Multi-energy Microgrids Incorporating EV Integration : Optimal Design and Resilient Operation

There are numerous opportunities and challenges in integrating multiple energy sources, for example, electrical, heat, and electrified transportation. The operation of multi-energy sources needs to be coordinated and optimized to achieve maximum benefits and reliability. To address the electrical, thermal, and transportation electrification energy demands in a sustainable and environmentally friendly multi-energy microgrid, this paper presents a mixed integer linear optimization model that determines an optimized blend of energy sources (battery, combined heat and power units, thermal energy storage, gas boiler, and photovoltaic generators), size, and associated dispatch. The proposed energy management system seeks to minimize total annual expenses while simultaneously boosting system resilience during extended grid outages, based on an hourly electrical and thermal load profile. This approach has been tested in a hospital equipped with an EV charging station in Okinawa, Japan through several case studies. Following a M1/M2/c queuing model, the proposed grid-tied microgrid successfully integrates EVs into the system and assures continued and economic power supply even during grid failures in different weather conditions.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Day-Ahead Scheduling of Electric Vehicles and Electrical Storage Systems in Smart Homes Using a Novel Decision Vector and AHP Method

The two-way communication of electricity and information in smart homes facilitates the optimal management of devices with the ability to charge and discharge, such as electric vehicles and electrical storage systems. These devices can be scheduled considering domestic renewable energy units, the energy consumption of householders, the electricity tariff of the grid, and other predetermined parameters in order to improve their efficiency and also the technical and economic indices of the smart home. In this paper, a novel framework based on decision vectors and the analytical hierarchy process method is investigated to find the optimal operation schedule of these devices for the day-ahead performance of smart homes. The initial data of the electric vehicle and the electrical storage system are modeled stochastically. The aim of this work is to minimize the electricity cost and the peak demand of the smart home by optimal operation of the electric vehicle and the electrical storage system. Firstly, the different decision vectors for charging and discharging these devices are introduced based on the market price, the produce power of the domestic photovoltaic panel, and the electricity demand of the smart home. Secondly, the analytical hierarchy process method is utilized to implement the various priorities of decision criteria and calculate the ultimate decision vectors. Finally, the operation schedule of the electric vehicle and the electrical storage system is selected based on the ultimate decision vectors considering the operational constraints of these devices and the constraints of charging and discharging priorities. The proposed method is applied to a sample smart home considering different priorities of decision criteria. Numerical results present that although the combination of decision criteria with a high rank of electricity demand has the highest improvement of technical and economic indices of the smart home by about 12 and 26%, the proposed method has appropriate performance in all scenarios for selecting the optimal operation schedule of the electric vehicles and the electrical storage system

Integration of AC/DC Microgrids into Power Grids

AC/DC Microgrids are a small part of low voltage distribution networks that are located far from power substations, and are interconnected through the point of common coupling to power grids. These systems are important keys for the flexible, techno-economic, and environmental-friendly generation of units for the reliable operation and cost-effective planning of smart electricity grids. Although AC/DC microgrids, with the integration of renewable energy resources and other energy systems, such as power-to-gas, combined heat and power, combined cooling heat and power, power-to-heat, power-to-vehicle, pump and compressed air storage, have several advantages, there are some technical aspects that must be addressed. This Special Issue aims to study the configuration, impacts, and prospects of AC/DC microgrids that enable enhanced solutions for intelligent and optimized electricity systems, energy storage systems, and demand-side management in power grids with an increasing share of distributed energy resources. It includes AC/DC microgrid modeling, simulation, control, operation, protection, dynamics, planning, reliability and security, as well as considering power quality improvement, load forecasting, market operations, energy conversion, cyber/physical security, supervisory and monitoring, diagnostics and prognostics systems

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles)

Multicarrier Microgrid Operation Model Using Stochastic Mixed Integer Linear Programming

