Two-stage Scheduling Strategy 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 Strategy for Electric Vehicles Based on Model Predictive Control

In recent years, with the rapid growth in the number of electric vehicles (EVs), the large-scale grid connection of EVs has had a profound impact on the power grid. As a flexible energy storage resource, EVs can participate in auxiliary services of the power grid via vehicle-to-grid (V2G) technology. Due to the uncertainty of EVs accessing the grid, it is difficult to accurately control their charging and charging behaviors at both the day-ahead and real-time stages. Aiming at this problem, this paper proposes a two-stage scheduling strategy framework for EVs. In the presented framework, according to historical driving data, a day-ahead scheduling model based on distributionally robust optimization (DRO) is first established to determine the total power plan. In the real-time scheduling stage, a real-time scheduling model based on model predictive control (MPC) is established to track the day-ahead power plan. It can reduce the impact of EVs’ uncertainties. This strategy can ensure the charging demand of users is under the control of the charging and discharging behaviors of EVs, which can improve the accuracy of controlling EVs. The case study shows that the scheduling strategy can achieve accurate and fast control of charging and discharging. At the same time, it can effectively contribute to the security and stability of grid operations.

A scheduling framework for VPP considering multiple uncertainties and flexible resources

This paper presents a two-stage scheduling framework for a virtual power plant (VPP) to address uncertainties in wind power plants (WPP), photovoltaic (PV), load, and price in the power system. The framework consists of a day-ahead (DA) scheduling stage and an intraday adjustment stage. The DA scheduling stage formulates the VPP's internal controllable resource scheduling strategy and the energy market's bidding plan. The intraday adjustment stage provides flexibility to adjust the scheduling strategy of controllable resources in the VPP to enhance power generation stability. To address profit distribution issues, the paper proposes a profit distribution strategy based on scheduling costs of each component unit of the VPP. The VPP considers various factors, such as battery capacity degradation, carbon credit trading, flexibility of electric vehicles (EVs), and novel demand response (DR) strategies. The case study demonstrates that accounting for battery capacity degradation reduces the utilization of energy storage systems (ESSs) and EVs, while considering controllable load and EVs increases the VPP's flexibility and decreases carbon emissions. Furthermore, the proposed two-stage scheduling strategy effectively handles multiple uncertainties. Finally, the implementation of new DR strategies leads to a reduction in cost by 6.83% (time-of-use) and 8.1% (real-time price) for the VPP.

Research on Two-level and Two-stage Scheduling Optimization Model of Distributed Photovoltaic Generation Energy System

Distributed PV (photovoltaic generation) is a distributed PV energy system that uses photovoltaic modules to convert solar energy into electric energy. In this paper, a two-level and two-stage scheduling optimization model and its solution algorithm under distributed PV energy system are constructed. Firstly, based on load forecasting and intermittent energy output forecasting data, this paper establishes a two-stage scheduling optimization model of distributed energy system under active distribution network. Then, the improved PSO (Particle swarm optimization) algorithm is introduced to optimize the scheduling model. Finally, the proposed model is validated by simulation results. The simulation results show that using the double-layer and two-stage scheduling strategy proposed in this paper to optimize the distributed PV energy system can not only optimize the power consumption structure, but also play the role of peak shaving and valley filling, and the state of charge of the battery has been higher than 50%, which can effectively increase its service life. Under the double-level and two-stage scheduling strategy, users get the most benefits, and the revenue growth rate is 20.07%.

Two-stage scheduling of integrated energy systems based on a two-step DCGAN-based scenario prediction approach

Integrated energy systems (IESs) are developing rapidly as a supporting technology for achieving carbon reduction targets. Accurate IES predictions can facilitate better scheduling strategies. Recently, a newly developed unsupervised machine learning tool, known as Generative Adversarial Networks (GAN), has been used to predict renewable energy outputs and various types of loads for its advantage in that no prior assumptions about data distribution are required. However, the structure of the traditional GAN leads to the problem of uncontrollable generations, which can be improved in deep convolutional GAN (DCGAN). We propose a two-step prediction approach that takes DCGAN to achieve higher accuracy generation results and uses a K-means clustering algorithm to achieve scenario reduction. In terms of scheduling strategies, common two-stage scheduling is generally day-ahead and intraday stages, with rolling scheduling used for the intraday stage. To account for the impacts on the prediction accuracy of scheduling results, Conditional Value at Risk (CVaR) is added to the day-ahead stage. The intra-day prediction process has also been improved to ensure that the inputs for each prediction domain are updated in real-time. The simulations on a typical IES show that the proposed two-step scenario prediction approach can better describe the load-side demands and renewable energy outputs with significantly reduced computational complexity and that the proposed two-stage scheduling strategy can improve the accuracy and economy of the IES scheduling results.

