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Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems
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Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: closed (10 June 2020) | Viewed by 53170

Special Issue Editors

Dr. Kody Powell

E-Mail Website
Guest Editor
Department of Chemical Engineering, The University of Utah, Salt Lake City, UT 84112, USA
Interests: energy systems; thermal energy storage; combined heat and power; energy storage; automation; optimization
Dr. Kasra Mohammadi

E-Mail
Guest Editor
Department of Chemical Engineering, The University of Utah, Salt Lake City, UT 84112, USA
Interests: multigeneration systems, solar towers, advanced thermodynamic analysis, solar radiation modeling, heat recovery

Special Issue Information

Dear Colleagues,

We invite you to make submissions to this Special Issue of Processes focused on “Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems”. Reliable and sustainable energy remains a major challenge today with no single solution. By combining energy technologies and resources in innovative ways, process synergies can be created and leveraged in ways that maximize energy efficiency, minimize total cost, minimize environmental impact, etc. This Special Issue seeks novel research contributions in, but not limited to, the following areas:

  • Simulation, control, and/or optimization of complex energy systems
  • Detailed analysis of novel energy system configurations
  • Combined heat and power systems
  • Multi-generation systems where multiple useful products are produced simultaneously (e.g., power and fresh water, power and chemicals, heat and cooling)
  • Renewable energy
  • Simulation and analysis of systems incorporating multiple energy types as inputs (e.g., solar and natural gas, wind and nuclear, etc.)
  • Systems and optimization/control techniques that leverage energy storage in novel ways

We welcome and look forward to your submissions.

Thank you,

Dr. Kody Powell
Dr. Kasra Mohammadi
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. Processes is an international peer-reviewed open access monthly 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 2400 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.

Published Papers (17 papers)

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Editorial

Jump to: Research

4 pages, 173 KiB  
Editorial
Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems
by Kody M. Powell and Kasra Mohammadi
Processes 2021, 9(7), 1125; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9071125 - 29 Jun 2021
Cited by 1 | Viewed by 1331
Abstract
As renewable energy technologies decrease in cost and become more prevalent, there is an increasing trend towards electrification of many energy systems [...] Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)

