Renewable Energy-based Distributed Generation Research Articles

A novel apparent power loss based distributed generator siting and sizing method considering dependent loads

In the present paper, it is first established that even though the standard Active Power Loss Sensitivity Factor (APLSF) based technique for Renewable Energy based Distributed Generation (REDG) system excels with respect to many available technical assessment indicators, its performance falls short in case of line loss reduction and power transfer capacity improvement indicators. On further investigation through Dynamic Fault Tree Analysis technique, it is found that the unsatisfactory performance of APLSF stem from neglecting reactive power loss and line reactance. Therefore, a novel apparent power loss augmented technique coined as Dynamic Loss Evaluation Indicator (DLEI) method is proposed here. The suitability of the proposed method is validated on IEEE 33 and 85 bus distribution systems for both voltage and voltage and frequency dependent load models and it is shown that the aforementioned indicators are improved along with the other indicators like voltage profile and voltage stability to name a few. For further validation, contingency and power quality analysis are also carried out and improved performance is observed in DLEI method when compared against the APLSF based method.

Multi-objective architecture for strategic integration of distributed energy resources and battery storage system in microgrids

Microgrids with integrated renewable energy-based distributed generation (RDG) and battery energy storage systems (BESS) should be effectively designed and controlled to reap the potential benefits. In this context, this study recommends a novel Multi-objective Artificial Hummingbird Algorithm (MOAHA) based framework for optimal RDG allocation and sizing, along with BESS operation strategy, to enhance the voltage stability margin (VSM) and reduce the overall yearly expenses. In this work, the developed formulation has been evaluated on the IEEE 33-bus system, IEEE 69-bus system, and the Masirah Island distribution grid of Oman. Furthermore, the proposed method’s Pareto fronts are concluded to be superior to four of the recent metaheuristics employed in this research domain: Multi-objective Multiverse Optimization method (MOMVO), Multi-objective Equilibrium Optimization Technique (MOEOT), Multi-objective Particle Swarm Optimization (MOPSO), and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). According to the findings, the MOAHA optimal Pareto solution candidates (PSC) for the three test systems satisfied the VSM constraints and cost objectives while providing substantial energy transfer during off-peak and peak demand hours. Additionally, all PSCs effectively avoided voltage violations, and the active power losses during each optimization period and the total energy losses were significantly reduced.

A novel unified planning model for distributed generation and electric vehicle charging station considering multi-uncertainties and battery degradation

Achieving the goal of sustainable development is dependent on the widespread integration of renewable energy sources, energy storage systems (ESSs), and electric vehicles (EVs). However, a continuous increase in the penetration of such elements would bring more complexities to the distribution network. Accordingly, this paper presents a unified planning model comprising renewable energy-based distributed generation (DG), ESS, and electric vehicle charging stations (EVCSs). In this regard, a Latin Hypercube Sampling method is utilized to generate multi-scenario for describing the uncertainty of renewable energy and load demand. The stochastic EV charging behaviors are represented by various probability density functions (PDF). In addition, an exploitable capacity loss of ESS and EV batteries is calculated by the battery degradation model based on the depth of discharge (DOD). Furthermore, the battery degradation cost is incorporated into the objective of the planning model to identify the optimal decision for candidate assets. A piecewise linearization approach is introduced to convert the problem into a mix-integer linear programming (MILP) model. Numerical results demonstrate that the exploitable capacity loss of batteries plays a key role in asset planning and provides potential contributions to the optimal decisions of the distribution network. In the meantime, by considering battery degradation in the optimization model, the sustainability and lifetime of the battery can be preserved.

태양광 시스템의 침투 및 스위칭에 따른 임계 주파수 제한 시간 결정 및 과도상태 영향 분석

For carbon neutrality, many countries are increasing the rate of renewable energy-based distributed generators (DGs). The DG has advantages such as voltage drop compensation and power loss reduction. However, reverse current flows and overvoltage phenomenons occur due to the penetration of DGs. DGs have different output characteristics from synchronous generators, so when DGs penetrate the grid, DGs inevitably affect the power flow and power quality of the grid. Therefore, it is necessary to check transient stability when DGs penetrate the power system. In this study, the IEEE 9-bus system, a looped system, was set as a testbed. In addition, inverter-based photovoltaic (PV) systems were used as an example of DGs. And the critical frequency limitation time was determined according to the penetration level of DGs. This study also checked the effect of reducing frequency variations through specific methodology of the PV system. Events such as fault occurrence, switch opening, fault clearing, and switch closing were set. And all the simulations for the transient analysis were conducted through DIgSILENT Power Factory.

