Special Issue "Advances in Theoretical and Computational Energy Optimization Processes"

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

Deadline for manuscript submissions: 31 July 2019

Special Issue Editors

Guest Editor
Prof. Dr. Ferdinando Salata

Department of Astronautics, Electrical and Energetics Engineering, University of Rome “Sapienza”, 00184 Rome, Italy
Website | E-Mail
Interests: energy efficiency; thermodynamics; heat transmission; buildings physics; human thermal comfort; urban microclimate; computational optimization; lighting systems; environmental acoustics
Guest Editor
Prof. Dr. Iacopo Golasi

Department of Astronautics, Electrical and Energetics Engineering, University of Rome “Sapienza”, 00184 Rome, Italy
Website | E-Mail
Interests: energy optimization; thermal comfort; microclimate; computational methods; heat transmission; thermodynamics; lighting; acoustics

Special Issue Information

Dear Colleagues,

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements. This is why over the past few years all the involved stakeholders have widely expressed the necessity of introducing a new approach to the analysis and management of those energy processes characterizing the aforementioned sectors. The objective is to guide production and energy processes to an approach aimed at energy savings and a decrease in the environmental impact. Indeed, all the ecosystems are stressed by obsolete production schemes deriving from an unsustainable paradigm of constant growth and related to the hypothesis of an environment able to absorb and accept all the anthropogenic changes.

Leading the production processes of industry, construction and transport to a revision of their energy requirements is necessary and research activity is called to carry out its natural innovative function.

The industrial sector is in full transition and transformation towards its version 4.0 and is therefore called to review its management and supply costs of energy and raw materials to limit the environmental impact. Research activity must support best practices in energy management and encourage the reduction of greenhouse gas emissions. The construction sector should apply retrofit solutions able to increase energy efficiency, taking into account the environment and climate change at the same time. The transport sector is moving towards a new mobility with respect to the past, thanks to the transition from fossil fuels to electrification and the use of artificial intelligence, thus increasing the level of automation. In this context of great attention towards a sustainable and respectful future for the planet, the study and the diffusion of the results provided by the scientific community concerning the most recent progress in energy optimization is expected to play a key role.

With the aim of proposing the next generation of energy processes and leading to positive implications for the environment, climate and sustainability, this Special Issue "Advances in Theoretical and Computational Energy Optimization Processes" aims to collect sophisticated contributions on all these aspects, highlighting the current state of the art with respect to the results of the main research groups. Studies on energy processes, production methods and innovative mechanisms related to research based on computational optimization methods are all invited to be a part of this scientific collection. This Special Issue also wants to encourage a debate on the future scenarios in each of those sectors currently characterized by significant energy requirements.

Thanks and we hope you will consider participating in this Special Issue.

Sincerely,

Prof. Dr. Ferdinando Salata
Prof. Dr. Iacopo Golasi
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 papers will be 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 1100 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2019 an APC of 1200 CHF applies. 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

  • Energy efficiency
  • Modelling and simulations
  • Complex systems analysis
  • Computational tools
  • Artificial intelligence
  • Optimized design
  • Process systems engineering
  • Industrial processes
  • Buildings energy systems
  • Transport infrastructures

Published Papers (18 papers)

