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Investment uncertainty analysis for smart grid adoption: A real options approach
Abstract
The growth of smart grid technologies is already defining energy in the 21st century: smart cities depend on the smart grid for resilient energy delivery and improved energy efficiency. Adopting the smart grid under conditions of uncertainty demands focused attention and innovative approaches. This paper employs the Real Options Approach (ROA) to study how utilities make investment decisions regarding smart grid information technology innovation, under conditions of investment uncertainty. We argue that investing in the smart grid is analogous to having the option rights in a call option in the US financial market. We propose a model in which the smart grid cost is taken as the primary decision variable to identify the optimal first time for utility suppliers to adopt the smart grid. This study demonstrates that ROA can be an important tool for simulating the impact of public policy on the adoption of the smart grid technology of electric utilities.
... 2 Campagna et al. (2020) describe the idea of smart grids as "the merge of digital technology, DES and ICT for energy consumption optimization, which provides and enhances the traditional power grid in terms of flexibility, reliability and safety". Feng et al. (2016) note the contribution of smart grids in "reducing power outage, lowering delivery costs, encouraging more energy conscious behaviors from consumers" as well as in the transition towards low-carbon economic growth. Moreno et al. (2017) detail the evolving energy landscape from conventional electricity systems to low-carbon smart grids, highlighting the transition of distribution networks from passive structures to active systems and the emergence of end-users, who "will become active participants in system and market operation", as well as the "opening up opportunities for aggregating and coordinating consumers and system needs". ...
... With reference to exchange P2P, we recall, among others, Angelidakis and Chalkiadakis (2015), Zafar et al. (2018), Ghosh et al. (2018), Liu et al. (2018, Gonzalez-Romera et al. (2019) and Hahnel et al. (2020). With respect to the real options literature, we complement studies pertaining to the energy sector, which include Boomsma et al. (2012), Ceseña et al. (2013), Martinez-Cesena et al. (2013), Feng et al. (2016, Kozlova (2017), Tian et al. (2017), , , Ioannou et al. (2017). ...
... 2 Campagna et al. (2020) describe the idea of smart grids as "the merge of digital technology, DES and ICT for energy consumption optimization, which provides and enhances the traditional power grid in terms of flexibility, reliability and safety". Feng et al. (2016) note the contribution of smart grids in "reducing power outage, lowering delivery costs, encouraging more energy conscious behaviors from consumers" as well as in the transition towards low-carbon economic growth. Moreno et al. (2017) detail the evolving energy landscape from conventional electricity systems to low-carbon smart grids, highlighting the transition of distribution networks from passive structures to active systems and the emergence of end-users, who "will become active participants in system and market operation", as well as the "opening up opportunities for aggregating and coordinating consumers and system needs". ...
... With reference to exchange P2P, we recall, among others, Angelidakis and Chalkiadakis (2015), Zafar et al. (2018), Ghosh et al. (2018), Liu et al. (2018), Gonzalez-Romera et al. (2019) and Hahnel et al. (2020). With respect to the real options literature, we complement studies pertaining to the energy sector, which include Boomsma et al. (2012), Ceseña et al. (2013), Martinez-Cesena et al. (2013), Feng et al. (2016, Kozlova (2017), Tian et al. (2017), , , Ioannou et al. (2017). ...
In this paper, we provide a real options model framing prosumers' investment in photovoltaic plants. This is presented in a Smart Grid context where the exchange of energy among prosumers is possible. We determine the optimal size of the photovoltaic installations based on the influence the self-consumption profiles on the exchange of energy among prosumers. We calibrate the model using figures relative to the Northern Italy energy market and investigate the investment decision allowing for different prosumer profiles and consider several combinations of their individual energy demand and supply. Our findings show that the shape of individual energy demand and supply curves is crucial to the exchange of energy among prosumers, and that there could be circumstances under which no exchange occurs.
... 23 18 See among others also D'Alpaos and Andreolli (2020) and Caramizaru and Uihlein (2020). 19 see Kumbaro §lu et al. (2008), Boomsma et al. (2012), Feng et al. (2016), , , Bertolini et al. (2018) 20 see also Frieden et al. (2019), Inês et al. (2020) 21 Among others see Brosch et al. (2014), Zhang et al. (2014), Zhang et al. (2015), Celik et al. (2017), Motalleb and Ghorbani (2017), Mei et al. (2019), Tarditi et al. (2020) 22 Among others, see Alam et al. (2013), Mandelli et al. (2016), Bakke et al. (2016), Gonzalez-Romera et al. (2019), Hahnel et al. (2020) 23 With reference in particular to the two models presented in Chapters 2 and 3, in both no storage possibility assumption is set, since both focuses on a context where prosumers are conceived as households and so far, storages are still far way to be adopted by this kind of agents. Of course, a possible extension of this current work could be the development of a model in a similar framework but with storage possibility. ...
