Pinned Repositories
236780
Microgrids_-labs-
micro grids lab
Optimal-Price-Based-control-of-heterogeneous-thermostatically-controlled-loads-under-uncertainty-usi
we consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
PPO
Proximal Policy Optimization
ppo-lstm-parallel
ppo-lstm-parallel
reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
smartgrid
mig se
SmartGridProject
A system for compressing smart grid data, and allowing queries on the compressed representation.
sockjs-tornado
WebSocket emulation - Python server
JohnSun23's Repositories
JohnSun23/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
JohnSun23/Optimal-Price-Based-control-of-heterogeneous-thermostatically-controlled-loads-under-uncertainty-usi
we consider the problem of thermostatically controlled load (TCL) control through dynamic electricity prices, under partial observability of the environment and uncertainty of the control response. The problem is formulated as a Markov decision process where an agent must find a near-optimal pricing scheme using partial observations of the state and action. We propose a long-short-term memory (LSTM) network to learn the individual behaviors of TCL units. We use the aggregated information to predict the response of the TCL cluster to a pricing policy. We use this prediction model in a genetic algorithm to find the best prices in terms of profit maximization in an energy arbitrage operation. The simulation results show that the proposed method offers a profit equal to 96% of the theoretical optimal solution.
JohnSun23/236780
JohnSun23/Microgrids_-labs-
micro grids lab
JohnSun23/ppo-lstm-parallel
ppo-lstm-parallel
JohnSun23/smartgrid
mig se
JohnSun23/SmartGridProject
A system for compressing smart grid data, and allowing queries on the compressed representation.
JohnSun23/PPO
Proximal Policy Optimization
JohnSun23/sockjs-tornado
WebSocket emulation - Python server