Varying Load Demand Research Articles

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

ENERGY MANAGEMENT IN HYBRID PV-WIND-BATTERY STORAGE-BASED MICROGRID USING DROOP CONTROL TECHNIQUE

The paper presents an efficient energy management system designed for a small-scale hybrid microgrid incorporating wind, solar, and battery-based energy generation systems using the droop control technique. The heart of the proposed system is the energy management system, which is responsible for maintaining power balance within the microgrid. The EMS continuously monitors variations in renewable energy generation and load demand and adjusts the operation of the energy conversion systems and battery storage to ensure optimal performance and reliability. The primary objective of the energy management system is to maintain power balance within the microgrid, even in the face of fluctuations in renewable energy generation and load demand. This involves dynamically adjusting the operation of the renewable energy sources and battery storage system to match the instantaneous power requirements of the microgrid. Overall, the paper presents a comprehensive approach to designing and implementing an efficient energy management system for a small-scale hybrid wind-solar-battery-based microgrid to extract maximum profit from electricity generation. By integrating renewable energy sources with energy storage and advanced control algorithms, the proposed system aims to enhance the reliability, stability, and sustainability of the microgrid's power supply.

A hybrid renewable energy system for Hassi Messaoud region of Algeria: modeling and optimal sizing

The growing global energy demand and the need to mitigate greenhouse gas emissions have driven the exploration of sustainable and efficient energy solutions. In Algeria, where the energy sector relies heavily on fossil fuels, integrating renewable energy systems is essential for enhancing energy security and reducing environmental impacts. This study focuses on optimizing a hybrid renewable energy system (HRES) for off-grid applications in the Hassi Messaoud region of Algeria to balance technical performance, economic viability, and environmental sustainability.A hybrid system consisting of photovoltaic (PV) panels, wind turbines (WT), fuel cells (FC), and diesel generators (DG) was modeled and optimized using a genetic algorithm (GA). The optimization process aims to minimize the annual cost of the system while ensuring high reliability, as measured by the loss of power supply probability (LPSP), and maximizing the use of renewable energy. A particle swarm optimization (PSO) approach was also implemented for comparison, highlighting the advantages of the GA in terms of cost distribution and system reliability. The optimized HRES demonstrated that renewable sources (PV and WT) provided 77% of the total energy demand, with an overall system cost of 0.18080$×kWh−1, significantly lower than recent studies, which reported costs between $0.213 and 0.609$×kWh−1. FCs contributed 14% to the load, whereas DGs were limited to 8% to minimize emissions, resulting in annual CO2 emissions of 10,865 kg and a relative emission rate of 3.608 gCO2eq×kWh−1. Economic analysis showed that DGs and FCs accounted for 44% and 24% of the annual cost, respectively, highlighting the impact of backup systems in ensuring reliability.Sensitivity analysis under varying load demands and renewable energy availability confirmed the robustness of the system, and the GA approach was found to be more effective than PSO in maintaining cost efficiency and reliability. Additionally, the social analysis highlighted a renewable fraction (RF) of 91.5%, emphasizing the contribution of the system to sustainable energy practices. These findings validate GA-based optimization as a superior method for designing cost-effective, reliable, and environmentally sustainable HRES, offering significant potential to reduce fossil fuel dependency in industrial applications. These results not only support the broader adoption of renewable energy systems in similar regions but also contribute valuable insights for future research and policy development in the field of energy sustainability.

Multivariate Deep Learning Long Short-Term Memory-Based Forecasting for Microgrid Energy Management Systems

In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynamic relationship between load demand variations and electricity price fluctuations within the microgrid EMSs. The investigation involves a comparative analysis and evaluation of recurrent neural networks’ performance to recognize the most effective technique for the forecasting module of microgrid EMSs. This study includes approaches based on Long Short-Term Memory Neural Networks (LSTMs), with architectures ranging from Vanilla LSTM, Stacked LSTM, Bi-directional LSTM, and Convolution LSTM to attention-based models. The empirical study involves analyzing real-world time-series data sourced from the Australian Energy Market (AEM), specifically focusing on historical data from the NSW state. The findings indicate that while the Triple-Stacked LSTM demonstrates superior performance for this application, it does not necessarily lead to more optimal operational costs, with forecast inaccuracies potentially causing deviations of up to forty percent from the optimal cost.

