Bugesera, a historically drought-prone region in Rwanda, is undergoing transformation through investment in modern irrigation and sustainable agricultural practices. However, extending the national electrical grid to numerous dispersed smallholder farms poses a major challenge. The persistent water scarcity and rising conventional energy costs necessitate the development of innovative and sustainable solutions. This study investigates the use of photovoltaic (PV) pumping systems as a green energy alternative for off-grid rural areas, supporting both agricultural irrigation and domestic water supply. A model system serving five one-hectare market-gardening plots and 25 inhabitants was analyzed, with a total daily water demand of 300.75 m3/day. A comprehensive technical and economic evaluation was conducted using MATLAB to optimize the system design, including PV array sizing and storage capacity, to ensure reliable operation under defined water and energy demands. A critical component of the analysis was the optimization of the piping network to balance hydrodynamic performance, energy consumption, and overall system cost. For a water requirement of 300.75 m3/day, the optimal PV system consisted of 12 panels, providing a cost-effective balance between energy generation and pumping demand. The results show a rapid decrease in total system cost as the pipe diameter increases from 0.15 to 0.30 m, primarily due to reduced friction losses that lower the total dynamic head and significantly decrease the required PV array size, which dominates the system cost. An optimal diameter of approximately 0.30 m was identified, beyond which further increases yield diminishing cost reductions as the total dynamic head becomes governed mainly by static head rather than hydraulic losses. This integrated technical and economic approach provides a practical framework for designing sustainable, cost-effective solar-powered irrigation and domestic water systems tailored to off-grid smallholder farmers in drought-prone regions.

Synchronous rectification (SR) is an effective method to improve the efficiency of the CLLC resonant converter. However, existing SR methods typically rely on calculating specific SR angles based on pulse frequency modulation (PFM) or phase shift modulation (PSM), which do not overcome the intrinsic efficiency drawbacks of these modulations. To address this, this paper proposes a hybrid modulation that integrates switching frequency and phase shift to achieve natural SR and modulation optimization simultaneously. Rather than SR angle calculation, the proposed modulation combines the two control variables to synchronize the operation of the primary side switches and secondary side rectifiers (diodes or SR switches). This alignment enables natural SR by gating the secondary switches concurrently with the primary switches. Furthermore, the proposed modulation reduces the RMS value of inductor current and extends the ZVS range compared to PSM. In contrast to PFM, the proposed method operates at a lower switching frequency, thereby reducing switching losses. The modulation is achieved by a closed-loop control for switching frequency and a precomputed fitting curve for phase shift. A series of experimental results have verified the effectiveness of the proposed modulation.

The growing energy demand of data centers, particularly those offering machine learning services, poses significant challenges to power system stability. As electricity grids become more dynamic due to the increasing penetration of renewable generation, data centers represent a promising but underutilized source of demand-side flexibility. This paper presents a quantitative framework to assess and activate the flexibility potential of data centers by deferring non-critical, high-latency IT workloads. Using real-world workload traces from the Alibaba Cluster Trace dataset, tasks are classified based on observed queuing latency, and a power model is developed to estimate energy consumption associated with different latency groups. A Latency-Aware Deferral for Flexibility (LAD-Flex) strategy is proposed to temporarily reduce modeled IT-side power demand in response to aggregator requests, while maintaining quality of service. The strategy accounts for estimated task duration, latency thresholds, and flexibility window constraints. Results from a one-week case study show that up to 22% of load can be deferred during flexibility windows, and that more than 20% of estimated GPU-side power is attributable to tasks with deferrable latency. Additionally, an optimization framework is introduced to identify the notification period that maximizes the value of flexibility under a time-sensitive pricing scheme. The findings demonstrate that data centers, coupled with an aggregator, can reliably participate in short-notice demand response by leveraging workload-aware deferral strategies, offering both operational and economic benefits.

Identifying faulty lines and their accurate location is key to the rapid restoration of distribution systems. Fault identification and its location will become more challenging as power electronics penetration increases and contingencies are seen in larger areas. This paper proposes a single terminal fault location methodology (i.e., no communication involved) that is robust to variations of key parameters (e.g., sampling frequency, fault resistance, etc.) for low voltage DC systems. The proposed method uses local measurements to estimate the current caused by the other remote terminals affected by the contingency. This mimics the strategy followed by double terminal methods that require communications and decouple the accuracy of the methodology from the fault resistance. The algorithm takes consecutive voltage and current samples, including the estimated current of the other terminal. This mathematical approach results in better accuracy than other single-terminal approaches in the literature. The robustness of the proposed strategy against different fault resistances and locations is demonstrated using PSCAD/EMTDC and Real-Time Digital Simulator (RTDS).