Research

Jump to: Editorial

19 pages, 1017 KiB  
Article
Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process
by Titus Quah, Derek Machalek and Kody M. Powell
Processes 2020, 8(11), 1497; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8111497 - 19 Nov 2020
Cited by 10 | Viewed by 3141
Abstract
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO [...] Read more.
One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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24 pages, 12871 KiB  
Article
A 140 MW Solar Thermal Plant in Jordan
by Wael Al-Kouz, Ahmad Almuhtady, Nidal Abu-Libdeh, Jamal Nayfeh and Alberto Boretti
Processes 2020, 8(6), 668; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8060668 - 04 Jun 2020
Cited by 4 | Viewed by 3345
Abstract
This paper aims to compute the performances of a smaller version of Solana power plant, with half the solar field, and 1 of 2 turbines in the power cycle, that can be built in Amman or Ma’an in Jordan. The climate conditions for [...] Read more.
This paper aims to compute the performances of a smaller version of Solana power plant, with half the solar field, and 1 of 2 turbines in the power cycle, that can be built in Amman or Ma’an in Jordan. The climate conditions for both Amman and Ma’an are discussed thoroughly in the paper. Furthermore, a preliminary validation exercise performed by using measured monthly average values of electricity production from existing plants, a system advisor model (SAM) is used to predict the performances of the proposed Solana-like plants in Ma’an and Amman. The validation shows a good agreement with the measured data for different existing power plants. The simulation results including the monthly capacity factors suggest the annual operation in Ma’an maybe even better than the operation in Gila Bend, for an annual average capacity factor of about 41% for Ma’an vs. a capacity factor of about 39% for Gila Bend. This is mainly due to the best combination of direct normal irradiance (DNI) and the dry bulb temperature across the year in Ma’an versus Gila Bend. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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26 pages, 3324 KiB  
Article
Assessment and Prediction of Complex Industrial Steam Network Operation by Combined Thermo-Hydrodynamic Modeling
by Kristián Hanus, Miroslav Variny and Peter Illés
Processes 2020, 8(5), 622; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8050622 - 22 May 2020
Cited by 8 | Viewed by 3796
Abstract
Steam network operation stability and reliability is vital for any industrial branch. A combined steam network model comprising a balance and a coupled thermo-hydrodynamic model, including seasonal variations impact and system specificities, is presented. A balance model can readily be used by a [...] Read more.
Steam network operation stability and reliability is vital for any industrial branch. A combined steam network model comprising a balance and a coupled thermo-hydrodynamic model, including seasonal variations impact and system specificities, is presented. A balance model can readily be used by a refinery’s operators. The thermo-hydrodynamic model identifies system bottlenecks and cold spots and evaluates proposed operation and investment measures including heat loss reduction. A three-pressure levels refinery steam network served for model testing and validation. Balance model results reveal significant misbalance in steam production and consumption, reaching 30.5% in the low-pressure steam system, and heat balance differences in the range of 9.2% to 29.5% on individual pressure levels, attributable both to flow measurement accuracy issues and to heat losses. The thermo-hydrodynamic model results differ from the measured steam parameters by less than 5% (temperature) and by less than 4% (pressure), respectively, with the estimated operational insulation heat conductivity exceeding 0.08 W/m/K. Its comparison with that of 0.03 W/m/K for dry insulation material yields the need for pipelines re-insulation and a partial revamp of the steam network. The model is sufficiently general for any type of industry, pursuing the goal of cleaner and energy-efficient steam transport and consumption. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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11 pages, 1576 KiB  
Article
An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms
by Shengran Chen and Shengyan Wang
Processes 2020, 8(4), 426; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8040426 - 03 Apr 2020
Cited by 15 | Viewed by 2346
Abstract
The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply [...] Read more.
The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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21 pages, 2331 KiB  
Article
Distributed Secondary Control for Islanded Microgrids Cluster Based on Hybrid-Triggered Mechanisms
by Shengxuan Weng, Yusheng Xue, Jianbo Luo and Yanman Li
Processes 2020, 8(3), 370; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8030370 - 23 Mar 2020
Cited by 9 | Viewed by 2304
Abstract
Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- [...] Read more.
Considering the communication resources limitation, the hybrid-triggered mechanism based distributed control of islanded microgrids cluster is proposed, which can restore the frequency to the rated value and realize the active power sharing when the disturbance occurs. The hybrid-triggered mechanism consists of the self- and event-triggered mechanisms, which are configured at each leader and follower distributed generation to determine the inter-microgrids and intra-microgrid information transmission, respectively. The communication burdens can be sharply reduced since the information is transmitted aperiodically only when the proposed triggering conditions are satisfied under the hybrid-triggered mechanism. Moreover, Zeno behavior is analyzed to be avoided to make the hybrid-triggered mechanism reasonable and practicable for practical islanded microgrids cluster. The simulation verifies the effectiveness of theoretical results. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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13 pages, 1777 KiB  
Article
Power Plant Optimisation—Effective Use of the Nelder-Mead Approach
by Paweł Niegodajew, Maciej Marek, Witold Elsner and Łukasz Kowalczyk
