wsxbjx,Chi ZHANG,github

admmbo

An implementation of "ADMMBO, An ADMM Framework for Bayesian Optimization with Unknown Constraints''

aequilibrae

Free QGIS add-on for transportation modeling

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。

cdcs

An open-source MATLAB® ADMM solver for partially decomposable conic optimization programs.

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。

convex.jl

A Julia package for disciplined convex programming

ensest

ENSESt is a module that uses Evolution Strategies (ES) instead of Genetic Algorithms (GA) as Evolutionary Algorithm (EA) in the NSGA-II procedure for multi-objective optimization.

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.

integrated-energy-systems-with-caes

The optimal dispatch of CAES in the integrated energy systems

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.

lustscenario

Luxembourg SUMO Traffic (LuST) Scenario

matpower

MATPOWER – steady state power flow simulation and optimization for MATLAB and Octave

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.

mostscenario

Monaco SUMO Traffic (MoST) Scenario

openstreetmap

Interface to OpenStreetMap (load maps, extract road connectivity, plot road network & find shortest path)

pglib-opf

Benchmarks for the Optimal Power Flow Problem

pomegranate

Fast, flexible and easy to use probabilistic modelling in Python.

powermodels.jl

A Julia/JuMP Package for Power Network Optimization

response-surface-method-for-assessing-energy-production-from-geopressured-geothermal-reservoirs

Response surface method for assessing energy production from geopressured geothermal reservoirs

rsm

R package for response-surface methodology

rsm-1

Response surface experimentation and coding.

rts-gmlc

Reliability Test System - Grid Modernization Lab Consortium

search-engine

A search-engine for document searching

stproxies

Short-term proxies for reliability management of power systems, written in Julia

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.

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