LMES_COLLECTOPIA_seasonal storage test system model implemntation - title

LMES COLLECTOPIA Seasonal Storage Test System Model Implementation

LMES_COLLECTOPIA_seasonal storage test system model implemntation - overview

Test System Model Implementation Overview

Author / organization: Stephan Seidelt, Daniel Fehrenbach / EIFER

Implemented Component Models / Implementation Tool:

Implementation Approach: Monolithic

Test Parameters of Test System Model :

  • Cost parameters, heat and electricity demands as well as CO2 grid factor from France are used

Outputs/Measured Parameters:

  • Overall system costs
  • Capacities of the technologies

Initial State of Test System Model: -

Temporal Resolution:

  • Hourly

Related System Configuration

Collectopia RES system configuration

Related Test Case

LMES_COLLECTOPIA_seasonalstorage

Related Use Case

UC14 – Optimal design of CHP and heat storage

LMES_COLLECTOPIA_seasonal storage test system model implemntation - Short description

Short Description

A Local multi energy system (LMES) can consist of many different energy production technologies, such as gas-driven boilers and CHPs, HPs, PV panels, DHN, electricity and heat storage facilities as well as imports and exports of electricity from the grid. The electricity and heat demands of the buildings located in a LMES have to be met at all times, either by self-production or by imports from the external grid.

The overall costs of the LMES are the discounted net total of all CAPEX, OPEX, fuel costs, electricity imports as well as revenues from exports. In addition, CO2 emissions can be considered with the help of a CO2 price. A least-cost planning approach determines the configuration of the LMES with the lowest overall costs.

LMES_COLLECTOPIA_seasonal storage test system model implemntation - source code

Source Code of the Implemented Result Object

Please contact:

Name: Stephan Seidelt, Daniel Fehrenbach / EIFER

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LMES_COLLECTOPIA_seasonal storage test system model implemntation - evaluation

Evaluation of System State and Test Signal

The evolution of the system state is determined by the simulation (i.e., system optimization over the full time range).