Variable Power Demand Research Articles

Advanced Power Management in CubeSats: Optimization of Solar Harvesting and Energy Storage Technologies

The miniaturization of satellite technology has led to the emergence of CubeSats as a cost-effective platform for a wide range of applications, including scientific research, Earth observation, and communication. These small satellites offer unique advantages, such as lower launch costs and rapid deployment; however, efficient power resource management remains a critical challenge. Limited energy supply and varying power demands complicate the operational effectiveness of CubeSats. This research paper explores advanced solar harvesting techniques and energy storage systems specifically tailored for CubeSats. We investigate innovative approaches to optimize power management systems through a combination of simulation models and experimental validation. Key areas of focus include the utilization of novel photovoltaic materials that promise higher efficiency and lighter weight, which are essential for the limited space available in CubeSats. Additionally, we delve into the implementation of maximum power point tracking (MPPT) algorithms, which enhance energy extraction from solar panels under varying environmental conditions. Moreover, we examine the latest advancements in battery technologies, including lithium-sulfur and solid-state batteries, which provide improved energy density and longevity compared to conventional options. Our findings indicate that the integration of these advanced solar harvesting and energy storage systems can significantly enhance energy efficiency, extend operational lifetimes, and improve overall mission outcomes for CubeSats. This research not only addresses existing power management challenges but also lays the groundwork for future developments in small satellite technology, ultimately contributing to the effectiveness of various space missions.

High-Capacity Energy Storage Devices Designed for Use in Railway Applications

This paper investigates the application of high-capacity supercapacitors in railway systems, with a particular focus on their role in energy recovery during braking processes. The study highlights the potential for significant energy savings by capturing and storing energy generated through electrodynamic braking. Experimental measurements conducted on a diesel–electric multiple unit revealed that approximately 28.3% to 30.5% of the energy could be recovered from the traction network, regardless of the type of drive used—whether electric or diesel. This research also explores the integration of starch-based carbon as an electrode material in supercapacitors, offering an innovative, sustainable alternative to traditional graphite or graphene electrodes. The carbon material was obtained through a simple carbonization process, with experimental results demonstrating a material capacity of approximately 130 F/g. To quantify the energy recovery, calculations were made regarding the mass and power requirements of the supercapacitors. For the tested vehicle, it was estimated that around 28.7% of the energy could be recovered during the braking process. To store 15 kWh of energy, the total mass of the capacitors required is approximately 245.1 kg. The study emphasizes the importance of increasing voltage levels in railway systems, which can enhance energy transmission and utilization efficiency. Additionally, the paper discusses the necessity of controlled energy discharge, allowing for the flexible management of energy release to meet the varying power demands of trains. By integrating high-voltage supercapacitors and advanced materials like starch-based carbon, this research paves the way for more sustainable and efficient railway systems, contributing to the industry’s goals of reducing emissions and improving operational performance. The findings underscore the crucial role of these capacitors in modernizing railway infrastructure and promoting environmentally responsible transportation solutions.

(Invited) Analyzing Effect of Battery Design Characteristics and Thermal Dynamics on Battery Performance in Electric Aircraft

