Towards Structuring Smart Grid: Energy Scheduling, Parking Lot Allocation, and Charging Management

Nowadays, the conventional power systems are being restructured and changed into smart grids to improve their reliability and efficiency, which brings about better social, economic, and environmental benefits. To build a smart grid, energy scheduling, energy management, parking lot allocation, and charging management of plug-in electric vehicles (PEVs) are important subjects that must be considered. Accordingly, in this dissertation, three problems in structuring a smart grid are investigated. The first problem investigates energy scheduling of smart homes (SHs) to minimize daily energy consumption cost. The challenges of the problem include modeling the technical and economic constraints of the sources and dealing with the variability and uncertainties concerned with the power of the photovoltaic (PV) panels that make the problem a mixed-integer nonlinear programming (MINLP), dynamic (time-varying), and stochastic optimization problem. In order to handle the variability and uncertainties of power of PV panels, we propose a multi-time scale stochastic model predictive control (MPC). We use multi-time scale approach in the stochastic MPC to simultaneously have vast vision for the optimization time horizon and precise resolution for the problem variables. In addition, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool. Further, we propose cooperative distributed energy scheduling to enable SHs to share their energy resources in a distributed way. The simulation results demonstrate remarkable cost saving due to cooperation of SHs with one another and the effectiveness of multi-time scale MPC over single-time scale MPC. Compared to the previous studies, this work is the first study that proposes cooperative distributed energy scheduling for SHs and applies multi-time scale optimization. In the second problem, the price-based energy management of SHs for maximizing the daily profit of GENCO is investigated. The goal of GENCO is to design an optimal energy management scheme (optimal prices of electricity) that will maximize its daily profit based on the demand of active customers (SHs) that try to minimize their daily operation cost. In this study, a scenario-based stochastic approach is applied in the energy scheduling problem of each SH to address the variability and uncertainty issues of PV panels. Also, a combination of genetic algorithm (GA) and linear programming (GA-LP) is applied as the optimization tool for the energy scheduling problem of a SH. Moreover, Lambda-Iteration Economic Dispatch and GA approaches are applied to solve the generation scheduling and unit commitment (UC) problems of the GENCO, respectively. The numerical study shows the potential benefit of energy management for both GENCO and SH. Moreover, it is proven that the GENCO needs to implement the optimal scheme of energy management; otherwise, it will not be effective. Compared to the previous studies, the presented study in this paper is the first study that considers the interaction between a GENCO and SHs through the price-controlled energy management to maximize the daily profit of the GENCO and minimize the operation cost of each SH. In the third problem, traffic and grid-based parking lots allocation and charging management of PEVs is investigated from a DISCO’s and a GENCO’s viewpoints. Herein, the DISCO allocates the parking lots to each electrical feeder to minimize the overall cost of planning problem over the planning time horizon (30 years) and the GENCO manages the charging time of PEVs to maximize its daily profit by deferring the most expensive and pollutant generation units. In both planning and operation problems, the driving patterns of the PEVs’ drivers and their reaction respect to the value of incentive (discount on charging fee) and the average daily distance from the parking lot are modeled. The optimization problems of each DISCO and GENCO are solved applying quantum-inspired simulated annealing (SA) algorithm (QSA algorithm) and genetic algorithm (GA), respectively. We demonstrate that the behavioral model of drivers and their driving patterns can remarkably affect the outcomes of planning and operation problems. We show that optimal allocation of parking lots can minimize every DISCO’s planning cost and increase the GENCO’s daily profit. Compared to the previous works, the presented study in this paper is the first study that investigates the optimal parking lot placement problem (from every DISCO’s view point) and the problem of optimal charging management of PEVs (from a GENCO’s point of view) considering the characteristics of electrical distribution network, driving pattern of PEVs, and the behavior of drivers respect to value of introduced incentive and their daily distance from the suggested parking lots. In our future work, we will develop a more efficient smart grid. Specifically, we will investigate the effects of inaccessibility of SHs to the grid and disconnection of SHs in the first problem, model the reaction of other end users (in addition to SHs) based on the price elasticity of demand and their social welfare in the second problem, and propose methods for energy management of end users (in addition to charging management of PEVs) and model the load of end users in the third problem

Clean Transportation: Effects of Heterogeneity and Technological Progress on EV Costs and CO2 Abatement, and Assessment of Public EV Charging Stations

