Multi-market Energy Optimization with Renewables via Reinforcement Learning

• URL: http://arxiv.org/abs/2306.08147v1
• Date: Tue, 13 Jun 2023 21:35:24 GMT
• Title: Multi-market Energy Optimization with Renewables via Reinforcement Learning
• Authors: Lucien Werner and Peeyush Kumar
• Abstract summary: This paper introduces a deep reinforcement learning framework for optimizing the operations of power plants pairing renewable energy with storage. The framework handles complexities such as time coupling by storage devices, uncertainty in renewable generation and energy prices, and non-linear storage models. It utilizes RL to incorporate complex storage models, overcoming restrictions of optimization-based methods that require convex and differentiable component models.
• Score: 1.0878040851638
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize revenue from energy markets while minimizing storage degradation costs and renewable curtailment. The framework handles complexities such as time coupling by storage devices, uncertainty in renewable generation and energy prices, and non-linear storage models. The study treats the problem as a hierarchical Markov Decision Process (MDP) and uses component-level simulators for storage. It utilizes RL to incorporate complex storage models, overcoming restrictions of optimization-based methods that require convex and differentiable component models. A significant aspect of this approach is ensuring policy actions respect system constraints, achieved via a novel method of projecting potentially infeasible actions onto a safe state-action set. The paper demonstrates the efficacy of this approach through extensive experiments using data from US and Indian electricity markets, comparing the learned RL policies with a baseline control policy and a retrospective optimal control policy. It validates the adaptability of the learning framework with various storage models and shows the effectiveness of RL in a complex energy optimization setting, in the context of multi-market bidding, probabilistic forecasts, and accurate storage component models.

Related papers

• Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes [0.0]
  This paper proposes a reinforcement learning (RL) approach to address the challenges of dynamic load scheduling. Our results show that the RL-based method provides a robust and scalable solution for real-time load scheduling.
  arXiv  Detail & Related papers  (2024-10-23T09:16:22Z)
• Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles [16.348774515562678]
  Learning-based intelligent energy management systems for plug-in hybrid electric vehicles (PHEVs) are crucial for achieving efficient energy utilization. Their application faces system reliability challenges in the real world, which prevents widespread acceptance by original equipment manufacturers (OEMs) This paper proposes a real-vehicle application-oriented control framework, combining horizon-extended reinforcement learning (RL)-based energy management with the equivalent consumption minimization strategy (ECMS) to enhance practical applicability.
  arXiv  Detail & Related papers  (2024-06-13T13:04:42Z)
• Interpretable Deep Reinforcement Learning for Optimizing Heterogeneous Energy Storage Systems [11.03157076666012]
  Energy storage systems (ESS) are pivotal component in the energy market, serving as both energy suppliers and consumers. To enhance ESS flexibility within the energy market, a heterogeneous photovoltaic-ESS (PV-ESS) is proposed. We develop a comprehensive cost function that takes into account degradation, capital, and operation/maintenance costs to reflect real-world scenarios.
  arXiv  Detail & Related papers  (2023-10-20T02:26:17Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• Diverse Policy Optimization for Structured Action Space [59.361076277997704]
  We propose Diverse Policy Optimization (DPO) to model the policies in structured action space as the energy-based models (EBM) A novel and powerful generative model, GFlowNet, is introduced as the efficient, diverse EBM-based policy sampler. Experiments on ATSC and Battle benchmarks demonstrate that DPO can efficiently discover surprisingly diverse policies.
  arXiv  Detail & Related papers  (2023-02-23T10:48:09Z)
• Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning [0.0]
  We propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to plan energy storage systems (ESS) A quantitative performance comparison proved that the DRL agent outperforms the scenario-based optimization (SO) algorithm. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology.
  arXiv  Detail & Related papers  (2022-12-12T02:24:50Z)
• Low Emission Building Control with Zero-Shot Reinforcement Learning [70.70479436076238]
  Control via Reinforcement Learning (RL) has been shown to significantly improve building energy efficiency. We show it is possible to obtain emission-reducing policies without a priori--a paradigm we call zero-shot building control.
  arXiv  Detail & Related papers  (2022-08-12T17:13:25Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• A Relearning Approach to Reinforcement Learning for Control of Smart Buildings [1.8799681615947088]
  This paper demonstrates that continual relearning of control policies using incremental deep reinforcement learning (RL) can improve policy learning for non-stationary processes. We develop an incremental RL technique that simultaneously reduces building energy consumption without sacrificing overall comfort.
  arXiv  Detail & Related papers  (2020-08-04T23:31:05Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.