Programming Optimization Research Articles

A centralized EMPC scheme for PV-powered alkaline electrolyzer

Photovoltaic (PV)-powered alkaline electrolyzer system (PVPAES) is an advanced technique to convert the off-grid and intermittent PV-based solar energy into storable and transportable electrolyzer-based hydrogen energy with zero carbon emissions. However, it is difficult to realize the coordinated control of the off-grid PV module and the alkaline electrolyzer, due to the multiple timescale dynamics. To address this challenge, the PVPAES is decomposed into the slow part and fast part based on the dynamic time scale. Exploiting the decomposed subsystems, the slow one is assumed to be managed well by the auxiliary controller. For the fast one, a centralized economic model predictive control (CEMPC) scheme is constituted. This CEMPC integrates the energy management system and local feedback control into a single optimal control framework. A mathematical model of the PVPAES is established, on the basis of which the CEMPC directly adopts the economic indices as the cost function to realize the flexible power point tracking, power supply-demand balance, and dynamic economic optimization. Moreover, the inherent strong nonlinearity of PVPAES results in the nonconvex mixed-integer nonlinear programming optimization problem in the CEMPC. The exhaustive search algorithm utilizing finite converter switching states is adopted to achieve the global economic optimum. The effectiveness of the proposed CEMPC controller is illustrated through simulations under varying irradiance conditions.

Robust Contact Computation in Non-Rigid Variation Simulation

Abstract Geometric variation is an inevitable element of any fabrication process. To secure the geometric quality of the assembled products, variation simulation is performed to control compliance with the set geometric requirements. In non-rigid variation simulation, contact modeling is used to avoid the virtual penetration of the components in the adjacent areas, enhancing the simulation accuracy. For frictionless contact models, numerical errors and convergence issues due to the deformation behavior of the interacting surfaces are still limiting the computational efficiency of solving this optimization problem. The optimization problem associated with a contact model is often large-scale, and in practice, fast and robust methods for achieving convergence are required. Previous implementations of contact modeling for non-rigid variation simulation have been prominently based on the Iterative or Penalty Methods. In this paper, a quadratic programming approach has been introduced, based on the Lagrangian multiplier method, for robust contact modeling in non-rigid variation simulation, and the performance of the proposed approach has been compared to the previously applied Iterative and Interior Point Method. The methods have been compared on three industrial reference cases, and the convergence and time-efficiency of each method are compared. The results show that robust optimization of the quadratic program associated with the contact model is highly dependent on the reduced stiffness matrix condition. Furthermore, it has been shown that robust and efficient contact modeling in non-rigid variation simulation is achievable through the proposed quadratic programming method.

A peer-to-peer energy trading market framework for single buyer model: An integration of decentralised finance instruments and cooperative game model

Single Buyer (SB) model has gained traction in many Southeast Asia, Middle Eastern, and North African countries for its blend of centralisation and controlled competition. The semi-liberalised regulatory environment has hindered the integration of cogeneration or combined heat and power (CHP) as a prosumer within the electricity value chain, despite its pivotal role in enhancing energy efficiency. There is a disconnection in the existing literature as electric supply configurations and enablement of peer-to-peer (P2P) electricity trading are individually studied. There also lacks the studies addressing electricity as a special commodity and its compatibility with the market. This paper bridges the research gaps by proposing a P2P energy trading market framework that integrates decentralised finance (DeFi) instruments and a cooperative game model. Malaysia, which implements the SB model in its electric supply structure, is selected for a case study. The comprehensive framework develops (1) a stableswap hybrid function automated market maker (HFMM) decentralised exchange (DEX) for P2P trading, (2) a mixed-integer linear programming (MILP) optimisation model to determine the optimal operational strategy with minimised total annualised cost (TAC) of the system, and (3) an allocation scheme based on a cooperative game model to gauge the individual and coalitional cost savings allocations. Overall, this study presents a novel approach to addressing the challenges with innovating the SB model, while also fostering competitions among prosumers. The SB structure of Malaysia is briefly inspected, and the proposed framework is expected to contribute to the growth of macro-level CHP integration in the nation's energy planning.

