Improved residential electricity demand management through analysis of the customer perspective

Peak electricity demand requires substantial investment to update transmission, distribution and generation infrastructure. A successful community peak demand reduction project was examined to identify residential consumer motivational and contextual factors involved in their decision to adopt/not adopt interventions. Energy professionals actively worked to achieve community 'peer' membership and by becoming a trusted information source, facilitated voluntary home energy assessment requests from over 80% of the residential community. By combining and tailoring interventions to the specific needs and motivations of individual householders and the community, interventions promoting energy conservation and efficiency can be effective in achieving sustained reduction in peak demand.

Modelling interregional links in electricity price spikes

Abnormally high price spikes in spot electricity markets represent a significant risk to market participants. As such, a literature has developed that focuses on forecasting the probability of such spike events, moving beyond simply forecasting the level of price. Many univariate time series models have been proposed to dealwith spikes within an individual market region. This paper is the first to develop a multivariate self-exciting point process model for dealing with price spikes across connected regions in the Australian National Electricity Market. The importance of the physical infrastructure connecting the regions on the transmission of spikes is examined. It is found that spikes are transmitted between the regions, and the size of spikes is influenced by the available transmission capacity. It is also found that improved risk estimates are obtained when inter-regional linkages are taken into account.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Compositional agent-based models for electricity distribution networks

This thesis presents a novel approach to building large-scale agent-based models of networked physical systems using a compositional approach to provide extensibility and flexibility in building the models and simulations. A software framework (MODAM - MODular Agent-based Model) was implemented for this purpose, and validated through simulations. These simulations allow assessment of the impact of technological change on the electricity distribution network looking at the trajectories of electricity consumption at key locations over many years.

Electricity pricing in New Zealand and the Australian State of Queensland: Accounting for the impact of sector restructuring

The electricity industries of New Zealand (NZ) and the Australian state of Queensland have undergone substantial structural and regulatory reform with the common intent to improve economic efficiency. Deregulation and privatisation have been key elements of the reform but have been approached differently by each jurisdiction. This study traces the link between structural and regulatory regimes and asset valuation, profits and, ultimately, pricing. The study finds that key drivers in recent price increases are the government-owned generation and retail sector in NZ and the government-owned distribution sector in Queensland. It is concluded that, contrary to the rationale for the imposition of regulatory controls in a nonmarket environment, the regulatory regimes appear to have contributed to higher rather than lower pricing structures.

Residential tariff structure and battery energy storage impact in queensland LV network

Distributed renewable energy has become a significant contender in the supply of power in the distribution network in Queensland and throughout the world. As the cost of battery storage falls, distribution utilities turn their attention to the impacts of battery storage and other storage technologies on the low voltage (LV) network. With access to detailed residential energy usage data, Energex's available residential tariffs are investigated for their effectiveness in providing customers with financial incentives to move to Time-of Use based tariffs and to reward use of battery storage.

On the tying of aid to tariff reform

This paper considers the welfare effects of foreign aid that is tied to changes in the recipient's tariff. By constructing a three-country model with tariffs, and by allowing for changes both in the amount of aid and in the tariff rates, we are able to consider the welfare implications of two different rules of aid conditionality: (i) a rule which leaves the donor's welfare unchanged, and (ii) a rule which leaves the recipient government's total revenue unchanged. It is shown that the tying of aid to a tariff reform can, inter alia, be used to ensure Pareto improvement.

Steepest ascent tariff reform

The policy reform literature is primarily concerned with the construction of reforms that yield welfare gains. By contrast, this paper’s contribution is to develop a theoretical concept for which the focus is upon the sizes of welfare gains accruing from policy reforms rather than upon their signs. In undertaking this task, and by focusing on tariff reforms, we introduce the concept of a steepest ascent policy reform, which is a locally optimal reform in the sense that it achieves the highest marginal gain in utility of any feasible local reform. We argue that this reform presents itself as a natural benchmark for the evaluation of the welfare effectiveness of other popular tariff reforms such as the proportional tariff reduction and the concertina rules, since it provides the maximal welfare gain of all possible local reforms. We derive properties of the steepest ascent tariff reform, construct an index to measure the relative welfare effectiveness of any given tariff reform, determine conditions under which proportional and concertina reforms are locally optimal and provide illustrative examples.

Tariff strategies and small open economies

In this paper we examine the issue of optimal tariffs for a small economy that trades with a large economy. We define ‘small’ and ‘large’ in the sense that the world prices are determined solely by the large country, and, therefore, the small country faces exogenously given world prices. Within this framework it is shown that there exist situations in which the small country has an incentive to behave as a Stackelberg leader by committing itself to a non-zero optimal tariff. Although the small country is unable to directly affect world prices, by pre-committing to a non-zero trade tax it may induce a reduction of the large country's optimal trade tax, thereby indirectly improving its terms of trade and welfare. JEL Classification: F13, F35

Tariff-tax reforms and market access

Reducing tariffs and increasing consumption taxes is a standard IMF advice to countries that want to open up their economy without hurting government finances. Indeed, theoretical analysis of such a tariff–tax reform shows an unambiguous increase in welfare and government revenues. The present paper examines whether the country that implements such a reform ends up opening up its markets to international trade, i.e. whether its market access improves. It is shown that this is not necessarily so. We also show that, comparing to the reform of only tariffs, the tariff–tax reform is a less efficient proposal to follow both as far as it concerns market access and welfare.

Forecasting day-ahead electricity load using a multiple equation time series approach

The quality of short-term electricity load forecasting is crucial to the operation and trading activities of market participants in an electricity market. In this paper, it is shown that a multiple equation time-series model, which is estimated by repeated application of ordinary least squares, has the potential to match or even outperform more complex nonlinear and nonparametric forecasting models. The key ingredient of the success of this simple model is the effective use of lagged information by allowing for interaction between seasonal patterns and intra-day dependencies. Although the model is built using data for the Queensland region of Australia, the method is completely generic and applicable to any load forecasting problem. The model’s forecasting ability is assessed by means of the mean absolute percentage error (MAPE). For day-ahead forecast, the MAPE returned by the model over a period of 11 years is an impressive 1.36%. The forecast accuracy of the model is compared with a number of benchmarks including three popular alternatives and one industrial standard reported by the Australia Energy Market Operator (AEMO). The performance of the model developed in this paper is superior to all benchmarks and outperforms the AEMO forecasts by about a third in terms of the MAPE criterion.