Distributed Generation Units Research Articles

Reliability enhancement through optimal placement of photovoltaic power plant and battery energy storage in distribution system

Integration of distributed generation (DG) units could reduce the duration of power outage for a certain number of consumers in distribution networks after a network failure. Prerequisites are that i) the DG unit has the technical characteristics that enable its islanded operation, the degree of automation of the distribution network allows it and ii) it is in accordance with the current technical regulations of the country. The extent to which integration of distributed sources can improve reliability depends on the DG size, type, and the point of common coupling. Besides, an energy storage system could be installed along with DG unit, so that energy supply availability during fault periods can be secured. This paper proposes an algorithm based on mixed-integer linear programming (MILP) approach for optimal placement of photovoltaic power plant (PVPP) and battery energy storage system (BESS). The optimal location is determined so that expected non supplied energy is minimized. The uncertainties in the estimation of production of PVPP, load and BESS state of charge (SoC) were taken into account by Monte Carlo simulations (MCS).

Islanded microgrid: hybrid energy resilience optimization

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.

A new fault detection and relay coordination technique for low voltage DC microgrid

Integration of various distributed generation units in the DC distribution network creates an outsized impact on magnitude and direction of fault current. The specific behaviour of fault current during a short circuit fault demands quick and reliable detection and isolation of fault, which poses a significant challenge in developing an effective protection and coordination scheme for DC microgrids. In this regard, a new fault detection and relay coordination algorithm is proposed for a Low voltage DC (LVDC) microgrid, which utilizes inverse time relay characteristics based on variance of fault current. While the relay responds and picks up during both internal and external faults, its operation is automatically limited upon operation of the downstream circuit breaker without the need for any communication channel. The efficacy of the proposed strategy is evaluated by modelling a mesh-connected DC microgrid network using PSCAD/EMTDC software. The protection coordination problem is developed as a constraint non-linear optimization problem to determine optimal relay settings. The proposed approach has been tested by simulating various types of faults at different fault locations and fault resistances along with various microgrid operating modes. The results suggest that the proposed strategy stands out by optimizing relay operating time and providing backup protection while upholding relay coordination. A comparative assessment of the proposed technique with existing ones proves its effectiveness in terms of swift relay tripping time, better immunity against noise, and independency against microgrid operating mode and topology.

Distributed Generation Approach with Helping of Charging Stations

This study addresses the optimization difficulties created by the combination of distributed generation (DG) and charging stations (CS), with the key difference that charging stations not only permit electric vehicle charging but also actively contribute to power supply. Using Convex Optimization (CVX) methods, the study formulates and solves optimization issues for coordinating and controlling DG units, taking into account charging stations' dual roles as power suppliers. The findings indicate that EVs can significantly contribute to grid stability, but challenges such as battery degradation and the need for advanced battery management systems (BMS) must be addressed to optimize these benefits. Simulation results show a potential improvement in grid stability the promise and the practical considerations of this innovative approach.

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Strategic Integration and Configuration of Distributed Generators Units in Distribution Networks: An Overview

Efficient reduction of power losses by allocating various DG types using the ZOA algorithm

The continuous challenge of ensuring energy supply is a significant concern. Traditionally, utilities have addressed increasing load demands by building new central power plants or expanding existing ones. However, these methods are time-consuming and costly, providing only short-term solutions. Additionally, power plants are often far from load centers, resulting in higher power losses during long-distance transmission. The integration of distributed generation (DG), characterized by smaller scale and lower capital costs, with nearby consumers can effectively address these challenges. Due to the significant influence of DG penetration and their specific locations on the distribution system's performance, this study offers a novel Zebra Optimization Algorithm (ZOA). The suggested ZOA is used to optimize the capacity and position of various DG units connected to a radial distribution system to reduce power losses and study the stability of the system after DG allocation using the Voltage Stability Index (VSI). The evaluation is conducted using the IEEE 33-bus test system, considering various scenarios including single and multiple DGs of different types. The results demonstrate that in the best scenario, the ZOA can reduce losses by up to 94.25 % and improve the VSI from 0.69643 to 0.9605. These findings highlight the superior performance of the ZOA over other techniques in optimizing DG placement and sizing for power system performance.

