Capacity Estimation for Vehicle-to-Grid Frequency Regulation Services with Smart Charging Mechanism

Due to various green initiatives, renewable energy will be massively incorporated into the future smart grid. However, the intermittency of the renewables may result in power imbalance, thus adversely affecting the stability of a power system. Frequency regulation may be used to maintain the power balance at all times. As electric vehicles (EVs) become popular, they may be connected to the grid to form a vehicle-to-grid (V2G) system. An aggregation of EVs can be coordinated to provide frequency regulation services. However, V2G is a dynamic system where the participating EVs come and go independently. Thus it is not easy to estimate the regulation capacities for V2G. In a preliminary study, we modeled an aggregation of EVs with a queueing network, whose structure allows us to estimate the capacities for regulation-up and regulation-down, separately. The estimated capacities from the V2G system can be used for establishing a regulation contract between an aggregator and the grid operator, and facilitating a new business model for V2G. In this paper, we extend our previous development by designing a smart charging mechanism which can adapt to given characteristics of the EVs and make the performance of the actual system follow the analytical model.Comment: 11 pages, Accepted for publication in IEEE Transactions on Smart Gri

Large-Scale Demand Management in Smart Grid

Future energy grids are expected to rely extensively on controlling consumers' demands to achieve an efficient system operation. The demand-side of the power network is usually constituted of a large number of low power loads, unlike energy production which is concentrated in a few numbers of high power generators. This research is concerned with supporting the management of numerous loads, which can be challenging from a computational point-of-view. A common approach to facilitate the management of a large number of resources is through resource aggregation (clustering). Therefore, the main objective of our research is to develop efficient load aggregation methodologies for two categories of demands: residential appliances and electric vehicles. The proposed methodologies are based on queueing theory, where each queue represents a certain category (class) of demand. Residential appliances are considered in the context of two demand management problems, where the first aims to minimize the energy consumption cost, while the second aims to reduce the magnitude of fluctuations in net demand, as a result of a large-scale integration of renewable energy sources (RESs). Existing models for residential demand aggregation suffer from two limitations:first, demand models ignore the inter-temporal demand dependence that is induced by scheduling deferrable appliances; Second, aggregated demand models for thermostatically-controlled loads are computationally inefficient to be used in DR problems that require optimization over multiple time intervals. Although the same aggregation methodology is applied to both problems, each one of them requires a different demand scheduling algorithm, due to the stochastic nature of RESs which is introduced in the second problem. The second part of our research focuses on minimizing the expected system time needed for charging electric vehicles (EVs). This target can be achieved by two types of decisions, the assignment of EVs to charging stations and the charging of EVs' batteries. While there exist aggregation models for batteries' charging, aggregation models for EVs' assignment are almost non-existent. In addition, aggregation models for batteries' charging assume that information about EVs' arrival times, departure times and their required charging energies are given in advance. Such assumption is non-realistic for a charging station, where vehicles arrive randomly. Hence, the third problem is concerned with developing an aggregation model for EVs' assignment and charging, while considering the stochastic nature of EVs' arrivals. Realistic models for residential demands and RES powers were used to develop the corresponding numerical results. The proposed scheduling algorithms do not require highly restrictive assumptions. The results proved that effectiveness of the proposed methodology and algorithms in achieving a significant improvement in the problems' objectives. On the other hand, the algorithm used in EV assignment requires restrictive Markovian assumptions. Hence, we needed to verify our proposed analytical model with a more realistic simulation model. The results showed a good compliance between both models. Our proposed methodology helped in improving the average system time significantly, compared to that of a near-station-assignment policy. This study is expected to have an important contribution from both research and application perspectives. From the research side, it will provide a tool for managing a large, diverse number of electric appliances by classifying them according to how much they can benefit the utility. From the application side, our work will help to include residential consumers in demand response (while current DR programs focus on the industrial sector only). It will also facilitate RESs and EVs on a large scale to help address environmental concerns

