The Modeling of the Electric Heating and Cooling System of the Integrated Energy System in the Coastal Area

Xue, Zuo; Dong, Minghao; Gao, Fengkai; Tian, Shiming

doi:10.2112/si103-213.1

Cited by 110 publications

(31 citation statements)

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“…Even though the RP load flow affects the line losses and the bus voltage and the active power load flow and its costs, unfortunately, the costs of producing and transmitting RP have received little attention [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. One reason for this lack of attention is the inherent difficulty in understanding this issue, particularly by economists [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Another reason for the lack of serious attention to RP is the low production price compared to active power [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

Syah

Mianaei

Elveny

et al. 2021

Sensors

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The power system planning problem considering system loss function, voltage profile function, the cost function of FACTS (flexible alternating current transmission system) devices, and stability function are investigated in this paper. With the growth of electronic technologies, FACTS devices have improved stability and more reliable planning in reactive power (RP) planning. In addition, in modern power systems, renewable resources have an inevitable effect on power system planning. Therefore, wind resources make a complicated problem of planning due to conflicting functions and non-linear constraints. This confliction is the stochastic nature of the cost, loss, and voltage functions that cannot be summarized in function. A multi-objective hybrid algorithm is proposed to solve this problem by considering the linear and non-linear constraints that combine particle swarm optimization (PSO) and the virus colony search (VCS). VCS is a new optimization method based on viruses’ search function to destroy host cells and cause the penetration of the best virus into a cell for reproduction. In the proposed model, the PSO is used to enhance local and global search. In addition, the non-dominated sort of the Pareto criterion is used to sort the data. The optimization results on different scenarios reveal that the combined method of the proposed hybrid algorithm can improve the parameters such as convergence time, index of voltage stability, and absolute magnitude of voltage deviation, and this method can reduce the total transmission line losses. In addition, the presence of wind resources has a positive effect on the mentioned issue.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

Syah

Mianaei

Elveny

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Secondly, resistance to changing energy technology. People concern more about their experience of comfort, cost and convenience than about what energy source brings this comfort (Mallaband and Lipson 2020 ; Zuo et al 2020 ). So familiarity with a specific energy source increases people’s preferences for this source.…”

Section: Resultsmentioning

confidence: 99%

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

Zhang

Abbas

Iqbal

2021

Environ Sci Pollut Res

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People’s sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people’s perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people’s interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people’s sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people’s interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people’s interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

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“…From a general point of view, the objective of many recent developments lies in facilitating complex analysis [7][8][9]. These efforts have resulted in finding promising approaches for various analyses, such as water treatment [10,11], energy [12,13], construction and waste reduction [14][15][16], optics-related simulations [17,18], environmental modeling [19][20][21], and remote sensing [22]. In this sense, different intelligent methods have been devised by experts to give a reliable simulation of engineering problems [23][24][25].…”

Section: Introduction 1problem Statement and Backgroundmentioning

confidence: 99%

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

2021

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Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

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The Modeling of the Electric Heating and Cooling System of the Integrated Energy System in the Coastal Area

Cited by 110 publications

References 0 publications

RETRACTED: A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

RETRACTED: A New Hybrid Algorithm for Multi-Objective Reactive Power Planning via FACTS Devices and Renewable Wind Resources

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

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