IEEE 33-bus Distribution System Research Articles

Cluster partition-fuzzy broad learning-based fast detection and localization framework for false data injection attack in smart distribution networks

The distributed renewable energy generations, as accessible and easily targets for attackers, introduce an extra false data injection attack (FDIA) threat in the smart distribution networks. Scattered attack points and complex attack features hinder the elimination of potential threats. In this context, an FDIA fast detection and pinpoint localization framework is proposed. This framework identifies abnormal signals and attacked nodes from the unique topology structure and status contiguity of smart distribution networks, namely, spatial-temporal correlations of power grids, by using a cluster partition-fuzzy broad learning system (CP-FBLS). Unlike most existing FDIA detection methods, which are dedicated to high accuracy but neglect the urgent need for rapid detection in smart distribution networks, the proposed CP-FBLS framework maintains the fast computational nature of a fuzzy broad learning system (FBLS), while avoiding the accuracy degradation caused by high-dimension of data in large-scale smart distribution networks. Moreover, the multi-layer structure of the proposed framework recognizes the location of FDIA, bridging the research gap of attack localization. To comprehensively evaluate the proposed strategy, datasets containing various FDIA types are constructed. Numerical simulations based on the above datasets in modified IEEE 34-bus and 123-bus distribution systems are implemented. The results of the case studies showed that the proposed method can achieve 98.43% accuracy with 0.34ms detection time, realizing rapid detection and localization of various FDIAs with satisfactory accuracy.

Voltage over-limit risk assessment of wind power and photovoltaic access distribution system based on day-night segmentation and Gaussian mixture model

The access of distributed power sources such as wind power and photovoltaic (PV) with randomness and uncertainty makes the operation of distribution system more complicated. It is particularly necessary to comprehensively and efficiently evaluate the risk of voltage over-limit in distribution systems with wind power and photovoltaic. Firstly, aiming at the problem of centralized sampling of Monte Carlo method in risk assessment of distribution system, a day-night segmented Monte Carlo (MC) sampling method is proposed to generate a large number of day-night sampling scenarios. Secondly, the Gaussian mixture model is used to fit the probability density of the voltage obtained from probabilistic power flow calculations, concurrently, the utility preference exponential function characterizes the severity of the over-limit, and a comprehensive evaluation model of voltage over-limit risk is constructed. Finally, the effectiveness and accuracy of the proposed method are verified by taking the improved IEEE 33-bus distribution system as an example. These results offer actionable insights for future risk assessments of new energy grid connections.

Service restoration in distribution systems considering priority customers and microgrids

Service restoration (SR) consists of automatically generating and executing a plan to restore the service in healthy zones using the least number of maneuvers after detecting and isolating a permanent fault in the distribution system zone. This component is essential to self-healing functionality in smart grids and allows customers to reconnect quickly to the distribution grid after a power outage. Distributed generation (DG) supports the distribution network when there is insufficient capacity to restore all zones out of service or supply the loads locally through microgrids. The power supply must be restored to the highest priority customers in case of partial restoration. Also, most research works use simplified or linearized models to propose restoration algorithms. This paper proposes a complete AC formulation for the service restoration problem in distribution systems considering network reconfiguration (NR), the integration of distributed generation (DG), and priority customers (PCs) into the solution. The optimization problem is solved by a centralized algorithm based on combining the Differential Evolution (DE) and Continuous Population-Based Incremental Learning (PBILc) metaheuristics techniques. Simulation results are presented for three case studies in which the IEEE 33-bus distribution system is tested for different fault scenarios. The numerical results show the robustness and efficiency of the proposed algorithm.

