Ramachandra Kota

Decentralized approaches for self-adaptation in agent organizations

Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70–90%) to that of an idealized centralized allocation method and is considerably better (10–60%) than the current state-of-the-art decentralized approaches.

Decentralised Structural Adaptation in Agent Organisations

Autonomic computing is being advocated as a tool for maintaining and managing large, complex computing systems. Self-organising multi-agent systems provide a suitable paradigm for developing such autonomic systems. Towards this goal, we demonstrate a robust, decentralised approach for structural adaptation in explicitly modelled problem solving agent organisations. Our method is based on self-organisation principles and enables the agents to modify the organisational structure to achieve a better allocation of tasks across the organisation in a simulated task-solving environment. The agents forge and dissolve relations with other agents using their history of interactions as guidance. We empirically show that the efficiency of organisations using our approach is close to that of organisations having an omniscient central allocator and considerably better than static organisations or those changing the structure randomly.

Cooperatives for demand side management

We propose a new scheme for efficient demand side management for the Smart Grid. Specifically, we envisage and promote the formation of cooperatives of medium-large consumers and equip them (via our proposed mechanisms) with the capability of regularly participating in the existing electricity markets by providing electricity demand reduction services to the Grid. Based on mechanism design principles, we develop a model for such cooperatives by designing methods for estimating suitable reduction amounts, placing bids in the market and redistributing the obtained revenue amongst the member agents. Our mechanism is such that the member agents have no incentive to show artificial reductions with the aim of increasing their revenues.

Decentralized Dynamic Task Allocation Using Overlapping Potential Games

This paper reports on a novel decentralized technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralized method for solving the approximating games that uses the distributed stochastic algorithm. Finally, we discuss an implementation of our approach to a task allocation problem in the RoboCup Rescue disaster management simulator. The results show that our technique performs comparably to a centralized task scheduler (within 6% on average), and also, unlike its centralized counterpart, it is robust to restrictions on the agents’ communication and observation ranges.

Voltage Correlations in Smart Meter Data

The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work shows that voltage time series measurements collected from customer smart meters exhibit correlations that are consistent with the hierarchical structure of the distribution network. These correlations may be leveraged to cluster customers based on common ancestry and help verify and correct an existing connectivity model. Additionally, customers may be clustered in combination with voltage data from circuit metering points, spatial data from the geographical information system, and any existing but partially accurate connectivity model to infer customer to transformer and phase connectivity relationships with high accuracy. We report analysis and validation results based on data collected from multiple feeders of a large electric distribution network in North America. To the best of our knowledge, this is the first large scale measurement study of customer voltage data and its use in inferring network connectivity information.

A generic agent organisation framework for autonomic systems

Autonomic computing is being advocated as a tool for managing large, complex computing systems. Specifically, self-organisation provides a suitable approach for developing such autonomic systems by incorporating self-management and adaptation properties into large-scale distributed systems. To aid in this development, this paper details a generic problem-solving agent organisation framework that can act as a modelling and simulation platform for autonomic systems. Our framework describes a set of service-providing agents accomplishing tasks through social interactions in dynamically changing organisations. We particularly focus on the organisational structure as it can be used as the basis for the design, development and evaluation of generic algorithms for self-organisation and other approaches towards autonomic systems.

Machine learning for inferring phase connectivity in distribution networks

The connectivity model of a power distribution network can easily become outdated due to system changes occurring in the field. Maintaining and sustaining an accurate connectivity model is a key challenge for distribution utilities worldwide. This work focuses on inferring customer to phase connectivity using machine learning techniques. Using voltage time series measurements collected from customer smart meters as the feature set for training classifiers, we study the performance of supervised, semi-supervised and unsupervised techniques. We report analysis and field validation results based on real smart meter measurements collected from three feeder circuits of a large distribution network in North America.

Blue Skies: A Methodology for Data-Driven Clear Sky Modelling

Solar energy is gaining prominence across the world in response to climate change, depleting reserves, lowering costs, and favorable legislation. However, a key challenge in integrating solar power into the grid is the ability to forecast solar energy production accurately. The variability and uncertainty of solar power can negatively impact grid stability and increase the cycling costs of conventional power plants.

Inferring non-outage events using meter voltage data

Power distribution utilities incur significant expenditures due to field operations. While restoration efforts following outages lead to truck rolls in order to identify and rectify faults, there exist several non-outage events that also result in generation of work orders and crew visits. In this paper, we discuss a new problem in the smart grid domain wherein data-driven approaches may be used to improve field operations and customer experience by minimizing the time required to diagnose, locate and rectify non-outage faults. In particular, we discuss how voltage datastream from customer smart meters can be leveraged to pre-inform the crew about such faults and proactively generate work orders. We also conduct a preliminary investigation based on meter data obtained from a large electrical utility in North America.