30-bus System Research Articles

A three-level robust scenario-based model for resilience-oriented placement of remote-controlled switches in distribution networks

This paper introduces an innovative strategy to improve the resilience of Distribution Networks (DN) following natural disasters. The approach involves a multi-stage model for the strategic placement of Remote-Controlled Switches (RCS) to expedite fault isolation and service restoration. The model integrates various resilience enhancement techniques, such as network reconfiguration and microgrid formation, throughout the pre-disaster, disaster progression, and post-disaster phases. To mitigate uncertainties related to line failures and ensure planning robustness, a novel three-level Robust Scenario-based Model (RSM) is introduced. The RSM optimizes the strategic RCS placement to enhance System Performance Criteria (SPC) in response to anticipated line failure scenarios, considering individual line vulnerabilities and disaster severity. Subsequently, the model identifies the most critical failures from each predicted scenario based on RCS placements and then recalibrates the placements to enhance DN resilience against both anticipated and critical failures. The RSM is formulated as a mixed-integer linear programming problem to maximize SPC by allocating resources optimally to serve maximum weighted loads while adhering to structural and operational constraints. The effectiveness and validity of the proposed model are demonstrated through case studies conducted on a modified IEEE 33-bus test system and a modified real DN.

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 power flow method with consideration of uncertainty sources of renewable energy and demand response

Optimal power flow (OPF) calculation methods are important for the power system operation and mainly focus on the deterministic power flow calculation, neglecting the impact of demand response on online security calculation of power systems with renewable energy sources. Therefore, this paper proposes an OPF calculation method that considers the uncertainties of wind power, photovoltaic (PV) power generation and demand-side response. Firstly, the research focuses on the renewable energy grid, considering the uncertainties of wind power and PV power generation, and establishes uncertainty models for wind power and PV output. Secondly, based on cloud model theory, an uncertainty model for demand response is established. According to the established models, an efficient OPF model is constructed with a linearized submodels considering multiple uncertainties. By testing on the IEEE 30-bus system as a typical example, we found the effectiveness and superiority of the proposed OPF calculation method can benefit the power system economic operation and demand side resource utilization.

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

Research on a Distributed Photovoltaic Two-Level Planning Method Based on the SCMPSO Algorithm

In response to challenges such as voltage limit violations, excessive currents, and power imbalances caused by the integration of distributed photovoltaic (distributed PV) systems into the distribution network, this study proposes at two-level optimization configuration method. This method effectively balances the grid capacity and reduces the active power losses, thereby decreasing the operating costs. The upper-level optimization enhances the distribution network’s capacity by determining the siting and sizing of distributed PV devices. The lower-level aims to reduce the active power losses, improve the voltage stability margins, and minimize the voltage deviations. The upper-level planning results, which include the siting and sizing of the distributed PV, are used as initial conditions for the lower level. Subsequently, the lower level feeds back its optimization results to further refine the configuration. The model is solved using an improved second-order oscillating chaotic map particle swarm optimization algorithm (SCMPSO) combined with a second-order relaxation method. The simulation experiments on an improved IEEE 33-bus test system show that the SCMPSO algorithm can effectively reduce the voltage deviations, decrease the voltage fluctuations, lower the active power losses in the distribution network, and significantly enhance the power quality.

An Identification Method for Anomaly Types of Active Distribution Network Based on Data Mining

With the increasing penetration of distributed generators (DGs) and the growing demand for reliable power sources, it has become imperative to promptly identify anomalies in active distribution networks (ADNs). Additionally, anomaly identification is also crucial to assisting in targeting maintenance to handle failures after the action of relay protections. This study presents a highly accurate and rapidly responsive approach to identifying anomalies in ADNs. The proposed method uses three layers of intricately interconnected modules. In the first module, an iterative clustering layer is devised to process the original data. In the second module, an association rule layer is designed to identify hidden patterns based on the types of anomalies and the post-fault voltage. In the third module, a regression training layer is employed to train accurate identification models. The case study utilizes data samples collected by an IEEE 33-bus system with distributed generations as well as post-fault data from a practical distribution network. The results of comparative tests demonstrate that the proposed data-mining structure achieves reliable performance with high identification accuracy and short reaction times, indicating its ability to be applied to the state awareness system of ADNs.

Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques

Abstract Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

Non-intrusive demand response management strategy to mitigate the impacts of residential electric vehicle charging on distribution systems

The proliferation of electric vehicles (EVs) is expected to significantly increase electricity demand. However, if left uncontrolled, the aggregated demands from EVs have the potential to affect the power balance and quality of distribution systems adversely. Given this complex framework, this work presents a non-intrusive demand response management (DRM) approach to mitigate the impacts of residential EV charging on distribution systems. After analyzing a database comprising 157,250 operations, 74,316 charging patterns described by 17 attributes are obtained. These patterns are used for sampling EV fleets distributed in the Institute of Electrical and Electronics Engineers (IEEE) 33-bus test system. From scenarios with different levels of EV penetration, critical regions in the demand curve are identified, while actions for demand response (DR) are taken. Despite the increase in total energy consumption, larger EV fleets have greater potential for participation in DR. At a penetration level of 5%, it is not possible to shift excess energy consumption during the peak period, even with the participation of about 47% of the fleet, which corresponds to all users involved in EV charging during the peak period. At a penetration level of 50%, the energy goal can be achieved with the participation of about 15% of the fleet. In this latter scenario, the proposed management strategy can achieve a peak demand reduction of 9–11%.

A multi-objective mixed integer linear programming approach for simultaneous optimization of cost and resilience of power distribution networks

In recent years, customers have experienced a significant increase in weather-related power outages. The power distribution network (PDN), a subset of the power system, in particular is more susceptible to extreme events. Therefore, ensuring the resilient and cost-effective operation of PDNs following extreme weather conditions poses a significant challenge for distribution network operators. This paper presents an approach for simultaneously optimizing the cost and load restoration for resilience enhancement of power distribution networks while determining the optimal positioning and generation levels of mobile emergency generators. The proposed method, in addition, employs a distribution network reconfiguration to improve the load restoration process, by optimally determining the status of switches. The multi-objective formulation involves the minimization of load shedding to increase the resilience of the system, while the other objective is formulated to minimize the cost of load restoration. A weighted sum method is employed to address the multi-objective mixed-integer linear programming (MILP) model. A set of non-dominated solutions determined using the proposed formulation provides opportunities to the distribution system operator in choosing a resilience improvement strategy according to the availability of the operational budget. The proposed model is implemented on 33-bus distribution system to validate the efficacy of the proposed model.

Power grid network security: A lightweight detection model for composite false data injection attacks using spatiotemporal features

The stability of power systems is paramount to industrial operations. The deleterious inherent characteristics of false data injection attacks (FDIA) have drawn substantial interest due to their severe threats to power grids. Contemporary detection systems face numerous challenges as attackers employ various tactics, such as injecting complex elements into measurement data and formulating quick attack strategies against critical nodes and transmission lines in the power grid network topology. Conventional models often fail to adapt to the intricacies of practical situations because they focus predominantly on detecting individual components. To overcome the above predicaments, this paper proposes a lightweight detection model integrating deep separable convolutional layers, squeeze neural networks, and a bidirectional long short-term memory architecture named DSE-BiLSTM. The acquisition process of network topological characteristics is accomplished through variable graph attention autoencoder (VGAAE). This approach leverages the effectiveness of the graph convolution (GCN) layer to acquire each node’s topological feature and the graph attention (GAT) module to identify and extract the topological features of critical nodes. Furthermore, the topology information obtained by the both techniques is embedded in one-dimensional vector space in the same form as measurement data. By combining the output of VGAAE with meter measurements, the feature fusion of temporal and spatial modalities is realized. DSE-BiLSTM with optimal hyperparameters achieves an F1-score of 99.56% and a row accuracy (RACC) of 93.10% on the conventional dataset. The experimental results of FDIA detection with composite datasets of IEEE 14-bus and IEEE 118-bus systems show that the F1-score and RACC of DSE-BiLSTM remain above 84.51% and 83.56% under various attack strengths and noise levels. In addition, as the power grid network scales up, noise level’s effect on detection performance decreases, while attack strength’s effect on recognition capability increases. DSE-BiLSTM can effectively process the composite data of spatiotemporal multimodes and provides a feasible solution for the localization and detection of FDIA in realistic scenes.

