Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

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

Semantic Networks for Hybrid Processes.

Simulation models are often used in parallel with a physical system to facilitate control, diagnosis and monitoring. Model based methods for control, diagnosis and monitoring form the basis for the popular sobriquets `intelligent', `smart' or `cyber-physical'. We refer to a configuration where a model and a physical system are run in parallel as a emph{hybrid process}. Discrepancies between the model and the process may be caused by a fault in the process or an error in the model. In this work we focus on correcting modeling errors and provide methods to correct or update the model when a discrepancy is observed between a model and process operating in parallel. We then show that some of the methods developed for model adaptation and diagnosis can be used for control systems design. There are five main contributions. The first contribution is an analysis of the practical considerations and limitations of a networked implementation of a hybrid process. The analysis considers both the delay and jitter in a packet switching network as well as limits on the accuracy of clocks used to synchronize the model and process. The second contribution is a semantic representation of hybrid processes which enables improvements to the accuracy and scope of algorithms used to update the model. We demonstrate how model uncertainty can be balanced against signal uncertainty and how the structure of interconnections between model components can be automatically reconfigured if needed. The third contribution is a diagnostic approach to isolate model components responsible for a discrepancy between model and process, for a structure preserving realization of a system of ODEs. The fourth contribution is an extension of the diagnostic strategy to include larger graphs with cycles, model uncertainty and measurement noise. The method uses graph theoretic tools to simplify the graph and make the problem more tractable and robust to noise. The fifth contribution is a simulation of a distributed control system to illustrate our contributions. Using a coordinated network of electric vehicle charging stations as an example, a consensus based decentralized charging policy is implemented using semantic modeling and declarative descriptions of the interconnection network.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99903/1/danand_1.pd

Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations

Wireless network architecture for future smart grid machine to machine communications

