energy management
31 papers with code • 0 benchmarks • 0 datasets
energy management is to schedule energy units inside the systems, enabling an reliable, safe and cost-effective operation
Benchmarks
These leaderboards are used to track progress in energy management
Most implemented papers
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Management
intelligent-environments-lab/CityLearn
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Rapid urbanization, increasing integration of distributed renewable energy resources, energy storage, and electric vehicles introduce new challenges for the power grid.
Peer-to-peer energy trading in smart grid through blockchain: A double auction-based game theoretic approach
With the aim of improving participants’ profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare.
A Verifiable Framework for Cyber-Physical Attacks and Countermeasures in a Resilient Electric Power Grid
In this paper, we investigate the feasibility and physical consequences of cyber attacks against energy management systems (EMS).
CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model
castacks/cvar-energy-risk-deep-model
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Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off.
Understanding the Safety Requirements for Learning-based Power Systems Operations
chennnnnyize/powersystem_rl_attacks
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Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach
This paper explores the impact of electric vehicles (EVs) on traffic congestion and energy consumption by proposing an integrated bi-level framework comprising of: a) a dynamic micro-scale traffic simulation suitable for modelling current and hypothetical traffic and charging demand scenarios and b) a queue model for capturing the impact of fast charging station use, informed by traffic flows, travel distances, availability of charging infrastructure and estimated vehicle battery state of charge.
A Cost-Effective, Scalable, and Portable IoT Data Infrastructure for Indoor Environment Sensing
The vast number of facility management systems, home automation systems, and the ever-increasing number of Internet of Things (IoT) devices are in constant need of environmental monitoring.
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles
bit-xjy/rl-based-transferable-ems
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Different exploration methods of DRL, including adding action space noise and parameter space noise, are compared against each other in the transfer learning process in this work.
Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling
shengrenhou/drl-for-energy-systems-optimal-scheduling
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This trade-off introduces extra hyperparameters that impact the DRL algorithms' performance and capability of providing feasible solutions.
Comparison of Forecasting Methods of House Electricity Consumption for Honda Smart Home
The electricity consumption of buildings composes a major part of the city's energy consumption.
