Bayesian Updating of Solar Panel Fragility Curves and Implications of Higher Panel Strength for Solar Generation Resilience

Solar generation can become a major and global source of clean energy by 2050. Nevertheless, few studies have assessed its resilience to extreme events, and none have used empirical data to characterize the fragility of solar panels. This paper develops fragility functions for rooftop and ground-mounted solar panels calibrated with solar panel structural performance data in the Caribbean for Hurricanes Irma and Maria in 2017 and Hurricane Dorian in 2019. After estimating hurricane wind fields, we follow a Bayesian approach to estimate fragility functions for rooftop and ground-mounted panels based on observations supplemented with existing numerical studies on solar panel vulnerability. Next, we apply the developed fragility functions to assess failure rates due to hurricane hazards in Miami-Dade, Florida, highlighting that panels perform below the code requirements, especially rooftop panels. We also illustrate that strength increases can improve the panels' structural performance effectively. However, strength increases by a factor of two still cannot meet the reliability stated in the code. Our results advocate reducing existing panel vulnerabilities to enhance resilience but also acknowledge that other strategies, e.g., using storage or deploying other generation sources, will likely be needed for energy security during storms.

Performance Investigation Based on Vital Factors of Agricultural Feeder Supported by Solar Photovoltaic Power Plant

Solar photovoltaic (SPV) installations are growing in the distribution network due to the continuously decreasing prices of solar photovoltaic panels. Installing the SPV Plant on the distribution feeder supplying to the agricultural pumps is a challenging task due to the varying agricultural load pattern of the Agricultural Feeder (AG Feeder). Supply of power and demand creates potential challenges in the low voltage (LV) distribution system. This paper presents a case study of a 2 MW SPV connected to an agricultural feeder in India. Performance analysis has been carried out using field measurement data. The key parameters such as PV Penetration and Capacity Utilization Factor (CUF) are calculated for analysis. Parameters such as Grid Dependency of the load and PV Contribution have been introduced in this paper, which relates to the SPV system behavior more aptly. It is recommended that the Time of Day (ToD) metering with the lowest cost during the solar generation hours will make agricultural consumers shift their demand matching with solar generation hours. Extensive analysis of agricultural feeder connected SPV power plant indicates that the power supply has improved for the feeder during winter and summer months.

Modelling and Analysis of Solar and Wind System Adequacy Assessment and Cost Optimization

The output of wind and the solar system is not constant; it is difficult to access the adequacy of the system. The Generation model is developed for 240 MW of different generation units in the Roy Billinton Test System (RBTS). Then a multi-state wind and solar generation model is developed based on different solar radiation and wind speed to evaluate the probability of the states. In this work, the wind and solar systems are studies for separate locations with each consist of 8 MW, 18 MW, 28 MW, and 38 MW generation capacities. This wind and solar generation model are applied to Roy Billinton Test System (RBTS) to evaluate the reliability indices like Loss of Load Expectation (LOLE). Based on the development of the MATLAB program for determining reliability indices, the capacity outage probability is developed for multiple solar and wind states. Based on standard load forecasting and the Time Series Load forecasting technique, the reliability of the system is analyzed. The results reveal the variation of risk indices in the system when additional generators are incorporated into the RBTS generation system. The cost optimization for Solar and Wind system were conducted using HOMER software to obtain the levelized cost of the proposed system.

Uncertainty Costs Optimization of Residential Solar Generators Considering Intraday Markets

The uncertainty of solar generation and the bull market are unavoidable in energy dispatch. The purpose of this research is to validate an uncertainty cost function of residential photovoltaic energy in a real microgrid by varying the number of auctions in intraday markets. Therefore, the following procedure is proposed. First, the variability of photovoltaic generation is quantified through Monte Carlo simulations. Second, a statistical function calculates the variability costs of photovoltaic generation. Third, the uncertainty costs are estimated by varying intraday auction markets. Other complementary services are added to the network, such as battery exchange stations for electric vehicles, demand response loads, market power restrictions, and energy storage systems, which are estimated as total costs in an index ranking. The total costs are optimized in a benchmark microgrid and take complimentary services as a black box. Only the uncertainty costs of residential solar generators are discriminated. The main findings were that (1) the uncertainty costs have an error of less than 0.0168% compared to the Monte Carlo simulations and that (2) the uncertainty costs of solar generation are reduced with a decreasing trend to a more significant number of auction markets in intraday markets.

Impact of Wind and Solar Generation on the Italian Zonal Electricity Price

This paper assesses the impact of increasing wind and solar power generation on zonal market prices in the Italian electricity market from 2015 to 2019, employing a multivariate regression model. A significant aspect to be considered is how the additional wind and solar generation brings changes in the inter-zonal export and import flows. We constructed a zonal dataset consisting of electricity price, demand, wind and solar generation, net input flow, and gas price. In the first and second steps of this study, the impact of additional wind and solar generation that is distributed across zonal borders is calculated separately based on an empirical approach. Then, the Merit Order Effect of the intermittent renewable energy sources is quantified in every six geographical zones of the Italian day-ahead market. The results generated by the multivariate regression model reveal that increasing wind and solar generation decreases the daily zonal electricity price. Therefore, the Merit Order Effect in each zonal market is confirmed. These findings also suggest that the Italian electricity market operator can reduce the National Single Price by accelerating wind and solar generation development. Moreover, these results allow to generate knowledge advantageous for decision-makers and market planners to predict the future market structure.