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Research

Open AccessArticle
Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid
Processes 2019, 7(5), 296; https://doi.org/10.3390/pr7050296 (registering DOI)
Received: 1 April 2019 / Revised: 28 April 2019 / Accepted: 15 May 2019 / Published: 19 May 2019
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Abstract
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system [...] Read more.
Optimal scheduling of a redundant residential microgrid (RR-microgrid) could yield economical savings and reduce the emission of pollutants while ensuring the comfort level of users. This paper proposes a novel multi-objective optimal scheduling method for a grid-connected RR-microgrid in which the heating/cooling system of the RR-microgrid is treated as a virtual energy storage system (VESS). An optimization model for grid-connected RR-microgrid scheduling is established based on mixed-integer nonlinear programming (MINLP), which takes the operating cost (OC), thermal comfort level (TCL), and pollution emission (PE) as the optimization objectives. The non-dominate sorting genetic algorithm II (NSGA-II) is employed to search the Pareto front and the best scheduling scheme is determined by the analytic hierarchy process (AHP) method. In a case study, two kinds of heating/cooling systems, the radiant floor heating/cooling system (RFHCS) and the convection heating/cooling system (CHCS) are investigated for the RR-microgrid. respectively, and the feasibility and validity of the scheduling method are ascertained. Full article
Open AccessArticle
Control Strategy of Electric Heating Loads for Reducing Power Shortage in Power Grid
Processes 2019, 7(5), 273; https://doi.org/10.3390/pr7050273
Received: 17 March 2019 / Revised: 13 April 2019 / Accepted: 24 April 2019 / Published: 9 May 2019
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Abstract
With the development of demand response technology, it is possible to reduce power shortages caused by loads participating in power grid dispatching. Based on the equivalent thermal parameter model, and taking full account of the virtual energy storage characteristics presented during electro-thermal conversion, [...] Read more.
With the development of demand response technology, it is possible to reduce power shortages caused by loads participating in power grid dispatching. Based on the equivalent thermal parameter model, and taking full account of the virtual energy storage characteristics presented during electro-thermal conversion, a virtual energy storage model suitable for electric heating loads with different electrical and thermal parameters is proposed in this paper. To avoid communication congestion and simplify calculations, the model is processed by discretization and linearization. To simplify the model, a control strategy for electric heating load, based on the virtual state ofcharge priority list, is proposed. This paper simulates and analyzes a control example, explores the relevant theoretical basis affecting the control effect, and puts forward an optimization scheme for the control strategy. The simulation example proved that the proposed method in this paper can reduce power storage in the grid over a long period of time and can realize a power response in the grid. Full article
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Open AccessArticle
Artificial Neural Networks Approach for a Multi-Objective Cavitation Optimization Design in a Double-Suction Centrifugal Pump
Processes 2019, 7(5), 246; https://doi.org/10.3390/pr7050246
Received: 4 April 2019 / Revised: 22 April 2019 / Accepted: 24 April 2019 / Published: 27 April 2019
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Abstract
Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. [...] Read more.
Double-suction centrifugal pumps are widely used in industrial and agricultural applications since their flow rate is twice that of single-suction pumps with the same impeller diameter. They usually run for longer, which makes them susceptible to cavitation, putting the downstream components at risk. A fast approach to predicting the Net Positive Suction Head required was applied to perform a multi-objective optimization on the double-suction centrifugal pump. An L32 (84) orthogonal array was designed to evaluate 8 geometrical parameters at 4 levels each. A two-layer feedforward neural network and genetic algorithm was applied to solve the multi-objective problem into pareto solutions. The results were validated by numerical simulation and compared to the original design. The suction performance was improved by 7.26%, 3.9%, 4.5% and 3.8% at flow conditions 0.6Qd, 0.8Qd, 1.0Qd and 1.2Qd respectively. The efficiency increased by 1.53% 1.0Qd and 1.1% at 0.8Qd. The streamline on the blade surface was improved and the vapor volume fraction of the optimized impeller was much smaller than that of the original impeller. This study established a fast approach to cavitation optimization and a parametric database for both hub and shroud blade angles for double suction centrifugal pump optimization design. Full article
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Open AccessArticle
Power Transmission Congestion Management Based on Quasi-Dynamic Thermal Rating
Processes 2019, 7(5), 244; https://doi.org/10.3390/pr7050244
Received: 20 March 2019 / Revised: 17 April 2019 / Accepted: 24 April 2019 / Published: 26 April 2019
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Abstract
Transmission congestion not only increases the operation risk, but also reduces the operation efficiency of power systems. Applying a quasi-dynamic thermal rating (QDR) to the transmission congestion alarm system can effectively alleviate transmission congestion. In this paper, according to the heat balance equation [...] Read more.
Transmission congestion not only increases the operation risk, but also reduces the operation efficiency of power systems. Applying a quasi-dynamic thermal rating (QDR) to the transmission congestion alarm system can effectively alleviate transmission congestion. In this paper, according to the heat balance equation under the IEEE standard, a calculation method of QDR is proposed based on the threshold of meteorological parameters under 95% confidence level, which is determined by statistical analysis of seven-year meteorological data in Weihai, China. The QDR of transmission lines is calculated at different time scales. A transmission congestion management model based on QDR is established, and the transmission congestion alarm system including conductor temperature judgment is proposed. The case shows that transmission congestion management based on QDR is feasible, which improves the service life and operation flexibility of the power grid in emergencies and avoids power supply shortages caused by unnecessary trip protection. Full article
