Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation

Palmer, Johannes; Sorda, Giovanni; Madlener, Reinhard

doi:10.2139/ssrn.2278120

Cited by 63 publications

(47 citation statements)

References 97 publications

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“…A number of agent-based modeling efforts are specifically targeted at the rooftop solar adoption domain [7,13,31,32,36,37,45]. Denholm et al [13] and Boghesi et al [7] follow a relatively traditional methodological approach (i.e., simple rule-based behavior model), and their focus is largely on financial considerations in rooftop solar adoption.…”

Section: Related Workmentioning

confidence: 99%

“…Denholm et al [13] and Boghesi et al [7] follow a relatively traditional methodological approach (i.e., simple rule-based behavior model), and their focus is largely on financial considerations in rooftop solar adoption. Palmer et al [31] and Zhao et al [45], likewise use a traditional approach, but consider several potentially influential behavioral factors, such as social influence and household income. Palmer et al calibrate their model using total adoption data in Italy (unlike our approach, they do not separate calibration from validation).…”

Section: Related Workmentioning

confidence: 99%

“…a framework for data-driven agent-based modeling; 2. methods for learning individual-agent models of solar adoption, addressing challenges posed by the market structure and the nature of the data; 3. an adaptation of a recent agent-based model of rooftop solar adoption, used as a baseline, with an improved means for systematic calibration (systemitizing the approach proposed by Palmer et al [31] entirely new addition compared to our preliminary work [44]); 4. a data-driven agent-based model of solar adoption in (a portion of) San Diego county, with forecasting efficacy evaluated on data not used for model learning; 5. a comparison of the data-driven approach to the baseline adoption model (a new addition compared to our preliminary work [44]); 6. a quantitative evaluation of the California Solar Initiative subsidy program (including a significantly improved and extended approach to optimizing the solar discount policy relative to our preliminary work [44]), a broad class of incentive policies, and a broad class of solar system "seeding" policies.…”

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confidence: 99%

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Data-driven agent-based modeling, with application to rooftop solar adoption

Zhang

Vorobeychik

Letchford

et al. 2016

Auton Agent Multi-Agent Syst

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Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple This paper is a significant extension of the following article: Haifeng Zhang, Yevgeniy Vorobeychik, Joshua Letchford, and Kiran Lakkaraju. Data-driven agent-based modeling, with applications to rooftop solar adoption.Auton Agent Multi-Agent Syst heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Data-driven agent-based modeling, with application to rooftop solar adoption

Zhang

Vorobeychik

Letchford

et al. 2016

Auton Agent Multi-Agent Syst

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“…Recent publications have investigated the role of certain technologies, like the market diffusion of PV [31,32] and biomass power plants [33], or the value of storage technologies [34]. Others have put their focus on certain aspects of the demand side of the energy system, as this aspect is associated with a need for a more "human" modelling and preference depiction: their works focus on demand response [ [35][36][37], adoption of dynamic tariffs [38], price elasticities [39], and smart meter diffusion [40], among others.…”

Section: Overview Of Agent-based Models In Energy Systemmentioning

confidence: 99%

Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration

et al. 2017

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The ongoing deployment of renewable energy sources (RES) calls for an enhanced integration of RES into energy markets, accompanied by a new set of regulations. In Germany, for instance, the feed-in tariff legislation for renewables has been successively replaced by first optional and then obligatory marketing of RES on competitive wholesale markets. This paper introduces an agentbased model that allows studying the impact of changing energy policy instruments on the economic performance of RES operators and marketers. The model structure, its components, and linkages are presented in detail; an additional case study demonstrates the capability of our sociotechnical model. We find that changes in the political framework cannot be mapped directly to RES operators as behaviour of intermediary market actors has to be considered as well. Characteristics and strategies of intermediaries are thus an important factor for successful RES marketing and further deployment. It is shown that the model is able to assess the emergence and stability of market niches.

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“…There are several examples of simulation research in social science, such as a simple agent model of an epidemic, a stochastic cellular automata model of innovation diffusion, and an agent-based simulation of policy-induced diffusion of smart meters [28][29][30]. Among the simulation models, there are diverse levels such as technology adoption at the national level, knowledge transfer at the economic level, or diffusion of a residential photovoltaic system in Italy at the national level, for example [31][32][33][34] x , is created at the time that t 1 decays as time goes by, so that at time t 2 , the performance becomes O (t 1 )…”

Section: Mathematical Modeling Of Open Innovationmentioning

confidence: 99%

Dismantling of the Inverted U-Curve of Open Innovation

et al. 2017

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Abstract:The purpose of this study is to address the following research question: What is the relationship between open innovation and firm performance? The study built up a research framework with three factors-i.e., open innovation strategy, time scope, and industry condition-to find out the concrete open innovation effects on firm performance. This study adopted four different research methods. Firstly, we applied the aforementioned factors to a game of life simulation in order to identify the concrete differences of open innovation effects on firm performance. Secondly, the study examined the real dynamics of open innovation effects on firm performance in the aircraft industry-one of the oldest modern industries-through a quantitative patent analysis. It then looked into the effects of major factors that impact open innovation effects. Thirdly, this study developed a mathematical model and tried to open the black box of open innovation effects on firm performance. Lastly, the study logically compiled research on open innovation effects on firm performance through the presentation of a causal loop model and derived the possible implications.

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Modeling the Diffusion of Residential Photovoltaic Systems in Italy: An Agent-Based Simulation

Cited by 63 publications

References 97 publications

Data-driven agent-based modeling, with application to rooftop solar adoption

Data-driven agent-based modeling, with application to rooftop solar adoption

Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration

Dismantling of the Inverted U-Curve of Open Innovation

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