Cost Value Research Articles

Sustainable inventory model with controlled temperature for deteriorating products under preservation technology

The disaster or epidemic has created a terrible situation in front of the whole world. This study saves the Corona vaccine's quality and keeps its demand in mind. This study develops an inventory model for deteriorating products with preservation technology investment under ramp-type demand. Reducing the deterioration rate of items reduces the number of waste items, which is beneficial for the environment. The demand increases at the ramp-type demand rate at the start, and then the market tends to a constant rate until the end of the inventory cycle. Retailers are attempting to reduce emissions, which impacts inventory levels. As a result, the impact of carbon emissions from holding and deterioration costs has been investigated in this study. This study allows for partially backlogging and shortages. The numerical part shows the minimized total cost of this study. The numerical findings indicate that the value of total cost increases due to carbon emissions. The study is analyzed for two cases: Case I shows a difference of 0.45% in total cost due to carbon emission, while Case II shows a difference of 0.20% in total cost due to carbon emission. Carbon emissions and preservation technology have a significant impact on environmental preservation. Finally, numerical examples and graph representation for convexity of total cost and sensitivity analysis are provided to illustrate.

Formal Safety Assessment (FSA) for ship collision in Musi River

Abstract Accident data from the Palembang Port Authority Of6ice Class II shows that the level of traf6ic accidents in the waters of the Musi River in Palembang is relatively high. Hazard identi6ication was conducted on the Musi River, resulting in 25 ship accidents caused by collisions and 4 ship accidents due to grounding. Subsequently, risk assessment was performed by analysing the collision frequency and its consequences on human casualties. The collision frequency was calculated using IWRAP, yielding results of 8.18 x 10-2, 9.74 x 10-3, and 8.18 x 10-4 for the head-on, overtaking, and crossing scenarios, respectively. An analysis of the consequences in terms of human casualties estimated an average of 11 victims. Six options were proposed as risk control options (RCOs): public awareness and regulation implementation, Automatic Identi6ication System (AIS) installation, crew education and training, standardization of safety equipment, utilization of pilotage services, and channel widening. Each RCO will be evaluated based on its cost and bene6its. The 6inal stage of the FSA involves recommendations aimed at selecting the best RCOs, considering the cost-bene6it analysis (CBA). The optimal choice to minimize ship collisions and human casualties in the waters of the Musi River is to conduct crew education and training. This option is the most cost-effective due to its lowest Gross Cost value. The bene6its gained amount to USD 890,273.02 with a 68% reduction in risk value.

From waste to sustainable production: Experimental design and optimization of sustainable concrete using response surface methodology and life cycle assessment

This study focuses on a holistic approach to investigate the engineering properties (microstructural, mechanical, environmental, and economic) of sustainable concretes produced using recycled aggregate and supplementary cementitious material (SCM). Compressive strength tests was conducted on 24 sustainable concrete series for the mechanical properties. Statistical analyses were performed using the response surface methodology, and mathematical models were developed considering the strength values obtained. Developed model-based optimization solutions were applied to determine the mix proportions of the optimization series containing lower cement dosage and SCM with the same hardened concrete properties as the reference concrete series. Considering these mix proportions, life cycle assessments were performed and environmental and economic properties were compared. A comparison of Ref-6 and Opt-6 series reveals that the global warming potential (GWP), energy consumption (EC), and waste generation (WG) values are 422.4-330.9 kg CO2 eq, 1294-1044 MJ, and 1294-1222 kg, respectively. This situation resulted in a reduction of 21.7% in GWP values, 19.3% in EC values, and 5.6% in WG values. Similarly, the economic comparison of these series indicates that the total cost values are 81.9-75.7 €, respectively, resulting in 7.7% reduction. Finally, Scanning Electron Microscopy analyses were conducted to examine the microstructural properties. The use of fly ash at 10% improved the microstructure properties and increased the C-S-H gel formation. It is thought that the process of transition from these waste materials to sustainable production is of critical importance, especially in places that are heavily damaged after devastating earthquakes and where large amounts of waste materials are generated.

