Distributed Generation Research Articles

A short-circuit protection scheme for active distribution networks based on improved adaptive current reliability coefficient

Abstract Addressing the issue of misoperation in traditional three-zone current protection caused by the assisted, drawdown, or reverse current due to the large-scale integration of distributed generation (DG) into distribution networks (DNs), an adaptive current short-circuit protection scheme based on local information is proposed for active DNs (ADNs). Firstly, the adaptive current protection reliability coefficients are improved for the setting of protection zones I and II of the lines in the ANDs. On the DG side, an improved reliability coefficient is constructed using an e-exponential function. Subsequently, the distance between the fault point and the busbar is calculated by analyzing the relation between the positive-sequence voltage and positive-sequence current at the measuring point when faults occur at various locations. Based on this distance parameter, the reliability coefficient is adjusted in real time. Finally, the modified adjustable reliability coefficient, along with the online-calculated equivalent impedance and equivalent electromotive force, is used to coordinate the setting of short-circuit protection zones I and II of the lines in the ADNs. Simulation results demonstrate that, compared with traditional adaptive current protection schemes, this proposed scheme is independent of the location of short-circuit fault points, the grid-connected capacity, or the number of DGs, exhibiting better selectivity and reliability.

Multi-Agent Deep Reinforcement Learning-Based Distributed Voltage Control of Flexible Distribution Networks with Soft Open Points

The increasing number of distributed generators (DGs) leads to the frequent occurrence of voltage violations in distribution networks. The soft open point (SOP) can adjust the transmission power between feeders, leading to the evolution of traditional distribution networks into flexible distribution networks (FDN). The problem of voltage violations can be effectively tackled with the flexible control of SOPs. However, the centralized control method for SOP may make it difficult to achieve real-time control due to the limitations of communication. In this paper, a distributed voltage control method is proposed for FDN with SOPs based on the multi-agent deep reinforcement learning (MADRL) method. Firstly, a distributed voltage control framework is proposed, in which the updating algorithm of the intelligent agent of MADRL is expounded considering experience sharing. Then, a Markov decision process for multi-area SOP coordinated voltage control is proposed, where the control areas are divided based on electrical distance. Finally, an IEEE 33-node test system and a practical system in Taiwan are used to verify the effectiveness of the proposed method. It shows that the proposed multi-area SOP coordinated control method can achieve real-time control while ensuring a better control effect.

Flexibility-Constrained Energy Storage System Placement for Flexible Interconnected Distribution Networks

Configuring energy storage systems (ESSs) in distribution networks is an effective way to alleviate issues induced by intermittent distributed generation such as transformer overloading and line congestion. However, flexibility has not been fully taken into account when placing ESSs. This paper proposes a novel ESS placement method for flexible interconnected distribution networks considering flexibility constraints. An ESS siting and sizing model is formulated aiming to minimize the life-cycle cost of ESSs along with the annual network loss cost, electricity purchasing cost from the upper-level power grid, photovoltaic (PV) curtailment cost, and ESS scheduling cost while fulfilling various security constraints. Flexible ramp-up/-down constraints of the system are added to improve the ability to adapt to random changes in both power supply and demand sides, while a fluctuation rate of net load constraints is also added for each bus to reduce the net load fluctuation. The nonconvex model is then converted into a second-order cone programming formulation, which can be solved in an efficient manner. The proposed method is evaluated on a modified 33-bus flexible distribution network. The simulation results show that better flexibility can be achieved with slightly increased ESS investment costs. However, a large ESS capacity is needed to reduce the net load fluctuation to low levels, especially when the PV capacity is large.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

Multi-faceted sustainability improvement in low voltage power distribution network employing DG and capacitor bank

