Introduction

Despite technological advances in small-scale hybrid renewable energy systems, there are very few studies that model the economic decision-making of a household which generates energy from multiple sources. Combining the concepts of energy production and consumption at the residential level, studies in energy economics refer to a household with an energy-generating capacity as a “prosumer” (see Sun et al. [1], MacGill and Smith [2], Green and Staffell [3], and Schill et al. [4]). There are several benefit–cost analyses that examine a prosumer’s decision to adopt a single renewable source (examples include Zarte and Pechmann [5], Jung and Tyner [6], and Swift [7]). For example, Ghaitha et al. [8] examine the economics of a residential wind turbine system while Ghaitha et al. [9] and Swift [7] study the economic feasibility of a residential solar panel system. Tervo et al. [10] examine the costs and benefits of a residential photovoltaic system with a lithium ion battery. However, there are only few benefit–cost analyses for hybrid energy systems in the residential sector. Following the literature, we refer to a household that produces and uses energy from multiple sources, including renewables, as a ‘hybrid-prosumer’. The system owned by the hybrid-prosumer is referred to as a ‘hybrid energy system’ (HES) or simply a ‘hybrid system’.

This study builds an economic model for hybrid-prosumers. We consider a hybrid system that produces energy onsite from multiple renewable energy sources [11]. The hybrid-prosumer’s simultaneous choice on energy efficiency, energy services, and energy consumption is included in the model. First, we derive the effective unit price of energy consumption for three types of hybrid-prosumers (net generator, net consumer, and net zero house). We examine the effect of a mismatch, if any, between production and consumption, on the level of energy consumption and its effective unit price. Second, we study the effect of increasing the number of renewable energy sources on the economically optimal level of energy consumption and explore possible rebound effects from generation. Finally, we use average prices from the US energy market to illustrate how the economic model can be used to guide the decision-making of an average hybrid-prosumer.

Why do residential units adopt a hybrid instead of a single renewable energy system? First, a hybrid system overcomes the inconsistent supply of a single renewable source. For instance, a photovoltaic-wind system is more likely to consistently produce power than a photovoltaic system alone because peak operating times for wind and solar systems occur at different times of the day and year. Thus, rather than wind and solar acting as substitute energy sources, as a hybrid system they could create synergy in the production of electricity [12]. The potential for generating electricity when needed will be higher with hybrid than a single energy source.

Second, a hybrid system can be integrated with an energy storage technology, providing a reliable back-up when and if consumption exceeds production. Energy storage can help reduce the size of other components (e.g., photovoltaic panels or wind turbines) and cut down costs. According to the US Department of Energy [13,14], most residential wind–solar systems can operate off-grid by providing power through batteries when the renewable component is not producing. Others may be connected to the grid via a smart grid allowing the homeowner to measure the electricity sent back to the grid [15,16]. Third, for remote locations, off-grid hybrid systems are cost-effective compared to extending the power line [16]. Finally, a HES can meet demand for energy with a lower environmental footprint and contribute to a distributed and diversified energy infrastructure [17].

A unifying aspect of the arguments presented above is the existence of the so-called technological gap. This gap is defined by Shove [18] as the difference between “current practice and recognized technical potential” and by Jaffe and Stavins [19] as the difference “between actual and optimal energy use”. In essence, the technological gap reflects a failure to optimize that has been identified in engineering studies as a slow transfer of technology; in economic analysis, as the result of market and non-market failures; and in psychological literature, as cognitive dissonance. In this paper, we use an economic model, based on production and utility theory, to address this issue in the context of a hybrid residential energy system. Our goal is to gain insight into the decision-making process of the hybrid-prosumer.

Several projects all over the world have demonstrated the application of small-scale hybrid energy technologies. For example, Frostburg State University in Maryland, US showcases a grid-tied residential size solar–wind system [20]; Yuan Ze University in Taiwan owns a small-scale photovoltaic–wind–fuel cell system [21]; and Pamukkale University in Turkey demonstrates a hybrid photovoltaic–hydrogen fuel cell–battery system designed to meet demand from non-fossil fuels [22]. In recent years, commercial HES developers have introduced products targeting the residential sector [12]. For example, WindStream Technologies Inc. is a US based developer of renewable energy generation products. Since May 2015, the company has commercialized a 1.2 kW system of solar panels and wind turbines that are suitable for grid-tied residential installations [23]. Another example is General Electric Company which has recently commercialized a solar–wind and a hydro–wind system in several countries including the US [24]. According to an industry research report conducted by Global Market Insights Inc. and authored by Gupta and Bais [25], the global hybrid solar–wind market was valued at $700 million in 2015 where the US market accounts for close to 28%. The report also finds that, from 2013 to 2015, the generation of energy from standalone or grid-connected hybrid solar–wind installations in the US has increased by 24% [25]. Given that hybrid energy technologies have a solid emerging demand, it is timely to consider their impact on other energy-related decisions such as energy-efficiency, energy services, and energy consumption.

