hustee's starred repositories
Reprinted_Applied_Energy
复刻论文Applied Energy的论文A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles，包含考虑电动汽车有序充放电的机组组合和最优潮流
ConvertChanceConstraint-ccc
ConvertChanceConstraint (ccc): a Matlab toolbox for Chance-constrained Optimization
smpc_example
Short example of MPC and specifically stochastic MPC (SMPC) with chance constraints for Matlab.
EnergyCircuitTheory-EnergyFlowCalculation
数值实验代码 for 《综合能源系统分析的统一能路理论(三)：稳态与动态潮流计算》
LPV
This algorithm exhibits a robust Energy Management Strategy (EMS) for battery-super capacitor (SC) Hybrid Energy Storage System (HESS). The proposed algorithm, dedicated to an electric vehicular application, it is based on a self-gain scheduled controller, which guarantees the H performance for a class of linear parameter varying (LPV) systems.
model-predictive-control-demand-response
MATLAB code on project: Role of Energy Positive Buildings in Future Low-Carbon Energy Systems.
IntelliHealer
IntelliHealer: An imitation and reinforcement learning platform for self-healing distribution networks
Grid_Scale_Energy_Storage_Q_Learning
Final Project for AA 228: Decision-Making under Uncertainty Abstract: Grid-scale energy storage systems (ESSs) are capable of participating in multiple grid applications, with the potential for multiple value streams for a single system, termed "value-stacking". This paper introduces a framework for decision making, using reinforcement learning to analyze the financial advantage of value-stacking grid-scale energy storage, as applied to a single residential home with energy storage. A policy is developed via Q-learning to dispatch the energy storage between two grid applications: time-of-use (TOU) bill reduction and energy arbitrage on locational marginal price (LMP). The performance of the dispatch resulting from this learned policy is then compared to several other dispatch cases: a baseline of no dispatch, a naively-determined dispatch, and the optimal dispatches for TOU and LMP separately. The policy obtained via Q-learning successfully led to the lowest cost, demonstrating the financial advantage of value-stacking.
gradient-energy-matching
Manuscript and code for the paper "Gradient Energy Matching for Distributed Asynchronous Gradient Descent".
microgrid-RL
Microgrid/distribution network level energy market managed by an RL agent
research-TCL
Simulation for residential load demand response
HiFi_ESD_PC
High Fidelity Energy Storage Dispatch in Production Costing Model
MILP_vs_Find_batt_scheduler
Uses data from the data driven battery scheduling competition; introduces battery scheduling using MILP; compares to the find optimisation method I developed for the competition
Solar-Cells-Fuel-Cells-Batteries-and-Supercapacitors
Energy storage and conversion systems
A-novel-DRO-model-for-self-scheduling-problem
This study is using distributionally robust optimization (DRO) algorithm with conditional value-at-risk (CVaR) to solve self-scheduling problem to obtain a suitable and adjustable self-scheduling strategy
StorageCode
GAMS and Python scripts underlying paper on how storage affects carbon dioxide emissions from a power system as it decarbonizes.
UGridAuction
Microgrid Multi Agent Auction Implementation using RIAPS
Reinforcement-learning-Notes
强化学习David Silver课程笔记