Satin bowerbird algorithm with an adaptive constriction factor for enhanced photovoltaic integration in distribution feeders

—This paper introduces an enhanced Satin bowerbird optimizer (SBO), specifically tailored for the integration of photovoltaic distributed generation (DG) units into radial distribution systems. The upgraded SBO version involves two pivotal adjustments. Firstly, the positional updating mechanism of the newly generated bower is modified to enable exploration around the iteration's elite bower. Secondly, an adaptive constriction factor is introduced, progressively decreasing during iterations to concentrate the search in promising areas. These modifications significantly amplify the exploration capacity of each new bower in the SBO algorithm. The proposed upgraded SBO aims at minimizing costs related to CO2 emissions from the grid and those linked with photovoltaic units, in addition to energy losses. The variability of photovoltaic DG units is represented using the Beta Probability Density Function (PDF) to portray distinct solar irradiation conditions experienced daily. The enhanced SBO version undergoes testing on a practical Nigerian distribution system, the Ajinde 62-bus network, and a standard IEEE 69 nodes system. Simulation results underscore the effectiveness of the upgraded SBO version, revealing substantial reductions in energy losses and emissions. Specifically, for the Ajinde 62-bus network, the proposed SBO version achieves a noteworthy 31 % reduction in the combined yearly costs of energy losses and emissions compared to the initial case. Additionally, for the IEEE 69-bus system, it attains a considerable reduction of 35 %. Furthermore, the simulation results illustrate the competitive performance of the suggested SBO version when compared to Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms, as well as the standard SBO.

Improvement of an AC microgrid system’s stability by using a fuzzy logic-based PSS controller

This research article presents the low-frequency oscillation damping control of an AC Microgrid. It is integrated with four distributed generation units in which two DGs are renewable energy source based solar PV and wind energy using DFIG and the other two DGs are conventional synchronous generator-based hydro and diesel generators. There is insufficient generation through renewable-based DG then a diesel generator is employed as an emergency unit to fulfil the requirements and maintain and control the deviation of the frequency of the system. The use of renewable-based DG affected the system's dynamic stability and the characteristics of an integrated microgrid differ from conventional sources of generation. This investigation explores the impression of the deployment of multiple forms of the power oscillation damping (POD) controllers to distributed generation sources of grid forming AC Microgrid. The main various types of POD controllers are conventional lead-lag PSSs, MBPSS-4B, and proposed robust fuzzy logic PSS (FLPSS) controllers are implemented on distributed generation units of AC Microgrid for the elimination of low-frequency oscillation. Low oscillation frequencies between 0.1 and 2 Hz are the focus of this work. The system's damping ratios and stability margins because of MG integration are observed using eigenvalues analysis. The results of the simulation test demonstrate the accomplishment of the robust fuzzy logic PSS controller regarding conventional lead-lag PSSs and the MBPSS-4B controller during circumstances of fault.

Simultaneous feeder reconfiguration and distributed generation planning in the presence of voltage-dependent and variable loads

In distribution systems, where distribution losses and the output of distributed generators (DGs) are significantly impacted by load power, an effective approach for reducing power loss is required, which the hybrid operation of DG units and grid reconfiguration can serve as a best alternative. Load power exhibits variability, altering alongside the voltage fluctuations which occur over time. Furthermore, the correlation between power demand and voltage relies on the type of load. Nonetheless, the incorporation of these important concerns into research on reconfiguration and distributed generation planning is rare. Only a limited number of papers have taken into account the voltage dependence and the type of time-varying loads in their respective models. Nevertheless, they proposed models with significant nonlinearity, requiring computation through nonlinear solvers or metaheuristic algorithms. Meanwhile, these models require the use of intensive linearization techniques to facilitate their implementation through linear solvers. High computational time is demanded by nonlinear solvers, while metaheuristic algorithms cannot guarantee the attainment of optimal solutions. Hence, the accurate modeling of load behavior holds importance in the active distribution system reconfiguration. In this paper, a proficient reconfiguration model is presented, which is both straightforward for implementation in conventional optimization tools and adept at identifying appropriate solutions for the reconfiguration and DG planning problem.