Decreasing environmental footprints of dairy production systems through optimization of feed rations and origins

Environmental impacts of livestock production systems are a global concern, because of the large greenhouse gas (GHG) emissions and nitrogen (N) and phosphorus (P) losses associated with feed production and livestock rearing. These losses are in part related to the system of livestock production and their feed rations and feed origin. This study aims to investigate the economic and environmental impacts of optimizing feed configurations of dairy farms in Inner Mongolia (China), using data from farm surveys, life cycle assessments and linear optimization programming. The findings of our study reveal that feed optimization may lower carbon footprints by 36–44%, nitrogen footprints by 35–42%, and phosphorus footprints by 42–71%, compared to the baseline. The need for cropland decreased by 11–38% per kilogram of milk, feed costs decreased by 15–48%, and net profit increased by 39–129%. Further, we found a statistically significant negative correlation between environmental footprints and feed self-sufficiency of dairy farms. Notably, energy-rich feeds, silage maize, and grass should be grown on-farm. These findings underscore the potential of adjusting feed rations and origin to decrease environmental footprints and enhance net profits of dairy farms. However, we observed several barriers to widespread adoption of optimal feed rations and origin, which relate to land scarcity, high land rental costs, and lack of trust and cooperation with nearby arable farms. Our research provides a scientific foundation for optimizing feed rations and origins for diverse dairy systems to achieve improved environmental and economic performance.

Dynamic responses of blood metabolites to nutrient depletion and repletion in broiler chicken nutrition

A total of 720 male Cobb 500 broiler chicks were used in a 5 treatment and 8 replicate experiment to explore dynamic changes in blood metabolites in response to short-term nutrient depletion and repletion. Day old chicks were offered a corn and soybean meal-based common starter diet from d1 to 14 that was formulated to meet all nutrient requirements of the birds. From d15 to 17, the experimental diets were offered, before returning all groups to a common diet from d18 to 20, at which point the experiment was terminated. A total of 5 experimental diets were designed. A standard grower diet served as a control and was offered to 1 of the 5 groups of chicks. The additional 4 experimental groups comprised diets that were low in digestible phosphorus (P), total calcium (Ca), crude protein and digestible amino acids (AA) or apparent metabolizable energy (AME). The common grower diet that was offered from d18-20 was designed to be nutritionally complete and was intended to explore dynamic response to nutrient repletion. Blood was drawn from 8 chicks per treatment at time 0 (immediately prior to introduction of the experimental diets) and then again 3, 6, 12, 24, and 48h after introduction of the nutrient depleted diets. Additionally, blood was drawn 3, 6, 12, 24, and 48h after the introduction of the nutritionally complete common grower diet. Chicks were not sampled more than once. Feed intake, body weight and feed conversion ratio (FCR) were assessed on d14, 17, and 20. Blood metabolites were analyzed using the iSTAT Alinity V handheld blood analyzer, the Vetscan VS2 Chemistry Analyzer and the iCheck Carotene Photometer. Live performance metrics were not affected by the short-term nutrient depletion and all chicks grew normally throughout the experiment. The diet with low digestible P generated a rapid temporary decrease in plasma P and an increase in plasma Ca, that were returned to baseline following the re-introduction of the common grower feed. Introduction of the diet with low total Ca resulted in a significant increase in plasma P, effects which were also mitigated during the nutrient repletion phase. Total plasma protein, albumin and uric acid (UA) were decreased, and plasma glucose increased, in the chicks that received the diet with low crude protein and digestible AA. There was a delayed increase in aspartate amino transaminase (AST) associated with the diets with low digestible P and low AME. These results demonstrate the capacity of blood biochemistry to adapt to quantitative and qualitative changes in nutrient intake. Point-of-care analysis of blood biomarkers offers nutritionists a valuable opportunity to calibrate nutritional matrices for common dietary ingredients, zootechnical feed additives and to optimize diet phase changes. It can be concluded that many blood biomarkers are plastic to changes in diet nutrient density and offer an objective index for optimization of nutritional programs for commercial broiler production.

