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6 pages, 202 KiB

Open AccessEditorial

Advances in Theoretical and Computational Energy Optimization Processes

by Ferdinando Salata and Iacopo Golasi

Processes 2020, 8(6), 669; https://doi.org/10.3390/pr8060669 - 4 Jun 2020

Viewed by 2012

Abstract

Industry, construction and transport are the three sectors that traditionally lead to the highest energy requirements [...] Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

Research

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19 pages, 4957 KiB

Open AccessArticle

Salp Swarm Optimization Algorithm-Based Controller for Dynamic Response and Power Quality Enhancement of an Islanded Microgrid

by Touqeer Ahmed Jumani, Mohd. Wazir Mustafa, Madihah Md. Rasid, Waqas Anjum and Sara Ayub

Processes 2019, 7(11), 840; https://doi.org/10.3390/pr7110840 - 10 Nov 2019

Cited by 49 | Viewed by 4704

Abstract

The islanded mode of the microgrid (MG) operation faces more power quality challenges as compared to grid-tied mode. Unlike the grid-tied MG operation, where the voltage magnitude and frequency of the power system are regulated by the utility grid, islanded mode does not [...] Read more.

The islanded mode of the microgrid (MG) operation faces more power quality challenges as compared to grid-tied mode. Unlike the grid-tied MG operation, where the voltage magnitude and frequency of the power system are regulated by the utility grid, islanded mode does not share any connection with the utility grid. Hence, a proper control architecture of islanded MG is essential to control the voltage and frequency, including the power quality and optimal transient response during different operating conditions. Therefore, this study proposes an intelligent and robust controller for islanded MG, which can accomplish the above-mentioned tasks with the optimal transient response and power quality. The proposed controller utilizes the droop control in addition to the back to back proportional plus integral (PI) regulator-based voltage and current controllers in order to accomplish the mentioned control objectives efficiently. Furthermore, the intelligence of the one of the most modern soft computational optimization algorithms called salp swarm optimization algorithm (SSA) is utilized to select the best combination of the PI gains (k_p and k_i) and dc side capacitance (C), which in turn ensures optimal transient response during the distributed generator (DG) insertion and load change conditions. Finally, to evaluate the effectiveness of the proposed control approach, its outcomes are compared with that of the previous approaches used in recent literature on basis of transient response measures, quality of solution and power quality. The results prove the superiority of the proposed control scheme over that of the particle swarm optimization (PSO) and grasshopper optimization algorithm (GOA) based MG controllers for the same operating conditions and system configuration. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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14 pages, 1319 KiB

Open AccessArticle

A Dispatching Optimization Model for Park Power Supply Systems Considering Power-to-Gas and Peak Regulation Compensation

by Yunfu Qin, Hongyu Lin, Zhongfu Tan, Qingyou Yan, Li Li, Shenbo Yang, Gejirifu De and Liwei Ju

Processes 2019, 7(11), 813; https://doi.org/10.3390/pr7110813 - 4 Nov 2019

Cited by 3 | Viewed by 2698

Abstract

To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of [...] Read more.

To ensure the stability of park power supply systems and to promote the consumption of wind/photovoltaic generation, this paper proposes a dispatching optimization model for the park power supply system with power-to-gas (P2G) and peak regulation via gas-fired generators. Firstly, the structure of a park power system with P2G was built. Secondly, a dispatching optimization model for the park power supply system was constructed with a peak regulation compensation mechanism. Finally, the effectiveness of the model was verified by a case study. The case results show that with the integration of P2G and the marketized peak regulation compensation mechanism, preferential power energy storage followed by gas storage had the best effect on the park power supply system, which minimized the clean energy curtailment to 11.18% and the total cost by approximately $120.190 and maximized the net profit by approximately $152.005. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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18 pages, 2951 KiB

Open AccessArticle

Reliability Evaluation Method Considering Demand Response (DR) of Household Electrical Equipment in Distribution Networks

by Hongzhong Chen, Jun Tang, Lei Sun, Jiawei Zhou, Xiaolei Wang and Yeying Mao

Processes 2019, 7(11), 799; https://doi.org/10.3390/pr7110799 - 3 Nov 2019

Cited by 5 | Viewed by 2516

Abstract

The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment [...] Read more.

The load characteristic of typical household electrical equipment is elaborately analyzed. Considering the electric vehicles’ (EVs’) charging behavior and air conditioning’s thermodynamic property, an electricity price-based demand response (DR) model and an incentive-based DR model for two kinds of typical high-power electrical equipment are proposed to obtain the load curve considering two different kinds of DR mechanisms. Afterwards, a load shedding strategy is introduced to improve the traditional reliability evaluation method for distribution networks, with the capacity constraints of tie lines taken into account. Subsequently, a reliability calculation method of distribution networks considering the shortage of power supply capacity and outages is presented. Finally, the Monte Carlo method is employed to calculate the reliability index of distribution networks with different load levels, and the impacts of different DR strategies on the reliability of distribution networks are analyzed. The results show that both DR strategies can improve the distribution system reliability. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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18 pages, 1193 KiB

Open AccessArticle

A Time-Sequence Simulation Method for Power Unit’s Monthly Energy-Trade Scheduling with Multiple Energy Sources

by Liang Sun, Qi Zhang, Na Zhang, Zhuoran Song, Xinglong Liu and Weidong Li

Processes 2019, 7(10), 771; https://doi.org/10.3390/pr7100771 - 21 Oct 2019

Cited by 5 | Viewed by 2984

Abstract

The uncertainty of new energy output from wind power is rarely considered in the monthly energy-trade scheduling. This causes many problems since the new energy penetration level increases. The fairness of the scheduled energy for the power suppliers is difficult to guarantee. Because [...] Read more.

