Life Cycle Cost Optimization Research Articles

Tackling the multitude of uncertainties in energy systems analysis by model coupling and high-performance computing

This paper identifies and addresses three key challenges in energy systems analysis—varying assumptions, computational limitations, and coverage of a few indicators only. First, results depend strongly on assumptions, i.e., varying input data. Hence, comparisons and robust results are hard to achieve. To address this, we use a broad range of possible inputs through an extensive literature review by scenario experts. Second, we overcome computational limitations using high-performance computing (HPC) and an automated workflow. Third, by coupling models and developing 13 indicators to evaluate the overall quality of energy systems in Germany for 2030, we include many aspects of security of supply, market impact, life cycle analysis and cost optimization. A cluster analysis of scenarios by indicators reveals three recognizable clusters, separating systems with a high share of renewables clearly from more conventional sets. Additionally, scenarios can be identified which perform very positive for many of the 13 indicators. We conclude that an automated, coupled workflow on supercomputers based on a broad parameter space is able to produce robust results for many important aspects of future energy systems. Since all models and software components are released as open-source, all components of a multi-perspective model-chain are now available to the energy system modeling community.

A stochastic optimization procedure to design the fair aggregation of energy users in a Renewable Energy Community

Optimizing unit sizes and operation within a Renewable Energy Community (REC) can match intermittent renewable energy generation with variable user energy demands. These uncertain variables are often represented by pre-defined stochastic scenarios, without searching for the “best” scenarios and testing the optimization models with these scenarios. Moreover, little work both optimized RECs under uncertainty and distributed optimal life-cycle costs (investment and operation) among members. Thus, the objectives are: i) identifying the “best” set of stochastic scenarios of solar irradiance and user electricity demands and ii) assessing the accuracy of the “stochastic forecasts” of the total system costs and unit sizes, obtained by solving a stochastic programming model based on the “best” scenarios. The proposed novel procedure shifts the “present moment” back in time to split historical data into “past” and “future” periods used to identify the “best” scenarios and compare the “stochastic forecasts” with the utopic “perfect forecasts” based on the perfect knowledge of real data, respectively. The small errors between these forecasts in the optimal life-cycle costs (less than 2 %) and sizes (3–13 %) indicate good effectiveness of the suggested procedure. Also, the optimal life-cycle costs of “stochastic forecasts” are fairly distributed among users by applying the Shapley value mechanism.

Optimal sizing of islanded microgrid using pelican optimization algorithm for Kutubdia Island of Bangladesh

This study proposes an optimal design approach, based on the Pelican Optimization Algorithm (POA), to configure the optimal sizing of design variables on an islanded microgrid: photovoltaic (PV) modules, wind turbines (WT), diesel generators (DG), and batteries in Kutubdia, Bangladesh based on optimal life cycle cost (LCC) and cost of energy (COE). Additionally, the economic analysis of three independent battery technologies, notably lead acid, lithium-ion, and nickel-iron are carried out to find the economically feasible technology, to ensure uninterrupted power supply. Moreover, reliability and sensitivity analyses of the optimized microgrid using POA were conducted for various reliability indices and variable interest rates. Results show that proposed POA method provides the optimal island microgrid configuration with lead acid (LA) batteries (PV/WT/LA/DG) based on a minimum LCC of $8334901, COE of 0.1080$/KWh and greenhouse gas emission amount of 19664 kgs/year. Furthermore, the outcomes generated by the POA are compared with genetic algorithm, particle swarm optimization, moth flame optimization algorithm, whale optimization algorithm and grey wolf optimization. It is found that POA method achieves more competitive results compared to other optimization techniques due to its ability to adjust parameters, fast convergence speed, and straightforward computations.

Optimization of Life Cycle Cost and Environmental Impact Functions of NiZn Batteries by Using Multi-Objective Particle Swarm Optimization (MOPSO)

Optimization of Life Cycle Cost and Environmental Impact Functions of NiZn Batteries by Using Multi-Objective Particle Swarm Optimization (MOPSO)

Multi-Objective Optimization in Support of Life-Cycle Cost-Performance-Based Design of Reinforced Concrete Structures

