Optimal System Design Research Articles

Optimal design of PV-SMES systems for power quality enhancement using pelicon optimized multi-level inverter model

In this article, a superconducting magnetic energy storage (SMES) system is integrated with a photovoltaic (PV) renewable energy source. The integrated system can achieve power balancing with voltage stability, high power quality, and reliability. Here, a Bonobo Optimization Algorithm (BOA)-based MPPT control has been developed to get the most electrical energy out of the solar PV cells under fluctuating weather conditions, including temperature and irradiance. The output voltage is greatly increased with minimal switching complexity by using the Zeta DC-DC converter, which helps to meet the energy requirements of load applications. Then, the Total Harmonics Distortions (THD) has been decreased with lower switching stress, power loss, and voltage ripples at the load using the sophisticated and simple five-level MLI circuit model. In addition, the performance of MLI has been greatly improved by tuning the controlling gain parameters with the use of Pelicon Optimization (PO) technique. Moreover, the Shunt Active Power Filter (SAPF) is used in order to achieve the best power quality results with the reduced overall harmonic distortions. The performance and results of the suggested circuit model are validated in this study through a complete simulation analysis using a variety of assessment indicators.

Optimal selection and design of grid-connected hybrid renewable energy system in three selected communities of Rivers State

The pursuit of electrifying rural areas has been a multi-decade quest, emblematic of Nigeria's longstanding efforts to diversify its energy sources and reduce dependence on traditional fossil fuels. Accordingly, this study aims to evaluate the electrification status and perform a techno-economic analysis of a hybrid system to ensure electricity reliability, bill reduction and reduced grid demand for three representative rural communities in Rivers State using HOMER Pro Software. Subsequently, five hybrid system scenarios that encompassed different combinations of the grid, PV modules, wind and battery, were examined using the CRITIC-PROMETHEE II methodologies. Each community was assessed based on its own hourly electricity demand, using data obtained from the injection substation. The Aluu, Choba and Rumuekini communities experienced an average daily electricity supply of 4.6, 5.4 and 6.2 h respectively from the national grid with energy usage of 5436.4 MW for Aluu, 3375.4 MW for Choba and 1626.1 MW for Rumuekini, in 2022. The findings indicate that the grid/PV module was the optimal choice for the three communities. Moreover, the cost of energy (COE) for the proposed systems ranged from 0.0181 $/kWh to 0.0185 $/kWh, significantly lower than the grid's COE of 0.08625 $/kWh in the respective communities. This research further includes a sensitivity analysis of the optimal system, examining variations in PV cost, discount rate, inflation rate, annual scaled solar irradiance and electrical demand. The results provide a comprehensive framework to support decision-making, optimize system design and align renewable energy solutions with project goals and stakeholder priorities.

Efficient Error-Rate Estimation for Optical Transmission Systems Using Artificial Neural Networks

ABSTRACT With the advancement in modulation formats, optical transmission systems are becoming more adaptable and dynamic. Predicting bit errors of higher-order modulation formats is a challenging problem for the optimum design of communication systems. The existing simulation methods have persisted by employing iterative procedures that frequently incur high computing expenses and time consumption. On the other hand, deep learning (DL) algorithms have demonstrated remarkable efficacy as practical computing tools, presenting a viable approach to accelerate modulation simulations. In this paper, an artificial neural network (ANN) based bit error rate (BER) estimation scheme is proposed for the popular modulation forms including 56 Gbps 16-quadrature amplitude modulation (16QAM), 100 Gbps 32QAM, and 120 Gbps 64QAM optical system. Based on constellation diagrams (CDs) acquired with different launch power, laser linewidth, transmission distance, and OSNR, amplitude histograms (AHs) are generated via a manual preprocessing method. The properly trained ANN architecture exhibits a computational speed that surpasses traditional simulation methods by a factor exceeding 347. Moreover, the design considerations including the number of layers, nodes, activation functions, learning rate, optimizers, and evolution epochs are also investigated in detail. This research paves the way for optical transmission systems to use fast DL-based optimization strategies.