The need to build resilient distribution systems provides the incentive to decentralize voltage stability responsibilities across the entire network instead of the main grid. This requires the system to operate in various modes depending on the availability and location of the voltage sources. To facilitate seamless transitions between different operation modes, this paper presents a unified control scheme designed for the interlink converter connecting multi-voltage buses in distribution systems. Under normal conditions, the proposed scheme efficiently manages power flow. In the event of loss of a voltage control unit at one bus, the scheme autonomously provides voltage support to this bus to ensure the stable operation of the whole network. This is achieved without the need for mode detection and communication, thereby preventing instability risk due to the inaccurate or delayed mode transition. Moreover, the proposed scheme inherently supports input-series output-parallel converter configurations, achieving input voltage sharing and output current sharing without additional control loops. Simulation results validate the effectiveness and robustness of the proposed scheme.

Accurate day-ahead demand forecasting is crucial for optimizing the performance of home energy management systems. Traditional forecasting methods often decouple the forecasting task and the subsequent decision marking, resulting in imbalanced economic penalties from load deviations. Furthermore, the rise of digitization has led to a massive increase in fine-grained smart meter data stored daily, posing significant challenges to customers' data privacy and security. To address these technical challenges, this study proposes a personalized federated learning methodology that incorporates a cost-oriented loss function. This methodology is designed to learn end-user-specific patterns, reduce penalization costs, and preserve customer privacy. Comparative analyses reveal that the proposed method, which utilizes a cost-oriented loss function and $L^{2}$ regularization, outperforms traditional symmetric loss functions in terms of efficiency and economic benefits. The results confirm that this personalized federated learning approach consistently achieves the lowest error rates and penalization costs compared to other methods. Additionally, sensitivity analyses indicate that even households with limited historical consumption data can achieve accurate load predictions using the personalized federated learning approach.

Modular multi-active-bridge (MMAB) converters have emerged as promising solutions for efficient power conversion in integrating various distributed energy resources, energy storage systems, and loads. However, existing studies predominantly rely on centralized controllers, which limit modular scalability due to the lack of software modularity. This also poses significant computational burdens for the centralized controllers. To address these challenges, this paper proposes a decentralized control method based on a decoupled MMAB converter. The decoupling is achieved by eliminating the inductance from one port, thereby simplifying the control complexity. Based on the decoupled MMAB converter, a decentralized control scheme is proposed, which is composed of a PI controller for frequency synchronization and a proportional controller for direct phase-shift regulation. This combination ensures accurate module synchronization and rapid transient responses. A detailed parameter design is obtained using small-signal analysis. Additionally, a thorough inductance design methodology is provided to maintain the phase shift within a stable operating range. Simulation results validate the effectiveness and superior dynamic performance of the proposed decentralized control strategy.

This paper proposes a data-driven algorithm for model order reduction (MOR) of large-scale wind farms and studies the effects that the obtained reduced-order model (ROM) has when this is integrated into the power grid. With respect to standard MOR methods, the proposed algorithm has the advantages of having low computational complexity and not requiring any knowledge of the high order model. Using time-domain measurements, the obtained ROM achieves the moment matching conditions at selected interpolation points (frequencies). With respect to the state of the art, the method achieves the so-called two-sided moment matching, doubling the accuracy by doubling the interpolated points. The proposed algorithm is validated on a combined model of a 200-turbine wind farm (which is reduced) interconnected to the IEEE 14- bus system (which represents the unreduced study area) by comparing the full-order model and the reduced-order model in terms of their Bode plots, eigenvalues and the point of common coupling voltages in extensive fault scenarios of the integrated power system.

