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- Optimal renewable distributed generation planning: an up-to-date
state-of-the-art review-
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Authors: Ali Tarraq, Faissal El Mariami, Abdelaziz Belfqih, Touria Haidi
Pages: 315 - 348
Abstract: Due to its multiple benefits, the integration of renewable-based distributed generation (RDG) into the distribution network (DN) is of great importance. Yet, given the complexity of this network, optimal distributed generation planning (ORDGP) is considered a complex combinatorial problem, which remains a real challenge for investigators, decision-makers, and investors. This issue involves finding the optimal locations, sizes, power factors, and number of RDGs to be incorporated into the DN to improve the network's overall efficiency while meeting a set of voltage, current, and power constraints. Moreover, the incorporation of uncertainties related to this type of source, especially wind turbine or PV system, decreases the ability of classical and analytical methods to solve the ORDGP problem. For this reason, meta-heuristic and hybrid methods have become very promising essentially because of their randomness. In this context, a systematic and comprehensive literature review on the different definitions, classifications, and interests of RDG, as well as on the different optimisation techniques, represents the main objective of this study. Concisely, this paper provides in-depth knowledge and serves as a useful guide for future researchers and investors in the ORDGP.
Keywords: renewable distributed generation; optimal renewable DG planning; distribution network; optimisation methods
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 315 - 348
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132011
Issue No: Vol. 45, No. 4/5 (2023)
- The prediction of carbon emissions from construction land in central
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Authors: Lede Niu, Jingzhi Lin, Lifang Zhou, Anlin Li, Yan Zhou
Pages: 349 - 365
Abstract: In order to clarify the quantitative relationship between construction land changes and carbon emissions, a prediction method for carbon emissions from construction land in central Yunnan urban agglomeration area based on multiple linear regression model was proposed. Taking the central Yunnan urban agglomeration area as the study area, based on the data of construction land from 2011 to 2020, the carbon emission of construction land was predicted by using the multiple linear regression model. There is a positive correlation between the carbon emissions of the central Yunnan urban agglomeration area and the level of construction land use. From 2011 to 2020, average annual growth rate of construction land area was 8.56%, and the average annual growth rate of carbon emissions was 5.75%. The annual growth rate of carbon emissions from 2021 to 2030 is 0.97%, indicating that the government's carbon emission control measures have achieved good results.
Keywords: multiple linear regression model; STIRPAT model; central Yunnan urban agglomeration area; construction land; carbon emission prediction
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 349 - 365
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132017
Issue No: Vol. 45, No. 4/5 (2023)
- Study on multi-step prediction method of passive energy-saving building
energy consumption based on energy consumption perception-
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Authors: Lede Niu, Jingzhi Lin, Lifang Zhou, Anlin Li, Yan Zhou
Pages: 366 - 382
Abstract: Passive energy-saving buildings have problems such as low energy consumption prediction accuracy and complex prediction process. A multi-step prediction method for energy consumption of passive energy-saving buildings based on energy consumption perception is proposed. Firstly, the related thermal parameters and building parameters of passive energy-saving buildings are calculated by steady-state calculation. Secondly, the equivalent thermal parameters of building air-conditioning load, air-conditioning load equivalent thermal parameters and air-conditioning load thermal parameters are determined by means of first-order differential equations, and the envelope structure is calculated. Finally, the ultrasonic sensor is set in the energy-saving building, and the energy consumption data of each part of the building is sensed to realise multi-step prediction. The experimental results show that the fluctuation of the predicted energy consumption value of the proposed algorithm is less than 1 J, the prediction accuracy is always above 90% and the time cost is about 1.54 s.
Keywords: energy consumption perception; passive energy-saving building; multi-step prediction of energy consumption; dynamic calculation; load equivalent thermal parameters
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 366 - 382
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132019
Issue No: Vol. 45, No. 4/5 (2023)
- Intelligent forecasting method of distributed energy load based on least
squares support vector machine-
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Authors: Yingwei Chen, Zhikui Chang
Pages: 383 - 394
Abstract: Aiming at the problems of long prediction time and low prediction accuracy of traditional distributed energy load intelligent prediction methods, a distributed energy load intelligent prediction method based on least squares support vector machine is proposed. The method of linear interpolation is used to process the missing load data of distributed energy, and the wrong load data of distributed energy are processed horizontally and vertically. On this basis, the t-test standard in probability and statistics method is used to identify the abnormal load of distributed energy. Using least squares support vector machine, a distributed energy load forecasting model is constructed to realise the intelligent forecasting of distributed energy load. The experimental results show that the average MAPE and RMSE of the proposed method are 1.008% and 1048 respectively, and the time of distributed energy load forecasting is 15.8 s. The proposed method can effectively improve the accuracy and efficiency of distributed energy load forecasting.
