Special Issue "Analysis and Development of Energy Management: Automotive and Stationary Applications"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Prof. Dr. Ramon Costa-Castelló
Website
Guest Editor
Escola Tècnica Superior d’Enginyeria Industrial de Barcelona
Interests: fuel cells; automatic control; control engineering; repetitive control; energy management; automatic control education
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

Energy consumption is growing day by day; in parallel, there is a transition to cleaner energy production systems. The new energy generation and consumption systems are increasingly complex, since they have a distributed nature, behavior that is not totally predictable, and hybrid characteristics, since different sources of energy usually coexist. Additionally, energy storage elements play a very important role. At present, there are a large number of them with very different characteristics. Handling these scenarios is a challenging problem.

Energy management systems play a decisive role in these new energy scenarios. Its correct operation is key to profit from available energy and minimize the use of fossil fuel.

The literature describes different ways to develop energy management systems; the main ones are heuristics, intelligent control, and optimal and predictive control, among others. An element that is usually present in all these algorithms is the technique for predicting generation and energy consumption. This prediction plays a key role in the optimal functioning of energy management systems.

In this special section, we intend to show energy management techniques applied to different energy systems.

Prof. Dr. Ramon Costa-Castelló
Guest Editor

Manuscript Submission Information

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Keywords

  • energy system modeling
  • production/consumption prediction
  • model predictive control in energy system
  • heuristics in energy systems
  • energy systems optimal control

Published Papers (3 papers)

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Research

Open AccessArticle
Optimal Energy Management in a Standalone Microgrid, with Photovoltaic Generation, Short-Term Storage, and Hydrogen Production
Energies 2020, 13(6), 1454; https://doi.org/10.3390/en13061454 - 20 Mar 2020
Abstract
This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management [...] Read more.
This paper addresses the energy management of a standalone renewable energy system. The system is configured as a microgrid, including photovoltaic generation, a lead-acid battery as a short term energy storage system, hydrogen production, and several loads. In this microgrid, an energy management strategy has been incorporated that pursues several objectives. On the one hand, it aims to minimize the amount of energy cycled in the battery, in order to reduce the associated losses and battery size. On the other hand, it seeks to take advantage of the long-term surplus energy, producing hydrogen and extracting it from the system, to be used in a fuel cell hybrid electric vehicle. A crucial factor in this approach is to accommodate the energy consumption to the energy demand and to achieve this, a model predictive control (MPC) scheme is proposed. In this context, proper models for solar estimation, hydrogen production, and battery energy storage will be presented. Moreover, the controller is capable of advancing or delaying the deferrable loads from its prescheduled time. As a result, a stable and efficient supply with a relatively small battery is obtained. Finally, the proposed control scheme has been validated on a real case scenario. Full article
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Open AccessFeature PaperArticle
Management and Activation of Energy Flexibility at Building and Market Level: A Residential Case Study
Energies 2020, 13(5), 1188; https://doi.org/10.3390/en13051188 - 05 Mar 2020
Abstract
The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by [...] Read more.
The electricity sector foresees a significant change in the way energy is generated and distributed in the coming years. With the increasing penetration of renewable energy sources, smart algorithms can determine the difference about how and when energy is produced or consumed by residential districts. However, managing and implementing energy demand response, in particular energy flexibility activations, in real case studies still presents issues to be solved. This study, within the framework of the European project “SABINA H2020”, addresses the development of a multi-level optimization algorithm that has been tested in a semi-virtual real-time configuration. Results from a two-day test show the potential of building’s flexibility and highlight its complexity. Results show how the first level algorithm goal to reduce the energy injected to the grid is accomplished as well as the energy consumption shift from nighttime to daytime hours. As conclusion, the study demonstrates the feasibility of such kind of configurations and puts the basis for real test site implementation. Full article
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Open AccessArticle
Prediction of the Energy Consumption Variation Trend in South Africa based on ARIMA, NGM and NGM-ARIMA Models
Energies 2020, 13(1), 10; https://doi.org/10.3390/en13010010 - 18 Dec 2019
Abstract
South Africa’s energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of [...] Read more.
South Africa’s energy consumption takes up about one-third of that in the whole African continent, ranking the first place in Africa. However, there are few researches on the prediction of energy consumption in South Africa. In this study, based on the data of South Africa’s energy consumption during 1998–2016, Autoregressive Integrated Moving Average (ARIMA) model, nonlinear grey model (NGM) and nonlinear grey model–autoregressive integrated moving average (NGM-ARIMA) model are adopted to predict South Africa’s energy consumption during 2017–2030. After using these NGM, ARIMA and NGM-ARIMA, the mean absolute percent errors (MAPE) are 2.827%, 2.655% and 1.772%, respectively, which indicates that the predicted result has very high reliability. The prediction results show that the energy consumption in South Africa will keep increasing with the growth rate of about 7.49% in the next 14 years. This research result will provide scientific basis for the policy adjustment of energy supply and demand in South Africa and the prediction techniques used in the research will have reference function for the energy consumption study in other African countries. Full article
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