Deep Reinforcement Learning Algorithm Based on Optimal Energy Dispatching for Microgrid

Bian, Haifeng; Tian, Xin; Zhang, Jun; Han, Xinyang

doi:10.1109/acpee48638.2020.9136221

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…In this paper, the DDPG algorithm was used to solve the microgrid optimal operation problem. The DDPG algorithm [20] consists of two independent neural networks fitting the policy function and the action-value function. The two neural networks are called the policy network and the evaluation network.…”

Section: Deep Deterministic Policy Gradient Algorithmmentioning

confidence: 99%

“…In terms of optimal operation, the deep reinforcement learning algorithms is used in [19] to solve the energy management problem of residential energy system with electricity, heat, and gas demand. In [20], a microgrid scheduling model is proposed and deep reinforcement learning algorithms is adopted to reduce the power purchase cost. However, this literature fail to consider the impact of hydrogen energy storage system on the microgrid operation.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

2022

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Microgrid with hydrogen storage is an effective way to integrate renewable energy and reduce carbon emissions. This paper proposes an optimal operation method for a microgrid with hydrogen storage. The electrolyzer efficiency characteristic model is established based on the linear interpolation method. The optimal operation model of microgrid is incorporated with the electrolyzer efficiency characteristic model. The sequential decision-making problem of the optimal operation of microgrid is solved by a deep deterministic policy gradient algorithm. Simulation results show that the proposed method can reduce about 5% of the operation cost of the microgrid compared with traditional algorithms and has a certain generalization capability.

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Section: Deep Deterministic Policy Gradient Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

2022

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“…Charge scheduling of EV is proposed using an advanced metering infrastructure (AMI)-based artificial neural network (ANN) in [28]. Deep learning (DL) and deep reinforced learning (DRL) methods are used for optimal dispatch in [29]. An Internet of Things (IoT)-based deep learning method is proposed for a group of microgrids with EV and RER for reduced carbon emission and maximum revenue [30].…”

Section: Introductionmentioning

confidence: 99%

Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV-, Wind-, and Biomass-Based Resources

et al. 2023

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The availability of sustainable, efficient electricity access is critical for rural communities as it can facilitate economic development and improve the quality of life for residents. Isolated microgrids can provide a solution for rural electrification, as they can generate electricity from local renewable energy sources and can operate independently from the central grid. Residential load scheduling is also an important aspect of energy management in isolated microgrids. However, effective management of the microgrid’s energy resources and load scheduling is essential for ensuring the reliability and cost-effectiveness of the system. To cope with the stochastic nature of RERs, the idea of an optimal energy management system (EMS) with a local energy transactive market (LETM) in an isolated multi-microgrid system is proposed in this work. Nature-inspired algorithms such as JAYA (Sanskrit word meaning victory) and teaching–learning based optimization algorithm (TLBO) can get stuck in local optima, thus reducing the effectiveness of EMS. For this purpose, a modified hybrid version of the JAYA and TLBO algorithm, namely, the modified JAYA learning-based optimization (MJLBO), is proposed in this work. The prosumers can sell their surplus power or buy power to meet their load demand from LETM enabling a higher load serving as compared to a single isolated microgrid with multi-objectives, resulting in a reduced electricity bill, increased revenue, peak-average ratio, and user discomfort. The proposed system is evaluated against three other algorithms TLBO, JAYA, and JAYA learning-based optimization (JLBO). The result of this work shows that MJLBO outperforms other algorithms in achieving the best numerical value for all objectives. The simulation results validate that MJLBO achieves a peak-to-average ratio (PAR) reduction of 65.38% while there is a PAR reduction of 51.4%, 52.53%, and 51.2% for TLBO, JLBO, and JAYA as compared to the unscheduled load.

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“…To solve the dimensional disaster problem posed by the discretization of the action space, DRL approaches that can learn policies with continuous action spaces are promoted. A DRL approach was proposed by Bian et al (2020) to optimize the day-ahead MG dispatching problem. The deterministic real-time electricity price was used, but the uncertainties of RESs were not taken into consideration.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

et al. 2023

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The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios.

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Deep Reinforcement Learning Algorithm Based on Optimal Energy Dispatching for Microgrid

Cited by 7 publications

References 4 publications

Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

Optimal Operation of a Microgrid with Hydrogen Storage Based on Deep Reinforcement Learning

Optimal Energy Management System of Isolated Multi-Microgrids with Local Energy Transactive Market with Indigenous PV-, Wind-, and Biomass-Based Resources

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

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