Meeting Global Cooling Demand with Photovoltaics during the 21st Century

Space conditioning, and cooling in particular, is a key factor in human productivity and well-being across the globe. During the 21st century, global cooling demand is expected to grow significantly due to the increase in wealth and population in sunny nations across the globe and the advance of global warming. The same locations that see high demand for cooling are also ideal for electricity generation via photovoltaics (PV). Despite the apparent synergy between cooling demand and PV generation, the potential of the cooling sector to sustain PV generation has not been assessed on a global scale. Here, we perform a global assessment of increased PV electricity adoption enabled by the residential cooling sector during the 21st century. Already today, utilizing PV production for cooling could facilitate an additional installed PV capacity of approximately 540 GW, more than the global PV capacity of today. Using established scenarios of population and income growth, as well as accounting for future global warming, we further project that the global residential cooling sector could sustain an added PV capacity between 20-200 GW each year for most of the 21st century, on par with the current global manufacturing capacity of 100 GW. Furthermore, we find that without storage, PV could directly power approximately 50% of cooling demand, and that this fraction is set to increase from 49% to 56% during the 21st century, as cooling demand grows in locations where PV and cooling have a higher synergy. With this geographic shift in demand, the potential of distributed storage also grows. We simulate that with a 1 m$^3$ water-based latent thermal storage per household, the fraction of cooling demand met with PV would increase from 55% to 70% during the century. These results show that the synergy between cooling and PV is notable and could significantly accelerate the growth of the global PV industry

Meeting Global Cooling Demand with Photovoltaics during the 21st Century

Space conditioning, and cooling in particular, is a key factor in human productivity and well-being across the globe. During the 21st century, global cooling demand is expected to grow significantly due to the increase in wealth and population in sunny nations across the globe and the advance of global warming. The same locations that see high demand for cooling are also ideal for electricity generation via photovoltaics (PV). Despite the apparent synergy between cooling demand and PV generation, the potential of the cooling sector to sustain PV generation has not been assessed on a global scale. Here, we perform a global assessment of increased PV electricity adoption enabled by the residential cooling sector during the 21st century. Already today, utilizing PV production for cooling could facilitate an additional installed PV capacity of approximately 540 GW, more than the global PV capacity of today. Using established scenarios of population and income growth, as well as accounting for future global warming, we further project that the global residential cooling sector could sustain an added PV capacity between 20-200 GW each year for most of the 21st century, on par with the current global manufacturing capacity of 100 GW. Furthermore, we find that without storage, PV could directly power approximately 50% of cooling demand, and that this fraction is set to increase from 49% to 56% during the 21st century, as cooling demand grows in locations where PV and cooling have a higher synergy. With this geographic shift in demand, the potential of distributed storage also grows. We simulate that with a 1 m$^3$ water-based latent thermal storage per household, the fraction of cooling demand met with PV would increase from 55% to 70% during the century. These results show that the synergy between cooling and PV is notable and could significantly accelerate the growth of the global PV industry

Integration of Photovoltaics into Building Energy Usage through Advanced Control of Rooftop Unit

As the United States sees the continued expansion of photovoltaic (PV) and other distributed solar generation technologies into the distribution grid, there is an increased need to find approaches to mitigate integration challenges associated with renewable resources. Depending on the renewable resource, the integration challenges will vary. Much of the challenge with integration is associated with the uncontrolled oscillations of output power, for example, from a PV array. Both solar and wind resources rely on environmental conditions to produce power. However compared to wind, solar generation resources such as PV typically produce more second to minute oscillations due to cloud patterns. With low levels of penetration, the impact is minimal. This paper focuses on developing advanced control strategies for building equipment like the rooftop units along with energy storage technologies to support seamless PV integration into buildings. A forecasting approach for PV is presented along with model-based control strategies for using load to support the integration of PV. The forecasting model takes as input solar irradiance and module temperature to estimate the output power of PV based on an interconnected voltage. The first step is to poll the cloud patterns for the day and utilize this information to project the cloud density each hour. The trained neural network defines relationship of this cloud cover to the amount of expected solar irradiance that is measured. Temperature data is collected from weather application and is inserted as an initial temperature to the PV model and thermal model. The model develops the corresponding PV curves based on the current module temperature reading and the solar irradiance data provided. The model predicts the average power output of the PV array over the next one-hour time window. A control algorithm for the rooftop unit is presented that utilizes this PV forecast to optimize the energy consumption to match the PV peak generation. The model is validated using irradiance, temperature, and PV output power measurements from Oak Ridge National Laboratory’s 50kW PV array

Microgrids for Improving Manufacturing Energy Efficiency

Thirty-one percent of annual energy consumption in the United States occurs within the industrial sector, where manufacturing processes account for the largest amount of energy consumption and carbon emissions. For this reason, energy efficiency in manufacturing facilities is increasingly important for reducing operating costs and improving profits. Using microgrids to generate local sustainable power should reduce energy consumption from the main utility grid along with energy costs and carbon emissions. Also, microgrids have the potential to serve as reliable energy generators in international locations where the utility grid is often unstable. For this research, a smart microgrid system was designed as part of an innovative load management option to improve energy utilization through active Demand-Side Management (DSM). An intelligent active DSM algorithm was developed to manage the intermittent nature of the microgrid and instantaneous demand of the site loads. The controlling algorithm required two input signals; one from the microgrid indicating the availability of renewable energy and another from the manufacturing process indicating energy use as a percent of peak production. Based on these inputs the algorithm had three modes of operation: normal (business as usual), curtailment (shutting off non-critical loads), and energy storage. The results show that active management of a manufacturing microgrid has the potential for saving energy and money by intelligent scheduling of process loads

How well does Learning-by-doing Explain Cost Reductions in a Carbon-free Energy Technology?

The incorporation of experience curves has enhanced the treatment of technological change in models used to evaluate the cost of climate and energy policies. However, the set of activities that experience curves are assumed to capture is much broader than the set that can be characterized by learning-by-doing, the primary connection between experience curves and economic theory. How accurately do experience curves describe observed technological change? This study examines the case of photovoltaics (PV), a potentially important climate stabilization technology with robust technology dynamics. Empirical data are assembled to populate a simple engineering-based model identifying the most important factors affecting the cost of PV over the past three decades. The results indicate that learning from experience only weakly explains change in the most important cost-reducing factors— plant size, module efficiency, and the cost of silicon. They point to other explanatory variables to include in future models. Future work might also evaluate the potential for efficiency gains from policies that rely less on ‘riding down the learning curve’ and more on creating incentives for firms to make investments in the types of cost-reducing activities quantified in this study.Learning-by-doing, Experience Curves, Learning Curves, Climate Policy

The Private and Public Economics of Renewable Electricity Generation

Generating electricity from renewable sources is more expensive than conventional approaches, but reduces pollution externalities. Analyzing the tradeoff is much more challenging than often presumed, because the value of electricity is extremely dependent on the time and location at which it is produced, which is not very controllable with some renewables, such as wind and solar. Likewise, the pollution benefits from renewable generation depend on what type of generation it displaces, which also depends on time and location. Without incorporating these factors, cost-benefit analyses of alternatives are likely to be misleading. However, other common arguments for subsidizing renewable power – green jobs, energy security and driving down fossil energy prices – are unlikely to substantially alter the analysis. The role of intellectual property spillovers is a strong argument for subsidizing energy science research, but less persuasive as an enhancement to the value of installing current renewable energy technologies.

Can grid-tied solar photovoltaics lead to residential heating electrification? A techno-economic case study in the midwestern U.S.

This study aims to quantify the techno-economic potential of using solar photovoltaics (PV) to support heat pumps (HP) towards the replacement of natural gas heating in a representative North American residence from a house owner\u27s point of view. For this purpose, simulations are performed on: (1) a residential natural gas-based heating system and grid electricity, (2) a residential natural gas-based heating system with PV to serve the electric load, (3) a residential HP system with grid electricity, and (4) a residential HP+PV system. Detailed descriptions are provided along with a comprehensive sensitivity analysis for identifying specific boundary conditions that enable lower total life cycle cost. The results show that under typical inflation conditions, the lifecycle cost of natural gas and reversable, air-source heat pumps are nearly identical, however the electricity rate structure makes PV costlier. With higher rates of inflation or lower PV capital costs, PV becomes a hedge against rising prices and encourages the adoption of HPs by also locking in both electricity and heating cost growth. The real internal rate of return for such prosumer technologies is 20x greater than a long-term certificate of deposit, which demonstrates the additional value PV and HP technologies offer prosumers over comparably secure investment vehicles while making substantive reductions in carbon emissions. Using the large volume of results generated, impacts on energy policy are discussed, including rebates, net-metering, and utility business models

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. High-quality EULPs are also critical for determining the time-sensitive value of efficiency and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs).For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, ultimately will use calibrated physics-based building energy models to create these EULPs

Factors for Measuring Photovoltaic Adoption from the Perspective of Operators

The diffusion of photovoltaic distributed generation is relevant for addressing the political, economic, and environmental issues in the electricity sector. However, the proliferation of distributed generation brings new administrative and operational challenges for the sustainability of electric power utilities. Electricity distributors operate in economies of scale, and the high photovoltaic penetration means that these companies have economic and financial impacts, in addition to influencing the migration of other consumers. Thus, this paper aims to systematically identify and evaluate critical factors and indicators that may influence electricity distributors in predicting their consumers’ adoption of photovoltaic technology, which were subjected to the analysis of 20 industry experts. Results show that the cost of electricity, generation capacity, and cost of the photovoltaic systems are the most relevant indicators, and it is possible to measure a considerable part of them using the internal data of the electricity distributors. The study contributes to the understanding of the critical factors for the forecast of the adoption of consumers to distributed photovoltaic generation, to assist the distribution network operators in the decision making, and the distribution sustainability. Also, it establishes the theoretical, political, and practical implications for the Brazilian scenario and developing countries.This research was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico [grant numbers 311926/2017-7, 142448/2018-4, 310594/2017-0 and 465640/2014-1], Coordenação de Aperfeiçoamento de Pessoal de Nível Superior [grant numbers 1773252/2018, 1845395/2019 and 23038.000776/2017–54] and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul [grant number 17/2551-0000517-1].info:eu-repo/semantics/publishedVersio