Hybrid stochastic/robust flexible and reliable scheduling of secure networked microgrids with electric springs and electric vehicles

Electric spring (ES) as a novel concept in power electronics has been developed for the purpose of dealing with demand-side management. In this paper, to conquer the challenges imposed by intermittent nature of renewable energy sources (RESs) and other uncertainties for constructing a secure modern microgrid (MG), the hybrid distributed operation of ESs and electric vehicles (EVs) parking lot is suggested. The proposed approach is implemented in the context of a hybrid stochastic/robust optimization (HSRO) problem, where the stochastic programming based on unscented transformation (UT) method models the uncertainties associated with load, energy price, RESs, and availability of MG equipment. Also, the bounded uncertainty-based robust optimization (BURO) is employed to model the uncertain parameters of EVs parking lot to achieve the robust potentials of EVs in improving MG indices. In the subsequent stage, the proposed non-linear problem model is converted to linear approximated counterpart to obtain an optimal solution with low calculation time and error. Finally, the proposed power management strategy is analyzed on 32-bus test MG to investigate the hybrid cooperation of ESs and EVs parking lot capabilities in different cases. The numerical results corroborate the efficiency and feasibility of the proposed solution in modifying MG indices.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Multistage Expansion Planning of Active Distribution Systems: Towards Network Integration of Distributed Energy Resources

Over the last few years, driven by several technical and environmental factors, there has been a growing interest in the concept of active distribution networks (ADNs). Based on this new concept, traditional passive distribution networks will evolve into modern active ones by employing distributed energy resources (DERs) such as distributed generators (DGs), energy storage systems (ESSs), and demand responsive loads (DRLs). Such a transition from passive to active networks poses serious challenges to distribution system planners. On the one hand, the ability of DGs to directly inject active and reactive powers into the system nodes leads to bidirectional power flows through the distribution feeders. This issue, if not adequately addressed at the design stage, can adversely affect various operational aspects of ADNs, specifically the reactive power balance and voltage regulation. Therefore, the new context where DGs come into play necessitates the development of a planning methodology which incorporates an accurate network model reflecting realistic operational characteristics of the system. On the other hand, large-scale integration of renewable DGs results in the intermittent and highly volatile nodal power injections and the implementation of demand response programs further complicates the long-term predictability of the load growth. These factors introduce a tremendous amount of uncertainty to the planning process of ADNs. As a result, effective approaches must also be devised to properly model the major sources of uncertainty. Based on the above discussion, successful transition from traditional passive distribution networks to modern active ones requires a planning methodology that firstly includes an accurate network model, and secondly accounts for the major sources of uncertainty. However, incorporating these two features into the planning process of ADNs is a very complex task and requires sophisticated mathematical programming techniques that are not currently available in the literature. Therefore, this research project aim to develop a comprehensive planning methodology for ADNs, which is capable of dealing with different types of DERs (i.e., DGs, ESSs, and DRLs), while giving full consideration to the above-mentioned two key features. To achieve this objective, five major steps are defined for the project. Step 1 develops a deterministic mixed-integer linear programming (MILP) model for integrated expansion planning of distribution network and renewable/conventional DGs, which includes a highly accurate network model based on a linear format of AC power flow equations. This MILP model can be solved using standard off-the-shelf mathematical programming solvers that not only guarantee convergence to the global optimal solution, but also provide a measure of the distance to the global optimum during the solution process. Step 2 proposes a distributionally robust chance-constrained programming approach to characterize the inherent uncertainties of renewable DGs and loads. The key advantage of this approach is that it requires limited information about the uncertain parameters, rather than perfect knowledge of their probability distribution functions. Step 3 devises a fast Benders decomposition-based solution procedure that paves the way for effective incorporation of ESSs and DRLs into the developed planning methodology. To this end, two effective acceleration strategies are proposed to significantly enhance the computational performance of the classical Benders decomposition algorithm. Eventually, Steps 4 and 5 propose appropriate models for ESSs and DRLs and integrate them into the developed planning methodology. In this regard, a sequential-time power flow simulation method is also proposed to incorporate the short-term operation analysis of ADNs into their long-term planning studies. By completing the above-defined steps, the planning model developed in Step 1 will be gradually evolved, so that Step 5 will yield the final comprehensive planning methodology for ADNs

Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

Actuators for Intelligent Electric Vehicles

This book details the advanced actuators for IEVs and the control algorithm design. In the actuator design, the configuration four-wheel independent drive/steering electric vehicles is reviewed. An in-wheel two-speed AMT with selectable one-way clutch is designed for IEV. Considering uncertainties, the optimization design for the planetary gear train of IEV is conducted. An electric power steering system is designed for IEV. In addition, advanced control algorithms are proposed in favour of active safety improvement. A supervision mechanism is applied to the segment drift control of autonomous driving. Double super-resolution network is used to design the intelligent driving algorithm. Torque distribution control technology and four-wheel steering technology are utilized for path tracking and adaptive cruise control. To advance the control accuracy, advanced estimation algorithms are studied in this book. The tyre-road peak friction coefficient under full slip rate range is identified based on the normalized tyre model. The pressure of the electro-hydraulic brake system is estimated based on signal fusion. Besides, a multi-semantic driver behaviour recognition model of autonomous vehicles is designed using confidence fusion mechanism. Moreover, a mono-vision based lateral localization system of low-cost autonomous vehicles is proposed with deep learning curb detection. To sum up, the discussed advanced actuators, control and estimation algorithms are beneficial to the active safety improvement of IEVs

Cross-border Mobility for Electric Vehicles: Selected results from one of the first cross-border field tests in Europe

This book provides selected results from the accompanying research of the project CROME. The vision of the project was to create and test a safe, seamless, user-friendly and reliable mobility with electric vehicles between France and Germany as a prefiguration of a pan-European electric mobility system. Major aims were contributions to the European standardisation process of charging infrastructure for electric mobility and corresponding services, and to provide an early customer feedback

Planeación de la expansión de la red de distribución considerando incertidumbre en la demanda y recursos energéticos distribuidos

Este documento contiene una metodología para el planeamiento de las redes de distribución de energía eléctrica considerando incertidumbre en la demanda y recursos energéticos distribuidos. En primer lugar, se plantea un modelo de caracterización horaria de la demanda que tiene en cuenta el comportamiento de la generación distribuida, programas de respuesta de la demanda, almacenamiento de energía y conexión de vehículos eléctricos. Posteriormente, se plantea la metodología de planeamiento basada en tres principios, optimizar el uso de la infraestructura, aplazar inversiones en la red y por último expandir la red cuando sea necesario. Se formula el problema de planeamiento y es solucionado utilizando un algoritmo híbrido GA-PSO. Finalmente, se presentan los resultados al aplicar la metodología en un sistema de prueba IEEE de 33 nodos radial, obteniendo reducciones de entre un 30 y 40% respecto a la expansión con alternativas tradicionales; además, se realiza un análisis de diferentes escenarios de penetración de recursos energéticos distribuidos y alternativas de expansión de la red.Abstract: This document contains a methodology for the planning of electricity distribution networks considering demand uncertainty and distributed energy resources. In the first place, a demand characterization model is proposed that takes into account the behavior of distributed generation, demand response programs, energy storage and connection of electric vehicles. Subsequently, the planning methodology is based on three principles, optimizing the use of infrastructure, deferring network investment and finally expanding when necessary. The planning problem is solved using a hybrid algorithm GA-PSO, and the results are presented when applying the methodology in an IEEE 33 radial bus test system, obtaining reductions between 30 and 40% compared to the expansion with traditional alternatives. In addition, an analysis is made of different levels of penetration of distributed energy resources.Maestrí