Optimal Supply Research Articles

Optimizing solar evaporation efficiency: integrating controllable water supply and efficient salt collection methods

Solar desalination presents a promising solution for providing high-purity fresh water in regions lacking sophisticated energy infrastructure. Specifically, the efficiency of solar-energy-driven interfacial evaporation relies on achieving a balance between supplied solar energy and the energy demand for evaporation through water supply management. Another significant challenge for practical application is salt crystallization on the photoabsorber surface. To address these issues, two strategies are proposed: I) Construction of a three-dimensional (3D) solar evaporator that achieves a balance between supplied energy and the heat energy required for interfacial evaporation (demand energy) by optimizing the height of the support structure and II) implementation of a tunable two-dimensional water supply path made of cotton. This not only facilitates optimal water supply for interfacial evaporation but also functions as a salt collection layer, enabling efficient salt collection post desalination. With the specific design of the proposed 3D solar evaporator, our device achieves a remarkable efficiency, approaching its performance limit (94%) with an evaporation rate of 2.30 kg/m/2/h, along with efficient salt collection and scalability. This work presents a novel approach for fabricating a cost-effective solar evaporator capable of consistently delivering affordable fresh water in remote areas.

Optimal Supply Calculation of Electric Vehicle Slow Chargers Considering Charging Demand Based on Driving Distance

Optimal Supply Calculation of Electric Vehicle Slow Chargers Considering Charging Demand Based on Driving Distance

Water, nitrogen, and phosphorus coupling improves gray jujube fruit quality and yield.

Irrigation and fertilization are indispensable links in the jujube planting industry in southern Xinjiang, China. Regulating the relationship between fertilization and irrigation can effectively reduce costs and improve economic efficiency. A 2-year water and fertilizer optimization coupling test was conducted to determine the optimal water and nutrient supply scheme. The three-factor randomized block experiment included water (W), nitrogen (N), and phosphorus (P). According to the principal component analysis of each index, each treatment's comprehensive score was obtained. Using yield and economic regression models, the theoretical value and yield value of the optimal economic benefit are inferred. When W, N, and P were applied together, the fruit quality and yield of each treatment significantly differed, and the vitamin C, soluble sugar, and sugar-acid ratio increased significantly with an increase in N fertilizer. However, the titratable acid decreased. An increase in irrigation and nitrogen application significantly increased fruit yield. The comprehensive score was the highest in the N4P3W2 treatment, which improved fruit quality, and the lowest in the N3P3W2 treatment. When the amounts of N, P, and W were 275.56 kg hm-2, 413. 66 kg hm-2, and 7278.19 m3 hm-2, respectively, the theoretical economic benefit was the best. The N4P3W2 treatment is the optimal treatment.

Hospital care direct costs due to ambulatory care sensitive conditions related to diabetes mellitus in the Mexican public healthcare system

BackgroundHospitalizations for ambulatory care sensitive conditions (ACSC) incur substantial costs on the health system that could be partially avoided with adequate outpatient care. Complications of chronic diseases, such as diabetes mellitus (DM), are considered ACSC. Previous studies have shown that hospitalizations due to diabetes have a significant financial burden. In Mexico, DM is a major health concern and a leading cause of death, but there is limited evidence available. This study aimed to estimate the direct costs of hospitalizations by DM-related ACSC in the Mexican public health system.MethodsWe selected three hospitals from each of Mexico’s main public institutions: the Mexican Social Security Institute (IMSS), the Ministry of Health (MoH), and the Institute of Social Security and Services for State Workers (ISSSTE). We employed a bottom-up microcosting approach from the healthcare provider perspective to estimate the total direct costs of hospitalizations for DM-related ACSC. Input data regarding length of stay (LoS), consultations, medications, colloid/crystalloid solutions, procedures, and laboratory/medical imaging studies were obtained from clinical records of a random sample of 532 hospitalizations out of a total of 1,803 DM-related ACSC (ICD-10 codes) discharges during 2016.ResultsThe average cost per DM-related ACSC hospitalization varies among institutions, ranging from $1,427 in the MoH to $1,677 in the IMSS and $1,754 in the ISSSTE. The three institutions’ largest expenses are LoS and procedures. Peripheral circulatory and renal complications were the major drivers of hospitalization costs for patients with DM-related ACSC. Direct costs due to hospitalizations for DM-related ACSC in these three institutions represent 1% of the gross domestic product (GDP) dedicated to health and social services and 2% of total hospital care expenses.ConclusionsThe direct costs of hospitalizations for DM-related ACSC vary considerably across institutions. Disparities in such costs for the same ACSC among different institutions suggest potential disparities in care quality across primary and hospital settings (processes and resource utilization), which should be further investigated to ensure optimal supply utilization. Prioritizing preventive measures for peripheral circulatory and renal complications in DM patients could be highly beneficial.