Impact of uncertainty in building operation patterns and electric energy demand on the design and techno-economic performance of solar photovoltaic systems in different climates

The optimal design of renewable energy systems for buildings requires accurately estimating building electric energy loads. While this energy demand highly depends on how occupants and facility managers use building systems, operation-related uncertainty is rarely accounted for in the renewable energy design workflow. This paper presents a comprehensive framework to quantify the impact of uncertainty in building operation patterns and electric demand on the techno-economic performance of grid-connected photovoltaic (PV) systems (with and without battery storage). A hybrid methodology is proposed, combining and comparing two approaches to studying building performance under uncertainty: (i) physics-based building performance simulations coupled with parametric variations of occupant behavioral profiles (e.g., austere vs. wasteful), and (ii) data-driven modeling using a large national building dataset. The approach is illustrated and validated on small, medium, and large-sized office buildings in different climate zones (hot dry, mixed marine, and cold humid). Results show that different building operational patterns could lead up to 60% increase in building electric demand, which translated to similar increases in PV energy capacity and the corresponding capital costs of the systems. The findings emphasize the urgent need to better account for uncertainty in building operational patterns in the design workflows of buildings with PV generation and energy storage capabilities.

Optimal Design of a Hybrid Renewable Power System for a Reverse Osmosis Desalination Plant in Jordan

Optimal Design of a Hybrid Renewable Power System for a Reverse Osmosis Desalination Plant in Jordan

Drivers of cocoa yield and growth in young monoculture and agroforestry systems

CONTEXTThe expansion of cocoa farming has been linked to deforestation and biodiversity loss in West Africa. Agroforestry systems could potentially increase the sustainability of cocoa production. However, despite the long history of cocoa cultivation in agroforestry systems, the exact mechanism or the combination of factors that drive cocoa growth and yield in these agroecosystems, especially at the cocoa establishment stage, is unclear. OBJECTIVEThe present study aims to analyse how resource availability, stand characteristics and cocoa tree morphology interact to determine the performance of cocoa during the establishment phase among different cocoa cultivation systems. METHODSWe studied seven different agroforestry systems and one monoculture system in 53 plots located in a 12-ha cocoa agroforestry trial recently established in Côte d'Ivoire. We characterized each system with 16 variables that described five system components: 1) cocoa yield (number of pods, pods weight, number of productive trees) and cocoa growth (basal diameter), 2) cocoa tree morphology (crown depth, diameter, area, and volume), 3) stand characteristics (number and size of shade trees), 4) light (leaf area index) and 5) water (soil volumetric water content). We used a Structural Equation Modelling (SEM) approach to understand and quantify interactions between the five components. RESULTS AND CONCLUSIONSThe models were able to largely explain cocoa early yield and juvenile growth variability (respective determination coefficients: 0.45 and 0.92). We observed improved cocoa establishment in certain designs of agroforestry systems compared with monoculture. However, SEM revealed that stand characteristics (i.e. increase in the number and height of shade trees) had a negative effect on cocoa yield and growth via changes in cocoa tree morphology, having path coefficients of −0.48 and − 0.72, respectively. Conversely, we found a positive relationship between the cocoa tree morphology and cocoa early yield, for which the path coefficient was 0.74, the strongest relation. Cocoa tree morphology was more important than stand characteristics, leaf area index, and soil volumetric water content in controlling cocoa yield and growth. Taken together, our results suggest a competition for space between shade trees and cocoa plants. Finding the optimal design of agroforestry systems can help enhance cocoa establishment in the first years, improving the long-term performance of the system. SIGNIFICANCEThis finding suggests that the competition effect depends on spatial planting pattern and species composition, offering an opportunity to meet the goals of a sustainable intensification of cocoa plantations through the design and management of optimal agroforestry systems.

Damping performance and optimal design of mid-story isolation system with tuned inerter damper

Damping performance and optimal design of mid-story isolation system with tuned inerter damper

A universal tool for estimating monthly solar radiation on tilted surfaces from horizontal measurements: A machine learning approach