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Open AccessArticle
Modeling of Future Electricity Generation and Emissions Assessment for Pakistan
Processes 2019, 7(4), 212; https://doi.org/10.3390/pr7040212
Received: 31 December 2018 / Revised: 5 April 2019 / Accepted: 8 April 2019 / Published: 12 April 2019
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Abstract
Electricity demand in Pakistan has consistently increased in the past two decades. However, this demand is so far partially met due to insufficient supply, inefficient power plants, high transmission and distribution system losses, lack of effective planning efforts and due coordination. The existing [...] Read more.
Electricity demand in Pakistan has consistently increased in the past two decades. However, this demand is so far partially met due to insufficient supply, inefficient power plants, high transmission and distribution system losses, lack of effective planning efforts and due coordination. The existing electricity generation also largely depends on the imported fossil fuels, which is a huge burden on the national economy alongside causing colossal loss to the environment. It is also evident from existing government plans that electricity generation from low-cost coal fuels in the near future will further increase the emissions. As such, in this study, following the government’s electricity demand forecast, four supply side scenarios for the study period (2013–2035) have been developed using Long-range Energy Alternatives Planning System (LEAP) software tool. These scenarios are Reference scenario (REF) based on the government’s power expansion plans, and three alternative scenarios, which include, More Renewable (MRR), More Hydro (MRH), and More Hydro Nuclear (MRHN). Furthermore, the associated gaseous emissions (CO2, SO2, NOX, CH4, N2O) are projected under each of these scenarios. The results of this study reveal that the alternative scenarios are more environmentally friendly than the REF scenario where penetration of planned coal-based power generation plants would be the major sources of emissions. It is, therefore, recommended that the government, apart from implementing the existing plans, should consider harnessing the renewable energy sources as indispensable energy sources in the future energy mix for electricity generation to reduce the fossil-fuel import bill and to contain the emissions. Full article
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Open AccessArticle
Numerical Investigation of Influence of Reservoir Heterogeneity on Electricity Generation Performance of Enhanced Geothermal System
Processes 2019, 7(4), 202; https://doi.org/10.3390/pr7040202
Received: 22 February 2019 / Revised: 3 April 2019 / Accepted: 5 April 2019 / Published: 9 April 2019
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Abstract
The enhanced geothermal system (EGS) reservoir consists of a heterogeneous fracture network and rock matrix, and the heterogeneity of the reservoir has a significant influence on the system’s electricity generation performance. In this study, we numerically investigated the influence of reservoir heterogeneity on [...] Read more.
The enhanced geothermal system (EGS) reservoir consists of a heterogeneous fracture network and rock matrix, and the heterogeneity of the reservoir has a significant influence on the system’s electricity generation performance. In this study, we numerically investigated the influence of reservoir heterogeneity on system production performance based on geological data from the Gonghe Basin geothermal field, and analyzed the main factors affecting production performance. The results show that with the increase of reservoir heterogeneity, the water conduction ability of the reservoir gradually reduces, the water production rate slowly decreases, and this causes the electric power to gradually reduce, the reservoir impedance to gradually increase, the pump power to gradually decrease and the energy efficiency to gradually increase. The fracture spacing, well spacing and injection temperature all have a significant influence on electricity generation performance. Increasing the fracture spacing will significantly reduce electric power, while having only a very slight effect on reservoir impedance and pump power, thus significantly decreasing energy efficiency. Increasing the well spacing will significantly increase the electric power, while having only a very slight effect on the reservoir impedance and pump power, thus significantly increasing energy efficiency. Increasing the injection temperature will obviously reduce the electric power, decrease the reservoir impedance and pump power, and thus reduce energy efficiency. Full article
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Open AccessArticle
A Rotor-Sync Signal-Based Control System of a Doubly-Fed Induction Generator in the Shaft Generation of a Ship
Processes 2019, 7(4), 188; https://doi.org/10.3390/pr7040188
Received: 20 February 2019 / Revised: 28 March 2019 / Accepted: 29 March 2019 / Published: 1 April 2019
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Abstract
A doubly-fed induction machine in generator-mode is popularly used for energy generation, particularly in the case of a variable speed, such as in the wind generator, the shaft generator of a ship, because the doubly-fed induction generator is able to maintain a stable [...] Read more.
A doubly-fed induction machine in generator-mode is popularly used for energy generation, particularly in the case of a variable speed, such as in the wind generator, the shaft generator of a ship, because the doubly-fed induction generator is able to maintain a stable frequency when changing the rotor speed. This paper aims to propose a novel method for controlling the shaft generation system of a ship using a doubly-fed induction generator. This method uses the rotor signals of a small doubly-fed induction machine as base components to create the control signal for the doubly-fed induction generators. The proposed method will be proven by both theory and a simulation model. The advantage of the proposed method is that the control system of the generator can be simply built, but it functions effectively. The generator voltage always coincides with the grid voltage, even when the grid voltage and the rotor speed are changed, and the reactive and active power of the generator fed into the grid can be separately controlled. Full article