Enhancing maintenance efficiency in energy assets through AI: a case study of maintAI

Energy assets demand periodic regular maintenance to ensure safe, reliable and efficient operations. The dynamic nature of operating conditions and evolving best practices necessitate frequent optimisation of maintenance programs. Traditionally, these programs were labour-intensive and costly to deploy, often yielding unclear outcomes due to subjective decision-making. The advent of artificial intelligence (AI) presents an opportunity to challenge traditional approaches. This paper presents a case study where asset knowledge, data, and AI capabilities are leveraged to streamline maintenance optimisation using our ‘maintAI’ approach. The program addresses maintenance strategy, backlog, spares, and predictive maintenance optimisation with a focus on value creation, data-driven decisions, and consistent recommendations. A systematic methodology employs AI to sift through and eliminate non-value-adding tasks, enabling prioritisation of work and enhancing reliability and productivity throughout the production facility lifecycle. AI, including Natural Language Processing and Generative AI algorithms, enhances the speed and accuracy of failure mode classification from operational maintenance data. Reliability modelling techniques provide insights into equipment reliability. Recommendations undergo expert review before integration into a Computerised Maintenance Management System. Implementation of this data-driven approach demonstrates rapid deployment and sustained efficiency, yielding substantial gains in production uptime, cost reduction, and safety. The user-centric design ensures agility and ease of configuration. A recent project, which took only 6 weeks to deliver, led to a ~28% reduction in maintenance backlog, freeing capacity for critical focus areas. The maintAI approach proves a meaningful change for energy producers, offering a new solution in maintenance optimisation for enhanced reliability and productivity.

A home health management app designed in the post-epidemic era using empirical evidence based on the demand collection of elderly users

In the context of the epidemic, the daily routines of China’s elderly populace underwent substantial transformations. The implementation of home-based pandemic containment strategies led to heightened levels of social isolation and loneliness, exerting a discernible influence on the subjective well-being of this demographic. Consequently, residing in retirement apartments has emerged as a more advantageous alternative for elderly individuals capable of self-care. To meet their life and emotional needs and improve their sense of well-being, we used the KANO model to design a health management app for the self-care of older persons. We summarised the functional requirements for the self-care of elderly people, distributed questionnaires to generate a list of requirements, and conducted data analysis. We optimised the low-fidelity prototype based on the usability test results combined with relevant interface design elements to implement emotional interaction design practices for older persons’ self-care. Furthermore, we completed a design evaluation and program optimisation of the design practice to guide the final visual design. The research findings identify the core principles of interactive design for elderly users to enable visual design specifications for improving the lives of the elderly population. This research highlights the need to design more appropriate products that meet demographic characteristics. This work contributes to strengthening the connection between elderly people and society while bridging the ‘digital divide’ based on this population’s cautious approach to technologies and declining abilities to learn.

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Hybrid simplicial-randomized approximate stochastic dynamic programming for multireservoir optimization

Pricing and Replenishment Prediction Model for Vegetable Commodities Based on Mixed Integer Linear Programming Optimization XGBoost

In fresh food superstores, the replenishment volume and pricing strategy of vegetables are affected by various factors such as seasonal seasons, supply demand, purchasing costs, and profits of vegetable categories. In this paper, we use the data related to the sales flow details and wholesale prices of each commodity from July 1, 2020 to June 30, 2023 as a dataset, and build a model based on the gradient boosting tree algorithm, XGBoost algorithm [1], to predict the total daily replenishment volume and pricing strategy of each vegetable category in the coming week (July 1-7, 2023) [2]. It is also optimized using a mixed-variable 0-1 linear programming (MILP) [3] model with decision variables including a binary variable of whether to order the vegetable category or not and a continuous variable of the number of vegetable categories ordered to compute the optimal replenishment volume and pricing strategy for July 1, 2023 for each item. Through the model in this paper, under the premise of meeting the market demand for each category of vegetable goods, while keeping the control within a specific range, the superstore can get the optimal replenishment plan and pricing strategy, reasonably allocate the resources of the warehouse space, improve the market competitiveness of the superstore, and maximize the revenue.

Enhancing Efficiency in Industrial Metal Roof Replacement: Utilizing Goal Programming for Cost Optimization

This study explores the use of Goal Programming (GP) to optimise project management decisions in the construction industry, focusing on a case study of a roof replacement project. The construction industry is crucial to a country's development, contributing significantly to economic growth and employment rates. However, construction projects are often complex and resource-intensive. Project management is essential in ensuring the successful completion of construction projects. The study uses GP to address the time-cost trade-off in project management, aiming to minimise project costs while meeting project deadlines and quality requirements. The GP model considers various constraints, such as the availability of workers and equipment, project duration, and budget limitations. The study presents two optimisation options: a time-focused option and a cost-focused ed option. The time-focused option prioritises timely project completion, while the cost-focused option aims to minimise project costs. In the case study, the time-focused option is the most suitable choice. This approach allows the project to be completed in time as the alternative option delay delays the project far too unacceptable. Overall, the study demonstrates the effectiveness of GP in optimising project management decisions in the construction industry. By carefully evaluating project constraints and objectives, GP can help construction companies achieve the best possible outcomes in terms of cost, time, and quality. Keywords: Goal Programming, Construction Industry, Project Management, Time-Cost Trade-off, Optimization