In this study, a universal method for estimating monthly solar radiation on tilted surfaces from horizontal measurements was developed and validated, targeting the optimal design and performance of solar energy systems. Using data from the Photovoltaic Geographical Information System (PVGIS) across 740 global locations, three machine learning models—linear regression, random forest, and k-nearest neighbors (KNN)—along with a multilayer perceptron deep learning model, were evaluated. The dataset spanned tilt angles from 0° to 90° at 1° increments and 11,511,773 rows with six input features for predicting irradiance on any tilt angle. The KNN model was selected for further testing analysis due to its superior accuracy, achieving the lowest root mean square error (RMSE) of 1.42 kWh/m2 and mean absolute error (MAE) of 0.7 kWh/m2 on the validation dataset. Testing was conducted with 16 years of data from five locations not included in the training dataset, obtained from PVGIS and Solcast. Results show that the KNN model consistently outperformed the traditional isotropic model in four out of five cities using PVGIS data and in two using Solcast data, especially excelling in regions with stable climates and consistent irradiance profiles. Moreover, better prediction performance was observed with PVGIS data compared to Solcast data across all five cities, with PVGIS data yielding an RMSE of 3.03 kWh/m2 and an MAE of 2.41 kWh/m2, while Solcast data exhibited a higher RMSE of 5.22 kWh/m2 and MAE of 4.14 kWh/m2. Finally, these results advocate for a shift towards data-driven methodologies highlighting their enhanced reliability.

Evaluation Method for Underwater Ultrasonic Energy Radiation Performance Based on the Spatial Distribution Characteristics of Acoustic Power.

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance-density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system.

Environmental Protection Control System Based on IoT and Deep Learning Intelligent Monitoring Sensors

In order to improve the multi-sensor intelligent control performance of environmental pollution detection, this paper proposes a design model of environmental pollution detection intelligent system based on Internet of Things technology. Firstly, this paper uses ZigBee’s Internet of Things networking technology to design the network of the pollution detection system. Secondly, in order to verify the effectiveness of the environmental pollution detection method, this paper carries out system debugging analysis and simulation test, and uses visual dsp++simulation system to debug the system. The network of ZigBee Internet of Things networking technology is adopted to realize the network design of pollution detection system, and VIX bus technology is adopted to carry out environmental pollution detection data communication and information processing, so as to realize the optimal design of intelligent system of environmental pollution detection system. Finally, the experimental test and analysis were performed. The collection period of environmental pollution data is 10 s and the monitoring time is 22 h. Finally, according to the environmental model and parameters, we determine that the system can accurately detect environmental pollution information, and the results are accurate and reliable.

Demand Response Power-Gas Interconnection Energy System and Power Metering Based on SOGA

Under the double background of global energy crisis and environmental pollution, China vigorously develops renewable energy while accelerating the construction of energy Internet. Taking demand response into account, this paper proposes the optimal design of power-gas interconnection energy system and power metering, and establishes the optimization research model of IEGES (integrated electricity-gas energy system) with demand response. Firstly, the structure model of the electric-gas interconnection energy system with CHP (Cogeneration, combined heat and power) as the core is constructed, and the energy conversion relationship and different energy flow directions of the coupling equipment are expounded from three aspects. The natural gas source point, pipeline equation, power side branch equation, voltage and current equation are modeled and sorted out, and the square term in the equation is linearized by second-order cone programming method, and the mixed integer nonlinear programming problem is transformed into mixed integer linear programming problem. A single objective genetic algorithm with “elite strategy” was selected to solve the equipment capacity optimization problem of IEGES system with system economy as the optimization objective. After a long time of parameter combination attempts, the current population size is 30, the number of iterations is 600, the crossover rate is 0.8, and the heritability is 0.3. The above parameters can obtain better convergence results on the basis of considering the operation time. Finally, a stochastic optimization method of energy Internet considering integrated demand response and uncertainty of wind power is proposed, which aims to meet the energy demand of end users while minimizing the operating cost of the system. The comprehensive demand response strategy including internal and external demand response is considered in the model. Internal demand response is realized by adjusting the internal operation mode of EH, while external demand response is implemented by the end user’s active response, and the load is time-shifted or interrupted under the guidance of external signals.

