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Optimal Design and Operation of Sustainable Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (18 December 2020) | Viewed by 17948

Special Issue Editor

Department of Simulation and Optimal, Processes, Technische Universitat Ilmenau, 98693 Ilmenau, Germany
Interests: dynamic optimization of large-scale systems; stochastic optimization under uncertainty; optimization of hybrid systems; real-time optimization, parameter identification; nonlinear model predictive control; applications: energy systems engineering; management of water resources systems; systems biology; chemical process engineering; autonomous driving
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Special Issue Information

Dear Colleagues,

Both the electrical energy demand and fossil fuel price have significantly increased in recent years. Therefore, relying only on conventional energy sources is not economically feasible to cover the current and future energy demands. Consequently, promoting the use of renewable energy sources is an important strategy to enhance the security and reliability of energy supply. However, power generation from renewable resources is intermittent because of their dependence on environmental conditions. This intermittent behavior has a direct impact on the voltage and frequency of the power system, and thus, on the quality and quantity of the power supply. Therefore, approaches to optimal design and operation of sustainable energy systems need to be investigated.

Battery storage systems (BSSs) were widely used at first to handle such problems. However, the installation of BSSs adds further complexities to the design and operation of power systems. Because of the high initial cost and replacement cost of a BSS, considering its lifetime becomes an important aspect.

Second, due to technology development in recent years, it has become possible to integrate small-scale renewable energy resources to build a local microgrid. However, many technical and economical complexities have to be handled for microgrids to provide a reliable and cost-effective energy source.

Third, electric vehicles (EVs) will be more and more in use. Since their charging is uncoordinated, EVs contribute to random loads, leading to a negative impact on the operation of the existing network. Therefore, developments of management strategies, scheduling processes, and optimization techniques are needed to deal with this issue.

I am writing to invite you to submit your original works to this Special Issue. I am looking forward to receiving your outstanding research.

Prof. Dr.-Ing. habil. Pu Li
Guest Editor

Manuscript Submission Information

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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

  • Microgrid
  • Distributed energy resources
  • Battery storage systems
  • Planning and operation of energy systems
  • Battery lifetime
  • Load and renewable energy forecasting
  • Lithium–ion battery modeling
  • Lead acid battery modeling
  • Complex hybrid energy systems
  • Grid integration of renewable energies
  • Optimal design and operation
  • Smart metering and power quality
  • Active and reactive power pricing
  • Demand side management
  • Optimization under uncertainty
  • Energy management
  • Electric vehicle

Published Papers (7 papers)

