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Energies
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Modeling and Control of Smart Energy Systems
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Modeling and Control of Smart Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 12254

Special Issue Editors

Prof. Dr. Marco Levorato

E-Mail Website
Guest Editor
Donald Bren School of Information and Computer Sciences, Computer Science Department, University of California, Irvine, CA, USA
Interests: Software-defined and cognitive networks; Smart energy systems; Distributed learning and processing for efficient data acquisition, transportation and analysis; Autonomous systems—unmanned aerial vehicles; (Urban) IoT architectures and signal processing for mobile healthcare; Modeling and control of smart energy systems
Dr. Roberto Valentini

E-Mail Website
Guest Editor
Universita’ degli Studi Dell’Aquila
Interests: Stochastic control and optimization; Stochastic modeling; Internet of Things; Intelligent energy storage

Special Issue Information

Dear Colleagues,

We are pleased to announce the Energies Special Issue on Modeling and Control of Smart Energy Systems. Despite the enormous effort from the research community, many critical challenges toward building effective smart energy systems remain unsolved. At different scales, smart energy systems can present an appalling complexity in terms of state dynamics and/or a large topological complexity. The integration of renewables, load control, and energy storage further complicates system modeling due to the need to jointly consider energy and external–logical, environmental or human–subsystems. We solicit contributions unveiling new tools toward a deeper understanding and control of these intricate systems. We encourage both new rigorous methodologies, theories, and analytical tools as well as insightful studies analyzing the application of modeling and control tools to practical systems. Machine learning and data analysis tools are also within the scope of the Special Issue. The Special Issue will accept contributions focusing on all the systems’ scales, ranging from residential systems, to microgrids and distribution systems.

Topics include but are not limited to:

  • Robust and scalable control methods
  • Real-time state estimation
  • Cyberphysical modeling and control
  • Transient and stability analysis
  • Load scheduling frameworks
  • Stochastic control methodologies for smart energy systems
  • Modeling and control for the integration renewables
  • Modeling and control of electric vehicle integration in micr-grids
  • Charging control and management
  • Modeling and control for residential demand response
  • Reinforcement learning and predictive control for smart energy systems
  • Deep Generative models
  • Experimentation and validation of modeling and control tools

Prof. Dr. Marco Levorato
Dr. Roberto Valentini
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

  • Robust control
  • Stochastic control
  • Cyberphysical modeling
  • Microgrids
  • Electric vehicles
  • Distribution systems
  • Renewable integration
  • Load scheduling
  • Reinforcement learning
  • Machine learning

Published Papers (5 papers)