The uncertainty of new energy output from wind power is rarely considered in the monthly energy-trade scheduling. This causes many problems since the new energy penetration level increases. The fairness of the scheduled energy for the power suppliers is difficult to guarantee. Because the actual power system operation is far away from scheduling when the monthly energy-trade schedule is carried out, unnecessary wind curtailment might occur, and even the feasibility of monthly energy-trade schedule might not be guaranteed. This affects the security and reliability of the power system operation. In this paper, a new time-sequence simulation method for the monthly energy-trade scheduling is proposed, which considers the new energy power forecasting characteristic and the computational load problem of hourly energy-trade simulation in the remaining months. The proposed method is based on a segment modelling strategy. The power generation in the scheduling month is optimized hourly, and the energy generation is optimized in the subsequent months on a monthly basis. For the scheduling month, accurate cost function is applied in the objective function, and detailed short-term operation constraints and the new energy forecasting results are considered, which can guarantee the feasibility of the new monthly energy-trade scheduling and lay a solid foundation for daily dispatching. For the subsequent months, since the load forecast accuracy is lower and no wind power forecasting results could be used, the rough cost function is applied, and only monthly constraints are considered. To ensure a balance in the execution progress of each power generating entity, the simulation time-scale is set as the remainder of the months in the study year. The new approach ensures the fairness of power execution progress and improves the new energy consumption level. A case study was used to verify the feasibility and effectiveness of the proposed method, which provides a theoretical reference for the monthly electrical energy-trade scheduling. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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29 pages, 6048 KiB

Open AccessFeature PaperArticle

Simulation-Based Design and Economic Evaluation of a Novel Internally Circulating Fluidized Bed Reactor for Power Production with Integrated CO₂ Capture

by Jan Hendrik Cloete, Mohammed N. Khan, Schalk Cloete and Shahriar Amini

Processes 2019, 7(10), 723; https://doi.org/10.3390/pr7100723 - 11 Oct 2019

Cited by 8 | Viewed by 3863

Abstract

Limiting global temperature rise to well below 2 °C according to the Paris climate accord will require accelerated development, scale-up, and commercialization of innovative and environmentally friendly reactor concepts. Simulation-based design can play a central role in achieving this goal by decreasing the [...] Read more.

Limiting global temperature rise to well below 2 °C according to the Paris climate accord will require accelerated development, scale-up, and commercialization of innovative and environmentally friendly reactor concepts. Simulation-based design can play a central role in achieving this goal by decreasing the number of costly and time-consuming experimental scale-up steps. To illustrate this approach, a multiscale computational fluid dynamics (CFD) approach was utilized in this study to simulate a novel internally circulating fluidized bed reactor (ICR) for power production with integrated CO₂ capture on an industrial scale. These simulations were made computationally feasible by using closures in a filtered two-fluid model (fTFM) to model the effects of important subgrid multiphase structures. The CFD simulations provided valuable insight regarding ICR behavior, predicting that CO₂ capture efficiencies and purities above 95% can be achieved, and proposing a reasonable reactor size. The results from the reactor simulations were then used as input for an economic evaluation of an ICR-based natural gas combined cycle power plant. The economic performance results showed that the ICR plant can achieve a CO₂ avoidance cost as low as $58/ton. Future work will investigate additional firing after the ICR to reach the high inlet temperatures of modern gas turbines. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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20 pages, 10373 KiB

Open AccessArticle

Numerical and Experimental Study of a Vortex Structure and Energy Loss in a Novel Self-Priming Pump

by Hao Chang, Ramesh K. Agarwal, Wei Li, Ling Zhou and Weidong Shi

Processes 2019, 7(10), 701; https://doi.org/10.3390/pr7100701 - 4 Oct 2019

Cited by 10 | Viewed by 3113

Abstract

The self-priming pump as an essential energy conversion equipment is widely used in hydropower and thermal power plants. The energy losses in the internal flow passage of the pump directly affect its work efficiency. Therefore, it is important to improve the internal flow [...] Read more.

The self-priming pump as an essential energy conversion equipment is widely used in hydropower and thermal power plants. The energy losses in the internal flow passage of the pump directly affect its work efficiency. Therefore, it is important to improve the internal flow characteristic of the pump. In the present work, a novel self-priming pump which starts without water is proposed; this pump can reduce the energy consumption as well as the time needed to start its operation. The spatial structure of the vortices in the pump is investigated by employing the Q criterion with the numerical solution of the vorticity transport equation. Based on the morphology, the vortices can be separated into three categories: Trailing Edge Vortex (TEV), Leading Edge Vortex (LEV) and Gap Leakage Vortex (GLV). Generally, the morphology of the TEV is more disorderly than that of LEV and GLV, and the intensity of TEV is significantly higher than that of the other two vortices. To determine the magnitude and distribution of energy loss in the pump, entropy production analysis is employed to study the influence of blade thickness on energy characteristics of the pump. It is found that with an increase in the flow rate, the location of energy loss transfers from the trailing edge to the leading edge of the blade, and viscous entropy production (VEP) and turbulence entropy production (TEP) are the dominant factors which influence the energy conversion in the pump. More importantly, employing the blade with a thin leading edge and a thick trailing edge can not only significantly reduce the impact of incoming flow under over-load condition (flow rate higher than the design condition) but can also increase the efficiency of the pump. Thus, an increase in thickness of the blade from the leading edge to the trailing edge is beneficial for improving the pump performance. The results of this paper can be helpful in providing guidelines for reducing the energy loss and in improving the performance of a self-priming pump. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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20 pages, 3556 KiB

Open AccessArticle

Multi-Agent Consensus Algorithm-Based Optimal Power Dispatch for Islanded Multi-Microgrids

by Xingli Zhai and Ning Wang

Processes 2019, 7(10), 679; https://doi.org/10.3390/pr7100679 - 1 Oct 2019

Cited by 12 | Viewed by 3433

Abstract

Islanded multi-microgrids formed by interconnections of microgrids will be conducive to the improvement of system economic efficiency and supply reliability. Due to the lack of support from a main grid, the requirement of real-time power balance of the islanded multi-microgrid is relatively high. [...] Read more.

