# Effect of Sampling Rate on Photovoltaic Self-Consumption in Load Shifting Simulations

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Related Work

#### 2.1. Sampling Rate Influence

#### 2.2. Load Shifting

#### 2.3. Event Detection

#### 2.4. Our Focus

## 3. Materials and Methods

#### 3.1. Aggregated Power Consumption of Several Appliances

#### 3.2. True Energy Self-Consumption

#### 3.3. Estimated Energy Self-Consumption

#### 3.4. Increasing Self-Consumption Using Load Shifting

#### 3.5. Activation Detection

#### 3.6. Load Shifting Algorithms

#### 3.6.1. Naive Load Shifting

#### 3.6.2. Optimal Load Shifting

#### 3.7. Simulation Workflow

## 4. Datasets and Pre-Processing

#### 4.1. Electrical Load Data

#### 4.2. Irradiance Data

#### 4.3. PV Simulation

#### 4.4. Combination and Resampling

#### 4.5. Availability of Data and Materials

- The Python code used to generate the results is available from https://github.com/ihomelab/effect-of-sampling-rate-on-PV-self-consumption
- REFIT: Electrical Load Measurements (Cleaned) can be obtained from https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements-cleaned
- The BSRN irradiance data can be obtained from PANGAEA as monthly files (example for December 2015: http://doi.pangaea.de/10.1594/PANGAEA.857816)
- The remaining weather data is available here: https://catalogue.ceda.ac.uk/uuid/6c441aea187b44819b9e929e575b0d7e

## 5. Results

#### 5.1. Theoretical Upper Bound for Improvements

#### 5.2. Results at Fastest Sampling Rate

#### 5.3. Influence of Sampling Rate

## 6. Discussion

#### 6.1. Comparison With Prior Research

#### 6.2. Strength of Influence of Sampling Rate

#### 6.3. Variation in Prior Research Considering Influence of Sampling Rate

#### 6.4. Further Considerations

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

ModelChain:

