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An interdisciplinary approach on efficient virtual microgrid to virtual microgrid energy balancing incorporating data preprocessing techniques

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Abstract

A way to improve energy management is to perform balancing both at the Peer-to-peer (P2P) level and then at the Virtual Microgrid-to-Virtual Microgrid (VMG2VMG) level, while considering the intermittency of available Renewable Energy Source (RES). This paper proposes an interdisciplinary analytics-based approach for the formation of VMGs addressing energy balancing. Our approach incorporates Computer Science methods to address an Energy sector problem, utilizing data preprocessing techniques and Machine Learning concepts. It features P2P balancing, where each peer is a prosumer perceived as an individual entity, and Virtual Microgrids (VMGs) as clusters of peers. We conducted several simulations utilizing clustering and binning algorithms for preprocessing energy data. Our approach offers options for generating VMGs of prosumers, prior to using a customized Exhaustive brute-force Balancing Algorithm (EBA). EBA performs balancing at the cluster-to-cluster level, perceived as VMG2VMG balancing. To that end, the study simulates on data from 94 prosumers, and reports outcomes, biases, and prospects for scaling up and expanding this work. Finally, this paper outlines potential ideal usages for the approach, either standalone or integrated with other toolkits and technologies.

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Availability of data and material (data transparency)

Data partly available in the appendices due to confidentiality clauses for the ongoing eDREAM project.

Code availability (software application or custom code)

N/A due to confidentiality clauses for the ongoing eDREAM project.

Notes

  1. In the simulations, historical data are utilized to have a realistic measure of outcomes and balancing goals.

Abbreviations

DR:

Demand response

P2P:

Peer-to-peer

PV:

Photovoltaic

RES:

Renewable energy source

DSO:

Distribution system operator

DER:

Distributed energy resource

DR:

Demand response

MG:

Microgrid

VMG:

Virtual microgrid

VMG2VMG:

VMG to VMG

VPP:

Virtual power plants

EBA:

Exhaustive balancing algorithm

P2G:

Peer-to-Grid

SG:

Smart grid

AI:

Artificial intelligence

ML:

Machine learning

ToU:

Time of use

RTP:

Real time pricing

CPP:

Critical peak pricing

KPI:

Key performance indicator

nZEB:

Nearly zero energy building

TAE:

Total active energy

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Acknowledgments

This work has been conducted within the eDREAM project Grant number 774478, co-funded by the European Commission as part of the H2020 Framework Programme (H2020-LCE-2017-SGS).

Funding

The project eDREAM has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N. 774478.

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Authors and Affiliations

  1. Information Technologies Institute, Centre of Research & Technology – Hellas, 6th km Harilaou - Thermi, 57001, Thessaloniki, Greece

    Paraskevas Koukaras, Christos Tjortjis, Paschalis Gkaidatzis, Napoleon Bezas, Dimosthenis Ioannidis & Dimitrios Tzovaras

  2. School of Science and Technology, International Hellenic University, 14th km Thessaloniki – N. Moudania, Thermi, 57001, Thessaloniki, Greece

    Paraskevas Koukaras & Christos Tjortjis

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  1. Paraskevas Koukaras
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Correspondence to Christos Tjortjis.

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Appendices

Appendix 1: k-means clustering on raw data for different timestamps

On the following figures, different coloring is applied for marked entries that were positioned to different clusters.

figure a

Appendix 2: Clustering and balancing on a P2P level example

Each table refers to the figure on its right, while different coloring is applied for marked entries that were positioned to different clusters.

figure b
figure c
figure d
figure e

Appendix 3: Binning overview on raw data on a specific timestamp

CUT: #create Bins by pandas.cut (binning) [47] specifically define the bin value edges.

figure f

QCUT: create Bins by pandas.qcut (binning) [47] "Quantile-based discretization function" equally sized bins.

figure g

Appendix 4: CUT and QCUT methods for “sunlight” timestamps’ example

CUT method output is depicted on the left side figures, while QCUT method output is depicted on the right-side figures.

figure h
figure i

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Koukaras, P., Tjortjis, C., Gkaidatzis, P. et al. An interdisciplinary approach on efficient virtual microgrid to virtual microgrid energy balancing incorporating data preprocessing techniques. Computing 104, 209–250 (2022). https://doi.org/10.1007/s00607-021-00929-7

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