Abstract
This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.
Highlights
To optimize energy and utilization configurations, the U.S Department of Energy (DOE) Office of Nuclear Energy (NE) established the Integrated Energy Systems (IES) program
The objective supports one of the key goals for integrated energy systems focused on optimizing the capacities in hybrid energy generation scenarios, and is carried out in a computationally efficient manner
Recognizing that a brute force optimization that relies on the analysis of numerous generation scenarios is infeasible, this work builds a workflow that employs a limited set of samples to train a Gaussian Process model, which is more amenable for optimization
Summary
To optimize energy and utilization configurations, the U.S Department of Energy (DOE) Office of Nuclear Energy (NE) established the Integrated Energy Systems (IES) program. According to [4], in 2017, U.S wind capacity increased by more than 8.3%, while solar capacity increased by 26% compared to 2016, accounting for more than 54% of newly installed renewable electricity capacity in 2017. This growing penetration of renewable energies will have unexpected impacts on the economic feasibility of traditional baseload technologies in the U.S and around the world
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