Optimal Placement and sizing of DG Units Using LCA Algorithm

The motivation of this paper is to present an approach that relies on the league championship algorithm (LCA) to determine the most effective number, placement, and size of distributed generations (DG) within distribution systems. The primary objective of this approach is to minimize the losses of the power system, as well as to enhance the voltage profiles and stability index of the voltage. The optimal location of the DG units is determined through the use of the Loss Sensitivity Factor (LSF), while the optimal size of the DG units is found through the use of LCA. The IEEE 33-bus and 69-bus radial distribution systems were both tested to validate the proposed method, and the results obtained from LCA were compared to those of other methods found in the literature. The simulated results have shown that the LCA method proposed in this paper is highly effective and performs exceptionally well when addressing the problem of optimal location and sizing of DG units in radial networks.

Impacts of Plug-in EVs and decentralized power generation on distribution system operation

Plug-in Electric Vehicles (PEVs) have gained popularity in recent years due to their environmentally friendly attributes. However, integrating PEVs into distribution systems poses novel challenges and opportunities. This research paper undertakes a comprehensive assessment of the repercussions of PEVs and distributed generation (DG) integration on the distribution system’s reliability, power loss, and voltage stability. In order to alleviate peak loads, select PEVs are deployed in Vehicle-to-Grid (V2G) mode. The integration of DG units, particularly solar Photovoltaic (PV) and wind turbine (WT) systems, is explored to mitigate the potential strain of PEV charging on the distribution system. The uncertainty of renewable DG and PEV charging-discharging, along with time-varying residential and commercial loads, are included in the analysis. Power interruption duration and frequency-based reliability index are considered in this work. The combined effects of PEVs’ Grid-to-Vehicle (G2V) and V2G mode and DG integration are comprehensively investigated, and the impact assessment is conducted on the modified IEEE 33 bus distribution system to ensure the practicality and applicability of the research findings.

ANALYSIS OF MICRO GAS TURBINE PERFORMANCE USING MODELLING APPROACH

Micro-gas turbine (MGT) is compact and self-sufficient distributed power generation units that exhibit reliability and has the capability to mitigate carbon monoxide (CO) emissions while conserving energy together with biomass used as a fuel sources. It is expected that they will have a noteworthy impact on securing the provision of upcoming energy resources for isolated areas, disaster condition and off-grid zone regardless of their connection to the power grid. However, the MGT performance using the various fuel still questionable. Databases and procedure of the MGT performance analysis should be established well to understand the overall MGT performance. In this study, MGT performance is determined by utilising the computer aided design (CAD) and solver software such as SOLIDWORKS and ANSYS-FLUENT. CAD drawing is used to design the combustion chamber and simulation performance is visualised in specific inside the combustion chamber (CC) of the MGT. The solver software was employed for the purpose of conducting computational fluid dynamics (CFD) analyses. The optimal configuration contained a flame holder with a diameter of 50 millimetres, a chamber height of 60 centimetres, four holes with diameters of 6, 8, and 10 millimetres, and a dead zone separating the combustion and dilution zones. The air inlet boundary conditions were established by utilising the specifications and compressor maps of the Garrett GT25 turbocharger in the simulation. The simulation employed the standard k-ε model of viscous model and energy equation to establish an optimal maximum airflow rate of 0.15 kg/s at a pressure of 1.4 bar, resulting in a compressor efficiency of approximately 70%. Three different fuels, diesel-air, wood-volatiles-air, and coal-hv-volatile, were used to determine the MGT's optimal performance. Successful investigation of this study opts to significant contribution to understand the MGT performance thus, give the awareness to choose as one of the alternative power generations which can be used in the future.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

AI-based optimal allocation of BESS, EV charging station and DG in distribution network for losses reduction and peak load shaving

In response to the growing integration of battery energy storage systems (BESS), electric vehicles (EV), and distributed generation (DG), planning frameworks have emerged to optimize the deployment of these units within distribution networks. In this context, this study introduces an artificial intelligence (AI) approach utilizing the genetic algorithm optimization technique to identify optimal installation nodes for BESS, EV charging stations, and DG units within the grid. The framework aims to minimize energy losses in the distribution system, incorporating a peak load shaving strategy in the BESS. The mathematical model encompasses technical and operational constraints from the resources and the grid. To address the complexity ensuing from exploring an extensive solution space, handling constraints, and managing the decision variables, a real coded genetic algorithm is employed to determine the optimal locations. Through simulations on a distribution feeder, the study demonstrates that resources installed based on the proposed approach significantly benefit the grid. This is evidenced by a reduction in energy losses, improved voltage levels, and demand mitigation during peak periods. An analysis of the BESS impact on losses, with respect to its installation nodes, is also conducted. Furthermore, the optimization approach is validated using benchmark functions and compared with another method.

