Flexible Scheduling Scheme Research Articles

Distributed robust cooperative scheduling of multi-region integrated energy system considering dynamic characteristics of networks

Due to the advantages of reliability and economy, multiple adjacent region integrated energy systems (IESs) are interconnected to form a multi-region IES through multi-energy network. However, the limitation on private data exchange, multi-uncertainty and the complex dynamic characteristics bring challenges to the optimal scheduling of the multi-region IES. This paper proposes a distributed robust cooperative scheduling method for the multi-region electricity–natural gas–heat IES considering the dynamic characteristics and the uncertainties of wind turbine (WT) and electricity load. Firstly, a robust scheduling model of the multi-region IES considering dynamic characteristics and the uncertainties is built. In particular, the dynamic transmission models of gas and heating networks are constructed, and a regulation model of virtual heat storage is built to control the virtual heat storage in district heating network (DHN). Further, to preserve the information privacy and decision-making independence for each sub-region IES, a distributed cooperative scheduling framework based on the consensus-based alternating direction method of multipliers (ADMM) is developed for the multi-region IES, where the original robust scheduling model is decomposed into several subproblems that are solved independently. Moreover, the nonconvex dynamic natural gas flow function is addressed by convex relaxation technique, and the max–min robust model with binary variables is solved by alternative optimization procedure (AOP) method and duality theory. Finally, the simulation results show that the proposed method can converge reliably, realize the balance between the operation cost and the heat loss and provide a flexible scheduling scheme for the multi-region IES.

Multi-Objective Optimization of Steam Power System Under Demand Uncertainty

There are many uncertain factors, which are critical for the efficient operation of steam power systems (SPSs). Due to the modelling inaccuracy and the changing environment, it is challenging to directly formulate the model for SPS operation. In this paper, a method is proposed to alleviate the influence of small-scale fluctuation by discretizing the uncertainty working condition on the SPS. Moreover, the exergy analysis method is proposed to evaluate the quality of different energy and develop a mixed-integer nonlinear model of SPS considering the economic and exergy objectives. Finally, the $\varepsilon $ -constraint method is used to obtain the Pareto front of the multi-objective optimization problem, and the fuzzy decision is used to determine the optimal strategy of the system. By introducing the multi-objective optimization of the SPS model with demand side uncertainty, a flexible scheduling scheme with good economic and exergy objectives is obtained. Comparing with the optimal scheduling without the demand-side uncertainty, numerical results indicate the feasibility and effectiveness of the model.

Flexible uplink MU-MIMO scheduling in unlicensed spectrum

Utilizing the unlicensed spectrum as supplementary is being studied to meet the increasingly growing demand of user equipment for rate, traffic and bandwidth, and thus to mitigate the current spectrum scarcity crisis. In this paper, we investigate the uplink multi-user multiple-input multiple-output (UL MU-MIMO) technique to achieve multiplexing gain in unlicensed spectrum. We first define a new parameter called channel busy radio (CBR) which is related to the probability of occupying the unlicensed channel, and find that the failure to occupy the unlicensed channel leads to the performance degradation in unlicensed spectrum. Then, according to the user-specific CBR of the unlicensed channel, we propose a flexible scheduling scheme for UL MU-MIMO in unlicensed spectrum, and formulate an optimization problem for the optimal scheduled user number. Furthermore, an adaptive scheduling algorithm is proposed to decide the optimal number of the scheduled users efficiently. The simulation results show that, based on statistical channel condition in unlicensed spectrum, adjusting the number of the scheduled user flexibly can offset the degraded throughput and form an optimal MU-MIMO transmission.

A hybrid and scalable multi-agent approach for patient scheduling based on Petri net models

Scheduling patients in a hospital is a challenging issue due to distributed organizational structure, dynamic medical workflows, variability of resources and the computational complexity involved. It calls for a sustainable architecture and a flexible scheduling scheme that can dynamically allocate available resources to promptly react to patients in a hospital and deliver healthcare services timely. The objectives of this paper are to propose a viable and systematic approach to develop a scalable and sustainable scheduling system based on multi-agent system (MAS) to shorten patient stay in a hospital and plan schedules based on the medical workflows and available resources. To develop a patient scheduling system, we combine MAS architecture, contract net protocol (CNP), workflow specification models based on Petri nets and the cooperative distributed problem solving concept. To achieve interoperability and sustainability, Petri Net Markup Language (PNML) and XML are used to specify precedence constraints of operations in medical workflows and capabilities of resource agents, respectively. Agent communication language (ACL) and CNP are used to achieve communication and negotiation/mutual selection of agents. A collaborative algorithm is invoked by individual agents to optimize the schedules locally based on a problem formulation automatically obtained by Petri net models. We have developed a scheduling system based on a FIPA compliant MAS platform to solve the dynamic patient scheduling problem. To illustrate the benefit of our approach, we compare the performance of our method with a heuristic rule commonly used in practice. In addition, we also analyze and verify scalability of our approach by experiments.

Efficient Implementation of Gaussian Belief Propagation Solver for Large Sparse Diagonally Dominant Linear Systems

We present an implementation-oriented algorithm for the recently developed Gaussian Belief Propagation solver that demonstrates 17× speedup over the prior algorithm for diagonally dominant matrices generated by typical Finite Elements applications. Compared to the diagonally-preconditioned conjugate gradient method, our algorithm demonstrates empirical improvements up to 6× in iteration count and speedups up to 1.8× in execution time. Also we present a new flexible scheduling scheme of the algorithm that is aimed for implementation on parallel architectures by reducing the iteration count of parallel GaBP and achieving better hardware parallelism.

The study of a dynamic dial-a-ride problem under time-dependent and stochastic environments

This paper studies a dynamic dial-a-ride problem bearing complex constraints on a time-dependent network. A flexible scheduling scheme is proposed to dynamically cope with different stochastic events, such as the travelling time fluctuation, new requests, absences of customers, vehicle breakdowns, cancellations of requests, traffic jams and so on. A fast heuristic is proposed to re-optimize the schedule when a new event occurs. This heuristic consists of a properly organized local search strategy and uses a secondary objective function to drive the search out of local optima. Intensive computational simulations were carried out to evaluate the performance of this scheduling scheme and the influence of different stochastic factors. The simulation results of different scenarios with different percentage of dynamic requests reveal that this scheduling scheme can generate high quality schedules and is capable of coping with various stochastic events.