Design optimization and sensitivity analysis of a biomass-fired combined cooling, heating and power system with thermal energy storage systems

Caliano, Martina; Bianco, Nicola; Graditi, Giorgio; Mongibello, Luigi

doi:10.1016/j.enconman.2017.07.048

Cited by 51 publications

(12 citation statements)

References 43 publications

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“…Power grids now consist of large-scale power plants based on nuclear power and fossil fuels. These power plants are considered to be the primary source of energy production [1,2]. In these forms of generation, unilateral energy transmission is conducted from centralized power plants (microgrids) to energy consumers.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting.

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Section: Introductionmentioning

confidence: 99%

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

et al. 2021

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“…Typically, among these facilities, the energy storage systems (ESS) are supplied in large quantities, and the frequency of fires related to these systems is on the rise. However, the research into this issue is insufficient [5][6][7]. The use of ESS has been rapidly increasing for the purpose of stabilizing the energy output, improving competitiveness, reducing the power bills in response to peak power demands, adjusting the frequency, and replacing emergency power generation in new renewable power generation systems.…”

Section: Introductionmentioning

confidence: 99%

A Study on The Safety Measures Against Fire Through An ESS Fire Analysis

et.al¹

2021

TURCOMAT

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Due to the increase in the frequency of fires due to the mass distribution of energy storage devices, following the introduction and operation of eco-friendly power generation facilities around the world, there is an urgent need to analyze the causes of such fires and to devise countermeasures against them. By analyzing the occurrence of ESS fires and reviewing the definition of ESS terminology, we intend to suggest some safety measures against ESS fires by understanding the related facilities and analyzing the causes. We would also like to propose new measures to prevent the spread of ESS fires. ESS fires occur most frequently during the discharge standby period after charging is completed in connected solar and wind power facilities. In addition, when we investigated the problems with the batteries, PCS manufacturing, ESS design, installation and construction, and the factors related to their usage and operation, we found such causes of fire as battery system defects, unsatisfactory protection systems against electrical shock, improper operations and environmental management, careless installations, and a lack of ESS integrated management systems. Therefore, a new approach is needed through the application of new technologies and safety measures including manufacturing standards, installation standards, operation and management standards, and firefighting standards for the purpose of ensuring the safety of these facilities and their operations, in order to prevent ESS fire accidents and the spread of fire.

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“…The energy parameters are either from the user side, namely the different kinds of energy demand [13], [14], or from the supply side of the renewable energies such as wind speed [15], solar irradiation [16], etc. As for economic parameters, the typical parameters for sensitivity analysis are the electricity price [17], [18], fuel price [17], [19], interest rate [19], and equipment investment cost (IC) [16], [20]. Technical parameters also affect the design results of DESs.…”

Section: Introductionmentioning

confidence: 99%

“…For example, [20] studies three values of thermal-to-electrical demand ratio to investigate their influence on the results of the optimal design. Reference [18] studies the influence of the size of absorption chiller (AC) and thermal storage device on the system performance. Compared with the two stages above, there are also many papers devoted to the sensitivity analysis of the operation strategy of DESs using the LSA method.…”

Section: Introductionmentioning

confidence: 99%

Multi-stage Sensitivity Analysis of Distributed Energy Systems: A Variance-based Sobol Method

Wei

Gao

et al. 2020

Journal of Modern Power Systems and Clean Energy

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In the face of the pressing environmental issues, the past decade witnessed the booming development of the distributed energy systems (DESs). A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations, in both aspects of optimal design and economic operation. It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties. This paper aims to give a comprehensive quantification, and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria, optimal design and economic operation. First, a mathematical model of a DES is developed to present the solutions to the three stages of the DES. Second, the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy, technical and economic parameters. Based on the simulation results, the variance-based Sobol method is applied to calculate the individual importance, interactional importance and total importance of various parameters. The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance. In addition, the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.

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Design optimization and sensitivity analysis of a biomass-fired combined cooling, heating and power system with thermal energy storage systems

Cited by 51 publications

References 43 publications

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

A Study on The Safety Measures Against Fire Through An ESS Fire Analysis

Multi-stage Sensitivity Analysis of Distributed Energy Systems: A Variance-based Sobol Method

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