Processes 2020, 8(3), 357; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8030357 - 20 Mar 2020
Cited by 14 | Viewed by 4284
Abstract
This paper demonstrates the use of a combined software package including IPSEpro and MATLAB in the optimisation of a modern thermal cycle. A 900 MW power plant unit (operating at ultra-supercritical conditions) was considered as the study object. The Nelder-Mead simplex-based, direct search [...] Read more.
This paper demonstrates the use of a combined software package including IPSEpro and MATLAB in the optimisation of a modern thermal cycle. A 900 MW power plant unit (operating at ultra-supercritical conditions) was considered as the study object. The Nelder-Mead simplex-based, direct search method was used to increase power plant efficiency and to find the optimal thermal cycle configuration. As the literature reveals, the Nelder-Mead approach is very sensitive to the simplex size and to the choice of method coefficients, i.e., reflection, expansion and contraction. When these coefficients are improperly chosen, the finding of the optimal solution cannot be guaranteed, particularly in such complex systems as thermal cycles. Hence, the main goal of the present work was to demonstrate the capability of an integrated software package including IPSEpro, MATLAB and MS Excel in the optimisation process of a complex thermal cycle, as well as to examine the effectiveness of the most popular sets of Nelder-Mead coefficients previously proposed by other researchers. For the investigation purposes, the bleed and outlet pressures from the turbines were considered as decision variables, and the power plant efficiency was used as an objective function. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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17 pages, 4795 KiB  
Article
Multi-Objective Optimal Configuration of the CCHP System
by Liukang Zheng, Xiaoli Wang and Baochen Jiang
Processes 2020, 8(3), 351; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8030351 - 19 Mar 2020
Cited by 8 | Viewed by 2284
Abstract
The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in [...] Read more.
The combined cooling, heating and power (CCHP) system not only has high energy efficiency but also has different load structures. Traditional separate production (SP) system and power supply system do not consider the land cost in terms of the environmental benefits, and in the aspect of the power supply reliability, the grid-connected inverter cost is also ignored. Considering the deficiency of the traditional energy supply system, this paper builds the CCHP system construction cost model. The particle swarm optimization (PSO) is adopted to find out the minimum value of the construction cost, and the optimal system construction scheme is constructed from three aspects which are system reliability, economic benefits and environmental benefits. In this paper, the typical daily data, as well as the meteorological data and the load data, in the last four years are taken as experimental dataset. The experimental results show that compared with the traditional SP system and power supply system, the CCHP system established in this paper not only achieves lower cumulative investment cost, but also has a good power supply reliability and environmental benefits. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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29 pages, 932 KiB  
Article
Integrated Process Design and Control for Smart Grid Coordinated IGCC Power Plants Using Economic Linear Optimal Control
by Jin Zhang, Sofia Garcia Fracaro and Donald J. Chmielewski
Processes 2020, 8(3), 288; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8030288 - 03 Mar 2020
Cited by 16 | Viewed by 2850
Abstract
The Integrated Gasification Combined Cycle (IGCC) possesses a number of advantages over traditional power generation plants, including increased efficiency, flex-fuel, and carbon capture. A lesser-known advantage of the IGCC system is the ability to coordinate with the smart grid. The idea is that [...] Read more.
The Integrated Gasification Combined Cycle (IGCC) possesses a number of advantages over traditional power generation plants, including increased efficiency, flex-fuel, and carbon capture. A lesser-known advantage of the IGCC system is the ability to coordinate with the smart grid. The idea is that process modifications can enable dispatch capabilities in the sense of shifting power production away from periods of low electricity price to periods of high price and thus generate greater revenue. The work begins with a demonstration of Economic Model Predictive Control (EMPC) as a strategy to determine the dispatch policy by directly pursuing the objective of maximizing plant revenue. However, the numeric nature of EMPC creates an inherent limitation when it comes to process design. Thus, Economic Linear Optimal Control (ELOC) is proposed as a surrogate for EMPC in the formulation of the integrated design and control problem for IGCC power plants with smart grid coordination. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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22 pages, 4465 KiB  
Article
A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System
by Yan Xu, Zhao Luo, Zhendong Zhu, Zhiyuan Zhang, Jinghui Qin, Hao Wang, Zeyong Gao and Zhichao Yang
Processes 2020, 8(2), 245; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020245 - 21 Feb 2020
Cited by 14 | Viewed by 2992
Abstract
With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve [...] Read more.
With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy). Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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19 pages, 2558 KiB  
Article
Distributed Optimal Frequency Regulation for Multiple Distributed Power Generations with an Event-Triggered Communication Mechanism
by Shiyun Xu, Huadong Sun, Bing Zhao, Jun Yi, Shengxuan Weng, Jianbo Chen and Chunxia Dou
Processes 2020, 8(2), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8020169 - 03 Feb 2020
Cited by 5 | Viewed by 2074
Abstract
This paper studied the distributed optimal frequency regulation for multiple power generations in an isolated microgrid under limited communication resource. The event-triggered mechanism is introduced in the construction of the regulation algorithm. Each power generation in the microgrid only transmits its own information [...] Read more.
