A Neural Network Based Prediction System of Distributed Generation for the Management of Microgrids

In the modern scenario of smart-grids, the concept of virtual power plant (VPP) is undoubtedly a cornerstone for the smooth integration of renewable energy sources into existing energy systems with a high penetration level. A VPP is the aggregation of decentralized medium-scale power sources, including photovoltaic and wind power plants, combined heat and power units, as well as demand-responsive loads and storage systems, with a twofold objective. On one hand, VPP relieves the stability and dispatchability problems on the external smart grid since it can be operated on an individual basis, appearing as a single system on the whole. On the other hand, VPP improves flexibility coming from all the networked units and enable traders to enhance forecasting and trading programs of renewable energies. This paper proposes a novel distributed decentralized prediction method for the management of VPPs. The novelty of the proposed technique is to effectively combine the concepts of neural networks and machine learning with a distributed architecture that is suitable for the aggregation purposes of the VPP.

[1]  P. Siano,et al.  Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid , 2011, IEEE Transactions on Sustainable Energy.

[2]  Henrik Madsen,et al.  Multi-site solar power forecasting using gradient boosted regression trees , 2017 .

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  Massimo Panella,et al.  A Distributed Algorithm for the Cooperative Prediction of Power Production in PV Plants , 2019, IEEE Transactions on Energy Conversion.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Gonzalo Mateos,et al.  Modeling and Optimization for Big Data Analytics: (Statistical) learning tools for our era of data deluge , 2014, IEEE Signal Processing Magazine.

[7]  Alex Graves,et al.  DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.

[8]  Jaime Lloret,et al.  Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .

[9]  Dong Liu,et al.  Research on Stochastic Optimal Operation Strategy of Active Distribution Network Considering Intermittent Energy , 2017 .

[10]  Tamer Khatib,et al.  A review of islanding detection techniques for renewable distributed generation systems , 2013 .

[11]  Martin Hasler,et al.  Distributed machine learning in networks by consensus , 2014, Neurocomputing.

[12]  Anzar Mahmood,et al.  Prosumer based energy management and sharing in smart grid , 2018 .

[13]  Dianhui Wang,et al.  Distributed music classification using Random Vector Functional-Link nets , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[14]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[15]  Samy Bengio,et al.  Show and tell: A neural image caption generator , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Simone Scardapane,et al.  Distributed semi-supervised support vector machines , 2016, Neural Networks.

[17]  B. Hodge,et al.  The value of day-ahead solar power forecasting improvement , 2016 .

[18]  Massimo Panella,et al.  Prediction in Photovoltaic Power by Neural Networks , 2017 .

[19]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[20]  Thomas Morstyn,et al.  Incentivizing Prosumer Coalitions With Energy Management Using Cooperative Game Theory , 2019, IEEE Transactions on Power Systems.

[21]  Alex Graves,et al.  Generating Sequences With Recurrent Neural Networks , 2013, ArXiv.

[22]  Vassilios G. Agelidis,et al.  Unified Distributed Control for DC Microgrid Operating Modes , 2016, IEEE Transactions on Power Systems.

[23]  Nikos D. Hatziargyriou,et al.  Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities , 2007 .

[24]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[25]  Viktor K. Prasanna,et al.  Submitted to Ieee Transactions on Parallel and Distributed Systems 1 Match for the Prosumer Smart Grid the Algorithmics of Real-time Power Balance , 2022 .

[26]  Anastasios G. Bakirtzis,et al.  Optimal Offering Strategy of a Virtual Power Plant: A Stochastic Bi-Level Approach , 2016, IEEE Transactions on Smart Grid.

[27]  Rahmat-Allah Hooshmand,et al.  A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems , 2017 .

[28]  Simone Scardapane,et al.  Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Bernhard Sick,et al.  Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[30]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[31]  Zoran Obradovic,et al.  The distributed boosting algorithm , 2001, KDD '01.

[32]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[33]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[34]  Qing Zhao,et al.  Distributed Learning in Wireless Sensor Networks , 2007 .

[35]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[36]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[37]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[38]  Jinyu Wen,et al.  Determining the Minimal Power Capacity of Energy Storage to Accommodate Renewable Generation , 2017 .

[39]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[40]  Hermann Ney,et al.  LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.

[41]  Sonia Leva,et al.  Physical and hybrid methods comparison for the day ahead PV output power forecast , 2017 .