Automatic detection of tomato leaf disease using an adopted deep learning algorithm

The detection of tomato leaf diseases is crucial for agricultural sustainability, impacting crop health, yield optimization, and global food supply. Despite the advancements in deep learning methods, a pressing challenge persists— achieving consistently high accuracy rates, particularly in the context of rigorous agricultural requirements. This study addresses this problem directly, introducing a novel approach by employing the Yolov8 architecture in a deep learning model for tomato leaf disease detection. The identified research challenge is precisely targeted, and the model is developed using a meticulously curated custom dataset. Through comprehensive training, validation, and testing phases, the study ensures the robust performance of the Yolov8 model. The novelty of this research lies in its focused solution to the specific accuracy challenge within deep learning-based tomato leaf disease detection. The proposed methodology is rigorously evaluated through extensive experimentation, showcasing its ability to surpass existing benchmarks and offering a highly effective solution. This innovative approach not only contributes a unique solution to the identified problem but also advances the field by providing a more accurate and reliable method for detecting tomato leaf diseases.

Multistage scenario-based planning for locating of pharmaceutic distribution centers for emergency patients by considering practical constraints under budget uncertainty conditions (case study: Kerman city in Iran)

In the realm of healthcare, medicine and the pharmaceutical supply chain exert notable influence. Recognized as a strategic commodity, any disruption in its supply chain can precipitate significant crises. The dispersed nature of pharmacies and the critical need for prompt medication delivery to patients underscore the paramount importance of designing an optimal pharmaceutical supply chain. This necessity is particularly pronounced when addressing the needs of diabetic and epilepsy patients, who, being more susceptible and in urgent need of medication, intensify the significance of a robust supply chain. Given the critical role of medicine and the pharmaceutical supply chain in healthcare, the repercussions of supply chain disruptions are far-reaching. A well-designed pharmaceutic supply chain is indispensable, especially in ensuring timely access to medications for vulnerable patient groups. To address this imperative, this study proposes a multi-stage scenario-based mathematical model aimed at enhancing the responsiveness of demand regions amidst uncertain budgets and feasible scenarios for meeting the insulin and phenobarbital requirements of patients. By incorporating uncertainties in both demand and procurement costs, the practicability of applying this model in real-world contexts has been strengthened. A case study focusing on Kerman City in Iran is conducted to evaluate the efficacy of the proposed model.

Economic and environmental benefits of e-waste management networks design in Yogyakarta province, Indonesia

Purpose: Electronic devices consumption, especially smartphones for communication, have increased significantly, causing the potential for electronic waste to increase too. However, the high potential of this flow has not been matched by good waste management. This study aims to develop a management network for e-waste that collaborates formal and informal channels to provide optimal supply chain benefits in terms of economic and environmental aspectsDesign/methodology/approach: This research designs an e-waste management network followed by building a single-objective mathematical model that considers economic and environmental aspects. This model was solved using Mixed Integer Linear Programming.Findings: The supply chain will benefit from the proposed management network by collaborating formal and informal channels. With an incentive of 10,000 IDR, it is hoped that it will be able to invite consumers and informal collectors to collect waste informal channels. The total profit from managing electronic waste supply every month from Yogyakarta Province is 5.529 x 1010 IDR with the composition, consumers 81.2%, informal 13.3% channel obtained from informal collectors and repairing centers and 2.1% for formal channels, and there is an intangible profit of 3.4%. The formal channel provides more significant benefits than the informal, but requires a significant investment. This formal channel feasible if at least 33% of the total supply goes to this channel.Research limitations/implications: The model designed is deterministic. Therefore, it can be developed into a probabilistic model for further research to represent more real cases in the field.Practical implications: Regulation is the factor that most influences consumers' intentions and behavior to participate in e-waste management programs. It will be able to change consumer behavior by forcing consumers to participate in e-waste collection programs. Therefore, a government regulation is needed that organizes and supervises the implementation of the proposed management model.Social implications: Currently, the practice developing in the community is that the informal sector carries out smartphone waste management, therefore in the designs made, informal actors are still given space in waste management for the repair process and the secondhand market. Meanwhile, further processing, such as recycling, must be carried out through formal channels taking into account the environmental impact.Originality/value: Few studies have developed an electronic waste management model by collaborating informal and formal channels that consider economic and environmental aspects, and its implementation is organized and supervised by government regulations.

