Sustainability

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25 pages, 6415 KiB

Open AccessArticle

Optimal Dispatch and Control Strategy of Park Micro-Energy Grid in Electricity Market

by Qunru Zheng, Ping Yang, Yuhang Wu, Zhen Xu and Peng Zhang

Sustainability 2023, 15(20), 15100; https://doi.org/10.3390/su152015100 - 20 Oct 2023

Viewed by 659

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. [...] Read more.

In the existing research on the dispatch and control strategies of park micro-energy grids, the dispatch and control characteristics of controllable energy units, such as response delay, startup and shutdown characteristics, response speed, and sustainable response time, have not been taken into account. Without considering the dispatch and control characteristics of the controllable energy units, substantial deviation will occur in the execution of optimized dispatch and control strategies, resulting in economic losses in the electricity market and adverse effects on the safe operation of power systems. This paper proposes a unified model to describe the dispatch and control characteristics of various types of controlled energy units, based on which we develop a three-tier optimization dispatch and control strategy for the micro-energy grid, involving day-ahead, intra-day, and real-time stages. The day-ahead and intra-day optimization dispatch strategy is implemented to obtain the optimal reference values in the real-time stage for each controllable energy unit. In the real-time stage, a minimum variance control strategy based on d-step prediction is proposed. By considering the multi-dimensional control characteristics of controllable energy units, the real-time predictive control strategy aims to ensure that the controllable energy units can precisely follow the optimized dispatch plan. The simulation results show that when compared with the dispatching method optimized by the improved quantum particle swarm algorithm, the adoption of the optimal dispatch and control strategy proposed in this paper resulted in a 45.79% improvement in execution accuracy and a 2.38% reduction in the energy cost. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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16 pages, 2397 KiB

Open AccessArticle

Benchmarking Evaluation of Building Energy Consumption Based on Data Mining

by Thomas Wu, Bo Wang, Dongdong Zhang, Ziwei Zhao and Hongyu Zhu

Sustainability 2023, 15(6), 5211; https://doi.org/10.3390/su15065211 - 15 Mar 2023

Cited by 3 | Viewed by 1422

Abstract

University building energy consumption is an important proportion of the total energy consumption of society. In order to work out the problem of poor practicability of the existing benchmarking management method of campus building energy consumption, this study proposes an evaluation model of [...] Read more.

University building energy consumption is an important proportion of the total energy consumption of society. In order to work out the problem of poor practicability of the existing benchmarking management method of campus building energy consumption, this study proposes an evaluation model of campus building energy consumption benchmarking management. By analyzing several types of feature data of buildings, this study uses random forest method to determine the building features that have outstanding contributions to building energy consumption intensity and building classification, and uses the K-means method to reclassify buildings based on the building features obtained after screening, to obtain a building category that is more in line with the actual use situation and to solve the problem that the existing building classification is not in line with the reality. Compared with the original classification method, the new classification method showed significant improvement in many indexes, among which DBI decreased by 60.8% and CH increased by 3.73 times. Finally, the quart lines of buildings in the category of new buildings are calculated to obtain the low energy consumption line, medium energy consumption line and high energy consumption line of buildings, so as to improve the accuracy and practicability of energy consumption line classification. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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11 pages, 3452 KiB

Open AccessArticle

Comparison of Dynamic Response Characteristics of Typical Energy Storage Technologies for Suppressing Wind Power Fluctuation

by Hong Qu and Ze Ye

Sustainability 2023, 15(3), 2437; https://doi.org/10.3390/su15032437 - 30 Jan 2023

Cited by 2 | Viewed by 1143

Abstract

The intermittence and randomness of wind speed leads to the fluctuation of wind turbine output power. In order to study the applicability of battery, super capacitor and flywheel energy storage technology in suppressing wind power fluctuation, this paper takes a 3 MW direct [...] Read more.

The intermittence and randomness of wind speed leads to the fluctuation of wind turbine output power. In order to study the applicability of battery, super capacitor and flywheel energy storage technology in suppressing wind power fluctuation, this paper takes a 3 MW direct drive wind turbine as an example, and, through the establishment of a wind storage system model, the dynamic response characteristics and application effects of the three typical energy storage technologies to suppress the power fluctuation of the wind turbine under two wind speed fluctuation scenarios are simulated and studied, and the stability of output power is quantitatively analyzed. Results show that all the three energy storage systems respond well to power command curves, but when the wind power fluctuation is large, the flywheel energy storage has a better effect on suppressing the wind power fluctuation, which can suppress about 70% of the power fluctuation. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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15 pages, 2389 KiB

Open AccessArticle

Energy-Saving Operation Strategy for Hotels Considering the Impact of COVID-19 in the Context of Carbon Neutrality

by Yutong Wu, Bin Xin, Hongyu Zhu and Zifei Ye

Sustainability 2022, 14(22), 14919; https://doi.org/10.3390/su142214919 - 11 Nov 2022

Cited by 2 | Viewed by 2906

Abstract

With the advent of the post-epidemic era, the energy consumption characteristics of hotels have changed, which has an important impact on urban energy conservation. In order to contribute to the goal of carbon neutrality, this study discusses the energy-saving operation strategy of hotels [...] Read more.