One the biggest challenges in achieving net-zero in the aviation sector through electrification of electric aircraft is the limitation of current battery systems in terms of simultaneously providing high gravimetric energy density and power density combined with suboptimal performance and safety issues at extreme temperatures. Much of the current literature related to electric aircraft focuses on aircraft design and optimization with overly simplified battery analysis such as linear voltage profile approximation or the use of equivalent circuit models which do not help understand and quantify material limitations of battery systems in electric aircraft application1-3. This is particularly relevant due to the fact that battery cells used in both fixed-wing electric aircraft and eVTOLs (electric vertical take-off and landing aircraft) see unique operating requirements that are often significantly more demanding than those seen in electric vehicles (EVs)4. Recently, there have been a few studies analyzing battery performance in more detail 5-7. However, there is a lack of investigations on the effect of battery design parameters and material properties on battery performance under real-world operating conditions, such as dynamically varying power demand, and the resulting effect on electric aircraft performance characterized by range, speed, and endurance. This type of study is necessary in guiding design of batteries and battery materials tailored to electric aircraft applications, particularly in dealing with the highly dynamic nature of such an application.In the present work, we perform coupled simulation studies of longitudinal flight dynamics and battery dynamics using high fidelity electrochemical battery models. First, we study the effect of model fidelity on the prediction of battery dynamics and key battery states, particularly in the context of studying the effect of battery design parameters. This results in the identification of an appropriate battery model for an in-depth analysis on the effect of battery design parameters and material properties on battery dynamics and the resulting effect on aircraft performance. Additionally, we consider the effect of dynamically changing battery temperature obtained from experimental testing under simulated flight conditions. This is particularly important due to the significant battery temperature variation expected during a typical flight and its implications on battery performance and safety. Overall, this work provides detailed analysis on the effect of battery design on its electrochemical performance and the overall system level performance considering real-world operating conditions.References M. Kaptsov and L. Rodrigues, Journal of Guidance, Control, and Dynamics, 41, 288 (2018).N. Biju and H. Fang, Applied Energy, 339, 120905 (2023).L. Kiesewetter, K. H. Shakib, P. Singh, M. Rahman, B. Khandelwal, S. Kumar and K. Shah, Progress in Aerospace Sciences, 142, 100949 (2023).X.-G. Yang, T. Liu, S. Ge, E. Rountree and C.-Y. Wang, Joule, 5, 1644 (2021).A. Ayyaswamy, B. S. Vishnugopi and P. P. Mukherjee, Joule, 7, 2016 (2023).Dixit, M., Bisht, A., Essehli, R., Amin, R., Kweon, C. B. M. and Belharouak, I., ACS Energy Letters, 9, 934-940 (2024).M. Wang, S. Kolluri, K. Shah, V. R. Subramanian and M. Mesbahi, IEEE Transactions on Aerospace and Electronic Systems, 59, 1084 (2022).

Simulation-Based Hybrid Energy Storage Composite-Target Planning with Power Quality Improvements for Integrated Energy Systems in Large-Building Microgrids

In this paper, we present an optimization planning method for enhancing power quality in integrated energy systems in large-building microgrids by adjusting the sizing and deployment of hybrid energy storage systems. These integrated energy systems incorporate wind and solar power, natural gas supply, and interactions with electric vehicles and the main power grid. In the optimization planning method developed, the objectives of cost-effective and low-carbon operation, the lifecycle cost of hybrid energy storage, power quality improvements, and renewable energy utilization are targeted and coordinated by using utility fusion theory. Our planning method addresses multiple energy forms—cooling, heating, electricity, natural gas, and renewable energies—which are integrated through a combined cooling, heating, and power system and a natural gas turbine. The hybrid energy storage system incorporates batteries and compressed-air energy storage systems to handle fast and slow variations in power demand, respectively. A sensitivity matrix between the output power of the energy sources and the voltage is modeled by using the power flow method in DistFlow, reflecting the improvements in power quality and the respective constraints. The method proposed is validated by simulating various typical scenarios on the modified IEEE 13-node distribution network topology. The novelty of this paper lies in its focus on the application of integrated energy systems within large buildings and its approach to hybrid energy storage system planning in multiple dimensions, including making co-location and capacity sizing decisions. Other innovative aspects include the coordination of hybrid energy storage combinations, simultaneous siting and sizing decisions, lifecycle cost calculations, and optimization for power quality enhancement. As part of these design considerations, microgrid-related technologies are integrated with cutting-edge nearly zero-energy building designs, representing a pioneering attempt within this field. Our results indicate that this multi-objective, multi-dimensional, utility fusion-based optimization method for hybrid energy storage significantly enhances the economic efficiency and quality of the operation of integrated energy systems in large-building microgrids in building-level energy distribution planning.

Optimal integration of PV generators and D-STATCOMs into the electrical distribution system to reduce the annual investment and operational cost: A multiverse optimization algorithm and matrix power flow approach

In this work, we address the problem of optimally integrating photovoltaic distributed generators (PV) and distributed static compensators (D-STATCOMs) in distributed electrical systems (DES). Previous methods have struggled with the complexities of non-linear mathematical models and the integration of operating and investment costs while meeting the operational constraints of these devices. We propose a solution using a master-slave methodology that combines a discrete-continuous version of the multiverse optimization algorithm (MVO), with a matrix power flow method based on successive approximation (MAX). This approach aims to reduce annual costs by efficiently managing the grid’s operating costs and the investment and operational costs of PV and D-STATCOMs. Various researchers have suggested methodologies for solving the problem of optimal D-STATCOM integration in DES, focusing on minimizing investment and operating costs as the objective function. However, these studies often lack comparative methodologies and statistical analysis to validate the performance of their proposed approaches. Additionally, they do not consider the impact of variable power demand. We compared our methodology with other master-slave approaches that utilize different optimization algorithms, including the vortex search algorithm (VSA), crow search algorithm (CSA), a continuous genetic algorithm (GA), and the particle swarm optimization algorithm (PSO). These comparisons were made using two test systems with 33 and 69 nodes, which incorporate variations in power demand and PV production from a region in Colombia. Our statistical analysis included evaluations of the best and average solutions, standard deviations, differences between the best and average solutions, and a dispersion analysis using box-and-whisker plots. The results demonstrate that MVO outperforms other methods, providing the best results with reduced processing times in both test systems.

