Energy Consumption Scheduling Research Articles

Optimising outbound scheduling in shipyard steel stockyard

ABSTRACT In shipyard operations, steel stockyards manage the intake, storage and distribution of raw materials. However, the inherent customisation of steel plates presents challenges such as inefficient outbound scheduling and elevated energy consumption. To address these issues, this study introduces a novel outbound scheduling model that comprehensively considers manufacturing orders and the current warehouse status during the outbound process. To solve this model, an improved ant colony optimisation algorithm, based on an energy consumption selection strategy (ECS-IACO), is proposed. The algorithm adopts a dynamic pheromone matrix and updates rules to address the complexity of high-dimensional state spaces. The energy consumption selection strategy is integrated into initialisation and state transitions, guiding and decisions throughout. Extensive numerical tests show that the ECS-IACO is robust and highly efficient in solving problems. In real-world scenarios tests, the ECS-IACO reduced energy use by up to 22% more than other algorithms, supporting sustainability efforts in business operations.

Task offloading scheme in Mobile Augmented Reality using hybrid Monte Carlo tree search (HMCTS)

Mobile Augmented Reality (MAR) applications enhance user experiences by providing realistic information about the current location through mobile devices. However, MAR applications are computationally intensive, leading to high energy consumption and latency issues. To address these challenges, this research presents a Hybrid Monte Carlo Tree Search (HMCTS) based task offloading scheme, combining a genetic algorithm with Monte Carlo tree search for efficient task management. The proposed method uses YoloV7 for object recognition and aims to reduce energy consumption, response time, and migration time. Experimental results demonstrate that the HMCTS approach significantly reduces energy consumption to 1290 kJ, response time to 24 ms, and migration time to 0.52 ms, outperforming existing techniques. These improvements highlight the potential of the HMCTS method for enhancing the performance of MAR applications. Proposed hybrid approach aims to improve the efficiency and effectiveness of task offloading in MAR applications. The HMCTS model dynamically offloads tasks to edge servers, optimizing scheduling time, response time, and energy consumption.

Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid

This study introduces a smart home load scheduling system that aims to address concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, which includes an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and the hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTPDR). The efficient integration of renewable energy into smart grids (SGs) is challenging due to its time-varying and intermittent nature. To address this, batteries were used in this study to mitigate the fluctuations in renewable generation. The simulation results validate the effectiveness of our proposed approach in optimally addressing the smart home load scheduling problem with photovoltaic generation and DR. The system achieves the minimization of utility bills, pollutant emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.

The concept of autonomous hydrogen energy complex: Adaptation of large nuclear power units to uneven energy consumption schedules

Evaluation of flexible operation of nuclear power plants (NPP) during load reduction hours shows inefficiency of the process with account for high capital investments in NPPs at relatively low fuel costs. The concept of producing hydrogen fuel at NPPs during the hours of low power demand in the energy grid will be an effective solution to the problem. Generating electricity through utilization of electrolysis hydrogen and oxygen production provides a possibility to significantly extend the flexibility range for power released by the NPP in the energy grid under a constant reactor capacity factor. Utilization of a low-power steam turbine can be a good solution to address the challenges of electricity production from hydrogen and oxygen. This will ensure high reliability of the main NPP steam turbine. In addition, in their previous works based on the experimental data relating the operation of NPPs, the authors showed that a low-power steam turbine and residual energy release from the reactor can be used for NPP auxiliary needs backup in case of a complete blackout. Thus, increased safety of NPPs will be ensured by installing an autonomous hydrogen energy complex. Moreover, this can reduce the risk of an accident when using both the newly developed and existing safety systems. This work presents an analysis of the current state of hydrogen production at NPPs, including the research results and rationale for efficiency of the developed energy complex. The calculations showed that development of NPPs in combination with autonomous hydrogen energy complexes will increase economic attractiveness of newly designed NPPs and will increase the competitiveness of existing nuclear plants on electricity market, will also provide their survival in the energy sector in the long term.

Sustainable location-routing problem for medical waste management using electric vehicles

The sustainable management of medical waste is essential for environmental protection and public health safety, contributing to urban development and societal well-being. This study focuses on the medical waste Location-Routing Problem with Electric Vehicles (EVs), aiming to mitigate infection risk while providing cost-effective and energy-efficient logistics solutions. It encompasses diverse elements such as disposal location selection, vehicle scheduling, recharge strategies, time windows, working duration, and load-dependent energy consumption. To tackle this complex problem, we introduce an Evolutionary Decomposition-based Adapted Large Neighborhood Search algorithm (E-ALNS/D), which surpasses existing methods such as MOEA/D-HGS, MOEA/D-LNS, MPGA, and NSGAII-LNS across various urban settings. Empirical analysis and a case study reveal opportunities for enhancing medical waste management with EVs. Key findings show that optimizing service times, adopting fast-charging infrastructure, and strategically placing temporary disposal facilities in response to unforeseen demand spikes significantly reduce energy consumption, lower infection risk, and cut charging cost. Furthermore, selecting peripheral over central disposal locations better minimizes infection risks. This research offers valuable insights for decision-makers in EV-based medical waste management, emphasizing the need to balance public health, environmental sustainability, and economic efficiency. Our methodology paves the way for future research in sustainable medical waste management with EVs.