Islanded multi-microgrids formed by interconnections of microgrids will be conducive to the improvement of system economic efficiency and supply reliability. Due to the lack of support from a main grid, the requirement of real-time power balance of the islanded multi-microgrid is relatively high. In order to solve real-time dispatch problems in an island multi-microgrid system, a real-time cooperative power dispatch framework is proposed by using the multi-agent consensus algorithm. On this basis, a regulation cost model for the microgrid is developed. Then a consensus algorithm of power dispatch is designed by selecting the regulation cost of each microgrid as the consensus variable to make all microgrids share the power unbalance, thus reducing the total regulation cost. Simulation results show that the proposed consensus algorithm can effectively solve the real-time power dispatch problem for islanded multi-microgrids. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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30 pages, 13849 KiB

Open AccessArticle

Energy Model for Long-Term Scenarios in Power Sector under Energy Transition Laws

by Gabriela Hernández-Luna, Rosenberg J. Romero, Antonio Rodríguez-Martínez, José María Ponce-Ortega, Jesús Cerezo Román and Guadalupe Diocelina Toledo Vázquez

Processes 2019, 7(10), 674; https://doi.org/10.3390/pr7100674 - 29 Sep 2019

Cited by 3 | Viewed by 2460

Abstract

High electricity demand, as well as emissions generated from this activity impact directly to global warming. Mexico is paying attention to this world difficulty and it is convinced that sustainable economic growth is possible. For this reason, it has made actions to face [...] Read more.

High electricity demand, as well as emissions generated from this activity impact directly to global warming. Mexico is paying attention to this world difficulty and it is convinced that sustainable economic growth is possible. For this reason, it has made actions to face this problem like as launching constitutional reforms in the power sector. This paper presents an energy model to optimize the grid of power plants in the Mexican electricity sector (MES). The energy model considers indicators and parameters from Mexican Energy Reforms. Electricity demand is defined as a function of two population models and three electricity consumption per capita. Prospectives are presented as a function of total annual cost of electricity generation, an optimal number of power plants—fossil and clean—as well as CO₂eq emissions. By mean of the energy model, optimized grid scenarios are identified to meet the governmental goals (energy and environment) to 2050. In addition, this model could be used as a base to identify optimal scenarios which contribute to sustainable economic growth, as well as evaluate the social and environmental impacts of employed technologies. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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23 pages, 10298 KiB

Open AccessArticle

Intelligent Energy Management for Plug-in Hybrid Electric Bus with Limited State Space

by Hongqiang Guo, Shangye Du, Fengrui Zhao, Qinghu Cui and Weilong Ren

Processes 2019, 7(10), 672; https://doi.org/10.3390/pr7100672 - 28 Sep 2019

Cited by 8 | Viewed by 2412

Abstract

Tabular Q-learning (QL) can be easily implemented into a controller to realize self-learning energy management control of a plug-in hybrid electric bus (PHEB). However, the “curse of dimensionality” problem is difficult to avoid, as the design space is huge. This paper proposes a [...] Read more.

Tabular Q-learning (QL) can be easily implemented into a controller to realize self-learning energy management control of a plug-in hybrid electric bus (PHEB). However, the “curse of dimensionality” problem is difficult to avoid, as the design space is huge. This paper proposes a QL-PMP algorithm (QL and Pontryagin minimum principle (PMP)) to address the problem. The main novelty is that the difference between the feedback SOC (state of charge) and the reference SOC is exclusively designed as state, and then a limited state space with 50 rows and 25 columns is proposed. The off-line training process shows that the limited state space is reasonable and adequate for the self-learning; the Hardware-in-Loop (HIL) simulation results show that the QL-PMP strategy can be implemented into a controller to realize real-time control, and can on average improve the fuel economy by 20.42%, compared to the charge depleting–charge sustaining (CDCS) strategy. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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12 pages, 2901 KiB

Open AccessArticle

Using PSO Algorithm to Compensate Power Loss Due to the Aeroelastic Effect of the Wind Turbine Blade

by Ying Zhao, Caicai Liao, Zhiwen Qin and Ke Yang

Processes 2019, 7(9), 633; https://doi.org/10.3390/pr7090633 - 18 Sep 2019

Cited by 3 | Viewed by 2620

Abstract

Power loss due to the aeroelastic effect of the blade is becoming an important problem of large-scale blade design. Prior work has already employed the pretwisting method to deal with this problem and obtained some good results at reference wind speed. The aim [...] Read more.

Power loss due to the aeroelastic effect of the blade is becoming an important problem of large-scale blade design. Prior work has already employed the pretwisting method to deal with this problem and obtained some good results at reference wind speed. The aim of this study was to compensate for the power loss for all of the wind speeds by using the pretwisting method. Therefore, we developed an aeroelastic coupling optimization model, which takes the pretwist angles along the blade as free variables, the maximum AEP (annual energy production) as the optimal object, and the smooth of the twist distribution as one of the constraint conditions. In this optimization model, a PSO (particle swarm optimization) algorithm is used and combined with the BEM-3DFEM (blade element momentum—three-dimensional finite element method) model. Then, the optimization model was compared with an iteration method, which was recently developed by another study and can well compensate the power loss at reference wind speed. By a design test, we found that the power loss can be reduced by pretwisting the origin blade, whether using the optimization model or the iteration method. Moreover, the optimization model has better ability than the iteration method to compensate the power loss with lower thrust coefficient while keeping the twist distribution smooth. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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14 pages, 1909 KiB

Open AccessArticle

The Direct Speed Control of Pmsm Based on Terminal Sliding Mode and Finite Time Observer

by Yao Wang, HaiTao Yu, Zhiyuan Che, Yuchen Wang and Yulei Liu

Processes 2019, 7(9), 624; https://doi.org/10.3390/pr7090624 - 16 Sep 2019

Cited by 5 | Viewed by 3733

Abstract

A non-singular terminal sliding mode control based on finite time observer is designed to achieve speed direct control for the permanent magnet synchronous motor (PMSM) drive system. Speed and current are regulated in one loop under the non-cascade structure, taking place of the [...] Read more.