The advent of Electric Vehicles (EV) in the private transportation sector is viewed as a means of reducing emissions and making significant efforts towards reducing climate change impacts. However, when it comes to adopting and/or promoting a new technology through subsidies, the consumers’ needs are seldom given significant attention. Moreover, most analyses informing policy making assess the potential of new and cleaner technologies like EVs based on an average consumer’s needs and behavior. Given heterogeneity, these analyses miss subpopulations that benefit (or lose) more than an average consumer. In fact, private transportation greatly depends upon how the diversity of consumers choose to commute and what kind of vehicles they choose to possess. Especially in the United States of America (U.S.), each consumer faces different needs for their daily commute, which dictates their preferences for vehicles. This behavioral heterogeneity in addition to the geographic locations of consumers makes the U.S. private transportation sector an intricate system. The locations of the U.S. define fuel prices as well as emissions from electricity production. Therefore, these behavioral and geographic heterogeneities are highly crucial while calculating the benefits and potentials of EVs. The analyses conducted for this dissertation consider these heterogeneities to accommodate the nuances in consumers. This consideration of heterogeneities is the most critical aspect of this work. Chapter 2 of this dissertation builds a Marginal Abatement Cost Curve (MACC) for Electric Technology Vehicles (ETVs) which incorporates these heterogeneities, behavioral and geographical. With current gasoline and battery cell prices, result indicate that without federal tax credits, about 1.9% of the population would receive direct financial benefits from purchasing an ETV. This subpopulation drives over 4 times (over 48,000 miles annually) more than the average consumer (11,700 miles). The consideration of the heterogeneities has made it possible to recognize this subpopulation. The scenario analyses are conducted for different fuel and battery cell prices. These analyses shed light on how different subpopulations benefit financially and environmentally from ETVs. In this chapter, the impacts of federal tax credits with and without considering heterogeneities are estimated, suggesting why policy analyses need to incorporate consumer heterogeneities while assessing benefits of government subsidies. Given these results on economic and carbon benefits of ETVs, Chapter 3 builds an integrated model of adoption that includes endogenous technological progress—through learning rates—where due to initial adopters the technology is made cheaper for the future ones. The feedback loop developed in this chapter takes into consideration the cumulative production of the technology and estimates price reductions using learning rates. Reduced capital costs then propel more consumers to adopt ETVs making the technology cheaper, again increasing the consumer base that benefits from them. The economic benefits of buying an ETV versus a conventional one costs depend on battery costs, non-battery EV costs, and the future of conventional vehicles. Results are that the future market penetration (share of consumers economically benefitting) is sensitive to two poorly understood quantities: non-battery EV costs and cost increases in conventional vehicles driven by future emission standards. Federal tax credits are also studied in how they stimulate adoption and in turn technological progress of ETVs. Governments are not only investing in subsidies for consumer purchase of ETVs but also in installing public EV charging stations. These charging stations are expected to motivate consumers to choose ETVs over conventional vehicles and help reduce range-anxiety. In Chapter 4 an assessment is conducted to understand how these public resources are being used. Results reveal the behavior of consumers at the public EV charging stations using empirical data collected in the City of Rochester. A data distillation is first conducted for the raw data to construct the daily charging profiles of the EV users. A pattern analysis is then performed to identify 5 distinct and homogenous clusters of daily charging profiles of the consumers. This work defines the operational inefficiency of the public charging station as the time spent in parking without charging out of the total time a PEV user accessed the public charging station. This analysis uncovers a significant inefficient operation of these public EV charging stations, i.e. EVs remained parked at stations long after charging is finished. An estimation of the opportunity cost of reducing this observed inefficiency in terms of Greenhouse Gas emissions savings is also conducted in this chapter. The main policy takeaways of this dissertation are that identifying key subpopulations who benefit from the ETVs is highly significant and possible only by incorporating behavioral and geographical heterogeneities. This allows a more precise estimation of impacts of policies such as the federal tax credits. Secondly, the initial adopters make the technology cheaper for the latter adopters. However, the future market parity of ETVs with conventional vehicles depends on poorly understood factors such as current costs and learning rates of non-battery EV technologies and future cost increases in conventional vehicles driven by stricter emissions requirements. Lastly, the use of public resources, such as public charging stations needs to be studied. They are expensive to create, and inefficient use may deter possible EV adopters. Furthermore, the possible opportunity cost of reducing emissions by using the charging station more efficiently allows better use of a public resource

Modeling a cooperation environment for flexibility enhancement in smart multi-energy industrial systems