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

An efficient modelling and hosting capacity analysis of a distribution system integrating PVs supported by the V2G technology

Over the past decade, the production of photovoltaic (PV) systems has increased owing to the rising demand for electricity generation. Recently, the vehicle-to-grid (V2G) technology of electric vehicles (EVs) has also gained interest as a clean and green energy source to support PVs providing sustainable energy. However, this necessitates the efficient modelling of PV output generation and V2G scheme for integration in the distribution system. Despite the advantages of PV and V2G technology, excessive integration potential affects the distribution system. To circumvent these problems, an accurate evaluation of hosting capacity (HC) is essential. This study proposes efficient modelling and a novel, improved analytical-optimal power flow method for HC analysis of distribution systems with PV and V2G integration considering uncertainties. Furthermore, the effects of increasing integration and optimal allocation of PVs and V2G technology on the voltage profile and power losses, respectively, were analysed. The IEEE 33-bus and 69-bus distribution systems were employed to perform the simulation test to obtain results of HC that validated the effectiveness and robustness of the proposed method compared with those of conventional methods. The proposed method provides an accurate, robust, and global-optimum solution. The integration of V2G technology enhances the overall HC of distribution system.

Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties

Climate change is exerting excessive strain on the environment, leading to an ever-increasing frequency of extreme weather events that negatively impact distribution systems. To mitigate these impacts, this paper presents a distributionally robust conditional value-at-risk (DR-CVaR) optimization approach for resilience-oriented distribution system planning. Firstly, a resilience-oriented distribution system planning model is developed, which incorporates flexible operational resources, such as mobile energy storage prepositioning and demand response program, in the emergency response phase. Then, the uncertainties associated with extreme weather events are addressed using a DR-CVaR optimization approach, where the short-term uncertainty in the occurrence of outages caused by specific extreme weather events is addressed through a distributionally robust optimization approach, while the long-term uncertainty regarding the risk of potential extreme weather events is addressed by a conditional value-at-risk optimization approach. The DR-CVaR model is eventually transformed into an equivalent three-level model and solved using a customized nested column-and-constraint generation algorithm. Finally, case studies are conducted based on the IEEE 33-bus distribution system under several extreme weather events. The numerical results show that the proposed model can reduce the load shedding cost as well as investment cost, which confirms the effectiveness and superiority of the proposed model.

A constrained price-based demand response framework employing utility functions in three-state Overlapping Generation and Gift and Bequest based model in distribution system

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

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.

Planning renewables in distribution system with electric vehicles to improve hosting capacity and energy losses: Two-stage stochastic optimization framework

Excessive penetration of renewables in distribution system can create several operational issues. One of the technologies that can maximize accommodation of renewables is electric vehicles (EVs). However, along with EVs additional measures are required while maximizing hosting capacity (HC) to avoid negative impact on system and account for uncertain parameters. In this regard, this paper proposes a two-stage stochastic optimization approach for planning of renewables in distribution system integrated with EVs. In the first stage, capacity of renewables is determined while maximizing HC whereas in the second stage operating strategy of EVs, renewables and grid power is evaluated to minimize expected energy losses. The proposed framework includes uncertainties in load demand, renewables and EVs characteristics through stochastic programming approach. The optimization problem is formulated as second order cone programming problem. The proposed model is tested on modified IEEE-33 bus and real-life 108-bus Indian distribution systems with different charging/discharging patterns and penetration rates of EVs. Moreover, an economic based cost analysis to compare planning cost and EV charging cost is also provided. Simulation results reveal that in 33-bus system with 500 EVs, the HC with vehicle-to-grid capability (V2G) is higher as compared to uncoordinated and coordinated charging by 29.86% and 6.36% respectively, while in 108-bus system with 1000 EVs, it is higher by 18.51% and 5.69% respectively. Further, the energy losses with V2G are 27.54% and 1.36% less in 33-bus system and 7.31% and 3.22% less in 108-bus system as compared to uncoordinated and coordinated charging respectively.

Multi-stage framework for optimal incorporating of inverter based distributed generator into distribution networks

