Two-stage Scheduling Method Research Articles

Data-driven two-stage scheduling of multi-energy systems for operational flexibility enhancement

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for enhancing operational flexibility. This paper investigates a data-driven two-stage scheduling strategy of multi-energy system to promote uncertain renewable energy integration and improve economic benefits. Dynamic models for DHNs and HCNG networks are established, and the flexibility of multi-energy system is quantified through distribution-level power aggregation. To compromise flexibility enhancement and operational cost reduction, the multiobjective day-ahead scheduling optimization is conducted based on adaptive batch-ParEGO method. Taking day-ahead scheduling as a baseline, a data-driven real-time dispatch method is proposed based on the deep deterministic policy gradient algorithm, which adaptively modifies the energy management scheme in smaller temporal dimension to address uncertainties on both the load and source sides. The performance of the proposed two-stage scheduling method is demonstrated through numerical tests.

A two-stage scheduling method for integrated community energy system based on a hybrid mechanism and data-driven model

The integrated community energy system (ICES) is an effective means to promote the synergies among multiple energy carriers. However, the off-design performance of equipment challenges the accurate and economical scheduling of the ICES. To solve this problem, a two-stage scheduling method for the ICES based on a hybrid mechanism and data-driven model is proposed in this paper. Combing the mechanism energy hub (EH) model with a data-driven efficiency correction model, a hybrid-driven dynamic energy hub (DEH) with variable equipment efficiency is built first. The EH describes the multi-energy coupling relationship; the embedded efficiency correction model adopts data-driven approaches of polynomial regression (PR) and backpropagation neural networks (BPNNs) to accurately extract nonlinear characteristics of equipment efficiency. On this basis, a two-stage scheduling model for the ICES is developed. In the day-ahead stage, the PR method is applied to calculate equipment efficiency which varies with load rate. The day-ahead scheduling model is established with the aim of minimizing the operating cost. In the intraday stage, considering the effects of load rate, temperature, and atmospheric pressure, the BPNNs method is employed to further correct equipment efficiency using the latest data. Furthermore, a rolling optimization (RO) strategy is used to address the uncertainties of equipment efficiency and load demand to improve the accuracy and economy of the scheduling scheme. Case studies demonstrate that the proposed method can improve the solution speed and accuracy of the scheduling model, and enhance the operating economy of the ICES.

A two-stage scheduling method for deadline-constrained task in cloud computing

A two-stage scheduling method for deadline-constrained task in cloud computing

A two-stage scheduling method for global humanitarian logistics in large-scale disaster response

Large-scale disaster response is a global issue. Planners must make effective logistics plans to deliver vital first-aid resources (e.g., medicine, food, clothing, machinery) to affected areas from all over the world. Furthermore, planners must consider surging demand and uncertain transportation situations. To explain the efficiency of distribution plans, this study addresses the resource allocation effectiveness losses, which are the losses caused by the mismatch between supply and demand in affected areas, and the emergency logistics time losses, which are the time losses caused by logistics processes under emergency conditions. A two-stage scheduling method is then proposed that considers random demand and travel time. In consideration of the incompleteness or lack of information in real situations, the scheduling method with Bayesian information updates is proposed by assuming that demand and transportation uncertainty are represented by population transfer rate (PTR) and road affected level (RAL), respectively. Next, a simulation study on Wenchuan earthquake response is conducted to illustrate the proposed method. Finally, the insights derived from the study are provided in the conclusion.

A two-stage scheduling method for global humanitarian logistics in large-scale disaster response

Large-scale disaster response is a global issue. Planners must make effective logistics plans to deliver vital first-aid resources (e.g., medicine, food, clothing, machinery) to affected areas from all over the world. Furthermore, planners must consider surging demand and uncertain transportation situations. To explain the efficiency of distribution plans, this study addresses the resource allocation effectiveness losses, which are the losses caused by the mismatch between supply and demand in affected areas, and the emergency logistics time losses, which are the time losses caused by logistics processes under emergency conditions. A two-stage scheduling method is then proposed that considers random demand and travel time. In consideration of the incompleteness or lack of information in real situations, the scheduling method with Bayesian information updates is proposed by assuming that demand and transportation uncertainty are represented by population transfer rate (PTR) and road affected level (RAL), respectively. Next, a simulation study on Wenchuan earthquake response is conducted to illustrate the proposed method. Finally, the insights derived from the study are provided in the conclusion.

A two-stage scheduling method for hot rolling and its application

Hot rolling scheduling is a difficult problem in the steel processing industry. It involves many objectives and constraints in both technical and practical respects. A two-stage scheduling method is proposed in this paper. Batch planning of staple material is formulated as a VRPTW, which is solved with a modified PGA. Then, batches of the established units are optimized by adjusting rolling sequences using intelligent search algorithms to reach higher hot charge ratios. This method has been applied to a hot strip mill belonging to Baosteel in China. Our results demonstrate that the proposed technique can improve production efficiency and offer significant economic benefits.