Energy optimization of smart home in electrical microgrids considering economic and technical multi-objective functions and demand response programs

Multi-objective optimization of PVDGs in power distribution networks using the self adaptive tyrannosaurus algorithm

With widespread proliferation of power electronic devices, harmonic pollution has become increasingly severe in power distribution network. Owing to its simple structure and high compensation accuracy, source-current-detected active power filter (APF) has been employed for harmonic cancellation. This type of APF generates the current reference through closed-loop control scheme without sensing the load current. However, this operational principle renders conventional current-limiting strategies, such as reference truncated limiting, impractical. Because they will destroy the inner closed-loop relationship. Effective residual capacity utilization can not be achieved while maintaining fast response. Therefore, this article proposes an improved fast current-limiting strategy for source-current-detected APF. By output current feedforward, closed-loop relationship is modified to achieve partial compensation. Further, this article optimizes multi-frequency feedforward coefficients for different cases. Fast-limiting control and total harmonic distortion (THD) optimization control are integrated in a cascade to form a hybrid strategy, which exhibits a more balanced performance. By instantaneous current triggering, proposed strategy substantially reduces the response time and overcurrent risk. On this basis, available capacity of APF is better utilized. Finally, proposed strategy is verified by simulations and experiments in which a 2.2kW load is compensated.

Power distribution network reconfiguration for distributed generation maximization

Enhanced protection scheme design of power distribution networks through optimal DG integration using MALO and ETAP

Hybrid sensitivity-driven enhanced modified ant lion optimizer framework for multi- distributed generation allocation in radial distribution networks

Adaptive machine learning framework for fair and resilient load shedding in smart power distribution networks

The paper proposes a wideband circularly polarized (CP) dielectric resonator antenna (DRA) array for X-band airborne SAR applications. The antenna element employs a dual-layer DRA excited by a T-shaped slot-coupled feeding. By placing a low-permittivity DRA beneath a high-permittivity one, both impedance and axial-ratio (AR) bandwidth are effectively improved. Then, a dual-feed sequentially rotated two-element subarray is developed, which provides the capability of forming a tilted main beam. In addition, grid-type metal walls are inserted to suppress mutual coupling and preserve polarization purity during beam tilting. To support broadband beam steering, wideband differential phase shifters are designed, while a Chebyshev power-distribution network is adopted to reduce sidelobes. A prototype was fabricated. Measurements demonstrate a 10-dB impedance bandwidth of 16.52% and a 3-dB AR bandwidth of 12.76% across 8.95–10.17 GHz. The array achieves a peak gain of 13.0 dBic, a tilted beam of 30°–35°, and sidelobe levels below −20 dB.

The difficulty in coordinating reconstruction and restoration resources significantly impacts fault recovery in distribution grids, with source-load uncertainty further affecting the load recovery process. This paper proposes a two-layer distributed robust resilience enhancement strategy that addresses fault repair and dynamic network reconstruction, aiming to resolve issues of resource coordination and source-load uncertainty in fault recovery. First, a coupled fault repair and dynamic reconstruction model for distribution network fault restoration is proposed by combining the spatiotemporal transfer process of fault restoration scheduling (FRS) with the dynamic reconstruction of isolated islands in the distribution network. Second, the DRCC model, based on Wasserstein distance, incorporates source-load uncertainty and utilizes dual theory and CVaR risk approximation to solve the model. To improve efficiency, a two-stage distributed robust optimization strategy is proposed: the first stage addresses network reconstruction and FRS scheduling, while the second solves the distribution network power decision plan based on the DRCC model. Finally, detailed simulations on the improved IEEE 33-bus system verify the effectiveness and feasibility of the proposed strategy, and an additional case study on the larger IEEE 123-bus system further confirms its scalability.