Evaluating the feasibility of coastal protection scenarios on a city scale across plausible pathways scenarios for both sea level rise and urban development

Coastal flood risk accelerates rapidly due to rising sea levels and urban expansion in low-lying areas. Many strategies based on the widespread application of hard protection have been developed. However, the implementation of such measures has raised concerns due to their environmental impact, high costs, and a potential increase in exposure.This study focuses on the economic implications of coastal flood protection at the local level, examining impacts on small to medium-sized cities, which reflects many coastal communities around the world. We introduce a framework for assessing the economic burden or benefit of flood protection strategies on local communities and municipalities. We derive standardized values for protection costs, flood risk, and socioeconomic development for typical coastal topologies, i.e., the data usually used for cost-benefit analyses of coastal protection.Based on two illustrative examples of coastal cities in different topographies, we demonstrate that cost-effective protection strategies can lead to lock-in situations because their implementation cost likely exceeds local willingness to pay, and we argue that this risk needs to be considered when designing flood adaptation pathways.

Optimal long-term planning of CCUS and carbon trading mechanism in offshore-onshore integrated energy system

The offshore-onshore integrated energy system (OOIES) interconnects the offshore oil extraction platforms, offshore wind farms (OWFs), onshore coal/gas-fired power plants and oil distillation factories, which has great potential in CO2 emission reduction. To investigate its long-term CO2 emission reduction and economic benefits in the subsequent operation, an optimal long-term planning (OLTP) model of carbon capture, utilization, and storage (CCUS) and carbon trading mechanism (CTM) in OOIES is proposed. In the model, a CCUS model using the geological reservoir model for CO2 enhanced oil recovery (CO2-EOR) is proposed, a price-based CTM with carbon emission allowance and Chinese certified emission reduction is included, multi-band intervals are used to describe the uncertain prices and OWF outputs, and GlueVaR is introduced to measure the risks caused by uncertainties for risk management. For the solution of the OLTP model, the geological reservoir model with partial differential equations is transformed into mix-integer linear constraints by using the implicit pressure explicit saturation method, the ReLU-based multi-layer perceptron method and the convex envelop relaxation technique. Moreover, by applying multi-band interval possibility degree and GlueVaR's coherence, the original uncertainty optimization model is transformed into a mixed-integer linear programming model. Case study on an actual OOIES on the east bank of the Pearl River estuary demonstrates the effectiveness of the proposed method. Compared to the optimal results without CCUS and CTM, the optimal results of the proposed method reduce the CO2 emission by 13.28 % and the expected value and GlueVaR of total cost by 2.381 % and 22.480 %, respectively.

A Novel Algorithm for Precise Orbit Determination Using a Single Satellite Laser Ranging System Within a Single Arc for Space Surveillance and Tracking

A satellite laser ranging (SLR) system uses lasers to measure the range from ground stations to space objects with millimeter-level precision. Recent advances in SLR systems have increased their use in space surveillance and tracking (SST). The problem we are addressing, the precise orbit determination (POD) using one-dimensional range observations within a single arc, is challenging owing to infinite solutions because of limited observability. Therefore, general orbit determination algorithms struggle to achieve reasonable accuracy. The proposed algorithm redefines the cost value for orbit determination by leveraging residual tendencies in the POD process. The tendencies of residuals are quantified as R-squared values using Fourier series fitting to determine velocity vector information. The algorithm corrects velocity vector errors through the grid search method and least squares (LS) with a priori information. This approach corrects all six dimensions of the state vectors, comprising position and velocity vectors, utilizing only one dimension of the range observations. Simulations of three satellites using real data validate the algorithm. In all cases, the errors of the two-line element data (three-dimensional position error of 1 km and velocity error of 1 m/s, approximately) used as the initial values were reduced by tens of meters and the cm/s level, respectively. The algorithm outperformed the general POD algorithm using only the LS method, which does not effectively reduce errors. This study offers a more efficient and accurate orbit determination method, which improves the safety, cost efficiency, and effectiveness of space operations.

Intelligent Forecast Model for Project Cost in Guangdong Province Based on GA-BP Neural Network