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Research

21 pages, 3153 KiB  
Article
Jump Linear Quadratic Control for Microgrids with Commercial Loads
by Maryam Khanbaghi and Aleksandar Zecevic
Energies 2020, 13(19), 4997; https://doi.org/10.3390/en13194997 - 23 Sep 2020
Cited by 4 | Viewed by 1385
Abstract
Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer [...] Read more.
Due to the aging power-grid infrastructure and increased usage of renewable energies, microgrids (μGrids) have emerged as a promising paradigm. It is reasonable to expect that they will become one of the fundamental building blocks of a smart grid, since effective energy transfer and coordination of μGrids could help maintain the stability and reliability of the regional large-scale power-grid. From the control perspective, one of the key objectives of μGrids is load management using local generation and storage for optimized performance. Accomplishing this task can be challenging, however, particularly in situations where local generation is unpredictable both in quality and in availability. This paper proposes to address that problem by developing a new optimal energy management scheme, which meets the requirements of supply and demand. The method that will be described in the following models μGrids as a stochastic hybrid dynamic system. Jump linear theory is used to maximize storage and renewable energy usage, and Markov chain theory is applied to model the intermittent generation of renewable energy based on real data. Although the model itself is quite general, we will focus exclusively on solar energy, and will define the performance measure accordingly. We will demonstrate that the optimal solution in this case is a state feedback law with a piecewise constant gain. Simulation results are provided to illustrate the effectiveness of such an approach. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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17 pages, 1727 KiB  
Article
Real-Time Active-Reactive Optimal Power Flow with Flexible Operation of Battery Storage Systems
by Erfan Mohagheghi, Mansour Alramlawi, Aouss Gabash, Frede Blaabjerg and Pu Li
Energies 2020, 13(7), 1697; https://doi.org/10.3390/en13071697 - 03 Apr 2020
Cited by 15 | Viewed by 2773
Abstract
In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale [...] Read more.
In this paper, a multi-phase multi-time-scale real-time dynamic active-reactive optimal power flow (RT-DAR-OPF) framework is developed to optimally deal with spontaneous changes in wind power in distribution networks (DNs) with battery storage systems (BSSs). The most challenging issue hereby is that a large-scale ‘dynamic’ (i.e., with differential/difference equations rather than only algebraic equations) mixed-integer nonlinear programming (MINLP) problem has to be solved in real time. Moreover, considering the active-reactive power capabilities of BSSs with flexible operation strategies, as well as minimizing the expended life costs of BSSs further increases the complexity of the problem. To solve this problem, in the first phase, we implement simultaneous optimization of a huge number of mixed-integer decision variables to compute optimal operations of BSSs on a day-to-day basis. In the second phase, based on the forecasted wind power values for short prediction horizons, wind power scenarios are generated to describe uncertain wind power with non-Gaussian distribution. Then, MINLP AR-OPF problems corresponding to the scenarios are solved and reconciled in advance of each prediction horizon. In the third phase, based on the measured actual values of wind power, one of the solutions is selected, modified, and realized to the network for very short intervals. The applicability of the proposed RT-DAR-OPF is demonstrated using a medium-voltage DN. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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18 pages, 3879 KiB  
Article
Design of Heat-Pump Systems for Single- and Multi-Family Houses using a Heuristic Scheduling for the Optimization of PV Self-Consumption
by Thomas Kemmler and Bernd Thomas
Energies 2020, 13(5), 1118; https://doi.org/10.3390/en13051118 - 02 Mar 2020
Cited by 11 | Viewed by 3354
Abstract
Heat pumps in combination with a photovoltaic system are a very promising option for the transformation of the energy system. By using such a system for coupling the electricity and heat sectors, buildings can be heated sustainably and with low greenhouse gas emissions. [...] Read more.
Heat pumps in combination with a photovoltaic system are a very promising option for the transformation of the energy system. By using such a system for coupling the electricity and heat sectors, buildings can be heated sustainably and with low greenhouse gas emissions. This paper reveals a method for dimensioning a suitable system of heat pump and photovoltaics (PV) for residential buildings in order to achieve a high level of (photovoltaic) PV self-consumption. This is accomplished by utilizing a thermal energy storage (TES) for shifting the operation of the heat pump to times of high PV power production by an intelligent control algorithm, which yields a high portion of PV power directly utilized by the heat pump. In order to cover the existing set of building infrastructure, 4 reference buildings with different years of construction are introduced for both single- and multi-family residential buildings. By this means, older buildings with radiator heating as well as new buildings with floor heating systems are included. The simulations for evaluating the performance of a heat pump/PV system controlled by the novel algorithm for each type of building were carried out in MATLAB-Simulink® 2017a. The results show that 25.3% up to 41.0% of the buildings’ electricity consumption including the heat pump can be covered directly from the PV-installation per year. Evidently, the characteristics of the heating system significantly influence the results: new buildings with floor heating and low supply temperatures yield a higher level of PV self-consumption due to a higher efficiency of the heat pump compared to buildings with radiator heating and higher supply temperatures. In addition, the effect of adding a battery to the system was studied for two building types. It will be shown that the degree of PV self-consumption increases in case a battery is present. However, due to the high investment costs of batteries, they do not pay off within a reasonable period. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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16 pages, 4160 KiB  
Article
A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network
by Peng Hou, Xiaojian Yi and Haiping Dong
Energies 2020, 13(3), 685; https://doi.org/10.3390/en13030685 - 05 Feb 2020
Cited by 5 | Viewed by 1990
Abstract
The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions [...] Read more.
