Enhanced Microgrid Control through Genetic Predictive Control: Integrating Genetic Algorithms with Model Predictive Control for Improved Non-Linearity and Non-Convexity Handling

\ua9 2024 by the authors.Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA’s USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA’s 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC’s potential as a versatile, efficient, and environmentally beneficial tool for power generation systems

A hybrid method based on logic predictive controller for flexible hybrid microgrid with plug-and-play capabilities

\ua9 2024 The Author(s). Controlling flexible hybrid microgrids (MGs) is difficult due to the system\u27s complexity, which includes multiple energy sources, storage devices, and loads. Although adding new components to the MG system through the plug-and-play (PnP) feature enables operating of the system in different modes, it adds to the system\u27s complexity, hence necessitates careful control system design. The most challenging aspect of designing the control system is ensuring that it can control the MG optimally in its various modes of operation. Previous methods based on logical control allow for synthesizing a controller capable of controlling the MG in its various operational modes. However, the resultant controller does not optimally operate the MG. Classical model predictive control allows optimal control of the MG only in specific operating modes. On the other hand, switched model predictive control (S-MPC) can optimally control the MG in its various modes. However, the design of S-MPC is complex, particularly for MGs with many operating modes or complex switching logic. Multiple factors contribute to the complexity, including model development, mode detection, and switching logic. This paper presents a hybrid method based on ɛ-variables and classical MPC for constructing the S-MPC for flexible hybrid MG with PnP capabilities. Our results show that the proposed controller synthesis approach provides an effective solution for optimally controlling flexible hybrid MGs with PnP capabilities as the proposed method enables: (i) an increase in the amount of energy export to the utility grid by 50.77% and (ii) a significant decrease in the amount of energy import from the grid by 46.7%

Multi-port coordination: Unlocking flexibility and hydrogen opportunities in green energy networks

\ua9 2024Seaports are responsible for consuming a large amount of energy and producing a sizeable amount of environmental emissions. However, optimal coordination and cooperation present an opportunity to transform this challenge into an opportunity by enabling flexibility in their generation and load units. This paper introduces a coordination framework for exploiting flexibility across multiple ports. The proposed method fosters cooperation between ports in achieving lower environmental emissions while leveraging flexibility to increase their revenue. This platform allows ports to participate in providing flexibility for the energy grid through the introduction of a green port-to-grid concept while optimising their cooperation. Furthermore, the proximity to offshore wind farms is considered an opportunity for the ports to investigate their role in harnessing green hydrogen. The proposed method explores the hydrogen storage capability of ports as an opportunity for increasing the techno-economic benefits, particularly through coupling them with offshore wind farms. Compared to existing literature, the proposed method enjoys a comprehensive logistics-electric model for the ports, a novel coordination framework for multi-port flexibility, and the potentials of hydrogen storage for the ports. These unique features position this paper a valuable reference for research and industry by demonstrating realistic cooperation among ports in the energy network. The simulation results confirm the effectiveness of the proposed port flexibility coordination from both environmental and economic perspectives

Integration of Supply Chains and Operational Performance: The Moderating Effects of Knowledge Management

Supply chain integration (SCI) is a strategic process management technique that may be used to boost an organizations performance and thereby gain a competitive edge. The purpose of this paper is to demonstrate both the direct effect of (SCI) on manufacturing firms operational performance and the moderating effect of knowledge management (KM) on the relationship between supplier integration (SI), customer integration (CI), internal integration (II), and operational performance (OP). The study analyzed survey data from 277 Jordanian manufacturing and industrial businesses using the PLS–Structural Equation method. According to the data, (CI), (II), and (SI) are all positively and significantly associated with operational success. (CI), (II), and (SI) all have a strong and beneficial moderate relationship with (OP). There is, however, no connection between (KM) and (OP). Furthermore, further research may be conducted to assess the applicability of the findings from this study to other populations of varied sizes in other countries. A long-term study that tracks the growth of different measures might provide further insight on the relationship between SCI and OP

Assessing the Moderating Effect of Innovation on the Relationship between Information Technology and Supply Chain Management: An Empirical Examination

This study examines how innovation (INN) influences the relationship between supply chain management and information technology in Jordan. 211 employees of Jordanian industrial enterprises who work in the Operations Department provided information for the study, which examines this subject. The findings indicate a close connection between information technology and supply chain management. Innovation also dramatically modifies the interaction between supply chain management and information technology. Management help may be the subject of future research

The Role of Business Intelligence adoption as a Mediator of Big Data Analytics in the Management of Outsourced Reverse Supply Chain Operations

