An Economic Model for Residential Energy Consumption, Generation, Storage and Reliance on Cleaner Energy
Economic models that provide a framework for understanding determinants of household energy consumption and the rate of reliance on cleaner energy are not available, despite the growing number of residential solar This study presents a theoretical framework for understanding the relationship between energy consumption, generation, storage and the reliance on cleaner Our findings suggest that, (i) households which generate energy could consume more energy than households which do not generate energy, (ii) the economically optimal reliance on solar energy depends on energy prices, income and the slope of the marginal energy storage cost, (iii) there is no relationship between reliance on cleaner energy (or energy demand) and the efficiency level of energy-conversion technologies, and (iv) when buy-back rates are equal to the retail rate of electricity, reliance on generated energy could be zero and energy demand of a generating household may be identical to its non-generating counterpart.
The use of solar energy in the US residential sector has slowly but steadily gained ground over the past two decades, making its study a relevant According to EIA (2016), solar energy generation as a percentage of all renewable energy produced in the residential sector has increased from 10% in 1994 to 39% in 2015 mainly due to the use of distributed solar photovoltaic (PV) systems installed at the premises of the Almost all of the PV modules shipped to the residential sector in 2012 were grid-connected distributed PV systems (EIA, 2012). This indicates that residential distributed generation systems are usually integrated to the electrical power grid.
The role of solar PV in the residential sector’s renewable energy use is expected to increase in the future (US EIA, 2017).
This paper establishes an economic model to understand the impact of income, retail rates, net-metering policies and storage cost on a household’s energy demand, and its optimal reliance on generated energy vis-à-vis buying from the We contribute to previous models by explicitly incorporating distributed generation and energy storage in the standard model of demand for energy.
Generating renewable energy such as solar energy makes a household not just a consumer of electricity from the utility company but also a producer of cleaner Several studies refer to a household with an energy-generating capacity as a “prosumer” building on the concept of production and For instance, Sun et al. (2013) present an economic model for distributed energy prosumers; and MacGill and Smith (2017) outline experiences of prosumers in Australia.
Another similar term used in the literature is “prosumage” which represents a prosumer with an energy storage capacity to increase self-consumption. Green and Staffell (2017) study British households which use storage to maximize self-consumption of their generated Schill et (2017) discuss the pros and cons of prosumage of solar electricity in Germany. IEA (2014) identifies different types of prosumers depending on their relationship with local utility companies.
Some prosumers could potentially cut their connection with the grid by producing 100% of their energy needs by using a combination of solar PV, storage batteries and energy efficiency However, studies like IEA (2014) and Borenstein (2017) suggest this scenario as less likely since currently it is not economically
The most realistic type of prosumers in current market conditions are homeowners who satisfy some of their energy demand from their own generation but continue to buy some energy from the grid (irrespective of ownership of battery). Through the use of net metering, they could send any excess energy back to the grid (Jansson, 2016).
Coughlin and Cory (2009) argue that the primary reason for homeowners to install solar PV systems is to reduce their utility bills by consuming less from the Additionally, there is the potential for net metering credits as a result of generating more energy than Likewise, Borenstein (2015) finds evidence that the steeply-tiered electricity price structure in California has contributed to the increase in the private value of solar energy generated by high-energy-consuming Declining installation cost of solar PV is another important driver of distributed generation in the residential Fu et al. (2016) show that the average inflation adjusted cost of a typical residential solar PV has declined by 5% from 2009 to 2016 where this reduction is mostly driven by decline in module Reductions in PV costs are expected to continue due to technological innovation, product optimization and learning by doing (IEA, 2014; Pillai, 2015).
Finally, favorable regulatory policy and financial incentives have allowed many homeowners to afford the installation of solar PV (Coughlin and Cory, 2009; Bauner and Crago, 2015). As of July 2016, 41 US states and DC, and three US territories (American Samoa, US Virgin Island and Puerto Rico) have mandatory state-developed net metering rules (DSIRE report, 2016) which require utility companies to buy back or credit prosumers for the excess electricity they send back to the However, Coughlin and Cory (2009) and IEA (2014) show that net metering by itself is not enough to make the economic case for distributed generation because, among other reasons, utility companies may not always buy back excess energy at the retail rate.