Recently, hydrogen-based distributed generators (DG) have gained significant attention for modern energy generation systems. These modem DGs are typically outfitted with power electronics converters, resulting in harmonic pollution. Furthermore, increasing the growth of modern nonlinear loads may result in exceeding the harmonic beyond the permitted level. This research proposes a framework for optimal incorporation of inverter-based distributed generation (a fuel cell connected to an AC distribution system) for minimizing power losses, enhancing the voltage profile, and limiting both total and individual harmonic distortion according to the IEEE-519 standard. In addition, for accounting system sustainability, the proposed framework considers load variation and the expected rise in demand. Therefore, the suggested framework comprises three stages, which include fundamental and harmonic power flow analysis. The first stage identifies the optimal size and location of the DG in relation to the base load operating condition. While, with the optimal DG of the first stage, the amount of harmonic pollution may violate the limits during a high level of nonlinear load penetration, as a result, the second stage resizes the DG, considering the connection bus of the first stage, to mitigate the harmonics and optimize the system at a higher level of nonlinear load penetration. Both the first and second stages are performed off-line, while the third stage optimizes the system operation during run time according to loading conditions, harmonic pollution, and the available DG capacity of the previous stages. DG’s harmonic spectrum is represented according to recently issued IEEE 1547-2018 for permissible DG’s current distortion limits. The suggested approach is applied and evaluated using an IEEE 33-bus distribution system for various combinations of linear and nonlinear loads. For run-time operation throughout the day, the presented framework reduces the energy losses from 5.281 to 2.452 MWh/day (about 53.57% energy savings). This saving is associated with voltage profile enhancement without violating the permissible standard levels of harmonics and other system constraints.

Optimal multi-objective sizing of renewable energy sources and battery energy storage systems for formation of a multi-microgrid system considering diverse load patterns

Optimal microgrid design is pivotal in planning active distribution networks (ADNs) with intermittent renewable energy sources (RESs) and battery energy storage systems (BESSs). This paper introduces an innovative approach to clustering existing ADN systems, incorporating RESs and BESSs into a set of microgrids (MGs) termed a multi-microgrid (MMG). The approach accommodates diverse load patterns, considering the dynamic nature of residential, commercial, and industrial loads. The proposed methodology employs an innovative k-means algorithm that utilizes end node characteristics based on the graph partitioning method and the Silhouette technique, transforming the existing system into an MMG framework. Furthermore, an iterative pareto-fuzzy (IPF) method is suggested to determine optimal RES and BESS sizes, considering reliability, total cost, and the unutilized surplus power function. The framework integrates total line losses into the generation adequacy assessment of energy management strategy (EMS) for each MG within the MMG system. The design and performance analysis of the proposed multi-objective optimization algorithm is tested on the IEEE 33-bus distribution system. The implementation of the independent operation index (IOI) and round-trip efficiency (RTE) metrics demonstrates the effectiveness of the proposed approach in constructing an MMG, with achieved values exceeding 90 percent for IOI and 77 percent for RTE.

Resolving Charging Station Placement Issues for Electric Vehicles: Hybrid Optimization-Assisted Multi-Objective Framework

As new energy technology has advanced to a respectable level, Electric Vehicles (EV) have gained recognition from people all over the world and have become increasingly popular in a number of nations. Effective charging station placement is critical for the rapid growth of electric vehicles, as it is vital to provide convenience for electric vehicles while also ensuring the effectiveness of traffic networks. The location of an EV charging station is simply an application situation for the facility location problem. Traditional works often pay attention to the mileage concerns of electric vehicle customers while ignoring their competitive and strategic charging tactics. This research aims to frame the allocation of charging stations issue as a multi-faceted venture by assessing the aspects economically along with the characteristics of the power grid like “cost, Voltage stability, Reliability, and Power loss (VRP) index, waiting time, and accessibility index”. Further, we proposed a hybrid artificial intelligence to resolve the allocation issue. The proposed new Artificial Intelligence (AI) based algorithm is termed as Hybridized Salp and Harris Algorithm (HS-HA). The effectiveness and scalability of the presented algorithm for the charging station placement issue are also assessed through different plans in the IEEE 33-bus distribution systems. Finally, the efficiency of the presented approach is proved over the existing approaches with respect to various measures.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point. Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.

A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches

The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators’ point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %.