Design of hydrogen production systems powered by solar and wind energy: An insight into the optimal size ratios

Green hydrogen is expected to play a crucial role in the future energy landscape, particularly in the pursuit of deep decarbonisation strategies within hard-to-abate sectors, such as the chemical and steel industries and heavy-duty transport. However, competitive production costs are vital to unlock the full potential of green hydrogen. In the case of green hydrogen produced via water electrolysis powered by fluctuating renewable energy sources, the design of the plant plays a pivotal role in achieving market-competitive production costs. The present work investigates the optimal design of power-to-hydrogen systems powered by renewable sources (solar and wind energy). A detailed model of a power-to-hydrogen system is developed: an energy simulation framework, coupled with an economic assessment, provides the hydrogen production cost as a function of the component sizes. By spanning a wide range of size ratios, namely the ratio between the size of the renewable generator and the size of the electrolyser, the cost-optimal design point (minimum hydrogen production cost) is identified. This investigation is carried out for three plant configurations: solar-only, wind-only and hybrid. The objective is to extend beyond the analysis of a specific case study and provide broadly applicable considerations for the optimal design of green hydrogen production systems. In particular, the rationale behind the cost-optimal size ratio is unveiled and discussed through energy (utilisation factors) and economic (hydrogen production cost) indicators. A sensitivity analysis on investment costs for the power-to-hydrogen technologies is also conducted to explore various technological learning paths from today to 2050. The optimal size ratio is found to be a trade-off between the utilisation factors of the electrolyser and the renewable generator, which exhibit opposite trends. Moreover, the costs of the power-to-hydrogen technologies are a key factor in determining the optimal size ratio: depending on these costs, the optimal solution tends to improve one of the two utilization factors at the expense of the other. Finally, the optimal size ratio is foreseen to decrease in the upcoming years, primarily due to the reduction in the investment cost of the electrolyser.

Environmental and economic valuation of user behavior in the optimal design of renewable energy systems

Environmental and economic valuation of user behavior in the optimal design of renewable energy systems

Optimal design of air distribution systems in large indoor spaces during the construction of walls: A case study on liquefied natural gas terminal storage tanks

Efficiently organizing indoor airflow is crucial for optimizing the working environment in large-scale wall construction projects, particularly when constructing liquefied natural gas membrane tanks. This helps prevent membrane corrosion, optimize construction time, and mitigate future safety risks. However, due to their size, these tanks often experience air stratification, posing challenges in meeting simultaneous temperature, humidity, and uniformity requirements during construction. To effectively address this issue, this study developed Computational Fluid Dynamics models that numerically simulated three commonly used ventilation systems: displacement ventilation system, stratified ventilation system, and fabric duct air supply system. Laboratory experiments were conducted to validate the accuracy of numerical simulation. Additionally, response surface methodology (RSM) was used to determine optimal combinations of air supply parameters for superior environmental uniformity control. The findings indicate that the fabric air supply system is more suitable than displacement and stratified ventilation systems for achieving environmental uniformity control in large spaces. It can limit wall temperature nonuniformity coefficient up to 9 % during summer and provide better uniformity in air velocity during winter conditions. Moreover, under summer conditions, the optimal range for air supply speed is between 9.62 m/s - 11.22 m/s with temperatures ranging from 16 °C to 19 °C, while under winter conditions, the optimal range is between 18.3 m/s −19.3 m/s with temperatures ranging from 33.0 °C to 35.0 °C.The optimal design of the ventilation system is advantageous for various building construction scenarios with stringent temperature and humidity requirements, particularly for large-scale buildings.

Efficiency Analysis of a Passive Daylighting System Based on Northern Malaysia’s Climate

Daylighting design strategy is important in order to have adequate lighting source in a room and necessary to decrease energy consumption for artificial lighting. Passive daylighting system utilizes daylight by collecting, reflecting and diffusing the natural light throughout a given area. The purpose for this study is to monitor, compare and analyse an optimum light pipe system design that can scatter daylight into a room based on three case studies. Lighting analysis was conducted using Autodesk 3ds Max Design software throughout the project based on the actual geographical parameters of Universiti Malaysia Perlis, Malaysia and also using the real sun azimuth on working hours. The results were compared according to the respective designs in order to observe the maximum internal illuminance and the average internal illuminance. The results show that the straight geometry with low aspect ratio produces the highest interior light intensity among other light pipe systems and the average internal illuminance values in the room was able to reach the minimum requirement of a small room which is 200 – 500 lux.

