The impact of residential photovoltaic power on electricity sales revenues in Cape Town, South Africa
Introduction
With a population of about 3.7 million people, the municipality of Cape Town represents one of the cultural, commercial, and political centers of South Africa (City of Cape Town (2012); Jenkins and Wilkinson, 2002). Similar to other South African cities, Cape Town still bears the legacy of apartheid through inequality and geographical separation (Lemanski, 2007, Smith, 2004). This leads to various efforts my municipal authorities to improve living conditions for underprivileged residents, such as seeking to improve housing infrastructure or provide basic services at low tariffs (Swilling, 2010). Access to affordable electricity is considered a basic need with high political importance in South Africa, as it has also been a central point in the government's Reconstruction and Development Program (ANC, 1994). The City of Cape Town has introduced a pro-favorable tariff structure for public services, such as electricity, based on its Equitable Services Policy Framework (Government of Western Cape (2003)).
Such a policy is relatively easy to implement in South Africa, since electricity is provided as public service by municipalities in contrast to privatized and liberalized power markets found in Europe and the USA. The municipalities purchase electricity at bulk-power tariffs mainly from the monopolistic power operator ESKOM, and then supply it to customers. In the case of Cape Town, the Electricity Services Department is in charge of designing different tariffs for customers depending on their consumption level as well as certain indigence criteria. This leads to a progressive tariff structure with high and middle-income households paying up to double the rate of the tariff compared to subsidized, low-income households (City of Cape Town (2014a)). With about 35% of the total budget, the electricity revenues are the largest share of general public revenues for the city (City of Cape Town (2013a)). According to officials and the city's annual book of budget1, revenues from electricity are also partly used for cross-subsidization of other public services such as water supply and sanitation (City of Cape Town (2013a); Swilling, 2010). In addition, electricity revenues entail the advantage of being sold mostly via a pre-paid system, which minimizes risks of non-payment problems (McDonald, 2009).
Apart from the progressive tariff structure, Cape Town's customers have also faced significant annual tariff increases in recent years, due mainly to rising demand for electricity caused not only by increasing living standards but in particular by the government's mass electrification programs over the past decades. Inadequate investment in infrastructure recently led to power shortages and calls for new capacities. The cost of new investments combined with traditionally low electricity prices caused an underfunding of ESKOM and eventually resulted in a sharp rise of electricity rates by about 16% annually (on average) over the last 10 years (City of Cape Town (2013a); Kohler, 2014, Pegels, 2010). This development evokes an under-discussed but relevant issue not confined to South Africa: with declining costs of residential photovoltaic technology (PV) and rising electricity rates, it becomes increasingly attractive for households to generate PV electricity. Even though this development supports the transition to a sustainable energy system, an increase of self-supply with PV electricity might lead to an eroding revenue base for South African municipalities such as Cape Town. The combination of (a) continuously increasing electricity rates, (b) the progressive end-user tariff design, and (c) the decreasing PV costs may incentivize especially higher-income households (with higher electricity consumption and sufficient financial means) to save electricity expenditures through residential PV electricity generation. From the city's perspective, this leads to a decline of electricity sales to higher-income households, which decreases the electricity revenues used to support the low tariff for indigent residents (South African Department of Energy (2011)). As illustrated in Fig. 1, this might result in a budget gap between electricity expenses and revenues from sales. Without any remuneration for excess PV electricity fed into the grid (such as a feed-in tariff), PV systems are not yet cost-effective, which is a disincentive for large-scale rollout of the technology by private households. However, if grid parity is achieved (in the early stages only for high-income households with higher rates), the city's electricity sales revenues are expected to decline steadily (Gets, 2013).
These trends reveal a potential trade-off between renewable electricity supply and maintaining the current poor-favorable energy policy. On the one hand, the city administration depends on electricity sales revenues, in particular from middle- and high-income households, for expanding electricity access and providing subsidized electricity to indigent households. On the other hand, as described by Becker and Fischer (2013), South Africa also identifies development of renewable energies as important. Rapid growth of residential PV would be in line with aims of decreasing CO2 emissions and increasing the share of renewable energies (Msimanga and Sebitosi, 2014, Winkler, 2007, Winkler et al., 2011). Both aspects of energy policy have to be considered carefully.
