1,721,057 research outputs found
Dynamic distributed clustering in wireless sensor networks via Voronoi tessellation control
Full text linkThis paper presents two dynamic and distributed clustering algorithms for Wireless Sensor Networks (WSNs). Clustering approaches are used in WSNs to improve the network lifetime and scalability by balancing the workload among the clusters. Each cluster is managed by a cluster head (CH) node. The first algorithm requires the CH nodes to be mobile: by dynamically varying the CH node positions, the algorithm is proved to converge to a specific partition of the mission area, the generalised Voronoi tessellation, in which the loads of the CH nodes are balanced. Conversely, if the CH nodes are fixed, a weighted Voronoi clustering approach is proposed with the same load-balancing objective: a reinforcement learning approach is used to dynamically vary the mission space partition by controlling the weights of the Voronoi regions. Numerical simulations are provided to validate the approaches
Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models
No full textThis paper presents an event driven model predictive control approach for a local energy management system, enabling residential consumers to the automated participation in demand side management (DSM) programs. We consider a household equipped with smart appliances, a storage unit, electric vehicles and photovoltaic micro-generation. Resources are coordinated according to the needs of maximizing self-consumption and minimizing the cost of energy consumption, in a contractual scenario characterized by designed or market indexed pricing models, with DSM options. The control action (appliances' start times, the storage charging profile and the IEC 61851 compliant charging profile of the electric vehicles) is updated every time an event triggers the controller, such as a user request, a price/volume signal or the notification of a new forecast of micro-generation from the photovoltaic unit. The control framework is flexible enough to meet the real dynamics of a household and short-term grid requirements, while taking into account user preferences, contractual and technical constraints. A proper set of simulations validates the proposed approach. © 2014 Elsevier Ltd
Decentralized Model Predictive Control of Plug-in Electric Vehicles Charging based on the Alternating Direction Method of Multipliers
Full text linkThis paper presents a decentralized Model Predictive Control (MPC) for Plug-in Electric Vehicles (PEVs) charging, in presence of both network and drivers' requirements. The open loop optimal control problem at the basis of MPC is modeled as a consesus with regularization optimization problem and solved by means of the decentralized Alternating Direction Method of Multipliers (ADMM). Simulations performed on a realistic test case show the potential of the proposed control approach and allow to provide a preliminary evaluation of the compatibility between the required computational effort and the application in real time charging control system
Decentralized PEV Control Based on a Subgradient Method for Mixed-Integer Programming Problems
No full textIn this paper, a subgradient method for solving mixed-integer linear programming problems is tailored and used to control in a distributed, and hence scalable way, the recharging process of a fleet of plug-in electric vehicles (PEVs). This makes possible to scale the problem to large PEV fleets, in a privacy-preserving fashion, something that cannot be done when relying on centralized optimization-based methods. A key challenge to face is given by the fact that the mathematical formulation of the PEV charging problem includes both real (i.e., continuous) and integer (in particular, Boolean) variables. This complicates significantly the mathematical theory, compared to the case in which all the involved variables are real. The PEV charging power is realistically modelled as a semi-continuous variable (while most of the works model it as a on/off variable), and the goal is to recharge the PEVs according to the user charging preferences, while letting the aggregated PEV power track a given power reference. Simulation results are discussed and possible directions for future research are outlined
Single Intersection MPC Traffic Signal Control in Presence of Automated Vehicles
No full textThis article presents a model predictive control (MPC) approach for the management of traffic lights (TLs) at a single road intersection. The proposed controller incorporates a microscopic traffic model, capturing the position, velocity, and acceleration of every single vehicle at the intersection. This allows us to achieve a detailed modeling of the dynamics of the queues. The proposed controller can adapt to work in scenarios that go from one in which vehicles are manually controlled by the drivers, to one in which some or all of the vehicles are automatically driven. In the former scenario, the dynamics of the vehicles' variables are intended to mimic the drivers' behavior, in the latter ones (i.e., semi or fully autonomous driving), vehicles' variables are references to the automated vehicles, sent by the TL controller. Numerical simulations on a real intersection with realistic traffic characteristics are discussed and results in the scenarios from the manual one to the fully automated one are compared, evaluating the performance in terms of queue length and waiting times. It is shown how the proposed controller can significantly improve the management of the intersection, leading to less traffic
Electric vehicles charging control in a smart grid: A model predictive control approach
No full textThe paper presents an event driven model predictive control (MPC) framework for managing charging operations of electric vehicles (EV) in a smart grid. The objective is to minimize the cost of energy consumption, while respecting EV drivers' preferences, technical bounds on the control action (in compliance with the IEC 61851 standard) and both market and grid constraints (by seeking the tracking of a reference load profile defined by the grid operator). The proposed control approach allows "flexible" EV users to participate in demand side management (DSM) programs, which will play a crucial role in improving stability and efficiency of future smart grids. Further, the natural MPC formulation of the problem can be recast into a mixed integer linear programming problem, suitable for implementation on a calculator. Simulation results are provided and discussed in detail. (C) 2013 Elsevier Ltd. All rights reserved
Optimal Stochastic Control of Energy Storage System Based on Pontryagin Minimum Principle for Flattening PEV Fast Charging in a Service Area
Full text linkThis letter discusses stochastic optimal control of an energy storage system (ESS) for reducing the impact on the grid of fast charging of electric vehicles in a charging area. A trade off is achieved between the objectives of limiting the charging power exchanged with the grid, and the one of limiting the fluctuation, around a given reference, of the ESS energy. We show that the solution of the problem can be derived from the one of a related deterministic problem, requiring the realistic assumption that the charging area operator knows an estimate of the aggregated charging power demand over the day. In addition, two alternative configurations of the charging area are discussed, and it is shown that, while they share the same solution, one better mitigates the demand uncertainty. Numeric simulations are provided to validate the proposed approach
A Model Predictive Control-Based Approach for Plug-in Electric Vehicles Charging: Power Tracking, Renewable Energy Sources Integration and Driver Preferences Satisfaction
No full textInterdependency modeling and analysis of critical infrastructures based on Dynamic Bayesian Networks
No full textThis paper presents a novel approach to the critical infrastructure (CI) interdependencies analysis, based on the Dynamic Bayesian Network (DBN) formalism. Our original modeling procedure divides the DBN in three levels: an atomic events level, which models the adverse events impacting on the analyzed CIs, a propagation level, which captures CI interdependencies, and a services level, which allows to monitor the state of provided services. Three types of analyses can be performed: a reliability study, an adverse events propagation study, and a failure identification analysis. A case study provided by Israel Electric Corporation is considered, and explicative simulations are presented and discussed in detail. © 2011 IEEE
Adaptive Model Predictive Control for Large-scale Coordinated PEV Recharging
No full textThis paper presents a centralised model predictive control (MPC) approach for the control of the recharging process of a fleet of plug-in electric vehicles (PEVs). The first control goal is to recharge the PEVs so that the user preferences on the final desired state of charge, and on the time available for recharging are respected. A second goal is from the perspective of the grid operator. The charging processes should be coordinated in such a way that the aggregated power profile tracks a proper power reference established by the grid operator to ensure the safe operation of the grid. We propose an adaptive MPC formulation, in which the weights of the objective function are changed based on the feedback on the current progress of the charging sessions. This allows to better adapt the control effort to the current PEVs' status, and to reduce the computation burden by allowing to decrease the MPC prediction horizon. We validate the proposed approach via simulations on a large scale realistic scenario randomly generated
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