Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Entezari, Ashkan; Aslani, Alireza; Zahedi, Rahim; Noorollahi, Younes

doi:10.1016/j.esr.2022.101017

Cited by 86 publications

(39 citation statements)

References 74 publications

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“…KD G , KD X , and KD E denote the capital demands, KD0 X , KD0 E , and KD0 G are the capital demands in the reference year, and

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{X}}}}

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{E}}}}

, and

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{G}}}}

represent the capital share parameters in the composite input of capital and labor. Taking the conditional demand for labor (LD) into account, the market‐clearing condition is obtained as 26 :

\sum _{{\rm{i}}}{\rm{L}}{{\rm{D}}}_{{\rm{i}}}\text{LD}{0}_{{\rm{i}}}=[\bar{{\rm{L}}}-\phantom{\rule{}{0ex}}(\text{FF})(\text{FF}0)](1-\text{UR}).

…”

Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

“…Indeed, by introducing the energy price index and CPI in the energy production and consumer goods sectors, the utility price index is presented within the utility production sector. The utility price index is a function of the consumer price and wage index 26 …”

Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

“…The utility price index is a function of the consumer price and wage index. 26 The CPI as the same composite price index of commodities is determined as 27 { }…”

Section: Zero-profit Condition or Unit Cost Functionsmentioning

confidence: 99%

“…KD G , KD X , and KD E denote the capital demands, KD0 X , KD0 E , and KD0 G are the capital demands in the reference year, and k X Ө , k E Ө , and k G Ө represent the capital share parameters in the composite input of capital and labor. Taking the conditional demand for labor (LD) into account, the marketclearing condition is obtained as 26 :…”

Section: Market Clearingmentioning

confidence: 99%

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Environmental, economic, and social impact of five COP26 policies: A computable general equilibrium analysis for Canada

Zahedi

Aslani

2023

Energy Science & Engineering

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Along with the main objective of politicians, other economic variables are variously affected by the environmental taxes, policies, and limitations like a price on pollution, clean electricity, the cap on emissions, methane emissions, and nature‐based solutions. The objective of the environmental solutions is to decrease pollutant emissions and energy consumption while reducing labor costs and taxes as the incentives for creating new occupations. An overall equilibrium model was considered in the present study as a nonlinear equations system, which was calibrated for the reference year of 2018 utilizing Canada's economy's data table. The effects of utilizing these environmental policies considered by Canada in COP26 are examined. In all scenarios, minimum, maximum, and optimum values for reducing pollutant emissions are calculated under these policies. According to the simulation results, welfare is reduced by the price of pollution policy. Moreover, the actual consumed budget of the household is reduced by 8%. However, such indices will be incremented by 2% in the nature‐based policy. In all scenarios, the gross domestic product is decreased. However, in the methane emission policy, this reduction is 1.05% in the lowest state. In all scenarios, the consumer price index will be incremented by 3.8%–9.8%. It is concluded that the clean electricity policy is an appropriate policy for reducing greenhouse gas emissions and at the same time adhering to international commitments.

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“…KD G , KD X , and KD E denote the capital demands, KD0 X , KD0 E , and KD0 G are the capital demands in the reference year, and

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{X}}}}

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{E}}}}

, and

{{\rm{\unicode{x004E8}}}}_{{{\rm{k}}}_{{\rm{G}}}}

represent the capital share parameters in the composite input of capital and labor. Taking the conditional demand for labor (LD) into account, the market‐clearing condition is obtained as 26 :

\sum _{{\rm{i}}}{\rm{L}}{{\rm{D}}}_{{\rm{i}}}\text{LD}{0}_{{\rm{i}}}=[\bar{{\rm{L}}}-\phantom{\rule{}{0ex}}(\text{FF})(\text{FF}0)](1-\text{UR}).

…”

Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

Section: Methodology and Mathematical Modelingmentioning

confidence: 99%

“…The utility price index is a function of the consumer price and wage index. 26 The CPI as the same composite price index of commodities is determined as 27 { }…”

Section: Zero-profit Condition or Unit Cost Functionsmentioning

confidence: 99%

Section: Market Clearingmentioning

confidence: 99%

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Environmental, economic, and social impact of five COP26 policies: A computable general equilibrium analysis for Canada

Zahedi

Aslani

2023

Energy Science & Engineering

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“…Artificial Intelligence (AI) has recently been used in the modeling and simulation of fluid and energy systems [3][4][5][6][7][8]. The term ML is also used synonymously.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Fluid Flow, Energy, and Heat Transfer

Makahleh,

Nassar

2023

Simulation Modeling - Recent Advances, New Perspectives, and Applications [Working Title]

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Engineering and science-related problems become more complicated as human knowledge evolves. This complication includes apparatus geometry and operational environment such as extreme variations in pressure and temperature. Analytical solution for such problems needs many assumptions that underestimate the problem under study and could lead to unrealistic results. Moreover, an experimental setup for a certain problem is constrained by the prototype size and each experiment is set up for certain operating conditions. This leads to building up many setups to deal with changes in size and operating conditions and, therefore, the prototype validation becomes very expensive and time-consuming. This calls for modeling and simulation approaches to deal with such engineering problems with the powerful computational capabilities available nowadays. Real-world patterns and processes are roughly modeled by scientific models. They may be refuted because they are representations, which are by definition imperfect. Models, however, are quite helpful for a variety of reasons. They first give us a method to comprehend procedures that would otherwise be incomprehensible to us. They also give scientists a base on which to build new research and theories. Finally, modeling and simulation reduce the time and cost of prototyping.

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Distributed online optimization for integrated energy systems: A multi‐agent system consensus approach

Wang,

Liu,

Zhang

et al. 2024

Adaptive Control & Signal

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SummaryThe integration of multi‐energy within distribution networks has escalated the need for efficient operation and control of integrated energy systems (IES). Addressing the complexities of real‐time scheduling and low‐carbon optimization, we propose a novel artificial intelligence driven multi‐agent system (MAS) approach for modeling the interactions and operations within the multi‐agent integrated energy systems (MA‐IES) framework. In this framework, distinct components such as electric, gas, and heat networks are conceptualized as autonomous agents, each responsible for managing its domain while interacting with other agents to achieve system‐wide efficiency and economical goals. The agents communicate and coordinate through a distributed online optimization framework, utilizing the alternating direction multiplier method (ADMM) to ensure effective consensus despite the inherent nontransparency of information exchange. This MAS based approach allows for dynamic adaptation of strategies based on local data and global objectives, significantly enhancing the responsiveness and adaptability of MA‐IES. We further integrate an objective function reliant on a tiered carbon pricing mechanism to assess and minimize the environmental impact of operations. Enhanced by adaptive penalty coefficients within the ADMM, our MA‐IES framework demonstrates improved convergence rates and robustness in operational scenarios. Empirical validation through detailed case studies confirms the superior performance of our MAS‐based model, demonstrating its potential to realize an efficient and economical low‐carbon operation of MA‐IES.

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Artificial intelligence and machine learning in energy systems: A bibliographic perspective

Cited by 86 publications

References 74 publications

Environmental, economic, and social impact of five COP26 policies: A computable general equilibrium analysis for Canada

Environmental, economic, and social impact of five COP26 policies: A computable general equilibrium analysis for Canada

Modeling and Simulation of Fluid Flow, Energy, and Heat Transfer

Distributed online optimization for integrated energy systems: A multi‐agent system consensus approach

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