Smart grid

About: Smart grid is a research topic. Over the lifetime, 37536 publications have been published within this topic receiving 627844 citations. The topic is also known as: intelligent grid.

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Abstract: Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.

Integrated Demand Side Management Game in Smart Energy Hubs

Abstract: The presence of energy hubs and the advancement in smart grid technologies have motivated system planners to deploy intelligent multicarrier energy systems entitled “smart energy hub” (S.E. Hub). In this paper, we model S.E. Hub, and propose a modern energy management technique in electricity and natural gas networks based on integrated demand side management (IDSM). In conventional studies, energy consumption is optimized from the perspective of each individual user without considering the interactions with each other. Here, the interaction among S.E. Hubs in IDSM program is formulated as a noncooperative game. The existence and uniqueness of a pure strategy Nash equilibrium (NE) is proved. Additionally, the strategies for each S.E. Hub are determined by proposing a distributed algorithm. We also address the IDSM game in a cloud computing (CC) framework to achieve efficient data processing and information management. Simulations are performed on a grid consisting of ten S.E. Hubs. We compare the CC framework with conventional data processing techniques to evaluate the efficiency of our proposed approach in determining NE. It is also shown that in the NE, the energy cost for each S.E. Hub and the peak-to-average ratio of the electricity demand decrease substantially.

Coordinated data-injection attack and detection in the smart grid: A detailed look at enriching detection solutions

Abstract: A smart grid improves the efficiency of power grids via the aid of modern communication, signal processing, and control technologies. While smart grid integration enables power grid networks to be smarter, it also increases the risk of cyberattacks due to the strong dependence on the cyberinfrastructure in the overall system. In this article, the coordinated datainjection attack detection problem in the smart grid is considered. Specifically, the data-injection attack model is first introduced and a thorough survey of existing detection methods is then given. Afterward, three important efforts to enrich the detection solution are presented in detail: 1) attacker versus defender dynamics, where possible interactive attack and defense strategies are discussed in the context of secure phasor measurement unit (PMU) placement 2) distributed attack detection and state recovery, where the focus is how to achieve the optimal centralized performance with a distributed approach 3) quickest detection (QD), where the trade off between the detection speed and detection performance is studied. A list of associated key open problems in this area is then presented to conclude this article.

A Distributed Algorithm for Managing Residential Demand Response in Smart Grids

Abstract: Demand response enabled by time-varying prices can propel the power industry toward a greater efficiency. However, a noncoordinated response of customers may lead to severe peak rebounds at periods with lower prices. In this regard, a coordinated demand response scheme can mitigate concerns about the peak rebounds. This paper presents a system-wide demand response management model to coordinate demand response provided by residential customers. The objective of the model is to flatten the total load profile that is subject to minimum individual cost of customers. The model is first formulated as a bi-level optimization problem. It is then casted into equivalent single-level problems, which are solved via an iterative distributed algorithm. Home load management (HLM) modules embedded in customers' smart meters are autonomous agents associated with the algorithm. In the algorithm, at first, HLM modules, in response to prices announced by the utility, optimize the daily operation of household appliances and send back the scheduled load profiles. Then, the total load profile is calculated and released by the utility. Thereafter, the HLM modules asynchronously update their schedule such that, given their least energy expenses, the most evenly distributed total load profile is achieved. The mutual interaction between the utility and HLM modules is continued to the point in which no further improvement is obtained. Convergence and optimality of the algorithm are proved.