Project cost forecasting is a complex and critical process, and it is of paramount importance for the successful implementation of engineering projects. Accurately forecasting project costs can help project managers and relevant decision-makers make informed decisions, thereby avoiding unnecessary cost overruns and time delays. Furthermore, accurately forecasting project costs can make important contributions to better controlling engineering costs, optimizing resource allocation, and reducing project risks. To establish a high-precision cost forecasting model for construction projects in Guangdong Province, based on case data of construction projects in Guangdong Province, this paper first uses the Analytic Hierarchy Process (AHP) to obtain the characteristic parameters that affect project costs. Then, a neural network training and testing dataset is constructed, and a genetic algorithm (GA) is used to optimize the initial weights and biases of the neural network. The GA-BP neural network is used to establish a cost forecasting model for construction projects in Guangdong Province. Finally, by using parameter sensitivity analysis theory, the importance of the characteristic values that affect the project cost is ranked, and the optimal direction for controlling the project cost is obtained. The results showed: (1) The determination coefficient between the forecasting and actual values of the project cost forecasting model based on the BP neural network testing set is 0.87. After GA optimization, the determination coefficient between the forecasting and actual values of the GA-BP neural network testing set is 0.94. The accuracy of the intelligent forecast model for construction project cost in Guangdong Province has been significantly improved after optimization through GA. (2) Based on sensitivity analysis of neural network parameters, the most significant factor affecting the cost of construction projects in Guangdong Province is the number of above-ground floors, followed by the main structure type, foundation structure, above-ground building area, total building area, underground building area, fortification intensity, and building height. The results of parameter sensitivity analysis indicate the direction for cost control in construction projects. The research results of this paper provide theoretical guidance for cost control in construction projects.

Costly involved? An examination of latent motivational profiles in physical education

ABSTRACT Situated Expectancy-Value Theory (SEVT) posits that expectancy for success, task value, and perceptions of cost are pivotal motivational factors influencing learning in a specific educational setting. The purpose of this study was to examine the profiles of expectancy, value, and cost beliefs pertaining to participation in physical education. Additionally, this study aimed to explore the longitudinal associations between these belief profiles and students’ demographics (gender, grade), class engagement and cardiorespiratory fitness throughout a school year. A total of 455 students from two urban public middle schools in China participated in this study, with latent profile analyses employed for the analysis. Three distinct profiles were identified: a high cost with moderate attainment value profile, a high expectancy-value with moderate effort cost profile, and a moderate mixed motivation profile. Further analyses demonstrated associations between gender and grade level with latent profile memberships (b = 1.13, SE = .31, p < .01). The high expectancy-value with moderate effort cost profile exhibited higher initial class engagement compared to the other two profiles (χ²(2) = 18.74, p < .01), while the decline in cardiorespiratory fitness over time was most pronounced in the high cost with moderate attainment value profile (χ²(2) = 5.10, p < .05). This study enhances our comprehension of the amalgamation of various motivational beliefs within student groups and emphasises the importance of taking into account diverse perceptions of cost. Tailored support may be crucial for students with differing motivational beliefs to foster continuous development in physical education.

Quantitative Convergence of a Discretization of Dynamic Optimal Transport Using the Dual Formulation

AbstractWe present a discretization of the dynamic optimal transport problem for which we can obtain the convergence rate for the value of the transport cost to its continuous value when the temporal and spatial stepsize vanish. This convergence result does not require any regularity assumption on the measures, though experiments suggest that the rate is not sharp. Via an analysis of the duality gap we also obtain the convergence rates for the gradient of the optimal potentials and the velocity field under mild regularity assumptions. To obtain such rates, we discretize the dual formulation of the dynamic optimal transport problem and use the mature literature related to the error due to discretizing the Hamilton–Jacobi equation.

Task Allocation and Sequence Planning for Human-Robot Collaborative Disassembly of End-of-Life Products Using the Bees Algorithm.

Remanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human-robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human-robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human-robot collaborative disassembly of EoL products. The adopted criteria for human-robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human-robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human-robot collaborative disassembly solutions.

Does reducing communication barriers promote diversity?

We study how a reduction in communication cost affects equilibrium diversity (in expertise) in hiring decisions. The key trade-off is that, while hiring a more diverse team may enlarge the team's pool of knowledge, it also makes communication between the hired experts more challenging. Equilibrium diversity balances this trade-off. We find that equilibrium diversity may exhibit a U-shape relationship with communication cost, reaching its lowest point at moderate cost values, where experts' incentives to communicate are most sensitive to changes in diversity.