... 2 Campagna et al. (2020) describe the idea of the smart grids as "the merge of digital technology, DES and ICT for energy consumption optimization, which provides and enhances the traditional power grid in terms of flexibility, reliability and safety". Feng et al. (2016) remark the contribution of smart grids in "reducing power outage, lowering delivery costs, encouraging more energy conscious behaviors from consumers" as well as in the transition towards low-carbon economic growth. Moreno et al. (2017) describe in details the evolving landscape from conventional electricity systems to low-carbon smart grids, underlining the transition of distribution networks from passive structures to active systems and the evolution of end-users, which "will become active participants in system and market operation" as well as remarking the "opening up opportunities for aggregating and coordinating consumers and system needs". ...
The purpose of this thesis is to investigate some key features of the Smart Grids topic, focusing on the agents' (i.e. prosumers) investment decisions in the photovoltaic technology, in a context characterized by uncertainty and where the investment decision is undertaken cooperatively. The effect of allowing prosumers to exchange of energy with each other (exchange P2P, hereafter) is analyzed with a special emphasis on the demand and supply matching in exchange P2P as well as the conditions assuring its economic optimality. Discussion related to Renewable Energy Communities (REC) is provided with the aim to understand how the findings of our models, can boost their diffusion.
... To establish a fully functional smart grid in the country, an estimated net investment ranging from $338 to $476 billion is projected (Gellings, 2011). Feng, Zhang, and Gao (2016) argue that certain smart grid projects may not be economically viable without adequate financial assistance, which can be attained through a combination of public funds and private investments. Other challenges arise from the absence of technology standards and regulatory frameworks, which can potentially hinder private investment and create risks and uncertainties (DOE, 2020). ...
Smart grids enable the alignment of energy supply and demand, enhance energy efficiency, and raise consumer awareness of energy conservation. Smart meter, a vital technological component of smart grids, enables bidirectional communication between consumers and utility companies. This paper employs the two-stage least squares panel data model to examine the effects of federal funding and state legislative actions on smart meter adoption and the resulting energy efficiency savings in the residential sector of the United States. Additionally, we use machine learning techniques to select the subset of control variables that are influential for smart meter adoption and energy efficiency savings. The findings suggest that both federal financial assistance and state legislative actions have positive effects on smart meter adoption, and the interaction of federal funding and state policy interventions have a significantly greater effect on adoption as compared to the sum of the individual policy instrument alone. Furthermore, there exists a positive association between the adoption rate and energy efficiency savings. This study presents empirical evidence that underscores the significance of multi-level governance as an effective means for policy integration regarding smart meter adoptions, and it discusses the policy implications for grid modernization and environmental policies.
... In their work, they evaluated the R &D decision on a smart city project. Feng et al. (2016) applied ROV to evaluate the decisions to adopt a smart grid technology. Focusing on the ICT field, other examples of how ROV can be applied in broadband investment analysis are given by Elnegaard (2002) and Elnegaard and Stordahl (2002). ...
In most cases, the valuation of the investments characterized by various stages with a high level of uncertainty is done through the compound real option valuation (ROV). This decision making support can consider various types of uncertainty that can affects these investment phases, such as that linked to technology. Specifically, within the category of uncertain investments there are the broadband opportunities that can be valued as real options in order to quantify the risks associated with the investment. However, since ROV theory has no definitive way to determine model parameters based on market information, we propose one that can adjust them dynamically. In this paper, to include this aspect in the project valuation, we have unified the ROV with the sentiment analysis, a natural language processing technique that allows us to quantify the polarity of expressions in natural language numerically. In particular, the inherent risks related to the different phases of the project can be extracted from the information present in the surrounding environment and published in newspapers. From there, we obtain a sentiment score which, through appropriate manipulations, manages to modify the evaluation of the success probabilities of each stage. Then, we embed these success probabilities in the ROV in order to provide a valuation methodology that includes the impact of information on the investment decision.