Microgrids Imitate Nature for Improved Performance—Use of Nature-Inspired Optimization Techniques in Future Power Systems

There is a constant push towards increasing use of renewable energy-based distributed generators around the globe. While they provide a clean and sustainable source of energy, they employ technologies that are unknown to traditional power systems. These generators are interfaced via inverters that lack the inertia of large synchronous machines. This manifests itself as a more volatile frequency profile that is susceptible to disturbances. This phenomenon is more amplified in stand-alone microgrids which are utilized as a popular electrification alternative in isolated or underserved communities. One solution approach takes its inspiration from nature, e.g., behavior of bees, butterflies, or ants. When employed in a suitable way, animals’ natural behavior helps optimize interaction between different renewable-energy based generators and create a more stable microgrid. There are different approaches to stabilizing such systems with novel optimization approaches. Some of them optimize the ratio between generators that utilize rotating machines and inverters. Penetration of renewable energy generation is about increasing the share of inverter-interfaced generators in the system without causing stability issues. Since renewable energy resources are intermittent and not dispatchable, it is important to create a diverse portfolio where the overall system achieves some stability. For instance, if a local grid is fed by PV panels, wind generation and a small-scale hydroelectric power plant, the varying nature of these resources may complement each other. On a sunny day, PV output might be very high, and wind may not be so significant. On the other hand, on a rainy day, clouds may reduce PV output while precipitation may feed the local hydro power plant. Similarly, wind generation might complement others on a windy day. While the idea is easy to comprehend qualitatively, finding the correct ratio is not trivial. Furthermore, there are many factors at play that are independently changing and impacting the outcome. For different sites, the available renewable energy resources, their profiles as well as the local load conditions would be different. Therefore, a systematic approach is required to optimize these systems at planning, operation and control levels. Nature-inspired optimization algorithms seem to have an edge in doing just that.

Impact Analysis of Distributed Solar Photovoltaic Power Generation System Integration on Power Grid Voltage Stability Using Static Techniques

Recently, the rise in consumption of electrical energy and power transmission between various utilities has resulted in the emergence of anxieties about the power system's network voltage stability. Additionally, the necessity for power systems to perform in a secure condition has increased due to an increase in load demand globally. Therefore, the use of renewable energy-based distributed generation is growing quickly to help meet electrical demand and disrupt environmental issues caused by the use of fossil fuels. This type of generation can have a positive or negative influence on the stability of the power system. This paper analyzes the effects of distributed PV power integration on the power grid's voltage stability using static techniques. In order to improve efficacy and accuracy, give a complete and in-depth understanding of the problem of voltage stability, and identify the causes of instability, a combination of four different techniques is employed for analysis. Analysis methodologies include modal analysis, sensitivity analysis, PV curve, and QV curve. The analysis of voltage stability is carried out on an IEEE 14-bus system by using NEPLAN software. The simulation results showed that the integration of a renewable energy resource-based distributed solar PV power system into the test system led to a noticeable improvement in stability degree, a decrease in sensitivity of buses, a significant improvement in system MW loading, an enhancement in voltage profile, and a perceptible increase in reactive power margin

Overview of Virtual Synchronous Generators: Existing Projects, Challenges, and Future Trends

The rapid growth in renewable energy-based distributed generation has raised serious concerns about the grid’s stability. Due to the intrinsic rotor inertia and damping feature and the voltage (reactive power) control ability, traditional bulk power plants, which are dominated by synchronous generators (SG), can readily sustain system instability. However, converter-based renewable energy sources possess unique properties, such as stochastic real and reactive power output response, low output impedance, and little or no inertia and damping properties, leading to frequency and voltage disturbance in the grid. To overcome these issues, the concept of virtual synchronous generators (VSG) is introduced, which aims to replicate some of the characteristics of the traditional synchronous generators using a converter control technique to supply more inertia virtually. This paper reviews the fundamentals, different topologies, and a detailed VSG structure. Moreover, a VSG-based frequency control scheme is emphasized, and the paper focuses on the different topologies of VSGs in the microgrid frequency regulation task. Then, the characteristics of the control systems and applications of the virtual synchronous generators are described. Finally, the relevant critical issues and technical research challenges are presented, and future trends related to this subject are highlighted.

