Decision tree-based security dispatch application in integrated electric power and natural-gas networks

Costa, Denis Carlos Lima; Nunes, Marcus Vinícius Alves; Vieira, Joao Paulo Abreu; Bezerra, Ubiratan Holanda

doi:10.1016/j.epsr.2016.08.027

Cited by 25 publications

(24 citation statements)

References 7 publications

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“…Costa et al (2016) [65] presented a security dispatch method based on decision trees, which can be applied to coupled natural gas and electric power networks against contingencies that may cause interruptions. Preventive measures to the optimal gas production and electric energy generation were performed based on boundaries of controllable variables and security regions determined by decision trees.…”

Section: Decision Treesmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Decision Treesmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…This model is also subject to some constraints including energy balance in the EHs and capacity limitations of the EHs equipment [20] which are expressed as Equations (2)- (6). Constraints (2), (3) represent electricity and natural gas power balance in each EH.…”

Section: Expansion Planning Of Energy Hubsmentioning

confidence: 99%

“…Capacity of EHs' components in each time stage is determined based on their capacity in previous time stage and newly added capacity using (5.a)-(5.c). Furthermore, the value of dispatch factors are limited by (6).…”

Section: Expansion Planning Of Energy Hubsmentioning

confidence: 99%

“…It is therefore essential to run integrated studies of electricity and gas systems to improve the overall efficiency of the energy system. Consequently, planning and operation studies of integrated gas and electricity systems have been of great interest in new publications [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. In Reference [2], an optimization model is proposed for multi-time period combined gas and electricity networks aimed at minimizing costs associated with gas supply, storage and line pack management as well as electricity generation and load shedding.…”

Section: Introductionmentioning

confidence: 99%

“…In Reference [5], a two-stage stochastic look-ahead dispatch is proposed for integrated electricity power and gas systems which can adjust the improper day-ahead dispatch plan (Stage 1) in the second stage. A datamining technique called decision tree (DT) is utilized in Reference [6] to develop a systemic security dispatch method for integrated natural gas and power systems. In Reference [7], a multi-area, multi-stage model is proposed to integrate the long-term expansion planning studies of generation and transmission of natural gas and electricity systems.…”

Section: Introductionmentioning

confidence: 99%

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Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems

et al. 2019

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This paper focuses on expansion co-planning studies of natural gas and electricity distribution systems. The aim is to develop a mixed-integer linear programming (MILP) model for such problems to guarantee the finite convergence to optimality. To this end, at first the interconnection of electricity and natural gas networks at demand nodes is modelled by the concept of energy hub (EH). Then, mathematical model of expansion studies associated with the natural gas, electricity and EHs are extracted. The optimization models of these three expansion studies incorporate investment and operation costs. Based on these separate planning problems, which are all in the form of mixed-integer nonlinear programming (MINLP), joint expansion model of multi-carrier energy distribution system is attained and linearized to form a MILP optimization formulation. The presented optimization framework is illustratively applied to an energy distribution network and the results are discussed.

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Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

Yang

2021

Energy Science & Engineering

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With the depletion of sources of primary energy across the world, the prospect of a global energy crisis in the near future has raised considerable concern. Among the many sources of clean energy, wind energy is considered to be the most effective means of sustainable and environmentally friendly energy. Traditional systems of energy based on oil and natural gas still operate in a discrete manner, and the promise offered by the mutual coordination between energy systems to draw complementary benefits has not yet been fully exploited. This has prompted all energy sectors to examine the optimal integration of interconnected gas–electricity energy systems. In this context, this study proposes a multi‐objective collaborative operational optimization model of P2G for an interconnected, integrated gas–electricity energy system in case of uncertain wind power. Based on the effect of carbon capture by using P2G, the effect of coupling gas and electricity, and the cost of low‐capacity reserves, the authors use conditional risk values to design four targets of optimization—the operating cost of the system, rate of consumption of the natural gas system, total carbon emissions and capacity stand‐by cost—according to the power system, natural gas system, thermal energy system, and their elements of coupling from multiple perspectives to clarify the constraints on the model. Moreover, to reduce the likelihood of the optimal solution falling into the local optimum, the improved cuckoo algorithm is used to optimize the multi‐objective scheduling model. The authors used a 10‐node power system and a six‐node natural gas system as examples for simulation to verify the proposed model. The results of empirical analysis show the following: (a) Once the P2G equipment had been connected, it reduced the total operating cost and improved the consumption of wind power of the natural gas system regardless of whether backup services were provided. (b) The P2G equipment could be combined with gas‐fired CHP units to “cut peaks and fill valleys” in the electrical load of the power system. These results verify the effectiveness of the proposed multi‐objective optimal dispatch model of an interconnected gas–electricity energy system.

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Decision tree-based security dispatch application in integrated electric power and natural-gas networks

Cited by 25 publications

References 7 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Multistage Expansion Co-Planning of Integrated Natural Gas and Electricity Distribution Systems

Multi‐objective optimization of integrated gas–electricity energy system based on improved multi‐object cuckoo algorithm

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