Exploiting Mobile Energy Storages for Overload Mitigation in Smart Grid

The advancement of battery and electronic technologies pushes forward transportation electrification, accelerating the commercialization and prevalence of plug-in electric vehicles (PEVs). The development of PEVs is closely related to the smart grid as PEVs are considered as high power rating electric appliances that require frequent charging. As PEVs become regular transportation options, charging stations (CSs) are also extensively deployed in the smart grid to meet the PEV charging demand. During peak traffic hours, the increasing PEV charging demand could exceed the loading capacities of CS-connected transformers, causing heavy charging overload in-station. Without proper overload mitigation, the energy imbalance issues will result in severe feeder degradation and power quality issue. Therefore, solutions for CS overload mitigation are in urgent demand. Considering the rechargeable nature of PEV batteries, PEVs can serve as potential mobile energy storages (MESs) to carry energy from power nodes with excess energy to overloaded CSs to compensate the overloads. Compared to infrastructure upgrade and installing stationary energy storages at CSs, the utilization of PEVs not only minimizes the additional upgrade/installation expenditure, but also maximizes the energy utilization in the smart grid with high flexibility. However, the PEV utilization for overload mitigation is confronted with a variety of challenges due to vehicular mobility and the fear of battery degradation. Because of vehicular mobility, the CS operation dynamics become stochastic processes, increasing the difficulty of the CS demand estimation. Without accurate demand estimation, the overload condition cannot be timely predicted and controlled. Moreover, the stochastic on-road traffic could impair the time-efficiency of the PEV overload mitigation service. Further, as the overload mitigation service demands frequent charging and discharging, the fear of battery degradation could impede PEV owners from providing the service, making the overload mitigation tasks harder to fulfill. In this thesis, we address the above challenges to effectively utilize PEVs for overload mitigation in the smart grid. In specific, different approaches are designed according to the PEV properties at different commercialization stages. First, at the early PEV commercialization stage, power utility company purchases large battery capacity PEVs as utility-owned MESs (UMESs) whose only responsibility is fulfilling the energy compensation task. The fleet of UMESs is rather small due to the company's limited budget, and therefore UMESs priorly serve the CSs with large energy imbalance (e.g., 500-1000kWh). Thus, the stochastic CS charging demand needs to be accurately estimated and then UMESs can be scheduled to these CSs for overload mitigation. To achieve this objective, we develop a two-dimensional Markov Chain model to characterize the stochastic process in-station so that the CS charging demand can be precisely estimated. Based on the estimated CS demand status, a two-tier energy compensation framework is designed to schedule UMESs to the heavily overloaded CSs in a timely and cost-efficient manner. Second, at the medium stage of PEV commercialization, vehicle-fleet based companies are motivated by legislation to purchase a large fleet of PEVs which can be served as potential MESs, referred to as legislation-motivated MESs (LMESs). To deliver energy to overloaded CSs using LMESs would introduce a large amount of additional traffics to the transportation network. When injecting these LMES traffics into an already busy transportation network, unexpected traffic delay could occur, delaying the overload mitigation service. To avoid the potential traffic delay incurred by LMES service, we develop an energy-capacitated transportation network model to measure the road capacity of accommodating additional LMES traffics. Based on the developed model, a loading-optimized navigation scheme is proposed to calculate the optimal navigation routes for LMES overload mitigation. To stimulate LMESs following the optimal navigation, we propose a dynamic pricing scheme that adjusts the service price to align the LMES service routes with the optimal routes to achieve a time-efficient service result. Third, when PEVs are prevalent in the automobile market and become regular transportation options for every household, on-road private-owned PEVs can be efficiently used as energy porters to deliver energy to overloaded CSs, named as private MESs (PMESs). As the primary objective of PMESs is to reach their planned destinations, the monetary incentive is demanded to stimulate them actively participating in the overload mitigation tasks. Therefore, a hierarchical decision-making process between the utility operator (UO) and PMESs is in demand. Moreover, considering PMESs have different service preferences (e.g., the fear of battery degradation, the unwillingness of long service time, etc.), individual PMES decision making process on the task should be carefully modelled. Thus, we propose to characterize the price-service interaction between the operator and PMESs as a Stackelberg game. The operator acts as the leader to post service price to PMESs while PMESs act as followers, responding to the posted price to maximize their utility functions. In summary, the analysis and schemes proposed in this thesis can be adopted by the local power utility company to utilize PEVs for overload mitigation at overloaded power nodes. The proposed schemes are applicable during different PEV commercialization stage and present PEVs as a flexible solution to the smart grid overload issue