A non-singular terminal sliding mode control based on finite time observer is designed to achieve speed direct control for the permanent magnet synchronous motor (PMSM) drive system. Speed and current are regulated in one loop under the non-cascade structure, taking place of the cascade structure control method in the vector control of PMSM. Based on the second-order speed function of the PMSM, the disturbance and parameters uncertainties are estimated by the designed finite time observer (FTO), and compensate to the drive system. The estimated value of the finite time observer will converge to the actual disturbance value in a finite time. A second-order non-singular terminal sliding mode controller is proposed to realize the speed and current single-loop, which can track the reference speed and reference current in a finite time. Rigorous stability analysis is established. Comparative results verified that the proposed method has faster speed tracking performance and disturbance rejection property. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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11 pages, 2013 KiB

Open AccessArticle

Extended State Observer-Based Predictive Speed Control for Permanent Magnet Linear Synchronous Motor

by Yao Wang, Haitao Yu, Zhiyuan Che, Yuchen Wang and Cheng Zeng

Processes 2019, 7(9), 618; https://doi.org/10.3390/pr7090618 - 11 Sep 2019

Cited by 15 | Viewed by 3664

Abstract

Combining the feedback of predictive function control and the feedforward of extended state observer, a composite control strategy is proposed for the permanent magnet linear synchronous motor (PMLSM). The mathematical model of the PMLSM vector control system is established based on the basic [...] Read more.

Combining the feedback of predictive function control and the feedforward of extended state observer, a composite control strategy is proposed for the permanent magnet linear synchronous motor (PMLSM). The mathematical model of the PMLSM vector control system is established based on the basic structure and operation mechanism of PMLSM. Then, a speed regulator based on predictive function control (PFC) is designed to improve the speed tracking performance of the PMLSM drive system. The state and disturbance of the PMLSM system estimated by the extended state observer (ESO) transferred to the PMLSM drive system, and the robustness of the drive system will be improved. Comparative simulation and experiment results show that the proposed method has better speed tracking performance and disturbance rejection property. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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17 pages, 457 KiB

Open AccessArticle

Economic Dispatch of Multi-Energy System Considering Load Replaceability

by Tao Zheng, Zemei Dai, Jiahao Yao, Yufeng Yang and Jing Cao

Processes 2019, 7(9), 570; https://doi.org/10.3390/pr7090570 - 28 Aug 2019

Cited by 7 | Viewed by 2767

Abstract

By integrating gas, electricity, and cooling and heat networks, multi-energy system (MES) breaks the bondage of isolated planning and operation of independent energy systems. Appropriate scheduling of MES is critical to the operational economy, and it is essential to design scheduling strategies to [...] Read more.

By integrating gas, electricity, and cooling and heat networks, multi-energy system (MES) breaks the bondage of isolated planning and operation of independent energy systems. Appropriate scheduling of MES is critical to the operational economy, and it is essential to design scheduling strategies to achieve maximum economic benefits. In addition to the emergence of energy conversion systems, the other main novelty of MES is the multivariate of load, which offers a great optimization potential by changing load replaceability (flexibly adjusting the composition of loads). In this paper, by designing load replaceability index (LRI) of composite load in MES, its interaction mechanism with scheduling optimum is systematically analyzed. Through case studies, it is proven that the optimum can be improved by elevating load replaceability. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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16 pages, 4121 KiB

Open AccessArticle

Mathematical Modeling and Simulation on the Stimulation Interactions in Coalbed Methane Thermal Recovery

by Teng Teng, Yingheng Wang, Xiang He and Pengfei Chen

Processes 2019, 7(8), 526; https://doi.org/10.3390/pr7080526 - 8 Aug 2019

Cited by 13 | Viewed by 4045

Abstract

Heat stimulation of coalbed methane (CBM) reservoirs has remarkable promotion to gas desorption that enhances gas recovery. However, coalbed deformation, methane delivery and heat transport interplay each other during the stimulation process. This paper experimentally validated the evolutions of gas sorption and coal [...] Read more.

Heat stimulation of coalbed methane (CBM) reservoirs has remarkable promotion to gas desorption that enhances gas recovery. However, coalbed deformation, methane delivery and heat transport interplay each other during the stimulation process. This paper experimentally validated the evolutions of gas sorption and coal permeability under variable temperature. Then, a completely coupled heat-gas-coal model was theoretically developed and applied to a computational simulation of CBM thermal recovery based on a finite element approach of COMSOL with MATLAB. Modeling and simulation results show that: Although different heat-gas-coal interactions have different effects on CBM recovery, thermal stimulation of coalbed can promote methane production effectively. However, CBM thermal recovery needs a forerunner heating time before the apparent enhancement of production. The modeling and simulation results may improve the current cognitions of CBM thermal recovery. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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30 pages, 6130 KiB

Open AccessArticle

A Modular Framework for Optimal Load Scheduling under Price-Based Demand Response Scheme in Smart Grid

by Ghulam Hafeez, Noor Islam, Ammar Ali, Salman Ahmad, Muhammad Usman and Khurram Saleem Alimgeer

Processes 2019, 7(8), 499; https://doi.org/10.3390/pr7080499 - 1 Aug 2019

Cited by 49 | Viewed by 4463

Abstract

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The [...] Read more.