An intelligent mechanism for energy consumption scheduling in smart buildings

An intelligent mechanism for energy consumption scheduling in smart buildings

Social welfare maximization with efficient energy management of community microgrid considering customer behavioral response using MDCLPIS

A novel residential demand response (DR) program is proposed to encourage microgrid customers to participate in demand reduction using a house energy consumption scheduler (HECS) to maximize social welfare by optimally reducing the cost of electricity and to receive maximum incentives. In this research, both Price-based and incentive-based DR programs are considered, and the demand-price elasticity approach is implemented to model them. To make the DR model more effective, customer behavioral response is taken into account, by leveling the customers according to their price elasticity. An optimized non-cooperative energy game theory framework and a Nash equilibrium strategy are employed to model customer response under a multi-time of use (TOU) pricing and incentive scheme, with the aim of achieving the best scheduling of home-controlled appliances while preserving customers’ privacy. This approach maximizes social welfare by minimizing the electricity consumption cost for customers while simultaneously saving on generation costs. The negative externality effect on price rate is internalized between the customers of different consumption levels using multi-dynamic consumption-level pricing and incentive scheme (MDCLPIS). The suggested model distinguishes between consumers’ behavior in response to changes in energy prices and their behavior in response to changes in incentives. Finally, numerical and simulation results demonstrate the suitability and effectiveness of the proposed model and its advantages in terms of customer saved cost by up to 21.26%, microgrid saved cost by up to 29.44%, and getting benefits by optimal scheduling of their load.

Integrating Life Cycle Principles in Home Energy Management Systems: Optimal Load PV–Battery–Electric Vehicle Scheduling

Energy management in the residential sector contributes to energy system dispatching and security with the optimal use of renewable energy systems (RES) and energy storage systems (ESSs) and by utilizing the main grid based on its state. This work focuses on optimal energy flow, ESS parameters, and energy consumption scheduling based on demand response (DR) programs. The primary goals of the work consist of minimizing electricity costs while simultaneously extending the lifetime of ESSs in conjunction with extracting maximum benefits throughout their operational lifespan and reducing CO2 emissions. Effective ESS and photovoltaic (PV) energy usage prices are modeled and an efficient energy flow management algorithm is presented, which considers the life cycle of the ESSs including batteries, electrical vehicles (EVs) and the efficient use of the PV system while reducing the cost of energy consumption. In addition, an optimization technique is employed to obtain the optimal ESS parameters including the size and depth of discharge (DOD), considering the installation cost, levelized cost of storage (LCOS), winter and summer conditions, energy consumption profile, and energy prices. Finally, an optimization technique is applied to obtain the optimal energy consumption scheduling. The proposed system provides all of the possibilities of exchanging energy between EV, battery, PV system, grid, and home. The optimization problem is solved using the particle swarm optimization algorithm (PSO) in MATLAB with an interval time of one minute. The results show the effectiveness of the proposed system, presenting an actual cost reduction of 28.9% and 17.7% in summer and winter, respectively, compared to a base scenario. Similarly, the energy losses were reduced by 26.7% in winter and 22.3% in summer, and the EV battery lifetime was extended from 9.2 to 19.1 years in the winter scenario and from 10.4 to 17.7 years in the summer scenario. The integrated system provided a financial contribution during the operational lifetime of EUR 11,600 and 7900 in winter and summer scenarios, respectively. The CO2 was reduced by 59.7% and 46.2% in summer and winter scenarios, respectively.

HVAC energy cost minimization in smart grids: A cloud-based demand side management approach with game theory optimization and deep learning

In this paper, we present a novel cloud-based demand side management (DSM) optimization approach for the cost reduction of energy usage in heating, ventilation and air conditioning (HVAC) systems in residential homes at the district level. The proposed approach achieves optimization through scheduling of HVAC energy usage within permissible bounds set by house users. House smart home energy management (SHEM) devices are connected to the utility/aggregator via a dedicated communication network that is used to enable DSM. Each house SHEM can predict its own HVAC energy usage for the next 24 h using minimalistic deep learning (DL) prediction models. These predictions are communicated to the aggregator, which will then do day ahead optimizations using the proposed game theory (GT) algorithm. The GT model captures the interaction between aggregator and customers and identifies a solution to the GT problem that translates into HVAC energy peak shifting and peak reduction achieved by rescheduling HVAC energy usage. The found solution is communicated by the aggregator to houses SHEM devices in the form of offers via DSM signals. If customers’ SHEM devices accept the offer, then energy cost reduction will be achieved. To validate the proposed algorithm, we conduct extensive simulations with a custom simulation tool based on GridLab-D tool, which is integrated with DL prediction models and optimization libraries. Results show that HVAC energy cost can be reduced by up to 36% while indirectly also reducing the peak-to-average (PAR) and the aggregated net load by up to 9.97%.

