With the increasing implementation of solar photovoltaic (PV) systems, comprehensive methods and tools are required to dynamically assess their economic and environmental costs and benefits under varied spatial and temporal contexts. This study integrated system dynamics modeling with life cycle assessment and life cycle cost assessment to evaluate the cumulative energy demand, carbon footprint, water footprint, and life cycle cost of residential grid-connected (GC) and standalone (SA) solar PV systems. The system dynamics model was specifically used for simulating the hourly solar energy generation, use, and storage during the use phase of the solar PVs. The modeling framework was then applied to a residential prototype house in Boston, MA to investigate various PV panel and battery sizing scenarios. When the SA design is under consideration, the maximum life cycle economic saving can be achieved with 20 panels with no battery in the prototype house, which increases the life cycle economic savings by 511.6% as compared to a baseline system sized based upon the engineering rule-of-thumb (40 panels and 40 batteries), yet decreases the demand met by 55.7%. However, the optimized environmental performance was achieved with significantly larger panel (up to 300 units) and battery (up to 320 units) sizes. These optimized configurations increase the life cycle environmental savings of the baseline system byup to 64.6%, but significantly decrease the life cycle economic saving by up to 6868.4%. There is a clear environmental and economic tradeoff when sizing the SA systems. When the GC system design is under consideration, both the economic and environmental benefits are the highest when no battery is installed, and the benefits increase with the increase of panel size. However, when policy constraints such as limitations/caps of grid sell are in place, tradeoffs would present as whether or not to install batteries for excess energy storage.
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On the Feasibility, Cost, and Carbon Emissions of Grid Defection
Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Nearly all of this solar generation feeds into the grid, since battery based energy storage is expensive to install and maintain. Unfortunately, accommodating unlimited intermittent solar power is challenging, since the grid must continuously balance supply and demand. Thus, governments and public utility commissions are increasingly limiting grid connections of new solar installations. These limitations are likely to become more restrictive over time in many areas as solar disrupts the utility business model. Thus, to employ solar without restrictions, users may increasingly need to defect from the grid. Unfortunately, batteries alone are unlikely to become cost-efficient at enabling grid defection for the foreseeable future. To address the problem, we explore using a mixture of solar, batteries, and a whole-home natural gas generator to shift users partially or entirely off the electric grid. We assess the feasibility and compare the cost and carbon emissions of such an approach with using grid power, as well as existing “net metered” solar installations. Our results show that the approach is trending towards cost-competitive based on current prices, reduces carbon emissions relative to using grid power, and enables users to install solar without restriction.
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- NSF-PAR ID:
- 10163747
- Date Published:
- Journal Name:
- IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Page Range / eLocation ID:
- 1 to 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Residential solar installations are becoming increasingly popular among homeowners. However, renters and homeowners living in shared buildings cannot go solar as they do not own the shared spaces. Community-owned solar arrays and energy storage have emerged as a solution, which enables ownership even when they do not own the property or roof. However, such community-owned systems do not allow individuals to control their share for optimizing a home’s electricity bill. To overcome this limitation, inspired by the concept of virtualization in operating systems, we propose virtual community-owned solar and storage—a logical abstraction to allow individuals to independently control their share of the system. We argue that such individual control can benefit all owners and reduce their reliance on grid power. We present mechanisms and algorithms to provide a virtual solar and battery abstraction to users and understand their cost benefits. In doing so, our comparison with a traditional community-owned system shows that our AutoShare approach can achieve the same global savings of 43% while providing independent control of the virtual system. Further, we show that independent energy sharing through virtualization provides an additional 8% increase in savings to individual owners.
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null (Ed.)The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment. To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework.more » « less
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As an alternative, cleaner energy source, solar power is becoming a much bigger player in how electricity is generated and consumed. To assess how economically-viable solar power is in comparison to other forms of electricity, factors such as the return on investment (ROI) can be calculated. In addition, based on the average lifetime of an average photovoltaic (PV) system, energy purchase reduction, and revenue that can be generated by selling excess electricity back to the power grid, the economic viability of solar energy can be determined. The capital and commissioning cost of a PV system will continue to be a major factor in how viable a PV system will be. Thus, observing the trend in PV system costs over the past years will yield a point of grid parity, when solar becomes competitive with existing forms of energy generation, and show the most beneficial time frame for the customer to install the PV system. This paper seeks to provide some insight on how economic using solar energy can be.more » « less
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Major innovations in computing have been driven by scaling up computing infrastructure, while aggressively optimizing operating costs. The result is a network of worldwide datacenters that consume a large amount of energy, mostly in an energy-efficient manner. Since the electric grid powering these datacenters provided a simple and opaque abstraction of an unlimited and reliable power supply, the computing industry remained largely oblivious to the carbon intensity of the electricity it uses. Much like the rest of the society, it generally treated the carbon intensity of the electricity as constant, which was mostly true for a fossil fuel-driven grid. As a result, the cost-driven objective of increasing energy-efficiency — by doing more work per unit of energy — has generally been viewed as the most carbon-efficient approach. However, as the electric grid is increasingly powered by clean energy and is exposing its time-varying carbon intensity, the most energy-efficient operation is no longer necessarily the most carbon-efficient operation. There has been a recent focus on exploiting the flexibility of computing’s workloads—along temporal, spatial, and resource dimensions—to reduce carbon emissions, which comes at the cost of either performance or energy efficiency. In this paper, we discuss the trade-offs between energy efficiency and carbon efficiency in exploiting computing’s flexibility and show that blindly optimizing for energy efficiency is not always the right approach.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SmartGridComm.2019.8909691