Manage morning commute for household travels with parking space constraints

Parking has always been a thorny problem in urban transport systems. Parking availability and parking reservation management may have a significant influence on the travel behavior of household travelers. This paper explores the morning commute for household travels by considering parking space constraint and the parking reservation mechanism in a two-mode transport system. Household travelers depart from home to their children’s school by car or by rail and then go to work alone. For household travelers who drive, some household travelers have reserved parking spaces while other household travelers must compete for public ones on a first-come-first-served (FCFS) basis. When household travelers do not have a parking spot, they use rail transit for their commute. We derive ten equilibrium departure patterns based on the school-work time difference, the number of parking spots and the ratio of reserved parking spots. With these equilibrium departure patterns, we thoroughly discuss the impact of school-work time difference on the total travel cost of auto travelers and parking reservation ratio. It is found that, given the school-work time difference, appropriate parking reservation allocation can minimize the total system travel cost. Then, the system performance, including total system travel cost and total congestion cost, is examined by regulating the parking supply, the parking reservation allocation and the school-work time difference. We found that there is a unique optimal parking supply to minimize the total system travel cost, and appropriate staggering policy can alleviate traffic congestion and thus improves social welfare. Finally, numerical analysis is conducted to verify the findings, and the Pareto frontier for system performance is analyzed. Traffic authorities can select the optimal solution based on the preferences of traffic indicators to develop effective strategies to manage morning commuting of household travels with parking space constraint. The study demonstrates that implementing reasonable staggering policy and efficient parking management can effectively promote efficiency of urban morning commuting systems and reduce social costs for household travelers.

Choosing Optimal Supply Radius of Transformer Substations (TSs) in Iraq’s Cities Using Geometric Programming with Neutrosophic Coefficients

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, then y is very likely to occur," "x should not be less than a," etc. To design an optimal radius of power supply in the electrical transformer substation, this article presents a new method for creating primal and dual neutrosophic geometric programming problems. It also provides a numerical example to evaluate the approximate optimal economic power supply radius.

A socio-economic and quality-oriented optimal fruit supply chain network design in a multi-market and multi-product environment: A real case study

This paper focuses on developing an optimal sustainable fruit Closed-Loop Supply Chain (CLSC). A novel multi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate a multi-product, multi-market, multi-period, multi-level, multi-mode selling problem which aims to minimize cost and emissions and maximize the whole social segments’ responsiveness to fruit, vinegar, and compost. The model is solved by the LP-metric method and implemented in a real case in Iran. The findings indicate that achieving the highest level of responsiveness comes with a substantial cost for the network. However, this does not apply to environmental considerations. For decision-makers prioritizing sustainability, the proposed decision support system strongly advises against buying fruit from fruit cars. Due to the absence of positive impacts on the level of responsiveness, buying from fruit cars results in increased costs and emissions. For a cost- and environmentally-sensitive decision-maker, it is recommended to provide higher-quality fruits. Because the lower the quality of the fruit, leads to increased waste rates and emissions. Meeting increased vinegar demand, with its elevated costs and emissions, becomes justifiable only when the policymaker is actively seeking to achieve maximum responsiveness.

