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Energies
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Forecasting and Management Systems for Smart Grid Applications
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Forecasting and Management Systems for Smart Grid Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (20 August 2021) | Viewed by 8055

Special Issue Editors

Prof. Dr. William Holderbaum

E-Mail Website
Guest Editor
Mathematics and Engineering, University of Reading, Reading, Berkshire RG6 6AY, UK
Interests: power management systems; control theory; hybrid dynamical systems; optimal control; Hamiltonian systems under Lie group; control and management of storage; wireless power transfer; remote monitoring and sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Stephen Haben

E-Mail Website
Guest Editor
Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK
Interests: probabilistic forecasting; data science; machine learning; control; energy analytics

Special Issue Information

Dear Colleagues,

The move towards a low carbon economy brings opportunities and challenges for energy systems and electricity networks, especially at the local level. Low carbon technologies such as photovoltaics, heat pumps, and electric vehicles will produce larger and more volatile demands on the network and increase the likelihood of violating the capacity of the network. At the same time, storage devices and energy management systems can provide opportunities to take advantage of the flexibilities in the network, utilise renewable energy, smooth demand, and encourage energy efficiency.

In many of these applications, a forecast will be essential to optimise the outcomes of the management systems and necessary for a true smart electricity grid. They will enable more optimal planning and help network operators and aggregators better anticipate and manage network disruption such as high PV generator or large spikes in demand.

This issue will be interested in all applications which use forecasting to enable, or support, management systems in low voltage smart grid applications. This will include but is not limited to:

  • Control of network storage devices from household up to low voltage level;
  • Home energy management systems;
  • Management of distributed generation;
  • Community control of high demand low carbon assets such as electric vehicles or heat pumps;
  • Demand side response applications;
  • Smart heating systems;
  • Forecasting of renewable generation;
  • Forecasting of electric vehicle charging demand;
  • Forecasting of low voltage connected commercial consumers.

Prof. Dr. William Holderbaum
Dr. Stephen Haben
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • load forecasting
  • forecasting
  • probabilistic load forecasting
  • demand forecasting
  • control
  • model predictive control
  • distributed control
  • demand management
  • smart grid
  • power management
  • low carbon technologies
  • distributed generation
  • renewable energy sources
  • local energy systems
  • smart energy systems

Published Papers (3 papers)

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Research

18 pages, 5773 KiB  
Article
Optimized Charge Controller Schedule in Hybrid Solar-Battery Farms for Peak Load Reduction
by Gergo Barta, Benedek Pasztor and Venkat Prava
Energies 2021, 14(22), 7794; https://doi.org/10.3390/en14227794 - 22 Nov 2021
Cited by 2 | Viewed by 2140
Abstract
The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and [...] Read more.
The goal of this paper is to optimally combine day-ahead solar and demand forecasts for the optimal battery schedule of a hybrid solar and battery farm connected to a distribution station. The objective is to achieve the maximum daily peak load reduction and charge battery with maximum solar photovoltaic energy. The innovative part of the paper lies in the treatment for the errors in solar and demand forecasts to then optimize the battery scheduler. To test the effectiveness of the proposed methodology, it was applied in the data science challenge Presumed Open Data 2021. With the historical Numerical Weather Prediction (NWP) data, solar power plant generation and distribution-level demand data provided, the proposed methodology was tested for four different seasons. The evaluation metric used is the peak reduction score (defined in the paper), and our approach has improved this KPI from 82.84 to 89.83. The solution developed achieved a final place of 5th (out of 55 teams) in the challenge. Full article
(This article belongs to the Special Issue Forecasting and Management Systems for Smart Grid Applications)
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11 pages, 556 KiB  
Article
Forecasting for Battery Storage: Choosing the Error Metric
by Colin Singleton and Peter Grindrod
Energies 2021, 14(19), 6274; https://doi.org/10.3390/en14196274 - 01 Oct 2021
Cited by 2 | Viewed by 1329
Abstract
We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct forecasting aims to maximise the daily evening peak reduction and using as [...] Read more.
We describe our approach to the Western Power Distribution (WPD) Presumed Open Data (POD) 6 MWh battery storage capacity forecasting competition, in which we finished second. The competition entails two distinct forecasting aims to maximise the daily evening peak reduction and using as much solar photovoltaic energy as possible. For the latter, we combine a Bayesian (MCMC) linear regression model with an average generation distribution. For the former, we introduce a new error metric that allows even a simple weighted average combined with a simple linear regression model to score very well using the competition performance metric. Full article
(This article belongs to the Special Issue Forecasting and Management Systems for Smart Grid Applications)
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22 pages, 4239 KiB  
Article
Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation
by Eugenio Borghini, Cinzia Giannetti, James Flynn and Grazia Todeschini
Energies 2021, 14(12), 3453; https://doi.org/10.3390/en14123453 - 10 Jun 2021
Cited by 15 | Viewed by 3190
Abstract
The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative [...] Read more.
The growing adoption of decentralised renewable energy generation (such as solar photovoltaic panels and wind turbines) and low-carbon technologies will increase the strain experienced by the distribution networks in the near future. In such a scenario, energy storage is becoming a key alternative to traditional expensive reinforcements to network infrastructure, due to its flexibility, decreasing costs and fast deployment capabilities. In this work, an end-to-end data-driven solution to optimally design the control of a battery unit with the aim of reducing the peak electricity demand is presented. The proposed solution uses state-of-the-art machine learning methods for forecasting electricity demand and PV generation, combined with an optimisation strategy to maximise the use of photovoltaic energy to charge the energy storage unit. To this end, historical demand, weather, and solar energy generation data collected at the Stentaway Primary substation near Plymouth, UK, and at other six locations were employed. Full article
(This article belongs to the Special Issue Forecasting and Management Systems for Smart Grid Applications)
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