The microgrid operation is addressed in this article based on a multicarrier energy hub. Natural gas, electricity, heating, cooling, hydrogen, carbon dioxide, and renewable energies are considered as the energy carriers. The designed microgrid optimizes and utilizes a wide range of resources at the same time including renewables, electrical storage, hybrid storage, heating-cooling storage, electric vehicles (EVs) charging station, power to gas unit, combined cooling-heating-power, and carbon capture-storage. The purpose is to reduce the environmental pollutions and operating costs. The resilience and flexibility of the energy hub is also improved. Vehicle to grid and fully-partial charge models are incorporated for EVs to improve the system resilience and supplying the critical loads following events. Different events are modeled to evaluate the system resilience. The model is expressed as a stochastic mixed integer linear programming problem. Both active and reactive powers are modeled. The microgrid is simulated under four different cases. The results show that the multitype energy storages reduce the annual cost of energy while the integrated charging station can decrease the load shedding.©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.fi=vertaisarvioitu|en=peerReviewed

Optimal Model for Energy Management Strategy in Smart Building with Energy Storage Systems and Electric Vehicles

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort

Optimization methods for developing electric vehicle charging strategies

Electric vehicles (EVs) are considered to be a crucial and proactive player in the future for transport electrification, energy transition, and emission reduction, as promoted by policy-makers, relevant industries, and the academia. EV charging would account for a non-negligible share in the future electricity demand. The integration of EV brings both challenges and opportunities to the electricity system, mainly from their charging profiles. When EV charging behaviors are uncontrolled, their potentially high charging rate and synchronous charging patterns may result in the bottleneck of the grid capacity and the shortage of generation ramping capacity. However, the promising load shifting potential of EVs can alleviate these problems and even bring additional flexibilities to the demand side for further applications, such as peak shaving and the integration of renewable energy. To grasp these opportunities, novel controlled charging strategies should be developed to help integrate electric vehicles into energy systems. However, corresponding methods in current literature often have customized assumptions or settings so that they might not be practically or widely applied. Furthermore, the attention of literature is more paid to explaining the results of the methods or making consequent policy recommendations, but not sufficiently paid to demonstrating the methods themselves. The lack of the latter might undermine the credibility of the work and hinder readers’ understanding. Therefore, this thesis serves as a methodological framework in response to the fundamental and universal challenges in developing charging strategies for integrating EV into energy systems. The discussions aim to raise readers’ awareness of the essential but often unnoticed concerns in model development and hopefully would enlighten future researchers into this topic. Specifically, this cumulative thesis comprises four papers and analyzes the research topic from two perspectives. With Paper A and Paper B, the micro perspective of the thesis is more applied and focuses on the successful implementation of charging scheduling solutions for each EV individually. Paper A proposes a two-stage scenario-based stochastic linear programming model to schedule EV charging behaviors and considers the uncertainties from future EVs. The model is calculated in a rolling window fashion with updated parameters. Scenario generation for future EVs is simulated by inhomogeneous Markov chains, and scenario reduction is achieved by a fast forward selection method to reduce the computational burden. The objective function is formulated as variance minimization so that the model can be flexibly implemented for various applications. Paper B applies the model proposed in Paper A to investigate how the generation of a wind turbine could be correlated with the EV controlled charging demand. An empirical controlled charging strategy is designed for comparison where EVs would charge as much as possible when wind generation is sufficient or would postpone charging otherwise. Although these two controlled charging strategies perform similarly in terms of wind energy utilization, the solutions from the proposed model could additionally alleviate the volatility of wind energy generation by matching the EV charging curve to the electricity generation profile. With Paper C and Paper D, the macro perspective of the thesis is more explorative and investigates how EVs as a whole would contribute to energy transition or emission reduction. Paper C investigates the greenhouse gas emissions of EVs under different charging strategies in Europe in 2050. Methodologically, the paper proposes an EV module that enables different EV controlled charging strategies to be endogenously determined by energy system models. The paper concludes that EVs would contribute to a 36% emission reduction on the European level even under an uncontrolled charging strategy. Unidirectional and bidirectional controlled charging strategies could further reduce emissions by 4% and 11%, respectively, compared with the original level. As a follow-up study of Paper C, Paper D develops, demonstrates, improves, and compares three different types of EV aggregation methods for integrating an EV module into energy system models. The analysis and demonstration of these methods are achieved by having a simplified energy system model as a testbed and the results from the individual EV modeling method as the benchmark. As different EV aggregation methods share the same data set as for the individual EV modeling method, the disturbance from parameters is minimized, and the influence from mathematical formulations is highlighted. These EV aggregation methods are compared from multiple aspects

