arXiv subjects

Neeraj Bokde

Publications and source records attributed to Neeraj Bokde.

5 recordsLinked to original sources

arXiv2024

Cost and CO2 emissions co-optimisation of green hydrogen production in a grid-connected renewable energy system

Green hydrogen is essential for producing renewable fuels that are needed in sectors that are hard to electrify directly. Hydrogen production in a grid-connected hybrid renewable energy plant necessitates smart planning to meet long-term hydrogen trading agreements while minimising costs and emissions. Previous research analysed economic and environmental impact of hydrogen production based on full foresight of renewable energy availabilty, electricity price, and CO2 intensity in the electricity grid. However, the full foresight assumption is impractical in day-to-day operation, often leading to underestimations of both the cost and CO2 emissions associated with hydrogen production. Therefore, this research introduces a novel long-term planner that uses historical data and short-term forecasts to plan hydrogen production in the day-to-day operation of a grid-connected hybrid renewable energy plant. The long-term planner co-minimises cost and CO2 emissions to determine the hydrogen production for the next day taking into account the remaining hydrogen production and the time remaining until the end of the delivery period, which can be a week, a month, or a year. Extended delivery periods provide operation flexibility, enabling cost and CO2 emissions reductions. Significant reductions in CO2 emissions can be achieved with relatively small increases in the levelised cost. Under day-to-day operation, the levelised cost of hydrogen is marginally higher than that of the full foresight; the CO2 emissions can be up to 60% higher. Despite a significant portion of the produced hydrogen not meeting the criteria for green hydrogen designation under current rules, CO2 emissions are lower than those from existing alternative hydrogen production methods. These results underscore the importance of balancing cost considerations with environmental impacts in operational decision-making.

eess.SY↗

arXiv2020

Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling

The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 hours of electricity consumption in a 24 hour interval can on average reduce the resulting CO2 emissions by 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day.

stat.AP↗

arXiv2020

A graphical approach to carbon-efficient spot market scheduling for Power-to-X applications

In the Paris agreement of 2015, it was decided to reduce the CO2 emissions of the energy sector to zero by 2050 and to restrict the global mean temperature increase to 1.5 degree Celcius above the pre-industrial level. Such commitments are possible only with practically CO2-free power generation based on variable renewable technologies. Historically, the main point of criticism regarding renewable power is the variability driven by weather dependence. Power-to-X systems, which convert excess power to other stores of energy for later use, can play an important role in offsetting the variability of renewable power production. In order to do so, however, these systems have to be scheduled properly to ensure they are being powered by low-carbon technologies. In this paper, we introduce a graphical approach for scheduling power-to-X plants in the day-ahead market by minimizing carbon emissions and electricity costs. This graphical approach is simple to implement and intuitively explain to stakeholders. In a simulation study using historical prices and CO2 intensity for four different countries, we find that the price and CO2 intensity tends to decrease with increasing scheduling horizon. The effect diminishes when requiring an increasing amount of full load hours per year. Additionally, investigating the trade-off between optimizing for price or CO2 intensity shows that it is indeed a trade-off: it is not possible to obtain the lowest price and CO2 intensity at the same time.

econ.GN↗

arXiv2017

PSF : Introduction to R Package for Pattern Sequence Based Forecasting Algorithm

This paper discusses about an R package that implements the Pattern Sequence based Forecasting (PSF) algorithm, which was developed for univariate time series forecasting. This algorithm has been successfully applied to many different fields. The PSF algorithm consists of two major parts: clustering and prediction. The clustering part includes selection of the optimum number of clusters. It labels time series data with reference to such clusters. The prediction part includes functions like optimum window size selection for specific patterns and prediction of future values with reference to past pattern sequences. The PSF package consists of various functions to implement the PSF algorithm. It also contains a function which automates all other functions to obtain optimized prediction results. The aim of this package is to promote the PSF algorithm and to ease its implementation with minimum efforts. This paper describes all the functions in the PSF package with their syntax. It also provides a simple example of usage. Finally, the usefulness of this package is discussed by comparing it to auto.arima and ets, well-known time series forecasting functions available on CRAN repository.

stat.ML↗

arXiv2016

R package imputeTestbench to compare imputations methods for univariate time series

This paper describes the R package imputeTestbench that provides a testbench for comparing imputation methods for missing data in univariate time series. The imputeTestbench package can be used to simulate the amount and type of missing data in a complete dataset and compare filled data using different imputation methods. The user has the option to simulate missing data by removing observations completely at random or in blocks of different sizes. Several default imputation methods are included with the package, including historical means, linear interpolation, and last observation carried forward. The testbench is not limited to the default functions and users can add or remove additional methods using a simple two-step process. The testbench compares the actual missing and imputed data for each method with different error metrics, including RMSE, MAE, and MAPE. Alternative error metrics can also be supplied by the user. The simplicity of use and significant reduction in time to compare imputation methods for missing data in univariate time series is a significant advantage of the package. This paper provides an overview of the core functions, including a demonstration with examples.

stat.ME↗