Cost-effectiveness analysis of atezolizumab plus bevacizumab and chemotherapy for the treatment of metastatic, persistent, or recurrent cervical cancer (BEATcc)

ABSTRACT Background The addition of atezolizumab to bevacizumab plus platinum regimen has demonstrated notable improvements in treating metastatic, persistent, or recurrent cervical cancer, but its cost-effectiveness requires further investigation. From a US payer perspective, we aimed to evaluate the cost-effectiveness of atezolizumab plus bevacizumab and chemotherapy vs. standard chemotherapy as a first-line treatment for metastatic, persistent, or recurrent cervical cancer. Methods A partitioned survival model based on the data from the BEATcc trial was used to calculate the incremental cost-effectiveness ratio (ICER), using cost and health utility information obtained from literature and publicly accessible databases. One-way and probabilistic sensitivity analyses were performed to evaluate the model’s responsiveness to variations in parameters. Results The addition of atezolizumab resulted in an additional 0.839 quality-adjusted life years (QALY) at an additional cost of $458,237, leading to an ICER of $545,943/QALY. One-way sensitivity analysis indicated that the cost of atezolizumab had the greatest impact on the ICER, followed by the utility value of progression-free survival (PFS) and follow-up costs. Probabilistic sensitivity analysis showed a 0% cost-effectiveness probability at the current willingness-to-pay (WTP) threshold of $150,000 per QALY. Conclusion Adding atezolizumab to chemotherapy is cost-prohibitive in the US and may not be cost-effective for patients.

Enhancing strategic decision-making in built asset management through BIM-Enabled asset information modelling (AIM) for public buildings in Ethiopia: A fuzzy-AHP analysis

BIM-Enabled Asset Information Modelling (AIM) entails incorporating extensive data, known as big data, into digital platforms for informed decision-making. However, the lack of accurate and reliable data and the immaturity of BIM integration in existing buildings lead to operational phase performance inefficiencies due to inadequate data access. A strategic approach using BIM-enabled AIM is proposed to address these challenges, with the goal of enhancing data accessibility and adequacy for the operational team's performance. This study aims to develop a framework of information requirements that supports operational strategic decisions in asset portfolio management. To develop the framework, we employed a methodology that combines the Analytic Hierarchy Process (AHP) a structured technique for organizing and analysing complex decisions with fuzzy logic, which helps handle uncertainty in experts' judgments. A comprehensive questionnaire based on the Analytic Hierarchy Process (AHP) methodology was developed to gather expert insights on prioritizing information requirements, and it was administered to 11 experts selected for their diverse expertise in problem area. Cost, risk, and business value as selection criteria, while technical, managerial, financial, legal, and commercial categories of information are considered as alternatives in the AHP hierarchy. Utilizing the described methodology, fuzzy-AHP analysis revealed distinct variations in information requirements across the strategic decisions of maintain/keep, improve/adapt, and deconstruct/disassemble. For decisions on whether to maintain/keep buildings, the primary information requirement is managerial (39.6 %), followed by legal (20.7 %) and commercial (20 %), guiding strategic decisions. In contrast, improve/adapt decisions prioritize technical information (39 %), with financial (15.5 %) and legal (13.5 %) considerations also being significant. For the deconstruct/disassemble decisions, technical information requirements are most critical (55.5 %), followed by legal (16.6 %) and commercial (12.8 %) information. The findings highlight the need for tailored data generation strategies in existing buildings to address specific decision requirements, aiding in planning and resource allocation towards efficient AIM. The primary limitation of this study is its reliance on a small pool of 11 experts, which may limit the generalizability of the findings. Future research should aim to broaden the expert base to enhance the applicability and robustness of the results.

Indirect Costs of Alzheimer’s Disease: Unpaid Caregiver Burden and Patient Productivity Loss

ObjectivesThe direct medical costs associated with Alzheimer’s disease (AD) in the United States have been estimated to be over US dollars $360 billion, but this value does not reflect the substantial financial burden on unpaid caregivers and society. We estimated the economic burden of unpaid caregivers and patient productivity loss because of AD across all disease severity stages to better understand the indirect impacts of AD. MethodsWe performed a narrative literature review to identify estimates of unpaid caregiver burden and market productivity loss. In addition, we leveraged a published algorithm to estimate non-market productivity loss because of AD. Patient-level estimates were scaled to the population on the basis of AD prevalence in the United States (approximately 12.5 million), weighted by disease severity. ResultsThe total annual indirect costs of unpaid caregiving and market and non-market productivity loss of AD increased with severity: $36 934 for mild cognitive impairment owing to AD, $65 565 for mild AD, $103 717 for moderate AD, and $145 250 for severe AD (2024 United States Dollars [USD]). Considering the current distribution of prevalent patients across severity stages, the total annual indirect cost was estimated at $832 billion, which includes $599 billion in unpaid caregiving costs and $233 billion in productivity losses. ConclusionsConventional cost estimates, which do not consider unpaid caregiver burden and patient productivity loss, significantly underestimate the total burden of AD. Both elements should be incorporated into cost estimates and value assessments to best capture the total indirect impact of AD and the value of new therapies.

