skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A Framework for Evaluating the Resilience Contribution of Solar PV and Battery Storage on the Grid
Motivated by decreased cost and climate change concerns, the penetration of solar photovoltaic (PV) energy generation and battery energy storage has been continually increasing. The variability in solar PV power generation has led to many new challenges for utilities and researchers. One challenge is the quantification of the resilience contribution to the grid from its assets and is the topic of this paper. In this work, we propose a framework for evaluating the resilience contribution of solar generation and battery storage assets on the grid. The metric provides a quantifiable adaptive capacity measure in terms of real and reactive power and includes uncertainty for solar PV assets. A case study using very short-term and short-term solar generation forecast demonstrates the framework and provides useful insight to the resilience solar and battery storage assets can contribute to the grid.  more » « less
Award ID(s):
1846493
PAR ID:
10222654
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
2020 Resilience Week (RWS)
Page Range / eLocation ID:
133 to 139
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, Science of the total environment)
    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. 
    more » « less
  2. ; ; (, IEEE)
    Microgrid is a small-scale grid where generation is close to the demand allowing more penetration of renewables, like photovoltaic (PV). However, the intermittent nature of PV power generation poses a significant challenge in microgrid operation, especially on days with highly variable weather conditions. In this paper, a deep reinforcement Q-learning long short-term memory (QLSTM) model is developed to predict the operation strategy of a microgrid for the next day at a 15-minute time interval. To address the uncertainty of PV power and demand, the previous three days’ PV and load data are added as inputs to the model since weather conditions on consecutive days may depend on similar atmospheric conditions. Also, to address the effect of propagation of error in the long forecasting horizon with multiple steps, a moving window training method is implemented. The moving window will be shifted by 15 minutes at each step once the actual PV and load data are available till the end of the day. The model is tested in a microgrid consisting of combined cooling, heating and power, heat pump, PV, battery, and heating and cooling energy storage systems. Results show that our model outperforms gated recurrent unit, LSTM, and Q-learning for testing data from different months. Also, it shows better performance than MATLAB 2023 Optimization Toolbox (the branch-and-bound method) which uses forecasted data, especially on a day with highly variable weather conditions. 
    more » « less
  3. (, International Journal of Renewable Energy Research)
    null (Ed.)
    Distributed power generation plants with combined photovoltaic (PV) systems and integrated energy storage for grid-connected applications have seen an increase in research interest in recent years. However, the combination of multiple energy sources requires numerous DC-DC converters and thus becomes more complex. To address this issue, a multiport bidirectional DC-DC LLC resonant converter for grid connected applications is presented in this research. In order to minimize the control complexity of the proposed system, a zone based controller approach with an integrated modified maximum power point tracking (MMPPT) method, which is based on the incremental conductance method, is also developed. This proposed controller is able to regulate the converter voltage and power flow while either delivering or taking power from the utility grid. The converter presented in this study contains a bidirectional buck-boost converter and an LLC resonant converter in addition to a voltage source grid-tied inverter which are interfacing the PV, the battery and the utility. Extensive simulation analyses through MATLAB/Simulink have proved the operations of the proposed topology. 
    more » « less
  4. ; ; ; ; (, 2018 IEEE Green Technologies Conference)
    Power grids based on traditional N-1 design criteria are no longer adequate because these designs do not withstand extreme weather events or cascading failures. Microgrid system has the capability of enhancing grid resilience through defensive or islanded operations in contingency. This paper presents a probabilistic framework for planning resilient distribution system via distributed wind and solar integration. We first define three aspects of resilient distribution system, namely prevention, survivability and recovery. Then we review the distributed generation planning models that comprehend moment estimation, chance constraints and bi-directional power flow. We strive to achieve two objectives: 1) enhancing the grid survivability when distribution lines are damaged or disconnected in the aftermath of disaster attack; and 2) accelerating the recovery of damaged assets through pro-active maintenance and repair services. A simple 9-node network is provided to demonstrate the application of the proposed resilience planning framework 
    more » « less
  5. ; ; ; ; ; (, Emerging Science Journal)
    Solar energy is a widely accessible, clean, and sustainable energy source. Solar power harvesting in order to generate electricity on smart grids is essential in light of the present global energy crisis. However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the output of solar power in order to assess the potential of smart grids. This paper presents a study that utilizes various machine learning models to predict solar photovoltaic (PV) power generation in Lubbock, Texas. Mean Squared Error (MSE) and R² metrics are utilized to demonstrate the performance of each model. The results show that the Random Forest Regression (RFR) and Long Short-Term Memory (LSTM) models outperformed the other models, with a MSE of 2.06% and 2.23% and R² values of 0.977 and 0.975, respectively. In addition, RFR and LSTM demonstrate their capability to capture the intricate patterns and complex relationships inherent in solar power generation data. The developed machine learning models can aid solar PV investors in streamlining their processes and improving their planning for the production of solar energy. Doi: 10.28991/ESJ-2023-07-04-02 Full Text: PDF 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email