Muriithi et al., 2022 - Google Patents

Deep Q‐network application for optimal energy management in a grid‐tied solar PV‐Battery microgrid

Muriithi et al., 2022

View PDF @Full View
Document ID
17072952055722856386
Author
Muriithi G
Chowdhury S
Publication year
Publication venue
The Journal of Engineering

External Links

Snippet

This paper presents a deep Q‐network (DQN) technique to optimally manage energy resources in a microgrid in which the algorithm learns tasks in the same way as humans do. Every move the agent makes in the environment generates feedback which then motivate …
Continue reading at ietresearch.onlinelibrary.wiley.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights

Similar Documents

Publication Publication Date Title
Muriithi et al. Deep Q‐network application for optimal energy management in a grid‐tied solar PV‐Battery microgrid
KR102707077B1 (en) Demand response management method for discrete industrial manufacturing system based on constrained reinforcement learning
Ovalle et al. Optimal management and integration of electric vehicles to the grid: Dynamic programming and game theory approach
Keerthisinghe et al. Energy management of PV-storage systems: ADP approach with temporal difference learning
Zhou et al. Correlated deep q-learning based microgrid energy management
Rezaeimozafar et al. A hybrid heuristic-reinforcement learning-based real-time control model for residential behind-the-meter PV-battery systems
Li et al. Energy trading of multiple virtual power plants using deep reinforcement learning
Poddubnyy et al. Online EV charging controlled by reinforcement learning with experience replay
Leo et al. Multi agent reinforcement learning based distributed optimization of solar microgrid
Mohamed et al. Battery scheduling control of a microgrid trading with utility grid using deep reinforcement learning
Li et al. Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems
Kumar et al. Explainable AI: Deep reinforcement learning agents for residential demand side cost savings in smart grids
Liu et al. Reinforcement learning-based approach for vehicle-to-building charging with heterogeneous agents and long term rewards
Liang et al. Energy optimization scheduling of grid-connected renewable energy hydrogen production system based on RGHS-PPO algorithm
Tajeddini et al. Decentralized charging coordination of plug-in electric vehicles based on reverse stackelberg game
Muriithi et al. Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach. Energies 2021, 14, 2700
Arwa Reinforcement learning for power scheduling in a grid-tied pv-battery electric vehicles charging station
Cardo-Miota et al. Deep reinforcement learning based electricity bill minimization strategy for residential prosumer
Yadav et al. Deep Reinforcement Learning and Energy Price Prediction
Selim et al. Safe Optimal Control of Battery Energy Storage Systems via Hierarchical Deep Reinforcement Learning
Rouzbahani et al. Energy scheduling in IoE-enabled smart grids using probabilistic delayed double deep Q-learning (P3DQL) algorithm
Khezri et al. Deep Learning‐Based Optimal Sizing of a Grid‐Tied Microgrid Under Real‐Time Pricing
Bui et al. Optimizing Islanded Microgrid Reliability with Demand Response-Driven Uncertainty Compensation
Başar et al. Economics of Networks
Narayana et al. A Study on Battery Storage Exploitation using Reinforcement Learning