Carbon neutral energy systems: Optimal integration of energy systems with CO2 abatement pathways

AbstractReducing emissions requires transitioning towards decarbonized systems through avoiding, processing, or offsetting. Decisions on system design are associated with high costs which can be reduced at the planning stage through optimization. The temporal variations in power demand and renewable energy supply significantly impact the design of a low‐emissions energy system. Effective decision‐making must consider such impact in a comprehensive framework that accounts for the potential synergies between different options. This work presents a mixed integer linear programming model that considers the impacts of energy supply and demand dynamics to optimize the design and operation of an integrated energy system while adhering to a set emissions limit. The model integrates renewable power with CO2 capture, utilization, and sequestration by considering H2 production and storage. The case study showed including negative emissions technologies and CO2 capture and processing with renewable energy allows achieving net zero emissions power.

Performance Analysis of Multiple Energy-Storage Devices Used in Electric Vehicles

Considering environmental concerns, electric vehicles (EVs) are gaining popularity over conventional internal combustion (IC) engine-based vehicles. Hybrid energy-storage systems (HESSs), comprising a combination of batteries and supercapacitors (SCs), are increasingly utilized in EVs. Such HESS-equipped EVs typically outperform standard electric vehicles. However, the effective management of power sources to meet varying power demands remains a major challenge in the hybrid electric vehicles. This study presents the development of a MATLAB Simulink model for a hybrid energy-storage system aimed at alleviating the load on batteries during periods of high power demand. Two parallel combinations are investigated: one integrating the battery with a supercapacitor and the other with a photovoltaic (PV) system. These configurations address challenges encountered in EVs, such as power fluctuations and battery longevity issues. Although batteries are commonly used in conjunction with solar PV systems for energy storage, they incur higher operating costs due to the necessity of converters. The findings suggest that the proposed supercapacitor–battery configuration reduces battery peak power consumption by up to 39%. Consequently, the supercapacitor–battery HESS emerges as a superior option, possibly prolonging battery cycle life by mitigating stress induced by fluctuating power exchanges during the charging and discharging phases.

Plug and Play control of Inverter-interfaced DERs for Power Management in Smart Grid

Productive and compelling utilization of sustainable energy resources, decentralized power generation, and energy storage can possibly take care of worldwide issues, such as the unavailability of energy and environmental change. A promising answer for interconnecting these Distributed Energy Resources (DERs) with the utility grid is the Microgrid. A key issue is the manner by which parallel inverters are being controlled in islanding mode and in grid-connected mode in order to operate these DERs at their maximum efficiency contributing power to electrical networks. This paper introduces a control strategy for productively working at least two single-phase parallel inverter-connected Distributed Energy Resources without any physical communication between them. This study will demonstrate the Photovoltaic (PV) interface of the microgrid under varying conditions, such as significant fluctuations in radiation levels, implementation of Maximum Power Point Tracking (MPPT), localized control for dynamic active power, coordinated dynamic control of active and reactive power with the decentralized operation, and multi-level control functionality of the PV source in conjunction with other microgrid sources and variable power demands. Simulation results will recommend the best control strategy for a single inverter’s active power control and voltage level.

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

Designing robust hybrid controllers for enhancing output voltage regulation in CPL feed boost converters through backstepping and nonsingular fast terminal sliding mode approaches

This paper presents a novel and robust hybrid controller that combines the backstepping controller with the nonsingular fast terminal sliding mode controller (NFTSMC) for DC-DC boost converters (DDBCs) operating alongside constant power loads (CPLs). The primary aim of the controller is to enhance the stability of the DC-bus voltage while consistently delivering power to the CPL. To achieve this objective, the exact feedback linearization approach is applied to transform the nonlinear dynamical model of the DDBC into Brunovsky’s canonical forms. The incorporation of matched uncertainties into the dynamical model illustrates the robustness of the proposed control approach. The hybrid controller is formulated based on this model, leveraging Lyapunov stability theory to ensure large signal stability under the designated control law. Significantly, the NFTSMC effectively addresses the singularity problem associated with sliding mode controllers (SMCs). The effectiveness and resilience of the proposed controller are validated under variations in input voltage, reference voltage, and power demand. Comparative analysis is conducted by contrasting simulation results with those obtained using conventional SMC (CSMC) and a typical PI controller (PIC). The results unequivocally highlight the superiority of the proposed controller in terms of ensuring system stability and providing a rapid response across various operating modes. Furthermore, to authenticate the practical applicability of the proposed controller, processor-in-loop (PIL) validation is employed, confirming its effectiveness in real-world scenarios.