Environmental aspects have been highlighted in architecting future energy systems where sustainable development plays a key role. Sustainable development in the energy sector has been defined as a potential solution for enhancing the energy system to meet the future energy requirements without interfering with the environment and energy provision. In this regard, studying the cross-impact of various energy vectors and releasing their inherent operational flexibility is main topic. Thecoordinationofvariousenergyvectorsundertheconceptofmulti-energysystem (MES)hasintroducednewsourcesofoperationalflexibilitytothesystemmanagers. MES considers both interactions among the energy carriers and the decision makers in an interdependent environment to increase the total efficiency of the system and reveal the hidden synergy among energy carriers. This thesis addresses a framework for modeling multi-energy players (MEP) that are coupled based on price signal in multi-energy system (MES) in a competitive environment. MEP is defined as an energy player who can consume or deliver more than one type of energy carriers. At first, the course of evolution for the energy system from today independent energy systems to a fully integrated MES is presented and the fractal structure is described for of MES architecture. Moreover, the operational behavior of plug-in electric vehicles’ parking lots and multi-energy demands’ external dependency are modeled in MES framework to enhance the operational flexibility of local energy systems (LES). In the fractal environment, there exist conflicts among MEPs’ decision making in a same layer and other layers. Realizing the inherent flexibility of MES is the main key for modeling the conflicts in this multi-layer structure. The conflict between two layers of players is modeled based on a bi-level approach. In this problem, the first level is the MEP level where the player maximizes its profit while satisfying LES energy exchange. The LES’s exchange energy price is the output of this level. In the lower level, the LESs schedule their energy balance, based on the upper level input price signal. The problem is transformed into a mathematical program with equilibrium constraint (MPEC) through duality theory. In the next step, high penetration of multi-energy players in the electricity market is modeled and their impacts on electricity market equilibrium are investigated. In such a model, MEP participates in the local energy and wholesale electricity markets simultaneously. MEP and the other players’ objectives in these two markets conflict with each other. Each of these conflicts is modeled based on bi-level programming. The bi-level problems are transformed into a single level mixed-integer linear problem by applying duality theory

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

17-07 Phase-II: Community-Aware Charging Station Network Design for Electrified Vehicles in Urban Areas: \u3c/i\u3e Reducing Congestion, Emissions, Improving Accessibility, and Promoting Walking, Bicycling, and use of Public Transportation

A major challenge for achieving large-scale adoption of EVs is an accessible infrastructure for the communities. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations due to mutual dependence of EV sales and public infrastructure deployment. Such infrastructure deployment also presents a number of unique opportunities for promoting livability while helping to reduce the negative side-effects of transportation (e.g., congestion, emissions, and noise pollution). In this phase, we develop a modeling framework (MF) to consider various factors and their associated uncertainties for an optimal network design for electrified vehicles. The factors considered in the study include: state of charge, dwell time, Origin-Destination (OD) pair

A general model for EV drivers' charging behavior

The increasing adoption of electric vehicles (EVs) due to technical advancements and environmental concerns requires wide deployment of public charging stations (CSs). In order to accelerate the EV penetration and predict the future CSs requirements and adopt proper policies for their deployment, studying the charging behavior of EV drivers is inevitable. This paper introduces a stochastic model that takes into consideration the behavioral characteristics of EV drivers in particular, in terms of their reaction to the EV battery charge level when deciding to charge or disconnect at a CS. The proposed model is applied in two case studies to describe the resultant collective behavior of EV drivers in a community using real field EV data obtained from a major North American campus network and part of London urban area. The model fits well to the datasets by tuning the model parameters. The sensitivity analysis of the model indicates that changes in the behavioral parameters affect the statistical characteristics of charging duration, vehicle connection time, and EV demand profile, which has a substantial effect on congestion status in CSs. This proposed model is then applied in several scenarios to simulate the congestion status in public parking lots and predict the future charging points needed to guarantee the appropriate level of service quality. The results show that studying and controlling the EV drivers’ behavior leads to a significant saving in CS capacity and results in consumer satisfaction, thus, profitability of the station owners

Elbilpolitikk fra et samfunnsøkonomisk perspektiv

This thesis focuses on the economics and polices for the electrification of transport. Over the last few years we have observed a rapid rise in the number of battery electric vehicles (BEVs) in Norway. This growth is the combined result of rapid technological change and a targeted national climate policy. The rising share of BEVs relative to the share of conventional vehicles could lead to socio-economic benefits such as reduced greenhouse gas emissions and local pollution, but it could also pose new challenges such as pressure on the capacity of the electricity distribution network. In addition, BEVs have similar negative externalities as fossil-fueled vehicles with regards to congestion, road wear and accidents. BEVs can mitigate some market failures and exacerbate others, creating a messy optimization problem for the social planner. This illustrates the need for new knowledge on mechanisms and welfare enhancing policies in the transport and electricity markets as they become more integrated. This thesis seeks to contribute to the body of knowledge on the subject, in the following introductory chapter and four independent chapters. The latter chapters are written as scientific papers that are either published or in the process of getting published in peer-reviewed journals.Denne avhandlingen tar for seg elbilpolitikk i et samfunnsøkonomisk perspektiv. De siste årene har vi opplevd en rask økning i antall elbiler i Norge. Denne veksten er et resultat av både rask teknologisk utvikling og en målrettet nasjonal klimapolitikk. Den økende andelen av elbiler i forhold til andelen konvensjonelle biler kan føre til samfunnsøkonomiske fordeler som reduserte klimagassutslipp og lokal forurensning, men det kan også gi nye utfordringer som press på kapasiteten til strømdistribusjonsnettet. I tillegg har elbiler tilsvarende eksterne kostnader som konvensjonelle biler med tanke på kø, veislitasje og ulykker. Elbiler kan dempe noen markedssvikt og forverre andre, og skape et rotete optimaliseringsproblem for samfunnsplanleggeren. Dette understreker behovet for ny kunnskap om den gjensidige påvirkningen mellom transport- og elektrisitetsmarkedet, og hva som kan være samfunnsmessig effektiv politikk. Denne avhandlingen bidrar til kunnskapen om emnet, i det følgende kappen og fire uavhengige kapitler. De siste kapitlene er skrevet som vitenskapelige artikler som enten er publisert eller i ferd med å bli publisert i fagfellevurderte tidsskrifter

