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Energies
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Renewable Energy Integration into Power Grids and Buildings
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Renewable Energy Integration into Power Grids and Buildings

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 (15 February 2023) | Viewed by 8224

Special Issue Editor

Prof. Dr. Javad Khazaei

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
Interests: smart grid optimization; cybersecurity of cyber–physical power systems; microgrids operation and control

Special Issue Information

Dear Colleagues,

The integration of highly uncertain renewable energy sources to the grid and buildings is one of the major challenges of sustainable energy systems. Although energy storage devices and modern application of vehicles to the grid can reduce the uncertainties of renewable energy sources and provide ancillary services to the grid, resource allocation and control of distributed generation become a challenge for real-time control purposes. In addition, the building sector is the largest energy-consuming sector and should be dynamically controlled and optimized to provide grid services. Therefore, it is vital to develop robust optimization and resource allocation frameworks that not only can manage multiple energy resources to support the loads in grids and buildings but also minimize the adverse impacts of uncertainties and contingencies in the system. This Special Issue aims at acting as a bridge between research and development groups for state-of-the-art application of intelligent methods in modeling, optimizing, controlling, and designing frameworks that consider the interlinks between buildings and smart grids. We are looking for solutions that result in efficient resource allocation and control of integrated energy-building systems, especially when a high penetration of renewables and storage units exists. Researchers from academia, industry, and government are invited to submit their original and unpublished work to this Special Issue. The topics of interest include but are not limited to: 

  • Grid interactive buildings;
  • Application of model-predictive control in grid optimization;
  • Demand response through building energy management;
  • Application of machine learning for optimization of grid and buildings;
  • Application of machine learning for control of grid-integrated buildings;
  • Real-time optimization and control of renewables integrated to buildings and grid.

Prof. Dr. Javad Khazaei
Guest Editor

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

  • Grid optimization
  • Grid-interactive buildings
  • Resource allocation
  • Machine learning in smart grids
  • Demand response
  • Building energy management system

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Published Papers (4 papers)