Efficient distribution of active and reactive power in distribution networks is crucial for ensuring that consumer demand is met and that electrical power quality is maintained. This requires careful planning, particularly in terms of optimally placing and sizing distributed generator (DG) and capacitor bank (CB) units. These units play a key role in minimizing power losses and voltage fluctuations within the network. However, implementing DG and CB units in unbalanced distribution networks presents unique challenges due to inherent system unbalances. To address these challenges, this article proposes a method for allocating and sizing DG and CB units in unbalanced distribution networks. The approach accounts for the network's unbalance and targets multiple objectives, such as reducing power losses, improving multi-phase voltage stability, and minimizing phase-to-phase voltage unbalance. The fast and flexible radial power flow (FFRPF) technique is employed to model complex interactions and constraints within the network, leading to a multi-objective optimization problem. This optimization problem is solved using the weight aggregated particle swarm optimization (WA-PSO) method, a variant of particle swarm optimization tailored for multi-objective functions. WA-PSO simplifies the process by combining multiple objectives into a single function using weighted aggregation. The efficacy of this approach is validated on 19, 34, and 123-node unbalanced radial distribution networks (URDNs). The results show significant improvements in power delivery efficiency across all tested networks, especially when DG and CB units are operated simultaneously. Specifically, DG and CB integration led to a reduction in system losses by 89.90 %, 88.42 %, and 86.87 %, and a decrease in three-phase voltage unbalance indices by 88.75 %, 81.68 %, and 39.05 %, for the 19, 34, and 123-node systems, respectively, while maintaining voltage stability within acceptable limits. Additionally, CO2 emissions were reduced by 52 %, 62 %, and 53 % when microturbines were utilized instead of coal-based thermal power plants, further highlighting the environmental benefits of the proposed approach.

Enhancement of Active Distribution Network Performance with Multiple Distributed Generators and DSTATCOMs using Reptile Search Algorithm

The integration of multiple DGs can introduce power quality issues and instability in the (DN) due to their intermittent and fluctuating nature. Distributed Static Compensators (DSTATCOMs) are devices that effectively manage and regulate both active and reactive powers, thereby maintaining the desired levels of reactive power in the DNs. This paper investigates the problem of determining the optimal number and placement of multiple DSTATCOMs within active DNs with multiple Distributed Generators (DGs) connected to them. To achieve the optimal sizing and allocation of multiple DSTATCOMs, a novel heuristic method called the reptile search algorithm (RSA) is introduced in this study. A combination of the RSA method and the loss sensitivity factor (LSF) are utilized to identify the optimal number, sizes, and locations of DSTATCOMs to enhance the performance of active DNs with different types of DGs and loads. The desired improvements include mitigating voltage deviation at nodes, minimizing system power losses, and alleviating overloading in feeders. The effectiveness of the algorithm is evaluated using an IEEE-33 bus DN, which is modified to incorporate different DGs and load types. To assess the efficiency of the proposed method, a modified particle swarm optimization (MPSO) algorithm is used for comparison. The results demonstrate that the RSA approach presented in this paper is robust in obtaining optimal solutions, offers fast and easy implementation, can be applied to large DNs, and outperforms the MPSO algorithm.

Control Performance Assessment of a Zero Harmonic Distortion Grid-Forming Converter for Medium Voltage Islanded Microgrids

The world is currently witnessing a rapid transformation in the production and utilization of electrical energy. The traditional centralized generation model for electric power is swiftly evolving into a more decentralized system, known as Distributed Generation (DG), which incorporates renewable energy sources situated closer to end-users. This shift towards DG has paved the way for the emergence of grid-forming converters, which play a pivotal role in enhancing voltage and frequency stability within microgrids (MGs) and isolated applications. This study focuses on assessing the performance of the Zero Harmonic Distortion (ZHD) in both stand-alone and parallel operation modes. The distinctive feature of this converter lies in its inherent ability to generate a sinusoidal voltage source without the need for capacitive filtering components, which can adversely affect cost, efficiency, and size while potentially contributing to resonance problems. This is achieved through a judicious combination of harmonic cancellation within a three-winding transformer and the utilization of Selective Harmonic Elimination Pulse Width Modulation (SHE PWM) dismissing a closed-loop control structure. Simulation and hardware-in-the-loop results presented in this work demonstrate the satisfactory performance of the ZHD grid-forming converter.

Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a Stochastic Mixed-Integer LP Model

AbstractThis paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

A Real-Time and Online Dynamic Reconfiguration against Cyber-Attacks to Enhance Security and Cost-Efficiency in Smart Power Microgrids Using Deep Learning

Ensuring the secure and cost-effective operation of smart power microgrids has become a significant concern for managers and operators due to the escalating damage caused by natural phenomena and cyber-attacks. This paper presents a novel framework focused on the dynamic reconfiguration of multi-microgrids to enhance system’s security index, including stability, reliability, and operation costs. The framework incorporates distributed generation (DG) to address cyber-attacks that can lead to line outages or generation failures within the network. Additionally, this work considers the uncertainties and accessibility factors of power networks through a modified point prediction method, which was previously overlooked. To achieve the secure and cost-effective operation of smart power multi-microgrids, an optimization framework is developed as a multi-objective problem, where the states of switches and DG serve as independent parameters, while the dependent parameters consist of the operation cost and techno-security indexes. The multi-objective problem employs deep learning (DL) techniques, specifically based on long short-term memory (LSTM) and prediction intervals, to effectively detect false data injection attacks (FDIAs) on advanced metering infrastructures (AMIs). By incorporating a modified point prediction method, LSTM-based deep learning, and consideration of technical indexes and FDIA cyber-attacks, this framework aims to advance the security and reliability of smart power multi-microgrids. The effectiveness of this method was validated on a network of 118 buses. The results of the proposed approach demonstrate remarkable improvements over PSO, MOGA, ICA, and HHO algorithms in both technical and economic indicators.

Active protection scheme based on high‐frequency current for distribution networks with inverter‐interfaced distributed generators

AbstractWith the high penetration and flexible access of inverter‐interfaced distributed generators (IIDGs), it is gradually becoming difficult for traditional protection schemes to meet the requirements for the safe operation of distribution networks (DNs). Active protection schemes based on power electronic equipment provide a new approach. On the basis of the controllability of voltage source converters, a method for active high‐frequency signal injection and a selection principle for the corresponding control parameters are proposed. Considering the impact of T‐connected load branches on the protected line, the high‐frequency current characteristics at the three terminals during internal and external faults are analysed. On this basis, an active protection scheme based on high‐frequency current is proposed for DNs with IIDGs. The performance of the proposed scheme is verified via PSCAD/EMTDC simulation software. The test results show that the proposed scheme can reliably trip during internal faults and identify faulty phases, which has better endurance to fault resistance.

PMU-based voltage estimation and distributed generation effects in active distribution networks

The integration of distributed generation (DG) and phasor measuring units (PMUs) has significantly impacted electrical distribution networks. This study focuses on real-time control of renewable energy intermittency and strategic PMU deployment. By optimizing the placement of PMUs and DGs, the study aims to achieve several goals: maximize power loss reduction, increase DG penetration, and maintain acceptable voltage profiles. The recommended PMU-based approach incorporates the optimal number of DGs into the distribution network for load flow analysis. Testing this technique on 12-bus, 33-bus, and 69-bus networks yielded substantial gains. For example, power loss was 81.044 % lower in the 33-bus system and 79.256 % lower in the 69-bus test system compared to the base-case scenario. We also compared these results with other methods found in the literature. The developed algorithm is recommended for application in a real electrical power distribution network for more efficient integration of PMUs and new distributed generation units. Integrating PMUs with DG benefits active distribution network management, enabling proactive grid management and stability.

Towards Enhancing Power System Protection in Distribution Systems with Distributed Generation: A Graph Theory-Based Systematic Relay Placement Approach

This manuscript presents an innovative approach to optimize power system protection through strategic relay placement in distribution systems with distributed generation (DG). Traditionally, distribution systems relied on radial configurations, assuming power flow from the grid feeder to downstream networks. However, with the integration of DG technologies, the complexity of relay placement and maintenance increases. The study aims to address protection system issues associated with connecting DGs, such as tripping of production units, blinding of protection, and undesirable islanding. The proposed methodology combines graph theory, energy not supplied (ENS) values, and relay coordination strategies to achieve reliable power system operation. The algorithm is implemented in MATLAB, utilizing data from DigSilent PowerFactory. The key constraints for relay placement, including islanding operation, relay coordination, and load priorities, are considered to minimize the number of power outages and increase overall system reliability. The effectiveness of the algorithm is demonstrated using IEEE 33-bus and 69-bus test systems under different conditions. Results show consistent and reliable relay placement locations, considering DG locations and load priorities. The algorithm's speed, effectiveness, and adaptability to different network topologies make it a promising approach for power system protection planning in distribution systems with distributed generation.

