Opportunities and Obstacles in the Transition to a Distributed Network of Rooftop Solar: A Multi-Method Approach

This paper investigates the feasibility and viability of providing power to Ada County, Idaho, using a distributed network of rooftop solar photovoltaic panels. Using a multi-disciplinary and multi-method modeling approach, a detailed simulation is performed where existing structures are retro-fitted with grid-tied solar photovoltaic systems using currently available technology. Feasibility is determined using simulated supply and demand per building, while viability is determined through standard financial metrics used in the energy sector. A major critique of solar energy comes from the vast amounts of space required to efficiently capture solar power, along with the inefficiencies created by transmission loss and intermittency. Under a system where structures become both producers and consumers of energy, with PV panels deployed in unused rooftop space, this paper mitigates those critiques and analyzes the results. Four case scenarios are discussed based on the perspectives of differing energy stakeholders; consumers, private firms, public utilities, and national governments

Optimizing the Implementation of Green Technologies Under Climate Change Uncertainty

In this study, we aim to investigate the application of the green technologies (i.e., green roofs (GRs), Photovoltaic (PV) panels, and battery integrated PV systems) under climate change-related uncertainty through three separate, but inherently related studies, and utilize optimization methods to provide new solutions or improve the currently available methodsFirst, we develop a model to evaluate and optimize the joint placement of PV panels and GRs under climate change uncertainty. We consider the efficiency drop of PV panels due to heat, savings from GRs, and the interaction between them. We develop a two-stage stochastic programming model to optimally place PV panels and GRs under climate change uncertainty to maximize the overall profit. We calibrate the model and then conduct a case study on the City of Knoxville, TN.Second, we study the diffusion rate of the green technologies under different climate projections for the City of Knoxville through the integration of simulation and dynamic programming. We aim to investigate the diffusion rates for PV panels and/or GRs under climate change uncertainty in the City of Knoxville, TN. We further investigate the effect of different and evaluate their effects on the diffusion rate. We first present the agent based framework and the mathematical model behind it. Then, we study the effects of different policies on the results and rate of diffusion.Lastly, We aim to study a Lithium-ion battery load connected to a PV system to store the excess generated electricity throughout the day. The stored energy is then used when the PV system is not able to generate electricity due to a lack of direct solar radiation. This study is an attempt to minimize the cost of electricity bill for a medium sized household by maximizing the battery package utilization. We develop a Markov decision processes (MDP) model to capture the stochastic nature of the panels\u27 output due to weather. Due to the minute reduction in the Li-ion battery capacity per day, we have to deal with an excessively large state space. Hence, we utilize reinforcement learning methods (i.e., Q-Learning) to find the optimal policy

Analysis of residential rooftop photovoltaic diffusion in India through a Bass model approach

In this paper, the analysis of the diffusion of photovoltaic systems is performed using the Bass model. The historical data of installed rooftop photovoltaic is not enough for the model, as the installation of photovoltaic was almost non-existent, hence data of solar water heaters is utilized to calculate the parameters for the model. The trajectory of growth for solar water heaters in the market presents a congruence for the growth of solar photovoltaic due to inherent similarities in the technologies and its application. India was used as a case study of the application of this borrowing approach in a market where photovoltaic is also used to provide electricity to local communities. Data from solar water heater market in India were used and they indicate innovator parameters of 0.00105 and imitator parameters of 0.12219. The study is significant as it forecasts the diffusion of photovoltaic in the market, which is essential for achieving India\u27s Intended Nationally Determined Contributions goals and Renewable Energy targets. The results indicate that residential rooftop photovoltaic diffusion will tend to present a slower pace in India than in other markets if no additional policies are implemented to foster this market

Over the Precipice: Transitional Pressures from Household PV Battery Adoption on Electricity Markets and the Potential for Decarbonisation

The electricity system is undergoing simultaneous change from the integration of large-scale wind and solar farms to the rapid growth of rooftop solar in over 3 million Australian households. This research establishes a range of future impacts that households with solar PV and battery systems could have on the wider power sector, the extent to which policymakers may influence their outcome, and their potential contribution to decarbonisation as an emerging source of renewable energy

