Distributed Generation Sources Research Articles

Micro Grid Integration of Distributed Power Generation Optimal Operating Methods

Microgrids, as localized energy systems comprising distributed power generation sources, offer resilience, flexibility, and efficiency to the modern power grid. Integrating various distributed energy resources (DERs) within microgrids poses challenges in optimizing their operation to ensure reliable and cost-effective energy supply. This paper presents a comprehensive review and analysis of optimal operating methods for the integration of distributed power generation within microgrids.The study explores various aspects of microgrid operation, including optimal dispatch strategies, demand-side management techniques, energy storage utilization, and renewable energy source integration. It investigates the application of advanced optimization algorithms, such as linear programming, mixed-integer linear programming, and metaheuristic algorithms, to address the complex optimization problems inherent in microgrid operation. Furthermore, the paper examines the role of smart grid technologies, advanced control systems, and communication infrastructure in facilitating the efficient operation of microgrids with distributed power generation. It discusses the importance of real-time monitoring, data analytics, and predictive modeling for enhancing decision-making processes and optimizing microgrid performance.

A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.

A comprehensive investigation of DG integration with DC microgrid

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

Distributed Generation and Demand Response Coordination for Optimal Operation of Microgrid Using Modified Elephant Herd Optimization

The optimal operation and coordination of distributed generation (DG) sources and demand response (DR) programs help to minimize the operating cost of microgrids along with other mutual benefits. The model is developed, and the multiobjective problem subjected to the set of constraints is solved by using the modified elephant herd optimization (MEHO) technique in this work. The expected output of the model is the optimal generation scheduling plan of both the dispatchable and non-dispatchable DG and optimized cost for the isolated microgrid operation. The other objectives related to the environmental aspects and reliability are also considered for optimization. DG operation is coordinated with the day ahead DR programs to minimize the costs while satisfying the operation limits of the DG sources and consumer demand. The novelty of the work lies in finding the optimal solution considering the multiobjective for optimal DG operation and analyzing the impact of different types of DR programs on microgrid operation by the MEHO technique. The effectiveness of the proposed model and validity of the algorithm is further tested on a 14 bus microgrid benchmark system. Through simulation and optimization results it is observed that the proposed algorithm has less computational time and better accuracy.

A Method for Assessing the Degradation of PVC-Insulated Low-Voltage Distribution Cables Exposed to Short-Term Cyclic Aging

The distribution grid comprises cables with diverse constructions. The insulating material used in low-voltage (LV) distribution cables is predominantly PVC. Furthermore, the presence of cables with different structures in the grid poses challenges in detecting the aging of the cable network. Finding a universal and dependable condition-monitoring technique that can be applied to various types of cables is indeed a challenge. The diverse construction and materials used in different cables make it difficult to identify a single monitoring approach that can effectively assess the condition of all cables. To address this issue, this study aims to compare the thermal aging behavior of different LV distribution cables with various structures, i.e., one cable contains a PVC belting layer, while the other contains filler material. The growing adoption of distributed generation sources, electric vehicles, and new consumer appliances in low-voltage distribution grids can lead to short, repetitive overloads on the low-voltage cable network. Hence, these cable samples were exposed to short-term cyclic accelerated aging in the climate chamber at 110 °C. The cable’s overall behavior under thermal stress was evaluated through frequency and time domain electrical measurements (including tan δ and extended voltage response) and a mechanical measurement (Shore D). The tan δ was measured in the frequency range of 20 Hz–500 kHz by using the Wayne-Kerr impedance analyzer. The extended voltage response measurement was conducted using a C# application developed in-house specifically for laboratory measurements in the .NET environment. The study observed a strong correlation between the different measurement methods used, indicating that electrical methods have the potential to be adopted as a non-destructive condition-monitoring technique.

A comprehensive overview of DC‐DC converters control methods and topologies in DC microgrids

AbstractMicrogrids with large‐scale photovoltaic systems constitute a large part of distributed renewable generation in many grids around the world. Managing the performance of such microgrids and especially their interaction with the main power grid is a challenging task, because it requires the control of renewable resources. This paper presents a comprehensive overview of DC‐DC converter structures used in microgrids and presents a new classification for converters. This paper also provides an overview of the control techniques of DC‐DC converters in DC microgrids and the advantages and disadvantages of the control methods are discussed. In connection with the increasing penetration of distributed generation sources (DGS) and renewable sources in power systems and their power management has been raised as a major concern and methods for power management have been investigated in this paper. Finally, a DC microgrid system, which includes a solar system, wind turbine, and battery, is simulated in MATLAB/Simulink software and its performance is analyzed.

Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm

This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.

Multi-Power Carriers-Based Integrated Control for Series-Cascaded Microgrid

Series-cascaded microgrids (SCMGs) indeed provide control flexibility and high-voltage synthesis capabilities. However, the power distribution in SCMGs based on distributed generation (DG) sources stays understudied. This paper proposes an SCMG topology using non-dispatchable DG sources and battery energy storage, with an integrated power-routing control. The objective is to address power distribution limitations and stabilize SCMG output voltages under varying conditions. A case study validates the control methodology, considering zero irradiation levels for photovoltaic (PV) and maximum power sharing. The battery modules play a crucial role by providing power, voltage support, and maintaining capacitor voltage at a reference value of a PV-integrated module. This is achieved through a third harmonic current injection in the fundamental frequency current, coupled with proportional power distribution using a third harmonic power signal. The effectiveness of the proposed SCMG topology and control is demonstrated through MATLAB/Simulink and hardware-in-loop analyses (Typhoon HIL). The results present an extended power distribution between series-cascaded DG sources-based units while ensuring stable SCMG output voltages, even in adverse conditions like PV module intermittency. Future work aims to extend the proposed topology to a ring/delta-connection SCMG, where third harmonic current aids power distribution among SCMG legs and between series-cascaded DG sources-based units.

Using Arithmetic Optimization Algorithm to Allocate and Size Wind Energy Systems in RDSs

Recent years have seen a marked increase in the world’s energy needs. Numerous studies have been conducted to examine distributed generation (DG) utilizing renewable energy sources (RESs) in order to address this need. The number of environmental problems that are raised by the usage of traditional power plants is also decreased by these renewable sources. The ideal position and size of the RESs-DG significantly influence the bus voltage profile, power quality, and efficiency of Radial Distribution Systems (RDS) because of power losses. In this study, the use of wind energy systems as a DG source in RDS is investigated. One of the most common RESs used as DG sources, the ideal location and size for wind system, was chosen to demonstrate this enquiry. The goal of this optimization work, which used the Arithmetic Optimization Algorithm (AOA), was to increase system efficiency by minimizing power losses and improving the voltage profile and power quality. Two widely used RDS, including the IEEE 31 and 69 bus systems, have been used to evaluate how well the recommended technique has been implemented. Genetic Algorithm (GA) is offered to examine the efficacy of the recommended AOA. The findings show that the used AOA approach can pinpoint the appropriate size and positioning of a wind farm in order to reduce power loss, enhance voltage profile, and outperform other existing tactics with superiority over GA.

OPTIMAL COORDINATED VOLTAGE CONTROL OF ELECTRICAL DISTRIBUTION NETWORKS IN THE PRESENCE OF DISTRIBUTED RENEWABLE ENERGY SOURCES

Recently, distributed generation (DG), as clean and renewable energy, has increased due to the global warming problems and depletion of fossil sources. Several DGs are installed near the end-user, with the feature that this will decrease power losses. However, without optimal control, many problems, such as voltage instability and power losses, may affect the performance and the good operation of the power system. This paper proposes an optimal coordinated voltage control (OCVC) method for the distribution networks with distributed generation sources. The method is based on a genetic algorithm (GA) approach in multi-core simulation with an open-source OpenDSS to obtain the optimal setting of voltage control devices adjusted remotely. This method takes into account time-varying load and generation based on day-ahead scheduling. To validate the performances of the proposed technique, simulations are performed in a distribution network test with DG integration.

Events Classification in Power Systems with Distributed Generation Sources Using an LSTM-Based Method with Multi-Input Tensor Approach

In this paper, a long short-term memory (LSTM)-based method with a multi-input tensor approach is used for the classification of events that affect the power quality (PQ) in power systems with distributed generation sources. The considered events are line faults (one line, two lines, and three lines faulted), islanding events, sudden load variations, and generation tripping. The proposed LSTM-based method was trained and tested using the signals produced by the events simulated in a study system with distributed generation sources via PSCAD®. Then, noise with different levels was added to the testing set for a thorough assessment, and the results were compared with other well-known methods such as convolutional and simple recurrent neuronal networks. The LSTM-based method with multi-input proved to be effective for event classification, achieving remarkable classification performance even in noisy conditions.

A Microgrid Test System for Protection Coordination Studies

This paper proposes a comprehensive 26-bus microgrid (MG) test system designed to validate or propose new protection coordination schemes. The proposed MG test system comprises various components facilitating the simulation of diverse configurations and operating modes. It also presents a predefined overcurrent protection scheme which is validated with and without Distributed Generation (DG), offering detailed specifications and settings for the test network. The MG test system outlined in this paper incorporates multiple DG sources in different buses, multiple voltage levels, and two operating modes (with and without DG). Additionally, the overcurrent protection scheme considers multiple functions and devices simultaneously. The detailed features of the proposed protection coordination scheme enable the simulation of various fault types and contingencies. The paper also presents specific details of the MG test system under different single-phase and three-phase faults, reporting bus voltages in nodes, transformer and line loadability, short-circuit currents, and operating times. The MG test system is meant to serve as a valuable tool for researchers to conduct comparative studies and develop new strategies regarding the operation and planning of MGs.

Optimal Energy Management of Parking Lots in Microgrid to Reduce Operation Cost by Dynamic Parameters Lightening Search Algorithm

In this paper, a dynamic parameters lightning search algorithm (DP-LSA) is proposed for optimal operation management of electric vehicle (EV) parking lots (PLs) and distributed generations (DGs) in a microgrid (MG). The MG comprises distributed generation sources such as diesel generators and fuel cells. The batteries of electric vehicles parked in the PL are also used for energy storage. A proposed objective function combines operating, maintenance, and replacement costs, considering inflation, motivation, and annual growth. Simulations are done in two scenarios, and the proposed algorithm results are compared with particle swarm optimization (PSO) algorithm results. Simulation results prove the better performance of the proposed LSA algorithm than the PSO algorithm in reducing the microgrid operation cost.

Analysis of "Source-Network-Load-Storage" Integrated Operation Mode for Park Microgrids

This article discusses the concept and characteristics of a park microgrid, as well as the principles and analysis of the integrated operation mode of "generation-network-load-storage". A park microgrid refers to the supply and management of energy within a park through distributed power generation sources, microgrid network architecture, load management, and energy storage technologies. By analyzing distributed power generation sources, optimizing microgrid network architecture, load forecasting and management, as well as the selection and application of energy storage technologies, the energy supply diversification and efficient optimization of the park microgrid can be achieved. This article summarizes the above content and emphasizes the importance of the integrated operation mode for the park microgrid.

Quantum annealing algorithm for fault section location in distribution networks

As the last link of the power system, the distribution network is responsible for ensuring stable power consumption and improving power quality. Therefore, a more reliable and fast fault section location(FSL) method is essential for the stable operation and optimization of distribution networks. In this context, this paper adopts an effective method to apply the quantum annealing algorithm(QA) based on the quantum tunneling mechanism to the distribution network fault section location problem. A quantum Hamiltonian function consisting of potential and kinetic energy terms is constructed based on the theoretical knowledge of QA. Among them, FSL objective function is mapped to the potential energy term, and the transverse magnetic field is introduced to construct the kinetic energy term, which can realize the quantum tunneling effect and approximate or even reach the global optimal solution. Based on the quantum Hamiltonian function construction, this paper modifies some parameters in the QA framework to propose an improved quantum annealing algorithm(IQA) to improve the accuracy. In the two test systems of IEEE 33-node distribution network and IEEE 33-node distribution network with distributed generation sources(DGs), QA and IQA are compared and analyzed with other intelligent algorithms using the average number of iterations and localization accuracy as indicators. We find that QA is more likely to obtain the global optimal solution compared with the simulated annealing algorithm(SA). IQA can search for faulty sections with 100% accuracy and the least number of average iterations in both single power distribution networks and distribution networks containing DGs. Under the scenarios of fault signal distortion and increasing fault sections, IQA shows superb competitive advantages by exhibiting good fault tolerance performance, global optimal search capability and stability.

New Passive Loss of Mains Detection Scheme Using Characteristics of Differential Impedance of Series Inductor

ABSTRACT Nowadays, renewable energy sources (RESs) are widely used as distributed generation sources, leading to issues such as unintentional islanding, protection concerns, and reverse power flow. Unintentional islanding is dangerous because it can endanger utility workers, damage DG units, and jeopardize grid stability. To overcome these issues, Researchers are developing islanding detection schemes. This article proposes a novel passive technique for islanding detection of multi-bust test systems, based on the differential impedance of a series inductor. The differential impedance changes during islanding and non-islanding events. However, in grid-connected mode, the voltage and frequency of the microgrid are handled by the utility grid. Therefore, no significant changes in differential impedance occur. The voltage and frequency of the microgrid are not handled by the power grid while in islanded mode. Therefore, the differential impedance varies significantly. The islanding is detected if the value of differential impedance is greater than the predefined threshold. This scheme detects islanding in 24 ms at zero mismatch power, has no impact on power quality, and is computationally simple. The proposed approach easily discriminates between islanding and non-islanding events. The distribution test system is simulated in MATLAB/Simulink environment. Additionally, the novel passive technique has been validated on an OPAL-RT real-time platform.

Reactive Power Sharing Among Distributed Generation Sources in Islanded Microgrids to Improve Voltage Stability

Reactive Power Sharing Among Distributed Generation Sources in Islanded Microgrids to Improve Voltage Stability

Power-Flow Simulations for Integrating Renewable Distributed Generation from Biogas, Photovoltaic, and Small Wind Sources on an Underground Distribution Feeder

The rapid expansion of distributed generation leads to the integration of an increasing number of energy generation sources. However, integrating these sources into electrical distribution networks presents specific challenges to ensure that the distribution networks can effectively accommodate the associated distributed energy and power. Thus, it is crucial to evaluate the electrical effects of power along the conductors, components, and loads. Power-flow analysis is a well-established numerical methodology for assessing parameters and quantities within power systems during steady-state operation. The University of São Paulo’s Cidade Universitária “Armando de Salles Oliveira” (CUASO) campus in São Paulo, Brazil, features an underground power distribution system. The Institute of Energy and Environment (IEE) leads the integration of several distributed generation (DG) sources, including a biogas plant, photovoltaic installations, and a small wind turbine, into one of the CUASO’s feeders, referred to as “USP-105”. Load-flow simulations were conducted using the PowerWorldTM Simulator v.23, considering the interconnection of these sources. This dataset provides comprehensive information and computational files utilized in the simulations. It serves as a valuable resource for reanalysis, didactic purposes, and the dissemination of technical insights related to DG implementation.

Decentralized self-stabilizing primary control of microgrids

The usage of distributed generation sources and the segmentation of power systems into microgrids brings several advantageous features, such as energy independence, lower transmission losses, and improved energy management flexibility. Still, the expected steady abolition of traditional centralized networks and their centralized control schemes introduces several operational difficulties, that can compromise (micro)grid’s stability and reliability. Consequently, several approaches to how microgrids should be operated have been proposed, albeit with several salient deficiencies. Precise power exchange, control of frequency and voltage excursions, applicability on different grid topologies, and the possibility to integrate grid codes, which are very important control aspects, are not jointly enabled in a general case. This paper proposes a novel approach, based on a specific goal function and corresponding decentralized control that addresses the mentioned problems and guarantees the microgrid’s stability by constraining the frequency and node voltage deviations, simultaneously supporting the desired active power exchange between prosumers. Unlike droop-based and droop-derived control schemes, the proposed approach aims to inherently preserve the frequency and voltage levels within the limits defined by the grid operator or future microgrid codes, thus preventing the generation unit’s unwanted disconnection during transients and disturbances. In addition, the control algorithm is independent of network topology, can be applied in both islanded and non-islanded microgrids, and secures system scalability. To confirm the effectiveness of the proposed control strategy, a number of simulation results are presented and thoroughly discussed.

Multi-Objective Operational Cost Management with Minimum Net Emission of a Smart Microgrid

This research article considers a microgrid (MG) with various distributed generation sources (DGs) such as microturbine (MT), fuel cell (FC), wind turbine (WT), and solar cell (PV) along with the battery storage system. This article aims to simultaneously minimize operational costs and emissions for a daily schedule, while considering uncertainties such as load demand, market prices, and electricity generated by different renewable energy sources (RESs). This article proposes the slime mould algorithm (SMA) to solve this complex optimization problem. Initially, SMA is tested for three scenarios considering the minimization of operational cost and emission, respectively, as two single objectives. In Scenario A, a reduction of 1.95% in operational cost and 4.57% in emissions is observed compared to the Grasshopper Optimization Algorithm (GOA). In scenarios B and C, SMA reduces operational cost by 0.78% and 2.65%, respectively, while also reducing emissions by 6.27% and 0.28%, respectively, when compared to GOA. The results obtained by other algorithms are worse than those obtained by GOA. This study also showed that SMA effectively considers multi-objective functions by using the weighted sum method and the fuzzy decision-maker to approach the Pareto-optimal solution, achieving a good tradeoff between operational cost and emissions in each scenario.