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

Retracted: Dynamic Combined Optimal Scheduling of Electric Energy and Natural Gas Energy Consumption in Data Center

A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms

A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches implemented for scheduling tasks are unable to cope with the complexity of optimizing multiple parameters. In this paper, we propose a multi-objective grey-wolf optimization based algorithm for scheduling tasks on cloud platforms. The proposed algorithm targets to minimize schedule length (overall execution time), energy consumption, and monetary cost required for executing tasks. For optimization, the algorithm incorporates steps that are performed iteratively for mimicking the behavior of grey wolves attacking their prey. It uses discrete values for positioning wolves for encircling and attacking the prey. The assignment of tasks to virtual machines is performed using the solution found after multi-objective optimization that incorporates weighted sorting for arranging solutions. Our experimentation performed using the CloudSim framework shows that the proposed algorithm outperforms other algorithms with performance improvement ranging from 3.98% to 16.07%, while considering the schedule length, monetary cost, and energy consumption.

Efficient economic energy scheduling in smart cities using distributed energy resources

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2–A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2–B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids’ peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing carbon emissions, CPP-PDC reduces the maximum demand for electricity from the grid and the overall demand above the predetermined level. The maximum power demand from the grid is minimized for points A5 and B5, reducing up to 22% compared to A2. The proposed method minimizes both energy costs as well as CO2 emissions. A Pareto curve illustrates the trade-off between cost and CO2 emissions.

Modeling method for optimal scheduling of Park comprehensive energy source load storage and consumption considering customer portrait

Aiming at the problems of energy consumption optimization of park integrated energy source load storage system in power grid dispatching, a modeling method of park integrated energy consumption optimization dispatching considering user portrait is proposed. Firstly, an improved k-means portrait method is proposed for park users; Secondly, on the premise of considering the constraints of equipment operation in the park, cluster users in the park, and establish an energy consumption optimization scheduling model with the objective function of user satisfaction, net income of wind power consumption and equipment operation cost; Finally, SAPSO algorithm and fuzzy theory are used for decision-making. The example simulation shows that the proposed model is correct and effective, which is more precise and objective than the traditional model.

A many-to-many pick-up and delivery problem under stochastic battery depletion of electric vehicles

ABSTRACT The study extends the traditional pick-up and delivery problems (PDPs) to address the specific challenges of urban logistics and electric vehicle (EV) adoption. These challenges include the limited range of EVs, energy consumption along the route, and uncertainty in traffic conditions. To overcome the limited range of EVs, the study includes battery swapping stations to ensure sufficient energy to complete delivery routes. Vehicle energy consumption is considered to reduce range anxiety and optimize energy use. The study also considers the unpredictability of traffic conditions that affect energy consumption and delivery schedules. To address these concerns, the study proposes an approximate Quadratic Chance-Constrained Mixed-Integer Programming (QC-MIP) model with a linear approximation, a constructive heuristic and a meta-heuristic. These quantitative models incorporate comprehensive EV energy estimation approaches, enabling more accurate energy predictions. The proposed approaches provide valuable insights and strategies for improving energy efficiency and delivery performance in urban logistics environments.

A data-driven framework for thermal comfort assessment method based on user interaction

The rapid development of smart home systems requires a transition of thermal comfort studies. A feasible, user-friendly, consumer-oriented thermal comfort assessment method for large-scale residential applications is extremely urgent. However, most studies of thermal comfort models have focused on large commercial buildings. The application and deployment of such methods often rely on multiple prerequisites like relatively steady thermal conditions, regular energy consumption schedule, and additional sensor information. While situations are different in residential scenarios. In this paper, over 100 thousand air conditioner users were analyzed and a fundamental but overlooked question in thermal comfort research was established. In the household environment, the thermal condition is more complicated. Difference in age, gender, and usage preference leads to diversity in individual thermal comfort. Furthermore, introducing more sensors for better comfort prediction performance is not acceptable both from the cost perspective and the user perspective. To bridge the above missing gap, a novel thermal comfort assessment framework combining user interaction and metric learning was constructed. The proposed framework can be used to construct thermal comfort assessment systems by exploiting user interaction actions, which is a low-cost alternative and complementary to the traditional methods based on thermal equilibrium. The ease of construction makes the framework easy to integrate into smart home systems, addressing the difficulty of applying thermal comfort studies in residential scenarios.

