Search Results (75)

Rural areas often lack convenient delivery logistics services, which has become a major obstacle to their economic development. Network design initiatives that synergize passenger and freight transport have been identified as effective solutions to address this challenge. Building upon this initiative, this study investigates a novel three-echelon location-routing problem that synergizes trucks and fixed-route buses (3E-LRP-TF). The model is designed with an innovative operational mode that enables fixed-route buses and trucks to travel in a parallel manner, representing a valuable extension to traditional integrated passenger–freight distribution network design. A mixed-integer nonlinear programming model with the objective of minimizing the total network cost is constructed to formulate the problem. Furthermore, a bottom-up three-phase adaptive large neighborhood search (ALNS) algorithm is designed to solve the problem. A final empirical study was conducted, with Qingchuan County in China serving as a case study, with the aim of validating the effectiveness of the proposed model and algorithm. The results show that, compared with using trucks alone, the synergistic network system has the potential to reduce costs by more than 5% for parcel pickup and delivery services. The proposed algorithm can address larger-scale problems and exhibits better performance with regard to solution quality and efficiency. Sensitivity analysis indicates that the parcel transport capacity of bus routes exerts a nonlinear effect on total costs, and changes in service radius result in trade-offs between cost and accessibility. These findings provide actionable insights for policymakers and logistics operators. Full article

Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and extensive public bus network usually leads to a optimization problem characterized by high-dimensionality and non-linearity. While classical computers struggle to deal with this kind of problems, quantum computers shed new light into this field. The coherent Ising machine (CIM), a specialized optical quantum computer using a photonic dissipative architecture, has shown its remarkable computational power in combinatorial optimization problems. We construct the classical model and the quadratic unconstrained binary optimization (QUBO) model of the bus route optimization problem, and solve it using a classical computer and CIM, respectively. Our experimental results demonstrate the significant acceleration capability of CIM over classical computers in finding the optimal or near-optimal solutions, albeit subject to the hardware limitations of the 100-qubit CIM. Full article

With increasing electrification and the connection of more renewable resources at the transmission level, bulk interconnected electric grids need to plan network expansion with new transmission facilities. The transmission expansion planning (TEP) problem is particularly challenging because of the combinatorial, integer optimization nature of the problem and the complexity of engineering analysis for any one possible solution. Network synthesis methods, that is, heuristic-based techniques for building synthetic electric grid models based on complex network properties, have been developed in recent years and have the capability of balancing multiple aspects of power system design while efficiently considering large numbers of candidate lines to add. This paper presents a methodology toward scalability in addressing the large-scale TEP problem by applying network synthesis methods. The algorithm works using a novel heuristic method, inspired by simulated annealing, which alternates probabilistic removal and targeted addition, balancing the fixed cost of transmission investment with objectives of resilience via power flow contingency robustness. The methodology is demonstrated in a test case that expands a 2000-bus interconnected synthetic test case on the footprint of Texas with new transmission to support 2025-level load and generation. Full article

This paper proposes a Distributed Cooperative Algorithm (DCA) that solves the windup problem caused by the saturation of the Distributed Energy Resource (DER) PI-based control unit. If the reference reactive current output by the PI exceeds the maximum reactive power capacity of the DER, the control unit saturates, preventing the optimal voltage regulation at the connection node of the Active Distribution Network (ADN). Instead of relying on a centralized solution, we proposed a cooperative approach in which each DER’s control unit takes part in the DCA. If a control unit saturates, the voltage regulation error is not null, and the algorithm is activated to assign a share of this error to all DERs’ control units according to a weighted average principle. Subsequently, the algorithm determines the control unit’s new value of the voltage setpoint, desaturating the DER and enhancing the voltage profile. The proposed DCA is independent of the design of the control unit, does not require parameter tuning, exchanges only the regulation error at a low sampling rate, handles multiple saturations, and has limited communication requirements. The effectiveness of the proposed DCA is validated through numerical simulations of an ADN composed of two IEEE 13-bus Test Feeders. Full article

