The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At every stage, new sequential decision-making challenges emerge, ranging from the operation of wind farms to the management of electrical grids or the scheduling of electric vehicle charging stations. All such problems are well suited for reinforcement learning, the branch of machine learning that learns behavior from data. Therefore, numerous studies have explored the use of reinforcement learning for sustainable energy. This paper surveys this literature with the intention of bridging both the underlying research communities: energy and machine learning. After a brief introduction of both fields, we systematically list relevant sustainability challenges, how they can be modeled as a reinforcement learning problem, and what solution approaches currently exist in the literature. Afterwards, we zoom out and identify overarching reinforcement learning themes that appear throughout sustainability, such as multi-agent, offline, and safe reinforcement learning. Lastly, we also cover standardization of environments, which will be crucial for connecting both research fields, and highlight potential directions for future work. In summary, this survey provides an extensive overview of reinforcement learning methods for sustainable energy, which may play a vital role in the energy transition.

Reinforcement Learning for Sustainable Energy: A Survey

Due to their dependence on intermittent renewable energy sources, island power systems, which are generally located in remote places or on islands, offer particular issues for day-ahead scheduling. Using the capabilities of neural networks, we offer a Seq2Seq-based technique for day-ahead scheduling, which increases the precision and flexibility of unit commitment choices. The attention mechanisms in the Seq2Seq model are trained with historical data that includes projections for intermittent generation, demand, and unit commitment choices. The model is tested for its capacity to incorporate dynamic temporal relationships and deal with regenerative uncertainty. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. In comparison to conventional scheduling approaches, the results show potential gains in prediction accuracy and operational efficiency. This study demonstrates how Seq2Seq models may be used to improve the longevity and dependability of isolated electrical grids and the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization. 

Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation

It is of great significance for the development of intermittent renewable energy and the marketization of electricity to study an efficient and accurate algorithm for solving the unit commitment problem with security constraints. Based on the existing constrained ordinal optimization (COO) algorithm, an improved ordinal optimization (OO) algorithm is proposed, which can be applied to solve SCUC problems. In order to solve the rough model and the accurate model, the discrete variable identification strategy and the effective safety constraint reduction strategy are adopted, respectively. Compared with the traditional object-oriented algorithm, the improved object-oriented algorithm not only gives full play to the computational efficiency of the traditional object-oriented algorithm but also further improves the compactness and reduces the computational redundancy. IEEE 118 simulation results verify the correctness and effectiveness of the algorithm.

Research on security-constrained unit commitment based on an improved ordinal optimization algorithm

The growing renewable energy sources have posed significant challenges to traditional power scheduling. It is difficult for operators to obtain accurate day-ahead forecasts of renewable generation, thereby requiring the future scheduling system to make real-time scheduling decisions aligning with ultra-short-term forecasts. Restricted by the computation speed, traditional optimization-based methods can not solve this problem. Recent developments in reinforcement learning (RL) have demonstrated the potential to solve this challenge. However, the existing RL methods are inadequate in terms of constraint complexity, algorithm performance, and environment fidelity. We are the first to propose a systematic solution based on the state-of-the-art reinforcement learning algorithm and the real power grid environment. The proposed approach enables planning and finer time resolution adjustments of power generators, including unit commitment and economic dispatch, thus increasing the grid's ability to admit more renewable energy. The well-trained scheduling agent significantly reduces renewable curtailment and load shedding, which are issues arising from traditional scheduling's reliance on inaccurate day-ahead forecasts. High-frequency control decisions exploit the existing units' flexibility, reducing the power grid's dependence on hardware transformations and saving investment and operating costs, as demonstrated in experimental results. This research exhibits the potential of reinforcement learning in promoting low-carbon and intelligent power systems and represents a solid step toward sustainable electricity generation.

Real-time scheduling of renewable power systems through planning-based reinforcement learning

A vehicle active suspension model with 1 / 2 ，four-degrees of freedom is established and by combining genetic algorithm with optimal control theory，the author presents a new control method of active suspension that is to optimize the value of K controlled by LQG in default of road input based on genetic algorithm and makes a simulation in the environment of Matlab / Simulink. By simulation and analysis，the result indicates that，this method has an obvious effect on improving comprehensive performance of vehicles，such as ride comfort and operate stability and so on.

Vehicle Active Suspension with Four-Degrees of Freedom of Optimizing the Value of K Based on Genetic Algorithm

This paper focuses on the application of electrical machines in public transportation and investigates the potential benefits of auxiliary slot design on this kind of high-speed low torque machine. A preliminary surrogate-based optimization is conducted to the shape of auxiliary slots on the stator for minimizing its cogging torque ripple while maintaining its Total Harmonic Distortion (THD) at an acceptable level. The results show the optimization achieves its best performance while the edges of the auxiliary slot are parallel to the edges of the stator teeth, which could reflect a deeper resonance among the geometries of the stator magnetic field.

Surrogate-based auxiliary slot optimization for a fractional slot IPM machine used in the public transportation

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Research on security-constrained unit commitment based on an improved ordinal optimization algorithm

Real-time scheduling of renewable power systems through planning-based reinforcement learning

Vehicle Active Suspension with Four-Degrees of Freedom of Optimizing the Value of K Based on Genetic Algorithm

Surrogate-based auxiliary slot optimization for a fractional slot IPM machine used in the public transportation