Вопросы комплексного развития молочно-мясного скотоводства Республики Коми

The study’s relevance stems from the increase in demand for livestock products, particularly meat, juxtaposed with the growing challenges in their production on an intensive scale. These challenges include environmental degradation, climate change, and an upswing in diseases linked to imbalanced nutrition. Also, there are ongoing discussions revolving around the merit of beef and milk with normalized fat content. This study aimed to analyze the current state of dairy and beef cattle breeding in the North and formulate growth strategies aligned with the principles of sustainable development. Innovative approaches to cattle breeding have been explored, focusing on enhancing environmental, social, and economic efficiency in the industry. The "less and better" concept, centered on producing livestock products that cause minimal environmental harm, improve animal welfare, and benefit human health, has been thoroughly examined. The study also discusses the challenges confronting cattle breeding in the Komi Republic. A shift towards intensive farming has been found to boost dairy cattle productivity while also resulting in increased cattle losses and a decline in fertility and overall health. Unresolved issues related to creating an optimal feed supply system and the uniform adoption of concentrate, hay, and silage feed mixtures also contribute to industry instability. Moreover, it has been found that concerns surrounding Holsteinization and the preservation of the Kholmogorsky breed of dairy cattle are particularly acute in the region. The study’s scientific novelty lies in introducing an integrated approach to cattle breeding in the Komi Republic, aiming to improve the quality of livestock products, enhance industry profitability, and boost farmland fertility. The practical significance of the study lies in proposing measures such as establishing cultivated pastures, reintroducing cattle grazing to improve the feed supply system, and safeguarding the Kholmogorsky breed. These proposals are valuable for governing bodies involved in shaping regional strategies and programs for agricultural production development.

Energy Harvest of Multiple Smart Sensors With Real-Time Fault-Detection

For multiple smart sensors with limited energy supply, the relationship between the energy supply and sensor fault-free region is often unknown, and neither of the interior structure nor exterior circumstance modeling the smart sensors is easy to accurately achieve, so how to guarantee the fault-free smart sensors to harvest the most energy in the fault-free way is a very challenging topic. To address this issue, this paper first formulates the individual smart sensor as a single-input single-output (SISO) model-free system (MFS), with its energy supply and sensing error as the input and output, respectively, then makes use of Lyapunov function to deduce an upper bound of the fault-free region to realize the fault-detection of any smart sensor and disclose the relationship between the energy supply and fault-free region, and finally achieves the optimal energy supply guiding the overall energy harvest of all fault-free smart sensors to converge to the maximum with the convergence rate no larger than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa$</tex-math> </inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa\in[0,1]$</tex-math> </inline-formula> , while enjoying the real-time fault-detection. An algorithm based on the sound theoretical foundations is further proposed to implement the optimal energy supply. Theoretical analysis, simulations and field experiments jointly verify the performance of our method. To our best knowledge, it is the initial work towards this issue. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper addresses the interesting issue of how to guarantee multiple smart sensors to harvest the most energy in the free-fault way. Through the insightful disclosure of relationship between the energy supply and sensor fault-free region, and the optimal control of energy supply, this paper facilitates the overall energy harvest of all fault-free smart sensors that work under the environments, where the available energy is limited, to converge to the maximum while enjoying the real-time fault-detection, which we believe could push the development of Internet of Things (IoT) or Cyber-Physical System (CPS) that employs multiple smart sensors to sense the physical world. Simulations and field experimental investigations jointly show that the proposed solution outperforms the existing solutions.

Artificial Intelligence (AI)-driven Transformation: Sustainable Development of Agro-based Industries in Bihar

Digital technologies particularly Artificial Intelligence (AI), are needed to transform agricultural sector in Bihar. There is a vast opportunity in adopting AI-driven solutions to enhance productivity and sustainability as well as increase the profitability of agriculture in Bihar. The current status and potential applications of AI in agriculture will be reviewed, challenges and opportunities specific to regions identified, together with development of strategies for maximum gain from AI technologies. We used the bibliographic methods and SWOT analysis. The growth of Bihar’s GDP as the adoption of Internet of Thing (IoT), precision farming and AI is projected at 5-6% per year that may create up to 30% more job opportunities in agricultural sector by 2024.Precision agriculture combined with crop yield forecasting is among key applications of AI in agro industries, labor optimization and efficiency; soil monitoring and management; disease detection and pest management; weather forecasting for climate risk management purposes among others include weed identification herbicide optimization supply chain optimization. SWOT analysis reveals strength such as enhanced productivity, cost reduction, improved decision-making ability on farm operations and climate resilience improvement strategy while helps farmers accessing information at their fingertips. However, weaknesses include infrastructure limitations, skill gaps and data accessibility issues. Opportunities arise from government support, research and development initiatives and market expansion. Threats encompass the digital divide, privacy and data security concerns, and dependence on external factors. The integration of AI holds substantial relevance for Bihar offering a pathway to overcome existing agricultural challenges and unlock new growth opportunities.