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Open AccessArticle
Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm
Processes 2019, 7(3), 177; https://doi.org/10.3390/pr7030177
Received: 22 February 2019 / Revised: 20 March 2019 / Accepted: 22 March 2019 / Published: 26 March 2019
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Abstract
The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based [...] Read more.
The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement. Full article
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Open AccessArticle
A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation
Processes 2019, 7(3), 169; https://doi.org/10.3390/pr7030169
Received: 15 February 2019 / Revised: 17 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
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Abstract
The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of [...] Read more.
The traditional effective variance weighted least squares algorithms for solving CMB (Chemical Mass Balance) models have the following drawbacks: When there is collinearity among the sources or the number of species is less than the number of sources, then some negative value of contribution will appear in the results of the source apportionment or the algorithm does not converge to calculation. In this paper, a novel robust algorithm based on enhanced sampling Monte Carlo simulation and effective variance weighted least squares (ESMC-CMB) is proposed, which overcomes the above weaknesses. In the following practical instances for source apportionment, when nine species and nine sources, with no collinearity among them, are selected, EPA-CMB8.2 (U.S. Environmental Protection Agency-CMB8.2), NKCMB1.0 (NanKai University, China-CMB1.0) and ESMC-CMB can obtain similar results. When the source raise dust is added to the source profiles, or nine sources and eight species are selected, EPA-CMB8.2 and NKCMB1.0 cannot solve the model, but the proposed ESMC-CMB algorithm can achieve satisfactory results that fully verify the robustness and effectiveness of ESMC-CMB. Full article
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Open AccessArticle
Numerical Investigation of SCR Mixer Design Optimization for Improved Performance
Processes 2019, 7(3), 168; https://doi.org/10.3390/pr7030168
Received: 1 February 2019 / Revised: 14 March 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
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Abstract
The continuous increase in the number of stringent exhaust emission legislations of marine Diesel engines had led to a decrease in NOx emissions at the required level. Selective catalyst reduction (SCR) is the most prominent and mature technology used to reduce NO [...] Read more.
The continuous increase in the number of stringent exhaust emission legislations of marine Diesel engines had led to a decrease in NOx emissions at the required level. Selective catalyst reduction (SCR) is the most prominent and mature technology used to reduce NOx emissions. However, to obtain maximum NOx removal with minimum ammonia slip remains a challenge. Therefore, new mixers are designed in order to obtain the maximum SCR efficiency. This paper reports performance parameters such as uniformity of velocity, ammonia uniformity distribution, and temperature distribution. Also, a numerical model is developed to investigate the interaction of urea droplet with exhaust gas and its effects by using line (LM) and swirl (SM) type mixers alone and in combination (LSM). The urea droplet residence time and its interaction in straight pipe are also investigated. Model calculations proved the improvement in velocity uniformity, distribution of ammonia uniformity, and temperature distribution for LSM. Prominent enhancement in the evaporation rate was also achieved by using LSM, which may be due to the breaking of urea droplets into droplets of smaller diameter. Therefore, the SCR system accomplished higher urea conversion efficiency by using LSM. Lastly, the ISO 8178 standard engine test cycle E3 was used to verify the simulation results. It has been observed that the average weighted value of NOx emission obtained at SCR outlet using LSM was 2.44 g/kWh, which strongly meets International Maritime Organization (IMO) Tier III NOx (3.4 g/kWh) emission regulations. Full article
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Open AccessArticle
Implementation of Maximum Power Point Tracking Based on Variable Speed Forecasting for Wind Energy Systems
Processes 2019, 7(3), 158; https://doi.org/10.3390/pr7030158
Received: 30 January 2019 / Revised: 6 March 2019 / Accepted: 8 March 2019 / Published: 15 March 2019
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Abstract
In order to precisely control the wind power generation systems under nonlinear variable wind velocity, this paper proposes a novel maximum power tracking (MPPT) strategy for wind turbine systems based on a hybrid wind velocity forecasting algorithm. The proposed algorithm adapts the bat [...] Read more.
In order to precisely control the wind power generation systems under nonlinear variable wind velocity, this paper proposes a novel maximum power tracking (MPPT) strategy for wind turbine systems based on a hybrid wind velocity forecasting algorithm. The proposed algorithm adapts the bat algorithm and improved extreme learning machine (BA-ELM) for forecasting wind speed to alleviate the slow response of anemometers and sensors, considering that the change of wind speed requires a very short response time. In the controlling strategy, to optimize the output power, a state feedback control technique is proposed to achieve the rotor flux and rotor speed tracking purpose based on MPPT algorithm. This method could decouple the current and voltage of induction generator to track the reference of stator current and flux linkage. By adjusting the wind turbine mechanical speed, the wind energy system could operate at the optimal rotational speed and achieve the maximal power. Simulation results verified the effectiveness of the proposed technique. Full article
