Chi ZHANG's repositories
ChineseDiachronicCorpus
ChineseDiachronicCorpus，中文历时语料库，横跨六十余年，包括腾讯历时新闻2000-2016，人民日报历时语料1946-2003，参考消息历时语料1957-2002。基于历时流通语料库，可用于历时语言变化计算、语言监测、社会文化变迁研究提供基础性的语料支持。
Integrated-Energy-Systems-with-CAES
The optimal dispatch of CAES in the integrated energy systems
CDCS
An open-source MATLAB® ADMM solver for partially decomposable conic optimization programs.
ADMMBO
An implementation of "ADMMBO, An ADMM Framework for Bayesian Optimization with Unknown Constraints''
PowerModels.jl
A Julia/JuMP Package for Power Network Optimization
pglib-opf
Benchmarks for the Optimal Power Flow Problem
AequilibraE
Free QGIS add-on for transportation modeling
User-Equilibrium-Solution
Use the Frank-Wolfe Algorithm to obtain the User Equillibrium Solution in urban traffic volume assignment
TrafficAssignment.jl
Julia package for finding traffic user equilibrium flow
matpower
MATPOWER – steady state power flow simulation and optimization for MATLAB and Octave
RTS-GMLC
Reliability Test System - Grid Modernization Lab Consortium
awesome-python-cn
Python资源大全中文版，包括：Web框架、网络爬虫、模板引擎、数据库、数据可视化、图片处理等，由伯乐在线持续更新。
MoSTScenario
Monaco SUMO Traffic (MoST) Scenario
Lagrangian-Relaxation-Algorithm-For-RRSLRP
To solve the RRS-LRP problem based on resource-space-time network, we developed a Lagrangian Relaxation Algorithm framework to decompose the origin problem into classic knapsack sub-problem and vehicle routing problem with recharging station (VRP-RS). The knapsack problem is solved by dynamic programming algorithm and a dynamic programming algorithm in RST network is developed to solve the VRP-RS. The dual problem of adjusting the Lagrangian multipliers was solved by an ascent method using sub-gradients approach. The algorithm framework is naturally suitable for parallel computing and distributed computing techniques due to the decomposition structure.
YALMIP
MATLAB toolbox for optimization modeling
RSM-1
Response surface experimentation and coding.
traci4matlab
An implementation of the Traffic Control Interface for Matlab
TransportationNetworks
Transportation Networks for Research
pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
sumo
SUMO is an open source, highly portable, microscopic and continuous road traffic simulation package designed to handle large road networks. It allows for intermodal simulation including pedestrians and comes with a large set of tools for scenario creation.
TrackerComponentLibrary
This is a collection of Matlab functions that are useful in the development of target tracking algorithms.
rsm
R package for response-surface methodology
LuSTScenario
Luxembourg SUMO Traffic (LuST) Scenario
GENCO-Investment-Strategies-by-Simulation-for-Demand-Side-Role-for-Investments-and-Capacity-Adequacy
This project will present an applied and game-like approach to simulating the load growth, investment decisions by two types of generation technologies, demand-price responsiveness, and reliability, of a test-case power system. The simulation begins as a 9-bus system with existing generation (3 generators) and transmission lines (8 lines). System topology can be viewed in a figure throughout the game with the yearly generation and load at each bus. In addition, dynamic color-coding is used to highlight transmission lines that exceed MVA ratings and highlight bus voltages that violate any limits. The winning objective of the player company (you) is to maximize his profit. Reliability can be tracked by viewing the N-1 generator and line contingencies every year, but this does not influence profits. There are two generation technologies used: coal and gas turbine. Each technology will have a similar competitor in the simulation. The competitor can bring down the market price and reduce the player’s profits significantly. The clock starts at T=0 in the investment game with a historical record of past prices and projected prices based on lack of investment. As time moves forward in yearly increments, the load, prices, investment costs, and other variables are adjusted to that of the player’s performance. The player has the opportunity to study various profitable and unprofitable investment alternatives each year of the simulation. If he invests at the right location, and in the right planning year, his company can make windfall profits. Competitors randomly participate in adding extra generation in random areas of the system based on the competition level settings. The challenge for the user is to study the effects of his investment decisions on market prices, reliability, and his profitability.
Response-surface-method-for-assessing-energy-production-from-geopressured-geothermal-reservoirs
Response surface method for assessing energy production from geopressured geothermal reservoirs
Convex.jl
A Julia package for disciplined convex programming
openstreetmap
Interface to OpenStreetMap (load maps, extract road connectivity, plot road network & find shortest path)
mepo
MEPO (Modular Energy Planning and Operations) model: A clustered integer formulation for electric power generation planning, unit commitment, and production cost modeling in GAMS/CPLEX.
STproxies
Short-term proxies for reliability management of power systems, written in Julia