Optimal production decision and supply chain coordination under different capital conditions

Optimal production decision and supply chain coordination under different capital conditions

Collaboration of Vague Theory and Mathematical Techniques for Optimizing Healthcare by Recommending Optimal Blockchain Supplier

The Internet of Medical Things (IoMTs) has the potential to revolutionize healthcare delivery by connecting medical devices and applications to healthcare IT systems via the internet. This connectivity enables the collection, transmission, and analysis of patient data, facilitating remote healthcare delivery and enhancing patient outcomes. However, the security and privacy of the data transmitted and stored within IoMT systems remain critical concerns. Blockchain technology offers a promising solution to address these challenges in IoMTs applications. Blockchain provides a decentralized and immutable ledger where transactions, in this case, patient data, are securely recorded and cannot be altered retroactively. This ensures the integrity and confidentiality of sensitive medical information while eliminating the need for a central authority to manage data exchange. This study proposes a comprehensive fuzzy decision-making framework for selecting the best blockchain provider in the IoMT field. The framework comprises two main steps: Determining the relative importance weights of the criteria utilizing the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Determining the most suitable supplier for blockchain services providers employing the Ranking Alternatives with the Fuzzy ROOT Assessment Method (RAM) technique. A comparison between Fuzzy Multi-Criteria Decision-Making (MCDM) methods is employed to ensure the robustness and validity of the proposed framework.

Automated component delivery management under uncertainty for prefabricated buildings to minimize cost and harmful emissions

Effectively coordinating and managing onsite construction, offsite prefabrication, and component delivery activities in an ever-changing environment is crucial for prefabricated or modular construction. This study develops an automated component delivery management system to identify the optimal supplier portfolio and delivery strategy, balancing the trade-offs among time, costs, and carbon emissions before construction. The system incorporates onsite activity rescheduling and delivery re-planning strategies to manage progress uncertainty, thereby minimizing crash and overstock costs. It automatically extracts project design data from building information model (BIM) digital files, schedule data from scheduling programs, and logistic data from the Web Map Service. The outcomes generated are then fed back into BIM platforms for enhanced visualization. An illustrative example showcases the system's effectiveness, demonstrating its ability to support lean construction practices while offering economic and environmental advantages to stakeholders.

Multi-scenario flexible contract coordination for determining the quantity of emergency medical suppliers in public health events.

Determining the optimal number of emergency medical suppliers for the government to contract with in the context of public health events poses a challenging problem. Having too many suppliers can result in increased costs, while having too few suppliers can potentially expose the government to supply risks. Striking the right balance between these two factors is crucial in ensuring efficient and reliable emergency response and management. This study examines the process of determining the appropriate number of suppliers in emergency medical supply chain. By incorporating option contracts and employing the total cost of government procurement as the objective function, the analysis focuses on the impact of relevant parameters on the optimal number of suppliers. Furthermore, the study investigates the optimal supplier quantities under different types of option contracts. The proposed decision model for determining the optimal number of suppliers in this paper considers three key factors: the supply risk associated with emergency medical supplies, the reserve cost of government procurement, and the responsiveness of emergency medical supplies. Additionally, a method is introduced for selecting the quantity of emergency medical suppliers based on flexible contracts. This approach offers a scientific foundation for the government to effectively address the challenge of supplier quantity selection when faced with risks related to shortages, expiration, and the combination of both.

Optimizing biomass supply for cofiring at power plants to minimize environmental impact: A case of oil palm empty fruit bunches in West Java