This paper presents a parallel genetic algorithm (GA) for the planning of power distribution networks considering harmonics. Power distribution systems are generally operated in a radial configuration, supplemented by tie switches that enable network reconfiguration during unexpected outages or planned maintenance. They can also include distributed generators (DGs), capacitor banks (CBs), and soft open points (SOPs) to lower distribution losses and improve the voltage profile. Some of the loads and DG units may be nonlinear, generating harmonic currents in the system, polluting the power, and increasing losses. This paper makes use of a parallel GA to find an optimized configuration, optimized location, and sizing of DGs, CBs, and SOPs to lower real power distribution losses while considering harmonics and the physical constraints of the network. The proposed algorithm uses a solution encoding based on the minimum spanning tree to guarantee the radial topology of candidate solutions. It uses the backward–forward power flow method to compute the fundamental voltages and a decoupled harmonic power flow for the harmonic components. The algorithm is parallelized on a small computer cluster using the Message Passing Interface (MPI) to reduce its execution time. The proposed solver is validated on distribution systems ranging from 16 to 880 buses. The results show that simultaneously optimizing the topology, the DGs, the CBs, and the SOPs results in reducing power losses by 37% to 93%, improving the overall efficiency of the distribution system. The parallelization using MPI allows for a 90.9× speedup on a 96-core cluster.

Physics-informed data-driven topology identification in power distribution networks with adversarial robustness enhancement

Advanced optimization of distributed generation and network reconfiguration in power distribution networks: Challenges, methodologies, and strategic insights

A multi-objective framework for siting of electric vehicle charging stations in power distribution networks

Environmental impact assessment of battery energy storage system in electrical power distribution network considering battery technology and power charging source

The Dynamic Voltage Restorer is a custom power device employed to alleviate voltage issues at load terminals. In today's world, power quality has emerged as a significant concern. This is particularly true with the advent of advanced devices that are highly sensitive to the quality of the power supply. Power quality issues manifest as deviations in voltage, current, or frequency, leading to failures in end-user equipment. A prominent issue addressed here is power sag. To tackle this challenge, custom power devices are implemented. Among these devices is the Dynamic Voltage Restorer (DVR), recognized as the most efficient and effective modern custom power device utilized in power distribution networks. The DVR injects the necessary voltage in series with the supply voltage via an injection transformer to correct the voltage amplitude, phase, and harmonic components in the line. This paper discusses the development, simulation, and analysis of a Dynamic Voltage Restorer (DVR) using MATLAB/SIMULINK. To improve the voltage sag restoration capability of the DVR, this paper focuses on the creation of a control structure utilizing a Discrete PWM pulse generator. Furthermore, this paper explores a new control algorithm based on the abc to dq0 transformation for pulse generation. The results indicate that the developed DVR possesses a strong capability to restore voltage levels during sag conditions.

This paper proposes a key vulnerable node identification method based on the trend of short-circuit capacity change, and establishes a complete set of node vulnerability evaluation index system by analyzing the change of node short-circuit capacity before and after distributed power supply access. At the same time, this paper improves the traditional port compensation method, fully considering the influence of parallel branches and the nonlinear characteristics of distributed power supply. Combined with complex network theory, this study introduces indicators such as the proportionality coefficient and the association Q-function to deeply analyze the topological destruction resistance of active distribution networks. The results show that the short-circuit capacity varies from 17.39% to 21.95% at distributed power access points and their neighboring areas, which constitute the key vulnerable nodes of the system. This paper also proposes a stochastic model node-equivalent voltage crossing probability calculation method to simplify the impact analysis of multi-point stochastic modeling of power distribution networks through node equivalence. The probabilistic security analysis method based on Latin hypercube-Monte Carlo sampling considers multiple uncertainties and establishes a time series probabilistic tidal current calculation model. The results show that the penetration rate of distributed power supply is the main factor affecting system safety. In addition, the complex affine analysis and operation optimization method proposed in this paper effectively solves the affine approximation problem of suboperations such as trigonometric and inverse trigonometric functions, and reduces the network loss and voltage deviation. This study provides important theoretical value and engineering application significance for the planning and design, operation control and fault handling of active distribution networks.

This work provides novel protection approaches to increase the dependability and safety of modern power systems, particularly microgrids. The proposed method emphasises the importance of microgrid resynchronisation, using effective control, communication and monitoring technologies to protect the renewables-integrated power system. The proposed approach focuses on seamless transitions between islanded and grid-connected modes, addressing essential challenges including voltage, frequency and phase angle synchronisation with the utility grid. This paper signifies the importance of battery energy storage systems in maintaining system stability and enabling effective resynchronisation. The proposed resynchronisation method provides optimal performance, ensuring low oscillations and fast stabilisation of grid parameters. Furthermore, the system's response to fault conditions proves its resilience in maintaining continuous operation in the power distribution network. The proposed approach enhances energy dependability and stability in distributed power systems, facilitating a sustainable and resilient power system. The proposed solutions are evaluated in a microgrid test system along with the integration of solar PV and battery systems in MATLAB 2024A Simulink environment. The proposed method achieves a maximum voltage variation of 1.2%, frequency deviation under 0.03 Hz and phase-angle difference of less than 2° during resynchronisation, indicating significant improvements when compared to conventional synchronisation techniques. These findings validate the efficacy of the proposed method for the effective operation of renewable-integrated microgrids.