Flexible control of grid-connected renewable energy systems inverters under unbalanced grid faults

The sustainable energy opportunity of the future lies in the renewable energy-based distributed Power Generation systems (DPGSs). However, there are different concerns and integration issues that surround them. To increase the rate at which renewable energy can be used, this paper focuses on an integrated utility grid system. A novel strategy to control the grid side inverter is developed for a DPGS when considering an unbalanced grid. First, an analysis of the consequences of the occurrence of a fault on the grid is performed. Secondly, a control strategy is proposed to ensure the operation of the DPGS connected to the grid even if the latter is unbalanced. The proposed approach consists in developing control loops for each of the three symmetrical sequences in a specific reference frame to ensure the operation of the grid-connected DPGS even during asymmetrical grid voltage fault. The found results are promising in terms of the guarantee of service continuity during faulty conditions.

Optimum allocation of multiple type and number of DG units based on IEEE 123-bus unbalanced multi-phase power distribution system

The increasing demand for clean energy has resulted in the growth of renewable energy-based distributed generation (DG) penetration in the power distribution networks (PDN). The crucial point here is to find the optimum allocation of DGs to minimize the power loss and enhance the system performance overall. In this study, the Equilibrium Optimizer (EO) algorithm-based method is proposed to determine the location, size, connection type, and power factor of three different types of multiple DGs based on a three-phase 123-bus unbalanced PDN. The complex 123-bus UPDN has been very little studied to date, and most existing research works on the DG allocation problem have been conducted using a co-simulation platform. In this work, an unbalanced three-phase backward forward load flow (UBFLF) algorithm is written and executed under the MATLAB environment. This approach has decreased the complexity of co-simulation and provided advantages like optimizing the power factor value and the connection type (Delta/Wye) of DGs easily. Six more recent and well-established optimization algorithms such as PSO, JFO, BO, SMA, FDA, and GBO are also applied under the same test conditions to solve the Type-III DGs allocation problem. The results obtained are compared with the proposed method to show the effectiveness of the method in terms of elapsed time, statistical variables (such as standard deviation, median, variance and so on), converge speed, and best/worst case active power loss values. The efficacy of the presented method is also validated by comparing the results obtained using three distinct platforms as MATLAB/Simulink, IEEE-PES data, and OpenDSS. The detailed and comprehensive analyses show that the proposed approach demonstrates the lowest power loss, more enhancement in voltage profile, and the superiority of the EO algorithm in the application of a 123-bus unbalanced power distribution network.

RETRACTED: Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application

Referred to by: Retraction notice to ‘Control of Bidirectional DC-DC Converter for Micro-Energy Grid’s DC Feeders' Power Flow Application' IJRED 11(2), 533-546International Journal of Renewable Energy Development, 12(6). https://doi.org/10.14710/ijred.2023.57139

Optimal Planning of Multiple Renewable Energy-Integrated Distribution System With Uncertainties Using Artificial Hummingbird Algorithm

Appropriate installation of renewable energy-based distributed generation units (RDGs) is one of the most important challenges and current topics of interest in the optimal functioning of modern power networks. Due to the intermittent nature of renewable energy sources, optimal allocation and sizing of RDGs, particularly photovoltaic (PV) and wind turbine (WT), remains a critical task. Based on a new metaheuristic known as the Artificial hummingbird algorithm (AHA), this paper provides a novel approach for addressing the problem of RDG planning optimization. Considering various operational constraints, the optimization problem is developed with multiple objectives including power loss reduction, voltage stability margin (VSM) enhancement, voltage deviation minimization, and yearly economic savings. Furthermore, using relevant probability distribution functions, the ambiguities related with the stochastic nature of PV and WT output powers are evaluated. The proposed algorithm was compared to two of the recent metaheuristics applied in this domain known as improved harris hawks and particle swarm optimization algorithm (HHO-PSO) and hybrid of phasor particle swarm and gravitational search algorithm (PPSOGSA). The IEEE 33-bus and 69-bus systems are assessed as the test systems in this study. According to the findings, AHA delivers superior solutions and enhances the techno-economic benefits of distribution systems in all the scenarios evaluated.

