Distributed Control of Electric Vehicle Charging: Privacy, Performance, and Processing Tradeoffs

As global climate change concerns, technological advancements, and economic shifts increase the adoption of electric vehicles, it is vital to study how best to integrate these into our existing energy systems. Electric vehicles (EVs) are on track to quickly become a large factor in the energy grid. If left uncoordinated, the charging of EVs will become a burden on the grid by increasing peak demand and overloading transformers. However, with proper charging control strategies, the problems can be mitigated without the need for expensive capital investments. Distributed control methods are a powerful tool to coordinate the charging, but it will be important to assess the trade-offs between performance, information privacy, and computational speed between different control strategies. This work presents a comprehensive comparison between four distributed control algorithms simulating two case studies constrained by dynamic transformer temperature and current limits. The transformer temperature dynamics are inherently nonlinear and this implementation is contrasted with a piece-wise linear convex relaxation. The more commonly distributed control methods of Dual Decomposition and Alternating Direction Method of Multipliers (ADMM) are compared against a relatively new algorithm, Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN), as well as against a low-information packetized energy management control scheme (PEM). These algorithms are implemented with a receding horizon in two distinct case studies: a local neighborhood scenario with EVs at each network node and a hub scenario where each node represents a collection of EVs. Finally, these simulation results are compared and analyzed to assess the methods’ performance, privacy, and processing metrics for each case study as no algorithm is found to be optimal for all applications

Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

Distributed Control of Charging for Electric Vehicle Fleets Under Dynamic Transformer Ratings

Due to their large power draws and increasing adoption rates, electric vehicles (EVs) will become a significant challenge for electric distribution grids. However, with proper charging control strategies, the challenge can be mitigated without the need for expensive grid reinforcements. This article presents and analyzes new distributed charging control methods to coordinate EV charging under nonlinear transformer temperature ratings. Specifically, we assess the tradeoffs between required data communications, computational efficiency, and optimality guarantees for different control strategies based on a convex relaxation of the underlying nonlinear transformer temperature dynamics. Classical distributed control methods, such as those based on dual decomposition and alternating direction method of multipliers (ADMM), are compared against the new augmented Lagrangian-based alternating direction inexact Newton (ALADIN) method and a novel low-information, look-ahead version of packetized energy management (PEM). These algorithms are implemented and analyzed for two case studies on residential and commercial EV fleets with fixed and variable populations. The latter motivates a novel EV hub charging model that captures arrivals and departures. Simulation results validate the new methods and provide insights into key tradeoffs

Maximising the benefit of distributed wind generation through intertemporal Active Network Management

The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component.The role of distribution networks is changing. There is a significant drive, influenced by climate change and security of supply issues, to move electricity generation towards renewable technologies. This is leading to an increase in demand for renewable generation connections at the distribution network level and putting pressure on distribution network operators to change the 'fit-and-forget' philosophy of network operation to include more active approaches. In the UK this is seen through the development of Active Network Management schemes which manage distributed generation in real-time, applying constraints when required to maintain network limits. In parallel, technologies have been developed that are capable of providing intertemporal flexibility, of which two particular examples are energy storage and flexible demand. The objective of the thesis is to answer the questions: How can energy storage and flexible demand be scheduled in a second-generation Active Network Management scheme? And how should they be operated to gain most benefit from distributed wind generation? To answer these questions, the thesis develops and uses tools to study the optimisation of second-generation Active Network Management schemes including intertemporal technologies. The tools developed include a Dynamic Optimal Power Flow algorithm for management of energy storage and flexible demand. The thesis provides the first fully flexible model of energy storage in this context, the first implementation of principles-of-access in an optimal power flow, and the first detailed study of the role of energy storage and flexible demand in managing thermal limits and reducing curtailment of distributed wind generation. The thesis also develops the theory of Dynamic Locational Marginal Pricing based on the economic information contained in an optimal solution to a Dynamic Optimal Power Flow. The thesis shows this to be a useful way of understanding the economic impact of intertemporal flexibility and monetary flows in markets which contain them. The thesis goes on to provide a detailed report of the application of Dynamic Optimal Power Flow and Dynamic Locational Marginal Pricing to an islanded Active Network Management scheme currently in deployment in the UK. This highlights the ability of the tools developed to contribute to future projects. A conclusions of the thesis is that DOPF provides a useful method of scheduling flexible devices such as energy storage and power systems. It takes full account of network constraints and limitations, and as applied in this thesis, the most complete models of the intertemporal effects of energy storage and flexible demand to date. The studies contained in the thesis show that energy storage and flexible demand can increase the benefit of distributed wind generation in Active Network Management by minimising curtailment and transferring generated electricity to periods during which the energy has greatest value in offsetting expensive, fossil fuel based generation. The thesis notes the importance of a useful definition of the 'benefit' of wind generation in terms of global objectives such as minimising emissions rather than interim objectives such as maximising generation from renewables. The thesis discusses the importance of losses in energy storage, and the relationship of storage and network losses with curtailment of wind and the lost opportunity of generating electricity. In terms of losses, the extension of existing economic analysis methods leads to the result that flexibility will only operate between time-steps where the ratio of prices is greater than the round-trip losses of the store. Within this constraint, effective use of energy storage is shown to result from regular charging and discharging. The comparison between energy storage and flexible demand shows that where there are few losses associated with flexibility in demand it is significantly more successful than energy storage at mitigating the effects of variability in wind. The final study of an islanded distribution network with wind curtailment, concludes that energy storage is less effective that flexible demand at reducing wind curtailment, but can provide benefit through management of peak demand. Flexible demand, in the form of flexible domestic electric heating, is shown to have the ability to provide a significant benefit in terms of reduced wind curtailment. This ability is further enhanced for island situations if demand has a frequency-responsive component