Business AI for Oil and Gas: Unlocking Efficiency Across the Value Chain

This article explores the transformative impact of artificial intelligence (AI) on the oil and gas industry, focusing on its applications across the value chain from exploration to end-user delivery. It examines the potential of AI, particularly generative AI, to optimize various aspects of the sector, including exploration, development, production, transportation, refining, and sales. The article discusses specific AI applications in refinery processes. It highlights the significant benefits of AI implementation, such as improved operational efficiency, reduced downtime, enhanced decision-making, cost reduction, and increased safety. Drawing on industry studies and research, the article presents quantitative data on the potential cost savings, productivity improvements, and value creation that AI technologies can bring to the oil and gas sector.

Pipe production cost management model based on graph theory

The pipe production process is a complex dynamical system with a significant number of production operations and interconnected cost localization centres. Purpose. To develop a mathematical model of the process of handling analytical information of a pipe enterprise using graph theory for the needs of cost management at each production stage. Methodology. Technological flow charts of the production job, where the list of mandatory activities and routing of the operating process are indicated, were used to organize cost accounting by operational centres of their localization with the help of computer modelling elements, namely the graph theory. The work used a complex of research methods, including analysis and scientific synthesis of scientific and technical information; theoretical studies; methods of mathematical and computer modelling, engineering developments. Findings. The cost accounting process is represented by a directed hypergraph. Each production operation is associated with a graph vertex. Each vertex is assigned to a series of interrelated marks: type of technological operation; weight factor; the value of direct costs for the operation; the value of indirect costs for the operation; the expense factor. The initial state of the hypergraph is specified by means of its initial marking, which corresponds to the set cipher of the batch of pipes and its weight. Further execution of the marked hypergraph is performed by running the allowed vertices. Marking of the graph ends if all the information from the technological flow charts of pipe products has been used. Originality. A model of cost management by operational centres is proposed which will allow dividing the prime cost of pipes which differ according to the production technology. It is recommended to rank pipe products within each technologically similar group for an economically feasible allocation of costs between batches of pipes. The proposed process model of processing analytical information of the pipe enterprise using graph theory will allow providing data governance as for the prime cost of each individual batch of pipe products, will contribute to the improvement in the order generation procedure taking into account the acquired information, and will allow making the appropriate adjustments when making management decisions. Practical value. Calculation of expenses for production operations allows one to identify the most cost-intensive of them. Relying on the results of the calculation, the enterprise management has the opportunity to manage costs at any stage of the production process. Moreover, the model allows determining the amount of consumed metal for each production operation, which is relevant for pipe enterprises. It is appropriate to focus further scientific developments in this direction on the possibility of managing non-manufacturing cost with the help of mathematical models in economics to optimize the supply and sales activities of the enterprise.

On-Farm Production of Renewable Energy in 2014–2022

The main purpose of this study is to present family farms as consumers and producers of renewable energies which provide them with an opportunity to reduce operating costs. The time scope of the study is 2014–2022, and the Farm Accountancy Data Network is used as the data source. The following research methods were employed: comparative and descriptive analysis, intensity indicators, ranking assignment and panel regression. Based on the values of energy output and energy costs, the rankings revealed a strong position of the Netherlands and Germany. As demonstrated by the study, energy production and consumption volumes depend on the farms’ economic size, but are not impacted by production type. Another finding is that energy production covers only one-third of its costs. Also, both production volumes and costs were on a growth path on a year-over-year basis, with similar growth ratios. The European Union’s leaders in energy consumption and production are the Netherlands, Germany, Belgium, Denmark, Czech Republic, Hungary, Luxembourg, Slovakia and Sweden. The study included the structuring of panel models for energy output and costs and identified their determinants. Energy output depends on total inputs and grows as they grow. Energy costs, in turn, are related to utilized agricultural area, total output and family farm income. An important limitation of this study is that FADN is a provider of high-level data. Hence, it is impossible to tell what specific sources of renewable energy are used by farms, and how they are affected by such exogenous factors as climate, earmarked subsidies or energy policy. The findings from this study are discussed in the context of the European Commission’s recommendations laid down in the Bioeconomy Strategy of the EU (2013), the Seventh Environment Action Program, the New Innovation Agenda of the European Union, the Report “Transforming Our World: the United Nations 2030 Agenda for Sustainable Development” and the Circular Economy Action Plan.