Two-stage optimization of a virtual power plant incorporating with demand response and energy complementation

The virtual power plant (VPP) breaks the geographical restrictions of environment and resource types by virtue of aggregating the distributed energy resources and dispatchable loads. In view of the impact of large-scale energy consumption by users on the operational safety of VPP, based on the existing demand response (DR) researches, this paper adds a penalty mechanism for the reasonable management of loads. For the difference of resources in multiple VPPs, the current researches ignore the energy interaction between different VPPs, and the external operator is introduced innovatively in this paper to coordinate the real-time complementation of regional energy. Considering the uncertainty from renewable energy and load, a two-stage scheduling strategy for VPP with multi-time scale optimization is proposed, including a long-time scale day-ahead scheduling and a short-time scale real-time scheduling. On the basis of DR and unit constraints, the resource scheduling plan is formulated in the day-ahead stage. In the real-time stage, the energy complementation between VPPs is considered under the framework of rolling optimization for the correction of the previous plan. The scheduling of both stages is aimed at the economic operation of the VPP. Simulation results show that the DR flexibly optimizes the load distribution while bringing economic benefits, and the total profit can be increased by up to 6.65%. Moreover, the results show that the energy interaction under the external operator coordination significantly reduces the total correction cost by up to 8.23%, and the correction cost is decreased with the extension of the rolling optimization scheduling period.

A Simulated Annealing Algorithm with Tabu List for the Multi-Satellite Downlink Schedule Problem Considering Waiting Time

In the multi-satellite and multi-ground station downlink task scheduling problem, the waiting time from the proposal of the task to the execution will affect its validity. If the satellite has multiple communicable ground stations when the downlink task is proposed, the selection problem needs to be solved first. After the selection, since the available time conflict between tasks of different satellites for the same ground station, the specific start time should be determined. To reduce the waiting time, a simulated annealing algorithm with a tabu list and start time decision (SATLD) is proposed. This method uses a two-stage scheduling strategy. In the first stage, the improved simulated annealing algorithm based on a tabu list is used to select the downlink ground station. The second stage combines downlink scheduling algorithm based on task arrival time (DSA-AT) method and downlink scheduling algorithm based on task requirement time (DSA-RT) method to determine the specific start time of each task of a single ground station. Simulation analysis prove the method has better selection efficiency of downlink task and shorter total task waiting time, and has practical value.

Two-Stage Scheduling Strategy for Integrated Energy Systems Considering Renewable Energy Consumption

With the crisis of energy and environment, the integrated energy systems (IES) have a bright prospect in future energy reform owing to the excellent economic and environmental performance. The IES combines a large amount of renewable energy (RE), which guarantees its economic and environmental benefits. Many uncertainties of RE threaten the performance of IES. How to promote RE consumption under strong uncertainties is a crucial problem in the scheduling of IES. In this work, a two-stage scheduling strategy for IES combining day-ahead scheduling and real-time scheduling is proposed to guide the system operation. The stage of day-ahead scheduling can obtain the optimal scheduling scheme one day in advance based on the forecast data of RE, and the stage of real-time scheduling is introduced to cope with the uncertainties of RE. The model of IES is established based on the energy hub (EH), and the entire strategy is implemented on this model. The model integrates various energy conversion equipment to give full play to the advantages of coordination and complementation of IES. The improved particle swarm optimization (IPSO) is proposed as the solution algorithm of the whole strategy, which improves the traditional PSO through random nonlinearity change inertia weight strategy and best solution perturbation operator (BSPO). Compared with the traditional PSO, IPSO has more excellent performance for solving IES scheduling model. Finally, different schemes under different situations are compared.

Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing

Edge computing is an emerging paradigm that settles some servers on the near-user side and allows some real-time requests from users to be directly returned to the user after being processed by these servers settled on the near-user side. In this paper, we focus on saving the energy of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for the providers of the edge nodes while meeting the resources and delay constraints. We propose a two-stage scheduling strategy which includes the scheduling and resource provisioning. In the scheduling stage, we first propose an efficient scheme based on the branch and bound method. In order to reduce complexity, we propose a heuristic algorithm that guarantees users’ deadlines. In the resource provisioning stage, we first approach the problem by virtualizing the edge nodes into master and slave nodes based on the sleep power consumption mode. After that, we propose a scheduling strategy through balancing the resources of virtual nodes that reduce the power consumption and guarantees the user’s delay as well. We use iFogSim to simulate our strategy. The simulation results show that our strategy can effectively reduce the power consumption of the edge system. In the test of idle tasks, the highest energy consumption was 27.9% lower than the original algorithm.

Two-stage scheduling strategy of air-conditioning load participating in PV absorption of distribution network

Aiming at the grid operation problems brought by large-scale photovoltaic(PV) connection to the distribution network, this paper proposes a two-stage scheduling strategy for air-conditioning load to participate in the PV absorption of distribution network, considering the schedulable capacity of air-conditioning load. First, in the first stage, in the micro-grid layer, each air-conditioning load aggregator(LA) makes a market bidding based on non-cooperative game to determine the day-ahead scheduling plan according to the plan of PV absorption formulated by the micro-grid operator in the past.; In the second stage, in the distribution network layer, when the micro-grid itself cannot fully absorb the PV power and the power is transferred to the main network, resulting in the voltage overlimit problem in the network, the grid company adjusts the voltage by using the schedulable capacity of air-conditioning load. The simulation results show that the strategy proposed in this paper can not only enhance the utilization of PV power and adjust the distribution network voltage, but also improve the profit of the air-conditioning LAs, reduces the regulation cost of the micro-grid operators and the grid company and has high economic efficiency.