A Stackelberg-Cournot model to harmonize participants' interests in a smart grid

A “smart grid” refers to an advanced and modernized electrical grid system that incorporates various digital technologies, communication networks, and automation. In a smart grid, each subscriber’s energy consumption is monitored and updated by their smart meter, and cost-effective energy consumption scheduling is determined by two-way communication with other participants. In general, the interests among participants are naturally conflicted, and therefore the game-theoretic approach is frequently used to solve the problem. However, most previous studies focused on the game between a service provider and subscribers or game among service providers or subscribers. In this study, we propose an extended model consisting of two sub-games: 1) the Stackelberg game between a service provider (a leader) and subscribers (followers), and 2) the Cournot game among subscribers. Our model conducts an innovative energy consumption scheduling (ECS), especially harmonizing all participants’ aspects. It draws a unique Nash Equilibrium and achieves a more balanced and cost-effective demand management in the smart grid. Compared to the non-scheduling, our model reduces the subscriber’s cost and its standard deviation, on average, 20.5 % and 12.7 %, respectively. Also, the peak-to-average ratio (PAR) of energy consumption, the ratio between the highest energy consumption and the average energy consumption within a certain time frame, is decreased, on average, 24.3 %.

Decision support model for PV integrated shading system: Office building case

Office buildings have a high amount of internal heat, solar gain, daytime energy consumption and occupancy schedules. Therefore, the increment in the cooling energy demand highlights the shading systems to provide efficient energy retrofit for office buildings. Shading surfaces, to prevent the high amount of solar radiation, are suitable for the collection of solar energy and the integration of photovoltaic systems onto the building envelope. However, the impact of the shading surface on the cooling, heating, and lighting energy consumption and the amount of energy produced by the PV system is a great task as a decision-making problem with multiple independent and dependent variables. This study searches for the installation of a PV integrated shading system to an office building through a decision support methodology. Independent variables such as the shading surface area, and angle and the dependent variables such as the energy, embodied carbon, and cost indicators are analysed within the decision support methodology. The results provide a definitive structure for such decision-making problems. Moreover, findings highlight that although Mono-Si PV options are more efficient in terms of energy generation, Poly-Si PV options are found to be the ideal solutions, due to the lower cost and embodied carbon.

An Energy-Efficient Dynamic Scheduling Method of Deadline-Constrained Workflows in a Cloud Environment

With the rapid development of cloud applications, the computing requests of cloud data centers have increased significantly, consuming a lot of energy, making cloud data centers unsustainable, which is very unfavorable from both the cloud provider’s point of view and the environmental point of view. Therefore, it is crucial to minimize energy consumption and improve resource utilization while ensuring user service quality constraints. In this paper, we propose a hybrid workflow scheduling algorithm (Online Hybrid Dynamic Scheduling, OHDS), which aims to minimize the energy consumption of tasks and maximize service resource utilization while satisfying the sub-deadline and data dependency constraints of workflow tasks. Firstly, the data dependencies between workflow tasks are considered for multi-task merging, and sub-deadline constraints are assigned to workflow tasks based on task priority. Secondly, based on the independent nature of the tasks of different workflows, a hybrid scheduling of multiple workflows is performed to reduce service idle time. Then, the workflow task scheduling priority and its sub-deadlines are dynamically adjusted, and the service status is sensed by the CPU utilization of the service, and the workload on the overloaded/underloaded service is balanced by dynamic migration of virtual machines. Finally, the OHDS method is compared with three existing scheduling methods to verify its better performance in terms of scheduling energy consumption, scheduling success rate and service resource utilization.

Scheduling of Multiple Energy Consumption in The Smart Buildings with Peak Demand Management

The global energy crisis and the depletion of fossil fuels have become pressing concerns, leading experts to search for alternative solutions. This paper presents an analysis of the day-ahead operation of the multi-carrier energy system (MCES) with the aim of minimizing operational costs, reducing pollution emissions, and maximizing consumers' comfort. The authors propose an optimal scheduling strategy called energy demand curtailment (EDCS), which aims at efficiently managing electrical energy consumption. Additionally, they consider an on-site generation strategy (OGS) for consumers to operate their own energy storages. Both EDCS and OGS are modeled based on demand-side management (DSM). To optimize these strategies and achieve their objectives, fuzzy logic is employed as an optimization approach along with objective functions. Finally, two scenarios are examined through numerical simulations to illustrate the effectiveness of this approach in optimizing energy utilization in MCE.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.