Operational assessment of solar-wind-biomass-hydro-electrolyser hybrid microgrid for load variations using model predictive deterministic algorithm and droop controllers

This study presents the operation and assessment of a solar-wind-biomass-hydro-electrolyser hybrid microgrid for different types of load variations by using a proprietary derivative-free algorithm (Model Predictive Deterministic Algorithm) and voltage IQ droop controller. The proposed hybrid microgrid integrates various renewable energy sources and an electrolyser to generate hydrogen and produce electricity. The objective is to optimize the microgrid’s performance and provide reliable power supply to meet varying load demands. The proprietary derivative-free algorithm is used to determine the optimal dispatch of power from each energy source to meet the load demand while minimizing the overall cost of energy. Additionally, a voltage IQ droop and voltage Q droop controller are employed to regulate the voltage, frequency and active power of the microgrid and maintain its stability during load variations. The proposed hybrid microgrid is assessed under different load scenarios for both grid tied and isolated modes. The results demonstrate that the proposed hybrid microgrid with the derivative-free algorithm and voltage droop controllers can effectively operate and provide reliable power supply under different load variations while maintaining the power system stability of the microgrid. Further, the mutual performances of the controller are compared to find the best controller for the specific microgrid. The findings of this study can contribute to the development of efficient and reliable hybrid microgrid systems for sustainable energy production and distribution.

A Wasserstein distributionally robust model for transmission expansion planning with renewable‐based microgrid penetration

AbstractThis article introduces a Wasserstein distance‐based distributionally robust optimization model to address the transmission expansion planning considering renewable‐based microgrids (MGs) under the impact of uncertainties. The primary objective of the presented methodology is to devise a robust expansion strategy that accounts for both long‐term uncertainty and short‐term variability over the planning horizon from the perspective of a central planner. In this framework, the central planner fosters the construction of appropriate transmission lines and the deployment of optimal MG‐based generating units among profit‐driven private investors. The Wasserstein distance uncertainty set is used to characterize the long‐term uncertainty associated with future load demand. Short‐term uncertainties, stemming from variations in load demands and production levels of stochastic units, are modeled through operating conditions. To ensure the tractability of the proposed planning model, the authors introduce a decomposition framework embedded with a modified application of Bender's method. To validate the efficiency and highlight the potential benefits of the proposed expansion planning methodology, two case studies based on simplified IEEE 6‐bus and IEEE 118‐bus systems are included. These case studies assess the effectiveness of the presented approach, its ability to navigate uncertainties, and its capacity to effectively optimize expansion decisions.

Modified control approach for MPP tracking and DC bus voltage regulation in a hybrid standalone microgrid

This paper proposes modified MPPT control technique for hybrid standalone microgrid. Standalone PV based microgrid has emerged as potential solutions to electricity challenges in the region where grid is not available. The battery energy storage system (BESS) is integrated into the system to facilitate synchronized load control and power flow. Variation in solar irradiation, load demand and fluctuation in battery SOC results in DC voltage fluctuation and maximum power point tracking challenges. Hybrid PV-Battery systems therefore must require control algorithms that can both flexibly adjust power flows as well as stabilize bus voltages. Proposed control technique is successful in regulating DC bus voltage and balancing the power flow in system under various operating conditions. A bidirectional converter is employed to maintain the nominal DC voltage conditions by charging/discharging the battery due to intermittent PV generation. It regulates the voltage of the DC bus to the maximum power point of the PV array. The main goal of this research article is to implement modified MPPT technique which includes Incremental conductance algorithm with double closed loop controller to track maximum power and to regulate the DC bus voltage of the system under different type of dynamic and atmospheric conditions.

