Total Energy Consumption Cost Research Articles

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Enhancing domestic hot water energy efficiency in High-Rise residential buildings through interconnected and sharing mode

Residential buildings in China are consuming over 20 % of their operating energy on domestic hot water (DHW), a percentage that has been increasing in recent years. The use of energy-efficient water heaters is a direct solution to reduce DHW energy consumption, but high costs and installation requirements often deter households from adopting them. This issue is particularly prevalent in high-rise residential buildings, resulting in a low adoption rate of energy-efficient water heaters in China's high-rise residential sector. To address this challenge, this study proposes a DHW interconnected and sharing mode as a novel approach to enhance DHW energy efficiency in high-rise residential buildings. A dynamic performance calculation model based on the China Energy Efficiency Label (CEEL) for various types of water heaters was developed to evaluate the energy efficiency of this mode. The total energy consumption, carbon emissions, and energy costs of the building were assessed under both decentralized mode and interconnected and sharing mode. The findings indicate that implementing the interconnected and sharing mode can significantly decrease the annual total energy consumption, carbon emissions, and energy costs of DHW. Among the two operational strategies examined (economic optimal and environmental optimal), the economic optimal strategy is more suitable for China's current energy landscape. Under the economic optimal strategy, the annual total energy consumption, carbon emissions, and energy costs of DHW in the target building are reduced by 41.58 %, 27.97 %, and 34.87%, respectively.

Performance Evaluation of On-farm Raw Milk Cooling System

This experiment was conducted with an aim to evaluate novel on-farm raw milk cooling system (OFMC). The system consisted of plate cooler, cooling cum storage tank and thermal storage tank. Conventional bulk milk cooling system was considered as control (CMC). The raw milk cooling systems were evaluated for overall heat transfer co-efficient, rate of cooling, coefficient of performance, total energy consumption and total cost of raw milk cooling. Raw milk cooling in OFMC was completed within 30 min whereas CMC took more than 2h to cool raw milk to 4°C. For OFMC, the U value of plate cooler when milk cooled to 4°C was 872.03 W/m2 °C. The average overall heat transfer coefficient of cooling cum storage tank for milk cooling in the evening and morning were 359.0 W/(m2 °C) and 350.2 W/(m2 °C) respectively. The average U value for thermal storage was 121.65 W/m2°C. The COP of the OFMC ranged from 3.97 to 2.36 during day time ice formation and 4.34 to 2.47 during night time ice formation. The average COP of the system was higher during night time compared to day time. When cost of electricity considered at ₹ 9.7 per kWh, cooling cost per litre of milk for OFMC and CMC were ₹ 0.2939 and ₹ 0.3589, respectively. The energy consumed in CMC was higher than the OFMC. Such on-farm raw milk cooling system can be a better option than conventional bulk milk coolers.

Research on energy-saving control of agricultural hybrid tractors integrating working condition prediction.

To address the issues of tractors using too much fuel and not being energy efficient, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed for agricultural hybrid tractors. The Dongfanghong 1804 tractor is being used for research. Firstly, the main parameters of the hybrid drive system are determined and modeled. Secondly, based on the adaptive cubic exponential forecasting method, the working condition information for a period of time in the future is predicted through historical working condition information. Furthermore, combining the predicted working conditions information, the goal is to minimize the total energy consumption cost of the entire machine. Motor power and diesel engine power are control variables. The battery state of charge is a state variable. Subsequently, a predictive control strategy based on Pontryagin's minimum principle integrating working condition prediction is proposed. Finally, the simulation test is carried out based on the MATLAB simulation platform. Research indicates: under plowing conditions, compared with the power following control strategy, the proposed predictive control strategy can effectively manage the performance of the diesel engine and motor, ensuring they operate at their most efficient level. The total energy consumption costs of the power following control and predictive control strategies are 37.17 China Yuan (CNY) and 33.67 CNY, respectively. The cost of energy used is decreased by 9. 42%, which helps make tractor field plowing more efficient and economical.

