewable energies making them more essential due to their advantages as reducing power losses and ameliorating system’s voltage profiles and reliability. This article addresses the optimal location and size of multiple distributed generations (DGs) based on solar photovoltaic panels (PV) connected to electrical distribution network (EDN) using the various proposed hybrid particle swarm optimization (PSO) algorithms based on chaotic maps and adaptive acceleration coefficients. These algorithms are implemented to optimally allocate the DGs based PV (PV-DG) into EDN by minimizing the multi-objective function (MOF), which is represented as the sum of three technical parameters of the total active power loss (TAPL), total voltage deviation (TVD), and total operation time (TOT) of overcurrent relays (OCRs). The effectiveness of the proposed PSO algorithms were validated on both standards IEEE 33-bus, and 69-bus. The optimal integrating of PV-DGs into EDNs reduced the TAPL percentage by 56.94 % for the IEEE 33-bus and by 61.17 % for the IEEE 69-bus test system, enhanced the voltage profiles while minimizing the TVD by 37.35 % and by 32.27 % for two EDNs, respectively.

Distributed Generation and Renewable Energy Integration into the Grid: Prerequisites, Push Factors, Practical Options, Issues and Merits

Power system operators are in search of proven solutions to improve the penetration levels of distributed generators (DGs) in the grid while minimizing cost. This transition is driven, among others, by global climate concerns, the growing power demand, the need for greater flexibility, the ageing grid infrastructure and the need to diversify sources of energy production. Distributed renewables would not easily substitute the conventional electric grid system, perhaps because the latter is a well-established technology and it would not be prudent to abandon it, while the new distributed renewable energy technologies are generally not adequately developed to support the total load. Thus, it is becoming increasingly necessary to consider sustainable options such as integrating renewable energy sources into the existing power grid. This study is a review that is mainly hinged on distributed generation (DG) classification, the challenges of DG to grid integration, practical options used in DG integration, lessons learned from some countries with successful DG to grid integration, push factors in the growth of DGs and the merits of DG to grid integration. These standpoints of DG to grid interconnection are critical in conducting grid planning and operational studies, which should be conducted in strict observance of aspects such as optimal technology selection, optimal capacity and a suitable connection point of DGs in the network. Therefore, the perspectives highlighted regarding DG can assist power system engineers, developers of DG plants and policymakers in developing a power network that is stable, efficient and reliable.

Optimal Long-Term Distribution System Planning: Network Reconfiguration and DG Integration

In this paper, a long-term Distribution System Planning Model (DSPM) considering network reconfiguration and Distributed Generation (DG) integration, is presented. The DSPM objective functions are power loss reduction and voltage stability enhancement. The proposed model handles long-term load increase and seeks to define when and where the reinforcements, such as network reconfiguration (NR) and DG integration, are required to meet the load increase. A Binary Particle Swarm Optimization algorithm (BPSO) is used for network reconfiguration while optimal DG allocation and sizing are performed using the Voltage Stability Margin Index (VSMI) and curve fitting approximation. The planning horizon of each solution is determined through load forecasting using a Nonlinear Autoregressive neuronal network with exogenous variables (NARX) technique. The performance of the proposed model is evaluated considering several case studies on the IEEE 33-bus test system. Results demonstrate the effectiveness of the proposed approach.

General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review

Distributed generators (DGs) have emerged as an advanced technology for satisfying growing energy demands and significantly mitigating the pollution caused by emissions. Microgrids (MGs) are attractive energy systems because they offer the reliable integration of DGs into the utility grid. An MG-based approach uses a self-sustained system that can operate in a grid-tied mode under normal conditions, as well as in an islanded mode when grid disturbance occurs. Islanding detection is essential; islanding may injure utility operators and disturb electricity generation and supply because of unsynchronized re-closure. In MGs, an energy management system (EMS) is essential for the optimal use of DGs in intelligent, sustainable, reliable, and integrated ways. In this comprehensive review, the classification of different operating modes of MGs, islanding detection techniques (IDTs), and EMSs are presented and discussed. This review shows that the existing IDTs and EMSs can be used when operating MGs. However, further development of IDTs and EMSs is still required to achieve more reliable operation and cost-effective energy management of MGs in the future. This review also highlights various MG challenges and recommendations for the operation of MGs, which will enhance the cost, efficiency, and reliability of MG operation for next-generation smart grid applications.

Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods

Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.