Keywords: least squares support vector machine; linear interpolation; t-test criterion; distributed energy; load forecasting
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 383 - 394
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132013
Issue No: Vol. 45, No. 4/5 (2023)
- Investment risk prediction method of renewable energy market under the
background of carbon neutralisation-
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Authors: Yingwei Chen, Zhikui Chang
Pages: 395 - 407
Abstract: To improve the accuracy of market investment risk prediction and reduce the time consumption of investment risk prediction, this paper proposes a renewable energy market investment risk prediction method under the background of carbon neutralisation. Firstly, based on the background of carbon neutrality, the earned value management theory is used to quantitatively describe the investment risk of renewable energy market. Secondly, aiming at carbon neutralisation, the system dynamics method is used to design the investment risk prediction function of renewable energy market. Finally, the residual test is used to verify the investment risk prediction results of the energy market, so as to realise the investment risk prediction of the renewable energy market. The results show that the accuracy of market investment risk prediction of this method is 96.5%, and the time of investment risk prediction is only 8.6 s. It can accurately predict the investment risk of renewable energy market.
Keywords: carbon neutralisation; system dynamics method; renewable energy; market investment; risk prediction
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 395 - 407
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132016
Issue No: Vol. 45, No. 4/5 (2023)
- A low carbon treatment technology of green building construction waste
based on genetic algorithm-
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Authors: Jinhua Kang, Guanglei Zhao
Pages: 408 - 420
Abstract: In this paper, a low-carbon treatment method of green construction waste based on genetic algorithm is proposed in this paper. Firstly, the group organisation method is used to search in the search solution space to calculate the carbon emission of green building construction waste. According to the calculation method of carbon emissions, the product of activity data and emissions is obtained. Then, through the model objectives and constraints, a single cycle multi-objective mathematical model with waste treatment cost and low-carbon emission as the objective function is established. Finally, the genetic algorithm is used to solve the model to realise the low-carbon treatment of green building construction waste. The experimental results show that the proposed low-carbon treatment time of green building waste is only 16.7 s and the carbon emission is only 0.13 g, which can effectively improve the low-carbon treatment efficiency of green building waste.
Keywords: genetic algorithm; emission factor method; green building; construction waste; low-carbon treatment
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 408 - 420
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132012
Issue No: Vol. 45, No. 4/5 (2023)
- An energy efficiency evaluation method of intelligent building based on
fuzzy clustering algorithm-
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Authors: Shuai Wang, Lei Sun, Xinjie Yuan
Pages: 421 - 435
Abstract: Aiming at the problems of low evaluation accuracy and long evaluation time in the traditional energy-saving evaluation methods of smart buildings, an energy-saving evaluation method of smart buildings based on fuzzy clustering algorithm is proposed. Firstly, according to the construction criteria of the evaluation index system, establish the energy-saving evaluation index system of smart buildings, obtain the evaluation indexes, then use the grey correlation theory to determine the weight of the energy-saving evaluation index of smart buildings, and measure the energy consumption of smart buildings separately. Finally, establish the fuzzy similarity matrix, and cluster with the network method in the direct clustering method to obtain the optimal clustering scheme and evaluate the energy-saving of smart buildings. The simulation results show that the accuracy of the proposed method is 100% and the evaluation time is within 7 s. The evaluation effect of the proposed method is good and the evaluation efficiency is high.
Keywords: fuzzy clustering algorithm; grey correlation theory; smart building; energy-saving evaluation; evaluation index system
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 421 - 435
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132015
Issue No: Vol. 45, No. 4/5 (2023)
- New energy industry investment risk assessment method based on fuzzy AHP
-
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Authors: Shuai Wang, Lei Sun, Xinjie Yuan
Pages: 436 - 447
Abstract: In order to improve the accuracy, efficiency and comprehensiveness of investment risk assessment, a new energy industry investment risk assessment method based on fuzzy AHP is proposed. Firstly, the random forest algorithm is used to predict the investment risk of new energy industry. Secondly, the fuzzy AHP method is used to construct the risk assessment system, the normalisation and consistency test are used to deal with the assessment indicators, and the weight of the assessment indicators is calculated. Finally, based on the evaluation index system, a new energy industry investment risk evaluation model based on multiple regression analysis is established to realise the new energy industry investment risk evaluation. The experimental results show that the highest accuracy of the evaluation results of the proposed method is more than 80%, the evaluation efficiency is high, and the evaluation results are more comprehensive.
