Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids

Tang, Rui; Wang, Shengwei; Li, Hangxin

doi:10.1016/j.apenergy.2019.04.177

Cited by 160 publications

(61 citation statements)

References 28 publications

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“…Each player's behaviors either can be known beforehand or remain concealed. An example of a game could be a smart grid environment where the attacker attempts to disrupt communication between a power system and a home, whereas the defender attempts to maintain connectivity between these various entities [30]- [32]. At each step of the game, the attacker and the defender would adopt strategies to be successful in their respective goals [33].…”

Section: ) Structured Query Language (Sql) Injection Attacksmentioning

confidence: 99%

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's potential in improving cybersecurity solutions, by identifying both its strengths and weaknesses. We also discuss future research opportunities associated with the development of AI techniques in the cybersecurity field across a range of application domains.

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Section: ) Structured Query Language (Sql) Injection Attacksmentioning

confidence: 99%

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

et al. 2020

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“…This will not only help the prosumers to earn money, but it can play an important role in PAR reduction. Authors in [23] proposed a fundamental and improved interaction strategy in which a grid and various buildings are produced using the Stackelberg game theory based on their recognized Nash equilibria, however, UC was ignored. The authors in [24] proposed mixed-integer quadratic programming (MIQP) to predict a control system established on the thermal building model and the building energy management system.…”

Section: Related Workmentioning

confidence: 99%

“…The delay time of the appliance is calculated by (23). Where undschd(t) represents the unscheduled time while Schd(t) represents the scheduled time.…”

Section: ) Parmentioning

confidence: 99%

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

Imran¹,

Hafeez²,

Khan³

et al. 2020

IEEE Access

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An operative and versatile household energy management system is proposed to develop and implement demand response (DR) projects. These are under the hybrid generation of the energy storage system (ESS), photovoltaic (PV), and electric vehicles (EVs) in the smart grid (SG). Existing household energy management systems cannot offer its users a choice to ensure user comfort (UC) and not provide a sustainable solution in terms of reduced carbon emission. To tackle these problems, this research work proposes a heuristic-based programmable energy management controller (HPEMC) to manage the energy consumption in residential buildings to minimize electricity bills, reduce carbon emissions, maximize UC and reduce the peak-to-average ratio (PAR). We used our proposed hybrid genetic particle swarm optimization (HGPO) algorithm and existing algorithms like a genetic algorithm (GA), binary particle swarm optimization algorithm (BPSO), ant colony optimization (ACO), wind-driven optimization algorithm (WDO), bacterial foraging algorithm (BFA) to schedule smart appliances optimally to attain our desired objectives. In the proposed model, consumers use solar panels to produce their energy from microgrids. We also perform MATLAB simulations to validate our proposed HGPO-HPEMC (HHPEMC), and results confirm the efficiency and productivity of our proposed HPEMC based strategy. The proposed algorithm reduced the electricity cost by 25.55%, PAR by 36.98%, and carbon emission by 24.02% as compared to the case of without scheduling.

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“…Increases in electricity prices rise by several decades due to the low latency in the communication level and the deployment of smart meters. However, some of the consumers, such as the residential sector, use day‐ahead pricing (DAP) or hourly‐ahead pricing (HAP) to respond to their consumers 26 . As a consequence, the industrial and commercial sectors are the two essential desired areas for RTP 27 .…”

Section: Dynamic Pricingmentioning

confidence: 99%

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

Alsalloum

Merghem-Boulahia

Rahim

2020

Int Trans Electr Energ Syst

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Summary The smart grid (SG) is an enhancement of the traditional electrical grid. Its main objectives are diverse, focusing mainly on minimizing the energy cost, as well as energy consumption. The smooth integration of renewable energy balances energy production and consumption. Dynamic pricing (DP) becomes one of the important integrated solutions, which comes in line with the global efficiency and reliability of smart grids. DP incentivizes the consumer to participate in the energy scheduling decision, mainly in the real‐time decision. Consumer represents the key challenge in the SG in perspective to make the energy system more efficient. Moreover, the success of smart grids relies mostly on the evolution of the widely used optimization methods. These include measures to encourage consumers and providers to be involved in the development of the SG. The main objective of this article is to review and analyze the recent research works regarding the pricing strategies and optimization methods utilized in the context of SG. A systematic analysis is thus performed by considering twofold views. First, it comprehensively approaches the pricing majors and visualizes the game theory used in real‐time pricing. Second, it describes the optimization methods used in the smart grid context and compares them in terms of their complexity.

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Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids

Cited by 160 publications

References 28 publications

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Harnessing Artificial Intelligence Capabilities to Improve Cybersecurity

Heuristic-Based Programable Controller for Efficient Energy Management Under Renewable Energy Sources and Energy Storage System in Smart Grid

A systematical analysis on the dynamic pricing strategies and optimization methods for energy trading in smart grids

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