Smart Grid

Smart Grid is conceptualized as a combination of electrical network and communication infrastructure. With the implementation of bidirectional communication and power flows, a smart grid is capable of delivering electricity more efficiently and reliably than the traditional power grid. A smart grid consists of a power network with ‘intelligent’ entities that can operate, communicate, and interact autonomously, in order to efficiently deliver electricity to the customers. This heterogeneity in architecture of a smart grid motivates the use of advanced technology for overcoming various technical challenges at different levels. Any smart grid infrastructure should support real-time, two-way communication between utilities and consumers, and should allow software systems at both the producer and consumer ends to control and manage the power usage.

We study the impact on smart grid from different perspectives: energy management and pricing mechanism.

 

Cooperative Energy Management in Smart Grid

In this paper, a community-based cooperative energy consumption (C2C) scheme in smart grid, which alleviates energy consumption cost to customers, is proposed. Furthermore, the proposed scheme also helps to reduce peak-to-average ratio of the energy demands from the customers in different time periods.

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In this paper, the problem of dynamic data aggregator unit selection in smart grid is studied using a dynamic evolutionary game theoretical model.

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In this paper, the problem of optimal energy distribution by dynamically changing the size of coalition, which consists of one micro-grid and several customers, is studied using the theory of Markov Decision Process (MDP) — a discrete optimization method.

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To improve reliability in energy management of smart grid, micro-grids provides electricity without any interruption and reduces the load on the main grid. In order to address this problem, we design a scheme for the dynamic coalition extension (DCoE) in smart grid using evolutionary game theory.

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In this paper,we propose a dynamic demand scheduling (D2S) scheme — an effort towards cost-effective energy consumption at customers’ end. The theory of Optimal Portfolio Selection is adopted to generate customers’ expected day-ahead energy demand graph called the weight graph, based on past days’ history — energy demand, profit return, and corresponding risk.

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In this paper, an energy-efficient smart metering scheme is proposed — an effort towards minimizing the energy consumption by the smart meters for green smart grid communication. We incorporate the use of coalition game to form multiple coalitions among smart meters to communicate with the service provider.

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Noncooperative Energy Management in Smart Grid

In this paper, the problem of energy scheduling and energy exchange between micro-grids and customers is studied as a multi-leader multi-follower non-cooperative Stackelberg game. We obtain that the proposed distributed energy management using scheduling (DEMANDS) scheme has a Nash equilibrium solution, which is also socially optimal.

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In this paper, we study the problem of allowing customers to use their storage energy, grid energy, as well as privately owned renewable sources of energy. The customer has three options — grid, storage, and self-generated energy, to fulfill the energy requirements. We use Markov Decision Process (MDP) to design this decision making policy of the customer.

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In this paper, we propose that a PHEV may get energy from any of the available micro-grids within a coalition instantaneously without paying higher price. In this work, the problem of energy trading network topology control for PHEVs is studied as a multi-leader multi-follower Stackelberg game.

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In this paper, the problem of green electric vehicle energy networks management is studied using a non-cooperative Stackelberg game theoretic model. Using the proposed electric vehicle energy networks management (EVENT) scheme, there exists Nash equilibrium solution for charging and discharging, and the satisfaction factors are high using proposed EVENT scheme.

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In this paper, the problem of distributed home energy management system with storage in a coalition, which consistsofmultiplemicro-gridsandmultiplecustomers,isstudied using the multiple leader multiple follower Stackelberg game theoretic model — a multi stage, and multi level game.

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In this paper,we present a Markov Decision Process(MDP)-based scheduling mechanism for residential energy management (REM) in smart grid. The aim of the proposed work is to reduce the energy expenses of a customer. In this mechanism, the Home Energy Management Unit (HEMU) acts as one of the players, the Central Energy Management Unit (CEMU) acts as another player.

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Energy Management with Uncertainty in Smart Grid

In this paper, energy trading for the distributed smart grid architecture is projected as an incomplete information game — a viewpoint that contrasts from all the existing pieces of literature available on the broader issue of energy management in smart grid.

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In this paper, real-time energy trading in smart grid is modeled as an optimization process under uncertainties of demand and price information — a problem perspective that is divergent from the ones in the existing literature.

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Cloud Architecture and Energy Management in Smart Grid

Future smart grids are expected to have reliable, efficient, secured, and cost-effective power management with the implementation of distributed architecture. To focus on these requirements, we provide a comprehensive survey on different cloud computing applications for the smart grid architecture, in three different areas — energy management, informationmanagement,and security.

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In this paper, the problem of energy distribution using virtual energy-cloud to the plug-in hybrid electric vehicles (PHEVs) is studied as a single leader multiple follower noncooperative Stackelberg game. Using variational inequality, it is shown that the proposed scheme, virtual energy cloud topology control (VELD), has a generalized Nash equilibrium, which is also socially optimal.

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Pricing Policy in Smart Grid

We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture -- an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides real-time dynamic price as home-price and roaming-price, depending on the supply-demand curve, to optimize its revenue.

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