Phased-Array Radar Task Scheduling Method for Hypersonic-Glide Vehicles

The radar task scheduling problem of phased-array radar (PAR) in detecting hypersonic-glide vehicle (HGV) targets has been studied. Based on the hypersonic-glide vehicle motion-model and detection model, a two-stage scheduling strategy was designed. Task scheduling is divided into two stages: prescheduling and formal scheduling. In the prescheduling stage, low-priority tasks beyond the radar resource capacity are sent into delay queues or delete queues in advance. In the formal scheduling stage, the quantization model of scheduling principles and the objective function of task scheduling are designed, and task requests are scheduled in parallel by using the group intelligence algorithm. A particle swarm-annealing algorithm was designed due to its ease of use and global search ability. The simulation results show that the proposed task scheduling method is superior to the traditional scheduling method in terms of the scheduling success rate, time utilization rate, realization value rate, task miss rate and discovery target number.

Two-Stage Optimal Scheduling Strategy for Large-Scale Electric Vehicles

This paper proposes a two-stage scheduling strategy for large-scale electric vehicles to reduce the adverse impact of the uncontrolled charging of the electric vehicles on the grid. Based on the statistical data of private car travel, the uncontrolled charging demand of individual electric vehicles and their aggregation are simulated. In the first stage, the electric vehicles and thermal power units are jointly scheduled. To minimize the total cost and standard deviation of the total load curve, the charging and discharging load guiding curve of the electric vehicles and the optimal output plans of the thermal power units in each period of the scheduled day are formulated. In the second stage, the electric vehicle load management and control centre formulates specific charging and discharging plans for the users through rolling optimization to follow the guiding load curve. The cost of vehicle discharge compensation is considered to improve the willingness of users to participate in scheduling and the user satisfaction. To avoid the “dimension disaster” caused by the centralized dispatching of large numbers of electric vehicles, the K-means clustering algorithm is used to divide the vehicles into different groups. Next, each group is scheduled as a unit, and the model is solved by using the particle swarm optimization algorithm. By comparing the optimization results of different scenarios, the feasibility and effectiveness of the proposed strategy are verified.

A two-stage taxi scheduling strategy at airports with multiple independent runways

Long taxiing times at large airports lead to fuel wastage and dissatisfied passengers. This paper investigates the 4D taxi scheduling problem in airports to minimize the taxiing time. We propose an iterative two-stage scheduling strategy. In the first stage, all aircrafts in a current schedule period are assigned initial 4D routes. In the second stage, landing aircrafts that are unavailable to fulfil their initially assigned routes are rescheduled using a shortest path algorithm based approach. In this paper, the simplified model used in most existing literature, that depicts a runway as having a single entrance and a single exit or even sets only one point to represent both of them has been discarded. Instead, we model the fact that a runway has multiple entrance and exit points and use an emerging concept—Runway Exit Availability (REA)—to measure the probability of clearing a runway from a specific exit during a specific time interval so that the taxiing scheduling model can be much higher approximation to the practical operation. An integer programming (IP) model factoring REA is proposed for assigning 4D taxiing routes in the first stage. The IP model covers most practical constraints faced in airport taxiing procedures, such as the rear-end/head-on conflict constraint, runway-crossing constraint, take-off/landing separation constraint, and taxi-out constraint. Besides, flight holding patterns at intersections are much more realistically modelled. Furthermore, to accelerate the solving process of the IP model, we have refined the formulation using several tricks. Simulation results by proposed scheduling approach for operations at the Beijing Capital International Airport (PEK) for an entire day demonstrate a surprising taxiing time saving against the empirical data and simulation results based on a strategy similar to what being used now days while showing an acceptable running time of our approach, which supports that our approach may help in real operation in the future.

Integrated Energy Exchange Scheduling for Multimicrogrid System With Electric Vehicles

Electric vehicles (EVs) can be considered as flexible mobile battery storages in microgrids. For multiple microgrids in an area, coordinated scheduling on charging and discharging are required to avoid power exchange spikes between the multimicrogrid system and the main grid. In this paper, a two-stage integrated energy exchange scheduling strategy for multimicrogrid system is presented, which considers EVs as storage devices. Then several dual variables, which are representative of the marginal cost of proper constraints, are utilized to form an updated price, thereby being a modification on the original electricity price. With this updated price signal, a price-based decentralized scheduling strategy is presented for the Microgrid Central Controller (MGCC). Simulation results show that the two-stage scheduling strategy reduces the electricity cost and avoids frequent transitions between battery charging/discharging states. With the proposed decentralized scheduling strategy, each microgrid only needs to solve its local problem and limits the total power exchange within the safe range.