Next Article in Journal
A Hybrid Non-Convex Compressed Sensing Approach for Array Diagnosis Using Sparse Promoting Norm with Perturbation Technique
Next Article in Special Issue
An Advanced Maximum Power Point Tracking Method for Photovoltaic Systems by Using Variable Universe Fuzzy Logic Control Considering Temperature Variability
Previous Article in Journal
Wide-Angle Beam Scanning Leaky-Wave Antenna Using Spoof Surface Plasmon Polaritons Structure
Previous Article in Special Issue
Virtual Inertia-Based Control Strategy of Two-Stage Photovoltaic Inverters for Frequency Support in Islanded Micro-Grid
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessFeature PaperArticle
Electronics 2018, 7(12), 349;

A Heuristics-Based Policy to Reduce the Curtailment of Solar-Power Generation Empowered by Energy-Storage Systems

Faculty of Computer Science and Mathematics, University of Passau, Innstrasse 43, 94032 Passau, Germany
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 17 November 2018 / Accepted: 20 November 2018 / Published: 24 November 2018
(This article belongs to the Special Issue Grid Connected Photovoltaic Systems)
Full-Text   |   PDF [1795 KB, uploaded 5 December 2018]   |  


Renewable energy sources, on one hand, are environmentally friendly, but on the other, they suffer from volatility in power generation, which endangers power-grid stability. A viable solution to circumvent the intermittent behavior of renewables is the usage of energy-storage systems. In this paper, we study the energy management of a proof-of-concept system consisting of solar panels, energy-storage systems, a power grid, and household loads. Using neural networks, we identify the most relevant parameters impacting the power generation of solar panels, and then train the corresponding network to derive forecasts. We also go one step further, and propose a heuristics-based energy-management policy for the purpose of reducing curtailments. We show that our proposed policy outperforms the naive policy by 8%, which does not consider any power-generation forecasts. View Full-Text
Keywords: neural network; solar-power generation predictions; energy-management policy neural network; solar-power generation predictions; energy-management policy

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Basmadjian, R.; De Meer, H. A Heuristics-Based Policy to Reduce the Curtailment of Solar-Power Generation Empowered by Energy-Storage Systems. Electronics 2018, 7, 349.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top