Two-stage Scheduling Approach Research Articles

Planning with the electricity market One day ahead for a smart home connected to the RES by the MILP method

This study proposes a novel framework for smart homes to optimize energy consumption and production, leading to reduced costs and a more reliable grid. The framework schedules the use of controllable appliances and renewable energy sources while considering uncertainties in production, real-time market prices, and uncontrollable household loads. By incorporating both incremental and real-time pricing models, the system discourages excessive consumption during peak hours. The core innovation lies in a two-stage scheduling approach implemented using GAMS software. This method minimizes the expected total cost while accounting for limitations on controllable loads, power supply, production resources, battery performance, and overall home energy balance. Additionally, the framework leverages the previous day’s bilateral contract and allows residents to adjust desired lighting levels based on current market fluctuations. Simulations demonstrate the program’s effectiveness in reducing both net energy costs and peak load on the electricity grid.

Demand response of large-scale residential load to concentrated variable renewable energy

Large-scale concentrated Variable Renewable Energy (VRE) sources pose a challenge to the balance and security of electricity systems. Studies have shown that the demand side may offer a greater responsiveness based on the shiftability of loads. However, there is currently no known literature on the demand response (DR) of large-scale shiftable loads in the residential sector. Firstly, there is no sufficiently large database of residential appliance-level load. Secondly, scheduling a large number of small residential shiftable loads simultaneously is a major challenge. Ultimately, the DR of large-scale residential buildings encounters the challenge of ensuring sufficient backup shiftable load to effectively cope with real-time VRE fluctuations during the response period. To address these issues, this paper proposes an appliance-level load data generation method based on diffusion neural networks. Meanwhile, a clustering method based on Generalized End to End (GE2E) loss neural networks is proposed to solve the complexity obstacles in simultaneous load swarm scheduling. Thirdly, a two-stage scheduling approach is proposed to address the challenges in the large-scale DR period through constructing day-ahead and real-time response models. The simulation results show that large-scale residential shiftable loads have a strong response capability to VRE fluctuations, and their response cost is lower than the conventional fuel-fired-power-plant-based-response within a certain user preference cost range. In practice, the clustering and scheduling methods proposed in this paper can be applied after the non-intrusive load monitoring (NILM) method is deployed to decompose the load data.

Large-scale EV charging scheduling considering on-site PV generation by combining an aggregated model and sorting-based methods

Large-scale electric vehicle (EV) charging scheduling is highly relevant for the growing number of EVs, while it can be complex to solve. A few existing studies have applied a two-stage scheduling approach to reduce computation time. The first stage approximates the optimal overall load, and the second prioritizes the charging. This work also attempts to apply such an approach for large-scale EV charging considering on-site photovoltaic (PV) generation at a workplace. However, validation and analysis are missing to address whether and why the two-stage approach is suitable. Besides, the existing studies lack exploring different methods to prioritize charging. This work investigates the two-stage approach. Simulation results show the non-uniqueness of the optimal solution from the optimal individual model, and guided by the optimal overall load, sorting-based methods can often lead to an optimal solution, while non-optimal solutions only cause decreases in the load-matching performance with a median value of less than 1%. The aggregated model usually cannot achieve the optimal overall load due to model simplifications. However, further applying sorting-based methods will reduce the differences between the final and the optimal overall load. Thus, the two-stage approach is suitable for this study, and further simulations show that it can achieve almost the optimal annual performance with around 1/57 of the computation time. Furthermore, this study explores different methods to prioritize charging. Simulation results show no difference in performance, while the Least Laxity First method leads to around 54.6% more switching.

Research on the scheduling method of ground resource under uncertain arrival time

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

Combined scheduling model to optimize tritium inventory in tritium plant of ITER

The goal of this study was to provide a discrete-continuous scheduling model for optimal operation of the fuel cycle of ITER. The model is designed to minimize the tritium working inventory in the Isotope Separation System (ISS) of the fuel cycle by adopting a two-stage scheduling approach. To consider the effect of pump regeneration schedule on tritium fluctuation in the ISS, the suggested solving algorithm is presented as a two-stage scheduling models: Stage I obtains the optimal pump-regeneration schedule, and Stage II suggests the optimal fuel-transition timing from the ISS to the Storage and Delivery System to minimize tritium working inventory in the ISS. The scheduling model uses discrete-time representation in Stage I and continuous-time representation in Stage II.

A Two-Stage Scheduling Approach on Hybrid Flow Shop with Dedicated Machine

A Two-Stage Scheduling Approach on Hybrid Flow Shop with Dedicated Machine

Resource-Aware Task Scheduling and Placement in Multi-FPGA System

With the development of high performance computing, Field Programmable Gate Arrays (FPGAs) are widely used for task acceleration. Partial reconfigurable technology provides the possibility of multi-task concurrent computation on FPGAs. However, it is still difficult to achieve high performance multi-task acceleration in multi-FPGA systems due to the resource contention in limited hardware resources and reconfiguration overhead. In this paper, we propose a two-stage task scheduling approach in multi-FPGA systems to optimize task execution efficiency. At the first scheduling stage, we select an appropriate computing unit for each task considering the subtask similarity and resource requirement similarity to reduce the possibility of reconfiguration and resource contention. At the second scheduling stage, we coordinate the subtask scheduling and placement to make full use of the hardware resources of FPGAs and improve the acceleration performance. Experiments show that our two-stage scheduling approach significantly reduces the tasks makespan and improves the utilization of resources compared with other traditional methods.

Data-aware optimization of bioinformatics workflows in hybrid clouds

Life Sciences have been established and widely accepted as a foremost Big Data discipline; as such they are a constant source of the most computationally challenging problems. In order to provide efficient solutions, the community is turning towards scalable approaches such as the utilization of cloud resources in addition to any existing local computational infrastructures. Although bioinformatics workflows are generally amenable to parallelization, the challenges involved are however not only computationally, but also data intensive. In this paper we propose a data management methodology for achieving parallelism in bioinformatics workflows, while simultaneously minimizing data-interdependent file transfers. We combine our methodology with a novel two-stage scheduling approach capable of performing load estimation and balancing across and within heterogeneous distributed computational resources. Beyond an exhaustive experimentation regime to validate the scalability and speed-up of our approach, we compare it against a state-of-the-art high performance computing framework and showcase its time and cost advantages.