Research on energy management strategy for fuel cell hybrid electric vehicles based on improved dynamic programming and air supply optimization

It is crucial to accurately calculate the cost function of the energy management strategy (EMS) of the hybrid powertrain to improve the hydrogen economy of the system. This paper proposes an EMS for fuel cell hybrid electric vehicles (FCHEV) based on improved dynamic programming (DP) and air supply optimization to improve economy and reliability. Taking the maximum net power output of the FC system as the target, the optimal oxygen excess ratio (OER) and cathode pressure of the FC system under different current densities are solved by using PSO. A velocity prediction method based on Bi-LSTM is developed to predict short-term velocity changes in real time. The DP algorithm is introduced and the EMS of the DP algorithm based on short-term velocity prediction is developed for real-time hybrid powertrain optimization and management. Based on the results of energy allocation and optimal gas supply conditions of FCs, the cost function of EMS is modified to reallocate the power of the FC system and battery. The results demonstrate that the proposed method achieves the lowest hydrogen consumption compared to the other two algorithms. Remarkably, it reduces the fuel cost by up to 8.85 % compared to the commonly used online DP algorithm.

Global, regional, and national burden of thalassemia, 1990–2021: a systematic analysis for the global burden of disease study 2021

Anemia is a significant contributor to the global disease burden, of which thalassemia is the most common hereditary anaemic disease. Previous estimates were based on data that were geographically limited and lacked comprehensive global analysis. This study provides the prevalence, incidence, mortality and disability-adjusted life years (DALYs) of thalassemia in 204 countries and regions of thalassemia between 1990 and 2021, focusing on the age structure and time trends of the disease burden. To provide effective information for health policy, allocation of medical resources and optimization of patient management programs. Using the standardised Global Burden of Disease (GBD) methodologies, we aimed to derive a more precise representation of the health burden posed by thalassemia by considering four distinct types of epidemiological data, namely the incidence at birth, prevalence, mortality and DALYs. The presented data were meticulously estimated and displayed both as numerical counts and as age-standardised rates per 100,000 persons of the population, accompanied by uncertainty interval (UI) to highlight potential statistical variability. The temporal trends spanning the years 1990-2021 were subjected to a rigorous examination utilizing Joinpoint regression analysis. This methodological approach facilitated the computation of the annual percentage change (APC) and the average annual percentage change (AAPC), along with their corresponding 95% confidence intervals (CIs). Globally, the age-standardized prevalence rates (ASPR), age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALYs rates for thalassemia in 2021 were 18.28 per 100,000 persons (95% UI 15.29-22.02), 1.93 per 100,000 persons (95% UI 1.51-2.49), 0.15 per 100,000 persons(95% UI 0.11-0.20), and 11.65 per 100,000 persons (95% UI 8.24-14.94), respectively. Compared to 1990, these rates have decreased by 0.18 (95% UI-0.22 to-0.14), 0.25 (95% UI-0.30 to-0.19), 0.48 (95% UI-0.60 to-0.28), and 0.49 (95% UI-0.62 to-0.29) respectively. In 2021, the ASIR of thalassemia was highest in East Asia at 7.35 per 100,000 persons (95% UI 5.37-10.04), and ASMR was highest in Southeast Asia at 0.37 per 100,000 persons (95% UI 0.29-0.45).Gender comparisons showed negligible differences in disease burden, with the highest prevalence noted in children under five, decreasing with age. The global ASPR and ASMR declined from 1990 to 2021 overall, though an increasing trend in prevalence was found among the elderly. Joinpoint analysis revealed that the global ASPR increased between 2018 and 2021 (APC=9.2%, 95% CI: 4.8%-13.8%, P<0.001), ASIR decreased (APC=-7.68%, 95% CI:-10.88% to-4.36%, P<0.001), and there was a significant rise in ASMR from 2019 to 2021 (APC=4.8%, 95% CI: 0.1%-9.6%, P<0.05). Trends in ASPR and ASMR varied across regions, with notable changes in South Asia. The global burden of thalassemia, reflected in its prevalence, incidence, mortality, and DALYs, exhibits significant disparities. Geographic and demographic shifts in disease distribution have been observed from 1990 to 2021, with an overall decrease in burden, yet an increase in cases among the elderly population. Analysis of epidemiological trends over time highlights the influence of health policies and significant public health interventions on thalassemia outcomes. There data are crucial for healthcare professionals, policymakers, and researchers to refine and enhance management strategies, aiming to further mitigate thalassemia's global impact. National Natural Science Foundation of China; Guizhou Province Science and Technology Project; Guizhou Province Science and Technology Foundation of Health Commission.