name: None

orientation_strategy: south_at_latitude_tilt

clearsky_model: ineichen

transposition_model: haydavies

solar_position_method: nrel_numpy

airmass_model: kastenyoung1989

dc_model: sapm

ac_model: snlinverter

aoi_model: sapm_aoi_loss

spectral_model: sapm_spectral_loss

temperature_model: sapm_temp

losses_model: no_extra_losses

## References

- Masson, G. Snapshot of Global PV Markets 2020; Snapshot IEA-PVPS T1-37: 2020; International Energy Agency IEA: Paris, France, 2020.
- Luthander, R.; Widén, J.; Nilsson, D.; Palm, J. Photovoltaic self-consumption in buildings: A review. Appl. Energy
**2015**, 142, 80–94. [Google Scholar] [CrossRef][Green Version] - Siano, P. Demand response and smart grids—A survey. Renew. Sustain. Energy Rev.
**2014**, 30, 461–478. [Google Scholar] [CrossRef] - Widén, J. Improved photovoltaic self-consumption with appliance scheduling in 200 single-family buildings. Appl. Energy
**2014**, 126, 199–212. [Google Scholar] [CrossRef] - Staats, M.; de Boer-Meulman, P.; van Sark, W. Experimental determination of demand side management potential of wet appliances in the Netherlands. Sustain. Energy Grids Netw.
**2017**, 9, 80–94. [Google Scholar] [CrossRef][Green Version] - Beck, T.; Kondziella, H.; Huard, G.; Bruckner, T. Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems. Appl. Energy
**2016**, 173, 331–342. [Google Scholar] [CrossRef] - Braun, M.; Büdenbender, K.; Landau, M.; Sauer, D.U.; Magnor, D.; Schmiegel, A.U. Charakterisierung von netzgekoppelten PV-Batterie-Systemen. In Proceedings of the 2010 25th Symposium Photovoltaische Solarenergie, Bad Staffelstein, Germany, 3–5 March 2010. [Google Scholar]
- Wyrsch, N.; Riesen, Y.; Ballif, C. Effect of the Fluctuations of PV Production and Electricity Demand on the PV Electricity Self-Consumption. In Proceedings of the 28th European Photovoltaic Solar Energy Conference and Exhibition, Paris, France, 30 September–4 October 2013; pp. 4322–4324, IMT-NE Number:733. [Google Scholar] [CrossRef]
- McKenna, E.; Pless, J.; Darby, S.J. Solar photovoltaic self-consumption in the UK residential sector: New estimates from a smart grid demonstration project. Energy Policy
**2018**, 118, 482–491. [Google Scholar] [CrossRef] - Leicester, P.A. Probabilistic analysis of solar photovoltaic self-consumption using Bayesian network models. IET Renew. Power Gener.
**2016**, 10, 448–455. [Google Scholar] [CrossRef][Green Version] - Swain, K.P.; De, M. Analysis of Effectiveness of Flexible Load Shifting Order on Optimum DSM. In Proceedings of the 2017 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), Uttarkhand, India, 18–19 December 2017; pp. 141–144. [Google Scholar]
- Ran, X.; Leng, S. Enhanced Robust Index Model for Load Scheduling of a Home Energy Local Network with a Load Shifting Strategy. IEEE Access
**2019**, 7, 19943–19953. [Google Scholar] [CrossRef] - Liao, Z.; Gu, X. Research on the peak load shifting plan optimization based on TABU search algorithm. In Proceedings of the 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Kowloon, Hong Kong, 7–10 December 2014; pp. 1–5. [Google Scholar]
- Balakumar, P.; Sathiya, S. Demand side management in smart grid using load shifting technique. In Proceedings of the 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, India, 27–28 April 2017; pp. 1–6. [Google Scholar]
- Lakshmanan, V.; Marinelli, M.; Kosek, A.M. Thermostat controlled loads flexibility assessment for enabling load shifting—An experimental proof in a low voltage grid. In Proceedings of the 2017 52nd International Universities Power Engineering Conference (UPEC), Crete, Greece, 28–31 August 2017; pp. 1–6. [Google Scholar]
- Yasin, A.; Khan, S.A. Unsupervised Event Detection and On-Off Pairing Approach Applied to NILM. In Proceedings of the 2018 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 19–21 December 2018; pp. 123–128. [Google Scholar]
- You, Z.; Raich, R.; Huang, Y. An inference framework for detection of home appliance activation from voltage measurements. In Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy, 4–9 May 2014; pp. 6033–6037. [Google Scholar]
- Murray, D.; Stankovic, L.; Stankovic, V. An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study. Sci. Data
**2017**, 4, 160122. [Google Scholar] [CrossRef][Green Version] - UK Census. 2011. Available online: http://www.nomisweb.co.uk/census/2011 (accessed on 1 October 2020).
- Driemel, A.; Augustine, J.; Behrens, K.; Colle, S.; Cox, C.; Cuevas-Agulló, E.; Denn, F.M.; Duprat, T.; Fukuda, M.; Grobe, H.; et al. Baseline Surface Radiation Network (BSRN): Structure and data description (1992–2017). Earth Syst. Sci. Data
**2018**, 10, 1491–1501. [Google Scholar] [CrossRef][Green Version] - Tamlyn, J. Basic Measurements of Radiation at Station Camborne (2014-01 to 2015-12, 24 Datasets); Met Office: Exeter, UK; PANGAEA: Exeter, UK, 2016. [Google Scholar]
- Met Office. MIDAS Open: UK Hourly Weather Observation Data, v201908; Centre for Environmental Data Analysis: Chilton, UK, 2019. [Google Scholar] [CrossRef]
- Holmgren, W.F.; Hansen, C.W.; Mikofski, M. pvlib python: A python package for modeling solar energy systems. J. Open Source Softw.
**2018**, 3, 884. [Google Scholar] [CrossRef][Green Version] - System Advisor Model Version 2020.2.29; SAM Source Code; National Renewable Energy Laboratory: Golden, CO, USA, 2020. Available online: https://github.com/NREL/ssc (accessed on 1 June 2020).
- Tounquet, F.; Alaton, C. Benchmarking Smart Metering Deployment in the EU-28; Publication Office of the EU: Luxembourg, 2020. [Google Scholar] [CrossRef]
- Pellegrini, M. Short-term load demand forecasting in Smart Grids using support vector regression. In Proceedings of the 2015 IEEE 1st International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI), Torino, Italy, 16–18 September 2015; pp. 264–268. [Google Scholar]

**Figure 1.**Illustration of our scenario. A house with photovoltaic (PV) and appliances, see (

**a**), produces load and PV curves illustrated in (

**b**). The dashed areas in (

**a**,

**b**) represent the amount of energy self-consumption. The shifted load in (

**b**) increases self-consumption with respect to (

**a**).

**Figure 2.**Illustration of the simulation workflow. The load and PV data define the baseline for self-consumption. Activations are extracted from load data and used to feed two load shifting algorithms. After load shifting, the improvement is calculated.

