scholarly journals The comparison of pricing methods in the carbon auction market via multi-agent Q-learning

2021 ◽  
Author(s):  
Akram Esmaeili Avval ◽  
Farzad Dehghanian ◽  
Mohammadali Pirayesh
Keyword(s):  
Trading Volume ◽  
Simulation Analysis ◽  
Carbon Trading ◽  
Government Revenue ◽  
Uniform Price ◽  
Auction Market ◽  
Q Learning ◽  
Trading Market ◽  
Multi Agent ◽  
The Government

In this paper, the uniform price and discriminative price method are compared in the carbon auction market using multi-agent Q-learning. The government and different firms are considered as agents. The government as auctioneer allocates initial permits in the carbon auction market, and the firms as bidders compete with each other to obtain a larger share of auction. The carbon trading market, penalty, reserve price, and bidding volume limitation are considered. The simulation analysis demonstrates that bidders have different behavior in two pricing methods under different amounts of carbon permits. In the uniform price, the value of bidding volume, firms’ profit, and trading volume for low permits and the value of the government revenue, clearing price, the trading price and auction efficiency for high permits are greater than ones in the discriminative price method. Bidding prices have a higher dispersion in the uniform price than the discriminative price method for different amounts of carbon permits.

scholarly journals Cost And Welfare: A Simulation of Establishing A Unified Carbon Market In China

2021 ◽  
Author(s):  
Xinru Ji ◽  
Lei Su
Keyword(s):  
Emission Reduction ◽  
Abatement Cost ◽  
Directional Distance Function ◽  
Carbon Market ◽  
Shadow Prices ◽  
Carbon Trading ◽  
Carbon Abatement ◽  
Trading Market ◽  
The Government ◽  
The Cost

Abstract BackgroundGlobal warming has aroused wide concern of international community, which has reached a consensus on the carbon abatement. In 2017, China should have established a unified market for carbon emission trading, while the government has postponed the establishment because the uncertainty of cost calculation and welfare. Therefore, the cost and welfare of carbon abatement in simulated scenarios could help the government in establishing a unified carbon market and setting suitable policy. In the national carbon trading market, the variations of different abatement cost are the precondition of carbon exchange. This paper set forth theories related to carbon market and used parametric directional distance function model to derive the shadow prices of 30 provinces from 2011 to 2017. Then the classic logarithmic model is used to simulate marginal abatement cost curves, which is further applied to empirically investigate the welfare of 30 provinces in two scenarios of carbon trading market in China. ResultsThe results indicate that marginal abatement cost would rise with the increasing of emission reduction and vary significantly among provinces, and undeveloped provinces have greater potential in emission reduction than developed regions. Moreover, all provinces could benefit from the establishment of the nationwide ETS.ConclusionsThis article combines the theoretical model of shadow prices with the analysis of China’s carbon trading market in an attempt to analyze the cost and welfare of Chinese provinces and cities on the unified carbon trading market, adding the time trend factor to the directional distance function, and then further combines the parameter method to estimate the shadow price of CO2. Finally, the paper gives some proposals regarding to China’s ETS and carbon reduction targets.

scholarly journals Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Chica-Pedraza ◽  
Eduardo Mojica-Nava ◽  
Ernesto Cadena-Muñoz
Keyword(s):  
Control Method ◽  
Optimization Problems ◽  
Replicator Dynamics ◽  
Boltzmann Distribution ◽  
Full Information ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Population Games ◽  
Distributed Approach ◽  
Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

Modeling and Simulation on Knowledge Transfer among Unofficial Groups Based on Communication Networks

2013 ◽  
Vol 411-414 ◽  
pp. 145-151
Author(s):  
Xiao Dong Kou ◽  
Bo Zhang ◽  
Lin Yang
Keyword(s):  
Knowledge Transfer ◽  
Complex Networks ◽  
Communication Networks ◽  
Simulation Analysis ◽  
China National Knowledge Infrastructure ◽  
Simulation Research ◽  
Knowledge Infrastructure ◽  
Learned Societies ◽  
Agent Simulation ◽  
Multi Agent

With features of good interactivity and fast spread speed, unofficial networks play a significant role in knowledge transfer. Based on theories of communication networks and computational modeling method, the transfer situation of complex networks theory within Chinas learned societies, including its rising, spread and development, was modeled and then made simulation analysis by using the Blanche software. By comparing the analysis results with periodicals data from China National Knowledge Infrastructure, the effectiveness of the built model and the reliability of Blanche in multi-agent simulation research are all validated. Furthermore, the future development of complex networks theory in China is predicted as well.