With the emergence of the smart grid (SG), real-time interaction is favorable for both residents and power companies in optimal load scheduling to alleviate electricity cost and peaks in demand. In this paper, a modular framework is introduced for efficient load scheduling. The proposed framework is comprised of four modules: power company module, forecaster module, home energy management controller (HEMC) module, and resident module. The forecaster module receives a demand response (DR), information (real-time pricing scheme (RTPS) and critical peak pricing scheme (CPPS)), and load from the power company module to forecast pricing signals and load. The HEMC module is based on our proposed hybrid gray wolf-modified enhanced differential evolutionary (HGWmEDE) algorithm using the output of the forecaster module to schedule the household load. Each appliance of the resident module receives the schedule from the HEMC module. In a smart home, all the appliances operate according to the schedule to reduce electricity cost and peaks in demand with the affordable waiting time. The simulation results validated that the proposed framework handled the uncertainties in load and supply and provided optimal load scheduling, which facilitates both residents and power companies. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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19 pages, 776 KiB

Open AccessArticle

Investigating the Dynamic Impact of CO₂ Emissions and Economic Growth on Renewable Energy Production: Evidence from FMOLS and DOLS Tests

by Muhammad Waris Ali Khan, Shrikant Krupasindhu Panigrahi, Khamis Said Nasser Almuniri, Mujeeb Iqbal Soomro, Nayyar Hussain Mirjat and Eisa Salim Alqaydi

Processes 2019, 7(8), 496; https://doi.org/10.3390/pr7080496 - 1 Aug 2019

Cited by 52 | Viewed by 10686

Abstract

Understanding the dynamic nexus between CO₂ emissions and economic growth in the sustainable environment helps the economies in developing resources and formulating apposite energy policies. In the recent past, various studies have explored the nexus between CO₂ emissions and economic growth. [...] Read more.

Understanding the dynamic nexus between CO₂ emissions and economic growth in the sustainable environment helps the economies in developing resources and formulating apposite energy policies. In the recent past, various studies have explored the nexus between CO₂ emissions and economic growth. This study, however, investigates the nexus between renewable energy production, CO₂ emissions, and economic growth over the period from 1995 to 2016 for seven Association of Southeast Asian Nations (ASEAN) countries. Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) methodologies were used to estimate the long- and short-run relationships. The panel results revealed that renewable energy production has a significant long term effect on CO₂ emissions for Vietnam (t = −2.990), Thailand (t = −2.505), and Indonesia (t = −2.515), and economic growth impact for Malaysia (t = 2.050), Thailand (t = −2.001), and the Philippines (t = −2.710). It is, therefore, vital that the ASEAN countries implement policies and strategies that ensure energy saving and continuous economic growth without forsaking the environment. This study, as such, recommends that ASEAN countries should take measures to decrease the reliance on fossil fuels for achieving these objectives. Future research should consider the principles of circular economy and clean energy development mechanisms integrated with renewable energy technologies. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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12 pages, 2044 KiB

Open AccessArticle

Temporal Feature Selection for Multi-Step Ahead Reheater Temperature Prediction

by Ning Gui, Jieli Lou, Zhifeng Qiu and Weihua Gui

Processes 2019, 7(7), 473; https://doi.org/10.3390/pr7070473 - 22 Jul 2019

Cited by 4 | Viewed by 3271

Abstract

Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for [...] Read more.

Accurately predicting the reheater steam temperature over both short and medium time periods is crucial for the efficiency and safety of operations. With regard to the diverse temporal effects of influential factors, the accurate identification of delay orders allows effective temperature predictions for the reheater system. In this paper, a deep neural network (DNN) and a genetic algorithm (GA)-based optimal multi-step temporal feature selection model for reheater temperature is proposed. In the proposed model, DNN is used to establish a steam temperature predictor for future time steps, and GA is used to find the optimal delay orders, while fully considering the balance between modeling accuracy and computational complexity. The experimental results for two ultra-super-critical 1000 MW power plants show that the optimal delay orders calculated using this method achieve high forecasting accuracy and low computational overhead. Moreover, it is argued that the similarities of the two reheater experiments reflect the common physical properties of different reheaters, so the proposed algorithms could be generalized to guide temporal feature selection for other reheaters. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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8 pages, 2188 KiB

Open AccessArticle

Optimized Energy Management Strategies for Campus Hybrid PV–Diesel Systems during Utility Load Shedding Events

by Jacques Maritz

Processes 2019, 7(7), 430; https://doi.org/10.3390/pr7070430 - 8 Jul 2019

Cited by 8 | Viewed by 3258

Abstract

The unique situation of utility power curtailment unveils opportunities in the fields of energy management and digital resource management. During utility load shedding events, campuses are typically driven as Photo Voltaic (PV)–diesel generator hybrid systems, of which the main fossil resource driver is [...] Read more.

The unique situation of utility power curtailment unveils opportunities in the fields of energy management and digital resource management. During utility load shedding events, campuses are typically driven as Photo Voltaic (PV)–diesel generator hybrid systems, of which the main fossil resource driver is diesel. With the appropriate Supervisory Control and Data Acquisition (SCADA) systems, discrete departmental energy policies along with control, forecasting and Internet of Things (IoT) infrastructure, the campus hybrid system could be optimized on a short timescale during the shedding event. In this paper the optimization methodology, required technology infrastructure, possible forecasting algorithms and potential implementation will be discussed. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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23 pages, 5398 KiB

Open AccessArticle

Wind Energy Generation Assessment at Specific Sites in a Peninsula in Malaysia Based on Reliability Indices

by Athraa Ali Kadhem, Noor Izzri Abdul Wahab and Ahmed N. Abdalla

Processes 2019, 7(7), 399; https://doi.org/10.3390/pr7070399 - 27 Jun 2019

Cited by 28 | Viewed by 5872

Abstract

This paper presents a statistical analysis of wind speed data that can be extremely useful for installing a wind generation as a stand-alone system. The main objective is to define the wind power capacity’s contribution to the adequacy of generation systems for the [...] Read more.

