High Penetration Solar PV Deployment Sunshine State Solar Grid Initiative (SUNGRIN)

Meeker, Rick; Steurer, M.; Langston, James; Schoder, Karl; Ravindra, Harsha; Hariri, Ali; Moaveni, Houtan; Click, Dave; Reedy, Bob

doi:10.2172/1235943

Cited by 11 publications

(5 citation statements)

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“…The databases used in this analysis were obtained from the Sunshine State Solar Grid Initiative (SUNGRIN) project data repository [33] (a DOE sponsored project), and the NREL (National Renewable Energy Laboratory) solar radiation database. The PV power data were obtained from a fixed ground-mounted solar plant located in northern Florida, with an AC power rating of 12.6 MW and a time resolution of 30 min.…”

Section: Training and Validation Of Resultsmentioning

confidence: 99%

Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model

Ospina

Newaz

Faruque

2019

IET Renewable Power Generation

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This paper proposes a novel forecasting model designed to accurately forecast the PV power output for both largescale and small-scale PV systems. The proposed model uses available temperature data, approximate and detailed coefficients obtained from the decomposed PV power time series using the stationary wavelet transform (SWT), and statistical features extracted from the historical PV data. The model is comprised of four long-short-term memory (LSTM) recurrent neural networks (RNN) designed to perform multi-step forecasting on the individual approximate and detailed coefficients decomposed by the SWT and a final deep neural network (DNN) designed to perform the next time step PV power forecast. The DNN makes use of the reconstructed values estimated by the four LSTM networks together with temperature data and statistical features to predict the final forecasted value of the next time step PV power. 30-min resolution data from a 12.6 MW PV system located in the state of Florida are used for testing and evaluating the proposed method against several models found in the literature. The results obtained suggest that the proposed model improved the forecasting accuracy significantly in the metrics used to compare with other models while reducing the number of features needed to perform the forecasting operation.

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Section: Training and Validation Of Resultsmentioning

confidence: 99%

Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model

Ospina

Newaz

Faruque

2019

IET Renewable Power Generation

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“…The solar data used to develop the solar PV forecasting model (training, validation, and testing) was obtained from the SUNGRIN report [32]. This report has solar PV generation data from different large-scale solar PV systems around the state of Florida.…”

Section: ) Solar Data (Sungrin and Nrel)mentioning

confidence: 99%

“…The load data and load profiles used were also obtained from the SUNGRIN project database, which has network model data and aggregated load profiles from various feeders located around the state of Florida [32]. Additional information was obtained from the City of Tallahassee energy consumption database.…”

Section: ) Load Data (Sungrin and City Of Tallahassee)mentioning

confidence: 99%

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Ospina

Gupta²,

Newaz

et al. 2019

IEEE Power Energy Technol. Syst. J.

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This paper proposes a novel control solution designed to solve the local and grid-connected distributed energy resources (DERs) management problem by developing a generalizable framework capable of controlling DERs based on forecasted values and real-time energy prices. The proposed model uses sampling-based model predictive control (SBMPC), together with the real-time price of energy and forecasts of PV and load power, to allocate the dispatch of the available distributed energy resources (DERs) while minimizing the overall cost. The strategy developed aims to find the ideal combination of solar, grid, and energy storage (ES) power with the objective of minimizing the total cost of energy of the entire system. Both offline and controller hardware-in-the-loop (CHIL) results are presented for a 7-day test case scenario and compared with two manual base test cases and four baseline optimization algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Quadratic Programming interior-point method (QP-IP), and Sequential Quadratic Programming (SQP)) designed to solve the optimization problem considering the current status of the system and also its future states. The proposed model uses a 24-hour prediction horizon with a 15-minute control horizon. The results demonstrate substantial cost and execution time savings when compared to the other baseline control algorithms. INDEX TERMSController Hardware-in-the-Loop (CHIL), distributed energy resources (DER), distributed generation (DG), distributed storage (DS), energy management, real-time pricing, model predictive control (MPC).

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“…The PV system is represented in the phasor‐domain by a simple current injection source that injects the necessary real and reactive powers at every time‐step. This representation is sufficient for most phasor‐based QSTS studies [2, 3].…”

Section: Implementation Of the Python‐opendss Hybrid Interfacementioning

confidence: 99%

“…Quasi‐static time series (QSTS) analysis is a form of dynamic analysis that relies on performing consecutive power flow solutions that account for the slow variations in solar and load profiles. QSTS analysis is used for PV impact studies such as voltage fluctuation, reactive power management, protection coordination and voltage regulators operation and interactions [2, 3]. QSTS simulation is fast and does not require large computational resources; however, it is not sufficient for capturing fast transients that are caused by fast power‐electronic switching devices.…”

Section: Introductionmentioning

confidence: 99%

Open‐source python‐OpenDSS interface for hybrid simulation of PV impact studies

Hariri

Newaz

Faruque

2017

IET gener. transm. distrib.

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High Penetration Solar PV Deployment Sunshine State Solar Grid Initiative (SUNGRIN)

Abstract: • Florida Reliability Coordinating Council (FRCC) • Gainesville Regional Utilities (GRU) • Jacksonville Electric Authority (JEA) • Lakeland Electric • Orlando Utilities Commission (OUC)

Cited by 11 publications

References 0 publications

Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model

Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model

Sampling-Based Model Predictive Control of PV-Integrated Energy Storage System Considering Power Generation Forecast and Real-Time Price

Open‐source python‐OpenDSS interface for hybrid simulation of PV impact studies

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