With the advent of the post-epidemic era, the energy consumption characteristics of hotels have changed, which has an important impact on urban energy conservation. In order to contribute to the goal of carbon neutrality, this study discusses the energy-saving operation strategy of hotels considering the impact of the COVID-19 epidemic. Based on the energy consumption characteristics of large public buildings, this paper analyzes the energy consumption distribution and operation characteristics of hotel buildings in detail. By collecting energy consumption data from five typical large hotel buildings in a tourist city in southern China from 2018 to 2022, the impact of COVID-19 on hotel energy consumption and hotel business characteristics was discussed in detail. Combined with the economic development characteristic in the post-epidemic era, this paper explores the energy-saving strategies that hotels can adopt in the context of normalized epidemic prevention and control and obtains the optimal path of low-carbon economic operation of hotel buildings. This study reveals the energy consumption characteristics and energy-saving potential of hotel buildings, and provides enlightenment for hotel management and low-carbon development in the post-epidemic era. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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29 pages, 5698 KiB

Open AccessArticle

The Key Technology of Smart Energy System and Its Disciplinary Teaching Reform Measures

by Dongdong Zhang, Jun Tian, Hui-Hwang Goh, Hui Liu, Xiang Li, Hongyu Zhu and Xinzhang Wu

Sustainability 2022, 14(21), 14207; https://doi.org/10.3390/su142114207 - 31 Oct 2022

Cited by 2 | Viewed by 2424

Abstract

Based on the rapid development of new energy technology, modern information technology, intelligent management technology and different countries’ strategic demand and deployment in the field of energy, the construction of intelligent energy systems is booming with the combination of new energy technology and [...] Read more.

Based on the rapid development of new energy technology, modern information technology, intelligent management technology and different countries’ strategic demand and deployment in the field of energy, the construction of intelligent energy systems is booming with the combination of new energy technology and Internet technology. The Energy Internet is the representative product of intelligent energy systems at the present stage. Its advantages are the effective promotion of energy saving, consumption reduction and optimization of deployment, thus improving the energy system. However, the large-scale construction of the Energy Internet requires a large number of professionals. In order to meet the needs of Energy Internet construction, the talent training mode of higher education is facing new challenges. To cultivate talents in Energy Internet construction, an effective measure is to reform the teaching system based on the current electrical engineering major in universities. This paper investigates the development and construction of the Energy Internet and the current situation of the electrical engineering discipline and puts forward teaching reform measures to transform the traditional electrical engineering discipline into an Energy Internet engineering discipline, considering course structure design, examination form, teacher allocation and teaching mode. This is important for promoting the large-scale construction of the Energy Internet and improving the competitiveness of graduates in the electrical engineering field. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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18 pages, 5062 KiB

Open AccessArticle

Study of Transformer Harmonic Loss Characteristic in Distribution Network Based on Field-Circuit Coupling Method

by Xiping Ma, Rong Jia, Chen Liang, Haodong Du, Xiaoyang Dong and Man Ding

Sustainability 2022, 14(20), 12975; https://doi.org/10.3390/su142012975 - 11 Oct 2022

Viewed by 1732

Abstract

One of the primary causes of additional losses in dry-type distribution transformers is harmonic disturbances in the distribution network. It is critical to investigate the change law of trans-former losses under harmonic conditions. The effect of harmonics on transformer core losses and winding [...] Read more.

One of the primary causes of additional losses in dry-type distribution transformers is harmonic disturbances in the distribution network. It is critical to investigate the change law of trans-former losses under harmonic conditions. The effect of harmonics on transformer core losses and winding losses is first investigated in this paper. The field-circuit coupling method is then used to create a finite element model of a three-phase phase dry type distribution transformer. Finally, the relationship between core loss and harmonic voltage, winding loss and harmonic current is calculated and analyzed for each harmonic frequency. The AC resistance factor model is found to be more accurate than the conventional model in calculating transformer harmonic winding losses. This paper’s findings have significant theoretical implications for the analysis of harmonic losses and loss reduction in distribution networks. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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27 pages, 9922 KiB

Open AccessArticle

Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction

by Kaiyan Wang, Haodong Du, Rong Jia and Hongtao Jia

Sustainability 2022, 14(19), 12683; https://doi.org/10.3390/su141912683 - 5 Oct 2022

Cited by 5 | Viewed by 2050

Abstract

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy [...] Read more.

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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20 pages, 5493 KiB

Open AccessArticle

Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China

by Kaiyan Wang, Xueyan Wang, Rong Jia, Jian Dang, Yan Liang and Haodong Du

Sustainability 2022, 14(17), 10473; https://doi.org/10.3390/su141710473 - 23 Aug 2022

Cited by 2 | Viewed by 1145

Abstract

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large [...] Read more.

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large extent. The multi-energy complementary operation coupling of the hybrid electricity market transactions can exploit the complementation and substitution between different energy sources, realize flexible energy production, consumption, storage, and transmission, and optimize the allocation of resources on a larger scale. In this paper, a mid-long-term spot transaction coordination scheduling (MTCS) model for a multi-energy system is constructed by considering the medium- and long-term electricity market uncertainty and the trial operation characteristics of the spot power market in China. A two-stage solution method is introduced to solve the complex multi-agent, multi-period, and multi-energy model. The results of testing this model on the Gansu region, one of the first eight spot pilot areas in China, are presented and discussed in detail. The results showed that this MTCS model could reduce the opening of thermal power units to a more considerable extent, prioritize the consumption of new energy power generation, and reduce the output uncertainty of new energy through the hybrid power market. Full article

(This article belongs to the Special Issue Research on Smart Energy Systems)

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