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.

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.

Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units

Renewable energy-based distributed generators are widely embedded into distribution systems for several economical, technical, and environmental tasks. The main concern related to the renewable-based distributed generators, especially photovoltaic and wind turbine generators, is the continuous variations in their output powers due to variations in solar irradiance and wind speed, which leads to uncertainties in the power system. Therefore, the uncertainties of these resources should be considered for feasible planning. The main innovation of this paper is that it proposes an efficient stochastic framework for the optimal planning of distribution systems with optimal inclusion of renewable-based distributed generators, considering the uncertainties of load demands and the output powers of the distributed generators. The proposed stochastic framework depends upon the scenario-based method for modeling the uncertainties in distribution systems. In this framework, a multi-objective function is considered for optimal planning, including minimization of the expected total power loss, the total system voltage deviation, the total cost, and the total emissions, in addition to enhancing the expected total voltage stability. A novel efficient technique known as the Equilibrium Optimizer (EO) is actualized to appoint the ratings and locations of renewable-based distributed generators. The effectiveness of the proposed strategy is applied on an IEEE 69-bus network and a 94-bus practical distribution system situated in Portugal. The simulations verify the feasibility of the framework for optimal power planning. Additionally, the results show that the optimal integration of the photovoltaic and wind turbine generators using the proposed method leads to a reduction in the expected power losses, voltage deviations, cost, and emission rate and enhances the voltage stability by 60.95%, 37.09%, 2.91%, 70.66%, and 48.73%, respectively, in the 69-bus system, while in the 94-bus system these values are enhanced to be 48.38%, 39.73%, 57.06%, 76.42%, and 11.99%, respectively.

Investigation on Hybrid Energy Storage Systems and Their Application in Green Energy Systems

Power systems all over the world have been under development towards microgrids integrated with renewable energy-based distributed generation. Due to the intrinsic nature of output power fluctuations in renewable energy-based power generation, the use of proper energy storage systems and integrated real-time power and energy control schemes is an important basis of sustainable development of renewable energy-based distributed systems and microgrids. The aim of this paper is to investigate the characteristics and application features of an integrated compound energy storage system via simulation and a small-scale hardware system implementation. This paper first discusses the main components, working principles and operating modes of the proposed compound energy storage system. Then, a detailed design example composed of supercapacitors, batteries, and various controllers used in two typical application scenarios, peak demand shaving and power generation smoothing, of a grid-connected microgrid is systematically presented. Finally, an experimental setup with proper power converters and control schemes are implemented for the verification of the proposed control scheme. Both simulation and implementation results prove that the proposed scheme can effectively realize desired control objectives with the proposed coordinated control of the two energy storage devices.

Reliability Evaluation of Smart Distribution Grids with Renewable Energy Sources and Demand Side Management

Technical advances and reduced technology costs have led to a significant increase in the use of renewable energy-based distributed generators (DGs) in the existing distribution grids. Modern distribution grid operation is also related to the efforts and incentives to influence changes in customers’ behavior and load use, known as demand side management (DSM). The effective implementation of renewable energy source (RES)-based DGs and DSM, particularly in microgrid operating modes, may benefit customers and distribution networks. This changes in the reliability performance of distribution networks owing to the integration of wind and photovoltaic DGs are evaluated in this study along with the implementation of load shifting and peak clipping DSM schemes for residential, commercial, and industrial load sectors. A reliability evaluation is based on energy not supplied and unavailability (hours per year), calculated by considering the seasonal changes in wind speed and solar irradiation, which are represented by their corresponding Weibull distribution functions. Load variations are incorporated using a normal distribution function. The reliability performances of different RES-based DGs and DSM schemes are evaluated using the non-sequential Monte Carlo simulation approach for modeling the random occurrence of faults in the system resulting in customer interruptions with the corresponding repair times required to restore the supply.