A MILP model and heuristic method for the time-dependent electric vehicle routing and scheduling problem with time windows

As an emerging and sustainable technology, electric vehicles (EVs) are becoming increasingly popular in the transportation system. However, they still have limitations in terms of energy capacity and high consumption. By implementing flexible scheduling for EV routings, it is hoped that improved efficiency and saved energy consumption can be achieved. This paper focuses on studying a time-dependent electric vehicle routing and scheduling problem with time windows (TDEVRSPTW). The goal is to minimize the total cost of energy consumption, travel distance, and the number of EVs, while considering vehicle travel process scheduling that allows stops at any nodes and along any arcs during periods of time-dependent congestion. For the first time, a mixed integer linear programming model is formulated for the problem, allowing for optimal solutions to small-scale problems using CPLEX. Meanwhile, a variable neighborhood search with partial model (VNS-PM) method is developed to handle the large-scale problem with 200 customers and obtain effective solutions. The numerical experiments demonstrate significant savings in energy consumption by the vehicle travel process scheduling. The proposed method further validates its strong performance by finding 11 new best solutions out of the 56 related EVRPTW benchmark instances. In addition, a case study is conducted to verify the application and energy consumption savings of the proposed problem, which also derives some additional recommendations by sensitivity analysis.

Control strategies and performance analysis for a novel nuclear heating system with heat storage

A nuclear heating system integrated with a heat storage tank (HST-NHS) is a promising technical solution for countries with large-scale heating demand. HST-NHS can greatly curtail carbon dioxide emissions and contribute to sustainable development. Appropriate control strategies are not only crucial to realize the real-time matching between HST-NHS heat supply and heat demand, but also ensure the stable and safe operation of HST-NHS. Until now, control strategies for HST-NHS have received limited attention. A refined dynamic simulation model for HST-NHS is established based on APROS software. The verification of the built model in steady state and transient state is completed. Two basic control strategies for the primary heating network (PHN) of HST-NHS, constant supply water temperature and variable flow rate (CT-VF) and variable supply water temperature and constant flow rate (VT-CF) are presented in detail. Based on various typical working conditions of a central heating region in Liaoyuan City, the control effects of two control strategies are investigated. Additionally, a comprehensive performance comparison between the two strategies is explored. Simulation results indicated that under the two control strategies, the indoor temperature can be effectively controlled at a high comfort level. During the same typical day, the total energy consumption cost under the VT-CF control strategy is 0.5 × 105 yuan lower than that under the CT-VF control strategy.

P2P transactions as a market innovation from a prosumer perspective: Level of potential involvement and behavioral incentives and disincentives of development

The development of sustainable energy not only initiates technological innovations but also affects organizational innovation. One of the manifestations of such intangible innovation is peer-to-peer (P2P) transactions used in prosumer energy trading. However, a crucial condition for the development of this market model is the willingness of participants to engage in this form of exchange. Bearing this in mind, the aim of this article is to identify the level of knowledge and potential involvement, as well as the incentives and disincentives for the development of the P2P market from the perspective of prosumers. To achieve this objective, the article uses the results of survey research conducted on a representative sample of 753 Polish prosumers. The analyses indicate that over half of the respondents are interested in and willing to participate in the P2P market. These prosumers are primarily motivated by the opportunity to minimize energy losses produced independently and the possibility of reducing fees (total energy consumption costs). On the other hand, the main barriers to the development of this form of market innovation in the energy sector are the need to dedicate additional time to settlements and transactions and a lack of confidence in state and regional institutions responsible for organizing such a market.