Keywords: fuzzy AHP; risk assessment; random forest algorithm; cart algorithm; multiple regression analysis
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 436 - 447
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132018
Issue No: Vol. 45, No. 4/5 (2023)
- Study on evaluation method of energy-saving potential of green buildings
based on entropy weight method-
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Authors: Wei Yuan, Zhigang Liu
Pages: 448 - 460
Abstract: For green buildings, the effect of energy-saving potential evaluation using the current method is poor and the evaluation efficiency is low. This paper uses the entropy weight method to study the energy-saving potential evaluation method of green buildings. Firstly, the evaluation index system of energy-saving potential of green buildings is established under the principles of scientificity, systematicness and operability, and the evaluation indexes are obtained. Then the entropy weight method is used to determine the index weight and calculate the energy-saving potential index. Finally, the energy-saving potential is evaluated based on the value on the basis of analysing the economic benefits. The simulation results show that the accuracy of the proposed method for energy-saving potential evaluation is up to 100%, the evaluation time is within 5 s, the evaluation accuracy is the highest and the evaluation time is the shortest.
Keywords: entropy weight method; economic performance; judgment matrix; G value
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 448 - 460
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132014
Issue No: Vol. 45, No. 4/5 (2023)
- Layered energy balance control method for renewable energy grid based on
island mode-
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Authors: Yao Li, Hui Liu, Wei Xia, Yanwu Ruan, Xiaochuan Guo
Pages: 461 - 473
Abstract: In order to improve load balance and energy efficiency of renewable energy grid, this study designed a layered energy balance control method based on island mode. Firstly, the operation characteristics of renewable energy grid in island mode are analysed, and then the energy balance control of renewable energy grid is divided into three layers, namely, renewable energy output control, grid reactive power control and grid voltage control. The experimental results show that the load waveform of bus voltage tends to be stable after a short abnormal change in the early stage, and the voltage stabilises around 220 kV after 6 s. The maximum renewable energy utilisation rate can reach 95%, indicating that the proposed method achieves the design expectation.
Keywords: island mode; renewable energy grid; energy control; layered control; voltage load; energy efficiency
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 461 - 473
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132021
Issue No: Vol. 45, No. 4/5 (2023)
- Minimisation of fuel cell electric vehicle cost using Cauchy particles
swarm optimisation-
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Authors: Intissar Darwich, Islem Lachhab, Lotfi Krichen
Pages: 474 - 488
Abstract: In this paper, enhanced particle swarm optimisation algorithm is suggested that uses mutated inertia weight which is based on Cauchy distribution in order to optimise fuel cell/ultra-capacitor electrical vehicle cost. This approach is dedicated to identify the optimal number of units of each energy source according to the vehicle performances. The proposed algorithm is based on Cauchy operator which substitutes the random function in classic PSO. Moreover, this method operates within constraints and inhibits to fall in local optimum problem. Cauchy distribution function permits to improve the convergence speed algorithm and to benefit global search ability of particle swarm optimisation. Simulation results show that the enhanced particle swarm optimisation contributes better in speed convergence and accuracy in comparison with classic PSO algorithm for solving traction system cost optimisation.
Keywords: FCHEV; PSO optimisation; Cauchy operator; hydrogen consumption
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 474 - 488
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132020
Issue No: Vol. 45, No. 4/5 (2023)
- An analysis of market power in Iran's electricity market with machine
learning-
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Authors: Intissar Darwich, Islem Lachhab, Lotfi Krichen
Pages: 489 - 502
Abstract: The Iranian electricity market was reformed over the last three decades primarily to promote competition and improve its production efficiency. This paper provides an analysis of competition in the Iranian electricity market. Although other works have provided similar assessments, none has provided a thorough probe over a long period. This paper analyses the Herfindahl Hirschman Index (HHI) of the market for the last decade which has not been done. Also, the paper forecasts the index in the market for the next year to project its direction. Long Short-Term Memory (LSTM) was implemented to forecast the indices in an efficient computational time. Grid search is used to select the optimal model for forecasting, and interactions analyses provide insights into the parameter options that lead to significantly improved accuracies. The results show that the market was unconcentrated from 2012 to 2021. Also, the forecasts show that the market will remain unconcentrated for the next year. Furthermore, the analysis shows that the entrance of new powerplants into the market could reduce the concentration in the market.
Keywords: market power analysis; Herfindahl Hirschman index; long short-term memory algorithm; hyperparameter optimisation; grid search
Citation: International Journal of Global Energy Issues, Vol. 45, No. 4/5 (2023) pp. 489 - 502
PubDate: 2023-07-06T23:20:50-05:00
DOI: 10.1504/IJGEI.2023.132009
Issue No: Vol. 45, No. 4/5 (2023)
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