A novel sequence component based fault detection index for microgrid protection

The paper introduces a novel protective relaying method using symmetrical components of current to detect asymmetrical faults in microgrid. This approach is focused on utilizing the complex magnitude-phase plane (MPP) of the fault detection index (FDI), which is computed using the positive and negative sequence components of the current signal retrieved at one end of the faulty line. The MPP obtained from the settling values of the FDI's magnitude (MFDI) and FDI's angle (AFDI) after the fault inception, is considered as the key indicator for fault detection. The scheme is comprehensively evaluated for a wide range of fault situations including variations in fault types, fault locations, fault resistances and DG penetrations in grid-connected (GC) and non-grid-connected (NGC) modes of operation. Several critical no fault cases are also tested to show the efficacy of the proposed logic for false operation. The fault study is conducted extensively on a modified low voltage standard Consortium for Electric Reliability Technology Solutions (CERTS) microgrid and a standard IEEE-34 bus distribution system including DGs. Furthermore, the proposed FDI-based scheme is tested on MATLAB/Simulink platform and validated on Typhoon HIL platform to assess the real-time performance. The test results show that this FDI-based scheme using the MPP concept can be a potent solution for the protection of microgrid.

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

Optimizing Solar PV Placement for Enhanced Integration in Radial Distribution Networks using Deep Learning Techniques

This study introduces a highly effective technique to address the load flow challenge in Radial Distribution Networks (RDNs). The proposed approach leverages two matrices derived from the topological features of distribution networks to provide an optimal solution to handle load flow challenges. To assess the efficacy of this technique, simulations were executed on an IEEE 33-bus radial distribution system using MATLAB. Deep Learning (DL) has become a powerful artificial intelligence technique that excels at interpreting power grid datasets. Thus, a data-driven methodology is presented that incorporates an advanced Long-Short-Term-Memory (LSTM) network. Employing the Recurrent Neural Network with the LSTM (RNN-LSTM) technique based on these simulations, the study precisely identifies the optimal placement of an integrated PV generator within the radial network. The application of DL techniques, specifically LSTM networks, exemplifies the potential of data-driven approaches in enhancing decision-making processes. The results of this study highlight the potential of RNN-LSTM for the optimal integration of PV generators and for ameliorating the reliability of RDNs.

Network partition and distributed voltage coordination control strategy of active distribution network system considering photovoltaic uncertainty

Aiming at the voltage exceeding problem caused by the introduction of Photovoltaic (PV) in Active distribution network (ADN), a comprehensive electrical distance index was established considering active and reactive voltage sensitivity, and the uncertainty of traditional PV probability density function (PDF) was optimized by random simulation method. The electrical distance expectation matrix between nodes was established by discretization of PV output probability distribution characteristics, and the ADN system was partitioned by affinity propagation (AP) algorithm. Taking active power loss as the objective function, the non-convex reactive power optimization control problem is transformed into a convex quadratic programming problem by LinDistFlow equation. An improved alternating direction method of multipliers (ADMM) which adaptively adjusts the penalty parameters is used to convert the finite boundaries of adjacent regions into data. By coordinating on-load tap-changer (OLTC), capacitor banks (CBs), and PV inverters on different time scales, the fast optimal control of global voltage in ADN is realized. The proposed method is tested on IEEE 33-bus and IEEE 123-bus distribution systems. After the optimization control algorithm described in this paper, the system network loss was reduced by 3.88%. Compared with the semi-definite programming relaxation, the calculation speed of LinDistFlow equation is increased by 72.2%. The results fully verify the feasibility and high efficiency of the control strategy described in this paper.

Sustainability of Dynamic Wireless Power Transfer Roadway for In-Motion Electric Vehicle Charging

Numerous articles have been written regarding the potential affects that electric vehicle (EV) charging stations can have on power distribution systems. With the advent to adopt a dynamic wireless power transfer (DWPT) roadway charging infrastructure, there lies a potential for hazardous effects to occur. Therefore, this paper presents a novel methodology to analyze the sustainability and the potential effects that a DWPT roadway can have on electric power distribution grid. Historical traffic flow (HTF) data is converted using a Monte Carlo method to develop an EV traffic flow (EV-TF). Then, a full DWPT simulation of a 1-mile and 10-mile roadway is performed to determine the power fluctuations that can occur as different densities of EVs traverse the roadway. This method creates simulations using an IEEE 33-Bus power distribution system to evaluate if a DWPT roadway for in-motion EV charging causes voltage magnitudes to fluctuate beyond operational standards.