Влияние распределенной генерации на базе возобновляемых источников энергии с использованием силовой электроники на резонанс на гармонических частотах

Влияние распределенной генерации на базе возобновляемых источников энергии с использованием силовой электроники на резонанс на гармонических частотах

Simultaneous Distribution Network Reconfiguration and Optimal Placement of Distributed Generation

A reliable, eco- and nature-friendly operation has been the major concern of modern power system (PS). To improve the PS reliability and reduce the adverse environmental effect of conventional thermal generation facilities, renewable energy based distributed generation (RDG) are being enormously integrated to low and medium voltage distribution networks (DN). However, if these systems are not properly deployed, the reliability and stability of the PS will be endangered and its quality can be dreadfully jeopardized. Among the measures taken to avoid such is optimizing the location and size of each RDG unit in the DNs. These networks are generally operated in a radial configuration, though they can be reconfigured to other topologies to achieve certain objectives. Both RDG placement/sizing and DN reconfiguration are highly non-linear, multi-objective, constrained and combinatorial optimization problems. In this study, a hybrid of Particle Swarm Optimization (PSO) and real-coded Genetic Algorithm (GA) techniques is employed for DN reconfiguration and optimal allocation (size and location) of multiple RDG units in primary DNs simultaneously. The objectives of the proposed technique are active power loss reduction, voltage profile (VP) and feeder load balancing (LB) improvement. It is carried out subject to some technical constraints, with the search space being the set of DN branches, DG sizes and potential locations. To ascertain the effectiveness of the technique, it is implemented on standard IEEE 16-bus, 33-bus and 69-bus test DNs. The proposed algorithm is implemented in MATLAB and MATPOWER environments. It is observed the power loss, voltage deviation and LB are found to be reduced by 32.84%, 12.33% and 24.03% of their respective inherent values in the biggest system when the system is reconfigured only. With the optimized RDGs placed in the reconfigured systems, a further reductions of 46.27%, 25.92% and 36.65% are observed respectively.

Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation

Multi-objective-based power system expansion planning considering distributed generation is presented in this study. An integrated model of electrical power system expansion planning with renewable energy-based distributed generation is proposed. Two objective functions, cost and the emission objective function, are implemented in the proposed model. The cost function comprises investment cost and operation cost. The investment cost covers the cost of the new generation unit, the new distributed generation unit, and the investment for the new transmission circuit. The operation cost covers the operation cost of the installed and new generation unit and the operation cost of the installed and new distributed generation unit. Two emission gasses, CO2 and NOx, are considered in the proposed model. These two gasses are expressed in the same unit as Global Warming Potential. The developed model is implemented into the IEEE 14 bus system. Lexicographic optimization combined with the epsilon constraint technique is used to solve the multi-objective problem. A Pareto optimal solution is generated by this method. Then, the fuzzy decision-making process is implemented to select the best solution from the Pareto set. The simulation results show that the distributed generation significantly reduces the overall cost of power system expansion planning. With a 30% DG penetration level, overall planning costs can be reduced by 12.62% compared to without DG penetration with an equal weighting factor for each objective function. Details of the capacity expansion of the generation unit, distributed generation unit, and transmission line are presented in this study.

Grid Voltage Sensorless Control of Single Phase Grid Tied Inverter for Renewable Energy Systems Applications

This paper introduces a grid voltage sensorless control scheme using damped resonant compensator for single phase grid interactive inverter interfacing renewable energy-based distributed generation plants with the grid. Elimination of grid voltage sensor reduces the cost and enhances the system reliability. A delay compensated model predictive current controller is also implemented with the estimation scheme to achieve the grid side control task, which includes reduction in harmonic contamination in the injected current and regulating the power factor during dynamic conditions like change in power demands and grid voltage distortions. The grid synchronization process is accomplished without a phase locked loop using the estimated grid voltage signal. A 1 kW grid tied single phase inverter is modeled and simulated for performance validation of the control scheme. The proposed control strategy is also corroborated by hardware implementation using ds1104 real time controller.