IEEE 30-bus Test System Research Articles

The Calibrated Safety Constraints Optimal Power Flow for the Operation of Wind-Integrated Power Systems

As the penetration of renewable energy sources (RESs), particularly wind power, continues to rise, the uncertainty in power systems increases. This challenges traditional optimal power flow (OPF) methods. This paper proposes a Calibrated Safety Constraints Optimal Power Flow (CSCOPF) model that uses the Improved Acceleration Coefficient-Based Bee Swarm algorithm (IACBS) in combination with the equivalent current injection (ECI) model. The proposed method addresses key challenges in wind-integrated power systems by ensuring preventive safety scheduling and enabling effective power incident safety analysis (PISA). This improves system reliability and stability. This method incorporates mixed-integer programming, with continuous and discrete variables representing power outputs and control mechanisms. Detailed numerical simulations were conducted on the IEEE 30-bus test system, and the feasibility of the proposed method was further validated on the IEEE 118-bus test system. The results show that the IACBS algorithm outperforms the existing methods in both computational efficiency and robustness. It achieves lower generation costs and faster convergence times. Additionally, the CSCOPF model effectively prevents power grid disruptions during critical incidents, ensuring that wind farms remain operational within predefined safety limits, even in fault scenarios. These findings suggest that the CSCOPF model provides a reliable solution for optimizing power flow in renewable energy-integrated systems, significantly contributing to grid stability and operational safety.

Dynamic adaptation in power transmission: integrating robust optimization with online learning for renewable uncertainties

IntroductionThe increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability and unpredictability. Traditional fossil-fuel-based power systems are ill-equipped to maintain stability and cost-effectiveness in this evolving energy landscape.MethodsThis study presents a novel framework that integrates robust optimization with online learning to dynamically manage uncertainties in renewable energy generation. Robust optimization ensures system resilience under worst-case scenarios, while the online learning component continuously updates operational strategies based on real-time data. The framework was tested using an IEEE 30-bus test system under varying levels of renewable energy integration.ResultsSimulation results show that the proposed framework reduces operational costs by up to 12% and enhances system reliability by 1.4% as renewable energy integration increases from 10% to 50%. Additionally, the need for reserve power is significantly reduced, particularly under conditions of high variability in renewable energy outputs.DiscussionThe integration of robust optimization with online learning provides a dynamic and adaptive solution for the sustainable management of power transmission systems. This approach not only improves economic and environmental outcomes but also enhances grid stability, making it a promising strategy for addressing the challenges posed by the increasing reliance on renewable energy.

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Resiliency-informed optimal scheduling of smart distribution network with urban distributed photovoltaic: A stochastic P-robust optimization

An increased worldwide emphasis on resilience has resulted in a greater concentration on the distribution networks' resiliency. Therefore, the conventional approach to advance scheduling for power distribution networks is no longer sufficient, thereby requiring the creation of resilient scheduling approaches. This paper examines a proactive day-ahead scheduling method for distribution networks that are linked with significant renewable energy sources. The suggested approach employs a probabilistic scenario-generating strategy to quantify the uncertainties related to wind speed, solar irradiation, catastrophe duration, and load fluctuations. This study utilizes a hybrid stochastic p-robust optimization technique to enhance the analysis and provide robust strategies against risks. The present methodology employs a regret-based metric to quantify the adverse impacts of uncertainty inside the proactive scheduling procedure. The suggested methodology is verified using an IEEE 33-bus test system that includes significant distributed photovoltaic sources. The findings indicate a significant decrease of 43% in the highest level of regret, accompanied by a relatively limited rise of 1.22% in the operational expenses of the distribution network. This framework efficiently manages natural occurrences in the day-ahead scheduling process, making it a very promising method for improving the robustness of power distribution networks.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Solving Optimal Power Flow Using New Efficient Hybrid Jellyfish Search and Moth Flame Optimization Algorithms