Integration of BESS in grid connected networks for reducing the power losses and CO[formula omitted] emissions: A parallel master-stage methodology based on PDVSA and PSO

This work addressed the problem of integration and operation of Ion-lithium batteries of different technical characteristics in grid-connected networks (GCN) for reducing the energy losses and CO2 emissions. To formulate the mathematical problem, nonlinear mathematical modeling was used, taking into account the previously mentioned objective functions and all the constraints that represent the operation of the grid in a scenario of variable photovoltaic generation and power demand. This paper proposed a master–slave methodology, which uses a parallel discrete version of the vortex search algorithm (PDVSA) for solving the problem of location and selection of the kind of battery in the GNC. The slave stage is entrusted to find the operation scheme of the batteries proposed for the master stage. This task is carried out by a particle swarm optimization algorithm (PSO) that defines the power to be supplied or stored for each battery. Using a matrix hourly power flow for calculating the effect in the objective function and constraints that represent the problem. As a test scenario, it was used a GCN of 33 buses and 32 lines that considered the photovoltaic generation and demand of Medellín-Colombia, and all technical and environmental indexes of the local grid operator. Six comparison methodologies were used from the literature and developed in this work to evaluate the effectiveness of the proposed methodology. Being all programming in MATLAB software, executing each one 1000 times with the aim to evaluate the performance in terms of solution, repeatability, and processing times. The results obtained demonstrating that the proposed PDVSA/PSO methodology achieved the best results in terms of solution, with reductions of 154.9094 kWh (6.23%) in energy losses and reductions of 0.0252 tons of CO2 (0.2548%) in pollutant gas emissions compared to the base case. Regarding repeatability, the PDVSA/PSO exhibits standard deviation values of 0.1383% for energy losses and 0.0034% for CO2 emissions.

Dynamic-Deep-Ensemble-Learning Scheme for Probabilistic Voltage Stability Margin Estimation to Enhance Resilient Power Grid Monitoring

Modern power grids are characterized by significant penetration of renewable energy sources (RES), variable power demand, and aging transmission infrastructure, all of which contribute to a high degree of operational uncertainty. Such uncertainty complicates the assessment of the static voltage stability in power grids. In response to this challenge, this paper proposes a novel deep ensemble learning-based approach to assess the probabilistic voltage stability margin (PVSM) for strengthening the resilience of power grid monitoring. First, the estimation of the PVSM is formulated as a quantile regression problem. Then, an improved deep quantile regression (iDQR) is utilized to generate a set of quantiles under specific nominal proportions. Next, a dynamic deep ensemble learning (D2EL) scheme based on diverse iDQR models and an improved Choquet fuzzy integral (iCFI) algorithm is proposed to enhance the overall performance of predictive quantiles for the PVSM. The proposed D2EL-based PVSM estimation approach is capable of accommodating system changes in a timely manner, thus providing higher estimation accuracy and stronger adaptability than conventional approaches. A comprehensive numerical study of several test systems is carried out, taking into account uncertain RES and loads, as well as topology changes. The results reveal the impressive performance of the proposed approach in the PVSM assessment.

A novel intelligent control approach for wind energy conversion systems with synchronous reluctance generators

SummaryThis study addresses a significant research gap in the field of wind energy, focusing on the underutilized potential of synchronous reluctance generators (SynRG) in wind power conversion systems (WPCS). Despite notable advancements in wind energy controls, the capabilities of SynRG within these systems have not been fully explored. In response, we introduce an innovative model‐free control (MFC) approach, termed intelligent proportional (iP), specifically designed and optimized for WPCS equipped with SynRG. This research encompasses the modeling, control, and simulation of SynRG‐based WPCS, aiming to optimize system performance and enhance the quality of grid power supply. The proposed vector control (VC) method utilizes a MFC approach (VC‐iP) to address the limitations inherent in traditional VC methods with proportional‐integral (PI) controllers (VC‐PI). Although widely used for their simplicity, VC‐PI methods often lead to power fluctuations and decreased power and current quality, consequently reducing overall system efficiency. In contrast, VC‐iP markedly enhances power quality and system efficiency, especially in situations involving fluctuating power demand and variable wind speeds. The efficacy of VC‐iP is demonstrated through simulations in two distinct test scenarios that include variations in power demand and random wind speed fluctuations. The results affirm the superiority of VC‐iP in maintaining stable, high‐quality power output from WPCS. This method notably minimizes fluctuations, improves current quality, reduces harmonic distortion and steady‐state error, ensures reliable grid integration, and contributes to a more efficient and sustainable energy system.