Placement of Infrastructure for Urban Electromobility: A Sustainable Approach

Over the last few years, electric vehicles (EVs) have turned into viable urban transportation alternatives. Charging infrastructure is an issue, since high investment is needed and there is a lot of demand uncertainty. Seeking to fill gaps in past studies, this investigation proposes a set of procedures to identify the most adequate places for implementing the EV charging infrastructure. In order to identify the most favorable districts for the installation and operation of electric charging infrastructure in São Paulo city, the following public available information was considered: the density of points of interest (POIs), distribution of the average monthly per capita income, and number of daily trips made by transportation mode. The current electric vehicle charging network and most important business corridors were additionally taken into account. The investigation shows that districts with the largest demand for charging stations are located in the central area, where the population also exhibits the highest purchasing power. The charging station location process can be applied to other cities, and it is possible to use additional variables to measure social inequality. Document type: Articl

Secure Large Scale Penetration of Electric Vehicles in the Power Grid

As part of the approaches used to meet climate goals set by international environmental agreements, policies are being applied worldwide for promoting the uptake of Electric Vehicles (EV)s. The resulting increase in EV sales and the accompanying expansion in the EV charging infrastructure carry along many challenges, mostly infrastructure-related. A pressing need arises to strengthen the power grid to handle and better manage the electricity demand by this mobile and geo-distributed load. Because the levels of penetration of EVs in the power grid have recently started increasing with the increase in EV sales, the real-time management of en-route EVs, before they connect to the grid, is quite recent and not many research works can be found in the literature covering this topic comprehensively. In this dissertation, advances and novel ideas are developed and presented, seizing the opportunities lying in this mobile load and addressing various challenges that arise in the application of public charging for EVs. A Bilateral Decision Support System (BDSS) is developed here for the management of en-route EVs. The BDSS is a middleware-based MAS that achieves a win-win situation for the EVs and the power grid. In this framework, the two are complementary in a way that the desired benefit of one cannot be achieved without attaining that of the other. A Fuzzy Logic based on-board module is developed for supporting the decision of the EV as to which charging station to charge at. GPU computing is used in the higher-end agents to handle the big amount of data resulting in such a large scale system with mobile and geo-distributed nodes. Cyber security risks that threaten the BDSS are assessed and measures are applied to revoke possible attacks. Furthermore, the Collective Distribution of Mobile Loads (CDML), a service with ancillary potential to the power system, is developed. It comprises a system-level optimization. In this service, the EVs requesting a public charging session are collectively redistributed onto charging stations with the objective of achieving the optimal and secure operation of the power system by reducing active power losses in normal conditions and mitigating line congestions in contingency conditions. The CDML uses the BDSS as an industrially viable tool to achieve the outcomes of the optimization in real time. By participating in this service, the EV is considered as an interacting node in the system-wide communication platform, providing both enhanced self-convenience in terms of access to public chargers, and contribution to the collective effort of providing benefit to the power system under the large scale uptake of EVs. On the EV charger level, several advantages have been reported favoring wireless charging of EVs over wired charging. Given that, new techniques are presented that facilitate the optimization of the magnetic link of wireless EV chargers while considering international EMC standards. The original techniques and developments presented in this dissertation were experimentally verified at the Energy Systems Research Laboratory at FIU

Survey of smart parking systems

The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject.Fil: Diaz Ogás, Mathias Gabriel. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; ArgentinaFil: Fabregat Gesa, Ramon. Universidad de Girona; EspañaFil: Aciar, Silvana Vanesa. Universidad Nacional de San Juan. Facultad de Ciencias Exactas, Físicas y Naturales; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Juan; Argentin