전기차의 하이브리드 에너지 저장장치를 위한 에너지 관리 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 장래혁.Electric vehicles (EV) are considered as a strong alternative of internal combustion engine vehicles expecting lower cost per mile, higher energy efficiency and low carbon emission. However, their actual benefits are not yet clearly verified while its energy storage system (ESS) can be improved in many ways. First, low cost per mile of EV is largely diminished if we charge EV with electricity from fossil fuel power plants due to power loss during generation, transmission, conversion and charging. On the other hand, regenerative braking is direct power conversion from the wheel to battery and one of the most important processes that can enhance energy efficiency of EV. Power loss during regenerative braking can be reduced by hybrid energy storage system (HESS) such that supercapacitors accept high power as batteries have small rate capability. Second, low cost per mile claimed by EV manufacturers does not take battery depreciation into account. Battery cost takes up to 50% of the total EV price, and its life is generally guaranteed for only 8~10 years. Harsh charge and discharge profiles of a battery results in reduced cycle life. Use of HESS and systematic charge management algorithms gives potential to mitigate the problems and improve various metrics of ESS such as cycle efficiency, cycle life, and so on. This dissertation discusses design-time and run-time issues in HESS for EVs in order to maximize the energy efficiency and minimize the operating cost. This dissertation performs extensive optimization based on elaborate component models to achieve the objectives. First, we proposed systematic algorithms to maximize the energy efficiency for a regenerative braking scenario, while most of the previous works relied on empirical and heuristic methods. We improve the energy efficiency by calculating the optimal charging power distribution between the supercapacitor bank and battery bank. Minimizing the cost per mile of an EV should consider optimization over a period of time including multiple acceleration and deceleration profiles. A little forecast on the future driving profiles helps prepare the supercapacitor state of charge (SOC) to the optimal level by systematic charge migration such that it can charge and discharge the electrical energy to enhance the energy efficiency. We also propose grid power source-aware EV charging technique to minimize the electricity bill from a home equipped with photovoltaic energy generation. Lastly, we implement an actual EV equipped with HESS to verify the proposed algorithms.Chapter 1 Introduction 1 1.1 Motivation for Electric Vehicle Energy Optimization 1 1.2 ResearchContributions 4 1.3 ThesisOrganization 7 Chapter 2 Background and Related Works 8 2.1 Electric Vehicle Powertrain Generals 8 2.2 Electric Vehicle Powertrain Modeling 13 2.2.1 Vehicle Modeling 13 2.2.2 Motor and Control Circuitry . 14 2.2.3 Energy Storage Elements . 19 2.3 Hybrid Energy Storage System 23 2.3.1 Architecture 23 2.3.2 HESS Management 25 2.3.3 HESS Managementfor EV 26 2.4 Electric Vehicle Charging. 27 Chapter 3 Maximum Power Transfer Tracking for Regenerative Braking 28 3.1 Regenerative Braking of Electric Vehicles 28 3.2 Battery-Supercapacitor HESS Benefits 30 3.3 Electric Vehicle HESS SOC Management 31 3.4 Maximum Power Transfer Tracking for Regenerative Braking 32 3.4.1 Concept of Maximum Power Transfer Tracking 32 3.4.2 RegenerativeBrakingFramework 34 3.5 Experiments 36 Chapter 4 Proactive Charge Management in Electric Vehicle HESS 42 4.1 PotentialsofProactiveChargeManagement 42 4.2 Hybrid Energy Storage Systems for Electric Vehicle 43 4.2.1 EV HESS Topology 43 4.2.2 EV HESS Charge Management 44 4.3 Battery Charging and Discharging Asymmetry and Charge Migration. 47 4.4 Charge Management Efficiency Enhancement Problem 48 4.5 EV HESS Management Policy 49 4.6 Experiments 51 Chapter 5 Electric Vehicle Charging Cost Reduction 55 5.1 Electric Vehicle Charging Standards. 56 5.2 Residential Photovoltaic Installations and EV charging 58 5.3 Grid-Connected PV System with a Battery 62 5.3.1 System Architecture 62 5.3.2 Component Models 62 5.4 Electricity Bill Reduction. 65 5.4.1 Power Generation and Usage Models 65 5.4.2 Battery Management for Electricity Bill Reduction 66 5.4.3 Problem Formulation 67 5.5 Electricity Bill Optimization Algorithm 68 5.6 Experiments 73 5.7 Summary 80 Chapter 6 Electric Vehicle HESS Implementation 82 6.1 EV Prototype 82 6.1.1 Design Specifications 82 6.1.2 Motor Driver Design 85 6.1.3 Motor and Gearbox 85 6.1.4 HESS 86 6.1.5 System Monitoring subsystem 86 Chapter 7 Conclusions 88 요약 96 감사의 글 98Docto

