@inproceedings{24540,
  abstract     = {{With its growing population and industrialization, DREs, and solar technologies in particular, provide a 
sustainable means of bridging the current energy deficit in Africa, increasing supply reliability and meeting future 
demand. Data acquisition and data management systems allow real time monitoring and control of energy systems as 
well as performance analysis. However commercial data acquisition systems often have cost implications that are 
prohibitive for small PV systems and installations in developing countries.
In this paper, a multi-user, multi-purpose microgrid database system is designed and implemented. MAVOWATT 
270 power quality analyzers by GOSSEN METRAWATT, raspberry pi modules and sensors are used for measuring, 
recording and storing electrical and meteorological data in East Africa. Socio-economic data is also stored in the
database. The designed system employs open source software and hardware solutions which are best suited to 
developing regions like East Africa due to the lower cost implications.
The expected results promise a comprehensive database covering different electro-technical and socio-economic 
parameters useful for optimal design of microgrid systems.}},
  author       = {{Kakande, Josephine Nakato and Philipo, Godiana Hagile and Krauter, Stefan}},
  booktitle    = {{Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021)}},
  isbn         = {{3-936338-78-7}},
  keywords     = {{Art-D, Afrika, Demand side management, MySQL, Raspberry pi, Data acquisition}},
  pages        = {{1505--1510}},
  title        = {{{Load Data Acquisition in Rural East Africa for the Layout of Microgrids and Demand–Side–Management Measures}}},
  doi          = {{10.4229/EUPVSEC20212021-6BV.5.38}},
  year         = {{2021}},
}

@inproceedings{19393,
  abstract     = {{To provide a simple instrument to operate residential Load-Shifting or Demand-Side-Management 
systems, the measurement of the actual grid frequency seems to be an appropriate method. Due to the present 
inflexibility and the lack of sufficient throttling capabilities of lignite and nuclear power plants, a surplus of 
electricity generation occurs during periods of high wind and solar power generation. While the specific CO2-
emission is decreasing then ‒ due to the increased share of Renewables, the grid frequency is increasing (to a certain 
limit). Using the grid frequency as an indicator to switch-on and off certain loads (loads that do not require power 
permanently (e.g. dishwashers, washing machines, dryers, fridges and freezers, heaters) could provide a simple, 
inexpensive demand-side management indicator to lower specific CO2‒emssions and costs (if a dynamic 
consumption tariff is available). To check the truthfulness of that hypothesis, the grid and frequency data of the 
German grid of the year 2018 have been collected and a the correlation between grid frequency, power surplus, share 
of renewables vs. CO2-contents and price at the European energy exchange (EEX) have been calculated. The results 
show: Correlation between frequency and share of renewables is quite low (r = 0.155) due to the fact that primary 
grid control quickly compensates deviations from the 50 Hz nominal frequency. There is a good anti-correlation (r = -
0.687) between the EEX‒prices and the share of renewables in the grid. Over the years, correlation between 
electricity trading prices (EEX) and CO2 emissions is quite good (r =0.665), within the one year (2018) that 
correlation almost doesn’t exist, possibly due to the inflexibility of the bulky lignite power plants that even operate at 
negative prices. 
}},
  author       = {{Krauter, Stefan and Zhang, L.}},
  booktitle    = {{Proceedings of the 37th European Photovoltaic Solar Energy Conference, 07 - 11 September 2020.}},
  issn         = {{	3-936338-73-6}},
  keywords     = {{Keywords: Load-Shifting, Demand-Side-Management, DSM, grid frequency, EEX, electricity trading prices, renewable share, flexibility, emissions, CO2}},
  location     = {{online}},
  pages        = {{1815 -- 1817}},
  title        = {{{Triggering Demand‒Side‒Management: Correlation of electricity prices, share of renewables, CO2‒contents, and grid‒frequency in the German electricity grid.}}},
  doi          = {{10.4229/EUPVSEC20202020-6BV.5.9}},
  year         = {{2020}},
}