Surveys on the optimum seismic design of structures reveal that many investigations focus on minimizing initial costs while satisfying performance constraints. Although reducing initial costs while complying with earthquake design codes significantly ensures occupant safety, it may still cause considerable economic losses and fatalities. Therefore, calculating potential earthquake damages over the structure’s lifetime is essential from an optimal Life-Cycle Cost (LCC) design perspective. LCC analysis evaluates economic feasibility, including construction, operation, occupancy, maintenance, and end-of-life costs. The population-based, meta-heuristic Ideal Gas Molecular Movement (IGMM) algorithm has proven effective in solving highly nonlinear mono- and multi-objective engineering problems. This paper investigates the LCC-based mono- and multi-objective optimum design of a 3D four-story concrete building structure using the Endurance Time (ET) method, which is employed for its efficiency in estimating structural responses under varying seismic hazard levels. The novelty of this work lies in integrating the ET method with the IGMM algorithm to comprehensively address both economic and performance criteria in seismic design. The results indicate that the proposed technique significantly reduces minor injury costs, rental costs, and income costs by 22%, 16%, and 16%, respectively, achieving a total reduction of 10% in all structural Life-Cycle Costs, which is considered significant.

Efficient neural network-aided seismic life-cycle cost optimization of steel moment frames

In this paper, a novel and efficient neural network-based methodology is proposed to achieve seismic total cost optimization of steel moment-resisting frames in a timely manner. The computational burden of an optimization process based on performing nonlinear time-history analysis is prohibitively high. To address this crucial issue, a new and efficient neural network model is proposed in this paper to accurately predict the nonlinear time history response of steel frames during the optimization process. In the proposed neural network model, an ensemble of parallel neural networks is used to provide excellent prediction accuracy. In addition, a new repairability constraint is proposed to check the seismic damage level of structures during the optimization process with the aid of the proposed neural network model. Moreover, an efficient metaheuristic algorithm is used to achieve the optimization task. Two numerical examples are illustrated to demonstrate the efficiency of the proposed methodology. The results show that the proposed neural network model outperforms the existing standard models in terms of prediction accuracy. Furthermore, it is shown that by using the proposed methodology, the optimal seismic total cost of steel frames increases by less than 2.5%, yet their seismic collapse capacity increases by at least 30%.

A novel heating strategy and its optimization of solar–air source heat pump heating system for rural buildings in northwest China

In the rural areas of Northwest China, the utilization of clean and renewable energy is deemed a crucial measure for reducing building energy consumption and environmental pollutant emissions. This paper focuses on constructing a simulation platform for a solar-assisted air source heat pump heating system. A rural residential building in Yongshou County, Shaanxi Province, serves as an illustrative example. A novel flexible temperature control method with a feedback controller in sub-area and period is proposed in this paper, alongside the selection of three distinct objective functions aimed at optimizing the heating system. The simulation results indicate an average temperature of 17.0 °C throughout the heating cycle, with a peak temperature of 18.7 °C. Moreover, the solar fraction is measured at 25.11%, underscoring the significance of collector area and heat storage tank volume as primary factors in system design. The results also demonstrate that across various optimization objectives, the life cycle cost optimization scheme yields greater economic benefits, while the target building unit heating cost optimization scheme boasts the shortest static payback period and lowest unit heating cost. Conversely, the solar fraction optimization scheme stands out for its superior environmental benefits. These findings offer valuable insights for the design of heating systems tailored to diverse objectives.

Exploring the characteristics of non-urban coworking spaces in Germany and their perceived benefits for corporate users: novel means for supporting corporate real estate management strategies

PurposeThis study aims to enrich our understanding of the characteristics of non-urban coworking spaces (CSs) that focus on corporate users, as well as the benefits that companies expect to gain from incorporating those CSs into their corporate real estate (CRE) portfolios.Design/methodology/approachThis study leverages a series of in-depth interviews with owners and managers of CSs in non-urban locales that focus on serving corporate clients.FindingsThe research reveals various CS characteristics and forms within non-urban areas, focusing on corporate clients. It suggests that implementing a CS in corporate premises is perceived to enhance CRE use-value strategies with a focus on the employee's well-being, innovation and the attraction of talents. Moreover, exchange-value strategies with a focus on portfolio flexibility may benefit from the implementation of a CS. However, strategies related to life-cycle cost optimization or gains are not perceived to be supported. Social events for the surrounding neighborhood and the choice of location emerge as critical success factors for non-urban CSs. Besides infrastructure and connectivity, non-urban corporate-centric CSs built their location decisions rather on a personal connection to the location and place of residence of potential users than on lower rental prices.Originality/valueThis research pioneers in providing a comprehensive understanding of non-urban CSs, particularly in the context of their perceived implications for corporate real estate management. The nuanced perspectives it offers are invaluable for stakeholders looking to leverage CSs as part of their corporate strategies.