Modular Production Control with Multi-Agent Deep Q-Learning

2021 ◽  
Author(s):  
Dennis Gankin ◽  
Sebastian Mayer ◽  
Jonas Zinn ◽  
Birgit Vogel-Heuser ◽  
Christian Endisch
Keyword(s):  
Production Control ◽  
Q Learning ◽  
Modular Production ◽  
Multi Agent

Improvement on Supporting Machine Learning Algorithm for Solving Problem in Immediate Decision Making

2012 ◽  
Vol 566 ◽  
pp. 572-579
Author(s):  
Abdolkarim Niazi ◽  
Norizah Redzuan ◽  
Raja Ishak Raja Hamzah ◽  
Sara Esfandiari
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Multi Agent Systems ◽  
Combined Model ◽  
Q Learning ◽  
Agent Systems ◽  
Multi Agent ◽  
Case Base ◽  
Case Base Reasoning ◽  
Robotic Tool

In this paper, a new algorithm based on case base reasoning and reinforcement learning (RL) is proposed to increase the convergence rate of the reinforcement learning algorithms. RL algorithms are very useful for solving wide variety decision problems when their models are not available and they must make decision correctly in every state of system, such as multi agent systems, artificial control systems, robotic, tool condition monitoring and etc. In the propose method, we investigate how making improved action selection in reinforcement learning (RL) algorithm. In the proposed method, the new combined model using case base reasoning systems and a new optimized function is proposed to select the action, which led to an increase in algorithms based on Q-learning. The algorithm mentioned was used for solving the problem of cooperative Markov’s games as one of the models of Markov based multi-agent systems. The results of experiments Indicated that the proposed algorithms perform better than the existing algorithms in terms of speed and accuracy of reaching the optimal policy.

scholarly journals Dynamic Analysis of Structural Shifts of Fiscal Revenue in Nigeria, 1999-2016

2016 ◽  
Vol 8 (11) ◽  
pp. 96
Author(s):  
Mustapha A. Akinkunmi
Keyword(s):  
Data Availability ◽  
Government Revenue ◽  
Excise Duty ◽  
Tax Revenues ◽  
Real Gdp ◽  
Crude Oil Prices ◽  
Long Run ◽  
Short Run ◽  
Oil Sector ◽  
The Government

The oil sector that eased the financial constraint of Nigerian government in the 1970s is presently acting as the source of financial constraints to the country due to a continuous decline in government revenue, arising from the recent drastic fall in world crude oil prices. This calls for the government to diversify its revenue base through improving taxation. This study examined the influence of economic performance on the government revenue as well as the various sources of tax revenues in Nigeria. Monthly data spanning 1999 to 2016 were utilized to estimate vector error correction models (VECM) for five sources of government tax revenues based on data availability. Empirical results revealed that there is a significant relationship between real GDP and real company income tax revenues, and between real GDP and real excise duty revenues in the long run. However, in the short run, the one-year lag of tax revenue varieties poses a significant influence on the various sources of tax revenues.

The Opportunities and Challenges of Chinese Renewable Energy Greenhouse Gas Emission Reduction Trade

2014 ◽  
Vol 1010-1012 ◽  
pp. 2094-2101
Author(s):  
Long Xi Han ◽  
Jia Jia Zhai ◽  
Lin Zhang
Keyword(s):  
Renewable Energy ◽  
Greenhouse Gas ◽  
Emission Reduction ◽  
Greenhouse Gas Emission ◽  
Carbon Trading ◽  
Ghg Emission ◽  
Trading System ◽  
Trading Market ◽  
The Future ◽  
The Impact

The opportunities and challenges in the field of Chinese renewable energy were analyzed through the impact of global greenhouse gas (GHG) emission reduction trade, especially CDM on Chinese renewable energy, combined with the enhancement of awareness of voluntary emission reduction, relationship between emission reduction trade and renewable energy, changes in the international trade environment and the rise of the domestic trading system. It is suggested that the renewable energy industry integrates with GHG emission reduction trading system in China and explores the huge double benefit of emission reduction and income increase with market means, providing a reference for the smooth implementation of nationwide CN ETS including varies industries in the carbon trading market in the future, and striving for the speaking right for China to set the marketing price of international GHG emission reduction trading in the future.

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