In this paper, we provide a real options model framing prosumers’ investment in photovoltaic plants. This is presented in a Smart Grid context where the exchange of energy among prosumers is possible. We determine the optimal size of the photovoltaic installations based on the influence the self-consumption profiles on the exchange of energy among prosumers. We calibrate the model using figures relative to the Northern Italy energy market and investigate the investment decision allowing for different prosumer profiles and consider several combinations of their individual energy demand and supply. Our findings show that the shape of individual energy demand and supply curves is crucial to the exchange of energy among prosumers, and that there could be circumstances under which no exchange occurs.
This paper employs the real option approach (ROA) to study the investment decision of a management accounting innovation—the case of activity-based costing (ABC)—adoption or discontinuation under uncertainty. We argue that investing in ABC is analogous to having the option rights in a financial (American) call option. We propose a model taking the firm’s total annual production as primary decision variable and using the added annual net profits after establishing ABC to identify the optimal threshold for adoption or discontinuation. We find that the optimal adoption (resp., discontinuation) threshold is higher (resp., lower) when obtained by the ROA than by the net present value (NPV) method; thus, the ROA is more conservative than the NPV approach. Their difference stems mainly from the ROA’s option value of delay before implementing the adoption/discontinuation decision. Further, recent empirical researches based on survey found inconclusive results about the adoption of ABC. For instance, there are studies found that manufacturing sector having a higher adoption rate of ABC than non-manufacturing sector, but the other studies find the opposite. Our theoretical model explains the inconclusive results from the empirical researches can be driven by the variations in mean and volatility of sectoral output growth, which are omitted in those studies.
While smart grid development promises benefits for utilities and consumers alike, the public policy surrounding its roll-out remains conflicted. Will regulation guide the structure and usability of the smart grid, or will the ways in which the smart grid is already being applied force specific types of regulation? Early decisions by regulators will surely influence the balancing of policy concerns later in the smart grid development process; yet these decisions will be made in a regulatory environment in which utilities may lack the proper incentives to promote energy efficiency and consumer awareness – both functions of the smart grid. This Article examines economic and legal constraints in current utility regulation and describes the policy concerns which impact regulators’ decisions. It then studies the interstate market for consumer usage data as a unique source of innovation within the electricity industry, as well as an example of the opposing interests that regulators will need to balance. In this case, such competing interests include consumer privacy protection and information access for edge service providers. Because of the disconnect between parties affected by smart grid development, exemplified by the information marketplace example, regulators need to act quickly and knowledgeably to prevent the death of innovation in this emerging field.
Although the field of study surrounding the “smart city” is in an embryonic phase, the use of information and communication technologies (ICT) in urban settings is not new (Dameri and Rosenthal-Sabroux, 2014; Toh and Low, 1993; Tokmakoff and Billington, 1994). Since ancient times, cities and metropolitan areas have propelled social transformation and economic
prosperity in many societies (Katz and Bradley, 2013). Many modern urban sites and metros have leveraged the success and competitiveness of ICTs (Caragliu, Del Bo and Nijkamp, 2011).
At least in part, the recent growth of smart city initiatives can be attributed to the rapid adoption of mobile and sensor technologies, as well as the diversity of available Internet applications (Nam and Pardo, 2011; Oberti and Pavesi, 2013).
The effective use of technological innovations in urban sites has been embraced by the
emergent term “smart city”, with a strong focus on improving living conditions, safeguarding the sustainability of the natural environment, and engaging with citizens more effectively and actively (Dameri and Rosenthal-Sabroux, 2014). Also known as smart city, digital city, or intelligent city, many of these initiatives have been introduced as strategies to improve the utilization of physical infrastructure (e.g., roads and utility grids), engage citizens in active local
governance and decision making, foster sustainable growth, and help government officials
learn and innovate as the environment changes.
The Digital Government landscape is continuously changing to reflect how governments are trying to find innovative digital solutions to social, economic, political and other pressures, and how they transform themselves in the process. Understanding and predicting such changes is important for policymakers, government executives, researchers and all those who prepare, make, implement or evaluate Digital Government decisions. This article argues that the concept of Digital Government evolves toward more complexity and greater contextualization and specialization, similar to evolution-like processes that lead to changes in cultures and societies. To this end, the article presents a four-stage Digital Government Evolution Model comprising Digitization (Technology in Government), Transformation (Electronic Government), Engagement (Electronic Governance) and Contextualization (Policy-Driven Electronic Governance) stages; provides some evidence in support of this model drawing upon the study of the Digital Government literature published in Government Information Quarterly between 1992 and 2014; and presents a Digital Government Stage Analysis Framework to explain the evolution. As the article consolidates a representative body of the Digital Government literature, it could be also used for defining and integrating future research in the area.
