Case: PV generation and Electricity demand at the Johan Cruijff Arena
Author: Jos Warmerdam
Date: 20-10-2020
Used in education: at AUAS: Engineering / Sustainable Energy Systems
Form: assignment
Time needed: instruction 1 hour; student work 24-48; presentations 2-4 hours Level: bachelor and up with knowledge of Python
ECTS credits: 1-2 ECTS (1 ECTS = 28 hour) Introduction
PV systems are used more and more. Not always is it possible to install them in the optimal direction for maximum energy output over the year. At the Johan Cruijff ArenA the PV panels are placed all around the roof in all possible directions. Panels oriented to the north will have a lower energy gain than those oriented to the south. The 42 panel groups are connected to 8 electricity meters. Of these 8 energy meters monthly kWh produced are available.
The first assignment is to calculate the energy gains of the 42 panel groups, and connect these in the correct way with the 8 energy meter readings, so simulated data is in accordance with measured data.
Of the year 2017 there are also main electricity meter readings available for every quarter of an hour.
A problem with these readings is that only absolute values are given. When electricity is taken of the grid this is a positive reading, but when there is a surplus of solar energy and electricity is delivered to the grid, this is also a positive reading. To see the effect on the electricity demand of future energy measures, and to use the Seev4-City detailed CO2 savings calculation with the electricity mix of the grid, it is necessary to know the real electricity demand of the building.
The second assignment is to use the calculations of the first assignment to separate the 15 minute electricity meter readings in that for real building demand and for PV production.
This document first gives information for teachers (learning goals, possible activities, time needed, further reading), followed by the assignment for students.
Notes for teachers:
This assignment can be used from bachelor level (3de year or higher) and up. This assignment can be used after some lessons on the energy system.
Students must have some background in programming and experience with programming in python.
Learning goals
- Pursue learning programming in Python -
- Understand the topology of the local low voltage distribution grid.
Possible activities:
- Students work on it independently
- In group: Have discussion and presentations in class
- In group: Put all results on the whiteboard and have them discuss all results from all groups
This case study is an extension of the Seev4-City basic Python lessons considering the power flow in a micro grid with 5 houses.
Time needed for assignment 1:
1 hour Introduction lessons on energy system and python coding (student + teacher) 8-16 hour work on assignment
2 hour presentations and discussion
Time needed for assignment 2:
1 hour Introduction lessons on energy system and python coding (student + teacher) 8-16 hour work on assignment
2 hour presentations and discussion
Student instruction:
Assignment 1: PV generation
- Make a table of the 42 PV panel groups on the roof of the JC ArenA, with amount of panels per groep;
o See overview picture below: in the inner ring are the 42 panel groups, in the outer ring the 8 electricity production meters.
o
- Each PV panel has an efficiency of 11.6%. North = orientation 0o, east = 90o.
Calculate in Python with the energy gain of each PV panel group with the PV_Module function. Use the KNMI meteodata file of 2017.
- Assign the 42 panel groups to the 8 electricity production meters in a logical way, such that the measured and calculated monthly and yearly energy PV productions have the least difference.
Files needed:
- Meteo irradiation data: KNMI_2017_hourly.txt - Measured PV production: Zonnepanelen2017.xlsx - python example code: Arena_Assignment_1.py
Assignment 2: Electricity demand
- Use the PV generation per production meter and per quarter of an hour of assignment 1.
- Use the PV installation scheme ArenA to assign the 8 production meters to the 4 trafos (A2, A3, B4, C2)
- Import the metered data of the 4 trafos (15 minute intervals).
- Use the PV production data and the trafo metered data to calculate the demand of the building, and the surplus to the grid, and export this to an excel file.
Files needed:
- Installation schema: PV installation scheme ArenA
- Metered data of the 4 trafos: Arena verbruik 2017 kwartierdata v20okt.xlsx - Python example code: ArenA_assignment_2.py