Reliability Analysis and Life Cycle Cost Optimization of Hydraulic Excavator

This study focuses on conducting a reliability analysis of an excavator from its field failure data and improve its reliability cost effectively. The aim of the research is to perform reliability estimation of systems and identify the critical subsystem with significant contributions to system unreliability. The reliability analysis was performed using repairable system data analysis approaches. Life cycle cost (LCC) was estimated for critical subsystems, and it was optimized to select cost effective reliability improvement strategy. The results of the study provide valuable insights into the performance and cost-effectiveness of the excavator and its subsystems, which can assist manufacturers and operators in optimizing their equipment’s reliability, availability while considering the cost implications over the life cycle of the equipment. The results show that the undercarriage has critical contributions to system unreliability. This study attempts deep down reliability analysis of critical undercarriage components for optimal selection of improvement method among feasible alternatives. LCC analysis and its optimization performed on the critical sub-system is expected to help OEM save approx. 15% of LCC. The empirical data used in the paper is based on field data gathered during the operational life of the hydraulic excavator over approx. six years in its Indian operations.

Guidance on subsidy strategies for optimum life cycle cost in government-owned and -operated assembly occupancy buildings in Kuwait

The challenging problem of energy subsidies and tariff policy is compounded in regions with hot climates, heavily subsidized energy, and lax energy regulations. Across the MENA region, mosques are the quintessential specimen of government-owned and -operated buildings. Their distinct intermittent operation presents an additional challenge. The objective is to determine configurations that result in optimum energy-savings and life-cycle cost, and propose subsidy strategies that maximize the former and minimize the latter, under a range of realistic tariffs. Three models for system initial costs (linear, exponential, and empirical) were explored. A hybrid workflow across EnergyPlus simulation engine and MATLAB subroutines were utilized to investigate interactions between all energy-related parameters for brute-force life-cycle cost optimization. Forty possible tariffs were considered for 6000 envelope-system combinations, with 11 different system cost increase scenario, totaling 2.64 million cases. When low tariffs were considered, the optimum structures that gave the highest energy savings were marked by low systems COPs. Meanwhile, high tariffs with high systems COPs resulted in twice the savings. Savings could be extended to both manufacturers and consumers, especially in the case of government-owned and operated buildings. These findings could carry a potential for broader insight into other building sectors, typologies and usages.

Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management.

A Systematic Review for Switchgear Asset Management in Power Grids: Condition Monitoring, Health Assessment, and Maintenance Strategy

Asset management process and techniques can improve the cost-efficiency, balance the cost and risk, and prolong the service life of the aging switchgears in power grids, which have become increasingly important for electric utilities. This paper provides an insight into the recent developments in switchgear asset management and explore future research stream. Accordingly, three directions: condition monitoring, health assessment, and maintenance strategy have been addressed. We first present switchgear condition monitoring indicators and condition evaluation methods utilizing these condition data. Subsequently, we explain health index methodologies to achieve switchgear health assessment and remaining useful life estimation. With these results, we then present the maintenance strategy and life cycle cost optimization methods to address cost-efficient decision making. Finally, the limitation of existing methods and future research trend are discussed. This paper covers various asset management domains, attempting to create a common understanding and fill the gaps among the academic, industry, and technology area.

Optimal sizing of supercapacitors for cost-effective hybridization of battery-alone energy storage systems

Battery-supercapacitor (SC) hybrid energy storage systems (HESS) are today known as an effective means to extend the service life of batteries that are prone to early failures, mainly caused by current-related stress. While this holds true in most cases, the overall economic costs and benefits of this architecture are either overlooked or incomplete in previous research works. This paper introduces a life cycle cost optimization model for cost-effective upgrade of battery-alone energy storage systems (BESS) into battery-SC HESS. The case study in this paper shows that the presence of SC can result in up to 1.95% reduction in LCC over the remaining five years of the plant’s lifespan. As side benefits, 5.2% decrease in energy consumption and CO2 equivalent emissions associated to the manufacturing and operation of the HESS components. The proposed model can therefore inform about the relevance of a BESS retrofit in both brownfield and greenfield projects.