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Research

25 pages, 4965 KiB  
Article
Predictive Control of District Heating System Using Multi-Stage Nonlinear Approximation with Selective Memory
by Marius Reich, Jonas Gottschald, Philipp Riegebauer and Mario Adam
Energies 2020, 13(24), 6714; https://doi.org/10.3390/en13246714 - 19 Dec 2020
Cited by 2 | Viewed by 1764
Abstract
Innovative heating networks with a hybrid generation park can make an important contribution to the energy turnaround. By integrating heat from several heat generators and a high proportion of different renewable energies, they also have a high degree of flexibility. Optimizing the operation [...] Read more.
Innovative heating networks with a hybrid generation park can make an important contribution to the energy turnaround. By integrating heat from several heat generators and a high proportion of different renewable energies, they also have a high degree of flexibility. Optimizing the operation of such systems is a complex task due to the diversity of producers, the use of storage systems with stratified charging and continuous changes in system properties. Besides, it is necessary to consider conflicting economic and ecological targets. Operational optimization of district heating systems using nonlinear models is underrepresented in practice and science. Considering ecological and economic targets, the current work focuses on developing a procedure for an operational optimization, which ensures a continuous optimal operation of the heat and power generators of a local heating network. The approach presented uses machine learning methods, including Gaussian process regressions for a repeatedly updated multi-stage approximation of the nonlinear system behavior. For the formation of the approximation models, a selection algorithm is utilized to choose only essential and current process data. By using a global optimization algorithm, a multi-objective optimal setting of the controllable variables of the system can be found in feasible time. Implemented in the control system of a dynamic simulation, significant improvements of the target variables (operating costs, CO2 emissions) can be seen in comparison with a standard control system. The investigation of different scenarios illustrates the high relevance of the presented methodology. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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18 pages, 2470 KiB  
Article
Bi-level Capacity Planning of Wind-PV-Battery Hybrid Generation System Considering Return on Investment
by Bowen Yang, Yougui Guo, Xi Xiao and Peigen Tian
Energies 2020, 13(12), 3046; https://doi.org/10.3390/en13123046 - 12 Jun 2020
Cited by 11 | Viewed by 1851
Abstract
Reasonable configuration of equipment capacity can effectively improve the economics of wind-photovoltaic-battery hybrid generation system (WPB-HGS). Based on the current needs of investors to pay more attention to the economic benefits of WPB-HGS, this paper proposes a capacity configuration method for WPB-HGS considering [...] Read more.
Reasonable configuration of equipment capacity can effectively improve the economics of wind-photovoltaic-battery hybrid generation system (WPB-HGS). Based on the current needs of investors to pay more attention to the economic benefits of WPB-HGS, this paper proposes a capacity configuration method for WPB-HGS considering return on investment (ROI). A bi-level planning model for integrated planning and operation of WPB-HGS was established. The lower-level model optimizes the system’s operating status with the goal of maximizing the daily power sales of the system. The upper-level model plans the equipment capacity of the WPB-HGS with the goal of maximizing the annual net income and return on investment. The model is solved using adaptive weighted particle swarm optimization. According to actual engineering examples, the specific equipment capacity is configured, and the configuration results are analyzed to verify the effectiveness of the method. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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18 pages, 1240 KiB  
Article
Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage
by Nadia Ahmed, Marco Levorato, Roberto Valentini and Guann-Pyng Li
Energies 2020, 13(9), 2201; https://doi.org/10.3390/en13092201 - 02 May 2020
Cited by 5 | Viewed by 2121
Abstract
This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the [...] Read more.
This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery and the grid to minimize a cost function capturing a user-defined tradeoff between battery degradation and financial expense by user preferences. Numerical results are based on real-world weather data for Golden, Colorado, and load traces. The results illustrate the ability of the system to limit battery degradation assessed using the Rain flow counting method for lithium ion batteries. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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21 pages, 5926 KiB  
Article
Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems
by Ana Carolina do Amaral Burghi, Tobias Hirsch and Robert Pitz-Paal
Energies 2020, 13(6), 1517; https://doi.org/10.3390/en13061517 - 23 Mar 2020
Cited by 6 | Viewed by 3323
Abstract
Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential [...] Read more.
Environmental and economic needs drive the increased penetration of intermittent renewable energy in electricity grids, enhancing uncertainty in the prediction of market conditions and network constraints. Thereafter, the importance of energy systems with flexible dispatch is reinforced, ensuring energy storage as an essential asset for these systems to be able to balance production and demand. In order to do so, such systems should participate in wholesale energy markets, enabling competition among all players, including conventional power plants. Consequently, an effective dispatch schedule considering market and resource uncertainties is crucial. In this context, an innovative dispatch optimization strategy for schedule planning of renewable systems with storage is presented. Based on an optimization algorithm combined with a machine-learning approach, the proposed method develops a financial optimal schedule with the incorporation of uncertainty information. Simulations performed with a concentrated solar power plant model following the proposed optimization strategy demonstrate promising financial improvement with a dynamic and intuitive dispatch planning method (up to 4% of improvement in comparison to an approach that does not consider uncertainties), emphasizing the importance of uncertainty treatment on the enhanced quality of renewable systems scheduling. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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25 pages, 4266 KiB  
Article
Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset
by Ana Carolina do Amaral Burghi, Tobias Hirsch and Robert Pitz-Paal
Energies 2020, 13(3), 616; https://doi.org/10.3390/en13030616 - 01 Feb 2020
Cited by 4 | Viewed by 2785
Abstract
Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to [...] Read more.
Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way. Full article
(This article belongs to the Special Issue Modeling and Control of Smart Energy Systems)
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