Aiming to solve optimization scheduling difficulties caused by the double uncertainty of source-side photovoltaic (PV) output and load-side demand response in active distribution networks, this paper proposes a two-stage distribution robust optimization method. First, the first-stage model with the objective of minimizing power purchase cost and the second-stage model with the co-optimization of active loss, distributed power generation cost, PV abandonment penalty, and load compensation cost under the worst probability distribution are constructed, and multiple constraints such as distribution network currents, node voltages, equipment outputs, and demand responses are comprehensively considered. Secondly, the second-order cone relaxation and linearization technique is adopted to deal with the nonlinear constraints, and the inexact column and constraint generation (iCCG) algorithm is designed to accelerate the solution process. The solution efficiency and accuracy are balanced by dynamically adjusting the convergence gap of the main problem. The simulation results based on the improved IEEE33 bus system show that the proposed method reduces the operation cost by 5.7% compared with the traditional robust optimization, and the cut-load capacity is significantly reduced at a confidence level of 0.95. The iCCG algorithm improves the computational efficiency by 35.2% compared with the traditional CCG algorithm, which verifies the effectiveness of the model in coping with the uncertainties and improving the economy and robustness. Full article

For a new distribution network with energy storage and a flexible multi-state switch (FMSS), several problems of multi-mode operation and switching, such as the unbalance of feeder loads and feeder faults, among others, should be considered. This paper forwards a coordination control strategy to address the above challenges faced by the FMSS under grid-connected operations. To tackle the multi-mode operation problem, the system’s operational state is divided into multiple working modes according to the operation states of the system, the positions and number of fault feeders, the working states of the transformers, and the battery’s state of charge. To boost the system’s operational reliability and load balance and extend the power supply time for the fault load, the appropriate control objectives in the coordination control layer and control strategies in the equipment layer for different working modes are established for realizing the above multi-directional control objectives. To resolve the phase asynchrony issue among the fault load and other normal working loads caused by the feeder fault, the off-grid phase-locked control based on the V/f control strategy is applied. To mitigate the bus voltage fluctuation caused by the feeder fault switching, the switching control sequence for the planned off-grid is designed, and the power feed-forward control strategy of the battery is proposed for the unplanned off-grid. The simulation results show that the proposed control strategy can ensure the system’s power balance and yield a high-quality flexible power supply during the grid-connected operational state. Full article

Fault recovery in distribution networks is a complex, high-dimensional decision-making task characterized by partial observability, dynamic topology, and strong interdependencies among components. To address these challenges, this paper proposes a graph-based multi-agent deep reinforcement learning (DRL) framework for intelligent fault restoration in power distribution networks. The restoration problem is modeled as a partially observable Markov decision process (POMDP), where each agent employs graph neural networks to extract topological features and enhance environmental perception. To address the high-dimensionality of the action space, an action decomposition strategy is introduced, treating each switch operation as an independent binary classification task, which improves convergence and decision efficiency. Furthermore, a collaborative reward mechanism is designed to promote coordination among agents and optimize global restoration performance. Experiments on the PG&E 69-bus system demonstrate that the proposed method significantly outperforms existing DRL baselines. Specifically, it achieves up to 2.6% higher load recovery, up to 0.0 p.u. lower recovery cost, and full restoration in the midday scenario, with statistically significant improvements ($p<0.05$ or $p<0.01$). These results highlight the effectiveness of graph-based learning and cooperative rewards in improving the resilience, efficiency, and adaptability of distribution network operations under varying conditions. Full article

In order to adapt to the development trend of large-scale access of distributed resource and power market reform, it has gradually become an industry consensus that multi-agent resources of a distribution network participate in regulation in the form of clusters. Based on the “centralized–distributed” regulation architecture, and relying on the regulation process of cluster partition, external characteristics calculation, command decomposition, and deaggregation, a cluster regulation strategy is proposed considering market factors. Firstly, the behavior characteristics of each agent are analyzed under the market trading mechanism. Then, the model of multi-agents participating in regulation in the form of a single point and a cluster is established. In the process of cluster partition, considering the active and reactive power–voltage coupling characteristics of the distribution network, a Monte Carlo random cluster partition sample generation method and screening mechanism are designed to deal with the problem of insufficient and inapplicable samples in the actual scene. At the same time, in order to reduce the difficulty of solving the cluster’s external characteristics, a multi-agent output range simplification method is proposed for the process of “external characteristics calculation”. Finally, the improved IEEE-33 bus system was taken as an example to verify the accuracy of the cluster regulation method when responding to the Automatic Generation Control (AGC) and Automatic Voltage Control (AVC) scheduling commands of the superior grid under market factors and different cluster partitions. The results show that the relative error of the command tracking of the proposed multi-agents in different cluster forms is less than 5.5%, which verifies the correctness of the proposed method. Full article