Optimal scheduling and management of grid‐connected distributed resources using improved decomposition‐based many‐objective evolutionary algorithm

AbstractThis paper emphasizes the integration of wind and photovoltaic (PV) generation with battery energy storage systems (BESS) in distribution networks (DNs) to enhance grid sustainability, reliability, and flexibility. A novel multi‐objective optimization framework is introduced in this study to minimize energy supply costs, emissions, and energy losses while improving voltage deviation (VD) and voltage stability index (VSI). The proposed framework comprising normal boundary intersection (NBI) and decomposition‐based evolutionary algorithms (DBEA) determines the optimal siting and sizing of renewable‐based distributed resources, considering load demand variations and the intermittency of wind and solar outputs. The comparative analysis establishes that the proposed strategy performs better than many contemporary algorithms, specifically when all the objective functions are optimized simultaneously. The validation of the proposed framework was carried out on the standard IEEE‐33 bus test network, which demonstrates significant percentage savings in energy supply costs (49.6%), emission rate (62.2%), and energy loss (92.3%), along with enormous improvements in VSI (91.9%) and VD (99.8953%). The obtained results categorically underline the efficiency, reliability, and robustness of the proposed approach when employed on any complex distribution network comprising multiple renewable energy sources and battery storage systems.

Data-driven MPPT techniques for optimizing vehicular fuel cell performance in hybrid DC microgrid

This paper aims to apply data-driven maximum power point tracking (MPPT) techniques specifically tailored for fuel cell vehicle (FCV) supported hybrid DC microgrids to enhance the power harvesting capability of fuel cell (FC) stacks. Compared to existing MPPT techniques, the current study focuses on developing and evaluating data-driven approaches for maximum power extraction by dynamically determining the operating point of FC power sources through a Zeta converter. An in-depth analysis is conducted by considering parameters such as efficiency, tracking accuracy, response time, and robustness to variations in load demand and operating conditions. The performance results validate that the developed three-layer neural network (TNN)-based MPPT gives better performance findings than Gaussian process regression (GPR), support vector regression (SVR), decision tree regression (DTR), and bagging ensemble decision tree (BEDT). In the performance evaluation phase, a vehicular FC with a rating of 1.26 kW is designed and operated within the temperature range of 320 K to 343 K for hydrogen pressure values ranging from 1 bar to 1.8 bar. For these operational conditions, the prediction accuracy value of the proposed TNN method is 99.6% while the performance values GPR, SVR, DTR, and BEDT are 99%, 98.6%, 97.2%, and 96%. In addition, system efficiency is increased by 0.98%, 1.25%, 2.51%, and 3.02% compared to GPR, SVR, DTR, and BEDT, respectively.

Seismic and Wind Resillence Study of a 15 Story High-Rise Building with Floating Columns in Seismic Zones II & V

The use of floating columns in the construction of high-rise buildings, specifically in the context of G+15 structures in metropolitan cities, as per Indian standards. The structural design of a building is crucial for its durability, strength, stability, and lifespan. Floating columns play a significant role in transferring loads from the floors to the beams, affecting the overall stability of the building.Several factors impact the structural design, including bending moments, shear forces, torsion, and deflection. Due to the growing population in metropolitan areas, there is a need for high-rise buildings that can accommodate both commercial and residential spaces on a single platform. Commercial and residential floors have different load requirements. Therefore, floating columns are specifically used for residential floors to address these varying load demands. The structural analysis considers seismic and wind forces as per Indian standards (IS 1893-2016 for seismic analysis and IS 875-2015 for wind analysis) for different seismic zones (Zone- Ⅱ, Ⅲ, Ⅳ, and Ⅴ). The materials used in the construction include concrete with a grade of M40 and steel with a grade of Fe550. These material grades are commonly used in construction for their respective strength properties. The floor height for residential floors is 3 meters, while commercial floors have a height of 4 meters. This distinction in floor height is considered in the structural design. The design of high-rise buildings, especially in seismic-prone regions, requires a thorough understanding of structural engineering principles and adherence to local building codes and standards to ensure the safety and stability of the structure. The use of floating columns can be a part of a structural design strategy to address varying load requirements in different parts of the building.Safety is paramount in the design and construction of high-rise buildings, particularly in areas susceptible to seismic activity and strong winds.

Enhancement of virtual inertia via delay designed GADRC in hybrid microgrid with communication delay

The lowering of inertia in a hybrid microgrid caused by the incorporation of renewable sources of energy leads to an increased rate of change of frequency and frequency deviation in the system. Further, the varying load demand and the presence of communication delay adversely affect the frequency regulation. In view of this, in this paper, a modified virtual inertia control strategy is formulated and implemented through the energy storage device to enhance the overall inertia of the hybrid microgrid. Secondly, a delay-designed generalized active disturbance rejection control (DD-GADRC) technique is proposed for the secondary frequency control of the power system under varying communication delay and rapidly fluctuating load demand in the system. The effectiveness of the proposed control scheme is evaluated across multiple scenarios, including situations when there are continuous changes in solar or wind power generations. The robustness of the proposed technique is also evaluated under parametric variations and the presence of non-linearities in the system. Finally, the effectiveness of the proposed technique is analyzed and compared to various pre-existing control strategies in the literature related to the hybrid microgrid.

