Decision making under uncertainty in energy systems: state of the art (1911.10905v1)

Abstract: The energy system studies include a wide range of issues from short term (e.g. real-time, hourly, daily and weekly operating decisions) to long term horizons (e.g. planning or policy making). The decision making chain is fed by input parameters which are usually subject to uncertainties. The art of dealing with uncertainties has been developed in various directions and has recently become a focal point of interest. In this paper, a new standard classification of uncertainty modeling techniques for decision making process is proposed. These methods are introduced and compared along with demonstrating their strengths and weaknesses. The promising lines of future researches are explored in the shadow of a comprehensive overview of the past and present applications. The possibility of using the novel concept of Z-numbers is introduced for the first time.

• The paper surveys and classifies state-of-the-art methodologies for handling uncertainty in energy systems, categorizing them based on technical and economic parameters.
• It discusses various uncertainty handling techniques including probabilistic, possibilistic, hybrid, IGDT, robust optimization, and interval analysis approaches.
• The authors introduce the Z-number concept for uncertainty modeling and highlight future research directions to enhance techniques and explore new parameters.

Decision Making Under Uncertainty in Energy Systems: State of the Art

The paper "Decision making under uncertainty in energy systems: state of the art" by Alireza Soroudi and Turaj Amraee explores various methodologies that have been developed to address uncertainty in energy systems. The authors provide a detailed classification of existing uncertainty modeling techniques, highlighting their strengths and weaknesses, while also identifying promising future research areas in this domain. The paper offers valuable insights for researchers focusing on decision-making processes in energy systems, especially under conditions characterized by significant uncertainty.

The paper begins by categorizing uncertain parameters in energy systems into two main types: technical parameters and economic parameters. Technical parameters encompass categories like topological changes due to failures or forced outages, and operational factors such as demand and generation values. Economic parameters, in contrast, include microeconomic factors affecting smaller business sectors and macroeconomic trends influencing the broader industry.

The authors classify and discuss various uncertainty handling techniques, including:

1. Probabilistic Approaches: Techniques like Monte Carlo Simulation (MCS), Point Estimate Method (PEM), and Scenario-Based Decision Making fall under this category, relying on the known probability distributions of input parameters to model uncertainty.
2. Possibilistic Approaches: Techniques such as fuzzy arithmetic are based on the fuzzy set theory introduced by Lotfi A. Zadeh. These models use membership functions to represent uncertainty and are particularly useful when the data is not well described by statistical distributions.
3. Hybrid Possibilistic-Probabilistic Approaches: Combining both probabilistic and possibilistic parameters, these models aim to leverage the advantages of both methodologies to handle uncertainty in energy systems more comprehensively.
4. Information Gap Decision Theory (IGDT): IGDT is employed for situations where data scarcity precludes the use of statistical and possibilistic methods. This approach aims to ensure robustness against undefined uncertainties by maximizing constraint satisfaction.
5. Robust Optimization: Originally proposed by Soyster, robust optimization methods provide solutions that remain optimal under the worst-case realizations of uncertain parameters, using defined uncertainty sets.
6. Interval Analysis: This approach determines the range of potential outcomes by analyzing the bounds on uncertain input parameters and is particularly effective when exact probability distributions are unavailable.

A distinctive contribution of the paper is the introduction of the Z-number concept for the first time in this context. Z-numbers, another innovation by Zadeh, represent uncertain parameters using both a fuzzy set and a reliability degree, thereby providing a new framework for enriching uncertainty modeling methodologies.

The applications of these methodologies are diverse, including Distributed Generation impact assessment, Plug-in Hybrid Electric Vehicle operation strategies, Renewable energy management, and Electricity market dynamics. Each method's applicability varies depending on the type of uncertainty and specific use case within the energy systems field.

The authors conclude that future research should focus on exploring new uncertain parameters germane to evolving technologies and market regulations, enhancing existing techniques to reduce computational demands, developing hybrid methods for more comprehensive treatment of uncertainties, and investigating new modeling techniques like Z-numbers.

In summary, this paper provides a comprehensive roadmap for researchers and practitioners dealing with decision-making under uncertainty in energy systems. The standard classification of various methodologies offered herein can serve as a foundation for identifying unexplored research areas, thereby guiding future advancements in the field.