This research is especially relevant in non-liberalized power markets where electricity is provided as a public service (generally by state-owned enterprises). While this is often the case in developing countries (Hall et al., 2010), the majority of published studies deals with the effects of distributed electricity generation in restructured or competitive supply markets (Fouquet, 1998, Haas et al., 2013, Menges, 2003, Milstein and Tishler, 2011). In particular, the impact of PV on retail electricity rates and consequently utilities is the focus of recent articles (Cai et al., 2013, Satchwell et al., 2014). Bode and Groscurth (2013) analyze PV grid parity in the German electricity market and find a substantial financial burden passed on to the public as self-generation and consumption is currently relieved from several costs, such as grid usage, electricity taxes, and concession fees.
A study by Jägemann et al. (2013) analyzes the economic efficiency of grid parity by combining a household optimization model with an electricity system optimization model. Similar to Bode and Groscurth (2013), they find that households with PV cause substantial excess costs for the network operators and for other market participants. Several discussions about the financial impact of a high penetration of PV on electricity sales revenues focus on South Africa, where progressive tariff policies make this issue more complicated (Gets, 2013, Reinecke et al., 2013, Sustainable Energy Africa, 2014, Trollip et al., 2012).
This research is relevant beyond South Africa, as electricity markets in developed and developing markets, liberalized or not, might experience revenue declines resulting from a high penetration of PV systems. We contribute to the literature by analyzing how expanded use of PV self-supply will affect revenues needed to ensure provision of electricity to indigent residents. We simulate returns on investment for various household investing in PV and battery storage systems as well as their respective impact on electricity revenues over the period from 2015 to 2030. Additionally, we analyze the effect of a simple change in the tariff structure; instead of charging for electricity exclusively on a per-kWh basis, the customer's bill is split into a per-kWh price and a fixed network-connection fee. Since a PV investment only affects per-kWh revenues through savings on the electricity bill, but not revenues from the fixed network-connection fee, this tariff-change potentially mitigates the negative impacts of PV expansion on electricity sales revenues (as further explained in Section 2.3).
In Section 2, we present the data and methods for our analysis. We report our results in Section 3 and further discussion of our approach and limitations in Section 4. In Section 5 we draw conclusions from our analysis.
Section snippets
Data and methods
This analysis simulates residential electricity consumption and corresponding effects on household electricity bills. Fig. 2 gives an overview of the approach, in which Cape Town's 570,000 residential electricity consumers are divided into groups G1 to G4, depending on their electricity consumption level and socio-economic status. Hourly measured load profiles are assigned to each group. In the household optimization model, the electricity bill is minimized for each load profile by considering
Household PV investment decision
The optimization model is applied with forecast economic data (such as PV cost or electricity tariffs) to analyze the household choices to invest in PV and ST.
Table 5 shows the share of households (represented by individual and unweighted load profiles per group) investing in PV in the BAU scenario. For the majority of households with high electricity consumption (G3 and G4), investments in PV become cost-effective at least by 2018. Our model simulates that by the year 2024, 100% of these
Discussion
An optimization model was developed to analyze the effects of household investment in PV and ST systems. Rising tariff rates, in combination with declining costs for PV and ST technologies, make these investments increasingly attractive for wealthy households. These households usually have high electricity consumption and thus the potential to see meaningful savings on electricity bills. Our model results show that for the majority of households (with the exception of Life-Line households)
Conclusion and policy implications
According to the results of our model distributed residential PV electricity generation is becoming increasingly cost-effective for residential customers in Cape Town. While lower-income households mainly consume electricity from the grid, the majority of high-income households will have a financial incentive to cover some of their electricity consumption by PV self-generation in the near future. Under the applied assumptions, the deployment of battery systems (ST) is not cost-effective until
Acknowledgments
This article has been supported by the Marietta-Blau-Scholarship provided by the Austrian Agency for International Mobility and Cooperation in Education, Science and Research (OeAD) as well as by the Austrian Federal Ministry of Science, Research and Economy. Appreciations also go to the University of Cape Town and its Energy Research Center (ERC), Professor Gaunt who provided household load profiles as well as the Electricity Service Department of the City of Cape Town supporting our research.
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