Operation and control of compact offshore combined cycles for power generation

Gas turbines are the main means for generating power in offshore installations. To increase energy efficiency, and thus reduce CO2 emissions, a steam bottoming cycle can be added to produce additional power by recovering surplus heat from the gas turbine exhaust. Due to space constraints, this solution is not widespread for offshore power generation. To enable deployment, the system must be compact and be able to provide varying power demands. In this paper, we analyze the operation and control problem for such compact combined cycles, consisting of two gas turbines and one steam bottoming cycle. We analyze the steady-state performance of the combined cycle with respect to efficiency and CO2 emissions for two control strategies for coordinating the power setpoint of the two gas turbines. Equal load allocation of the gas turbines showed a higher thermal efficiency and lower emissions compared to keeping one gas turbine close to nominal and letting the second one handle load variations. The main controlled variables for the steam bottoming cycle are the superheated steam pressure and temperature. We implement decentralized control strategies based on standard PID-controllers and nonlinear feedforward. Due to reduced throttle losses, sliding pressure shows higher efficiency and lower CO2 emissions compared to keeping a constant steam pressure, which conversely provides a better temperature dynamic response and may be necessary with highly varying power demand.

Working Duration Prediction of Electric Tractors According to Weight Reduction

Highlights A method for weight reduction of the tractor's structure through topology optimization was presented. A dynamic model of an electric tractor was developed to predict working duration for various agricultural works. The increase in working duration due to the weight reduction of the tractor structure was predicted. A method for predicting the appropriate battery weight of an electric tractor was proposed. Abstract. Recently, the demand for electric tractors has grown substantially in efforts to achieve carbon neutrality. To successfully introduce electric tractors into the market, ensuring sufficient working duration on a single charge is crucial. Increasing the battery capacity is one approach to extend the working duration. However, structural weight reduction strategies for tractors should be explored to mitigate potential issues associated with excessive weight, such as soil compaction. To increase the working duration of electric tractors, this paper proposes methods for structural weight reduction, to predict working duration per agricultural work based on tractor weight reduction, and to predict the appropriate battery weight for electric tractors. The proposed method involves implementing topology optimization on the rear axle housing of tractors to achieve weight reduction. The allocation of weight reduction resulting from structural improvements to the battery weight is determined using a dynamic model of a tractor to calculate the increase in working duration. Additionally, the extended working duration for various agricultural works is estimated, considering different types of batteries and battery weight ratios. Notably, light-weighting achieved a 20.21% reduction in weight when compared with the initial model. The findings of this study indicate an increase in the working duration, with approximately 56.2 and 7.1 min additional for road driving and rotavator work when using Li-S batteries, respectively. Furthermore, the battery weight required to achieve the targeted working duration is calculated, accounting for variations in power demand across different agricultural works. Finally, a method for predicting the optimal battery weight for electric tractors based on the specific power needs and targeted working duration for various agricultural activities is proposed. Keywords: Battery weight, Electric tractor, Topology optimization, Weight reduction, Working duration prediction.

Control design and multimode power management of WECS connected to HVDC transmission line through a Vienna rectifier

This paper deals with nonlinear control and optimal management of wind energy conversion systems connected to HVDC transmission lines. Specifically, the energy system under study consists of a wind turbine driving a permanent magnet synchronous generator, the latter supplies energy to a power grid through a Vienna rectifier and a DC link. To compensate for intermittent nature of wind power and load demand variations, we design a voltage controller as well as a novel energy management strategy that accounts for the available wind power and DC load demands. In the considered management, distinction is made between two operation modes: i) the maximum power point tracking mode using an intelligent adaptive optimizer, where only electrical parameter measurements are used to ensure maximum power extraction; ii) the adaptive power point tracking mode employing demand-driven production. In both modes, the power factor requirement is ensured, regardless the weather conditions and load demand variations, enhancing thus the aerogenerator’s efficiency. One more novelty of the proposed control and management system is the online estimation of the DC voltage, avoiding thus the use of a physical sensor. The performances of the control and management system are highlighted by several simulations, showing the achievement all control objectives.

