George Grozev

The challenge of adapting centralised electricity systems: peak demand and maladaptation in South East Queensland, Australia

South East Queensland’s (SEQ’s) centralised electricity system is under great pressure to adapt. Climate change is converging with socio-economic, demographic and technological changes to create a ‘perfect storm’ for the region’s electricity system. Distribution networks are particularly affected, with these factors contributing to tremendous peak demand growth, about double the rate of growth in average demand in recent years. This paper reviews how Australia’s electricity system is adapting to multiple drivers of peak electricity demand. We use socio-technical transitions theory to understand the temporal interconnected social and technical dimensions of adaptation in this setting. Specifically, we present an historical narrative of the emergence of centralisation in Australia and outline the peak demand problem in SEQ and review adaptation options from the international literature. We also analyse the interactions between key social groups and their adaptation responses over the past decade. Our analysis shows that adaptation has become a contested process between supply-chain actors and end-users, each with different economic objectives, adaptation needs and capacities. The resulting adaptation dynamic that is emerging shows worrying signs of maladaptation. Implications for market governance and urban policy and research are discussed.

Knowledge-Mining the Australian Smart Grid Smart City Data: A Statistical-Neural Approach to Demand-Response Analysis

Large scale field trials of smart grid technologies provide important insights as they capture the complex interdependencies of all the key variables, including consumer behaviours, which are needed for their effective evaluation. We present the Australian Smart Grid Smart City program and describe its big data using a narrative approach to hasten understanding and further analyses by others. Then we present a novel statistical-neural approach to maximise knowledge extraction from large datasets of diurnal load profiles, and demonstrate its use in evaluating the effectiveness of two cost-reflective product offerings, a Network-type and a Retail-type product bundle. The methods of analyses include Principal Component Analysis and Self-Organising Mapping. The results for the mid-winter electricity consumption profiles of participating households in July 2013 in New South Wales showed consumption behaviour changes with up to 12 % reduction in relative peak demand at 700 households who accepted the offerings compared to the control group. The resultant load factor of the high consuming outliers improved by about 18 % under demand-response compared to the control group. The feature-based classifier also revealed which behavioural components change due to users’ demand-response activities; results compared favourably with third party consumer survey results.

Analysis of household electricity consumption behaviours

Adoption of renewable electricity generation technologies such as photovoltaic (PV) systems is at the early majority stage in most developed countries. Depending on solar capacity, applied feed-in tariff, and other factors, households exhibit different electricity consumption behaviours which can potentially assist in Demand Side Management (DSM) of electricity usage. This article presents three univariate analysis methods to infer deliberative behavioural patterns at households with solar electricity generation capacity. Analysis methods include qualitative Principal Component Analysis (PCA), unsupervised Hebbian-based clustering, and clustering using a semi-supervised Self-Organising Map (SOM). The techniques are individually applied to 300 sample households with rooftop PV panels operating under a Gross Metering (GM) scheme. According to the PCA, the dominant behaviours are often general among most households, and therefore reveal themselves on first and second principal components. However, on the third and fourth components some specific household behaviours related to load-shifting and self-consumption, are observed. The Hebbian model differentiates between at least eight behaviour types, some of which indicate deliberative behaviours by the households. Most effectively, SOM clustering clearly detects a self-consumption behaviour attributed to domestic electricity generation. A control group of 400 households is analysed to ensure uniqueness of the self-consumption behaviour to customers with solar PV installed. The techniques developed herein may be able to be used by electricity utilities to assess the influence that future tariff and technology offerings will have on behavioural aspects of customer electricity consumption.

Development of application-specific adjacency models using fuzzy cognitive map

Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause–effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Managing energy futures and greenhouse gas emissions with the help of agent-based simulation

Managing energy futures and greenhouse gas emissions with the help of agent-based simulation

A Neural Approach to Electricity Demand Forecasting

Electricity demand forecasting is significant in supply-demand management, service provisioning, and quality. This chapter introduces a short-term load forecasting model using Fuzzy Cognitive Map, a popular neural computation technique. The historic data of intraday load levels are mapped to network nodes while a differential Hebbian technique is used to train the network’s adjacency matrix. The inferred knowledge over weekly training window is then used for demand projection with Mean Absolute Percentage Error (MAPE) of 5.87 % for 12 h lead time, and 8.32 % for 24 h lead time. A Principal Component Analysis is also discussed to extend the model for training using big data, and to facilitate long-term load forecasting.

How to Characterise and Parameterise Agents in Electricity Market Simulation Models: The Case of Genersys

This chapter describes the process of characterisation and parameterisation of computer agents in the case of decision making of profit-driven companies in a competitive electricity market. It focuses on adaptive behaviour of generation and investment companies in Australia’s National Electricity Market (NEM) as modelled by Genersys. Through initiatives such as formal focus group meetings, gathering observations of industry experts, analysing market data and a selective approach in representing real systems, modellers can improve the design and potential future use of their simulation systems.

Electric Vehicles: New Problem, or Distributed Energy Asset?

Abstract In an evolving decentralized future with growing intermittent renewables and distributed generation, any type of storage becomes a valuable resource. While they contain significant energy storage, electric vehicles (EVs) are rarely considered as a potential resource to the electricity system—rather, as they represent a very significant additional load, they are usually considered a potential challenge. This need not be the case—with careful management of their battery charging and the ability to discharge in to the grid, EVs may in fact be a very significant distributed energy resource. This chapter explores the impact and potential of EVs—what is clear is that such impacts are quite localized, and careful analysis is needed to determine exactly where and how EVs will impact the grid. With this in mind, this chapter presents a modeling methodology that analyzes the impacts of EVs at an unprecedented level of detail. Such detailed analysis will ultimately be critical to understand the full impact of EVs and whether they will remain a challenge to grid operations or a resource of greater benefit.

Simulating cost based bidding models in genersys

This paper demonstrates the usefulness of an agent based model in studying the dynamics and economic performance of a competitive electricity market. An auction based market exhibits the characteristics of a complex adaptive system. An agent based model has been designed and implemented to mimic the operation of an electricity market. By running different simulation scenarios, we discover computational emergence on price and emission when generators employ different bidding models.

Modeling the Impacts of Disruptive Technologies and Pricing on Electricity Consumption

This chapter examines scenarios for rapid uptake of new distributed energy resources and energy storage, and explores a hypothesis that adoption of residential “network tariffs” will reduce the distortional effects of volume-based tariffs on residential energy consumption patterns. The modeling methodology examines annual electricity consumption for specific dwelling types in Townsville in Northern Queensland, Australia, using network-based tariffs, feed-in-tariffs and time-of-use rates for solar photovoltaic generation, and battery storage. The analysis demonstrates that electricity consumption could drop by more than 10% in the next decade. The scenario results demonstrate that cost-reflective tariffs can improve network utilization, and potentially put downward pressure on retail prices.