A Hybrid Energy System Workflow for Energy Portfolio Optimization

Journal Article · Wed Jul 21 00:00:00 EDT 2021 · Energies

DOI:https://doi.org/10.3390/en14154392· OSTI ID:2222409

Zhou, Jia ^[1]; Abdel-Khalik, Hany ^[1]; Talbot, Paul ^[2]; Rabiti, Cristian ^[2]

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1. Purdue University, West Lafayette, IN (United States)
2. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

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.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 2222409
• Report Number(s): INL/JOU--21-61831-Rev000
• Journal Information: Energies, Journal Name: Energies Journal Issue: 15 Vol. 14; ISSN 1996-1073
• Publisher: MDPI
• Country of Publication: United States
• Language: English

Related Subjects

29 ENERGY PLANNING, POLICY, AND ECONOMY
Economic Optimization
Gaussian Process
HERON
Integrated Energy Systems
RAVEN
Synthetic Time History Generation