This article introduces a dataset designed for the detection of partial discharges in transmission power lines using covered conductors through a contact galvanic method, sourced from real environments across 23 different power lines in various locations. Though partially introduced in a Kaggle competition (only 3% of data), its full extent is disclosed here for the first time. The dataset is distinguished by its rich, imbalanced distribution across seven classes, derived from signals processed via a sophisticated voltage-based method, and supplemented with extracted features to aid analysis. Its scale, detailed labeling, and real-world basis offer unparalleled opportunities for developing machine learning algorithms aimed at fault detection. This contribution holds vast potential for reuse in electrical engineering research focused on enhancing power distribution network reliability and safety, particularly in the context of predictive maintenance and understanding partial discharge behaviors.

Active power distribution networks are evolving with the integration of distributed energy resources (DERs) and advanced optimization techniques to enhance grid flexibility and efficiency. However, radial distribution networks suffer from significant voltage drops and high-power losses due to their inherent topology and unidirectional power flow.This study proposes a novel Classification-based Global Optimization (CGO) approach that integrates electrical engineering principles with a structured optimization framework-a departure from conventional metaheuristic methods. Unlike black-box algorithms, CGO classifies distribution buses based on voltage sensitivity and power flow characteristics before applying a deterministic global optimization function for optimal placement and sizing of distributed generation (DG) and capacitor banks (CBs). The methodology is validated on IEEE 33-bus and IEEE 69-bus test systems. For the IEEE 33-bus system, simultaneous DG and CB integration achieved a 94.75% reduction in active power losses, while for the IEEE 69-bus system, losses were reduced by 98.061%. Voltage stability was significantly improved, with the voltage stability index (VSI) increasing to 0.9740 and 0.9773 for the 33-bus and 69-bus systems, respectively. The proposed CGO approach demonstrates superior computational efficiency, with average simulation times of 18.62s (33-bus) and 21.45s (69-bus) for combined DG and CB optimization. By enhancing energy efficiency and renewable integration, the method directly supports Sustainable Development Goals (SDGs) 7, 9, 11, and 13, offering a scalable and interpretable solution for modern active distribution networks.

The increasing interdependence between power distribution networks and transportation systems introduces unprecedented operational complexity under high renewable penetration and stochastic electric vehicle (EV) mobility. This study proposes a spatiotemporally coupled distributionally robust topology–dispatch co-optimization framework to coordinate network reconfiguration, dispatch scheduling, and EV charging operations under dual-layer uncertainty from renewable generation and transportation flows. A Wasserstein metric–based distributionally robust optimization (DRO) model is developed to capture ambiguity in the joint probability distributions of wind–solar availability and EV traffic intensity, ensuring robust feasibility against distributional shifts. The upper-level problem minimizes the expected operational cost, switching losses, and travel-related energy cost, while the lower-level subproblem represents the worst-case realization of uncertain parameters within a data-driven ambiguity set. The resulting min–max structure is decomposed through a column-and-constraint generation algorithm augmented by Benders cuts, enabling tractable convergence for large-scale mixed-integer nonlinear decision spaces. Case studies on a 10-node distribution network coupled with 20-route EV mobility scenarios demonstrate that the proposed framework achieves 12.4% lower operational cost variance and 45% voltage deviation reduction compared with deterministic optimization. The Wasserstein ambiguity radius varepsilon is shown to critically shape the robustness–efficiency trade-off, with varepsilon = 0.05 yielding a near-optimal balance– improving out-of-sample reliability by 39% with only a 12.7% cost penalty. Moreover, scenario-wise analyses reveal adaptive reconfiguration patterns that dynamically align switching actions with renewable curtailment and traffic congestion, leading to coordinated mitigation of spatiotemporal imbalances. These findings confirm that integrating topology flexibility with distributionally robust dispatch and mobility coordination can substantially enhance the resilience and economic efficiency of renewable-dominated urban energy systems.