Hosting capacity maximization of distributed energy resources through simultaneous optimized volt–var curve and network reconfiguration

Distributed Energy Resources (DERs) integration into distribution systems becomes problematic when the penetration level exceeds the system DER hosting capacity. Although several studies propose to quantify DER hosting capacity, methodologies to maximize it in distribution systems are scarce. Some approaches propose installing var compensators, incurring in costly solutions; others propose reduced cost solutions like network reconfiguration or optimized Volt–Var curve. Nevertheless, the combination of these approaches has not been proposed. Therefore, this paper proposes the simultaneous network reconfiguration and optimized setting of grid-tie inverter Volt–Var curve to maximize DER hosting capacity and minimize power losses. The uncertainties are addressed using Monte Carlo simulation, whereas Non-Dominated Sorting Genetic Algorithm II handles the multi-objective problem. The simulations are performed using Matlab and OpenDSS software, considering the IEEE 33-bus test system. Finally, the results demonstrate the superiority of the proposed approach against previous studies that performed network reconfiguration and Volt–Var optimization separately.

Distributed optimization for EVs integrated power system considering flexible ramping requirement

More flexible ramping service is required due to the increase of renewable power generation in power systems. Electric vehicles (EVs) could provide such flexible ramping products (FRPs) at low cost while participating in the electricity market through aggregation. However, EVs’ dispatching capability cannot be fully utilized without the right incentives. This paper addresses a distributed optimal model developed between EV aggregators (EVAs) and the independent system operator (ISO). To make such concept, a cloud-edge collaborated market structure is adopted. At the edge level, EVAs assess the dispatching capability and solve the market bidding subproblem. At the cloud level, ISO solves the market clearing subproblem considering system economy and security. The overall problem is solved by the analytical target cascading (ATC) method. Heuristic constraints are also introduced into the model to improve convergence performance. The model is tested on a modified IEEE 30-bus system. Results demonstrate that the proposed method can incentivize EVAs with different owners to shift load and provide FRPs accurately, meanwhile reducing the cost and increasing the consumption of renewable energy effectively.

Load redistribution attack for power systems with high penetration of EVs

This paper provides insights for cyber defenders of power systems with high penetration of electric vehicles (EVs) by proposing a novel consecutive attack model based on load redistribution for price control in both transmission networks (TNs) and distribution networks (DNs). The target of the attack is to induce massive EV users in DNs to charge simultaneously and cause a spike of loads in the TN at peak hours. The problem is formulated as a multi-slot bi-level optimization problem, where the upper level describes the hacker behavior and attacking constraints. The lower level explains the TN operator and DN operators’ behaviors and the relationship between the local marginal price and retail charging price. The bi-level problem is converted into an equivalent single-level mix-integer linear problem and is solved based on greedy algorithm. Simulations on IEEE 30-bus system prove the effectiveness of the attack strategy.

Enhancing Coordination Efficiency with Fuzzy Monte Carlo Uncertainty Analysis for Dual-Setting Directional Overcurrent Relays Amid Distributed Generation.

In the contemporary context of power network protection, acknowledging uncertainties in safeguarding recent power networks integrated with distributed generation (DG) is imperative to uphold the dependability, security, and efficiency of the grid amid the escalating integration of renewable energy sources and evolving operational conditions. This study delves into the optimization of relay settings within distribution networks, presenting a novel approach aimed at augmenting coordination while accounting for the dynamic presence of DG resources and the uncertainties inherent in their generation outputs and load consumption-factors previously overlooked in existing research. Departing from conventional methodologies, the study proposes a dual-setting characteristic for directional overcurrent relays (DOCRs). Initially, a meticulous modeling of a power network featuring distributed generation is undertaken, integrating Weibull probability functions for each resource to capture their probabilistic behavior. Subsequently, the second stage employs the fuzzy Monte Carlo method to address generation and consumption uncertainties. The optimization conundrum is addressed using the ant lion optimizer (ALO) algorithm in the MATLAB environment. This thorough analysis was conducted on IEEE 14-bus and IEEE 30-bus power distribution systems, showcasing a notable reduction in the total DOCR operating time compared to conventional characteristics. The proposed characteristic not only achieves resilient coordination across a spectrum of uncertainties in both distributed generation outputs and load consumption, but also strengthens the resilience of distribution networks overall.