Research on Green Vehicle Path Planning of AGVs with Simultaneous Pickup and Delivery in Intelligent Workshop

In this study, we present and discuss a variant of the classic vehicle routing problem (VRP), the green automated guided vehicle (AGV) routing problem, which involves simultaneous pickup and delivery with time windows (GVRPSPDTW) in an intelligent workshop. The research object is AGV energy consumption. First, we conduct a comprehensive analysis of the mechanical forces present during AGV transportation and evaluate the overall operational efficiency of the workshop. Then, we construct a green vehicle path planning model to minimize the energy consumption during AGV transportation and the standby period. Hence, the problems considered in this study are modeled based on asymmetry, making the problem solving more complex. We also design a hybrid differential evolution algorithm based on large neighborhood search (DE-LNS) to increase the local search ability of the algorithm. To enhance the optimal quality of solutions, we design an adaptive scaling factor and use the squirrel migration operator to optimize the population. Last, extensive computational experiments, which are generated from the VRPSPDTW instances set and a real case of an intelligent workshop, are designed to evaluate and demonstrate the efficiency and effectiveness of the proposed model and algorithm. The experimental results show that DE-LNS yields competitive results, compared to advanced heuristic algorithms. The effectiveness and applicability of the proposed algorithm are verified. Additionally, the proposed model demonstrates significant energy-saving potential in workshop logistics. It can optimize energy consumption by 15.3% compared with the traditional VRPSPDTW model. Consequently, the model proposed in this research carries substantial implications for minimizing total energy consumption costs and exhibits promising prospects for real-world application in intelligent workshops.

Single machine parallel-batch scheduling under time-of-use electricity prices: New formulations and optimisation approaches

Electricity costs account for a relatively large proportion of total production costs in many energy-intensive manufacturing industries. And energy-efficient scheduling is getting increasing attention from researchers owing to its wide real-world applications and computational challenges. In this study, the single-machine parallel-batch scheduling problem with non-identical job sizes under time-of-use electricity prices is investigated. The objective is to minimise the total energy consumption cost such that the makespan does not exceed a given deadline. First, an improved time-slot-indexed formulation is proposed to eliminate symmetric solutions. Subsequently, a new set partition-based formulation is developed. To the best of our knowledge, there is no exact algorithm for this problem, except for some formulations solved by off-the-shelf solvers. To address this problem, a branch-and-price algorithm is developed with a novel and efficient branching rule. In addition, a column-generation-based heuristic is proposed to solve larger-scale instances. Extensive numerical experiments show that the efficiency of the branch-and-price algorithm is significantly better than that of off-the-shelf solvers, and many instances with up to 100 jobs could be solved to optimality within a 20-minute computational time limit for the first time. The column-generation-based heuristic can efficiently provide better solutions than existing heuristics (2.75% average gap in three minutes on instances with 100 jobs and 3.12% average gap in ten minutes on instances with 200 jobs).

An Energy Saving Control Strategy Based on Multi-Agent Q-Learning Algorithm for Data Center

In recent years, the application of green renewable energy to data centers has become an important trend. Traditional solutions lack the consideration of matching tasks to renewable energy supplies. Therefore, in the face of diverse real-time computing tasks, how to reduce the total energy cost while ensuring the quality of service is an important challenge for the data center in the future. In this paper, our focus is on using the information on renewable energy supply and task characteristics as input states to assign tasks that maximize user satisfaction while meeting the minimum total cost of energy consumption. We consider the diversity of real-time tasks and design three different task types: the most crucial task, the crucial task and the non-crucial task. According to the different characteristics of these tasks, we propose a scheduling algorithm based on multi-agent, which uses multiple sets of agents with different initial positions to parallel search in different dimensions of the parameter space to find the optimal solution. To further optimize the algorithm, we eliminate the centralized noise solution based on the Pareto sorting method and sort the multiple optimal solutions to highlight the most suitable solution. The experimental results show that the proposed algorithm compared with other algorithms can reduce the total energy consumption by 11% and increase the customer satisfaction by 13% on average, and has better performance and applicability.

Correlations between Hotel Size and Gas Consumption with a Feasibility Analysis of a Fuel Switch—A Coastal Case Study Croatia Adriatic

This paper analyses gas consumption in hotels on the Adriatic coast, comparing data on natural gas and liquefied petroleum gas (LPG) consumption by hotel size. The research hypothesis is that by switching from LPG to natural gas, not only can a reduction in emissions be achieved in the hospitality industry, but there are also significant economic benefits. The research objectives included a regression analysis for various factors affecting gas consumption. The analysis showed a medium–strong relationship between the variables, which is a novelty for energy trends in the hospitality sector. By converting from heating oil to natural gas, hotels can achieve significant financial savings. It was also calculated that this would reduce the total energy consumption costs. Measures taken by the hospitality industry will have a positive impact on guest perception and could be used as a promotional tool under the “green hospitality” label.