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Open AccessArticle
A Flexible Responsive Load Economic Model for Industrial Demands
Processes 2019, 7(3), 147; https://doi.org/10.3390/pr7030147
Received: 18 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract
The best pricing method for any company in a perfectly competitive market is the pricing scheme with regards to the marginal cost. In contrast to this environment, there is a market with imperfect competition. In this market, the price can be affected by [...] Read more.
The best pricing method for any company in a perfectly competitive market is the pricing scheme with regards to the marginal cost. In contrast to this environment, there is a market with imperfect competition. In this market, the price can be affected by some players in the generation/demand side (i.e., suppliers and/or buyers). In the economic literature, “market power” refers to a company that has the power to affect prices. In fact, market power is often defined as the ability to divert prices from competitive levels. In the electricity market, especially because of the integration of intermittent renewable energy resources (RESs) along with the inflexibility of demand, there are levels of market power on the supply side. Hence, implementation of demand response (DR) programs is necessary to increase the flexibility of the demand side to deal with the intermittency of renewable generations and at the same time tackle the market power of the supply side. This paper uses economic theories and mathematical formulations to develop a flexible responsive load economic model (FRLEM) based on real-time pricing (RTP) to show modification of the load profile and mitigation of the energy costs for an industrial zone. This model was developed based on constant elasticity of the substitution utility function, known as one of the most popular utility functions in microeconomics. Full article
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Open AccessArticle
An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms
Processes 2019, 7(3), 142; https://doi.org/10.3390/pr7030142
Received: 29 January 2019 / Revised: 24 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
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Abstract
Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. [...] Read more.
Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme. Full article
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Open AccessArticle
An Integrated Delphi-AHP and Fuzzy TOPSIS Approach toward Ranking and Selection of Renewable Energy Resources in Pakistan
Processes 2019, 7(2), 118; https://doi.org/10.3390/pr7020118
Received: 31 December 2018 / Revised: 14 February 2019 / Accepted: 15 February 2019 / Published: 25 February 2019
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Abstract
Pakistan has long relied on fossil fuels for electricity generation. This is despite the fact that the country is blessed with enormous renewable energy (RE) resources, which can significantly diversify the fuel mix for electricity generation. In this study, various renewable resources of [...] Read more.
Pakistan has long relied on fossil fuels for electricity generation. This is despite the fact that the country is blessed with enormous renewable energy (RE) resources, which can significantly diversify the fuel mix for electricity generation. In this study, various renewable resources of Pakistan—solar, hydro, biomass, wind, and geothermal energy—are analyzed by using an integrated Delphi-analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (F-TOPSIS)-based methodology. In the first phase, the Delphi method was employed to define and select the most important criteria for the selection of RE resources. This process identified four main criteria, i.e., economic, environmental, technical, and socio-political aspects, which are further supplemented by 20 sub-criteria. AHP is later used to obtain the weights of each criterion and the sub-criteria of the decision model. The results of this study reveal wind energy as the most feasible RE resource for electricity generation followed by hydropower, solar, biomass, and geothermal energy. The sensitivity analysis of the decision model results shows that the results of this study are significant, reliable, and robust. The study provides important insights related to the prioritizing of RE resources for electricity generation and can be used to undertake policy decisions toward sustainable energy planning in Pakistan. Full article
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Open AccessArticle
Cogeneration Process Technical Viability for an Apartment Building: Case Study in Mexico
Processes 2019, 7(2), 93; https://doi.org/10.3390/pr7020093
Received: 29 December 2018 / Revised: 5 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
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Abstract
The objective of this paper is to evaluate and to simulate the cogeneration process applied to an apartment building in the Polanco area (Mexico). Considering the building’s electric, thermal demand and consumption data, the cogeneration process model was simulated using Thermoflow© software [...] Read more.
The objective of this paper is to evaluate and to simulate the cogeneration process applied to an apartment building in the Polanco area (Mexico). Considering the building’s electric, thermal demand and consumption data, the cogeneration process model was simulated using Thermoflow© software (Thermoflow Inc., Jacksonville, FL, USA), in order to cover 1.1 MW of electric demand and to supply the thermal needs of hot water, heating, air conditioning and heating pool. As a result of analyzing various schemes of cogeneration, the most efficient scheme consists of the use of a gas turbine (Siemens model SGT-100-1S), achieving a cycle with efficiency of 84.4% and a heat rate of 14,901 kJ/kWh. The economic results of this evaluation show that it is possible to implement the cogeneration in the building with a natural gas price below US$0.014/kWh. The use of financing schemes makes the economic results more attractive. Furthermore, the percentage of the turbine load effect on the turbine load net power, cogeneration efficiency, chimney flue gas temperature, CO2 emission, net heat ratio, turbine fuel flow and after burner fuel flow was also studied. Full article
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Open AccessArticle
Energy-Efficient Train Driving Strategy with Considering the Steep Downhill Segment