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Research

17 pages, 3734 KiB  
Article
Optimizing Energy Management in Microgrids Based on Different Load Types in Smart Buildings
by Mohammad Zareein, Jalal Sahebkar Farkhani, Amirhossein Nikoofard and Turaj Amraee
Energies 2023, 16(1), 73; https://doi.org/10.3390/en16010073 - 21 Dec 2022
Cited by 4 | Viewed by 1855
Abstract
This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids [...] Read more.
This paper presents an energy management strategy (EMS) based on the Stackelberg game theory for the microgrid community. Three agents or layers are considered in the proposed framework. The microgrid cluster (MGC) refers to the agent that coordinates the interactions between the microgrids and the utility grid. The microgrid agent manages the energy scheduling of its own consumers. The third agent represents the consumers inside the microgrids. The game equilibrium point is solved between different layers and each layer will benefit the most. First, an algorithm performs demand response in each microgrid according to load models in smart buildings and determines the load consumption for each consumer. Then, each microgrid determines its selling price to the consumers and the amount of energy required to purchase from the utility grid to achieve the maximum profit. Finally, the balance point will be obtained between microgrids by the microgrid cluster agent. Moreover, the proposed method uses various load types at different times based on real-life models. The result shows that considering these different load models with demand response increased the profit of the user agent by an average of 22%. The demand response is implemented by the time of use (TOU) model and real-time pricing (RTP) in the microgrid. Full article
(This article belongs to the Special Issue Renewable Energy Integration into Power Grids and Buildings)
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27 pages, 8357 KiB  
Article
How Multi-Criterion Optimized Control Methods Improve Effectiveness of Multi-Zone Building Heating System Upgrading
by Ahmad Esmaeilzadeh, Brian Deal, Aghil Yousefi-Koma and Mohammad Reza Zakerzadeh
Energies 2022, 15(22), 8675; https://doi.org/10.3390/en15228675 - 18 Nov 2022
Cited by 3 | Viewed by 1678
Abstract
This paper aims to develop multi-objective optimized control methods to improve the performance of retrofitting building heating systems in reducing consumed energy as well as providing comfortable temperature in a multi-zone building. While researchers evaluate various controllers in specific systems, providing a comprehensive [...] Read more.
This paper aims to develop multi-objective optimized control methods to improve the performance of retrofitting building heating systems in reducing consumed energy as well as providing comfortable temperature in a multi-zone building. While researchers evaluate various controllers in specific systems, providing a comprehensive controller for retrofitting the existing heating systems of multi-zone buildings is less investigated. A case study approach with a four-story residential building is simulated. The building energy consumption is modeled by EnergyPlus. The model is validated with energy data. Then, the building steam system model is upgraded, and in the other case, renewed by a hydronic system instead of a steam one. Three optimized controller groups are developed, including Model Predictive Controller (MPC), fuzzy controllers (Fuzzy Logic Controller (FLC) and an Optimized Fuzzy Sliding Mode Controller (OFSMC)), and optimized traditional ones. These controllers were applied to the upgraded steam and hydronic heating systems. The control methods affected the tuning of the boiler feed flow by regulating the condensing cycle and circulating the pump flow of the hydronic system. Accordingly, renewing the heating system improves energy efficiency by up to 29% by implementing a hydronic system instead of the steam one. The fuzzy controllers increased renewing effectiveness by providing comfortable temperatures and reducing building environmental footprints by up to 95% and 12%, respectively, compared with an on/off controller baseline. Full article
(This article belongs to the Special Issue Renewable Energy Integration into Power Grids and Buildings)
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18 pages, 4751 KiB  
Article
High-Efficiency Power Cycles for Particle-Based Concentrating Solar Power Plants: Thermodynamic Optimization and Critical Comparison
by Miguel Angel Reyes-Belmonte and Francesco Rovense
Energies 2022, 15(22), 8579; https://doi.org/10.3390/en15228579 - 16 Nov 2022
Cited by 3 | Viewed by 1277
Abstract
This paper investigates and compares several highly efficient thermodynamic cycles that are suitable for coupling with particle-in-tube fluidized-bed solar receiver technology. In such a receiver, high-temperature particles are used as both a heat transfer fluid and a storage medium. A dense particle suspension [...] Read more.
This paper investigates and compares several highly efficient thermodynamic cycles that are suitable for coupling with particle-in-tube fluidized-bed solar receiver technology. In such a receiver, high-temperature particles are used as both a heat transfer fluid and a storage medium. A dense particle suspension (DPS) is created through an upward bubbling fluidized-bed (UBFB) flow inside the receiver tubes, which constitutes the “particle-in-tube” solar receiver concept. Reaching higher temperatures is seen as a key factor for future cost reductions in the solar plant, as this leads to both higher power conversion efficiency and increased energy storage density. Three advanced thermodynamic cycles are analyzed in this work: the supercritical steam Rankine cycle (s-steam), supercritical carbon dioxide cycle (s-CO2) and integrated solar combined cycle (ISCC). For each one, 100% solar contribution, which is considered the total thermal input to the power cycle, can be satisfied by the solar particle receiver. The main findings show that the s-CO2 cycle is the most suitable thermodynamic cycle for the DPS solar plant, exhibiting a net cycle efficiency above 50% for a moderate temperature range (680–730 °C). For the other advanced power cycles, 45.35% net efficiency can be achieved for the s-steam case, while the efficiency of the ISCC configuration is limited to 45.23% for the solar-only operation mode. Full article
(This article belongs to the Special Issue Renewable Energy Integration into Power Grids and Buildings)
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25 pages, 6611 KiB  
Article
Thermal Design of a Biohydrogen Production System Driven by Integrated Gasification Combined Cycle Waste Heat Using Dynamic Simulation
by Mohammad Fakhrulrezza, Joon Ahn and Hyun-Jin Lee
Energies 2022, 15(9), 2976; https://doi.org/10.3390/en15092976 - 19 Apr 2022
Cited by 2 | Viewed by 2046
Abstract
Utilizing biological processes for hydrogen production via gasification is a promising alternative method to coal gasification. The present study proposes a dynamic simulation model that uses a one-dimensional heat-transfer analysis method to simulate a biohydrogen production system. The proposed model is based on [...] Read more.
Utilizing biological processes for hydrogen production via gasification is a promising alternative method to coal gasification. The present study proposes a dynamic simulation model that uses a one-dimensional heat-transfer analysis method to simulate a biohydrogen production system. The proposed model is based on an existing experimental design setup. It is used to simulate a biohydrogen production system driven by the waste heat from an integrated gasification combined cycle (IGCC) power plant equipped with carbon capture and storage technologies. The data from the simulated results are compared with the experimental measurement data to validate the developed model’s reliability. The results show good agreement between the experimental data and the developed model. The relative root-mean-square error for the heat storage, feed-mixing, and bioreactor tanks is 1.26%, 3.59%, and 1.78%, respectively. After the developed model’s reliability is confirmed, it is used to simulate and optimize the biohydrogen production system inside the IGCC power plant. The bioreactor tank’s time constant can be improved when reducing the operating volume of the feed-mixing tank by the scale factors of 0.75 and 0.50, leading to a 15.76% and 31.54% faster time constant, respectively, when compared with the existing design. Full article
(This article belongs to the Special Issue Renewable Energy Integration into Power Grids and Buildings)
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