Real-time Control Layer Research Articles

Collision-Free Ecological Cooperative Robust Control for Uncertain Vehicular Platoons With Communication Delay

The varying real-world road conditions (eg. slopes, speed limits) have great effects on the vehicle dynamics and fuel characteristics. The lack of consideration for these effects may lead to deteriorative fuel economy and even collisions. This paper presents a hierarchical control framework for a connected and automated vehicular platoon subject to time-vary uncertain dynamics and uniform communication delays. This control method minimizes the fuel consumptions and ensures the tracking performance on the premise of collision-free property. Firstly, a centralized ecological speed planning (CESP) algorithm based on dynamic programing (DP) is designed as the upper layer. This layer produces the fuel-optimal and feasible speed profiles, which can be followed by all vehicles, for the entire platoon based on the preview information of road conditions (eg. slopes, speed limits). Secondly, the distributed collision-free speed tracking control (DCST) method is proposed in the lower real-time control layer. The collision-free property is guaranteed by the uniform boundedness performance of the well-designed potential function of the spacing error. The convergences of all speed tracking errors are proved through barbalat's lemma. By modifying the desired spacing model, the application range of DCST is extended to the conditions with large initial deviations. Finally, the main theoretical results are verified through numerical simulations.

A Hierarchically Coordinated Operation and Control Scheme for DC Microgrid Clusters Under Uncertainty

In the existing works of microgrid clusters, operation and real-time control are normally designed separately in a hierarchical architecture, with the real-time control in the primary and secondary levels, and operation in the tertiary level. This article proposes a hierarchically coordinated control scheme for DC MG clusters under uncertainty. In each MG, the tertiary level controller optimizes the operating cost in the MG by taking into account the real-time uncertainties of renewable generations and loads deviated from the forecasting data; and the primary controller responds to the real-time power fluctuations through an optimised droop curve. The hierarchically coordinated optimization problem is formulated to optimize the power set points and droop curve coefficients simultaneously under uncertainties using an adjustable robust optimization model. For the MG cluster, the energy sharing of each MG in the cluster is optimized to minimize the total operating cost and the transmission loss. The overall optimization problem is solved in a distributed manner by alternating direction method of multipliers (ADMM) where each MG entity only exchanges boundary information (i.e. the power exchange of MG entity with the MG cluster), thus information privacy and plug-and-play feature of each MG are guaranteed. The proposed approach optimally coordinates the operation and real-time control layers of a DC MG cluster with uncertainties; it achieves decentralized power sharing at the real-time control layer and distributed optimization at the operation layer, featuring high scalability, reliability and economy. Case studies of a DC MG cluster are conducted in Matlab/Simulink in order to demonstrate the effectiveness of the proposed approach.

Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system

An energy management system for stand-alone microgrid composed of diesel generators, wind turbine generator, biomass generator and an ESS (energy storage system) is proposed in this paper. Different operation objectives are achieved by a hierarchical control structure with different time scales. Firstly, the optimal schedules of the diesel generators, wind turbine generator, biomass generator and ESS are determined fifteen minutes ahead according to the super short-term forecast of load and wind speed in the optimal scheduling layer. Comprehensive analysis which takes the uncertainty of load and wind speed into account is conducted in this layer to minimize the operation cost of the system and ensure a desirable range of the state of charge of the ESS. Secondly, the operation points of each unit are regulated dynamically to guarantee real-time power balance and safety range of diesel generation in the real-time control layer, based on which the response capability when suffering significant forecast deviation and other emergency issues, e.g. sudden load-up can be improved. Finally, the effectiveness of the proposed energy management strategy is verified on an RT_Lab based real-time simulation platform, and the economic performances with different types of ESS are analyzed as well.

Development and Empirical Study of Real-Time Simulation-Based Dynamic Traffic Assignment Model

This research aims at developing a system of real-time simulation-based dynamic traffic model under mixed traffic flow conditions. The system consists of two layers, namely, simulation layer and real-time control layer. The system is implemented based on rolling horizon approach, which is advanced for each stage; thus real-time data can be incorporated within the framework. In order to predict normative, as well as predictive, information, a simulation-based dynamic traffic assignment model is employed within each stage. Empirical data for a real city network, such as flows from vehicle detectors, are used to validate the model in a real-time environment. The values of mean absolute percentage error and root-mean-squared percentage error are within 15%, and the results show promising agreements between observed and simulated flows.

IMPACT OF REAL-TIME INFORMATION ON DYNAMIC VEHICULAR FLOWS UNDER MIXED TRAFFIC CONDITIONS

This research aims at studying impacts of real-time information on mixed traffic flows through an integrated dynamic simulation-assignment model, DynaTAIWAN. The DynaTAIWAN model is composed of 2 layers, namely simulation-layer and real-time control layer. The simulation layer is designed to simulate traffic flow patterns according to assumed tripmaker characteristics and/or under a set of given conditions; the real-time control layer receives real-time vehicle information and forecast short-term traffic flow patterns. In the simulation process, each vehicle is moved and tracked individually. Four different vehicle types are explicitly considered in DynaTAIWAN, including car, bus, motorcycle, and truck. Vehicles are moving along the link through macroscopic flow relationships, speeds of each type of vehicle are adjusted. Numerical experiments based on real-time information are conducted in a 50-node test network and a Taichung City Network.