This paper investigates a control method for a three-level bipolar DAB converter with different voltage levels in each pole for bipolar LVDC distribution systems. The topology modifies a conventional DAB converter by splitting the high-voltage side into two series half-bridges and adding a single blocking capacitor, enhancing power density and cost-effectiveness compared to methods requiring additional switches and magnetic components. The two constrained outputs form a quartic equation, making the optimal solution space non-convex and challenging for analytical solutions or traditional PID control due to the nonlinear input-output relationship. To address this, a long-horizon finite control set model predictive control (FCS-MPC) is proposed to handle the non-convex solution space, control output voltages, and optimize switching frequency. Simulations verify the effectiveness of the proposed converter and control method.

A large-scale wind farm model is presented as a benchmark model for various model order reduction (MOR) methods. Firstly, the detailed mathematical description of a wind turbine generator is provided, including the aerodynamic and mechanical sub-blocks of the wind turbine, the electrical sub-block of the generator, and the associated controllers. Based on the wind turbine generator model, a large-scale wind farm model is created whose dimension can be easily changed to test MOR methods on different scales. Finally, the wind farm model is utilised as a benchmark model to show the performance of a nonlinear MOR method and a linear MOR method. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Interconnected Microgrid (IMG) networks have been suggested as the best to build electrical networks in remote villages far from the main electricity grid by interconnecting the nearby distributed energy resources (DERs) through power electronic converters. Interconnecting different DERs results in voltage deviation with unequal power-sharing, while voltage performance is a significant challenge. The control strategies for these converters are essential in the operational stability of any IMG network under study. In this paper, we propose an improved droop control method aiming to manage the power flow among the IMGs by maintaining the constant desired voltages in the network with minimum voltage deviation, resulting in the minimization of power losses. We found that the minimum voltage deviation at the load side (converter-3) was between 0.58 and 0.56 V, while the voltage deviation for both converter-1 and converter-2 remained below 0.5 V. This leads to efficient voltage regulation, resulting in the stability of an IMG network. To verify the feasibility of this method, MATLAB/SIMULINK has been used.

There are obstacles to the widespread use of small electric vehicles (EVs) in Rwanda, including concerns regarding the battery range and lifespan. Lithium-ion batteries (LIBs) play an important role in EVs. However, their performance declines over time because of several factors. To optimize battery management systems and extend the range of EVs in Rwanda, it is essential to understand the influence of the driving profiles on lithium-ion battery degradation. This study analyzed the degradation patterns of a lithium-ion battery cell that propels an E-bike using various real-world E-bike driving cycles that represent Rwandan driving conditions under deep discharge (>80%). By being aware of these variables, battery failure can be slowed and improved battery performance can be achieved to promote the transition to cleaner transportation in Rwanda for the productive use of energy. The analyzed parameters that affect battery performance are temperature, driving cycles, and state of charge. It was found that the higher the temperature, the higher was the rate of fading. On the other hand, the EVs that operate in the region with higher elevation (hilly region) combined with a flat surface where the riders use their physical forces to propel the E-bike and their batteries lose their capacity rapidly compared to those operating in regions where the energy from the lithium-ion battery assists for the entire mileage. By draining the battery to 10% and charging it to 90% of its initial capacity, the capacity fading decreased by 5%.

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

Modular multiactive-bridge (MMAB) converter is increasingly recognized as a potential solution for efficient power conversion in the integration of distributed energy resources, storage systems, and loads. However, optimizing MMAB converters for high-efficiency operation remains a challenge due to the inherent coupling across ports. To address this problem, this article proposes an inductance-current-minimization optimization scheme based on a hardware decoupling method. The decoupling across ports is achieved by eliminating the inductance at one of the ports, which simplifies the complexity of the system significantly. Based on the decoupled MMAB converter, an optimization scheme is proposed to minimize the root mean square value of the inductor current. Compared with the conventional modulation scheme, this scheme can reduce the inductor current and expand the soft-switching region across various power ranges and voltage ratios, thereby enhancing the operation efficiency. Furthermore, the proposed scheme is based on analytical solutions, enabling online implementation without the need for lookup tables and scalable to accommodate any number of ports. The effectiveness of the proposed scheme has been verified by a series of simulations and experiments based on a four-port MMAB converter prototype.