Implementation and proficiency analysis of enhanced graph algorithm for DC microgrid applications

Integrating renewable energy generation with the conventional grid supports reduces carbon emissions in the atmosphere. Despite technical advancements in protection strategies, critical issues concerning renewable integration in microgrid structures require standardized solutions. The essential aspects that need to be concentrated during securing the grids are rapid fault interruption, false tripping and blinding of protection. This study proposes an innovative approach to enhance fault isolation speed through the implementation of a grid monitoring system (GMS) coupled with a fault identification method based on Kosaraju’s algorithm. This algorithm operates on the principles of overvoltage and overcurrent detection. The study assesses the efficacy of this approach by examining its integration with a Z-source circuit breaker and conducting tests on different fault types within a 13-bus system. Real-time simulations using Opal RT software are employed to experimentally validate the proposed methodology, ensuring its efficacy in fault interruption and isolation.

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.

Optimizing local electricity markets: A bi-level primal-dual approach for integrating price-based demand response and distribution locational marginal pricing

This paper addresses the conundrum of optimizing electricity consumption patterns in response to fluctuations in demand and price, a task managed by both load aggregators and the distribution system operator (DSO). The conventional approaches in the literature to integrate demand response (DR) into optimal power flow (OPF) problems typically overlook the price responsiveness of consumers or simplify power flow equations to account for price-elastic demand.In this paper, we strive to close this gap by introducing a bi-level primal-dual optimization framework that incorporates aggregators' objectives into the market-clearing process while preserving the precision of the OPF equations. At the upper level, the model seeks to minimize both the total payment and peak load. The lower level, structured as a second-order conic (SOC) problem, aims to reduce overall generation costs subject to constraints of the second-order conic (SOC) branch flow model (BFM). The two levels interact through the optimal demand response and distribution locational marginal prices (DLMPs). The principles of convex duality principles together with the strong duality constraint are leveraged to transform the market-clearing problem into a single primal-dual problem. We also circumvent the non-linearity that stems from the DR payment term by incorporating discretizing loads and the big-M method, thus converting the problem into a mixed-integer SOC (MI-SOC) formulation. The merits of the proposed MI-SOC framework are validated through case studies conducted on the IEEE 33-bus test system, showcasing its potential to enforce price-elasticity demand response in distribution system's electricity markets.

Cost–Performance Optimization of a Renewable Energy Resources Based Multimicrogrid System

The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world.The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost –performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER.A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized.

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

A novel nature-inspired nutcracker optimizer algorithm for congestion control in power system transmission lines

In the restructured power system, where uncertainties are common, managing congestion becomes a crucial aspect of power system operation and control. Congestion management aims to alleviate the power system transmission line congestion while meeting the system constraints at minimal cost. This research introduces a generation rescheduling method for congestion management in the electricity market, leveraging an innovative nutcracker optimizer algorithm. The nutcracker optimizer algorithm, inspired by nutcrackers’ food accumulation mechanisms, is a recently developed nature-inspired algorithm. The efficacy of this proposed approach is assessed across modified IEEE 30-bus, and IEEE 118-bus test systems, considering the system parameters. The effectiveness of the proposed congestion management with the nutcracker optimizer algorithm is analyzed by comparing its results with those generated by other recent optimization techniques. Results demonstrated that the nutcracker optimizer algorithm surpasses other comparative methods, requiring fewer fitness function evaluations, avoiding local optima, and displaying encouraging convergence traits. Implementing this approach can assist the system operators in swiftly addressing contingencies, ensuring secure and reliable power system operation within a deregulated environment.