The development of smart grids has led to an increased focus by transmission and distribution network operators on the Optimal Power Flow (OPF) problem. The solutions identified for an OPF problem are vital to ensure the real-time optimal control and operation of electric networks and can help enhance their efficiency. In this context, this paper proposed an original solution to the OPF problem, represented by optimal voltage control in electric networks integrating wind farms. Based on a fuzzy inference system (FIS) built in the Fuzzy Logic Designer of the Matlab environment, where the fuzzification process was improved through fuzzy K-means clustering, two approaches were developed, representing novel tools for OPF analysis. The decision-maker can use these two approaches only successively. The FIS-based first approach considers the load requested at the PQ-type buses and the powers injected by the wind farms as the fuzzy input variables. Based on the fuzzy inference rules, the FIS determines the suitable tap positions for power transformers to minimise active power losses. The second approach (I-FIS), representing an improved variant of FIS, calculates the steady-state regime to determine power losses based on the suitable tap positions for power transformers, as determined with FIS. A real 10-bus network integrating two wind farms was used to test the two proposed approaches, considering comprehensive characteristic three-day tests to thoroughly highlight the performance under different injection active power profiles of the wind farms. The results obtained were compared with those of the best methods in constrained nonlinear mathematical programming used in OPF analysis, specifically sequential quadratic programming (SQP). The errors calculated throughout the analysis interval between the SQP-based approach, considered as the reference, and the FIS and I-FIS-based approaches were 5.72% and 2.41% for the first day, 1.07% and 1.19% for the second day, and 1.61% and 1.33% for the third day. The impact of the OPF, assessed by calculating the efficiency of the electric network, revealed average percentage errors between 0.04% and 0.06% for the FIS-based approach and 0.01% for the I-FIS-based approach. Full article

Recently, data-driven methods have been widely assessed by researchers in the field of power system transient stability assessment (TSA). The differences in prediction difficulty among the samples are ignored by most previous studies. To address this problem, anchor loss (AL) is introduced, which can dynamically reshape loss values based on the prediction difficulty of samples. Thereby, easy samples are suppressed by reducing their loss values to avoid being paid too much attention when they are misclassified. Meanwhile, hard samples are emphasized by increasing their loss values, in order to be predicted correctly as much as possible. On basis of the AL, historical information in the model training process is considered. A novel loss function named historical information anchor loss (HIAL) is designed. The loss values can be adaptively rescaled according to the previous prediction results as well as the prediction difficulty of samples. Finally, the HIAL is combined with the deep brief network (DBN) and applied in the IEEE 39-bus system, and a realistic system is produced to verify its effectiveness. By incorporating prediction difficulty and historical training information, the accuracy (with a reduction in misjudgment rate exceeding 30%) and convergence speed of the TSA model can be significantly improved. Full article

In the context of the global response to climate change and the promotion of an energy transition, the Internet of Things (IoT), sensor technologies, and big data analytics have been increasingly used in power systems, contributing to the rapid development of distributed energy resources. The integration of a large number of distributed energy resources has led to issues, such as increased volatility and uncertainty in distribution networks, large-scale data, and the complexity and challenges of optimizing security and economic dispatch strategies. To address these problems, this paper proposes a day-ahead scheduling method for distribution networks based on an improved multi-agent proximal policy optimization (MAPPO) reinforcement learning algorithm. This method achieves the coordinated scheduling of multiple types of distributed resources within the distribution network environment, promoting effective interactions between the distributed resources and the grid and coordination among the resources. Firstly, the operational framework and principles of the proposed algorithm are described. To avoid blind trial-and-error and instability in the convergence process during learning, a generalized advantage estimation (GAE) function is introduced to improve the multi-agent proximal policy optimization algorithm, enhancing the stability of policy updates and the speed of convergence during training. Secondly, a day-ahead scheduling model for the power distribution grid containing multiple types of distributed resources is constructed, and based on this model, the environment, actions, states, and reward function are designed. Finally, the effectiveness of the proposed method in solving the day-ahead economic dispatch problem for distribution grids is verified using an improved IEEE 30-bus system example. Full article