Processes 2019, 7(2), 77; https://doi.org/10.3390/pr7020077
Received: 13 January 2019 / Revised: 27 January 2019 / Accepted: 31 January 2019 / Published: 3 February 2019
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Abstract
Implementation of energy-efficient train driving strategy is an effective method to save train traction energy consumption, which has attracted much attention from both researchers and practitioners in recent years. Reducing the unnecessary braking during the journey and increasing the coasting distance are efficient [...] Read more.
Implementation of energy-efficient train driving strategy is an effective method to save train traction energy consumption, which has attracted much attention from both researchers and practitioners in recent years. Reducing the unnecessary braking during the journey and increasing the coasting distance are efficient to save energy in urban rail transit systems. In the steep downhill segment, the train speed will continue to increase without applying traction due to the ramp force. A high initial speed before stepping into the steep downhill segment will bring partial braking to prevent trains from overspeeding. Optimization of the driving strategy of urban rail trains can avoid the partial braking such that the potential energy is efficiently used and the traction energy is reduced. This paper presents an energy-efficient driving strategy optimization model for the segment with the steep downhill slopes. A numerical method is proposed to calculate the corresponding energy-efficient driving strategy of trains. Specifically, the steep downhill segment in the line is identified firstly for a given line and the solution space with different scenarios is analyzed. With the given cruising speed, a primary driving strategy is obtained, based on which the local driving strategy in the steep slope segment is optimized by replacing the cruising regime with coasting regime. Then, the adaptive gradient descent method is adopted to solve the optimal cruising speed corresponding to the minimum traction energy consumption of the train. Some case studies were conducted and the effectiveness of the algorithm was verified by comparing the energy-saving performance with the classical energy-efficient driving strategy of “Maximum traction–Cruising–Coasting–Maximum braking”. Full article
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Open AccessArticle
A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy
Processes 2019, 7(2), 69; https://doi.org/10.3390/pr7020069
Received: 17 December 2018 / Revised: 26 January 2019 / Accepted: 27 January 2019 / Published: 1 February 2019
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Abstract
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise [...] Read more.
Influenced by the complexity of ocean environmental noise and the time-varying of underwater acoustic channels, feature extraction of underwater acoustic signals has always been a difficult challenge. To solve this dilemma, this paper introduces a hybrid energy feature extraction approach for ship-radiated noise (S-RN) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with energy difference (ED) and energy entropy (EE). This approach, named CEEMDAN-ED-EE, has two main advantages: (i) compared with empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN has better decomposition performance by overcoming mode mixing, and the intrinsic mode function (IMF) obtained by CEEMDAN is beneficial to feature extraction; (ii) the classification performance of the single energy feature has some limitations, nevertheless, the proposed hybrid energy feature extraction approach has a better classification performance. In this paper, we first decompose three types of S-RN into sub-signals, named intrinsic mode functions (IMFs). Then, we obtain the features of energy difference and energy entropy based on IMFs, named CEEMDAN-ED and CEEMDAN-EE, respectively. Finally, we compare the recognition rate for three sorts of S-RN by using the following three energy feature extraction approaches, which are CEEMDAN-ED, CEEMDAN-EE and CEEMDAN-ED-EE. The experimental results prove the effectivity and the high recognition rate of the proposed approach. Full article
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Open AccessArticle
Smart Community Energy Cost Optimization Taking User Comfort Level and Renewable Energy Consumption Rate into Consideration
Processes 2019, 7(2), 63; https://doi.org/10.3390/pr7020063
Received: 14 January 2019 / Revised: 23 January 2019 / Accepted: 24 January 2019 / Published: 26 January 2019
Cited by 1 | PDF Full-text (2365 KB) | HTML Full-text | XML Full-text
Abstract
With the rapid development of smart community technologies, how to improve user comfort levels and make full use of renewable energy have become urgent problems. This paper proposes an optimization algorithm to minimize daily energy costs while considering user comfort level and renewable [...] Read more.
With the rapid development of smart community technologies, how to improve user comfort levels and make full use of renewable energy have become urgent problems. This paper proposes an optimization algorithm to minimize daily energy costs while considering user comfort level and renewable energy consumption rate. In this paper, the structure of a typical smart community and the output models of all components installed in the community are introduced first. Then, the characteristics of different types of loads are analyzed, followed by defining the coefficients of user comfort level. In this step, the influence of load-scheduling on user comfort level and the renewable energy consumption rate is emphasized. Finally, based on the time-of-use gas price, this paper optimizes the daily energy costs for an off-grid community under the constraints of the comfort level and renewable energy consumption rate. Results show that scheduling transferable loads and interruptible loads are not independent to each other, and improving user comfort level requires spending more money as compensation. Moreover, fully consuming renewable energy has side effects on energy bills and battery lifetime. It is more conducive to system economy and stability if the maximum renewable energy consumption rate is restricted to 95%. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: An Integrated Delphi-AHP and Fuzzy TOPSIS Approach Towards Ranking and Selection of Renewable Energy Resources in Pakistan