Presenting a new method for optimal placement of reliability-based distributed generation units in the transmission system considering the demand response schedule

Presenting a new method for optimal placement of reliability-based distributed generation units in the transmission system considering the demand response schedule

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

OPTIMAL SCHEDULING OF RENEWABLE ENERGY RESOURCES IN ENERGY MANAGEMENT SYSTEMS USING HYBRID GENETIC ALGORITHM AND PARTICLE SWARM OPTIMIZATION

Emphasizing the importance of Energy Management (EM) systems, the rise in Distributed Generation (DG) and the introduction of multicarrier energy networks have become key factors. An EM is a novel concept introduced in multicarrier energy networks. It enables the transmission, reception, and storage of various types of energy. Thus, this paper presents an enhanced energy hub incorporating various renewable energy-based DG units and heating and power storage systems. It focuses on modeling the operational and organizing elements of the system. In addition, the modeling of optimal planning and scheduling for a multicarrier EM system considers the unpredictable nature of wind and Photovoltaic (PV) units. An effective solution to the EM problem, cost reduction, peak-to-average ratio (PAR), and carbon emission can be achieved through a seamless combination of Renewable Energy Sources (RES) and Power Storage Systems (PSS). This work presents an Optimal Scheduling and Energy Management System utilizing Hybrid Genetic Algorithm and Particle Swarm Optimization (OSEMS-HGA-PSO). This approach combines the strengths of both GA and PSO, resulting in better convergence and superior solutions for optimal scheduling of RES in EM systems. The numerical evaluation assesses the effectiveness of the heuristic algorithms and the proposed system. The results show that the HGA-PSO EM system significantly decreases the cost, PAR, and carbon emission by 58.74%, 57.19%, and 90%, respectively.

Optimal Allocation of Capacitor Banks and Distributed Generation: A Comparison of Recently Developed Metaheuristic Optimization Techniques on the Real Distribution Networks of ALG-AB-Hassi Sida, Algeria

Recent advancements in renewable energy technologies, alongside changes in utility infrastructure and progressive government policies, have bolstered the integration of renewable-based distributed generation units within distribution systems. This paper introduces the Energy Valley Optimizer, a novel tool designed for the strategic placement of distributed generation units and capacitor banks. This placement is crucial not only for optimizing energy loss and enhancing bus voltage stability but also for promoting sustainable energy use and reducing environmental impact over the long term. By minimizing energy loss and voltage fluctuations, the optimizer contributes to a more sustainable and resilient energy system. It achieves this through the optimal allocation of resources across various load patterns within a 24 h period and is tested on the ALG-AB-Hassi-Sida 157-bus distribution network in South Algeria. Comparative analysis with existing algorithms—such as the Liver Cancer Algorithm, Walrus Optimization Algorithm, and Zebra Optimization Algorithm—demonstrates the superior performance of the Energy Valley Optimizer. It not only enhances technical and economic efficiencies but also significantly lowers the total cost of energy over 24 years, thus supporting sustainable development goals in energy management.

An Enhanced Power Allocation Strategy for Microgrids Considering Frequency and Voltage Restoration

In a microgrid, load power should be properly shared among multiple distributed generation (DG) units, not only for fundamental power but also for negative sequence and harmonic power. In this paper, the operation of a microgrid under imbalance and nonlinear load conditions is studied, and a consensus algorithm-based distributed control strategy is proposed for the microgrid power allocation, frequency, and voltage restoration. First of all, the output current of DG unit is decomposed by second-order generalized integrator (SOGI) modules to obtain the fundamental power and harmonic power through the power calculation formula. Then, state values of DG units, such as local power, frequency, and voltage, are transmitted on a sparse communication network. Under the action of a consensus algorithm, the real power of DG units is allocated following the equal increment principle; the reactive power, imbalance, and harmonic power are allocated according to the capacities of DG units; and the frequency of the microgrid and the voltage at the point of common coupling (PCC) are rated. In the consensus-based strategy, DG units only communicate with their neighbor units; thus, the “plug and play” function is reserved. Compared with the centralized control strategy, the proposed strategy with a distributed consensus protocol can simplify the maintenance and possible expansions of the system, making the microgrid more flexible. Moreover, as the structure of the detailed network is not required, it is easy to apply in practice. Simulation and experiment results are presented to verify the proposed method.