New electric utility management and control systems : proceedings of conference, held in Boxborough, Massachusetts, May 30-June 1, 1979

"This work was supported by the Center for Energy Policy Research and the Electric Power Systems Engineering Laboratory of the Massachusetts Institute of Technology.

Architecture of a Microgrid and Optimal Energy Management System

With the growing population trends, the demand for electricity is accelerating rapidly. The policy planners and developers have great focus to utilize renewable energy resources (RERs) to encounter the scarcity of energy since they offer benefits to the environment and power systems. At present, the energy generation is evolving into a smart distribution system that assimilates several energy resources assuring to generate clean energy, to have reliable operational procedures, and to enhance the energy supervision and management arrangements. Therefore, the model of a distributed microgrid (DMG) with optimal energy management strategies based on multi-agent systems (MASs) technique has been focused in this chapter. Distributed energy resources (DER) have been considered for the generation of electrical power to fulfill the consumer’s load demands. Thus, a fully controlled architecture of a grid along with concept of MAS and its development platforms, implementation, and operational procedures have been discussed in detail. In addition, agent’s operations and their coordination within the MG arrangements have been focused by considering the supervision of the entire system autonomously. Moreover, optimal procedures of a microgrid (MG) energy supervision and power distribution system have also been presented considering the cost control and optimal operations of the entire MG at the distributed level

Enabling Privacy in a Distributed Game-Theoretical Scheduling System for Domestic Appliances

Demand side management (DSM) makes it possible to adjust the load experienced by the power grid while reducing the consumers' bill. Game-theoretic DSM is an appealing decentralized approach for collaboratively scheduling the usage of domestic electrical appliances within a set of households while meeting the users' preferences about the usage time. The drawback of distributed DSM protocols is that they require each user to communicate his/her own energy consumption patterns, which may leak sensitive information regarding private habits. This paper proposes a distributed privacy-friendly DSM system that preserves users' privacy by integrating data aggregation and perturbation techniques: users decide their schedule according to aggregated consumption measurements perturbed by means of additive white Gaussian noise. We evaluate the noise power and the number of users required to achieve a given privacy level, quantified by means of the increase of the information entropy of the aggregated energy consumption pattern. The performance of our proposed DSM system is compared to the one of a benchmark system that does not support privacy preservation in terms of total bill, peak demand, and convergence time. Results show that privacy can be improved at the cost of increasing the peak demand and the number of game iterations, whereas the total bill is only marginally incremented

Distributed Path Reconfiguration and Data Forwarding in Industrial IoT Networks

In today's typical industrial environments, the computation of the data distribution schedules is highly centralised. Typically, a central entity configures the data forwarding paths so as to guarantee low delivery delays between data producers and consumers. However, these requirements might become impossible to meet later on, due to link or node failures, or excessive degradation of their performance. In this paper, we focus on maintaining the network functionality required by the applications after such events. We avoid continuously recomputing the configuration centrally, by designing an energy efficient local and distributed path reconfiguration method. Specifically, given the operational parameters required by the applications, we provide several algorithmic functions which locally reconfigure the data distribution paths, when a communication link or a network node fails. We compare our method through simulations to other state of the art methods and we demonstrate performance gains in terms of energy consumption and data delivery success rate as well as some emerging key insights which can lead to further performance gains

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine