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1 – 10 of over 13000The purpose of this paper is to derive the analytical expression of fractional order reducing generation operator (or inverse accumulating generating operation) and study its…
Abstract
Purpose
The purpose of this paper is to derive the analytical expression of fractional order reducing generation operator (or inverse accumulating generating operation) and study its properties.
Design/methodology/approach
This disaggregation method includes three main steps. First, by utilizing Gamma function expanded for integer factorial, this paper expands one order reducing generation operator into integer order reducing generation operator and fractional order reducing generation operator, and gives the analytical expression of fractional order reducing generation operator. Then, studies the commutative law and exponential law of fractional order reducing generation operator. Lastly, gives several examples of fractional order reducing generation operator and verifies the commutative law and exponential law of fractional order reducing generation operator.
Findings
The authors pull the analytical expression of fractional order reducing generation operator and verify that fractional order reducing generation operator satisfies commutative law and exponential law.
Practical implications
Expanding the reducing generation operator would help develop grey prediction model with fractional order operators and widen the application fields of grey prediction models.
Originality/value
The analytical expression of fractional order reducing generation operator, properties of commutative law and exponential law for fractional order reducing generation operator are first studied.
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Wei Meng, Qian Li, Bo Zeng and Yingjie Yang
The purpose of this paper is to unify the expression of fractional grey accumulating generation operator and the reducing generation operator, and build the FDGM(1,1) model with…
Abstract
Purpose
The purpose of this paper is to unify the expression of fractional grey accumulating generation operator and the reducing generation operator, and build the FDGM(1,1) model with the unified fractional grey generation operator.
Design/methodology/approach
By systematically studying the properties of the fractional accumulating operator and the reducing operator, and analyzing the sensitivity of the order value, a unified expression of the fractional operators is given. The FDGM(1,1) model with the unified fractional grey generation operator is established. The relationship between the order value and the modeling error distribution is studied.
Findings
The expression of the fractional accumulating generation operator and the reducing generation operator can be unified to a simple expression. For −1<r < 1, the fractional grey generation operator satisfies the principle of new information priority. The DGM(1,1) model is a special case of the FDGM(1,1) model with r = 1.
Research limitations/implications
The sensitivity of the unified operator is verified through random numerical simulation method, and the theoretical proof was not yet possible.
Practical implications
The FDGM(1,1) model has a higher modeling accuracy and modeling adaptability than the DGM(1,1) by optimizing the order.
Originality/value
The expression of the fractional accumulating generation operator and the reducing generation operator is firstly unified. The FDGM(1,1) model with the unified fractional grey generation operator is firstly established. The unification of the fractional accumulating operator and the reducing operator improved the theoretical basis of grey generation operator.
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Changhai Lin, Zhengyu Song, Sifeng Liu, Yingjie Yang and Jeffrey Forrest
The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the…
Abstract
Purpose
The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.
Design/methodology/approach
The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.
Findings
Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.
Practical implications
The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.
Originality/value
The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.
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Yufeng Lian, Wenhuan Feng, Pai Li, Qiang Lei, Haitao Ma, Hongliang Sun and Binglin Li
The purpose of this paper is to propose a fractional order optimization method based on perturbation bound and gamma function of a DGM(r,1).
Abstract
Purpose
The purpose of this paper is to propose a fractional order optimization method based on perturbation bound and gamma function of a DGM(r,1).
Design/methodology/approach
By analyzing and minimizing perturbation bound, the sub-optimal solution on fractional order interval is obtained through offline solving without iterative calculation. By this method, an optimized fractional order non-equidistant ROGM (OFONEROGM) is applied in fitting and prediction water quality parameters for a surface water pollution monitoring system.
Findings
This method can narrow fractional order interval in this work. In a surface water pollution monitoring system, the fitting and prediction performances of OFONEROGM are demonstrated comparing with integer order non-equidistant ROGM (IONEROGM).
Originality/value
A method of offline solving the sub-optimal solution on fractional order interval is proposed. It can narrow the optimized fractional order range of NEROGM without iterative calculation. A large number of calculations are eliminated. Besides that, optimized fractional order interval is only related to the number of original data, and convenient for practical application. In this work, an OFONEROGM is modeled for predicting water quality trend for preventing water pollution or stealing sewage discharge. It will provide guiding significance in water quality parameter fitting and predicting for water environment management.
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Bingjun Li, Weiming Yang and Xiaolu Li
The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.
Abstract
Purpose
The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.
Design/methodology/approach
Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.
Findings
The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.
Practical implications
The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.
Originality/value
This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.
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Li Ji, Yiwei Zhang, Ruifeng Shi, Limin Jia and Xin Zhang
Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation…
Abstract
Purpose
Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO2 emission reduction of highways.
Design/methodology/approach
To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO2 emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.
Findings
The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO2 emissions.
Originality/value
This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.
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B. Kirwan, B. Martin, H. Rycraft and A. Smith
Human error data in the form of human error probabilities should ideally form the corner‐stone of human reliability theory and practice. In the history of human reliability…
Abstract
Human error data in the form of human error probabilities should ideally form the corner‐stone of human reliability theory and practice. In the history of human reliability assessment, however, the collection and generation of valid and usable data have been remarkably elusive. In part the problem appears to extend from the requirement for a technique to assemble the data into meaningful assessments. There have been attempts to achieve this, THERP being one workable example of a (quasi) database which enables the data to be used meaningfully. However, in recent years more attention has been focused on the PerformanceShaping Factors (PSF) associated with human reliability. A “database for today” should therefore be developed in terms of PSF, as well as task/ behavioural descriptors, and possibly even psychological error mechanisms. However, this presumes that data on incidents and accidents are collected and categorised in terms of the PSF contributing to the incident, and such classification systems in practice are rare. The collection and generation of a small working database, based on incident records are outlined. This has been possible because the incident‐recording system at BNFL Sellafield does give information on PSF. Furthermore, the data have been integrated into the Human Reliability Management System which is a PSF‐based human reliability assessment system. Some of the data generated are presented, as well as the PSF associated with them, and an outline of the incident collection system is given. Lastly, aspects of human common mode failure or human dependent failures, particularly at the lower human error probability range, are discussed, as these are unlikely to be elicited from data collection studies, yet are important in human reliability assessment. One possible approach to the treatment of human dependent failures, the utilisation of human performance‐limiting values, is described.
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Rebecca Weir, Joleen Hadrich, Alessandro Bonanno and Becca B.R. Jablonski
Beginning Farmer and Rancher programs are available for operators with ten years of experience or less on any farm. These programs support farmers who are starting operations…
Abstract
Purpose
Beginning Farmer and Rancher programs are available for operators with ten years of experience or less on any farm. These programs support farmers who are starting operations, often without an initial asset allocation. However, some beginning farmers acquire operations that are already established, with substantial assets in place. The authors investigate whether a profitability gap exists between beginning farmers entering the industry ex novo and those operating a preexisting operation and if so, what factors contribute to the gap.
Design/methodology/approach
The authors utilize the Blinder-Oaxaca decomposition to determine what drives financial differences between first-generation beginning farmers, second-generation beginning farmers and established farmers using a unique farm-level panel dataset from 1997 to 2021.
Findings
Results indicate that first- and second-generation beginning farmers have similar operating profit margins, but first-generation beginning farmers have a statistically higher rate of return on assets than second-generation beginning farmers. Established farmers outperform second-generation beginning farmers on both the operating profit margin and rate of return on assets. These results suggest that economic viability for beginning farmers differs depending upon the initial status of their operation, suggesting that heterogenous policies may be more impactful in supporting various pathways to enter agriculture.
Originality/value
This analysis is the first to identify beginning farmers that enter the industry without an asset base and those that take over a principal operator role on an established farm through an assumed farm transition. The authors quantify differences in financial performance using detailed accrual-based financial data that tracks farms over time in one dataset.
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Adela Bâra and Simona Vasilica Oprea
This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV…
Abstract
Purpose
This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.
Design/methodology/approach
One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.
Findings
Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.
Research limitations/implications
One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.
Practical implications
An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.
Originality/value
As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.
Xing Yao, Shao-Chao Ma, Ying Fan, Lei Zhu and Bin Su
The ongoing urbanization and decarbonization require deployment of energy storage in the urban energy system to integrate large-scale variable renewable energy (VRE) into the…
Abstract
Purpose
The ongoing urbanization and decarbonization require deployment of energy storage in the urban energy system to integrate large-scale variable renewable energy (VRE) into the power grids. The cost reductions of batteries enable private entities to invest energy storage for energy management whose operating strategy may differ from traditional storage facilities. This study aims to investigate the impacts of energy storage on the power system with different operation strategies. Two strategies are modeled through a simulation-based regional economic power dispatch model. The profit-oriented strategy denotes the storage system operated by private entities for price arbitrage, and the nonprofit-oriented strategy denotes the storage system dispatched by an independent system operator (ISO) for the whole power system optimization. A case study of Jiangsu, China is conducted. The results show that the profit-oriented strategy only has a very limited impact on the cost reductions of power system and may even increase the cost for consumers. While nonprofit-oriented energy storage performs a positive effect on the system cost reduction. CO2 emission reduction can only be achieved under a high VRE scenario for energy storage. Integrating energy storage into the power system may increase CO2 emissions in the near term. In addition, the peak-valley spread is crucial to trigger operations of profit-oriented energy storage, and the profitability of energy storage operator is observed to be decreasing with the total storage capacity. This study provides new insights for the energy management in the smart city, and the modeling framework can be applied to regions with different resource endowments.
Design/methodology/approach
The authors characterize two battery storage operating strategies of profit- and nonprofit-oriented by adopting a simulation-based economic dispatch model. A simulation from 36 years of hourly weather data of wind and solar output from case study of Jiangsu, China is conducted.
Findings
The results show that the profit-oriented strategy only has a very limited impact on the cost reductions of power system and may even increase the cost for consumers. While nonprofit-oriented energy storage performs a positive effect on the system cost reduction. CO2 emission reduction can only be achieved under high VRE scenario for energy storage. Integrating energy storage into the power system may increase CO2 emissions in the near term. In addition, the peak-valley spread is crucial to trigger operations of profit-oriented energy storage, and the profitability of energy storage operator is observed to be decreasing with the total storage capacity.
Originality/value
This study provides new insights for the energy management in the smart city, and the modeling framework can be applied to regions with different resource endowments.
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