Energy-efficient Optimization of Life-cycle Costs Based on BIM

This article deals with a methodology for the economic development of energy-efficient buildings from an early planning or development phase based on building information modelling (BIM). In this context, both geometrically and energetically relevant parameters of a building are derived from a digital building model, already in the early phase of a project. The subsequent definition of building components for the building envelope and the performance of an energy demand calculation provides the basis for the selection of reference buildings suitable for the respective application. This enables the determination of practical costs, which include both annuity costs and total costs arising in the life cycle of the building for the cost groups of the building structures and the technical building equipment. By taking a holistic view of all costs and focusing specifically on energy efficiency, the methodology presented in this article can be used to identify both ecological and economic advantages for planning in the early stages of a project. By incorporating energy efficiency and economic efficiency, a sustainable and successful project can be achieved.

4E analysis and multi-objective optimization of compression/ejection transcritical CO2 heat pump with latent thermal heat storage

To promote the goal of peak carbon dioxide emissions and carbon neutrality, low-energy consumption buildings require innovative technologies and efficient energy management. In this paper, the multi-objective optimization and the energy, exergy, economic and environmental (4E) analyses of the compression/ejection transcritical CO2 heat pump with latent thermal storage (TPE-LTES) system are conducted, and the influences of crucial parameters on the system performance are evaluated. The results show that the optimal comprehensive COP and annual hot water production (mDHW) of the TPE-LTES system are 3.59 and 3110 tons, respectively. Then, the comprehensive exergy efficiency of the TPE-LTES system owns 48.93 % higher than that of the heat pump unit. Furthermore, the optimal life-cycle cost (LCC) and life-cycle CO2 emissions (LCCE) of the TPE-LTES system are 708,073 CNY and 575,512.79 kg, respectively. Moreover, with constant discharge pressure, the evaporative superheat can be preferentially increased to reduce the system LCC and LCCE. Finally, the comprehensive COP and exergy efficiency of the system can be improved by increasing the inlet water temperature, while the increasing inlet water flow rate is the least desirable. This work is helpful to promote the research and application of latent heat storage unit integrated into air source heat pump.

Design optimization integrating energy, economic, and environmental evaluation of a hybrid solar heating system for detached buildings in rural China

This study investigated the techno-economic-environmental feasibility of solar heating systems for supplying power to detached buildings in the rural context. A hybrid solar–electromagnetic heating technology and a time-of-use pricing-based energy management technique were proposed. The developed model was validated through experiments on solar collectors and the phase-change storage tank based on the proposed time-of-use strategy. A comprehensive techno-economic-environmental single-criterion optimization scheme was constructed through coupled modeling and optimizations with TRNSYS and GENOPT. Furthermore, the comprehensive evaluation model was used to conduct a multiattribute assessment based on 13 specific sub-indicators of the studied cases. The post-operation costs and life cycle cost of the existing system under time-of-use pricing reduced by 12.2% and 7.16%, respectively. The case with optimal configuration, that is, life cycle cost optimization goal, exhibited levelized cost of energy and solar fraction of 0.82 RMB/kW·h and 64%, respectively. In severe cold regions of China with general abundant solar resource, cases utilizing a high solar energy proportion exceeding 64% perform better. This comprehensive evaluation of different scenarios provides valuable references to stakeholders for advancing renewable energy utilization in sustainable, low-carbon heating systems in rural areas of China.

Considering embodied CO2 emissions and carbon compensation cost in life cycle cost optimization of carbon-neutral building energy systems

Renewable energy (RE) technologies are regarded as a solution to the climate impact of buildings, but some of these technologies contain embodied CO2 emissions, which have been ignored in most studies. Previous studies found carbon compensation to be necessary for reaching carbon neutrality as even the least carbon-intensive energy production technologies contain emissions related to manufacturing. The novelty of this study was to combine emission and cost calculations into a single method by including the compensation cost for the carbon footprint in the total life cycle cost (LCC) of carbon-neutral building energy systems. Embodied emissions were included in the carbon footprint of the building energy system in addition to operational emissions. The implemented RE technologies were dimensioned in an optimization loop by minimizing the total LCC of the energy system. The method was tested in a case study. The embodied emissions of the studied RE technology options for the case building accounted for up to 48% of the total carbon footprint, and carbon compensation accounted for 0.3–1.9% of the total LCC. The method was proven to be applicable to the cost-optimal dimensioning of carbon-neutral energy system options and is recommended for use in further studies regarding carbon-neutral building energy systems.