In the following section, we present a summary of past studies which examine the technological and economic feasibility of a HES. In Section 3, we introduce a theoretical framework to understand the several layers of decision-making that a typical hybrid-prosumer faces. We consider three types of hybrid-prosumers: net energy generator, net consumer, and a net zero house. In Section 4, we present discussions and examine insights gained from the model.

Techno-Economic Studies on HES

Studies on the financial and economic feasibility of residential HES are widely dispersed across many engineering and energy focused journals. Despite the substantial research on the techno-economic analysis of residential HES, economic approaches have been restricted to cost-analysis. Deshmukh and Deshmukh [26] find that cost analysis is the most popular tool used to select among different types and sizes of HES. Specifically, the life-cycle cost of a system is calculated as the present value of life-time costs. Life-time costs include initial installation (e.g., component and system cost), replacement cost (e.g., batteries and/or inverters may need to be replaced), and operating and maintenance cost less any salvage value. Typically, calculations are made on an annual basis and the life-times assumed for the systems differ widely across studies [27,28].

Many studies rely on the calculation of the levelized cost of energy (LCOE) as an indicator for the financial performance of a small-scale HES. The LCOE is usually computed as the ratio of the total annualized cost of a system to either the annual electricity generated by the system or the annual electricity consumed by the household. The present value of life-time cost is divided by the capital recovery factor to find total annualized cost. A system with the lowest LCOE is considered to be cost-effective relative to others [27,28]. In the following paragraphs, we present a discussion of a few representative studies that apply the LCOE method in the analysis of a small-scale HES.

Li et al. [29] present a techno-economic analysis of three types of stand-alone systems for a typical household in Urumqi, China. A typical household is defined as a two-bedroom house with three people. The three systems are a hybrid photovoltaic–wind–battery system, a photovoltaic–battery system, and a wind–battery system. The photovoltaic–wind–battery system is composed of a 5 kW of photovoltaic arrays, a wind turbine of 2.5 kW, eight unit batteries each of 6.94 kWh, and a 5 kW power converter. The authors calculate and compare the LCOE of the three systems by taking the ratio of total annualized cost of a system divided by annual electricity consumption. They find that the photovoltaic–wind–battery system has the lowest LCOE of $1.045 per kWh of electricity consumption. The photovoltaic–battery and wind–battery systems’ LCOEs were calculated as $1.150 and $1.173 per kWh respectively. The photovoltaic–wind–battery system reduces the need for a larger battery because the two energy sources, solar and wind, often complement each other: when one is at a lower supply the other is usually at a higher supply. Ashok [30] calculates LCOE in a similar fashion as the ratio of total annualized cost to total electricity consumption. He finds that a micro-hydro–wind system with a small battery backup provides the lowest LCOE of Rs 6.5 per kWh of load served to an Indian rural village. Likewise, Shaahid and Elhadidy [31–33] perform a techno-economic assessment of a grid independent photovoltaic–diesel–battery system in Saudi Arabia. The cost of generating energy from the system is calculated to be $0.149/kWh [31].

Lv et al. [34] assess the techno-economic feasibility of a photovoltaic–wind–storage system owned by a Chinese household in Hangzhou, Zhejiang Province. Based on the household’s annual energy use of 5935 kWh and local weather data, the authors find a 5 kW photovoltaic panel, 7 kW wind turbine, 5760 Ah battery, and a 6.2 kW converter system to be optimal for the household. Even if the specified hybrid system produces excess energy in aggregate, there is a 0.86 percent unmet load in the months of February and August (due to heavy load and maximum discharge depth of battery). This illustrates that even if a hybrid system significantly reduces the mismatch between consumption and production, it may not completely eliminate it. The system’s calculated LCOE is $0.146 per each kWh of electrical energy generated by the system. This is less that the local retail price of electricity ($0.08 per kWh). With the possibility of selling all excess energy back to the grid, the system can generate $8079 over 25 years, with an adjusted LCOE of −$0.062/kW. Since electricity purchased from the grid is generated in thermal power plants, by providing almost all the energy needs of the household, the specified system contributes to reducing pollution. The authors use local emission coefficients (emissions per kWh) to calculate the annual average savings of atmospheric pollutants attributable to the system and consider this as an environmental benefit.