On the optimal design of peer monitoring systems in capital budgeting

AbstractThis paper considers peer monitoring in a capital budgeting setting with information asymmetry between principal and agents. The paper demonstrates in a two‐agent, two‐project setting that the principal can have each agent reveal their private information on the other agent's project, at an arbitrarily low cost. Next, an examination of monitoring resource allocations is made. Specifically, a comparison is made of broad monitoring, which spreads monitoring resources equally across agents' projects, and focused monitoring, which applies all monitoring resources to a single agent's project. Focused monitoring is preferred within an intermediate range of profitability, above which broad monitoring is preferred and below which neither is preferred. If project profitability is heterogeneous and focused monitoring is preferred, the more profitable project receives the focus. Both of these results occur because there is a threshold of monitoring resources that must be deployed for monitoring to provide any benefit, and the threshold is higher (more resources must be deployed) for lower profitability projects. That is, even if all monitoring resources are devoted to a low profitability project, they may provide no benefit to the principal. A discussion of how peer monitoring can be engineered by the firm is included.

Optimal Design of Data Acquisition System for Fault Diagnosis of Hydraulic Machinery

Abstract Fault diagnosis of hydraulic machinery has high requirements on the synchronization of monitoring data and the anti-interference of monitoring equipment. The performance and data quality of the existing operation monitoring equipment of the unit cannot meet the requirements. At the same time, due to the variety of sensors and the different wiring methods of different types of sensors, the preparation work such as wiring of data acquisition equipment in the test is complicated. This paper analyzes the application requirements of fault diagnosis data and the problems existing in the conventional data collection process. A data collection solution specified for diagnosis of hydraulic machinery, which better solves the problem of data synchronization, is designed. The new designed system has high sampling accuracy and anti-interference characteristics, as well as the advantages of strong capability and simple equipment deployment. The proposed system has been applied in multiple power stations, pumping stations and pump test platforms. The relevant functions have been effectively verified.

Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model

Adaptive learning-based optimal tracking control system design and analysis of a disturbed nonlinear hypersonic vehicle model

Feasibility study of hybrid renewable energy systems for off-grid electrification in Kuwait’s rural national park reserve

ABSTRACT This study demonstrates the optimal design of a hybrid renewable energy system for the electrification of a potential rural national park reserve. The objective is to evaluate the feasibility of utilising renewable energy sources (RESs) to reduce GHG emissions. The core components studied are photovoltaic solar (PV) panels, wind turbines (WTs), diesel generators (DGs), and battery banks (BBs). The research involves estimating the reserve’s load profile, assessing the potentials of RESs, and designing various system configurations. Each configuration is evaluated to derive the most efficient option. For a building with an energy demand of 832,640 kWh/yr, it is found that PV-WT-BB is the best configuration which comprises of 500-kW PV, 200-kW WT, and 1424-kW BBs and contributes an annual generation of 1,821,732 kWh/year. With a net present cost of $2,206,308 for the lifetime of the project, it saves 757,162 kg/year of total GHG emissions if the reserve operated using DGs.

Thermal convective transport of nanofluids in a microannulus under electromagnetohydrodynamic and time-periodic pressure gradient

The study of the unsteady flow and heat transfer characteristics of nanofluids in microannulus is crucial for the optimal design and precise operation of microfluidic systems, and microfluidic technology has an important application value in microelectronic engineering. In this article, a theoretical analysis of the heat transfer characteristics of unsteady flow of Water-Al2O3 nanofluids through a microannulus under the coupling of an alternating electric field, a magnetic field, and a time-periodic pressure gradient is presented. Both the thermally completely developed flow assumption and the Debye–Hückel linear approximation are taken into consideration. The microannulus wall is subjected to a consistent heat flow while taking into account the effects of Joule heating and viscous dissipation. A semi-analytical solution of the temperature field is efficiently obtained using the Green’s function method. In addition, the Nusselt number and total entropy generation are further analyzed. The results show that the nanoparticle volume fraction and dimensionless frequency have a significant effect on the heat transfer characteristics of nanofluids. The increase of dimensionless frequency decreases the convective heat transfer and improves the cooling performance of microannulus. On the contrary, increasing the nanoparticle volume fraction improves the heat transfer performance of microannulus. Therefore, the results of this study can be used for electronic cooling of compact and micro-sized circuits. This study is instructive for unsteady flow and heat transfer of nanofluids in microchannels.