Furthermore, IEA (2014) argue that any mismatch between peak PV production and higher self-consumption would discourage the adoption of solar PVs in the residential For instance, if a prosumer without a storage capability is always away from home during the mid-day when production is the highest, then higher amounts of solar energy will continue to be sent back to the grid, reducing self-consumption. This suggests that affordable energy storage is another factor that could encourage distributed generation onsite (Cunha and Ferreira, 2014; Grantham et, 2017). Because of the mismatch between energy consumption and generation, households may have to rely on a combination of net metering and energy
Energy storage allows the household to use its peak generation at later times in the evening, whereas net metering allows the household to sell any excess generation back to the utility company for some compensation (Perez et , 2004). The study by Provost (2014) finds evidence for declining cost of energy storage, allowing residential households to consume their generated energy with less need to sell back excess Such energy storage systems are expected to reduce energy bills and provide some protection against outages (Ton et , 2008; National Renewable Energy Laboratory, 2014). Energy storage for grid-tied residential PVs increases the household’s ability to consume a higher percentage of cleaner energy and naturally reduces the amount fed back to the grid (Grantham et al., 2017).
Applications of energy storage to residential grid-tied PV systems are fast progressing.
However, there are no economic models that provide a framework for understanding the economically optimal level of energy consumption and rate of reliance on cleaner energy for households with energy generating and storage capabilities (Timmons et , 2014; Heal, 2009).
The contribution of this study is at the intersection of two branches of the existing literature on residential distributed solar generation: studies that estimate households’ demand for energy services and studies that examine economic drivers for adoption of solar PVs.
Studies like Hunt and Ryan (2014) and Sanstad (undated) adopt models to examine energy consumption of a household; Guertin et (2003) finds some evidence that demand for residential energy services is affected by prices and None of these studies introduce the possibility of energy generation and storage by the Households with generating and storage capabilities (e.g. PVs and batteries respectively) have additional choice variables and other relevant parameters in their energy decision-making when compared to their fully grid-dependent counterparts.
Such additional information may alter their energy consumption behavior and yield a different load Borenstein (2017) and Borenstein and Davis (2015) examine characteristics that are more likely to make households adopt solar They find that higher income households and the highest electricity-users are more likely to adopt solar Other studies point to the role of government incentives and the declining price of PVs (Coughlin and Cory, 2009; Fu et al 2016).
Once households’ adopt PVs, it is not clear how these (e.g. higher income, higher demand) and other variables affect how much of the generated energy ends up being consumed, stored and sold by the household.
The objective of this study is to present the much-needed economic framework for understanding the relationship between energy consumption, energy generation, storage and reliance on cleaner We introduce a cost-minimization and utility-maximization model that can be used to understand the impact of household income, rate structures (retail price of electricity and the buy-back price) and storage cost on energy demand, the ability to consume a higher percentage of the generated energy and energy sent back to the grid.
In Section 2, we present a benchmark model of energy consumption for a household which does not generate In Section 3 we introduce the energy generating household (e.g. distributed solar energy) and analyze it both under the assumption that energy storage is free and when energy storage involves some cost, the more realistic Section 4 discusses main results and implications by outlining a method for validating the Section 5 concludes by forwarding questions for future work.
This study presents a theoretical framework for understanding the relationship between energy consumption, energy generation, storage and reliance on cleaner energy. Our findings suggest that, (i) under certain conditions, households which generate energy could consume more energy than households which do not generate energy. (ii) the economically optimal reliance on solar energy depends on energy prices, income and the slope of the marginal energy storage cost, (iii) there is no relationship between reliance on cleaner energy (or energy demand) and the efficiency level of energy-conversion technologies, and (iv) when buy back rates are equal to the retail rate of electricity, reliance on generated energy is zero and energy demand of a generating household may be identical to its non-generating counterpart.
Our model is not without limitations. We have excluded other relevant dimensions to the optimal choice of a. For example, the model presents a one-time cost-minimization and utility maximization choice. If the household were to make the decision in an inter-temporal fashion, this might alter the main outputs of the model such as optimal energy demand. Thus, in future studies we will adopt a time relevant model and study its implications. Another important way the current model can be extended is by explicitly accounting for the additional utility gained from going ‘greener’. For instance, investment in solar PV usually comes with ‘qualitative benefits’ such as sustainability, resilience and a sense of independence; and these aspects could directly affect the individual’s utility maximization. We will extend our theoretical model (specifically, the utility 600 function of households which generate cleaner energy) to measure the additional satisfaction 601 derived from such ‘qualitative goods’ and compare it to its marginal opportunity cost. Within this 602 framework, we will derive the economically optimal levels of energy demand, energy generation, 603 and reliance on cleaner energy (in our case solar energy) for individual houses.