Multiclass transient event classification in hybrid distribution network based on co-training of fine KNN and ensemble KNN classifier

ABSTRACT A new machine learning-based method to classify the different transient events in distributed generation (DG) system has been proposed in this article. An existing hybrid DG-based network which consists of three microgrids (MGs), i.e. thermal, wind, and solar power, is used as test network to create transient conditions for both the islanding and grid-connected circumstances. The transient case studies include the symmetrical and unsymmetrical fault at distribution line, intentional islanding, variation of power demand, switching of capacitor bank, addition of nonlinear load, motor starting condition, etc. This recommended methodology starts with generating the sampled voltage signals of three different phases of different locations, and each signal has been decomposed using discrete wavelet transform. The significant features are extracted from the computed energy values of detailed wavelet coefficient for co-training of fine K-nearest neighbor (KNN) and ensemble KNN classification in the following stage. The results and the performance indices of the trained classifiers prove that the proposed method has been detected and classified all the transient events with 98% accuracy. Such type of multiple transient event classification in MG by a single algorithm is truly beneficial with respect to the power quality issues of modern power system.

DESIGN OF CLOSED LOOP MULTIPORT DC DC CONVERTER

In recent years, there has been lots of emphasis put on the development of renewable energy. While considerable improvement on renewable energy has been made, there are some inherent limitations for these renewable energies The Closed-Loop Multiport DC-DC Converter with Adaptive Sliding Mode Control (ASMC) is difficult in power electronics system designed to efficiently manage and control energy flow between multiple energy sources and loads in various applications, including renewable energy integration, electric vehicles, and microgrids. This paper provides an overview of the key features and benefits of this innovative converter system. Multiport DC-DC converters have gained significant attention due to their ability to interface multiple energy sources, such as solar panels and batteries with different voltage levels and power ratings, providing a versatile platform for energy management. The ASMC controller enhances the converter's performance by dynamically adjusting the control parameters based on system conditions and load requirements, thereby improving efficiency and reliability .The ASMC employs a sliding mode control strategy, a robust and adaptable control method, which ensures that the converter operates effectively under various operating conditions, including rapid changes in load and source characteristics. This control technique offers inherent advantages, such as fast transient response and reduced sensitivity to parameter variations, contributing to superior system performance. The ASMC controller optimizes power conversion by minimizing switching losses and ensuring that the converter operates close to its peak efficiency. The controller continuously adapts to changes in input voltage, output voltage, and load conditions, ensuring stable and reliable operation even in dynamic environments. The sliding mode control strategy enables rapid response to sudden load changes, making it suitable for applications with varying power demands. Key Words: DC-DC converter, Sliding-mode control, Battery, Solar PV.

Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.

Parametric analysis and optimization of 660 MW supercritical power plant

The newly set up power plant has been committed to fulfilling the power supply demand of the world. Therefore, optimizing operating variables within constraints of varying power demand becomes necessary. The research aims to identify and optimize several parameters influencing the performance and efficiency of a 660 MW supercritical power plant at different operating conditions, such as steam temperature, pressure, feedwater flow rate, and fuel consumption. Ultimately, the research aims to contribute to developing sustainable and environmentally friendly power generation technologies. The present study covers the multi-objective optimization of a 660 MW capacity fossil fuel-fired SUPP. The overall plant efficiency, cost of electricity, and exergetic efficiency are taken as objective functions. The Particle Swarm Optimization (PSO) technique and a semi-empirical model of energy, economic, and exergy analysis of fossil fuel-fired SUPP have been employed. The varying power outputs, coal calorific value, amount of coal consumption, inlet temperature, and pressure conditions of turbines set are decision variables taken for the study. The parametric study was carried out with the variation in plant load and mass of coal consumption concerning the variation of the objective function. The lower temperature at the inlet of the low-pressure turbine is preferred for lowing the cost of electricity. The maximum value of plant efficiency of 41.643% and exergy efficiency of 39.834% with a minimum cost of electricity of 3.1456 INR/Unit have been evaluated using multi-objective PSO. The outcome of the present study is that the optimized value of decision variables will reduce the dependency on high-grade coal from an energy, exergy, and economic point of view. The outcome of the present study will explore the scope for future researchers and engineers.