In remote areas of Sub-Saharan Africa, as well as in Rwanda, communities face the problem of finding an affordable and suitable way of transport for their daily life. Currently, the short-range transportation of people and goods rely on manual traction (e.g., bicycles, handmade wooden bicycle, etc.) and on small combustion engines. However, this region has a large renewable energy source (solar) which can help to mitigate this problem. Electric bicycles may be used to tackle the problem, although their use is still not largely widespread despite its potential. In addition, the local society does not have an easy way to accurately estimate the amount of energy consumed for a certain itinerary to be sure of how the electric vehicles perform. Thus, characteristic analysis of consumed energy is very important to analyze the performance of electric vehicle storage systems, model charging infrastructure, size vehicles, planning for itineraries, etc. This paper presents a methodology to estimate the power consumed by electric bikes for specific itineraries. In order to accomplish so, the WebPlotDigitizer tool and Google Maps were used to produce driving profile patterns. The results are compared against experimental data, showing the accuracy of the methodology presented. The simulated results show that the consumed power value differs by only 1.68% from the experimental recorded value; the difference can be explained by traffic congestion, the density of traffic, and the intersection that occurred during the experiment. The presented method of driving energy requirement estimation using WebPlotDigitizer is attractive, affordable, and easy to use.

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

Grid forming (GFM) controllers provide grid-tied voltage source converters (VSC) with dynamic characteristics similar to synchronous generators. One limitation of conventional GFM is that it ties the frequency droop with the provision of synthetic inertia (SI). This makes it impossible for GFM to offer a SI without a frequency droop, which becomes a problem if the VSC can’t source enough energy on the long term (eg a STATCOM). This paper presents a new GFM formulation that decouples these settings. The paper presents the derivation of the controller along with a series of simulation studies to demonstrate its characteristics compared to the conventional GFM.

Identifying faulty lines and their accurate location is key for rapidly restoring distribution systems. This will become a greater challenge as the penetration of power electronics increases, and contingencies are seen across larger areas. This paper proposes a single terminal methodology (i.e., no communication involved) that is robust to variations of key parameters (e.g., sampling frequency, system parameters, etc.) and performs particularly well for low resistance faults that constitute the majority of faults in low voltage DC systems. The proposed method uses local measurements to estimate the current caused by the other terminals affected by the contingency. This mimics the strategy followed by double terminal methods that require communications and decouples the accuracy of the methodology from the fault resistance. The algorithm takes consecutive voltage and current samples, including the estimated current of the other terminal, into the analysis. This mathematical methodology results in a better accuracy than other single-terminal approaches found in the literature. The robustness of the proposed strategy against different fault resistances and locations is demonstrated using MATLAB simulations.

With Europe dedicated to limiting climate change and greenhouse gas emissions, large shares of Renewable Energy Sources (RES) are being integrated in the national grids, phasing out conventional generation. The new challenges arising from the energy transition will require a better coordination between neighboring system operators to maintain system security. To this end, this paper studies the benefit of exchanging primary frequency reserves between asynchronous areas using the Supplementary Power Control (SPC) functionality of High-Voltage Direct-Current (HVDC) lines. First, we focus on the derivation of frequency metrics for asynchronous AC systems coupled by HVDC interconnectors. We compare two different control schemes for HVDC converters, which allow for unilateral or bilateral exchanges of reserves between neighboring systems. Second, we formulate frequency constraints and include them in a unit commitment problem to ensure the N-1 security criterion. A data-driven approach is proposed to better represent the frequency nadir constraint by means of cutting hyperplanes. Our results suggest that the exchange of primary reserves through HVDC can reduce up to 10% the cost of reserve procurement while maintaining the system N-1 secure.

Fault detection and isolation are important tasks to improve the protection system of low voltage direct current (LVDC) networks. Nowadays, there are challenges related to the protection strategies in the LVDC systems. In this paper, two proposed methods for fault detection and isolation of the faulty segment through the line and bus voltage measurement were discussed. The impacts of grid fault current and the characteristics of protective devices under pre-fault normal, under-fault, and post-fault conditions were also discussed. It was found that within a short time after fault occurrence in the network, this fault was quickly detected and the faulty line segment was efficiently isolated from the grid, where this grid was restored to its normal operating conditions. For analysing the fault occurrence and its isolation, two algorithms with their corresponding MATLAB/SIMULINK platforms were developed. The findings of this paper showed that the proposed methods would be used for microgrid protection by successfully resolving the fault detection and grid restoration problems in the LVDC microgrids, especially in rural villages.