Sizing HPand Storage To Maximize Self Consumption Test System Model Implementation
SizingHPandStorageToMaximizeSelfConsumption test system model implementation - title
Sizing HPand Storage To Maximize Self Consumption Test System Model Implementation
SizingHPandStorageToMaximizeSelfConsumption test system model implementation - overview
Test System Model Implementation Overview
Author / organization: Sami Ghazouani and Mickael Rousset / EDF
Implemented Component Models / Implementation Tool:
Implementation Approach: Monolithic
Test Parameters of Test System Model :
Test paprameters of Greenhouse, Residential building, Tertiary building, Solar photovoltaic panel, further details see D4-3 Description of optimization strategies.pdf
Outputs/Measured Parameters:
Output parameters of District heating network, Greenhouse utility, Buildings utility, Heat pump, Storage tank, further details see D4-3 Description of optimization strategies.pdf
Initial State of Test System Model: -
Temporal Resolution:
For each consumer (residential and tertiary buildings, greenhouse) and producer (PV) an hourly data set over one year is given. Out of this data set, averaged generic days for 3 significant periods of time are extracted (winter, mid-season, summer). The resulting data sets have an hourly resolution
Related System Configuration
Collectopia System Configuration
Related Test Case
Sizing HPand Storage To Maximize Self Consumptionn
Related Use Case
SizingHPandStorageToMaximizeSelfConsumption test system model implementation - short description
Short Description
This system aims to optimize the design of a district’s infrastructure in order maximize the use of renewable energy sources – such as waste heat from a greenhouse and solar PV – in the energy mix of the district’s energy supply (thermal and electrical).
In order to find the optimal storage tank and heat pump designs to maximize self-consumption and heat recovery, data for a full year is required. However, given the complexity of the optimization model, it in infeasible to use a whole year worth of data. Therefore, the data is aggregated into a reduced set of relevant, generic days that most accurately represent the general behaviour of each consumer and renewable energy production system (greenhouse and PV).
The more generic days are used, the bigger the problem gets (which potentially implies longer computational times), but the solution gets more accurate. The objective is to evaluate the relevance of using 3 relevant generic days per year.
Media Gallery
SizingHPandStorageToMaximizeSelfConsumption test system model implementation - source code
Source Code of the Implemented Result Object
Please contact:
Name: Sami Ghazouani and Mickael Rousset / EDF