Abstract: Pakistan for long is relying on fossil fuels for electricity generation. It is despite the fact that country has an enormous renewable energy resources which can significantly diversify the fuel
mix for electricity generation. In this study, various renewable resources of Pakistan; solar, hydro, biomass and wind are analyzed by using integrated Delphi, Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution techniques (F-TOPSIS) methodologies. In the first phase, Delphi method was employed to define and select the most important criteria for the selection of renewable energy resources. The Delphi analysis identified 4 main criteria, i.e., economic, environmental, technical, and socio-political aspects which are further supplemented by 20 sub-criteria. AHP is employed later to obtain the weights of each criteria and sub-criteria of the decision model. Finally, the Fuzzy TOPSIS is used to prioritize the 4 renewable energy resources of Pakistan. The results of this study reveal wind energy as the most feasible renewable energy resource for electricity generation followed by hydropower, solar and biomass energy. Moreover, the sensitivity analysis reveals that results are significant, reliable and robust. The study provides important insights pertaining prioritizing the renewable energy resources for the electricity generation and can be used to undertake policy decisions towards sustainable energy planning in Pakistan.


Keywords: Renewable energy resources; Delphi; AHP; Fuzzy TOPSIS; sensitivity analysis; sustainable energy planning.

 

2. Title: Power System Oscillation Improvement Using an Optimal Adaptive Intelligent Controller for STATCOM in a Series Compensated Wind Farm

Author: Dr. Lu

Abstract: This paper proposed a Static Synchronous Compensator (STATCOM) for use with a Self-Excited Induction Generator (SEIG)-based wind farm, which applies a damping controller that is based on an optimal adaptive intelligent controller (OAIC). The proposed OAIC consists of the critic network, the Functional Link based Elman Neural Network (FLENN) and Genetic Algorithm Hybrid Time Varying Particle Swarm Optimization (GAHTVPSO) algorithm. The SEIG-based series compensated wind farm system can improve the damping power system oscillations by a STATCOM using the proposed OAIC. The node connecting weights of the proposed FLENN and critic network are trained online by backpropagation (BP) algorithms. GAHTVPSO is used to adjust the learning rates in the BP algorithm to improve the learning ability of the neural network. Analysis of the performance of the proposed controller shows that it can achieve better damping characteristics. The internal power fluctuations to the power system can also be effectively alleviated under variable wind power generation conditions.

 

3. Title: Managing Energy plus Performance in Data Centers and Battery-based devices using the process of Online Non-clairvoyant Speed-bounded Multiprocessor Scheduling

Author: Dr. Khan

Abstract: An efficient scheduling reduces the time required to process the jobs, and energy management decreases the service cost as well as increases the lifetime of a battery. A balanced trade-off between the energy consumed and processing time gives an ideal objective for scheduling jobs in data centers and battery based devices. In this paper, an online multiprocessor scheduling Multiprocessor with Bounded Speed (MBS) is proposed. The objective of MBS is to minimize the importance-based flow time plus energy (IbFt+E), wherein the jobs arrive arbitrarily with arbitrary importance and the jobs’ sizes are known only at their completion time. Every processor can execute at a different speed, to reduce the energy consumption. MBS uses the tradition power function and bounded speed model. The functioning of MBS is evaluated by utilizing potential function analysis against an offline adversary. For processors m ≥ 2, MBS is O(1)-competitive. The working of a set of jobs is simulated to compare MBS with the best known non-clairvoyant scheduling. The comparative analysis shows that the MBS outperforms other algorithms. The competitiveness of MBS is the least to date.

