electricity-prices

The open source repository for Electricity Maps App and data parsers that enables a real-time visualisation of the CO2 emissions of electricity consumption

Integration for Home Assistant to fetch day ahead energy prices from European countries via ENTSO-e Transparency Platform

Transforms electricity spot price into thermostat control signal. Home Assistant custom component.

Simple aio library to download Spanish electricity hourly prices (PVPC) from esios.ree.es

Harvard CS109: A predictive model for electricity prices in the midwest, and more specifically, the prices of nodes where nuclear plants are located

Python package to import data from OMIE (Iberian Peninsula's Electricity Market Operator): https://www.omie.es/

Dynamic electricity carbon emission factors and prices for Europe

Home Assistant Component to pull the latest energy prices from Amber Electric

Predicts the CAISO day-ahead market hourly prices using different forecasting methods including ARIMA and LSTM.

Proposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers.

Parses electric utility companies (Cez)

ENTSO-e transparency platform API Client built for Deno. Complete. Easy to use. Minimal.

A Geo-Distributed Cloud Simulator.

Dynamic Grid Prices for Ecopower

Allows you to do automation based on the current electricity price

An interactive and up-to-date map for exploring electricity prices in Europe

Dockerized Deno application that gets and caches electricity prices (spot price) from ENTSO-e.

This repo features a deep reinforcement learning Home Energy Management System for cost-effective heating. It optimizes energy consumption using advanced algorithms, outperforming an optimal linear programming solution.

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.

Electrical billing for small consumers in Spain using PVPC