Diaf et al. [35] study the sizing and techno-economical optimization of a stand-alone photovoltaic–wind–battery for typical residential houses located in three different sites in Corsica Island, a region of France. The three sites have similar solar radiation but different wind potentials. The authors calculate LCOE as the ratio of the total annualized cost of the system to the annual electricity delivered from the photovoltaic arrays and wind turbines. They find that for a given battery size, the LCOE of the system decreases with an increase in load up to a certain load size, after which the decrease becomes very small. For instance, for a three days battery capacity the average LCOE for the three regions is $2.68/kWh for a one kWh of load and the average LCOE is $1.513/kWh for a 10 kWh of load. They also find that, for a given load, a change in battery capacity generally affects the LCOE positively. Ani [36] calculates the present value of costs associated with a photovoltaic–diesel–battery system (15 kW photovoltaic array, 21.6 kWh worth of battery storage, and a 5.4 kW generator) in a remote off-grid house located in Nigeria. Besides the most common cost items, the author estimates the emission cost of the system. The study shows that even if a photovoltaic–diesel–battery system has a higher initial capital cost, the present value of its life-time cost is lower than that of a diesel–battery system. This is because including solar energy in the system reduces fuel consumption and the need for larger batteries. In addition, emission costs are higher when the operating hour of the diesel generator is higher.

Unlike Li et al. ’s [29], Ani’s [36], and Diaf et al.’s [35] cost analyses, Syed et al. [37] focus on identifying and monetizing the annual benefits of a photovoltaic–wind system for a representative house in Canada. The system generates a total of 7720 to 8832 kWh of energy annually, but since it does not have storage, excess energy is sent back to the grid. They find that the photovoltaic–wind system generates $381.7 CAD annually in electricity bill savings and $340.7 CAD annually in credit for sending surplus energy back to the grid. The study also finds that a house with the photovoltaic–wind hybrid system generates 56% less greenhouse gases compared to a fully grid-dependent house. This is because greenhouse gas emissions from the generation of electricity at fossil fuel-based plants are reduced with the hybrid system, due to the reduction in the electricity import from the grid. More benefit–cost studies are needed to comprehensively evaluate the economic costs and benefits of adopting HES in the residential sector.

The LCOE is an approach that can be used to rapidly evaluate different types and sizes of HES; it has practical applications for the hybrid energy industry. However, it has several drawbacks when used to evaluate a household’s energy decision-making process. First of all, according to Bazilian et al. [38], the metrics used in the economics of renewable energy production are not standardized because they are defined in different ways based on the type of available data. For example, in Diaf et al. [35], LCOE is defined as the ratio of total annualized cost to the annual electricity delivered from the photovoltaic arrays and wind turbines; in Li et al. [29] the LCOE is defined as the ratio of total annualized cost to annual electricity consumption.

Second, cost-analyses simplify the hybrid-prosumer’s decision-making to choosing the least cost option. Options are either framed as hybrid versus a single renewable energy source (e.g., photovoltaic–wind–battery versus photovoltaic–battery), or different types of HES (e.g., photovoltaic–wind–battery versus hydro-wind–battery). However, when a hybrid-prosumer considers the adoption of a HES, he/she is actually making several other decisions simultaneously or close to each other. These other decisions may have a confounding effect on the choice of the type and size of a given system. In addition, the adoption of a HES with a specific combination of renewable sources may affect the hybrid-prosumer’s other energy-related decisions. In the following paragraphs, we discuss three other relevant decisions that are not fully captured by a cost-analysis. These are energy efficiency improvements, energy service production and consumption, and the level of energy consumption.

Energy-efficiency improvements: For the residential sector, demand for energy efficiency and the market for renewable energy generation are not independent. Oftentimes, when households consider onsite generation of a renewable energy, they also consider improving the energy efficiency of their homes [39]. For example, in California a majority of solar photovoltaic homeowners upgraded the energy efficiency of their homes and/or appliances before, or in conjunction with installing solar photovoltaic panels [40]. Thus, one can think of energy generation as a demand shifter in the market for energy efficiency. In the context of policy-making, McAllister [41] argues that both energy efficiency and renewable energy programs are needed to achieve a net zero energy objective.

