PEV Charging Infrastructure Integration into Smart Grid

Plug-in electric vehicles (PEVs) represent a huge step forward in a green transportation system, contribute to the reduction of greenhouse gas emission, and reduce the dependence on fossil fuel. With the increasing popularity of PEVs, public electric-vehicle charging infrastructure (EVCI) becomes indispensable to meet the PEV user requirements. EVCI can consist of various types of charging technologies, offering multiple charging services for PEV users. Proper integration of the charging infrastructure into smart grid is key to promote widespread adoption of PEVs. Planning and operation of EVCI are technically challenging, since PEVs are characterized by their limited driving range, long charging duration, and high charging power, in addition to the randomness in driving patterns and charging decisions of PEV users. EVCI planning involves both the siting and capacity planning of charging facilities. Charging facility siting must ensure not only a satisfactory charging service for PEV users but also a high utilization and profitability for the chosen facility locations. Thus, the various types of charging facilities should be located based on an accurate location estimation of the potential PEV charging demand. Capacity planning of charging facilities must ensure a satisfactory charging service for PEV users in addition to a reliable operation of the power grid. During the operation of EVCI, price-based coordination mechanisms can be leveraged to dynamically preserve the quality-of-service (QoS) requirements of charging facilities and ensure the profitability of the charging service. This research is to investigate and develop solutions for integrating the EVCI into the smart grid. It consists of three research topics: First, we investigate PEV charging infrastructure siting. We propose a spatial-temporal flow capturing location model. This model determines the locations of various types of charging facilities based on the spatial-temporal distribution of traffic flows. In the proposed model, we consider transportation network dynamics and congestion, in addition to different characteristics and usage patterns of each charging facility type. Second, we propose a QoS aware capacity planning of EVCI. The proposed framework accounts for the link between the charging QoS and the power distribution network (PDN) capability. Towards this end, we firstly optimize charging facility sizes to achieve a targeted QoS level. Then, we minimize the integration cost for the PDN by attaining the most cost-effective allocation of the energy storage systems and/or upgrading the PDN substation and feeders. Additionally, we capture the correlation between the occupation levels of neighboring charging facilities and the blocked PEV user behaviors. Lastly, we investigate the coordination of PEV charging demands. We develop a differentiated pricing mechanism for a multiservice EVCI using deep reinforcement learning (RL). The proposed framework enhances the performance of charging facilities by motivating PEV users to avoid over-usage of particular service classes. Since customer-side information is stochastic, non-stationary, and expensive to collect at scale, the proposed pricing mechanism utilizes the model-free deep RL approach. In the proposed RL approach, deep neural networks are trained to determine a pricing policy while interacting with the dynamically changing environment. The neural networks take the current EVCI state as input and generate pricing signals that coordinate the anticipated PEV charging demand

Service Revenue Evaluation Methodologies to Maximize the Benefits of Energy Storage

The objective of this research is to develop novel methodologies and tools for service revenue evaluation of electrical energy storage systems. Energy storage systems can provide a wide range of services and benefits to the entire value chain of the electricity industry and, therefore, are becoming a favorable technology among stakeholders. The U.S. Government and various states have set initiatives and mandated energy storage deployment as part of their grid modernization roadmap. The key to an increased deployment of energy storage projects is their economic viability. Because of the significant potential value of energy storage as well as the complexity of the decision-making problem, sophisticated service evaluation methodologies and service optimization tools are highly needed. The maximum potential value of energy storage cannot be captured with the evaluation methodologies that have been developed for conventional generators or other distributed energy resources. Previous research studies mostly operational strategies for energy storage coupled with renewable energy sources and the benefits and business models of privately-owned energy storage systems are not well understood. Most of the existing literature focuses on evaluating energy storage systems providing a single service while multiservice operation and evaluation is often not considered. The few available methods for multiservice evaluation study a limited number of services and cannot be readily implemented into a computational tool due to complexity and scalability issues. Accordingly, this research proposes novel service evaluation methodologies with two main objectives: a. Discover the maximum value of energy storage systems for single and multiservice applications, b. Provide flexibility, scalability and tractability of implementation. In order to meet these objectives, various methodologies based on statistical analysis, dynamic control, mixed integer linear programming, convex optimization and decomposition have been proposed. The challenges, complexities, and the benefits of modeling energy services using a scalable approach are analyzed, solutions are proposed and simulated with realistic data in three main chapters of this research: a) energy storage in wholesale energy markets, b) generic multiservice revenue analysis of energy storage, and c) temporal complexities of energy storage optimization models: value and decomposition. Simulation results show the feasibility of the proposed approaches, and significant added values to the economic viability of energy storage projects using the proposed methodologies. Energy storage decision makers including public utility commissioners, transmission/distribution system operators, aggregators, private energy storage owners/investors, and end-use customers (residential and commercial loads) can benefit from the proposed methodologies and simulation results. A software tool has been developed for multiservice benefit cost analysis of energy storage projects. It is hoped that with the significant unlocked value of energy storage systems using the proposed tools and methodologies, more of these technologies be deployed in the future grids to help communities with their sustainability and environmental goals.Ph.D

