Decentralised demand response market model based on reinforcement learning

Shafie‐khah, Miadreza; Talari, Saber; Wang, Fei; Catalão, João P.S.

doi:10.1049/iet-stg.2019.0129

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…In [119], a Q-learning algorithm was used as a reinforcement learning approach to minimise the costs and protect the privacy when the EV owners exchange energy, while the physical constraints for the vehicle-to-grid (V2G) could be further considered. Analogously, Shafie-Khah et al [120] designed a Q-learning algorithm to optimally submit the bids of demand response for the end-user. The numerical results proved that the proposed model could reduce the costs of using electricity and improve the load balance.…”

Section: Research and Implementationsmentioning

confidence: 99%

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review

Hua¹,

Chen²,

Qadrdan³

et al. 2022

Preprint

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Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced.

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Section: Research and Implementationsmentioning

confidence: 99%

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review

Hua¹,

Chen²,

Qadrdan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…(2) The application of control, for instance grid control, load scheduling and demand response has seen a significant amount of promising research. Particular in the application of decentralised control [54,73,88]. With the advent of distributed data collection and internet of things, it has become possible to achieve various welfare objectives through a distributed control algorithm.…”

Section: Applicationsmentioning

confidence: 99%

“…Shafie-Khah et al [73] develop a novel decentralised demand response model. In their model, each agent submits bids according to the consumption urgency and a set of parameters by the RL algorithm, Q-Learning.…”

Section: Literature Review 41 Reinforcement Learningmentioning

confidence: 99%

“…[82] International/National Market investigation Q-Learning 2020 Tomin N. [80] Microgrid Electricity grid control Q-Learning 2020 Liang Y. [39] International/National Bidding strategies DDPG 2020 Kim J.-G. [35] International/National Peer to peer trading Deep Q-Learning 2020 Shafie-Khah M. [73] Local energy market Demand response Q-Learning 2020 Sousa J.C. [76] International/National Bidding strategies Q-Learning 2020 Poplavskaya K. [64] International/National Bidding strategies Q-Learning 2020 Liu Y. [43] Local energy market Bidding strategies DQN 2020 Nunna H.S.V.S.K.…”

Section: Year First Authormentioning

confidence: 99%

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Machine learning applications for electricity market agent-based models: A systematic literature review

Kell¹,

McGough²,

Forshaw³

2022

Preprint

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The electricity market has a vital role to play in the decarbonisation of the energy system. However, the electricity market is made up of many different variables and data inputs. These variables and data inputs behave in sometimes unpredictable ways which can not be predicted a-priori. It has therefore been suggested that agentbased simulations are used to better understand the dynamics of the electricity market. Agent-based models provide the opportunity to integrate machine learning and artificial intelligence to add intelligence, make better forecasts and control the power market in better and more efficient ways. In this systematic literature review, we review 55 papers published between 2016 and 2021 which focus on machine learning applied to agent-based electricity market models. We find that research clusters around popular topics, such as bidding strategies. However, there exists a long-tail of different research applications that could benefit from the high intensity research from the more investigated applications. CCS CONCEPTS• Computing methodologies → Modeling and simulation; Machine learning; Artificial intelligence.

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A Load Threshold Allocation Approach: Tackling Blackouts Through Brownout

Agarwal¹

2023

Communications in Computer and Information Science

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Decentralised demand response market model based on reinforcement learning

Cited by 8 publications

References 35 publications

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review

Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review

Machine learning applications for electricity market agent-based models: A systematic literature review

A Load Threshold Allocation Approach: Tackling Blackouts Through Brownout

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