Realistic Peer-to-Peer Energy Trading Model for Microgrids using Deep Reinforcement Learning

Chen, Tianyi; Bu, Shengrong

doi:10.1109/isgteurope.2019.8905731

Cited by 29 publications

(27 citation statements)

References 12 publications

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“…Our intelligent agent strategies managed to achieve annual average saving of 7,56ce/kWh (25,3%) with only 10% DR. As for prosumers, they made a profit with average annual MCP of 25,4ce/kWh with trading instead of putting it in the grid and achieving a feed-in tariff 16,83ce/kWh i.e. 51% annual increase in the revenue which is comparable to that of Chen and Bu (2019). However, it must be noted that the results from Chen and Bu (2019) corresponds to the regulations of United States of America but in our case, it corresponds to Germany.…”

Section: Evaluation Of the Results Of The Modelmentioning

confidence: 62%

“…An annual decrease of 30% cost of electricity was reported for the community through peer-to-peer trading. Chen and Bu (2019) has explored the self-learning prosumer behaviour of developing intelligent agent strategies through deep reinforcement learning method in a LEM of 200 households. The average annual revenue saved through this method for the LEM with only trading was reported as 33% saving with trading in LEM and 54% with trading and storage.…”

Section: Evaluation Of the Results Of The Modelmentioning

confidence: 99%

“…The KPIs we apply are: Chen and Bu (2019) have used the MCP to determine the economic benefits and setup the constraints for their learning algorithm of their model. Mengelkamp et al (2018a) and Marzband et al (2013) have utilised RPD to evaluate the optimum flexibility that can be offered by a LEM to a transmission grid.…”

Section: Key Performance Indicators (Kpis)mentioning

confidence: 99%

See 2 more Smart Citations

Reinforcement learning in local energy markets

Bose

Kremers

Mengelkamp³

et al. 2021

Energy Inform

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Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility.

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Section: Evaluation Of the Results Of The Modelmentioning

confidence: 62%

Section: Evaluation Of the Results Of The Modelmentioning

confidence: 99%

Section: Key Performance Indicators (Kpis)mentioning

confidence: 99%

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Reinforcement learning in local energy markets

Bose

Kremers

Mengelkamp³

et al. 2021

Energy Inform

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show abstract

“…In [52], Dominguez-Barbero et al proposed a DQN-based energy management algorithm for an isolated residential microgrid to minimize the operating cost, which is the sum of DG generation cost and the penalty of non-served power demand. In [53], Chen et al proposed a DQN-based energy trading strategy for a microgrid to maximize the utility function, which is related to trading profit, retail profit, battery wear cost, demand penalty and virtual penalty. In [54], Ji et al proposed a DQN-based energy management algorithm for a microgrid to minimize daily operating cost.…”

Section: Applications Of Drl In Building Microgridsmentioning

confidence: 99%

Deep Reinforcement Learning for Smart Home Energy Management

Xie

et al. 2020

IEEE Internet Things J.

307

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In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, nonshiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.

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“…Deep reinforcement learning (DRL) [6] is applied in the MG energy management field in several prior works. The DRL is used for Peer-to-Peer energy trading in [7], and an improvement is shown when compared with rulebased strategy. Furthermore, energy management problem is formulated to be a markov decision process (MDP) in [8], and actor-critic based DRL is proposed to minimize the energy cost.…”

Section: Introductionmentioning

confidence: 99%

Correlated Deep Q-learning based Microgrid Energy Management

Zhou

Erol‐Kantarci

2020

2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)

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Microgrid (MG) energy management is an important part of MG operation. Various entities are generally involved in the energy management of an MG, e.g., energy storage system (ESS), renewable energy resources (RER) and the load of users, and it is crucial to coordinate these entities. Considering the significant potential of machine learning techniques, this paper proposes a correlated deep Q-learning (CDQN) based technique for the MG energy management. Each electrical entity is modeled as an agent which has a neural network to predict its own Q-values, after which the correlated Q-equilibrium is used to coordinate the operation among agents. In this paper, the Long Short Term Memory networks (LSTM) based deep Qlearning algorithm is introduced and the correlated equilibrium is proposed to coordinate agents. The simulation result shows 40.9% and 9.62% higher profit for ESS agent and photovoltaic (PV) agent, respectively.

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Realistic Peer-to-Peer Energy Trading Model for Microgrids using Deep Reinforcement Learning

Cited by 29 publications

References 12 publications

Reinforcement learning in local energy markets

Reinforcement learning in local energy markets

Deep Reinforcement Learning for Smart Home Energy Management

Correlated Deep Q-learning based Microgrid Energy Management

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