This paper studied the distributed optimal frequency regulation for multiple power generations in an isolated microgrid under limited communication resource. The event-triggered mechanism is introduced in the construction of the regulation algorithm. Each power generation in the microgrid only transmits its own information to its neighbors through a communication network when the event-triggered condition is satisfied, and the communication burden can be reduced significantly. Moreover, Zeno behavior is excluded to make the event-triggered regulation algorithm reasonable and realistic for practical microgrids. The proposed regulation method can restore the frequency and retain the economic efficiency simultaneously when some disturbances occur in isolated microgrids. The experimental result shows the effectiveness of the theoretical method. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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15 pages, 2977 KiB  
Article
Nonlinear Structural Control Analysis of an Offshore Wind Turbine Tower System
by Y. S. Hamed, Ayman A. Aly, B. Saleh, Ageel F. Alogla, Awad M. Aljuaid and Mosleh M. Alharthi
Processes 2020, 8(1), 22; https://0-doi-org.brum.beds.ac.uk/10.3390/pr8010022 - 22 Dec 2019
Cited by 12 | Viewed by 3676
Abstract
This paper investigates the vibration control, stability, and energy transfer of the offshore wind turbine tower system with control force and nonlinearity terms. A nonlinear proportional derivative (NPD) controller was connected to the system to reduce a high oscillation amplitude and to transfer [...] Read more.
This paper investigates the vibration control, stability, and energy transfer of the offshore wind turbine tower system with control force and nonlinearity terms. A nonlinear proportional derivative (NPD) controller was connected to the system to reduce a high oscillation amplitude and to transfer the energy in the wind turbine system. Furthermore, the averaging method and Poincaré maps were used with respect to the controlled system to study the stability and bifurcation analysis in the worst resonance cases. The curves of force response and frequency response were plotted before and after the control unit was added to the wind turbine system. In addition, we discuss the performances of the control parameters on the vibration magnitudes. Numerical simulations were carried out with Maple and Matlab algorithms to confirm the analytical results. The results show the effectiveness of the NPD controller in suppressing the nonlinear oscillations of the wind turbine system. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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13 pages, 1878 KiB  
Article
Multi-Time-Scale Rolling Optimal Dispatch for Grid-Connected AC/DC Hybrid Microgrids
by Zhao Luo, Zhendong Zhu, Zhiyuan Zhang, Jinghui Qin, Hao Wang, Zeyong Gao and Zhichao Yang
Processes 2019, 7(12), 961; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7120961 - 16 Dec 2019
Cited by 5 | Viewed by 2227
Abstract
In order to reduce the impact of the randomness and volatility of renewable energy on the economic operation of AC/DC hybrid microgrids, a multi-time-scale rolling optimization strategy is proposed for the grid-connected AC/DC hybrid microgrids. It considers the source-load uncertainty declined with time [...] Read more.
In order to reduce the impact of the randomness and volatility of renewable energy on the economic operation of AC/DC hybrid microgrids, a multi-time-scale rolling optimization strategy is proposed for the grid-connected AC/DC hybrid microgrids. It considers the source-load uncertainty declined with time scale reduction, and the scheduling cooperation problem of different units on different time scales. In this paper, we propose a three-time-scale optimal strategy of the day-ahead, intraday and real-time dispatching stage and a two-level rolling optimal strategy of the intraday and real-time stage, aiming at minimizing the operating cost. We added the power penalty cost in the rolling optimization model to limit the energy state of the energy storage system in the constraint, and improve the power correction and tracking effect of the rolling optimization. A typical-structure AC/DC hybrid microgrid is analyzed in this paper and the simulation results are shown to demonstrate the feasibility and effectiveness of the proposed multi-time-scale rolling optimal dispatch. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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34 pages, 22542 KiB  
Article
Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts
by Cody R. Simmons, Joshua R. Arment, Kody M. Powell and John D. Hedengren
Processes 2019, 7(12), 929; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7120929 - 05 Dec 2019
Cited by 12 | Viewed by 4000
Abstract
This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to [...] Read more.
This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. Heating, ventilation, and air conditioning (HVAC) on/off behaviors are simulated by using Mathematical Program with Complementarity Constraints (MPCCs) and solved in near real time with a non-linear solver. Removing HVAC on/off as a discrete variable and replacing it with an MPCC reduces solve time. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. This simulated home contributes less to the grid peak load and therefore improves grid stability and reduces the amplitude of load-following cycles for utilities. The case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real time, and mixed empirical/physics-based machine-learning methods to guide the model structure. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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26 pages, 7070 KiB  
Article
Using Real-Time Electricity Prices to Leverage Electrical Energy Storage and Flexible Loads in a Smart Grid Environment Utilizing Machine Learning Techniques
by Moataz Sheha and Kody Powell
Processes 2019, 7(12), 870; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7120870 - 21 Nov 2019
Cited by 24 | Viewed by 3769
Abstract
With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with [...] Read more.