Our consulting experiences indicate that many New Zealand businesses and their managers are increasingly aware of the shortcomings of conventional methods for evaluating capital investment projects, particularly in situations where there is considerable flexibility subsequent to the project's commencement. With the busy executive in mind, we offer a brief and intuitive introduction to recently developed methods of project evaluation that explicitly incorporate managerial flexibility.
To address the issue of reducing emissions of greenhouse gases, organizations are involved with emergent markets for trading emission permits. Investment in equipment that reduces emissions may generate emission credits for sale in the market. This article applies real options analysis to actual case study information from British Petroleum-Amoco of a particular project that would generate emissions credits. We conclude that unless permits have a faster price rise than is generally anticipated, certain projects are not economically feasible. The policy implication is that planners may need to set more stringent regulations to bring about their desired result. Additionally, real options analysis in this market based regulatory policy is an especially important tool for the energy industry, which is disproportionately impacted by greenhouse gases policies.
The real options approach is frequently advocated as an ap-proach that offers a positive and radical reassessment of the value of risk and exploration. We examine a recent case where Merck used the real options approach to justify an investment in an R&D project. This case is used to highlight some of the problems associated with using real options. We note that the assumptions incorporated in most standard option valuation models can conflict with the conclusions reached by strategic analysis. As a result, users of real options models should un-derstand the quantitative aspects of these models, and may often need to create a customized model for each situation. The dif-ficulty of developing customized models may explain, in part, the limited use of the real options approach in strategic analysis.
This paper presents a valuation study of hydropower investment opportunities in the Norwegian context. According to NVE (Norwegian Water Resources and Energy Directorate, the regulator), there is a potential of 39 TWh not yet developed (generation in a normal year is approximately 120 TWh).By using the conceptual real option framework of Dixit and Pindyck [1994. Investment Under Uncertainty. Princeton University Press, Princeton, NJ] one can estimate the value of investment opportunities to NOK 11 million/GWh (EUR 1.4 million/GWh). Furthermore, the optimal trigger price for initiating an investment based on electricity forward prices is calculated to NOK 0.32/kWh (EUR 0.04/kWh). The analysis shows consistency between real option theory and aggregate investment behaviour in Norwegian hydropower.
Collateral impacts of LULUCF projects, especially those concerning social and environmental aspects, have been recognised as important by the Marrakech Accords. The same applies to the necessity of assessing and, if possible, of quantifying the magnitude of these impacts. This article aims to define, clarify and structure the relevant social, economic and environmental issues to be addressed and to give examples of indicators that ought to be included in the planning, design, implementation, monitoring, and ex post evaluation of LULUCF projects. This is being done by providing a conceptual framework for the assessment of the sustainability of such projects that can be used as a checklist when dealing with concrete projects, and that in principle is applicable to both Annex I and non-Annex I countries. Finally, a set of recommendations is provided to further develop and promote the proposed framework.
We introduce adaptive learning behavior into a general-equilibrium life-cycle economy with capital accumulation. Agents form forecasts of the rate of return to capital assets using least-squares autoregressions on past data. We show that, in contrast to the perfect-foresight dynamics, the dynamical system under learning possesses equilibria that are characterized by persistent excess volatility in returns to capital. We explore a quantitative case for theselearning equilibria. We use an evolutionary search algorithm to calibrate a version of the system under learning and show that this system can generate data that matches some features of the time-series data for U.S. stock returns and per-capita consumption. We argue that this finding provides support for the hypothesis that the observed excess volatility of asset returns can be explained by changes in investor expectations against a background of relatively small changes in fundamental factors.
This study presents a policy planning model that integrates learning curve information on renewable power generation technologies into a dynamic programming formulation featuring real options analysis. The model recursively evaluates a set of investment alternatives on a year-by-year basis, thereby taking into account that the flexibility to delay an irreversible investment expenditure can profoundly affect the diffusion prospects of renewable power generation technologies. Price uncertainty is introduced through stochastic processes for the average wholesale price of electricity and for input fuel prices. Demand for electricity is assumed to be increasingly price-sensitive, as the electricity market deregulation proceeds, reflecting new options of consumers to react to electricity price changes (such as time-of-use pricing, unbundled electricity services, and choice of supplier). The empirical analysis is based on data for the Turkish electricity supply industry. Apart from general implications for policy-making, it provides some interesting insights about the impact of uncertainty and technical change on the diffusion of various emerging renewable energy technologies.
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