Research on Global Optimal Energy Management Strategy of Agricultural Hybrid Tractor Equipped with CVT

This paper presents a proposed global optimal energy management strategy based on dynamic programming to enhance the energy consumption efficiency of an agricultural hybrid tractor that is equipped with a continuously variable transmission (CVT). Firstly, using a diesel-electric parallel agricultural hybrid tractor as the research object, a tractor-rotary tillage coupling dynamics model is constructed. Secondly, with the torque and speed of the motor, the torque and speed of the diesel engine, and the CVT speed ratio as the control variables, the state of charge (SOC) of the power battery as the state variable, and the goal of minimizing the total energy consumption of the whole machine, a global optimal energy management model based on dynamic programming is established. Finally, the field operation measured data is injected into the MATLAB simulation model, and experiments are carried out to verify the effectiveness of the energy management strategy. The results show that compared with the power-following energy management strategy, the proposed energy management strategy can make the diesel engine and electric motor work in the optimal area, and effectively reduce the total cost of energy consumption of the tractor during field operations. Under the condition of rotary tillage, the total cost of energy consumption is decreased by 16.89%.

Energy-saving strategy for separation of methylcyclohexane/tert-butanol azeotrope

With the increase in industrialization, researchers are striving to pursue energy-saving and environment-friendly separation technologies. In this regard, effective separation of azeotropes is one of the focus areas in recent years. According to the characteristics of methylcyclohexane (MCH) and tert-butanol (TBA) azeotropes, a pressure-swing distillation (PSD) process is established in the current work. Combined with steam recompression and heat integration, two improved separation processes for reduced energy consumption are designed. The operating parameters had a significant effect on the efficiency and cost of the process. Therefore, the selection of parameters was discussed in detail. The proposed PSD processes were evaluated according to the assessment indices including total energy consumption (TEC), CO2 emissions and total annual cost (TAC). The results showed that the improved processes obviously decreased the energy consumption and improved the economy whether it is heat pump pressure swing distillation (HP-PSD) or heat integrated pressure swing distillation (HI-PSD). The HI-PSD process can achieve a TAC savings of 30.86%, TEC reduction of 37.59% and CO2 reduction of 36.34%. The HP-PSD process is found to be the most economical and environment-friendly process among the studied processes, and could achieve 34.92% TAC savings, 42.89% TEC reduction and 50.98% CO2 reduction.

Task offloading based on edge collaboration in MEC-enabled IoV networks

Benefiting from its abundant computing resources and low computing latency, mobile edge computing (MEC) is a promising approach for enhancing the computing capacity of the 5G Internet of vehicles (IoV). Because of the high mobility, handover is frequent and inevitable in IoV networks. In this paper, we investigate an edge collaborative task offloading and splitting strategy in MEC-enabled IoV networks, in which the task is splitted on the edge and paralleling executed by each part of the task on several MEC servers when handover is occured. Applications in IoV networks have flexible requirements on latency and energy consumption. To realize the tradeoff between latency and energy consumption, we formulate the task offloading and splitting as an optimization problem with the aim of minimizing the total cost of latency and energy consumption by jointly optimizing the task splitting ratio and uplink transmit power of vehicle terminal (VT). Because the proposed problem is non-smooth and non-convex, we divide the original problem into two convex subproblems, and apply an alternate convex search (ACS) algorithm to obtain the optimized solution with low computational complexity. Numerical simulation results show that the proposed method can adjust the offloading strategy properly according to task preference, and obtain a lower total cost compared with the baseline algorithms.