The identification of high risk regions is an important aim of risk-based inspections (RBIs) in pipeline networks. As the most vital part of risk-based inspections, risk assessment makes a significant contribution to achieving this aim. Accurate assessment can target high risk inspected regions so that limited resources can mitigate considerable risks in the face of increased spatial distribution of a pipeline network. However, the existing approaches for risk assessment face grave challenges due to a lack of sufficient data and an assessment’s vulnerability to human biases and errors. This paper attempts to tackle those challenges through spatial statistics, which is used to estimate the uncertainty of risk based on a dataset of locations of pipeline network failure events without having to acquire additional data. The consequence of risk in each inspected region is measured by the total cost caused by the failure events that have occurred in the region, which is also calculated in the assessment. Then, the risks of the different inspected regions are obtained by integrating the uncertainty and consequences. Finally, the feasibility of our approach is validated in a case study. Our results in the case study demonstrate that uncertainty is less instructive for prioritizing pipeline inspections than the consequences of risk due to the low significant difference in risk uncertainty in different regions. Our results also have implications for understanding the correlation between the spatial location and consequences of risk. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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23 pages, 1930 KiB  
Article
A Framework for the Synthesis of Optimum Operating Profiles Based on Dynamic Simulation and a Micro Genetic Algorithm
by Erik Rosado-Tamariz, Miguel A. Zuniga-Garcia, Alfonso Campos-Amezcua and Rafael Batres
Energies 2020, 13(3), 677; https://doi.org/10.3390/en13030677 - 05 Feb 2020
Cited by 4 | Viewed by 2913
Abstract
This paper presents an approach to managing the thermal power plant’s flexible operation based on the steam generation process optimization. A strategy at the process level, as a first step in the operational optimization of the entire power plant, is proposed. The proposed [...] Read more.
This paper presents an approach to managing the thermal power plant’s flexible operation based on the steam generation process optimization. A strategy at the process level, as a first step in the operational optimization of the entire power plant, is proposed. The proposed approach focuses on minimizing the drum boiler startup time, since it is considered the most critical element in the steam generation process and in the thermal power plant’s efficient operation. An approach that addresses the problem to find the optimal sequences of control valves that minimize the drum boiler startup time as a dynamic optimization problem is proposed. To solve the optimization problem, a dynamic optimization framework based on a micro genetic algorithm (mGA) coupled with a dynamic simulation model is implemented. The dynamic simulation model is validated against data available in the literature, and the proposed optimization algorithm is characterized by the use of variable length chromosomes and the use of small population sizes. The results show that optimized operating profiles minimize the drum boiler startup time by at least 35 percent and generate control valve operating sequences that must be carried out to achieve the desired profile, while the structural integrity constraints are fulfilled at all times. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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21 pages, 2832 KiB  
Article
Optimization of Cold Pressing Process Parameters of Chopped Corn Straws for Fuel
by Tianyou Chen, Honglei Jia, Shengwei Zhang, Xumin Sun, Yuqiu Song and Hongfang Yuan
Energies 2020, 13(3), 652; https://doi.org/10.3390/en13030652 - 04 Feb 2020
Cited by 12 | Viewed by 1883
Abstract
Pressed condensation is a key process before the reclamation of loose corn straws. In this study, the effects of stabilization time on the relaxation density and dimensional stability of corn straws were studied firstly, and then the stabilization time was determined to be [...] Read more.
Pressed condensation is a key process before the reclamation of loose corn straws. In this study, the effects of stabilization time on the relaxation density and dimensional stability of corn straws were studied firstly, and then the stabilization time was determined to be 60 s by comprehensively considering the compression effect, energy consumption, efficiency and significance. On this basis, the effects of the water content (12%, 15%, 18%), ratio of pressure maintenance time to stabilization time (0, 0.5, 1), maximum compression stress (60.4, 120.8, 181.2 kPa) and feeding mass (2.5, 3, 3.5 kg) on the relaxation density, dimensional stability coefficient, and specific energy consumption of post-compression straw blocks were investigated by the Box–Behnken design. It was found that the water content, ratio of pressure maintenance time to stabilization time, maximum compression stress, and feeding mass all very significantly affected the relaxation density, dimensional stability coefficient and specific energy consumption. The interaction between water content and maximum compression stress significantly affected both relaxation density and specific energy consumption. The interaction between the ratio of pressure maintenance time to stabilization time and feeding mass significantly affected the dimensional stability coefficient. The factors and the indices were regressed by quadratic equations, with the coefficients of determination larger than 0.97 in all equations. The optimized process parameters were water content of 13.63%, pressure maintenance time of 22.8 s, strain maintenance time of 37.2 s, maximum compression stress of 109.58 kPa, and raw material feeding mass of 3.5 kg. Under these conditions, the relaxation density of cold-pressed straw blocks was 145.63 kg/m3, the dimensional stability coefficient was 86.89%, and specific energy consumption was 245.78 J/kg. The errors between test results and predicted results were less than 2%. The low calorific value of cold-pressed chopped corn straw blocks was 12.8 MJ/kg. Through the situational analysis method based on the internal and external competition environments and competition conditions (SWOT analysis method), the cold-pressed chopped corn straw blocks consumed the lowest forming energy consumption than other forming methods and, thus, are feasible for heating by farmers. Our findings may provide a reference for corn straw bundling, cold-press forming processes and straw bale re-compressing. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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23 pages, 2987 KiB  
Article
An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks
by Azadeh Sadeghi, Roohollah Younes Sinaki, William A. Young II and Gary R. Weckman
Energies 2020, 13(3), 571; https://doi.org/10.3390/en13030571 - 24 Jan 2020
Cited by 30 | Viewed by 3021
Abstract
As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several [...] Read more.
As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures. Full article
(This article belongs to the Special Issue Optimal Design and Operation of Sustainable Energy Systems)
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