In the DC distribution networks, DC bus voltage is maintained by the grid-connected converter; the controllability and reliability of the grid-connected converter significantly affect the bus voltage characteristic. To address the problem of limited stability and frequent oscillations, this paper proposes a state feedback control method for the AC/DC converter. Conventional AC/DC converter adopts the voltage-current double-closed-loop control structure with the proportional-integral (PI) controllers, which is the equivalent of the typical type II control system, but the typical type II control system cannot fully settle the stability and immunity problems. In contrast, the state feedback control strategy not only achieves the control objectives of the traditional double-closed-loop control but also reduces the AC/DC converter system model to a typical Type I system, which improves stability and thus enhances the oscillation suppression capability of the bus voltage. By selecting multiple state variables and designing the converter pole configuration range, the proposed single-loop state feedback control method manages to optimize both the dynamic and steady-state performances of the grid-connected AC/DC converter. Finally, the effectiveness of the proposed single-loop state feedback control strategy is verified through MATLAB (2018b)/Simulink software simulation and experiments on a DC distribution network platform. Full article

The transient stability control for disturbances in microgrids based on a lithium-ion battery–supercapacitor hybrid energy storage system (HESS) is a challenging problem, which not only involves needing to maintain stability under a dynamic load and changing external conditions but also involves dealing with the energy exchange between the battery and the supercapacitor, the dynamic change of the charging and discharging process and other factors. This paper focuses on the bus voltage control of HESS under load mutations and system uncertainty disturbances. A BP Neural Network-based Active Disturbance Rejection Controller (BP-ADRC) is proposed within the traditional voltage-current dual-loop control framework, leveraging uncertainty quantification. Firstly, system uncertainties are quantified using system-identification tools based on measurable information. Subsequently, an Extended State Observer (ESO) is designed to estimate the total system disturbance based on the quantified information. Thirdly, an adaptive BP Neural Network-based Active Disturbance Rejection Controller is studied to achieve transient stability control of disturbances. Robust controllers, PID controllers and second-order linear Active Disturbance Rejection Controllers are employed as benchmark strategies to design simulation experiments. Simulation results indicate that, compared to other benchmark strategies, the BP-ADRC controller based on uncertainty quantification exhibits superior tracking and disturbance-rejection performance in transient stability control within microgrids of hybrid energy storage systems. Full article

Reactive power dispatch (RPD) in electric power systems, integrated with renewable energy sources, is gaining popularity among power engineers because of its vital importance in the planning, designing, and operation of advanced power systems. The goal of RPD is to upgrade the power system performance by minimizing the transmission line losses, enhancing voltage profiles, and reducing the total operating costs by tuning the decision variables such as transformer tap setting, generator’s terminal voltages, and capacitor size. But the complex, non-linear, and dynamic characteristics of the power networks, as well as the presence of power demand uncertainties and non-stationary behavior of wind generation, pose a challenging problem that cannot be solved efficiently with traditional numerical techniques. In this study, a new fractional computing strategy, namely, fractional hybrid particle swarm optimization (FHPSO), is proposed to handle RPD issues in electric networks integrated with wind power plants (WPPs) while incorporating the power demand uncertainties. To improve the convergence characteristics of the Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), the proposed FHPSO incorporates the concepts of Shannon entropy inside the mathematical model of traditional PSOGSA. Extensive experimentation validates FHPSO effectiveness by computing the best value of objective functions, namely, voltage deviation index and line loss minimization in standard power systems. The proposed FHPSO shows an improvement in percentage of 61.62%, 85.44%, 86.51%, 93.15%, 84.37%, 67.31%, 61.64%, 61.13%, 8.44%, and 1.899%, respectively, over ALC_PSO, FAHLCPSO, OGSA, ABC, SGA, CKHA, NGBWCA, KHA, PSOGSA, and FPSOGSA in case of traditional optimal reactive power dispatch(ORPD) for IEEE 30 bus system. Furthermore, the stability, robustness, and precision of the designed FHPSO are determined using statistical interpretations such as cumulative distribution function graphs, quantile-quantile plots, boxplot illustrations, and histograms. Full article

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network. Full article