Date of Award

2013

Document Type

Thesis

Publisher

Edith Cowan University

Degree Name

Doctor of Philosophy

School

School of Engineering

Faculty

Faculty of Health, Engineering and Science

First Supervisor

Professor Daryoush Habibi

Second Supervisor

Dr Octavian Bass

Abstract

The existing power grids that form the basis of the respective electrical power infrastructures for various states and nations around the world, are expected to undergo a period of rapid change in the near future. The key element driving this change is the emergence of the Smartgrid. The Smartgrid paradigm represents a transition towards an intelligent, digitally enhanced, two-way power delivery grid. The aim of the Smartgrid is to promote and enhance the e_cient management and operation of the power generation and delivery facilities, by incorporating advanced communications, information technology, automation, and control methodologies into the power grid proper. Smartgrid's are currently an active topic for research, where the research is strongly focused on developing new technologies such as: demand response, power generation management, pricing modelling and energy markets participation, power quality, and self-healing scenarios. In recent times, in both the United States of America and Europe, many new projects have begun which are specifically directed towards developing “Smartgrid” technologies. In Australia, the Federal Government has recently initiated funding plans to promote the commercialisation of renewable energy. In order to exploit these developments, Edith Cowan University (ECU); which is a High Voltage (HV) customer for the major utility network of Western Australia, and which owns its own transformers and Low Voltage (LV) network; is planning to integrate renewable energy suppliers within its LV network.

The aim of this research is to introduce a smart decision making system, which can manage the operation of disparate power generation sources installed on a LV network (microgrid); such as that owned by ECU on its campuses. The proposed energy management system is to gather data in real-time, and it must be capable of anticipating and optimising energy needs for each operational scenario that the microgrid might be expected to experience. The system must take into account risk levels, while systematically favouring low economic and environmental costs. A management system application, based on autonomous and distributed controllers, is investigated in a virtual environment. The virtual environment being a full-scale simulation of ECU's microgrid; with solar panels, wind turbines, storage devices, gas gen-sets, and utility supply. Hence the simulation studies were conducted on the basis of realistic demand trends and weather conditions data.

The major factors for reducing the cost of generation in the case study, were identified as being: 1) demand forecasting; 2) generation scheduling; 3) markets participation; and 4) autonomous strategies configuration, which is required to cope with the unpredictable operation scenarios in LV networks. Due to the high uncertainty inherent within the operational scenarios; an Artificial Intelligence (AI) deployment for managing the distributed sub-systems was identified as being an ideal mechanism for achieving the above mentioned objectives. Consequently it is proposed that Multi-Agent System (MAS) technology be deployed, to enable the system to respond dynamically to the unpredictable operational conditions by updating the method of analysis. The proposed system is to behave in a strategic manner when dealing with the expected operational scenarios, by aiming to achieve the lowest possible cost of power generation for the microgrid. The simulated system is based on realistic operational scenarios, which have been scaled to suit the size and type of load in the case study. The distributed intelligent modules have proven to be successful in achieving the potential benefits of the dynamic operational conditions, by minimising the cost of power generation.

The distributed intelligent modules, which form the basis of the proposed management systems, have been designed to perform the following functions:

1. Provide accurate demand forecasts through the utilisation of an AI-based adaptive demand forecasting model. The novel demand-forecast modelling technique, which was introduced to model demand in the case study, has been utilised to supply reasonably accurate demand forecasts to other stages of processing in the management system. The forecasts are generated from this model, by monitoring and controlling the forecasting error to ensure consistent and satisfactory forecasts.

2. Make optimum decisions concerning the operation of the power generators by considering the economic and the environmental costs. In order to deal with the complexity of the operational conditions, a smart and adaptive generation scheduling method was implemented for the case study. The method was primarily applied to control the charging/ discharging process of the Storage Devices (SDs) among the other generators. The proposed method aims at controlling the resources, and extracting the benefit of having an hourly based variable generation cost.

3. Integrate the microgrid into the electricity market, in order to enable the microgrid to offer its spinning and non-spinning power generation reserve as Ancillary Services (AS) to the grid. To this end, studying the operational mechanisms of the Australian market was essential prior to building the proposed market participation rules which form an integral part of the proposed management system. As a result we used the market data, by approaching the market operators to create a semi-realistic competitive market environment for our simulations. Consequently, a smart and adaptive pricing mechanism, that adapts the AS prices to the amount of electricity on offer, and the level of demand in the market has been presented.

The motivation for introducing the proposed management system, is to achieve a transition plan for current microgrids, so that they can have a commercial connection to the future Smartgrid. The results obtained in this work show that there is a signi_cant economic and environmental advantage to be gained from utilising intelligence when managing electricity generation within a power grid. As a consequence, selecting the appropriate management strategy is fundamental to the success of the proposed management system. In conclusion, modelling of the proposed strategies using MAS technology has proven to be a successful approach, and one that is able to reflect the human attitude; in making critical decisions and in reducing the cost of generation.

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