With exposure to real-time market pricing structures, consumers would be incentivized to invest in electrical energy storage systems and smart predictive automation of their home energy systems. Smart home automation through optimizing HVAC (heating, ventilation, and air conditioning) temperature set points, along with distributed energy storage, could be utilized in the process of optimizing the operation of the electric grid. Using electricity prices as decision variables to leverage electrical energy storage and flexible loads can be a valuable tool to optimize the performance of the power grid and reduce electricity costs both on the supply and demand sides. Energy demand prediction is important for proper allocation and utilization of the available resources. Manipulating energy prices to leverage storage and flexible loads through these demand prediction models is a novel idea that needs to be studied. In this paper, different models for proactive prediction of the energy demand for an entire city using different machine learning techniques are presented and compared. The results of the machine learning techniques show that the proposed nonlinear autoregressive with exogenous inputs neural network model resulted in the most accurate predictions. These prediction models pave the way for the demand side to become an important asset for grid regulation by responding to variable price signals through battery energy storage and passive thermal energy storage using HVAC temperature set points. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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22 pages, 5505 KiB  
Article
Energetic and Exergetic Investigations of Hybrid Configurations in an Absorption Refrigeration Chiller by Aspen Plus
by Xiao Zhang, Liang Cai and Tao Chen
Processes 2019, 7(9), 609; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7090609 - 10 Sep 2019
Cited by 9 | Viewed by 3595
Abstract
In the present study, a steady-state simulation model was built and validated by Aspen Plus to assess the performance of an absorption refrigeration chiller according to the open literature. Given the complex heat transfer happening in the absorbers and the generator, several assumptions [...] Read more.
In the present study, a steady-state simulation model was built and validated by Aspen Plus to assess the performance of an absorption refrigeration chiller according to the open literature. Given the complex heat transfer happening in the absorbers and the generator, several assumptions were proposed to simplify the model, for which a new parameter ε l i q was introduced to describe the ratio of possible heat that could be recovered from the absorption and heat-transferring process in the solution cooling absorber. The energetic and the exergetic investigations of a basic cycle and hybrid cycles were conducted, in which the following parameters were analyzed: coefficient of performance (COP), exergetic efficiency, exergy destruction, and irreversibility. According to the results, the basic cycle exhibited major irreversibility in the absorbers and the generator. Subsequently, two proposed novel configurations were adopted to enhance its performance; the first (configuration 1) involved a compressor between a solution heat exchanger and a solution cooling absorber, and the second (configuration 2) involved a compressor between a rectifier and a condenser. The peak COP and the overall exergetic efficiency (η) of configuration 1 were found to be better, increasing by 15% and 5.5%, respectively, and those of configuration 2 were also upregulated by 5% and 4%, respectively. The rise in intermediate compressor ratio not only reduced the driving generator temperature of both configurations but also expanded the operating range of the system under configuration 1, thus proving their feasibility in waste heat sources and the superiority of configuration 1. Detailed information about the optimal state for hybrid cycles is also presented. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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14 pages, 5286 KiB  
Article
Cooling Performance Assessment of a Slinky Closed Loop Lake Water Heat Pump System under the Climate Conditions of Pakistan
by Muhammad Kashif Shahzad, Mirza Abdullah Rehan, Muzaffar Ali, Azeem Mustafa, Zafar Abbas, Muhammad Mujtaba, Muhammad Imran Akram and Muhammad Rabeet Yousaf
Processes 2019, 7(9), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/pr7090553 - 22 Aug 2019
Cited by 2 | Viewed by 4059
Abstract
This paper presents an experimental evaluation of a closed loop lake water heat pump (LWHPs) system based on the slinky coiled configuration. Initially, a mathematical model is developed in the Engineering Equation Solver (EES) for the heat pump system and the submerged coils [...] Read more.
This paper presents an experimental evaluation of a closed loop lake water heat pump (LWHPs) system based on the slinky coiled configuration. Initially, a mathematical model is developed in the Engineering Equation Solver (EES) for the heat pump system and the submerged coils in a lake. System performance is determined for the submerged slinky copper coils under the various operating conditions. Afterwards, parametric analysis is performed considering different influencing parameters, such as the lake water temperature, ambient temperature, and mass flow rate of the circulating fluid at constant lake depth of 4 ft. The experimental setup is developed for 3.51 kW cooling capacity after cooling load calculation for a small room. In the current study, slinky copper coils are used to exchange heat with lake water. The experimental setup is installed in Taxila, Pakistan, and the system’s performance is analyzed during selected days. After experimentation based on hourly and daily operation characteristics, it is observed that the lake water temperature has significant influence on the heat transfer rate between slinky coil and lake water. While the lake water temperature in summer decreases and increases in winter with the depth. The resulted daily average coefficient of performance (COP) of the system is within the range of 3.24–3.46 during the selected days of cooling season. Based on these results, it can be concluded that the LWHP systems can be considered a viable solution for Pakistan having a well-established canal system. Full article
(This article belongs to the Special Issue Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems)
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