Energy Management and Smart Charging of PEVs in Isolated Microgrids

Microgrids are defined as a cluster of loads and micro-resources operating as a single controllable entity that provides both power and heat to its local area. Typically, these rely on conventional diesel generators, but with recent developments are expected to include more renewable energy sources (RESs), battery energy storage systems (BESSs), and plug-in electric vehicles (PEVs). Both RESs, such as wind and solar, and PEVs can reduce greenhouse gas (GHG) emissions significantly such as carbon dioxide (CO_2) which are released from burning fuel by generators or conventional vehicles. Energy management in isolated microgrids is an important task since these have limited generation capacity and are expected to rely on various uncontrollable resources to match and balance the demand-supply gap. Moreover, PEVs present a promising solution to GHG emissions but on the other hand, their increased penetration can impact power system operation, particularly so in isolated microgrids. Therefore, PEV load management is considered to be a crucial issue. Similarly, demand response (DR) has the potential to provide significant flexibility in operation of isolated microgrids with limited generation capacity, by altering the demand and introducing an elasticity effect. The present research work examines the impact of uncontrolled and controlled (smart) charging of PEVs using a comprehensive mathematical optimization model for short-term operation of the isolated microgrid. This model determines optimal energy management solutions combining generation from different resources such as diesel generators, wind turbines, solar panels, and BESSs, and incorporates the DR options as well. Furthermore, the thesis presents a stochastic optimization model after creating several probabilistic operational scenarios for energy management and smart charging of PEVs in short-term operation of the isolated microgrid considering fixed and optimal DR options. The proposed stochastic optimization model studies the impact of wind and solar generation output variability as well as the effect of uncertain energy consumption patterns of customers; and also the stochastic nature of the state of charge (SOC) of the PEV battery at the start of charging. Several case and scenario studies considering a modified CIGRE isolated microgrid benchmark test system, and using the proposed models are presented and evaluated, to obtain insights into the effect of smart charging vis-`a-vis uncoordinated charging accompanied by DR options in overall energy management of the isolated microgrid.4 month

Innovation in Energy Systems

It has been a little over a century since the inception of interconnected networks and little has changed in the way that they are operated. Demand-supply balance methods, protection schemes, business models for electric power companies, and future development considerations have remained the same until very recently. Distributed generators, storage devices, and electric vehicles have become widespread and disrupted century-old bulk generation - bulk transmission operation. Distribution networks are no longer passive networks and now contribute to power generation. Old billing and energy trading schemes cannot accommodate this change and need revision. Furthermore, bidirectional power flow is an unprecedented phenomenon in distribution networks and traditional protection schemes require a thorough fix for proper operation. This book aims to cover new technologies, methods, and approaches developed to meet the needs of this changing field