Energy services: Another important factor for hybrid-prosumers is the level of energy services they can produce from their investments in energy. Fell [42] defines energy services as “those functions performed using energy which are means to obtain or facilitate desired end services.” The most common examples of energy services include space heating, cooling, lighting, water heating, and refrigeration. Hybrid-prosumers are producers of energy services in a sense that they transform energy (renewable and non-renewable sources) into energy services by using conversion technologies such as furnaces, space heaters, and pumps [43,44]. Hybrid-prosumers also derive utility from the consumption of energy services and their demand for such services is directly affected by the amount and type of energy used as an input. The implicit value of energy services rarely enter into cost-analyses such as the LCOE.

Energy consumption: The decision to adopt an onsite energy generating system may directly affect energy consumption in the residential sector. After households invest in an energy generating system, such as a HES, they may exhibit a different load profile. On the one hand, households may increase energy consumption post-generation similar to the rebound effect of energy-efficiency [45,46]. According to the rebound effect, improvement in the technical efficiency of technologies reduces the shadow price of energy services, which in turn increases demand for energy input. With respect to renewable energy generation, this implies that renewable generation may reduce the marginal or average cost of energy input, and hence increase its use. McAllister [41] argues that this is entirely due to an income effect, where households “facing reduced total and/or marginal cost of electricity due to the installation of an energy generating system would, in theory, increase overall electricity consumption (presumably prioritizing those end uses with the highest marginal utility).” The income effect indicates redistribution of the savings from the electricity bill to the overall use of more energy.

Based on large dataset from San Diego, California, McAllister [41] finds that the overall electricity consumption trend among solar photovoltaic adopters is that long-term consumption (two and three years post-generation) may be higher than consumption before generation. McAllister [41] finds that this increase in energy consumption post-generation is not very large overall (less than 5%), and although it is hard to determine causation it is observed for households which install relatively large sized generation systems. Such households are “more interested in covering most or all of their consumption and may not be interested in reducing consumption, or may also be involved in home expansion or other energy intensive activities” [41]. Fikru et al. [47] find that energy consumption of a prosumer could be higher than the energy consumption of a comparable grid dependent household. This is because households that generate energy have a lower valuation for energy services because of their ability to generate energy onsite. Lower shadow price for energy services implies higher demand for such services which requires higher energy input, keeping other factors constant. On the other hand, after adopting an onsite energy generating system, the household may decrease energy consumption. For example, the household may make efforts to coordinate the timing of generation with consumption. Energy storage may also contribute to the most efficient use of energy.

It is worth noting that when it comes to residential energy use and the decision-making process of a household, there has always been an interest in improving energy efficiency, increasing technological transfer, and modeling consumer behavior so as to identify behavioral drivers that can be targeted through policy intervention. While the reasons may have changed over time, from reducing dependency on increasingly pricier exhaustible natural resources (oil, gas, or coal) to reducing the household’s carbon footprint in order to limit climate change, the research on the determinants of household behavior when it comes to energy use has remained topical. Wilson and Dowlatabadi [48] present a critical review of research done on this subject from four perspectives: conventional and behavioral economics, technology adoption theory, and attitude-based decision-making, social and environmental psychology, and sociology. They advocate for integrating research findings across disciplines and they underline the difficulty of developing an all-encompassing model. One challenge is to reconcile the individually centered decision models (economics, psychology) with the social construction of technology, energy use, and climate. The second is the trade-off between the need to understand behavior (which increases model complexity), and the purpose of designing and evaluating policies (requires simplicity). The authors’ suggestions are threefold: recognize the existence of heterogeneity, especially when moving from individual to social level of decision; match the models to decision types and contexts; and consider the use of nested decision models.

Stern [49] stresses the need for interdisciplinary, collaborative efforts when it comes to developing realistic behavioral models designed to understand household behavior in the context of energy. He discusses the necessity of contextualizing the problem, identifying the barriers to changing behavior and targeting it by using combined approaches that incorporate financial incentives and non-financial features (feed-back, communication of information), which psychology can help with.

In the following section, we develop an economic model for the hybrid-prosumer by directly capturing demands for energy-efficiency, energy consumption, and energy services while accommodating for the possibility of selling excess energy (if any) back to the grid.