The fluctuating and disorganized state of todays global markets is the result of several factors. COVID-19 is an illustration. Supply chain managers should re-evaluate their competitive strategy and leverage big data analytics in light of the rising volatility in demand and supply, rivalry among supply chain partners, and the requirement to deliver tailored goods and services (BDA). Supply chain firms require sophisticated BDA processes and procedures to provide useful insights from big data to better decision-making and supply chain operations, as many leaders in the sector have acknowledged the necessity for improving with data (SCO). This research gives theoretical justification for the influence that BDA has on SCO

A robust Logistics-Electric framework for optimal power management of electrified ports under uncertain vessel arrival time

\ua9 2024Maritime transport is responsible for producing a considerable amount of environmental pollution due to the reliance of ports and ships on the carbon-based energy sources. With the increasing trend towards port electrification to reduce carbon emissions, the operation of ports will be increasingly relying on the electricity network. This interconnection creates multiple challenges due to the complexity of power flow in the port network, uncertainty of vessel arrival time and fluctuation of power generation of renewable energy sources. These uncertainties can lead to an overload in electricity networks and delays in cargo-handling activities, resulting in increased vessel handling times and environmental emissions. This paper presents a joint logistics-electric framework for optimal operation and power management of electrified ports, considering multiple uncertainties in the arrival time of vessels, network demand, and renewable power generation. An optimal power flow method is developed for a real-life port, with consideration for multiple port logistic assets such as cargo handling equipment, reefers, and renewable energy sources. The proposed model ensures feasible port operation for all uncertainty realisations defined by robust optimisation, while minimising operational costs. Simulation results demonstrate that the probability of a network constraint violation can be as high as 70% for an electrified major UK port if the uncertainty in the port operation is neglected, presenting an unacceptable risk of disruption to port activities. Furthermore, such uncertainty can cause 150% increase in emissions if the ships use their auxiliary engine instead of using shore power. The numerical study shows that such challenges can be handled by a 0.3% increase in the robustness in face of uncertainty, while the cost increase in the worst case does not exceed 4.7%. This shows the effectiveness of the proposed method enhancing robustness against uncertainty at the minimum cost

Z-Scan Measurements of the Third-Order Optical Nonlinearity of a C60C_{60} Doped Poly(dimethylacetylendicarboxylate)

The third-order nonlinear optical properties of $C_{60}$/poly(dimethylacetylendicarboxylate) have been studied using Z-scan technique. Experiments are performed using a CW diode laser at 635 nm wavelength and 26 mW power. The nonlinear absorption coefficient β, nonlinear refractive index n₂, Reχ³, and Imχ³ in $C_{60}$ doped poly(dimethylacetylendicarboxylate) are measured using Z-scan data. Our results show that the values of the nonlinear optical parameters (β, n₂, Reχ³, and Imχ³) of $C_{60}$ doped poly(dimethylacetylendicarboxylate) are smaller than the polymer itself

Study of dissolution kinetics for poorly water-soluble drugs from ternary interactive mixtures in comparison with commercially available capsules

Objective: The main objective of this work was to study the dissolution kinetics of poorly water-soluble drugs, indomethacin and ibuprofen, from formulated capsules or interactive mixtures containing fine lactose (FL), as ternary additive, and coarse lactose as carrier compared with selected commercially indomethacin capsules and to investigate the role of FL-drug size ratio on the dissolution. Results and Discussion: It was found that the addition of FL in lactose-indomethacin capsules enhanced the dissolution of indomethacin while it has decreased the dissolution of ibuprofen from the lactose-ibuprofen mixtures. The particle size distributions for drugs and fine lactose used in this study suggested that the difference in dissolution behaviour for the two drugs could be due to the FL-drug ratio. Results obtained from the application of different dissolution kinetic equations showed that the first-order equation can best describe the kinetic of the dissolution for Rothacin®, Indylon®, Indomin® and ternary-formulated capsules of indomethacin, while the dissolution from the binary-formulated indomethacin capsules showed that the dissolution cannot be described by zero-order or first-order equations. For ibuprofen mixtures, the results showed that the release followed the first-order kinetic for both systems, binary and ternary mixtures. Results obtained from Peppas equation showed that all indomethacin formulations used in this study released the drug by Fickian release with release exponent (n) < 0.45, while all ibuprofen formulations used in this study released the drug by non-Fickian (anomalous) release with release exponent (n) > 0.45 and > 0.89. Conclusion: The FL-drug ratio could give an explanation to the enhanced dissolution of indomethacin and decreased dissolution of ibuprofen from interactive mixtures