This paper presents a statistical analysis of wind speed data that can be extremely useful for installing a wind generation as a stand-alone system. The main objective is to define the wind power capacity’s contribution to the adequacy of generation systems for the purpose of selecting wind farm locations at specific sites in Malaysia. The combined Sequential Monte Carlo simulation (SMCS) technique and the Weibull distribution models are employed to demonstrate the impact of wind power in power system reliability. To study this, the Roy Billinton Test System (RBTS) is considered and tested using wind data from two sites in Peninsular Malaysia, Mersing and Kuala Terengganu, and one site, Kudat, in Sabah. The results showed that Mersing and Kudat were best suitable for wind sites. In addition, the reliability indices are compared prior to the addition of the two wind farms to the considered RBTS system. The results reveal that the reliability indices are slightly improved for the RBTS system with wind power generation from both the potential sites. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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13 pages, 2504 KiB

Open AccessArticle

A Model for Optimizing Location Selection for Biomass Energy Power Plants

by Chia-Nan Wang, Tsang-Ta Tsai and Ying-Fang Huang

Processes 2019, 7(6), 353; https://doi.org/10.3390/pr7060353 - 8 Jun 2019

Cited by 28 | Viewed by 5956

Abstract

In addition to its potential for wave power, wind power, hydropower, and solar power, it can be said that Vietnam is a country with great potential for biomass energy derived from agricultural waste, garbage, and urban wastewater, which are resources widely available across [...] Read more.

In addition to its potential for wave power, wind power, hydropower, and solar power, it can be said that Vietnam is a country with great potential for biomass energy derived from agricultural waste, garbage, and urban wastewater, which are resources widely available across the country. This huge amount of biomass, however, if left untreated, could become a major source of pollution and cause serious impacts on ecosystems (soil, water, and air), as well as on human health. In this research, the authors present a fuzzy multicriteria decision-making model (FMCDM) for optimizing the site selection process for biomass power plants. All of the criteria affecting location selection are identified by experts and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized so as to identify the weight of all of the criteria in the second stage. Furthermore, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is applied for ranking potential locations in the final stage of this research. As a result, Long An (DMU/005) was found to be the best location for building biomass energy in Vietnam. The main contributions of this work include modeling the site selection decision process under fuzzy environment conditions. The proposed approaches also can address the complex problems in site selection; it is also a flexible design model for considering the evaluation criteria, and is applicable to location selection for other industries. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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13 pages, 1597 KiB

Open AccessArticle

The Influence and Optimization of Geometrical Parameters on Coast-Down Characteristics of Nuclear Reactor Coolant Pumps

by Yuanyuan Zhao, Xiangyu Si, Xiuli Wang, Rongsheng Zhu, Qiang Fu and Huazhou Zhong

Processes 2019, 7(6), 327; https://doi.org/10.3390/pr7060327 - 1 Jun 2019

Cited by 3 | Viewed by 3684

Abstract

Coast-down characteristics are the crucial safety evaluation factors of nuclear reactor coolant pumps. The energy stored at the highest moment of inertia of the reactor coolant pump unit is utilized to maintain a normal coolant supply to the core of the cooling loop [...] Read more.

Coast-down characteristics are the crucial safety evaluation factors of nuclear reactor coolant pumps. The energy stored at the highest moment of inertia of the reactor coolant pump unit is utilized to maintain a normal coolant supply to the core of the cooling loop system for a short period of time during the coast-down transition. As a result of the high inertia moment of the rotor system, the unit requires a high reliability of the nuclear reactor coolant pump and consumes considerable energy in the start-up and normal operation. This paper considers the operational characteristics of the coast-down transition process based on the existing hydraulic model of the nuclear reactor coolant pump. With the implementation of an orthogonal test, the hydraulic performance of the nuclear reactor coolant pump was optimized, and the optimal combination of impeller geometrical parameters was selected using multivariate linear regression to prolong the coast-down time of the reactor coolant pump and to avoid serious nuclear accidents. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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18 pages, 3300 KiB

Open AccessArticle

Productivity Models of Infill Complex Structural Wells in Mixed Well Patterns

by Liang Sun, Baozhu Li and Yong Li

Processes 2019, 7(6), 324; https://doi.org/10.3390/pr7060324 - 31 May 2019

Cited by 3 | Viewed by 2884

Abstract

The mathematical models of productivity calculation for complex structural wells mainly focus on the single well or the regular well pattern. Previous research on the seepage theory of complex structural wells and vertical wells in mixed well pattern is greatly insufficient. Accordingly, this [...] Read more.

The mathematical models of productivity calculation for complex structural wells mainly focus on the single well or the regular well pattern. Previous research on the seepage theory of complex structural wells and vertical wells in mixed well pattern is greatly insufficient. Accordingly, this article presents a methodology of evaluating the productivity of infill complex structural wells in mixed well patterns. On the basis of the mirror-image method and source–sink theory, two semi-analytical models are established. These models are applied to the productivity prediction of an infill horizontal well inhorizontal-vertical well pattern and an infill multilateral well inmultilateral-vertical well pattern, respectively, in which the interference of other wells, the randomicity of well patterns, and the pressure drawdown along the horizontal laterals are taken into account. The semi-analytical models’ results are consistent with those calculated by the Eclipse reservoir simulator with the relative error of less than 15%. Results indicate that the bottom hole flowing pressure decreases logarithmically while the wellbore flow rate increases monotonically from the toe to the heel of the horizontal well. Due to the pseudo-hemispherical flow at each endpoint and the pseudo-linear flow at the center of the horizontal well, the drainage area at each endpoint is relatively larger than that at the center. The radial inflow at each endpoint of the horizontal segment is considerably greater than that at the center, which presents the U-shape distribution. The proposed methodology enhances and promotes the theory of productivity evaluation for complex structural wells in mixed well patterns. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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16 pages, 3421 KiB