Hybrid switching : converging packet and TDM flows in a single platform

Optical fibers have brought fast and reliable data transmission to today’s network. The immense fiber build-out over the last few years has generated a wide array of new access technologies, transport and network protocols, and next-generation services in the Local Area Network (LAN), Metropolitan Area Network (MAN), and Wide Area Network (WAN). All these different technologies, protocols, and services were introduced to address particular telecommunication needs. To remain competitive in the market, the service providers must offer most of these services, while maintaining their own profitability. However, offering a large variety of equipment, protocols, and services posses a big challenge for service carriers because it requires a huge investment in different technology platforms, lots of training of staff, and the management of all these networks. In today’s network, service providers use SONET (Synchronous Optical NETwork) as a basic TDM (Time Division Multiplexing) transport network. SONET was primarily designed to carry voice traffic from telephone networks. However, with the explosion of traffic in the Internet, the same SONET based TDM network is optimized to support increasing demand for packet based Internet network services (data, voice, video, teleconference etc.) at access networks and LANs. Therefore the service providers need to support their Internet Protocol (IP) infrastructure as well as in the legacy telephony infrastructure. Supporting both TDM and packet services in the present condition needs multilayer operations which is complex, expensive, and difficult to manage. A hybrid switch is a novel architecture that combines packets (IP) and TDM switching in a unified access platform and provides seamless integration of access networks and LANs with MAN/WAN networks. The ability to fully integrate these two capabilities in a single chassis will allow service providers to deploy a more cost effective and flexible architecture that can support a variety of different services. This thesis develops a hybrid switch which is capable of offering bundled services for TDM switching and packet routing. This is done by dividing the switch’s bandwidth into VT1.5 (Virtual Tributary -1.5) channels and providing SONET based signaling for routing the data and controlling the switch’s resources. The switch is a TDM based architecture which allows each switch’s port to be independently configured for any mixture of packet and TDM traffic, including 100% packet and 100% TDM. This switch allows service providers to simplify their edge networks by consolidating the number of separate boxes needed to provide fast and reliable access. This switch also reduces the number of network management systems needed, and decreases the resources needed to install, provision and maintain the network because of its ability to “collapse” two network layers into one platform. The scope of this thesis includes system architecture, logic implementation, and verification testing, and performance evaluation of the hybrid switch. The architecture consists of ingress/egress ports, an arbiter and a crossbar. Data from ingress ports is carried to the egress ports via VT1.5 channels which are switched at the cross point of the crossbar. The crossbar setup and channel assignments at ingress port are done by the arbiter. The design was tested by simulation and the hardware cost was estimated. The performance results showed that the switch is non-blocking, provide differentiated service, and has an overall effective throughput of 80%. This result is a significant step towards the goal of building a switch that can support multiprotocol and provide different network capabilities into one platform. The long-term goal of this project is to develop a prototype of the hybrid switch with broadband capability

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

On greening optical access networks

With the remarkable growth of fiber-based services, the number of FTTx subscribers has been dramatically increasing in recent years. Owing to the environmental concern, reducing energy consumption of optical access networks has become an important issue for network designers. In Ethernet passive optical network (EPON), the optical line terminal (OLT) located at the central office broadcasts the downstream traffic to all optical network units (ONUs), each of which checks all arrival downstream packets to obtain those destined to itself. Since traffic of ONUs changes dynamically, properly defining the sleep mode for idle ONUs can potentially save a significant amount of energy. However, it is challenging to shut down an ONU receiver as the ONU needs to receive some downstream control packets to perform upstream transmission. In this framework, a novel sleep control scheme is proposed to address the downstream issue which can efficiently put ONU receivers to sleep. This dissertation further defines multiple levels of power saving in which the ONU disables certain functions based on the upstream and downstream traffic load. The proposed schemes are completely compatible with the multi-point control protocol (MPCP) and EPON standards. Elimination of the handshake process makes the sleep control schemes more efficient. Currently, OLTs also consume a significant amount of energy in EPON. Therefore, reducing energy consumption of OLT is as important as reducing energy consumption of ONUs; such requirement becomes even more urgent as OLT keeps increasing its provisioning data rate, and higher data rate provisioning usually implies higher energy consumption. Thus, a novel energy-efficient OLT structure, which guarantees services of end users with a smallest number of power-on OLT line cards, is proposed. More specifically, the number of power-on OLT line cards is adapted to the real-time incoming traffic. Also, to avoid service disruption resulted by powering off OLT line cards, a proper optical switch is equipped in OLT to dynamically configure the communications between OLT line cards and ONUs. By deploying a semi-Markov based technique, the performance characteristics of the sleep control scheme such as delay and energy-saving are theoretically analyzed. It is shown that, with proper settings of sleep control parameters, the proposed scheme can save a significant amount of energy in EPON

Charging Autonomous Electric Vehicle Fleet for Mobility-on-Demand Services: Plug in or Swap out?

This paper compares two prevalent charging strategies for electric vehicles, plug-in charging and battery swapping, to investigate which charging strategy is superior for electric autonomous mobility-on-demand (AMoD) systems. To this end, we use a queueing-theoretic model to characterize the vehicle waiting time at charging stations and battery swapping stations, respectively. The model is integrated into an economic analysis of the electric AMoD system operated by a transportation network company (TNC), where the incentives of passengers, the charging/operating shift of TNC vehicles, the operational decisions of the platform, and the planning decisions of the government are captured. Overall, a bi-level optimization framework is proposed for charging infrastructure planning of the electric AMoD system. Based on the proposed framework, we compare the socio-economic performance of plug-in charging and battery swapping, and investigate how this comparison depends on the evolving charging technologies (such as charging speed, battery capacity, and infrastructure cost). At the planning level, we find that when choosing plug-in charging, increased charging speed leads to a transformation of infrastructure from sparsely distributed large stations to densely distributed small stations, while enlarged battery capacity transforms the infrastructure from densely distributed small stations to sparsely distributed large stations. On the other hand, when choosing battery swapping, both increased charging speed and enlarged battery capacity will lead to a smaller number of battery swapping stations. At the operational level, we find that improved charging speed leads to increased TNC profit when choosing plug-in charging, whereas improved charging speed may lead to smaller TNC profit under battery swapping. The above insights are validated through realistic numerical studies

A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given