**Figure 3.**Data availability of the houses from REFIT dataset [18] selected for this study. Gaps in the lines correspond to periods where data were unavailable for more than a quarter day. The data range selected for this study is marked with a blue background. See text for details on excluded houses.

**Figure 4.**Self-consumption upper bound for each appliance and household. Values are the difference between the upper bound and the self-consumption before load shifting. The orange dots mark the mean.

**Figure 5.**Distributions of the improvements from load shifting grouped by load shifting algorithm and sampling rate. Blue and orange areas relate to the naive and optimal algorithm respectively. The distribution values are normalized by the corresponding upper bound.

**Figure 6.**Mean improvement from load shifting grouped by machine type: (

**a**) Naive shifting; (

**b**) Optimal shifting.

**Table 1.**Mean and median results for various sampling rates. The first column contains the sampling period in minutes. Columns marked with (a) contain absolute improvements over the self-consumption before load shifting, measured in Wh/day. Columns marked with (r) contain relative values, which are normalized with the upper bound improvement.

Upper Bound (a) | Naive (a) | Optimal (a) | Naive (r) | Optimal (r) | ||||||
---|---|---|---|---|---|---|---|---|---|---|

Mean | Median | Mean | Median | Mean | Median | Mean | Median | Mean | Median | |

1 | 325 | 253 | 36.6 | 19.2 | 103 | 64.4 | 0.115 | 0.114 | 0.304 | 0.324 |

2 | 322 | 252 | 37.6 | 19.6 | 125 | 101 | 0.119 | 0.119 | 0.389 | 0.410 |

3 | 320 | 251 | 38.6 | 19.9 | 131 | 109 | 0.123 | 0.124 | 0.411 | 0.433 |

4 | 318 | 249 | 39.0 | 20.9 | 135 | 113 | 0.125 | 0.122 | 0.426 | 0.452 |

5 | 316 | 247 | 40.0 | 22.8 | 139 | 115 | 0.129 | 0.134 | 0.441 | 0.467 |

6 | 314 | 245 | 40.5 | 23.0 | 140 | 115 | 0.132 | 0.131 | 0.445 | 0.473 |

7 | 312 | 242 | 41.0 | 23.5 | 143 | 117 | 0.135 | 0.114 | 0.456 | 0.484 |

8 | 310 | 240 | 41.7 | 24.9 | 145 | 117 | 0.136 | 0.118 | 0.467 | 0.488 |

9 | 309 | 238 | 42.5 | 23.8 | 148 | 120 | 0.139 | 0.117 | 0.476 | 0.501 |

10 | 307 | 235 | 42.6 | 24.2 | 149 | 114 | 0.140 | 0.121 | 0.489 | 0.515 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Voinov, P.; Huber, P.; Calatroni, A.; Rumsch, A.; Paice, A.
Effect of Sampling Rate on Photovoltaic Self-Consumption in Load Shifting Simulations. *Energies* **2020**, *13*, 5393.
https://doi.org/10.3390/en13205393

**AMA Style**

Voinov P, Huber P, Calatroni A, Rumsch A, Paice A.
Effect of Sampling Rate on Photovoltaic Self-Consumption in Load Shifting Simulations. *Energies*. 2020; 13(20):5393.
https://doi.org/10.3390/en13205393

**Chicago/Turabian Style**

Voinov, Philippe, Patrick Huber, Alberto Calatroni, Andreas Rumsch, and Andrew Paice.
2020. "Effect of Sampling Rate on Photovoltaic Self-Consumption in Load Shifting Simulations" *Energies* 13, no. 20: 5393.
https://doi.org/10.3390/en13205393