1. Introduction - UNSW-CEEM/TDA_Python GitHub Wiki

Cost reflective tariffs

Recent regulatory reform efforts in the Australian National Electricity Market (NEM) have included a number of rule changes aiming to contain electricity price rises driven by network investment by distributed network service providers (DNSPs). One focus area has been the economic inefficiencies of current network tariff arrangements, particularly for residential and small business consumers. These tariffs are typically shaped by limited metering capabilities and equity considerations and have generally involved a major volumetric consumption component. This tariff structure doesn’t clearly reflect the role of consumer contributions to network peak demand and hence in overall DNSP expenditure.

As part of the regulatory reform efforts, a distribution network pricing rule change has been implemented, effective for the current regulatory periods. The new rule requires that network tariffs should be more ‘cost reflective’, motivated by the idea that efficient prices will change consumer behaviour, which in turn will improve load factors, reduce network congestion and lower average costs for consumers. DNSPs are given considerable discretion over the specific implementation of the rule, which provides broad pricing principles only.

The rule states that network tariffs should be based on the long-run marginal costs (LRMC) of providing the service, and that the revenue to the network should reflect the efficient costs of providing the services to each consumer class. However, DNSPs are able to determine how to calculate their LRMC, how this is reflected in the tariff design and how residual costs should be collected. As DNSP tariff structures must balance efficient pricing considerations with fairness and the ability of consumers to understand and respond to the tariffs, there is considerable scope for a variety of tariff designs to emerge. In recent submissions, DNSPs have put forward a number of tariffs of varying structure and complexity. It is challenging to assess how each tariff will impact on different consumer groups, and how well they can provide efficient price signals and address existing cross-subsidy issues.

Why we need to assess the tariffs

Preliminary analysis indicates that many of the tariff proposals now being put forward by DNSPs under the new rule might not provide appropriate price signals to consumers regarding their investment and behaviour, and may disadvantage some consumer groups. For instance, in the majority of proposed network tariffs, the fixed daily charge component is being increased, resulting in high unavoidable costs, particularly for low energy consuming (often vulnerable) customers. Special tariffs are also being proposed for certain classes of customers (e.g. Solar PV System owners), while in some areas, customers are being transferred to new tariffs under opt-out arrangements, leaving them vulnerable to potentially disadvantageous new tariffs.

In addition, there are concerns that even what are claimed to be cost-reflective tariffs might not effectively target peak network loads (either local or region wide), and therefore may not provide an appropriate price signal regarding the associated network costs of consuming energy at different times and locations. Poorly designed tariffs that do not appropriately align benefits and costs may lead to inefficient investment in both networks and demand side options, and hence not be in the best interests of consumers. Specifically, they might act to reduce the consumer incentives to deploy solar PV, energy efficiency, and other load management systems that can reduce network expenditure while also delivering wider economic and environmental benefits.

Advocacy groups have expressed concern that many of the new tariffs appear to be better designed to protect the revenue of DNSPs in a context of falling demand and uptake of demand-side technologies, rather than to encourage efficient use of the network and future investment. This is of particular concern as these distributed energy technologies have the potential to provide competition for network services in the interests of consumers, enabling new participants, opportunities for innovation, and reduced emissions.

It is possible for groups or individuals to make submissions to the regulator regarding tariff design. However, given significant information asymmetry between network service providers and other stakeholders, including both consumers and regulators; advocacy organisations lack the resources to put forward robust, evidence-based analysis of the impact of proposed tariffs on different electricity consumer groups and the incentives these tariffs might provide to customers. The tariff design and assessment tool (TDA) is developed by CEEM, UNSW, to assist stakeholders, including consumer advocates and researchers, to investigate how different tariff structures impact on the expected bills of different types of residential consumers, while also estimating how well the tariffs align these customer bills with their impact on longer-term network costs.

Tariff Design and Analysis Tool

The tool builds on research and analysis currently being done by CEEM and APVI, and aims to support submissions to network pricing and tariff structure proposals. This version of the TDA uses load and survey data from over 4000 homes, collected under the Smart Grid Smart City program, to:

  • Under existing and proposed tariffs, calculate and compare for different user groups, the distribution of:
  • electricity bills,
  • annual demand
  • seasonal variations
  • Select consumer groups on the basis of annual demand, peak load, income level, dwelling type, household size, air conditioning type or if they use gas or have a solar PV system.
  • Assess price signals from different tariffs: i.e. how well bills are correlated with variables such as annual demand and peak load under different tariff designs.
  • Compare outcomes for different consumer groups, and under different tariffs.
    This document introduces different parts of the tool and provides some example analyses which can be undertaken.