Open AccessArticle

Off-Grid Solar PV Power Generation System in Sindh, Pakistan: A Techno-Economic Feasibility Analysis

by Li Xu, Ying Wang, Yasir Ahmed Solangi, Hashim Zameer and Syed Ahsan Ali Shah

Processes 2019, 7(5), 308; https://doi.org/10.3390/pr7050308 - 22 May 2019

Cited by 63 | Viewed by 9715

Abstract

The off-grid solar photovoltaic (PV) system is a significant step towards electrification in the remote rural regions, and it is the most convenient and easy to install technology. However, the strategic problem is in identifying the potential of solar energy and the economic [...] Read more.

The off-grid solar photovoltaic (PV) system is a significant step towards electrification in the remote rural regions, and it is the most convenient and easy to install technology. However, the strategic problem is in identifying the potential of solar energy and the economic viability in particular regions. This study, therefore, addresses this problem by evaluating the solar energy potential and economic viability for the remote rural regions of the Sindh province, Pakistan. The results recommended that the rural regions of Sindh have suitable solar irradiance to generate electricity. An appropriate tilt angle has been computed for the selected rural regions, which significantly enhances the generation capacity of solar energy. Moreover, economic viability has been undertaken in this study and it was revealed that the off-grid solar PV power generation system provides electricity at the cost of Pakistani Rupees (PKR) 6.87/kWh and is regarded as much cheaper than conventional energy sources, i.e., around PKR 20.79/kWh. Besides, the off-grid solar PV power generation system could mitigate maximum CO₂ annually on the condition that all of the selected remote rural regions adopt the off-grid solar PV system. Therefore, this study shall help the government to utilize the off-grid solar PV power generation system in the remote rural regions of Pakistan. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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7 pages, 1157 KiB

Open AccessArticle

Determination of the Acidity of Waste Cooking Oils by Near Infrared Spectroscopy

by Juan Francisco García Martín, María del Carmen López Barrera, Miguel Torres García, Qing-An Zhang and Paloma Álvarez Mateos

Processes 2019, 7(5), 304; https://doi.org/10.3390/pr7050304 - 21 May 2019

Cited by 20 | Viewed by 6070

Abstract

Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this [...] Read more.

Waste cooking oils (WCO) recycling companies usually have economic losses for buying WCO not suitable for biodiesel production, e.g., WCO with high free acidity (FA). For this reason, the determination of FA of WCO by near infrared (NIR) spectroscopy was studied in this work to assess its potential for in situ application. To do this, FA of 45 WCO was measured by the classical titration method, which ranged between 0.15 and 3.77%. Then, the NIR spectra from 800 to 2200 nm of these WCO were acquired, and a partial least squares model was built, relating the NIR spectra to FA values. The accuracy of the model was quite high, providing r² of 0.970 and a ratio of performance to deviation (RPD) of 4.05. Subsequently, a model using an NIR range similar to that provided by portable NIR spectrometers (950–1650 nm) was built. The performance was lower (r² = 0.905; RPD = 2.66), but even so, with good accuracy, which demonstrates the potential of NIR spectroscopy for the in situ determination of FA of WCO. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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24 pages, 5952 KiB

Open AccessArticle

Multi-Objective Optimal Scheduling Method for a Grid-Connected Redundant Residential Microgrid

by Weiliang Liu, Changliang Liu, Yongjun Lin, Kang Bai, Liangyu Ma and Wenying Chen

Processes 2019, 7(5), 296; https://doi.org/10.3390/pr7050296 - 19 May 2019

Cited by 9 | Viewed by 3377

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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18 pages, 4870 KiB

Open AccessArticle

Control Strategy of Electric Heating Loads for Reducing Power Shortage in Power Grid

by Siyuan Xue, Yanbo Che, Wei He, Yuancheng Zhao and Ruiping Zhang

Processes 2019, 7(5), 273; https://doi.org/10.3390/pr7050273 - 9 May 2019

Cited by 11 | Viewed by 3504

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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23 pages, 9739 KiB

Open AccessArticle

Artificial Neural Networks Approach for a Multi-Objective Cavitation Optimization Design in a Double-Suction Centrifugal Pump

by Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Xingcheng Gan and Tingyun Yin

Processes 2019, 7(5), 246; https://doi.org/10.3390/pr7050246 - 27 Apr 2019

Cited by 35 | Viewed by 5556

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 0.6Q_d, 0.8Q_d, 1.0Q_d and 1.2Q_d respectively. The efficiency increased by 1.53% 1.0Q_d and 1.1% at 0.8Q_d. 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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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17 pages, 4360 KiB

Open AccessArticle

Power Transmission Congestion Management Based on Quasi-Dynamic Thermal Rating

by Yanling Wang, Zidan Sun, Zhijie Yan, Likai Liang, Fan Song and Zhiqiang Niu

Processes 2019, 7(5), 244; https://doi.org/10.3390/pr7050244 - 26 Apr 2019

Cited by 2 | Viewed by 3259

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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25 pages, 5868 KiB

Open AccessArticle

Modeling of Future Electricity Generation and Emissions Assessment for Pakistan

by Abdullah Mengal, Nayyar Hussain Mirjat, Gordhan Das Walasai, Shoaib Ahmed Khatri, Khanji Harijan and Mohammad Aslam Uqaili

Processes 2019, 7(4), 212; https://doi.org/10.3390/pr7040212 - 12 Apr 2019

Cited by 40 | Viewed by 7903

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, NO_X, 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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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24 pages, 7178 KiB