Capacity analysis in different systems exploiting mobility of VANETs

Improving road safety and traffic efficiency has been a long-term endeavor for not only government but also automobile industry and academia. After the U.S. Federal Communication Commission (FCC) allocated a 75 MHz spectrum at 5.9 GHz for vehicular communications, the vehicular ad hoc network (VANET), as an instantiation of the mobile ad hoc network (MANET) with much higher node mobility, opens a new door to combat the road fatalities. In VANETs, a variety of applications ranging from safety related (e.g. emergency report, collision warning) to non-safety-related (e.g. infotainment and entertainment) can be enabled by vehicle-to-vehicle (V2V) and vehicle-to-roadside (V2R) communications. However, the flourish of VANET still hinges fully understanding and managing the challenges that the public concerns, for example, capacity and connectivity issues due to the high mobility of vehicles. In this thesis, we investigate how vehicle mobility can impact the performance in three important VANET-involved systems, i.e., pure VANET, VANET-enhanced intelligent transportation systems (ITS), and fast electric vehicle (EV) charging systems. First, in pure VANET, our work shows that the network data-traffic can be balanced and the network throughput can be improved with the help of the vehicle mobility differentiation. Furthermore, leveraging vehicular communications of VANETs, the mobility-aware real-time path planning can be designed to smooth the vehicle traffic in an ITS, through which the traffic congestion in urban scenarios can be effectively relieved. In addition, with the consideration of the range anxiety caused by mobility, coordinated charging can provide efficient charging plans for electric vehicles (EVs) to improve the overall energy utilization while preventing an electric power system from overloading. To this end, we try to answer the following questions: Q1) How to utilize mobility characteristics of vehicles to derive the achievable asymptotic throughput capacity in pure VANETs? Q2) How to design path planning for mobile vehicles to maximize spatial utility based on mobility differentiation, in order to approach vehicle-traffic capacity in a VANET-enhanced ITS? Q3) How to develop the charging strategies based on mobility of electric vehicles to improve the electricity utility, in order to approach load capacities of charging stations in VANET-enhanced smart grid? To achieve the first objective, we consider the unique features of VANETs and derive the scaling law of VANETs throughput capacity in the data uploading scenario. We show that in both free-space propagation and non-free-space propagation environments, the achievable throughput capacity of individual vehicle scales as $\Theta (\frac{1}{{\log n}}) with$n$denoting the population of a set of homogenous vehicles in the network. To achieve the second objective, we first establish a VANET-enhanced ITS, which incorporates VANETs to enable real-time communications among vehicles, road side units (RSUs), and a vehicle-traffic server in an efficient way. Then, we propose a real-time path planning algorithm, which not only improves the overall spatial utilization of a road network but also reduces average vehicle travel cost for avoiding vehicles from getting stuck in congestion. To achieve the third objective, we investigate a smart grid involved EV fast charging system, with enhanced communication capabilities, i.e., a VANET-enhanced smart grid. It exploits VANETs to support real-time communications among RSUs and highly mobile EVs for real-time vehicle mobility information collection or charging decision dispatch. Then, we propose a mobility-aware coordinated charging strategy for EVs, which not only improves the overall energy utilization while avoiding power system overloading, but also addresses the range anxieties of individual EVs by reducing the average travel cost. In summary, the analysis developed and the scaling law derived in$Q1$of this thesis is practical and fundamental to reveal the relationship between the mobility of vehicles and the network performance in VANETs. And the strategies proposed in$Q2$and$Q3$ of the thesis are meaningful in exploiting/leveraging the vehicle mobility differentiation to improve the system performance in order to approach the corresponding capacities

