Comparison of 7 measures to integrate PV in the low voltage grid, using stochastic irradiance data Paris, Tuesday, 1 October, IEA PVPS & SHC Workshop
About me _ 2003: Matura in Solothurn (CH) _ 2003 2008: Electrical Engineering and Information Technology, ETH (Zürich) _ 2008 present: Project manager for Photovoltaic Systems, Enecolo AG / Basler & Hofmann AG Christof Bucher, Basler & Hofmann _ 2010 present: Ph.D. student at ETH, Distribution Grid Analysis and Simulation with Photovoltaics 2
Table of Contents _ Introduction, Project Background _ Methodology _ Results of 7 Simulation Methods _ Monte Carlo Method _ Conclusion and Further Developments 3
Introduction _ DiGASP: Distribution Grid Analysis and Simulation with Photovoltaics. _ Aim of the project: Find out, how much PV can be integrated into a distribution power system. _ Compare several different methods to increase the PV hosting capacity. 4
Project Partners _ ETH Zurich _ Oldenburg University _ AIT (Austrian Institute of Technology) _ ewz (DSO of Zürich) _ SFOE (Swiss Federal Office of Energy) 5
Methodology: Irradiance Generator _ Randomisation of a historic data set _ Creation of one minute data: Skartveit and Olseth method, expected intra-hour standard deviation as a function of the standard deviation of three hours 6
Methodology: Load Profile Generator _ Flow chart of the load profile generator 7
Methodology: Load Profile Generator _ Load profile generated _ Upper graph: load profile _ Lower graph: power factor cos(φ) Source: Generation of Domestic Load Profiles an Adaptive Top-Down Approach 8
Methodology: Simulation Procedure _ Flow chart of the simulation procedure 9
Feeder Methodology: Test Feeder Node Line 1 2 9 10 _ Ten nodes _ n = 1...20 households connected to each node _ Design: minimal grid to supply loads 10
Methodology: Compared Measures 1. DACHCZ: no measures 2. Correlation with load 3. RPC: Reactive power control 4. APC: Active power curtailment 5. Orientation of PV-modules 6. Storage 7. DSM: Demand side management 8. OLTC: On Load Tap Changer transformer 11
1. & 2. DACHCZ & Correlation with Load _ Weakest possible grid has a PV hosting capacity of ~20% - 50% _ Considering loads results in up to 1.5 times more PV HC 12
1. & 2. DACHCZ & Correlation with Load Losses in the distribution grid _ Minimising losses @ 25% penetration _ Valid for all investigated cases! 13
3. Reactive Power Control (RPC) _ Highly dependent on grid properties (R/X-ratio) _ No general statement possible _ From almost no effect to doubling HC 14
4. Active Power Curtailment (APC) _ Sacrifice 3% energy to increase the PV-hosting capacity by 50%. 15
4. APC Optimised APC _ Save 15-20% APC losses _ valid for all multi-node systems (without theoretical proof) Source: DiGASP 16
5. Different Orientation of PV Generator _ Benefit of different orientation is only small. _ No benefit for tilt angles smaller than 30 17
6. Storage _ 250 % more PV in the grid with a storage of 4 nominal operation hours 18
7. Demand Side Management (DSM) _ Assumption: water boiler in Switzerland _ Results highly dependent on assumptions _ Design your own results 19
8. OLTC Case Study ewz _ 3% TC-ratio doubles PV hosting capacity _ Test for current limitations! Source: DiGASP 20
Monte Carlo Simulation: DACHCZ _ Only PV, no loads _ Distribution of maximum voltage during one summer day 21
Monte Carlo Simulation: Correlation _ PV and loads _ Distribution of maximum voltage during one summer day 22
Conclusion _ A simulation tool to simulate distribution power grids with PV and loads based on stochastic input data has been presented. _ Benefits of various scenarios could be quantified. 23
Further Developments _ Validation of results with measured household load patterns. _ Publication of case study 24
Christof Bucher, Project Manager at Basler & Hofmann and Ph.D. student at ETH Zürich christof.bucher@baslerhofmann.ch, +41 44 387 13 80 Thank you for your interest and attention! Christof Bucher 25