Transformation of the conventional power grid into an efficient power delivery network is an important advance that will benefit consumers, business and the environment by providing improved integration of renewable energy, including solar and wind. A reliable, low latency communication system is a fundamental requirement for smart power grids. To achieve bidirectional energy distribution capability and to support diverse Smart Grid (SG) applications, the modern SG requires the capacity to handle the traffic generated by machine to machine (M2M) communication infrastructure. Successful integration of numerous SG applications, renewable energy sources and Electric Vehicles (EVs) into a conventional power grid would not be possible without a communication network that has been designed to support the needs of the new and innovative renewable power generation, distribution and storage technologies. While the legacy communication infrastructure, utilized to support the existing power network, fails to support all of the SG functionalities, Software Defined Networking (SDN), based on wireless communication systems, has the potential to provide an effective solution. SDN offers a range of features that fulfill the unique requirements of the SG applications. Being a new networking paradigm, SDN remains to be implemented for SG M2M communication scenarios and there remain a number of challenges that need to be overcome. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. The aim of this research was to carry out an in-depth study on the future SG communication networks and to propose solutions to identified limitations of existing communication networks. Keeping this intention in mind, the study first focuses on the SG application modeling techniques based on the traffic requirements and power supply load profiles. To address the dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. SG application models were developed and evaluated using a range of scenarios reflecting typical usage. Heterogenous network architectures and efficient traffic models were developed to identify an appropriate wireless communication technology and to maximize the network performance for major SG applications. However, a careful observation of the communication networks ability to manage and control the diverse M2M communications reveals that the inadequate dynamic communication network configuration capability would be a problem for future SG applications. M2M communication protocols and standards provide a starting point for the broader development of SG communication networks that can be enhanced by abstracting high-level network functionalities. To realize the full potential of the SGs and deployment scenarios it is essential to analyze the major applications and key requirements to develop those applications. Also, it might be necessary to select an appropriate communication technology for each of the power system domains. The study first focuses on the SG application modeling techniques based on the traffic requirement and load supply profiles of the power system. To address dynamicity of the traffic model and demand load curve, a series of analytical models and smart algorithms were developed. The developed SG application models were further evaluated using simulation scenarios and a test bed model. The challenge of selecting an appropriate wireless communication technology and maximizing network performance for major SG applications was handled by developing multiple heterogenous network architectures and efficient traffic models. A comprehensive literature review of the state of the art of SG applications and standards was carried out to develop robust network models utilizing diverse communication technologies. The literature survey immensely helped to develop two novel SG application models, Zigbee based Pilot protection scheme for a smart distribution grid and Vehicle to Grid (V2G) smart load management scheme. Application modelling included detail traffic modelling, developing smart algorithms, analytical models, user load profile analysis, simulation models and test bed setups. Furthermore, a novel WiMax Ranging scheme is presented to improve the random-access mechanism for various periodic M2M applications supported by extensive simulation based performance analysis. Future SGs will be overwhelmed by an excessive number of sensor devices that collect various data related to the power system. In a SG Neighborhood Area Network (NAN), wireless sensor networks (WSNs) will play a key role in the development of major SG applications. The application centric WSNs require complex configurations such as well-defined access techniques, transmission and security protocols. Challenges also include development of appropriate routing protocols to tackle resource limitations and delay caused by decentralized WSNs and ad hoc based packet forwarding techniques. A careful observation of manageability and controllability of the diverse M2M network reveals that the inadequate dynamic network configuration capability of the existing SG communication network would be a key bottleneck for future SG. Thus, a novel WSN based communication framework is presented exploiting the emerging SDN networking paradigm. SDN would be beneficial for SGs in many ways. By decoupling the control plane and data forwarding plane, SDN facilitates real-time control and integration of network services and applications that can reach down into the network through the controller hierarchy. A higher degree of control over the overall SG communication network would be achievable via the dynamic programmability provided by SDN. The SDN based WSN network must be robust enough to support the adaptive energy dispatching capacity of the modern power system. The proposed communication framework incorporates novel communication features to separate the control plane and data forwarding plane within the SG communication network. This includes detailed modeling of the control and data plane communication parameters to support both delay sensitive and delay tolerant SG applications. The unique SDN features offers a platform to accommodate maximum number of SG applications with highest controllability and manageability. The performance of the SDN based future SG network is evaluated using a simulation scenario that considers realistic user load profiles, wireless standards, the SG premises geographical area and the state of the art of the SG standards. Although the control plane enables a global view of the data plane and provides a centralized platform to control and deploy new services, physically a single controller in the controller would not be practical for SG networks. The challenges arise in terms of scalability, security and reliability, particularly in a SG environment. To increase the efficiency of the proposed SDN based WSNs for the SG NAN, the study proposed distributed controllers with a comprehensive analytical model that optimizes the number of distributed controllers to enhance performance of the proposed communication framework in the NAN domain. The proposed framework along with the analytical model derive several solutions, such as the minimum number of controllers to support the switches and M2M devices, accommodate SG applications and a differentiated flow processing technique to support all traffic types within the network. Lastly, the study focuses on developing SDN-based application specific traffic models for the smart distribution grid. The thesis focuses on three major issues while developing a future SG communication system. Firstly, its identifies major applications and their traffic requirements at different domains of the SG. Appropriate traffic models were developed by designing robust wireless communication network models. Also, application centric smart optimization techniques are adopted to achieve maximum performance and presented with simulation results, statistical analysis and a test bed result analysis. Secondly, to facilitate the centralized controllability and programmability for supporting diverse SG applications within the SG, a novel WSNs communication framework is presented exploiting the next generation SDN paradigm. Both delay sensitive and delay tolerant SG applications were considered based on the traffic requirement to develop the SDN based WSN communication framework in the SG NAN. Smart algorithms were developed at the SDN based WSN application layer to accommodate a large number of SG applications. The framework feasibility is demonstrated by the simulations carried out to verify the model and provide a statistical analysis. Thirdly, the thesis focuses on developing a novel analytical model that can be used to determine the optimal number of distributed controllers and switches in a SG NAN domain. The proposed application centric traffic modelling techniques, SDN based wireless communication framework and analytical models in this thesis can be adapted for research into other communication networks, particularly those that are begin developed for the Internet of Things and other forms of M2M communications. Also, due to the technology agonistic characteristics of the analytical and traffic models, they can be used in the development of various wireless networks, particularly those that focus on wireless sensor networks, more generally than the broader Internet of Things