Radio Resource Management in LTE-Advanced Systems with Carrier Aggregation

In order to meet the ever-increasing demand for wireless broadband services from fast growing mobile users, the Long Term Evolution -Advanced (LTE-A) standard has been proposed to effectively improve the system capacity and the spectral efficiency for the fourth-generation (4G) wireless mobile communications. Many advanced techniques are incorporated in LTE-A systems to jointly ameliorate system performance, among which Carrier Aggregation (CA) is considered as one of the most promising improvements that has profound significance even in the upcoming 5G era. Component carriers (CCs) from various portions of the spectrum are logically concatenated to form a much larger virtual band, resulting in remarkable boosted system capacity and user data throughput. However, the unique features of CA have posed many emerging challenges as well as span-new opportunities on the Radio Resource Management (RRM) in the LTE-A systems. First, although multi-CC transmission can bring higher throughput, it may incur more intensive interference for each CC and more power consumption for users. Thus the performance gain of CA under different conditions needs fully evaluating. Besides, as CA offers flexible CC selection and cross-CC load balancing and scheduling, enhanced RRM strategies should be designed to further optimize the overall resource utilization. In addition, CA enables the frequency reuse on a CC resolution, adding another dimension to inter-cell interference management in heterogeneous networks (HetNets). New interference management mechanisms should be designed to take the advantage of CA. Last but not least, CA empowers the LTE-A systems to aggregate the licensed spectrum with the unlicensed spectrum, thus offering a capacity surge. Yet how to balance the traffic between licensed and unlicensed spectrum and how to achieve a harmony coexistence with other unlicensed systems are still open issues. To this end, the dissertation emphasizes on the new functionalities introduced by CA to optimize the RRM performance in LTE-A systems. The main objectives are four-fold: 1) to fully evaluate the benefits of CA from different perspectives under different conditions via both theoretical analysis and simulations; 2) to design cross-layer CC selection, packet scheduling and power control strategies to optimize the target performance; 3) to analytically model the interference of HetNets with CA and propose dynamic interference mitigation strategies in a CA scenario; and 4) to investigate the impact of LTE transmissions on other unlicensed systems and develop enhanced RRM mechanisms for harmony coexistence. To achieve these objectives, we first analyze the benefits of CA via investigating the user accommodation capabilities of the system in the downlink admission control process. The LTE-A users with CA capabilities and the legacy LTE users are considered. Analytical models are developed to derive the maximum number of users that can be admitted into the system given the user QoS requirements and traffic features. The results show that with only a slightly higher spectrum utilization, the system can admit as much as twice LTE-A users than LTE users when the user traffic is bursty. Second, we study the RRM in the single-tier LTE-A system and propose a cross-layer dynamic CC selection and power control strategy for uplink CA. Specifically, the uplink power offset effects caused by multi-CC transmission are considered. An estimation method for user bandwidth allocation is developed and a combinatorial optimization problem is formulated to improve the user throughput via maximizing the user power utilization. Third, we explore the interference management problem in multi-tier HetNets considering the CC-resolution frequency reuse. An analytical model is devised to capture the randomness behaviors of the femtocells exploiting the stochastic geometry theory. The interaction between the base stations of different tiers are formulated into a two-level Stackelberg game, and a backward induction method is exploited to obtain the Nash equilibrium. Last, we focus on the mechanism design for licensed and unlicensed spectrum aggregation. An LTE MAC protocol on unlicensed spectrum is developed considering the coexistence with the Wi-Fi systems. The protocol captures the asynchronous nature of Wi-Fi transmissions in time-slotted LTE frame structure and strike a tunable tradeoff between LTE and Wi-Fi performance. Analytical analysis is also presented to reveal the essential relation among different parameters of the two systems. In summary, the dissertation aims at fully evaluating the benefits of CA in different scenarios and making full use of the benefits to develop efficient and effective RRM strategies for better LTE-Advanced system performance