Physics-informed deep learning and linear programming for efficient optimization of combined cycle power plants

Efficient operation of combined cycle power plants (CCPP) involves solving mixed-integer nonlinear optimization problems, constrained by thermodynamic differential equations, thermal constraints, and temporal dependencies between variables. The optimization problem aims to maximize profit for day-ahead operation by minimizing short-term and long-term costs, and accounting for volatility in ambient conditions and energy price. In operational terms, the challenge lies in developing an efficient and robust optimization algorithm capable of determining optimal values for thousands of temporal variables. It must also have the capacity to evaluate a multitude of scenarios encompassing various weather and market conditions. To address this challenge, this paper proposes a hybrid optimizer that utilizes deep learning models trained on historical operational data of a real CCPP, along with a surrogate physics-driven piecewise-linear model of CCPP, to determine the optimal values of thousands of control setpoints, with strong temporal co-dependencies, within a two-minute runtime required by industry constraints. A multi-resolution architecture is proposed to efficiently implement this optimizer. The algorithm sequentially solves the problem in coarse and fine resolutions to accelerate convergence. Final results are checked for feasibility using pre-trained deep learning models, ensuring accuracy and validity. The study shows that the proposed model provides comparable results to the exact solution while reducing computational costs, up to 95%. This research has practical implications for power plant system operators, as well as traders and dispatchers, enabling them to make optimal decisions for day-ahead contracting purposes and CCPP operation in a reasonable time, ultimately leading to more efficient and cost-effective power plant management. Furthermore, the methodology introduced in this work could potentially be extended to other industrial applications that require large-scale, complex optimizations, where the precise governing principles of a process can be approximated by piecewise-linear functions.

Impact of Under Voltage Load Shedding Approach for Voltage Security Control in Power Quality Environment

Contingencies in power system are an ever-present problem for electrical engineers. They may cause the system to have low voltages due to active and reactive power deficiencies. Engineers shold consider the many solutions to approach this problem. The solution has to be appropriate to the contingency faced so as to be able to solve the issue without major damage to a power system. A drastic measure that can be used is by removing low voltage loads or ‘shedding’ the problematic loads. This method of Under Voltage Load Shedding is done by shedding or ‘islanding’ buses that have fallen to a predetermined minimum voltage for a predetermined time to prevent a cascade of blackouts in the widespread power system. The method to automate the process is by using two optimization techniques, Evolutionary Programming (EP) and Particle Swarm Optimization (PSO). This paper proposes a new hybrid algorithm, named Integrated Chaotic Swarm Based-Evolutionary Programming Optimization (ICSBEP) Technique for Voltage Security Control using Under Voltage Load Shedding (UVLS) approach. Voltage security is indicated by the reduction in voltage stability index, FVSI. In this study, EP, PSO and ICSBEP optimizations algorithms were developed and tested on a IEEE 30-bus reliability test system (RTS). Comparative studies were conducted to observe the advantages of the hybrid algorithm over traditional EP and PSO algorithms. The results obtained from the study can help manage the power quality of the system in ensuring that the customer receive secure electricity supply.

Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) Technique for Loss Minimization in Distribution System

Distribution system is the final section of the electrical power system, apart from the transmission and generation system. The distribution system acts as the interface that connects the high-voltage network to the low-voltage consumer service point. Consequently, the distribution system contributes the most of the overall losses in the electrical power system. Hence, evolutionary computation was introduced to accomplish a reduction of the previously said losses. The evolutionary computation involves optimization techniques to allocate distributed generation (DG) to the distribution system. Unfortunately, several optimization techniques are not capable of achieving accurate results and are trapped at local optima. This paper presents the Integrated Index Vector Method and Hybrid Ant Lion Chaotic Evolutionary Programming Optimization (HALCEP) technique for loss minimization in a distribution system. In this study, Evolutionary Programming (EP), Ant Lion Optimization (ALO) and HALCEP optimization engines are developed. The validation process was simulated on the IEEE 33-bus radial distribution system (RDS) model. Comparative studies were performed to gain a clear observation of the advantage of the developed HALCEP over the conventional EP and the ALO techniques, effectively showing its capability to outperform them.

A novel metaheuristic based on object-oriented programming concepts for engineering optimization

This paper presents a novel, robust, efficient, and simple optimization algorithm called the Object-Oriented Programming Optimization Algorithm (OOPOA) for tackling constrained and unconstrained optimization problems. The algorithm is inspired by the inheritance concept of Object-Oriented programming languages, where the features of a class are classified into three types according to inheritance probability: public, private, and protected. The object-oriented programming inheritance concept is implemented in the algorithm to update the population for the next generations. The proposed algorithm ensures exploitation by selecting the solution with the highest fitness to be inherited in each iteration. It ensures exploration by applying a mutation process that helps explore wide regions in the search space. The performance of this technique is demonstrated by solving 34 different optimization tasks, including 20 standard benchmark problems, ten IEEE Congress of Evolutionary Computation benchmark test functions, and four constrained real-world engineering design problems.

Transition status and influencing factors of newly graduated nurses: A descriptive survey design

AimsTo assess the transition status of newly graduated nurses in China and identify its influencing factors. BackgroundNewly graduated nurses are the indispensable part of nursing human resource. The successful transition of into clinical work is crucial for their future career development. However, the transition status of new nurses in China remains inadequately explored. DesignA descriptive survey design was employed in this study. MethodsFrom October 2022 to January 2023, 1261 newly graduated nurses were surveyed online with the Transition Status Scale for Newly Graduated Nurses. Description statistical analysis was adopted to evaluate the transition status of new nurses. Independent-samples t-test, Analysis of Variance and Multiple Regression Analysis was used to explore the influencing factors of the transition status. ResultsThe total mean score of Transition Status Scale for Newly Graduated Nurses was 4.00 (SD=0.61). Competence for nursing work (Mean=4.20; SD=0.57) was rated the highest among the five dimensions of the scale, while the dimension of balance between work and life (Mean=3.65; SD=0.89) was rated the lowest. Mentored by senior nurses, night shift, attribute of working hospital, educational background, interned in the same department, tertiary general hospital, reasons for choosing nursing and working time can affect the transition status of new nurses, accounting for 17.9% of the variance in transition status (R²= 0.179, P＜0.001). ConclusionThe transition status of newly graduated nurses in China is at a relatively high level, especially in the dimension of competence for nursing work. However, newly graduated nurses are in a relatively poor status of work-life balance. Nurse educators and managers need to pay more attention to the transitional training of highly educated nursing talents and the optimization of clinical transition training programs to prevent talent loss. Experienced tutors should be allocated to provide guidance for newly graduated nurses.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

PROMPT: A Fast and Extensible Memory Profiling Framework

Memory profiling captures programs’ dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various memory profiler types have been developed. Yet, designing practical memory profilers often requires extensive compiler expertise, adeptness in program optimization, and significant implementation effort. This often results in a void where aspirations for fast and robust profilers remain unfulfilled. To bridge this gap, this paper presents PROMPT, a framework for streamlined development of fast memory profilers. With PROMPT, developers need only specify profiling events and define the core profiling logic, bypassing the complexities of custom instrumentation and intricate memory profiling components and optimizations. Two state-of-the-art memory profilers were ported with PROMPT where all features preserved. By focusing on the core profiling logic, the code was reduced by more than 65% and the profiling overhead was improved by 5.3× and 7.1× respectively. To further underscore PROMPT’s impact, a tailored memory profiling workflow was constructed for a sophisticated compiler optimization client. In 570 lines of code, this redesigned workflow satisfies the client’s memory profiling needs while achieving more than 90% reduction in profiling overhead and improved robustness compared to the original profilers.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.