Flexible Voyage Scheduling and Coordinated Energy Management Strategy of All-Electric Ships and Seaport Microgrid

Maritime transportation takes a major responsibility in public travel between islands while producing a large quantity of greenhouse gas (GHG) emissions. All-electric ships (AESs) can be applied to mitigate GHG emissions through energy storage systems (ESS), renewable integration, and cold ironing. In this paper, we propose a flexible voyage scheduling strategy for AESs based on the temporal-spatial dynamics (TSD) to satisfy the transportation demand while mitigating the burden of the AESs on the power grids during charging. The interaction between the AESs and island-based microgrids is modeled. Furthermore, the AESs are utilized to enhance the resilience of the power grids. The AESs can be dispatched to realize the load restoration under contingencies. The proposed methodology is verified on a three-island system. It can be concluded that under normal operation, the proposed voyage scheduling can reduce the total energy consumption cost of the AESs and the islands. Besides, the voltage violation can be improved. Under the emergency, the proposed method can help the grids restore more critical and normal loads.

Dynamic energy-efficient scheduling of multi-variety and small batch flexible job-shop: A case study for the aerospace industry

In the context of the global implementation of carbon peaking and carbon neutrality, energy-aware production scheduling has attracted world-wide attention. The optimal scheduling problem for a multi-variety and small-batch dynamic flexible job shop scheduling is challenging in aerospace industry. This paper considers an energy-efficient scheduling problem with multi-resource constraints of multi-variety and small-batch dynamic flexible job shop (α-shop for short). First, it formulates a mathematical model of energy efficient α-shop with the objectives of minimum total energy consumption (TEC), makespan and machining cost. Next, a novel multi-objective bi-population differential artificial bee colony (BDABC) algorithm is proposed to solve the model. Then, the design of experiment Taguchi method is performed to calibrate the parameter setting of the proposed algorithm. Finally, comparative experiments of the proposed algorithm with other popular algorithms such as large-scale multi-objective optimization framework (LSMOF), large-scale multi-objective competitive swarm optimizer (LMOCSO), as well as some known algorithms including multi-objective evolutionary algorithm based on decomposition (MOEA/D), multi-objective particle swarm optimization (MOPSO) and multi-objective artificial bee colony (MOABC) are carried out to assess the effectiveness of the proposed algorithm in an aerospace plant of China based on a number of well-recognized metrics. The experimental results demonstrate that our proposed algorithm is effective reported for this real-life scheduling problem.

Negative Effect Prediction and Refueling Traffic Flow Equilibrium of Urban Energy Supply Network during Weekends and Holidays

With the continuous improvement of people’s living standards, the travel demand for vehicles increases rapidly during weekends and holidays. This situation leads to a number of negative impacts on the transport network, such as traffic congestion, carbon emissions, and long queues at refueling stations. The impact of vehicles requiring energy replenishment on the negative effect is nonlinear. Therefore, the negative effect data of working days cannot be directly used to evaluate the cost, and the traffic equilibrium allocation problem also needs to be solved. An equilibrium allocation model of hybrid vehicles considering the energy replenishment demand in holidays is established, aiming at minimizing the total time cost and energy consumption cost. The Frank–Wolfe algorithm is used to solve the traffic allocation problem in advance, and the negative effect of the urban energy network during holidays is predicted by the Genetic Algorithm—Back Propagation (GA-BP) algorithm. Then, the energy-replenishing vehicles in the traffic network are induced to reduce the total cost. Finally, taking the actual road network in Jiulongpo District of Chongqing City, southwest of China, as an example, the negative effects of multiple stations are predicted. The results show that the prediction method proposed in this paper is effective during holiday periods. In addition, as the market share of electric vehicles increases, the negative cost can decrease gradually. The predicted results can provide a reference for traffic managers.