Novel approaches to optimize and mitigate the impact of high penetration level of electric vehicles on the distribution network

In recent years, the integration of Electric Vehicles (EVs) into the distribution network is studied intensively. One of the major concerns is that EVs consume lots of energy during a short period when most of them are simultaneously connected to the grid (during the night or during working hours). Therefore, in case the consequence of simultaneous charging is not resolved, undesired peak loads may appear on the distribution network. This is the reason why research is actually focusing on optimization and control algorithms to be executed at different levels of the network. Since the number of EVs increases drastically, the power demand during specific periods of the day would cause severe issues on the network. As a matter of fact, high peak demand may exponentially reduce the lifetime of the transformers and may damage some elements on the network. Therefore, severe voltage drop and blackouts of some regions or on the complete network can be the consequences. To solve the problem, many studies were conducted to reduce the impact of integrating EVs on the network. Their main goal was to limit the peak demand created by the EVs in order to protect the distribution grid from any damages. To do so, many optimization techniques and control strategies were used to mitigate the impact of EVs on the network. The main goal of using optimization techniques is to schedule the charging and discharging of EVs during their connection period, in which their charging will be shifted to periods where the demand on the network is low. For this purpose, Demand Response Programs (DRP) are used to incite the end-users consuming during low electricity prices and reducing their consumptions during high prices. As its name indicates, the DRP uses the supply and demand models to price the electricity depending on the power consumption of the end-users and the available power generated by the power utility. The electricity price may vary in time, in which in periods when the consumption is high, the electricity price will be high in order to let the end-users shift their power consumption to other periods when the price and the consumption are low. This strategy will help the power utility to control the power demand of the end-users and reduce the burden in certain periods in a day. Despite the advantages of using the DRP in controlling the total load demand on the distribution network, it is still limited by its long-time response (one day ahead pricing, hourahead pricing, etc.). Therefore, DRPs are useful when the speed of response to a certain unfavorable situation does not require an instant action or intervention, in other words, when losses are to be reduced, customer expenses are to be minimized, and operator benefits are to be maximized. DRPs can’t eliminate any sudden variation of the load which may produce a blackout or damage some components or even reduce their lifetime. Researchers have indeed suggested multiple methods for finding fast response solutions in order to prevent any technical or economic undesired phenomena occurring on the network. Literature review shows that these existing studies offer a solution for a partial aspect of the problem. It is seen that published results show the improvement on either technical or economic or implementation issues, but to the best of our knowledge, existing approaches cannot find a global optimum, considering all the electrical distribution aspects (pricing, maintenance, customer satisfaction, technical losses, stability, availability, etc.). The suggested approach has a wider view of the problem since it considers EV penetration at different levels of the network, it considers if the network’s infrastructure is conventional or modernized, it considers the lifetime of the distribution transformers, it minimizes losses and it introduces a collaborative algorithm for finding the global optimum solution. Comparative studies show the advantages of the suggested approach compared to the existing ones. Major findings in this thesis can be summarized as follows (i) the total load demand on the transformer respects its limit, which will increase its lifetime, (ii) the voltage profile respects the limits on the network and the transformers, (iii) the energy losses on the network are reduced, (iv) the depreciation cost of the network is reduced, (v) the revenue of the power utility and distribution system operator is increased, (vi) and finally the end-users are satisfied because the proposed strategies help them to reduce their electricity cost. Therefore, both end-users and power utility are satisfied