Multihazard life-cycle cost optimization of active mass dampers in tall buildings

This article proposes an optimization-based-methodology for designing multiple active mass dampers (AMDs) in tall buildings in multihazard environments. A realistic cost of the AMD system is formulated as the objective-function, and the multihazard Life-Cycle-Cost (LCC) that represents the long-term hazard-related losses, is selected as the main performance-constraint. This allows joining winds and earthquakes in a single decision parameter. Additional constraints limit the dampers forces and strokes. To ensure stability of the actively controlled building, the LQR algorithm is nested within the formal optimization problem. In addition, for the first time in the context of AMDs, their masses are considered as design variables, emerging directly as an optimization output. As the LQR control law is not aimed at optimizing costs and LCC, the lower triangular matrices related to the LQR weighting matrices are used as additional design variables. Thus, their values converge throughout the optimization to optimize the formulated cost function while satisfying the constraints. As this strategy produces large-scale problems, an efficient gradient-based algorithm is developed, using the adjoint sensitivity analysis method. The framework is applied to several examples for a benchmark-building, providing consistent results. One example demonstrates that AMDs can offer similar performance to its passive TMDs counterpart, requiring a mass 40% lower.

Lifecycle Cost Optimization for Electric Bus Systems With Different Charging Methods: Collaborative Optimization of Infrastructure Procurement and Fleet Scheduling

Battery electric buses (BEBs) have been regarded as effective options for sustainable mobility while their promotion is highly affected by the total cost associated with their entire life cycle from the perspective of urban transit agencies. In this research, we develop a collaborative optimization model for the lifecycle cost of BEB system, considering both overnight and opportunity charging methods. This model aims to jointly optimize the initial capital cost and use-phase operating cost by synchronously planning the infrastructure procurement and fleet scheduling. In particular, several practical factors, such as charging pattern effect, battery downsizing benefits, and time-of-use dynamic electricity price, are considered to improve the applicability of the model. A hybrid heuristic based on the tabu search and immune genetic algorithm is customized to effectively solve the model that is reformulated as the bi-level optimization problem. A numerical case study is presented to demonstrate the model and solution method. The results indicate that the proposed optimization model can help to reduce the lifecycle cost by 7.77% and 6.64% for overnight and opportunity charging systems, respectively, compared to the conventional management strategy. Additionally, a series of simulations for sensitivity analysis are conducted to further evaluate the key parameters and compare their respective life cycle performance. The policy implications for BEB promotion are also discussed.

Machine learning for performance prediction in smart buildings: Photovoltaic self-consumption and life cycle cost optimization

The application of Photovoltaic (PV) system in buildings is growing rapidly in response to the need for clean energy sources and building decarbonization targets. Nonetheless, enhancing PV self-consumption through technical solutions such as Energy Storage Systems (ESS) is getting higher importance to increase the profitability of PV plants, by minimizing the building-grid interaction. In this context, analyzing PV self-consumption of different energy storage configurations becomes more relevant and crucial in building energy modeling although it is heavily time-consuming and complicated, particularly within a multi-objective optimization related to the ESS design. As a solution to resolve this issue, this paper evaluates the accuracy, training, and prediction speed of 24 Machine Learning (ML) models to be used as surrogate models for analyzing PV self-consumption in smart buildings. Furthermore, the performance of short-term Thermal Energy Storage (TES) to increase PV self-consumption is assessed and presented using ML models. The results showed the Gaussian Process Regression (GPR), Neural Networks (NN) including bilayered and trilayered NN models, Support Vector Machines (SVM) including the fine gaussian and cubic SVM models, and Ensembles of Trees (EoT) as superior ML models. The results also revealed that TES systems can efficiently increase PV self-consumption in the building equipped with electric heat pumps to provide heating, cooling, and domestic hot water. Moreover, the TES size optimization regarding the Life Cycle Cost (LCC) showed that the LCC-based optimum TES size can yield 7.1% savings within 30 years of the building service life. The novelties of this research are first to provide a reference to select the most suitable ML models in predicting PV self-consumption, second to implement machine learning for analyzing the performance of short-term thermal energy storage to enhance PV self-consumption in buildings, and third to carry out an LCC-based optimization on TES size using ML-based prediction models.