Influence of Technical Reasons on Cost Overruns of Infrastructural Projects: A Sustainable Development Perspective

Cost overruns are a significant issue in infrastructure projects, adversely affecting not only economic performance but also sustainability goals by straining resources and extending project timelines. There is consensus among researchers about the persistence of cost overruns and the importance of accurate estimates. However, there are significant differences in the explanation of the reasons for the overruns. While we recognize the importance of understanding the reasons for cost overruns at all stages of project development, we have focused on the reasons for cost overruns after contract award due to the rarely available real and valid data collected at the source of the information. We analyze the deviations between actual and contracted costs, as well as the dependence of cost deviations of individual project types on the size of the contract. For example, the size of the tunnel significantly affects relative cost deviations, while for standard viaducts or road sections this effect is minimal. The results confirmed that cost overruns are less frequent in standard facilities than in those where the experience factor has little influence on the final cost estimate. For example, deviations in the average value of the contracted costs for highway sections range between −12.1% and 27.9% of the contracted value, and for standardized viaduct projects they range between −16.73% and 6.27% of the contracted value. The type of distribution function of deviations between actual and contractually agreed costs was investigated, and a predictive model for estimating future cost deviations of project programs was presented. The goal is to improve economic sustainability and the related optimization of resources in the development of infrastructure, which contributes to the broader agenda of sustainable development. The results encourage the adoption of improved project documentation and forecasting tools, which are essential for sustainable project management.

Incorporating costs and benefits to the evaluation of uncertain research results: Applications to cancer research funding

Abstract Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verification. Proposals to fix these issues typically ask for tighter statistical controls (e.g., stricter p-value thresholds or higher statistical power). However, these approaches often overlook the importance of contemplating research outcomes’ potential costs and benefits. Here, we develop a framework grounded in Bayesian decision theory that seamlessly integrates cost-benefit analysis into evaluating research programs with potentially uncertain results. We derive minimally acceptable pre-study odds and positive predictive values for cost and benefit levels. We show that tolerance to inaccurate results changes dramatically due to uncertainties posed by research. We also show that reducing uncertainties (e.g., by recruiting more subjects) may have limited effects on the expected benefit of continuing specific research programs. We apply our framework to several types of cancer research and their funding. Our analysis shows that highly exploratory research designs are easily justifiable due to their potential benefits, even when probabilistic models suggest otherwise. We discuss how the cost and benefit of research could and should always be part of the toolkit used by scientists, institutions, or funding agencies.

Stochastic optimization of a hybrid photovoltaic/fuel cell/parking lot system incorporating cloud model

The interaction and optimization of electric parking lots (EPLs) with hybrid renewable energy to exchange power and improve the system load reliability has been welcomed systems in energy industry, but it is accompanied by challenges such as uncertain parameters, and also implementing the optimal energy management. This paper proposes a novel stochastic optimization framework for a hybrid energy system including photovoltaic resources, hydrogen storage based-fuel cell integrated with an EPL (PV/FC/EPL) aiming for minimization of the total net present cost (TNPC) while ensuring the maximum probability of power shortage (PPSHmax) to meet a commercial load in Tianjin, China using the real meteorological data. A new improved kepler optimization algorithm (IKOA) utilizing the golden sine strategy is applied to optimize system component sizes and EPL charge/discharge. In a deterministic scenario, the optimal system configuration ensures cost-efficiency and reliability for load supply. Also, a new stochastic modeling framework is proposed using the Cloud Model (CM) to evaluate the effect of uncertainties in irradiance, temperature, and system demand on TNPC and PPSH. Deterministic findings reveal enhanced reliability and reduced TNPC through FC and EPL overlap during power shortages, compared to systems lacking EPL. Specifically, TNPC and PPSH are $8,632,643 and 1.45 %, respectively, for PPSHmax = 2 % over 20 years of operation. However, stochastic optimization demonstrates a 10.82 % increase in TNPC and an 8.67 % decrease in PPSH compared to the deterministic model under PPSHmax = 2 %, indicating heightened cost and reduced reliability when uncertainties are considered. This underscores the efficacy of the cloud model-based stochastic approach in addressing uncertainty in hybrid system optimization. Also, it reveals fluctuations in optimal cost and reliability values with changes in cloud droplet distribution around the expected value, emphasizing the significance of stochastic modeling for robust decision-making in hybrid energy systems. Moreover, evaluating the incorporating the penalty for the system's inability to supply the load, changing the storage investment cost, and interest rate variations on the system optimization cleared considerable effect of the system cost and reliability.