This paper presents a new optimization technique based on the hybridization of two meta-heuristic methods, Jellyfish Search (JS) and Moth Flame Optimizer (MFO), to solve the Optimal Power Flow (OPF) problem. The JS algorithm offers good exploration capacity but lacks performance in its exploitation mechanism. To improve its efficiency, we combined it with the Moth Flame Optimizer, which has proven its ability to exploit good solutions in the search area. This hybrid algorithm combines the advantages of both algorithms. The performance and precision of the hybrid optimization approach (JS-MFO) were investigated by minimizing well-known mathematical benchmark functions and by solving the complex OPF problem. The OPF problem was solved by optimizing non-convex objective functions such as total fuel cost, total active transmission losses, total gas emission, total voltage deviation, and the voltage stability index. Two test systems, the IEEE 30-bus network and the Mauritanian RIM 27-bus transmission network, were considered for implementing the JS-MFO approach. Experimental tests of the JS, MFO, and JS-MFO algorithms on eight well-known benchmark functions, the IEEE 30-bus, and the Mauritanian RIM 27-bus system were conducted. For the IEEE 30-bus test system, the proposed hybrid approach provides a percent cost saving of 11.4028%, a percent gas emission reduction of 14.38%, and a percent loss saving of 50.60% with respect to the base case. For the RIM 27-bus system, JS-MFO achieved a loss percent saving of 50.67% and percent voltage reduction of 62.44% with reference to the base case. The simulation results using JS-MFO and obtained with the MATLAB 2009b software were compared with those of JS, MFO, and other well-known meta-heuristics cited in the literature. The comparison report proves the superiority of the JS-MFO method over JS, MFO, and other competing meta-heuristics in solving difficult OPF problems.

Strategic investments and portfolio management in interdependent low-carbon electricity and natural gas markets

Addressing global warming necessitates carbon emissions reduction and renewable energy integration within the energy sector. Gas-Fired Power Plants (GFPP) are appealing to investors due to their low emissions and operational flexibility, which are considered necessary characteristics within low-carbon power systems with increasing renewable energy uncertainty. Investing in GFPPs presents intricate challenges due to the increasingly interdependent electricity and natural gas markets, especially in the light of a low-carbon economy. This work addresses these challenges for a strategic agent by proposing a bi-level optimization framework. The upper-level model derives the optimal electricity portfolio management regarding new investments and strategic biddings, while in the lower-level model, the electricity and gas markets are cleared sequentially under a Carbon Emission Trading Scheme (CETS). Case studies on a Pennsylvania-New Jersey- Maryland (PJM) 5-bus power system and an IEEE 24-bus test system demonstrate the applicability and efficacy of the proposed model in capturing the impact of a transitional integrated market framework on GFPPs investments. Also, introducing stochasticity to the model provides a better insight into the contrasting effects of emission allowance trading and gas prices on investment and bidding strategies.

Hybrid local energy markets: Incorporating utility function-based peer risk attributes, renewable energy integration, and grid constraints analysis

This study proposes a novel hybrid Local Energy Market (LEM) trading framework meticulously designed for efficient energy trading among distribution network participants. The proposed LEM hybrid modeling framework aims to maximize social welfare by considering the behavior of market participants and incorporating network constraints into the transactional framework. To address the consumption behavior of market participants, the concept of the utility function with peer risk attributes is employed, differentiating between flexible and inflexible demands. Additionally, it adeptly manages various generation types, including diesel generators, wind and solar generators, and storage, each characterized by a unique cost function. The investigation extends to the analysis of the Japan Electric Power Exchange market (JEPX), taking into account the residential electricity demand in Tokyo, Japan, by comparing with community-based (CB) and peer-to-peer (P2P) markets and scrutinizing market player behavior across multiple scenarios. These scenarios include comparisons of different market structures, variations in prices, considerations of grid constraints, and diverse weather scenarios. The proposed framework undergoes rigorous evaluation utilizing the IEEE 33-bus and 69-bus standard test systems, with results demonstrating its practicality, superiority of the proposed market compared to the CB and P2P markets, and providing valuable insights into the intricate interplay between market players’ behavior and the dynamics of diverse market structures, price fluctuations, grid constraints, and weather scenarios.

Effect of DG penetration on hybrid relay coordination using user-defined dual-setting directional overcurrent relays and distance relays

ABSTRACT The efficient coordination of the distance and directional overcurrent relays (DOCRs) is jeopardized by the massive deployment of renewable distributed generation (DG) into electrical power networks. The two key aspects influencing the relay coordination are the bidirectional flow of current and increased short-circuit currents due to DG integration. This study investigates the consequences of variable DG penetration on the distance and dual-setting DOCRs (DS-DOCRs) coordination. As a result, common optimal relay settings have been obtained that can be utilized for variable DG penetration levels in modified IEEE-14 bus test system. For the distance relays (DRs), a fixed zone 1 and variable zone 2 operation have been considered. The proposed combined protection scheme is tested on a modified IEEE-14 bus test system using DS-DOCRs and DRs. The relay coordination problem is formulated as a constrained non-linear optimization problem and solved by genetic algorithm (GA) and gray wolf optimization (GWO). In this paper, DS-DOCRs with user-defined relay characteristics have demonstrated superior performance in line protection compared to conventional DOCRs with normal inverse (NI) characteristics. In addition, a comprehensive comparative analysis of optimization algorithms has been performed between GA and GWO algorithms.

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

To mitigate the impact of wind power uncertainty and power-communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology-function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%.

A collaborative restoration strategy of resilient distribution system with the support of electric bus clusters

The widespread deployment of electric buses (EBs) offers substantial flexibility resources to support the service restoration and enhance the resilience of distribution system (DS). Therefore, this paper presents a comprehensive and collaborative service restoration strategy for resilient DS, considering the support of EBs. First, the restoration flexibility model for EBs is derived, which quantifies the restoration capability of EBs by dividing effective EB clusters. This model is integrated into the DS restoration process. Then, a two-step collaborative restoration method of DS is proposed with the aim of minimizing the load curtailment cost. The first step addresses the restoration planning problem to determine the dispatch decisions for EB clusters. Based on above planning solutions, the second step focuses on the real-time restoration problem of DS, obtaining optimal restoration results through coordinating EB clusters and other local resources. In this stage, the impact of natural disasters on the EB cluster dispatch in transportation system is considered, and a rolling optimization method is employed to deal with uncertainties from renewables, load, and EB cluster dispatch time. Finally, numerical simulations are conducted using the modified IEEE 33-bus distribution test system and a 29-node transportation system to verify the effectiveness of the proposed restoration method.

An active protection method based on superimposed phase currents for active distribution systems

To enhance the fault current amplitude of active distribution systems during the weak fault period and decrease the influence of the measurement equipment noise, this paper proposes an active protection method based on superimposed phase currents for active distribution systems. This method is shown as follows. First, the reference value of the filter inductor current of each distributed generator (DG) is set as different values when the fault intensity of the active distribution system changes. Second, the superimposed phase current in each relay is obtained by subtracting each sample of phase current from its corresponding sample of phase current in the previous cycle. Third, the fundamental phase angle of the superimposed phase current is extracted. This is because the fundamental current component of the superimposed current cannot be affected by the active control strategy, which is proved in this paper in detail. Meanwhile, the feature direction is built by fundamental phase angles at both ends of the line to detect faulty zone. Finally, the proposed protection method has been validated in a modified IEEE 13-bus test system, and the test results prove the effectiveness of the proposed active protection method that can enhance the faulty feature to detect faults.

MMD-DRO based economic dispatching considering flexible reserve provision from concentrated solar power plant

To achieve real-time power balance between uncertain resource and demand consumptions in locally standalone power systems with a high penetration of new energy, it is promising to build concentrated solar power (CSP) plants through the establishment of power generation and flexible reserve provision. To address that, a refined maximum mean discrepancy-distributionally robust optimization (MMD-DRO) based economic dispatching model considering flexible reserve provision from CSP plants is designed in this work. Remarkably, a maximum mean discrepancy (MMD)-based ambiguity set representing new energy uncertain outputs is carried out. This proposed MMD-based ambiguity set mathematically keeps the shape of an ellipsoid through kernel mean embedding and related reformulation, and its center is equivalated as a group of empirical distributions extracted from historical data. On this basis, the impact of CSP and relevant flexible reserve capabilities in combination with conventional thermal power units is analyzed, and the joint up-forward/down-forward reserve provision produced from thermal power units and CSP is derived. Furthermore, a two-stage economic dispatching model considering flexible reserve configurations is constructed, which can be solved by the column & constraint generation algorithm. Some results on a modified IEEE 30-bus test system have demonstrated the effectiveness of the proposed model.

Mitigation of Photovoltaics Penetration Impact upon Networks Using Lithium-Ion Batteries

The paper conducts a comprehensive analysis of the impact of very large-scale photovoltaic generation systems on various aspects of power systems, including voltage profile, frequency, active power, and reactive power. It specifically investigates IEEE 9-bus, 39-bus, and 118-bus test systems, emphasizing the influence of different levels of photovoltaic penetration. Additionally, it explores the effectiveness of battery energy storage systems in enhancing system stability and transient response. The transition to PV generation alters system stability characteristics, impacting frequency response and requiring careful management of PV plant locations and interactions with synchronous generators to maintain system reliability. This study highlights how high penetration of photovoltaic systems can improve steady-state voltage levels but may lead to greater voltage dips in contingency scenarios. It also explores how battery energy storage system integration supports system stability, showing that a balance between battery energy storage system capacity and synchronous generation is essential to avoid instability. In scenarios integrating photovoltaic systems into the grid, voltage levels remained stable at 1 per unit and frequency was tightly controlled between 49.985 Hz and 50.015 Hz. The inclusion of battery energy storage systems further enhanced stability, with 25% and 50% battery energy storage system levels maintaining strong voltage and frequency due to robust grid support and sufficient synchronous generation. At 75% battery energy storage system, minor instabilities arose as asynchronous generation increased, while 100% battery energy storage system led to significant instability and oscillations due to minimal synchronous generation. These findings underline the importance of synchronous generation for grid reliability as battery energy storage system integration increases.

Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems

As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods.

The Efficacy of a Cellular Approach to Resilience-Oriented Operation of Distribution Grids Based on Eigenvectors of the Laplacian Matrix

This study addresses the critical question for distribution system operators: how to maintain power supply during an impending disaster that could disrupt the grid? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This approach benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution grids of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (around 96%) and a substantial reduction in load-shedding costs (around 80%), making the network considerably more robust.

Multi-objective optimal reconfiguration of distribution networks using a novel meta-heuristic algorithm

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution.

Coordination of medium-voltage distribution networks and microgrids based on an aggregate flexibility region approach

The large-scale integration of distributed energy resources (DERs) presents operational challenges for medium-voltage distribution networks (MVDNs) and microgrids (MGs) because the conventional centralized scheduling framework lacks of an effective information exchange mechanism for coordinating the scheduling between MVDNs and MGs while supporting privacy concerns. The present work addresses these issues by leveraging a convex hull-based aggregate flexibility region (AFR) associated with the scheduling capability of massive DERs to coordinate the scheduling between an MVDN and MGs efficiently with very limited information exchange. Specifically, the AFR associated with the DERs in an MG is constructed based on the convex hull fitting method, and coordinated scheduling is facilitated by introducing safety operation constraints for the MVDN based on the AFRs of the MGs. Moreover, the coordination model is transformed into a readily solvable quadratically constrained quadratic programming problem by applying second-order cone transformations. The results of numerical computations applied to an IEEE 33-bus test system demonstrate that the obtained AFRs effectively characterize the flexible scheduling capability of DERs, and the proposed coordinated scheduling mechanism between the MVDN and MGs reduces the network losses and voltage deviations of the MVDN, while preserving information privacy.

Northern goshawk optimization for optimal reactive power compensation in photovoltaic low-voltage radial distribution networks

In recent years, photovoltaic systems (PVs) have been increasingly integrated into radial distribution networks (RDNs). However, network operators experience significant issues because they are sporadic. To reduce the load on the main grid and improve the network performance, capacitor banks (CBs) are frequently employed for power factor correction and VAr compensation. However, a large number of CBs must be switched at once in accordance with variations in load and PV generation to maintain feeder voltage regulation and improve its performance. This study's optimization strategy for CB control considers a variety of goals. Using seasonal load profiles and PV generation patterns, a novel and efficient northern goshawk optimization (NGO) was created to determine the appropriate control of CBs. By resolving the typical CBs allocation problem in EDNs and comparing the findings with those in the literature, the effectiveness of NGO was initially cross-verified. In the second stage, an NGO is used to maximize annual net savings while maintaining the best possible control over CBs. Considering the PVs and CBs of the network, a modified IEEE 33-bus test system was used to evaluate the effectiveness of the suggested methodology.

Coordinated energy storage and network expansion planning considering the trustworthiness of demand-side response

The enhancement of economic sustainability and the reduction of greenhouse gas (GHG) emissions are becoming more relevant in power system planning. Thus, renewable energy sources (RESs) have been widely used as clean energy for their lower generation costs and environmentally friendly characteristics. However, the strong random uncertainties from both the demand and generation sides make planning an economic, reliable, and ecological power system more complicated. Thus, this paper considers a variety of resources and technologies and presents a coordinated planning model including energy storage systems (ESSs) and grid network expansion, considering the trustworthiness of demand-side response (DR). First, the size of a single ESS was considered as its size has a close effect on maintenance costs and ultimately affects the total operating cost of the system. Second, it evaluates the influence of the trustworthiness of DR. Third, multiple resources and technologies were included in this high-penetration renewable energy integrated power system, such as ESSs, networks, DR technology, and GHG reduction technology. Finally, this model optimizes the decision variables such as the single size and location of ESSs and the operation parameters such as thermal generation costs, loss load costs, renewable energy curtailment costs, and GHG emission costs. Since the problem scale is very large not only due to the presence of various devices but also both binary and continuous variables considered simultaneously, we reformulate this model by decomposition. Then, we transform it into a master problem (MP) and a dual sub-problem (SP). Finally, the proposed method is applied to a modified IEEE 24-bus test system. The results show computational effectiveness and provide a helpful method in planning low-carbon electricity power systems.