This study addresses the supply flow optimization to reduce global warming in a cofiring system, using the example of oil palm empty fruit bunches converted into pellets for use in power plants in Indonesia. Four oil palm mills, seven pellet plants and five coal power plants in the West Java Region were considered in this study to select the optimal supply chain. The study was performed using a life cycle assessment (LCA) for evaluating impact of single firing of coal and linear programming (LP) for optimizing the impact reduction of cofiring of coal and pellets. Our study indicates that the optimal environmental reduction can be achieved by constructing six of the seven pellet plants, which can substitute up to 1 % of the coal supply in five power plants. Accordingly, the emissions could be decreased by an average of 0.018 kg CO2eq/kWh for the power plants, which is equal to 0.2 Mt CO2eq/y or a 1.57 % reduction per year. By varying the emission factor of the pellet plants, our analysis showed a tolerable range in which the supply would not be affected. The sensitivity analysis at a 1 % cofiring rate showed that the increase in biomass supply from the oil palm mills had the highest reduction, whereas the reduction in transportation emissions provided much less impact. By adjusting the cofiring rate, the impact would not change after the 3 % cofiring rate because of the limited availability of Empty Fruit Bunch (EFB) feedstock. However, if sufficient supply could be provided, the environmental impact would be further reduced significantly. Furthermore, the scenario expansion indicated that only three pellet plants would be required for the minimum economic scale of production, although the environmental impact increased by 0.47 % compared to using six pellet plants. Therefore, this study can be used to determine the optimal supply chain scenarios by constructing an appropriate number of economically feasible pellet plants according to the biomass demand and cofiring rate of the power plant.

Comprehensive evaluation of the working mode of multi-energy complementary heating systems in rural areas based on the entropy-TOPSIS model

BackgroundThe establishment of a multi-energy complementary heating system (MECHS) using abundant renewable energy is currently an effective way to solve the rapid growth of heating demands in rural areas. However, coupling schemes for different energy sources have become a critical barrier to the MECHS development. ObjectivesThe entropy-TOPSIS method was used to conduct a comprehensive evaluation of MECHS, which provided a basis for optimizing system operation strategy. MethodsWe built a test platform for MECHS based on solar-biomass-electricity energy in Tumxuk, Northwest China. Frist, six working modes were established according to the system operation control strategy. Second, we constructed the evaluation indexes of MECHS considering energy, environment and economy and determined their weights using entropy weight method. Third, the TOPSIS model was used to perform a comprehensive evaluation of the different working modes to identify optimal energy supply schemes. ResultsOur case study showed that the MECHS operated stably and met the indoor temperature demands of rural houses in cold regions. To maximize the heating efficiency of the system, it is necessary to reduce the types of energy appropriately. Next, based on entropy-TOPSIS model, the recommended order of energy supply mode for heating systems were solar-biomass energy (0.6202), solar-biomass-electric energy (0.6152), solar-electric energy (0.5895), biomass energy (0.4075), biomass-electric energy (0.3680), and electrical energy (0.3603). We found that the optimal energy supply mode was solar-biomass. ConclusionOur results indicated that the entropy-TOPSIS model could provide practical recommendations for the design and operation strategies of MECHS in cold rural areas.

Enhancement of the Power-to-Heat Energy Conversion Process of a Thermal Energy Storage Cycle through the use of a Thermoelectric Heat Pump

The principal strategy for achieving a neutral climate entails enhancing the share of renewable energies in the energy mix, in conjunction with promoting innovation in efficient technologies. Thermal energy storage systems have the potential to efficiently handle the intermittent nature of renewable energy sources. Furthermore, these systems can effectively handle shifts in both heat and electrical demand. Thus, efficient power-to-heat technologies are needed to boost thermal energy storage. This manuscript explores the potential of utilising a thermoelectric heat pump system in conjunction with electric resistances for charging a thermal energy storage. In order to achieve elevated temperatures, the thermoelectric system integrates thermoelectric heat pump blocks in a two-stage configuration. Air has been employed as a heat transfer medium for sensible heat storage. Higher airflow rates improve the performance of thermoelectric heat pump system. Moreover, its impact on the optimal voltage supply of the thermoelectric system is observed when it is combined with an electric resistance to achieve elevated temperatures. In comparison to the basic charging process that solely relies on the electric resistance of a thermal energy storage at 120 °C, a significant 30 % increase in power-to-heat energy conversion has been achieved by including the thermoelectric heat pump system. In fact, it efficiently elevates the temperature from the initial ambient temperature of 25 °C to a remarkable 113.1 °C, achieving a coefficient of performance of 1.35 with an airflow rate of 23 m3/h. Therefore, the use of this technology to enhance a complete process of storing excess renewable energy in the form of heat for subsequent use in both heat and electricity through a combined heat and power cycle is demonstrated.