Joint planning for PV-SESS-MESS in distribution network towards 100% self-consumption of PV via consensus-based ADMM

The integration of distributed photovoltaic (PV) has become a prominent characteristic of distribution network (DN). However, achieving 100% PV self-consumption poses challenges due to intermittent generation and varying load demands. To address these challenges, this paper proposes an innovative joint planning method for PV, stationary energy storage system (SESS), and mobile energy storage system (MESS) using the consensus-based alternating directional multiplier method (ADMM). First, to account for multiple scenarios with real-world variations, a two-stage clustering method with clustering by fast search and finding of density peaks and k-means is employed. Second, a joint planning model for PV, SESS, and MESS is developed to minimize the total cost of DN, including installation, operation, and maintenance costs. The model optimizes the PV capacity, SESS capacity, and MESS capacity, as well as their locations. Third, a distributed planning framework is proposed using consensus-based ADMM, which decomposes the problem into a PV planning subproblem under the worst-case scenario and energy storage system planning subproblems under multiple scenarios. Finally, the proposed method is demonstrated on an IEEE-33 node system, and the results show the method effectively enhances the access capacity of PV by 11.80% and leads to a reduction in the total cost.

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

A price-regulated scheduling of electric vehicle with grid supporting photovoltaic and battery storage

With solar photovoltaic (PV) sources integrated into microgrids and electric vehicle (EV) charging stations, users have a variety of charging mode options to meet their needs. However, overloading of the distribution transformer, higher peak demand, and voltage instability are all potential drawbacks associated with EV charging loads. Microgrid optimisation becomes more difficult when dealing with intermittent PV generation and uncertain EV charging and discharging profiles. A Smart Home Energy Management System (SHEMS) is proposed for efficient power flow from PV sources, battery energy storage system (BESS), and an EV to maximise economic advantages for a consumer using the Artificial Hummingbird Algorithm (AHBA)-based optimisation approach. This approach considers the EV's unpredictable energy consumption, variations in residential load demand and seasonal variations in the solar insolation profile throughout the year. Consequentially, potential EV requirements such as initial and predetermined state of charge (SOC) arrangements and arrival and departure hours are also considered. Three energy management scenarios satisfying the allowable loading limit of the distribution transformer are examined, namely power transfers from home to electric vehicle (H2EV), EV to home (EV2H), and EV to grid (EV2G). The reduction in yearly operating costs (YOC) of 49.5 %, 53.2 %, and 70 % was achieved in H2EV, EV2H, and EV2G modes, respectively. This demonstrates that an effective EV and BESS management strategy can reduce the annual operating costs of a residential consumer by up to 70 % while simultaneously achieving peak load shaving.

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

N − 1 Security Criteria Based Integrated Deterministic and Probabilistic Framework for Composite Power System Reliability

Unpredictable variations in load demand and unanticipated component failures are progressively impacting the operation of modern power systems, making system evaluation more stochastic in nature. Although deterministic approaches were formerly the norm for determining system status, probabilistic approaches have greatly improved the capacity to capture the stochastic behavior characteristic of power system operations. The presented work in the paper recommends the use of probabilistic modelling approaches with deterministic approaches, highlighting their crucial function in augmenting the reliability and security of contemporary power systems to unanticipated failures. In this paper, N − 1 security criteria based reliability of the composite power system (CPS) is proposed using an integrated deterministic and probabilistic framework (D-P) considering outage of the transmission line. For the deterministic approach (DA), line overloading on available lines is determined using the static security index (SSI). For the probabilistic approach (PA), reliability indices such as expected loss of power (ELOP), expected frequency of contingency (EFOC), expected loss of load (ELOL), probability of load curtailment (PLC), and expected duration of load curtailments (EDLC) are calculated. Further, for each contingency, a performance index is determined using both approaches to assess the severity of the contingency that occurred on the power system. Based on the N − 1 security criteria based reliability analysis using an integrated D-P framework, a credible critical set of transmission lines is obtained, which can serve as important information to system operators. The proposed techniques have been tested on IEEE 24 bus reliability test system (RTS).