A game-based power system planning approach considering real options and coordination of all types of participants

With the ongoing advancement of global power marketization, the increasing diversity of stakeholders in the power market and their interactions have a significant impact on power system planning and operation. Traditional methods employ game theory to describe the interactions among market participants. However, these approaches fail to include all relevant participant types and overlook the long-term uncertainty value of planning schemes. Therefore, this paper proposes a game theory-based power system planning approach considering real options (RO) and coordination of all types of participants. First, the dynamic gaming interaction among supply-transmission-demand is studied, and the independent system operator is integrated into the game planning framework based on the overall perspective of power market operation. Moreover, the new power supply, grid planning, and operation of distributed generation (DG) are taken as decision variables based on the RO theory, and a game decision model of all participants in the power system planning considering the investment uncertainty is constructed. Finally, the iterative search algorithm is employed to solve the model, and the framework is validated based on the IEEE 30-bus system. The results indicate that through collaborative planning involving various market participants, Generation Companies and Transmission Companies expand their investments to maximize profits, while large power consumers reduce their investment in DG and instead increase their direct power purchases to lower costs. Moreover, these participants are inclined to pursue projects with higher asset value volatility, which further enhances their profit. By integrating RO theory with game theory, the proposed approach establishes a comprehensive planning framework that effectively addresses investment uncertainty. This integration enables market participants to make more optimal decisions, ultimately leading to an enhancement in the long-term economic performance of the power system.

Faulty section location method for high impedance grounding fault in resonant grounding system based on discrete Fréchet distance

When a High Impedance Fault (HIF) occurs in a resonant grounding system, the differentiation of the current flowing upstream and downstream of the fault point is inconspicuous due to the weak electrical signal. This paper proposes a faulty section location approach based on Discrete Fréchet Distance (DFD) to address this issue. Initially, taking the zero-sequence current as the characteristic quantity, the instantaneous value of the zero-sequence current at each sampling point and its previous sampling moments are accumulated to amplify the fault signal, and furthermore to obtain the cumulative waveform of the zero-sequence current at each monitoring point. The DFD values between the cumulative waveforms of zero-sequence currents flowing through each section are also calculated. For a branchless section, if the Coefficient of Variation (CV) of the DFD in each section does not exceed the threshold value, it is identified as an end-of-line fault, otherwise, the section with the maximum DFD is identified as a faulty section. For a section with branches, the branching coefficient is calculated to determine whether it is a faulty section or not. Finally, MATLAB/Simulink simulation results and field-recorded data demonstrate the validity and robustness of the approach under various fault conditions, despite the connection of Distributed Generators (DGs).

Data-model driven probability model of branch power flow for distribution networks and its application to analysis of overload and line loss

The fluctuation of highly penetrated distributed generations (DGs) in distribution networks (DNs) increases branch overload risks, which makes the analysis uncertainty of branch power flow more complex. The probability analysis of branch power can grasp the power flow operation characteristics under uncertain conditions, which supports the power flow optimization management and ensures the safe and economic operation of DNs. Therefore, this paper proposes a data-model driven probability analysis method for branch power flow in DNs. An approximate branch power model is first derived to reveal the analytical relationship between branch power and node power. Then, based on the branch power approximation model and the forecasting error, the datasets of branch apparent power are constructed to capture the complex nonlinear characteristics of the power flow. The non-Gaussian distribution characteristics of branch apparent power and line loss are described by the optimal probability fitting method. Finally, the probability level of branch overload is evaluated and the probability distribution of line loss is analyzed. The case studies demonstrate that the branch power approximation model is highly accurate, and the probability distribution of the branch apparent power presents different characteristics. The proposed method can quickly and accurately calculate the branch overload probability level.

Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain

The huge computational cost required to test whether a number is prime and the inefficiency of the known sieving algorithms for extremely large inputs have posed significant challenges in computational number theory. Traditional deterministic prime generation methods struggle to maintain performance when the input sizes increase exponentially. In this work, we show that, through multiscale distribution and deterministic prime number generation, it is possible to create a multiscale sieve with drastically better performance than the deterministic algorithms known to date, providing a more efficient solution for large-scale prime number generation, demonstrated by several benchmarks that highlight the potential of our approach. Consequently, we can gain some advantages in cryptography and in info-security, such as in IoT and blockchain environments.

Optimization of shared energy storage configuration for village-level photovoltaic systems considering vehicle charging management

Distributed renewable energy is more abundant in rural areas, and a large amount of distributed photovoltaic grid-connected power brings challenges to the stable of the power grid. How to promote the self-generation and self-consumption of distributed renewable energy has become an urgent problem. In this paper, a village-level distributed photovoltaic power generation system including energy storage and electric vehicles is constructed. With the goal of minimizing the photovoltaic grid-connected power and maximizing the annual comprehensive revenue, the planning model of energy storage capacity allocation for village-level distributed power generation system is constructed. Considering the charging management for different numbers of electric vehicles, the optimal energy storage capacity allocation strategy is solved using the improved particle swarm algorithm.Five scenarios are set up as examples to be analyzed.The conclusions are:(1)After the configuration of a reasonable energy storage,the grid-connected generation of the system is significantly reduced,and the annual comprehensive revenue of the system is significantly increased.(2)After considering charging management for different numbers of electric vehicles, the capacity and power of the energy storage configuration can be significantly reduced, thus reducing the energy storage investment cost and operation and maintenance cost, and further significantly improving the annual comprehensive revenue of this system.

Efficient utilization of enriched oxygen gas in residential PEMFC-CHP system with hydrogen energy storage

The oxygen byproduct generated from water electrolysis can be utilized on-site in residential combined heat and power (CHP) systems to enhance economic efficiency. This study explores solutions for optimizing the use of enriched oxygen gas (EOG) and determining the ideal oxygen fraction to maximize economic viability. A novel EOG supply system is designed to regulate oxygen fraction, oxygen excess ratio (OER), and humidity. A dynamic model of a PEMFC integrated with the EOG supply system is developed using MATLAB/Simulink® software and partially validated. The dual effects of EOG on system efficiency and remaining useful lifetime (RUL) are analyzed using a comprehensive economic optimization algorithm, leading to the identification of optimal oxygen fractions for various hydrogen production modes. Additionally, a feedforward static decoupling controller is designed to independently control the OER and oxygen fraction. The results indicate that, under rated conditions (10 kW), system power increased by 15 %, along with a 10 % improvement in system efficiency, from 48 % to 58 %. In conclusion, the efficient use of enriched oxygen gas demonstrates significant economic feasibility and practical viability, making it highly suitable for distributed power generation applications.

A methodology for preliminary benefit evaluation of Distributed Generation to drive private investments

Private investments in distributed generation (DG) are typically driven by the interests of private investor, often overlooking possible benefits for the distribution operator. This work presents a methodology that enables power distribution utilities to assess the potential for attracting investments in their networks, considering a regulatory framework where utilities could actively encourage private DG investors. The methodology involves calculating and integrating the benefits associated with investment deferral, losses reduction and reliability improvement into the energy price negotiated between the utility and the DG owner. The proposed approach was applied to an adapted test grid, considering real regulatory aspects related to technical losses and reliability. The results provide support for utilities to perform preliminary financial analyses to determine the appropriate incentives for private DG investors, identifying cases with competitive energy prices for generators. Key contributions of this methodology include a clear and reasonable metric for quantifying the capacity to attract investments based on standard utility data and the importance of incorporating regulatory aspects related to technical losses and reliability, that are often neglected in similar studies. Furthermore, the methodology not only promotes new opportunities for DG investors but also addresses challenges associated with network congestion.