Optimal Stochastic Planning of DERs in a Game Theory Framework Considering Demand Response and Pollution Issues

2020 ◽  
pp. 193-214
Author(s):  
Pouya Salyani ◽  
Mehdi Abapour ◽  
Kazem Zare ◽  
Tohid Babri
Keyword(s):  
Game Theory ◽  
Demand Response ◽  
Stochastic Planning ◽  
Theory Framework

scholarly journals A game theory framework for ISP streaming traffic management

2011 ◽  
Vol 68 (11) ◽  
pp. 1162-1174 ◽  
Author(s):  
V. Bioglio ◽  
R. Gaeta ◽  
M. Grangetto ◽  
M. Sereno ◽  
S. Spoto
Keyword(s):  
Game Theory ◽  
Traffic Management ◽  
Streaming Traffic ◽  
Theory Framework

The Formulation of Subsidy Price for Demand Response Based on Game Theory

2018 ◽  
Author(s):  
Xuntao Shi ◽  
Bo Huang ◽  
Ciwei Gao ◽  
Yanlu Huang ◽  
Jinyong Lei ◽  
...  
Keyword(s):  
Game Theory ◽  
Demand Response

A game theory framework for cooperative management of refillable and disposable bottle lifecycles

2007 ◽  
Vol 15 (17) ◽  
pp. 1618-1627 ◽  
Author(s):  
Hilary G. Grimes-Casey ◽  
Thomas P. Seager ◽  
Thomas L. Theis ◽  
Susan E. Powers
Keyword(s):  
Game Theory ◽  
Cooperative Management ◽  
Theory Framework

scholarly journals Monetary Consequences of Fiscal Stress in a Game Theoretic Framework

2020 ◽  
Vol 9 (s1) ◽  
pp. 125-164
Author(s):  
Dat Thanh Nguyen ◽  
Viet Anh Hoang
Keyword(s):  
Game Theory ◽  
Crucial Role ◽  
Fiscal Stress ◽  
Ageing Population ◽  
Population Problem ◽  
The Public ◽  
Inflation Expectation ◽  
Game Theoretic ◽  
Government Transfers ◽  
Theory Framework

AbstractThis paper maps Leeper and Walker (2011) model into a game theory framework to study about the strategic aspects of monetary and fiscal interaction under a fiscal stress caused by an ageing population problem. The paper reveals that the outcomes of the game depend on the parameters of the underlying model, the size of the projected transfers and the public inflation expectation. The findings show that commitment to the target (inflation, government transfers) plays a crucial role in the policy interaction.

scholarly journals Demand Response Management Algorithm for Distributed Multi-Utility Environment in Smart Grid

2020 ◽  
Vol 15 ◽  
Keyword(s):  
Game Theory ◽  
Smart Grid ◽  
Information And Communication Technologies ◽  
Energy Management ◽  
Demand Response ◽  
Communication Technologies ◽  
Energy Market ◽  
Grid Environment ◽  
Utility Companies ◽  
Information And Communication

Effective usage of Information and Communication Technologies (ICT) has started with a paradigm shift in the energy management and functioning of the conventional power grid. It also aids in the maintenance of the complete information about consumer usage pattern, power storage, supply and regulation. Blending of information and communication technologies with energy management creates a smart grid environment which makes it move to the next horizon. The smart grid environment, uplifts renewable energy sources and brings out novel strategies in the energy market. The new functioning of the energy market attracts more utility companies for decentralized power generation and optimizes the power price for the consumer. The consumer plays an active role in the demand response modelling to maximize the welfare of the utility and to obtain the optimized price for their demand. In this paper, a novel demand response management scheme is proposed for multi-utility environment. The utility companies function in a peer to peer manner to communicate effectively and to select a specific utility from a set of utilities for the power supply. The selection of single utility is based on a non-cooperative game theory algorithm where the demand and generated power should be balanced to maximize the welfare of the utility and the residential consumers. The power price can be updated in an equal interval to allow all the utilities to participate in the Distributed Multi-Utility Demand Response Management (DMDRM) system. The simulated results justify that the distributed noncooperative game theory algorithm certainly maximizes the welfare of the utility companies and residential consumers.

Game theory framework for agent decision-making in communication constrained environments

2021 ◽  
Author(s):  
Steven Dennis ◽  
Frederick E. Petry ◽  
Donald Sofge ◽  
Daniel Yang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Constrained Environments ◽  
Theory Framework
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