Open AccessArticle

Numerical Investigation of Influence of Reservoir Heterogeneity on Electricity Generation Performance of Enhanced Geothermal System

by Yuchao Zeng, Liansheng Tang, Nengyou Wu, Jing Song and Zhanlun Zhao

Processes 2019, 7(4), 202; https://doi.org/10.3390/pr7040202 - 9 Apr 2019

Cited by 7 | Viewed by 3178

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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16 pages, 7548 KiB

Open AccessArticle

A Rotor-Sync Signal-Based Control System of a Doubly-Fed Induction Generator in the Shaft Generation of a Ship

by Trong-Thang Nguyen

Processes 2019, 7(4), 188; https://doi.org/10.3390/pr7040188 - 1 Apr 2019

Cited by 7 | Viewed by 3769

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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14 pages, 2531 KiB

Open AccessArticle

Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm

by Zhufeng Lei and Wenbin Su

Processes 2019, 7(3), 177; https://doi.org/10.3390/pr7030177 - 26 Mar 2019

Cited by 15 | Viewed by 4880

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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14 pages, 1477 KiB

Open AccessArticle

A Novel Robust Method for Solving CMB Receptor Model Based on Enhanced Sampling Monte Carlo Simulation

by Wen Hou, Yunlei Yang, Zheng Wang, Muzhou Hou, Qianhong Wu and Xiaoliang Xie

Processes 2019, 7(3), 169; https://doi.org/10.3390/pr7030169 - 23 Mar 2019

Cited by 3 | Viewed by 3902

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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22 pages, 6750 KiB

Open AccessArticle

Numerical Investigation of SCR Mixer Design Optimization for Improved Performance

by Ghazanfar Mehdi, Song Zhou, Yuanqing Zhu, Ahmer Hussain Shah and Kishore Chand

Processes 2019, 7(3), 168; https://doi.org/10.3390/pr7030168 - 22 Mar 2019

Cited by 28 | Viewed by 6015

Abstract

The continuous increase in the number of stringent exhaust emission legislations of marine Diesel engines had led to a decrease in NO_x 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 NO_x emissions at the required level. Selective catalyst reduction (SCR) is the most prominent and mature technology used to reduce NO_x emissions. However, to obtain maximum NO_x 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 NO_x emission obtained at SCR outlet using LSM was 2.44 g/kWh, which strongly meets International Maritime Organization (IMO) Tier III NO_x (3.4 g/kWh) emission regulations. Full article

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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18 pages, 2210 KiB

Open AccessArticle

Implementation of Maximum Power Point Tracking Based on Variable Speed Forecasting for Wind Energy Systems

by Yujia Zhang, Lei Zhang and Yongwen Liu

Processes 2019, 7(3), 158; https://doi.org/10.3390/pr7030158 - 15 Mar 2019

Cited by 38 | Viewed by 4847

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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13 pages, 1978 KiB

Open AccessArticle

A Flexible Responsive Load Economic Model for Industrial Demands

by Reza Sharifi, Amjad Anvari-Moghaddam, S. Hamid Fathi and Vahid Vahidinasab

Processes 2019, 7(3), 147; https://doi.org/10.3390/pr7030147 - 8 Mar 2019

Cited by 17 | Viewed by 6296

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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17 pages, 689 KiB

Open AccessArticle

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

by Ibrar Ullah, Zar Khitab, Muhammad Naeem Khan and Sajjad Hussain

Processes 2019, 7(3), 142; https://doi.org/10.3390/pr7030142 - 7 Mar 2019

Cited by 34 | Viewed by 4748

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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31 pages, 2489 KiB

Open AccessArticle

An Integrated Delphi-AHP and Fuzzy TOPSIS Approach toward Ranking and Selection of Renewable Energy Resources in Pakistan

by Yasir Ahmed Solangi, Qingmei Tan, Nayyar Hussain Mirjat, Gordhan Das Valasai, Muhammad Waris Ali Khan and Muhammad Ikram

Processes 2019, 7(2), 118; https://doi.org/10.3390/pr7020118 - 25 Feb 2019

Cited by 110 | Viewed by 9858

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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24 pages, 7905 KiB

Open AccessArticle

Cogeneration Process Technical Viability for an Apartment Building: Case Study in Mexico

by Hugo Valdés and Gabriel Leon

Processes 2019, 7(2), 93; https://doi.org/10.3390/pr7020093 - 13 Feb 2019

Cited by 13 | Viewed by 6287

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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17 pages, 601 KiB

Open AccessArticle

Energy-Efficient Train Driving Strategy with Considering the Steep Downhill Segment

by Wentao Liu, Tao Tang, Shuai Su, Jiateng Yin, Yuan Cao and Cheng Wang

Processes 2019, 7(2), 77; https://doi.org/10.3390/pr7020077 - 3 Feb 2019

Cited by 18 | Viewed by 4114

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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13 pages, 2128 KiB

Open AccessArticle

A Hybrid Energy Feature Extraction Approach for Ship-Radiated Noise Based on CEEMDAN Combined with Energy Difference and Energy Entropy

by Yuxing Li, Xiao Chen and Jing Yu

Processes 2019, 7(2), 69; https://doi.org/10.3390/pr7020069 - 1 Feb 2019

Cited by 47 | Viewed by 4434

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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17 pages, 2365 KiB

Open AccessArticle

Smart Community Energy Cost Optimization Taking User Comfort Level and Renewable Energy Consumption Rate into Consideration

by Kun Shi, Dezhi Li, Taorong Gong, Mingyu Dong, Feixiang Gong and Yajie Sun

Processes 2019, 7(2), 63; https://doi.org/10.3390/pr7020063 - 26 Jan 2019

Cited by 18 | Viewed by 3563

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

(This article belongs to the Special Issue Advances in Theoretical and Computational Energy Optimization Processes)

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