Integrated Framework for Analyzing Clean Energy Technology Subsidies

Renewable energy technologies can significantly reduce fossil fuel consumption and greenhouse gas emissions associated with the energy sector. In US, both federal and state governments have implemented numerous policies and programs to support these technologies. But these policies require a substantial amount of public spending. In this study, an integrated model to identify optimal subsidy schedules for clean energy technologies that maximize social benefits less subsidy costs is developed. The national flexible optimal subsidy schedule for residential solar PV begins at $585/kW and declines to zero in 14 years. An alternative analytical model is also presented to analyze technological features affecting subsidy design. Three important factors determining the social benefits of subsidizing the use of clean energy technology are examined: the price sensitivity of adoption, induced cost reductions through learning, and environmental benefits. Results show that optimal subsidy schedules for utility wind are roughly constant over time. In contrast, optimal residential solar subsidies either decline over time or are not desirable (subsidy of zero). The results imply that the optimal subsidy for utility wind is justified mainly through the direct environmental benefits, unlike residential solar PV, in which indirect technological progress benefits primarily justify the subsidy. The effects of multiple adoption modeling and parameter choice alternatives on optimal subsidy design are also explored. The study considers three different model structures for rooftop solar adoption consisting of a combination of single and multiple explanatory variables. Results show that the scale of sensitivity of optimal subsidy designs to technology learning rate assumptions depends on the model choice. This dissertation shows that analytical inputs can be instrumental in informing policymakers deciding on subsidy schedules promoting renewable technologies. These tools can integrate environmental benefits and the complex interaction between the subsidy, diffusion patterns, and technology cost trajectories to ensure socially optimal policy designs

Sunspots that matter: The effect of weather on solar technology adoption

This paper tests for the presence of behavioral biases in household decisions to adopt solar photovoltaic installations using exogenous variation in weather. I find that residential technology uptake responds to exceptional weather, defined as deviations from the long-term mean, in line with the average time gap between decision-making and completion of the installation. In particular, a one standard deviation increase in sunshine hours during the purchase period leads to an approximate increase of 4.7% in weekly solar PV installations. This effect persists in aggregate data. I consider a range of potential mechanisms and find suggestive evidence for projection bias and salience as key drivers of my results.I would like to thank Jerome Adda, Stefan Ambec, Bryan Bollinger, Sylvain ChabeFerret, Antonia Diaz, Andreas Gerster, Ken Gillingham, Sebastian Houde, Andrea Ichino, Martin Kesternich, Matt Kotchen, Matt Neidell, Francois Salanie, Fabiano Schivardi, Joe Shapiro, and seminar participants at the Atlantic Workshop on Energy & Environmental Economics, EMEE, Energy and Climate Conference Toulouse, FAERE, IAERE, Northeast Workshop on Energy Policy & Environmental Economics, Workshop on Economic Theories & Low-Carbon Policies, World Congress of the Econometric Society, European University Institute, Goteborg University, RWI Essen, Toulouse School of Economics, Yale University, and ZEW Mannheim for helpful comments and suggestions. I am also indebted to the Editor and two anonymous referees for their insightful feedback. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant Agreement No 772331). A previous version of this article has been circulated under the name "Projection Bias in Solar Electricity Markets". Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Analysis of Rooftop Photovoltaics Diffusion in Energy Community Buildings by a Novel GIS- and Agent-Based Modeling Co-Simulation Platform

The present work introduces an empirically ground agent-based modeling (ABM) framework to assess the spatial and temporal diffusion of rooftop photovoltaic (PV) systems on existing buildings of a city district. The overall ABM framework takes into account social, technical, environmental, and economic aspects to evaluate the diffusion of PV technology in the urban context. A city district that includes 18 720 households distributed over 1 290 building blocks and a surface area of 2.47 km2 is used to test the proposed ABM framework. Results show how the underlying regulatory framework (i.e., the rules of the internal electricity market) influences the pattern and intensity of adoption, thus realizing different shares of the available potential. Policies that support the establishment of `prosumers' within Condominiums (i.e., energy community buildings), and not in single-family houses only, is key to yield high diffusion rates. The installed capacity increases by 80% by switching from the one-to-one configuration to the one-to-many paradigm, i.e., from 5.90 MW of rooftop PV installed on single-family households and/or single PV owners to 10.64 MW in energy community buildings. Moreover, the possibility to spread the auto-generated solar electricity over the load profile of the entire population of Condominium results in self-consumption rates greater than 50% and self-sufficiency ratios above 20% for the majority of the simulated buildings

A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries