165 research outputs found
Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts
The climate changes that are visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this book presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on energy internet, blockchain technology, and smart contracts, we hope that they are of interest to readers working in the related fields mentioned above
Consensus problems and the effects of graph topology in collaborative control
In this dissertation, several aspects of design for networked
systems are addressed. The main focus is on combining approaches
from system theory and graph theory to characterize graph
topologies that result in efficient decision making and control.
In this framework, modelling and design of sparse graphs that are
robust to failures and provide high connectivity are considered.
A decentralized approach to path generation in a collaborative
system is modelled using potential functions. Taking inspiration
from natural swarms, various behaviors of the system such as
target following, moving in cohesion and obstacle avoidance are
addressed by appropriate encoding of the corresponding costs in
the potential function and using gradient descent for minimizing
the energy function. Different emergent behaviors emerge as a
result of varying the weights attributed with different components
of the potential function. Consensus problems are addressed as a
unifying theme in many collaborative control problems and their
robustness and convergence properties are studied. Implications of
the continuous convergence property of consensus problems on their
reachability and robustness are studied. The effects of link and
agent faults on consensus problems are also investigated. In
particular the concept of invariant nodes has been introduced to
model the effect of nodes with different behaviors from regular
nodes. A fundamental association is established between the
structural properties of a graph and the performance of consensus
algorithms running on them. This leads to development of a
rigorous evaluation of the topology effects and determination of
efficient graph topologies.
It is well known that graphs with large diameter are not efficient
as far as the speed of convergence of distributed algorithms is
concerned. A challenging problem is to determine a minimum number
of long range links (shortcuts), which guarantees a level of
enhanced performance. This problem is investigated here in a
stochastic framework. Specifically, the small world model of Watts
and Strogatz is studied and it is shown that adding a few long
range edges to certain graph topologies can significantly increase
both the rate of convergence for consensus algorithms and the
number of spanning trees in the graph. The simulations are
supported by analytical stochastic methods inspired from
perturbations of Markov chains. This approach is further extended
to a probabilistic framework for understanding and quantifying the
small world effect on consensus convergence rates: Time varying
topologies, in which each agent nominally communicates according
to a predefined topology, and switching with non-neighboring
agents occur with small probability is studied. A probabilistic
framework is provided along with fundamental bounds on the
convergence speed of consensus problems with probabilistic
switching. The results are also extended to the design of robust
topologies for distributed algorithms.
The design of a semi-distributed two-level hierarchical network is
also studied, leading to improvement in the performance of
distributed algorithms. The scheme is based on the concept of
social degree and local leader selection and the use of
consensus-type algorithms for locally determining topology
information. Future suggestions include adjusting our algorithm
towards a fully distributed implementation.
Another important aspect of performance in collaborative systems
is for the agents to send and receive information in a manner that
minimizes process costs, such as estimation error and the cost of
control. An instance of this problem is addressed by considering a
collaborative sensor scheduling problem. It is shown that in
finding the optimal joint estimates, the general tree-search
solution can be efficiently solved by devising a method that
utilizes the limited processing capabilities of agents to
significantly decrease the number of search hypotheses
Self-management for large-scale distributed systems
Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management.
In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving
self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research
on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying
the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed
by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic
managers. In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic
and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a
management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck. In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by
presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control
Reliable and Robust Cyber-Physical Systems for Real-Time Control of Electric Grids
Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintain safe and optimal grid operation by exchanging messages over a communication network.
We focus on CPSs with a centralized controller that receives measurements from the various resources in the grid, performs real-time computations, and issues setpoints. Long-term deployment of such CPSs makes them susceptible to software agent faults, such as crashes and delays of controllers and unresponsiveness of resources, and to communication network faults, such as packet losses, delays, and reordering. CPS controllers must provide correct control in the presence of external non-idealities, i.e., be robust, and in the presence of controller faults, i.e., be reliable. In this thesis, we design, test, and deploy solutions that achieve these goals for real-time CPSs.
We begin by abstracting a CPS for electric grids into four layers: the control layer, the network layer, the sensing and actuation layer, and the physical layer. Then, we provide a model for the components in each layer, and for the interactions among them. This enables us to formally define the properties required for reliable and robust CPSs.
We propose two mechanisms, Robuster and intentionality clocks, for making a single controller robust to unresponsive resources and non-ideal network conditions. These mechanisms enable the controller to compute and issue setpoints even when some measurements are missing, rather than to have to wait for measurements from all resources. We show that our proposed mechanisms guarantee grid safety and outperform state-of-the-art alternatives.
Then, we propose Axo: a framework for crash- and delay-fault tolerance via active replication of the controller. Axo ensures that faults in the controller replicas are masked from the resources, and it provides a mechanism for detecting and recovering faulty replicas. We prove the reliable validity and availability guarantees of Axo and derive the bounds on its detection and recovery time. We showcase the benefits of Axo via a stability analysis of an inverted pendulum system.
Solutions based on active replication must guarantee that the replicas issue consistent setpoints. Traditional consensus-based schemes for achieving this are not suitable for real-time CPSs, as they incur high latency and low availability. We propose Quarts, an agreement mechanism that guarantees consistency and a low bounded latency- overhead. We show, via extensive simulations, that Quarts provides an availability at least an order of magnitude higher than state-of-the-art solutions.
In order to test the effect of our proposed solutions on electric grids, we developed T-RECS, a virtual commissioning tool for software-based control of electric grids. T-RECS enables us to test the proper functioning of the software agents both in ideal and faulty conditions. This provides insight into the effect of faults on the grid and helps us to evaluate the impact of our reliability solutions.
We show how our proposed solutions fit together, and that they can be used to design a reliable and robust CPS for real-time control of electric grids. To this end, we study a CPS with COMMELEC, a real-time control framework for electric grids via explicit power setpoints. We analyze the reliability issues..
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