skip to main content

Title: 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 more » users to install solar without restriction. « less
Authors:
;
Award ID(s):
1645952 1405826 1534080
Publication Date:
NSF-PAR ID:
10163747
Journal Name:
IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Page Range or eLocation-ID:
1 to 7
Sponsoring Org:
National Science Foundation
More Like this
  1. 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 thenmore »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.« less
  2. 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 effectmore »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.« less
  3. 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 theirmore »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.

    « less
  4. 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 amore »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.« less
  5. In this paper, we consider the problem of dynamic programming when supremum terms appear in the objective function. Such terms can represent overhead costs associated with the underlying state variables. Specifically, this form of optimization problem can be used to represent optimal scheduling of batteries such as the Tesla Powerwall for electrical consumers subject to demand charges - a charge based on the maximum rate of electricity consumption. These demand charges reflect the cost to the utility of building and maintaining generating capacity. Unfortunately, we show that dynamic programming problems with supremum terms do not satisfy the principle of optimality.more »However, we also show that the supremum is a special case of the class of forward separable objective functions. To solve the dynamic programming problem, we propose a general class of optimization problems with forward separable objectives. We then show that for any problem in this class, there exists an augmented-state dynamic programming problem which satisfies the principle of optimality and the solutions to which yield solutions to the original forward separable problem. We further generalize this approach to stochastic dynamic programming problems and apply the results to the problem of optimal battery scheduling with demand charges using a data-based stochastic model for electricity usage and solar generation by the consumer.« less