4.Title: Reliability evaluation method in distribution network considering demand response of household electrical equipment

Author: Dr. Sun

Abstract: The load characteristic of the typical household electrical equipment is studied, and the electric vehicle (EV) loads and the air-conditioning loads are chosen as controllable loads. Considering the EV charging behavior and thermodynamic property of air-conditioning loads, the demand response (DR) models for two typical high-power electrical equipment are presented based on the electricity price and incentive mechanism separately, then the load curve considering two different kinds of DR mechanism is obtained. In order to enhance the computational accuracy of the reliability index, an improved fuzzy clustering algorithm is employed to cluster the annual load curves attained by different DR strategies. Besides, with the line capacity and the transfer capability constraints into consideration, the load shedding strategy is introduced and an improved reliability evaluation method for distributed networks taking insufficient power supply capacity and fault outage into consideration is proposed. Afterwards, the Monte Carlo method is employed to calculate the distribution network reliability index under different load levels. Besides, the impact of different DR strategies on the reliability of distribution networks is analyzed. The results show that the proposed DR strategies could greatly improve the distribution system reliability.

Keywords: demand response; household electrical equipment; time-of-use electricity price; incentive mechanism; capacity constraint; reliability evaluation

 

5. Title: Study of the isentropic efficiency of fluids refrigerants in scroll expander of an Organic Rankine Cycle

Author: Dr. Rios-Moreno

Abstract: According to the International Energy Agency (IEA), the main sources of energy worldwide come from oil, coal and natural gas. The industrial sector uses approximately one third of that energy in its production processes, with a significant loss of heat associated, which is normally wasted and not recovered. However, this residual heat, generated at low temperatures (between 70 and 100 ° C), and then discarded to the environment, has a tremendous potential to be reused in the generation of electrical energy. Research on new techniques applied to innovate the generation of energy using residual heat obtained by its thermal processes has been reported in the literature. The Organic Rankine Cycle systems (ORC) are technologies that allow residual heat to be used to heat up organics fluid, generating electrical energy with the help of a turbine. The thermodynamics behavior of fluids must be known before a selection and application in ORC plants can be implemented. The isentropic efficiency is an important parameter for the comparison of the working fluids, defined as the relation between the electric output power of the system and the ideal expansion power. This paper studies the behavior of the R245fa fluid in a scroll expander integrated to an ORC plant for the generation of electrical energy. SolidWorks Flow Simulation was performed and then compared to the actual performance of the system. The results show that the overall isentropic efficiency of the fluid in the prototype test for ORC in the generation of 1000 W was about 60%, a promising result for the generation of electrical energy using residual heat from the industry.

Keywords: Organic Rankine Cycle; organic fluid; scroll expander; isentropic efficiency; residual heat recovery; flow simulation.

 
6. Title: Optimizing the Process of Wind Energy Generation in Urban Areas: Case Study in México

Abstract: The population growth and the need for a better comfort require a greater energy consumption in cities. The solution should prompt to the use of renewable resources, such as wind. However, the fundamental challenge of the wind turbine for the generation of energy is how to obtain the maximum power in a wide range of wind speeds. This paper presents and optimized decision making process for the selection and installation of a wind turbine in an urban area with a statistically defined range of wind speeds. Dynamic models and simulations were made for a 14kW wind turbine installed at the Autonomous University of Querétaro (UAQ) campus, and the use of a pitch control that will turn the turbine at its nominal speed was also optimized. The results were obtained as a percentage of the use of the turbine according to the frequency of the wind speeds and an estimate of the annual generation.

Keywords: renewable resources; wind energy; generation of energy; wind turbine; urban area; pitch control

7. Title: Investigating dynamic impact of Renewable energy production on CO2 emissions and economic growth: Evidence from FMOLS and DOLS test

Author: Dr. Waris

Abstract: This paper investigates the dynamic relationship between renewable energy production, CO2 emission and economic growth over the period of 1995-2016 from a panel of seven ASEAN countries. The panel co-integration results reveal that sustainable renewable energy production, CO2 emissions, and economic growth are statistically significant for Malaysia, Vietnam, and Indonesia. Furthermore, the study also reveals the harmful impacts on the economic growth of Malaysia and Singapore owing to the increase in CO2 emissions. It is, therefore, recommended to undertake the pedagogical campaigns to accelerate the renewable energy usage in these high carbon emission countries for a more environmentally friendly and sustainable future. In order to decrease the reliance on fossil fuels, which impact the environment adversely, it is suggested that future research should consider and focus the principles of the circular economy and clean development mechanisms integrated with renewable energy technologies

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