Ценовые параметры поставки электроэнергии как базис управления спросом на электропотребление в регионе

Energy cost management (more than 45 % in the structure of total costs) can significantly reduce own energy supply costs and improve operational efficiency of an enterprise. The study examines regional electricity price parameters, affected by retail and wholesale electricity markets, in terms of the possibilities for industrial enterprises to implement price-dependent demand management to reduce energy purchase costs. The article aims to distribute Russian regions according to the prospects for reducing energy purchase costs by managing electricity demand schedules. The following methods were utilised: statistical analysis of hourly average electricity prices per year, month and day in the regional context; mathematical modelling and calculation of authors’ coefficients of average electricity prices and coefficients of daily price volatility for assessing prospects for effective energy demand management in Russian regions; construction of positioning maps for grouping and identifying regions where the implementation of price-dependent demand management is possible. Electricity price parameters of all Russian regions were examined. The paper analysed principles of electricity pricing for domestic industrial enterprises, researched the impact of price volatility on energy purchase costs, assessed the contribution of electricity costs to total energy consumption costs of enterprises. Coefficients of average electricity prices, coefficients of daily price volatility and coefficients of efficiency of price-dependent electricity consumption were applied to study electricity price parameters in the regional context. As a result, the article presented a map of electricity price parameters, where regions are grouped according to the possibility of implementing demand management mechanisms. Additionally, specific practical recommendations on price-dependent demand management for electricity consumption of industrial enterprises were given for each identified regional group.

Effects of various connectors on the whole-life-cycle energy consumption of sandwich wall panels in five thermal zones of China

Connectors are not only an important mechanical component of sandwich wall panels (SWPs), but they also contribute to SWP energy loss because they penetrate thermal insulation layers. Reducing the energy consumption (EC) generated by connectors is critical for improving the energy-saving efficiency of SWPs throughout their life cycle. The mechanical and thermal parameters of connectors, structural layers, and insulation layers all have a coupling effect on the EC of connectors in the SWPs studied in this study. First, a connector diameter calculation model and a three-dimensional finite element (3-DFE) model were developed to investigate the mechanism of various connectors' effects on the thermal performance (TP) of SWPs with different structural and insulation layers. Due to the high shear strength of steel, the diameter of the steel connector was found to be 59% smaller than that of the FRP connector, and the thermal bridge effect of the steel connector is reduced by 78% at most when the coupling effect is considered. Then, based on TP, a life cycle cost assessment (LCCA) model was developed. Using five thermal zones in China as research subjects, it was discovered that the effects of connectors on the whole-life-cycle EC of SWPs depend on the value of degree days (DD). Steel connectors can reduce the total energy consumption cost of SWPs by up to 13.7% in Guangzhou when compared to GFRP connectors. The connector manufacturing EC dominated the whole-life-cycle EC in an area with a low DD value. Reduced thermal conductivities of structural layers could significantly improve the effects of connectors on the whole-life-cycle EC of SWPs in an area with a high DD value. These findings are critical for the development of effective SWPs.

Thermo-economic optimization of a multi-source (air/sun/ground) residential heat pump with a water/PCM thermal storage

The main target of this paper is to numerically study a multi-source (air/sun/ground) heat pump with the implementation of a thermal energy storage, using either water or PCM, for residential space heating. The system was modelled considering several sub-models for each of the components (compressor, solar panels, storage tank, heat exchangers etc.). A control strategy has been established to decide which operating mode of the system provides the highest coefficient of performance (COP). A multi-objective optimization through genetic algorithm of several decisional variables of the system was carried out, in different configurations and climate conditions, by considering different scenarios in terms of total investment and energy consumption costs, to optimize seasonal performances and investment cost of the entire system. Results show that solar thermal and solar photovoltaic collectors coupled with water storage tank give higher seasonal energy performance, especially in warmer climates, whereas the exploitation of the ground source can be more advantageous for colder climates. From the optimization analysis, it results that optimal non-dominated solutions characterized by a SCOP increase between 50% and 250% are characterized by higher investment costs between 215% and 730%, depending on the climate conditions. None of the solutions employing a PCM storage tank results economically feasible, due to a slight effect on system performance, and a much higher effect on investment costs. Finally, several cost scenarios in terms of incentives on investment costs and increased energy prices were analysed, for which the employment of scenarios with higher capital investment can be more advantageous in terms of lower total costs.