Geoff James

A deployed multi-agent framework for distributed energy applications

In this paper, we describe the adaptation of an open-source multi-agent platform for distributed energy applications and the trial deployment of resource-controller agents. The platform provides real-time, two-way communication and decision making between distributed energy resources --- loads and generators --- in electricity distribution networks. Such a decentralized architecture improves grid reliability, allows consumers to play a more active role in their energy usage, benefits the network by alleviating the effects of peak wholesale prices and network constraints, and creates new business opportunities in a deregulated market. Agents have been deployed to control hardware at trial locations in Australia, providing a realistic test environment for the platform, and medium-scale trials are anticipated in the near future.

Partial Carbon Permits Allocation of Potential Emission Trading Scheme in Australian Electricity Market

Emission trading is widely considered to be the most effective policy to minimize the overall costs for CO2 abatement. However, the political feasibility of an emission trading scheme may crucially depend on the free initial allocation of emission permits to carbon-intensive industries in order to offset the reduction in profits. This paper aims to analyze these potential profit impacts and the possible compensation to affected generation companies through modeling the Australian National Electricity Market under a potential emission trading scheme. Historical emission-based and historical generation-based allocation approaches are used in this paper to calculate and compare the percentages of carbon permits that should be freely allocated. Two carbon permit price scenarios are used to analyze the sensitivity of the optimal percentage of free allocation to carbon permit price.

Evolutionary optimisation of distributed energy resources

Genetic optimisation is used to minimise operational costs across a system of electrical loads and generators controlled by local intelligent agents and connected to the electricity grid at market rates. Experimental results in a simulated environment show that coordinated market-sensitive behaviours are achieved. A large network of 500 loads and generators, each characterised by different randomly selected parameters, was optimised using a two-stage genetic algorithm to achieve scalability.

Designing self-assembly for 2-dimensional building blocks

In this paper we present a genetic algorithm-based approach towards designing self-assembling objects comprised of square smart blocks. Each edge of each block can have one of three polarities (+1, -1 or 0) which defines how blocks stick together - opposite polarities attract, like polarities repel, and a 0 face neither attracts nor repels. In addition to this property, the block contains an internal state machine which can change the polarity of any number of its sides following the detection of an event (for example, two blocks sticking or unsticking). The aim of this work is to evolve block parameters and rule sets of the state machine which allow the self-assembly of desired basic structures that can be used as primitive building blocks for the assembly of more complicated objects. We detail a genetic algorithm-based approach that can be used to evolve the rule sets of interaction for a number of interacting blocks, so that the final shape or states of a structure formed by the blocks can approximate some target shapes or satisfy some global goals. We have assumed a list of simple identical properties for each block, and observed that a great diversity of complex structures can be achieved.

Integrating learning into a BDI Agent for environments with changing dynamics

We propose a framework that adds learning for improving plan selection in the popular BDI agent programming paradigm. In contrast with previous proposals, the approach given here is able to scale up well with the complexity of the agents plan library. Technically, we develop a novel confidence measure which allows the agent to adjust its reliance on the learning dynamically, facilitating in principle infinitely many (re)learning phases. We demonstrate the benefits of the approach in an example controller for energy management.

Sensor/actuator networks supporting agents for distributed energy management

We have demonstrated the use of low-cost sensing devices for intelligent control of typical commercial energy loads: cool rooms for keeping produce fresh and heating, ventilation, and air-conditioning (HVAC) systems for buildings. We provide the sensing devices - Berkeley Motes and X10 sensing and switching equipment - with a presence on the Internet to permit sophisticated applications to access their data and control their electrical loads. The ad hoc sensor network is connected to the mobile phone network through a light-weight gateway for long-haul communications. Local control provides failsafe default behaviour in the absence of remote control or when communication is lost. We propose to use this network to deploy agent-based software to manage distributed energy resources, including local generation plants as well as loads. Our aim is to provide new ways for small-to-medium enterprises, energy retailers, and energy network businesses to obtain commercial benefits in a deregulated energy market.

Approaches for semantic interoperability between domain ontologies

: Domain ontologies and knowledge-based systems have become very important in the agent and semantic web communities. As their use has increased, providing means of resolving semantic differences has also become very important. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies. We also discuss some key issues that still need to be addressed if we are to move from semi-automated to fully automated approaches to providing consensus among heterogeneous ontologies.

Set-Points Based Optimal Multi-Agent Coordination for Controlling Distributed Energy Loads

The management of a very large number of distributed energy resources, energy loads and generators, to create aggregated quantity of power is a hot research topic. We consider a multi-agent system comprising multiple energy loads, each with a dedicated controller. This paper introduces our latest research in self-organization of coordinated behavior of multiple agents. Energy resource agents coordinate with each other to achieve a balance between the overall consumption by the multi-agent collective and the stress on the community. In order to reduce the overall communication load while permitting efficient coordinated responses, information exchange is through indirect communications between resource agents and a broker agent. It gives a decentralized coordination approach that does not rely on intensive computation by a central processor. The algorithm presented here can coordinate different types of loads by controlling their set-points. The coordination strategy is optimized by a genetic algorithm. A fast coordination convergence has been achieved.

Dynamic zone modelling for HVAC system control

This paper presents the development and validation of a dynamic zone model used for improved control of a heating, ventilation and air conditioning (HVAC) system to reduce energy consumption and improve the quality of the indoor environment. In particular, the paper focuses on a zone modelling technique that uses physical-principles based real-time model fitting and prediction methodology, taking advantage of genetic algorithm based problem solving. An air-conditioning zone model is deduced from an energy and mass balance and then expressed in terms of electric circuit theory, where the electric circuit is used to represent functions of the building elements. Experimental results for real-time zone model fitting and prediction are given. The results show that our model is capable of accurately predicting the indoor temperature of a dynamic zone. This dynamic model is useful for control strategies that require knowledge of the dynamic characteristics of HVAC systems.

Directed self-assembly of 2-dimensional mesoblocks using top-down/bottom-up design

In this paper we present a general design methodology suitable for a class of complex multi-agent systems which are capable of self-assembly. Our methodology is based on a top-down, bottom-up approach, which has the potential to achieve a range of global design goals whilst retaining emergent behaviour somewhere in the system, and thereby allowing access to a richer solution space. Our experimental environment is a software system to model 2-dimensional self-assembly of groups of autonomous agents, where agents are defined as square smart blocks. The general design goal for such systems is to direct the self-assembly process to produce a specified structure. The potential of this design methodology has been realised by demonstrating its application to a toy problem - the self-assembly of rectangles of different sizes and shapes in a two-dimensional mesoblock environment. The design procedure shows different choices available for decomposing a system goal into subsidiary goals, as well as the steps needed to ensure a match to what is achievable from the bottom-up process. Encouraging results have been obtained, which allows mesoblock rectangles of specified size to be assembled in a directed fashion. Two different approaches to the same problem were presented, showing the flexibility of the method.