Twin-Delayed Deep Deterministic Policy Gradient Algorithm to Control a Boost Converter in a DC Microgrid

A stable output voltage of a boost converter is vital for the appropriate functioning of connected devices and loads in a DC microgrid. Variations in load demands and source uncertainties can damage equipment and disrupt operations. In this study, a modified twin-delayed deep deterministic policy gradient (TD3) algorithm is proposed to regulate the output voltage of a boost converter in a DC microgrid. TD3 optimizes PI controller gains, which ensure system stability by employing a non-negative, fully connected layer. To achieve optimal gains, multi-deep reinforcement learning agents are trained. The agents utilize the error signal to obtain the desired output voltage. Furthermore, a new reward function used in the TD3 algorithm is introduced. The proposed controller is tested under load variations and input voltage uncertainties. Simulation and experimental results demonstrate that TD3 outperforms PSO, GA, and the conventional PI. TD3 exhibits less steady-state error, reduced overshoots, fast response times, fast recovery times, and a small voltage deviation. These findings confirm TD3’s superiority and its potential application in DC microgrid voltage control. It can be used by engineers and researchers to design DC microgrids.

Multi-Objective Optimal Allocation of Hybrid Photovoltaic Distributed Generators and Distribution Static Var Compensators in Radial Distribution Systems Using Various Optimization Algorithms

In recent years, considerable growth was about the integration of renewable energy sources in the Radial Distribution Systems (RDS), as Photovoltaic Distributed Generators (PVDG) due to their importance in achieving plenty desired technical and economic benefits. Implementation of the Distribution Static Var Compensator (DSVC) in addition to the PVDG would be one of the best choices that may provide the maximum of those benefits. Hence, it is crucial to determine the optimal allocation of the devices (PVDG and DSVC) into RDS to get satisfactory results and solutions. This paper is devoted to solve the allocation problem (locate and size) of hybrid PVDG and DSVC units into the standards test systems IEEE 33-bus and 69-bus RDSs. Solving the formulated problem of the optimal integration of hybrid PVDG and DSVC units is based on minimizing the proposed Multi-Objective Function (MOF) which is represented as the sum of the technical-economic parameters of Total Active Power Loss (TAPL), Total Reactive Power Loss (TRPL), Total Voltage Deviation (TVD), Total Operation Time (TOT) of the overcurrent relays (OCRs) installed in the RDS, the Investment Cost of PVDGs (ICPVDG) and the Investment Cost of DSVCs (ICDSVC), by applying various recent metaheuristic optimization algorithms. The simulation results reveal the superiority and the effectiveness of the Slime Mould Algorithm (SMA) in providing the minimum of MOF, including minimization of the power losses until 16.209 kW, and 12.11 kVar for the first RDS, 4.756 kW and 7.003 kVar for the second RDS, enhancing the voltage profiles and the overcurrent protection system. Moreover, the ability to reach the optimal allocation of PVDG and DSVC and maintain the voltage profiles in the allowable limit, whatever the load demand variation.

Research into the operating modes of a stand-alone dual-channel hybrid power system

<abstract> <p>The article describes the development and simulation of a stand-alone hybrid power system based on a variable-speed diesel generator and a hydrogen fuel cell generation system. The goal of the research was to investigate the electromagnetic processes of this power system, which supplies power to autonomous energy consumers with varying load demand. MATLAB Simulink was used to simulate the proposed hybrid power system and check its operating capacity. The results of the simulation include the dependencies of current and voltage changes in the critical components of the hybrid system at stepwise load rate changes. In the future, the developed models and simulation results will allow researchers to select semiconductor devices and create microprocessor-based control systems for electric power installations that meet specific requirements. The dual-channel power system can provide a required power output of 3 kW when powered by a diesel generator and 1 kW when powered by a hydrogen fuel cell. At the same time, the total harmonic distortion (THD) at a load between 100 W and 3 kW varies within acceptable limits between 3.6% and 4.4%. It is worth noting that these higher power complexes can be incorporated into stand-alone electrical grids as well as centralized distribution systems for power deficit compensation during peak loads.</p> </abstract>