Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Sadeghian, Omid; Moradzadeh, Arash; Mohammadi‐Ivatloo, Behnam; Abapour, Mehdi; Márquez, Fausto Pedro Garcı́a

doi:10.3390/en13112840

Cited by 39 publications

(14 citation statements)

References 51 publications

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“…In these forms of generation, unilateral energy transmission is conducted from centralized power plants (microgrids) to energy consumers. Due to several factors, including the possible lack of fossil fuels and the dangers of rising greenhouse gases, renewable energies like solar energy and wind energy are becoming much more popular as clean and novel energy sources [3,4]. To address this trend, many efforts were made to create a novel type of power system by combining communication and optimization theory to have optimal power management.…”

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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“…To face such problems and better demand response, power systems must increase their generation capacity. However, there are other problems, such as increased fossil fuel consumption and environmental pollutions [1,2]. As the energy crisis and the environmental crisis become more serious, Distributed Generations (DGs), as the main forms of Renewable Energy Sources (RESs), have attracted much attention in issues related to energy management and sustainability of the power systems.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

et al. 2020

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Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

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“…Therefore, the utilization of biomass-derived fuels like biodiesel, biogas, hydrogen, producer gas, etc., dramatically contributes to sustainable waste management systems to eliminate or reduce the use of fossil fuels [13]. Energy management with high efficiency and lesser emission is significant for country development [14][15][16]. The government of India has been put into practice for the biomass power/co-generation program.…”

Section: Introductionmentioning

confidence: 99%

Hydrogen Injection in a Dual Fuel Engine Fueled with Low-Pressure Injection of Methyl Ester of Thevetia Peruviana (METP) for Diesel Engine Maintenance Application

et al. 2020

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The present work is mapped to scrutinize the consequence of biodiesel and gaseous fuel properties, and their impact on compression-ignition (CI) engine combustion and emission characteristics in single and dual fuel operation. Biodiesel prepared from non-edible oil source derived from Thevetia peruviana belonging to the plant family of Apocynaceaeis. The fuel has been referred as methyl ester of Thevetia peruviana (METP) and adopted as pilot fuel for the effective combustion of compressed gaseous fuel of hydrogen. This investigation is an effort to augment the engine performance of a biodiesel-gaseous fueled diesel engine operated under varied engine parameters. Subsequently, consequences of gas flow rate, injection timing, gas entry type, and manifold gas injection on the modified dual-fuel engine using conventional mechanical fuel injections (CMFIS) for optimum engine performance were investigated. Fuel consumption, CO, UHC, and smoke formations are spotted to be less besides higher NOx emissions compared to CMFIS operation. The fuel burning features such as ignition delay, burning interval, and variation of pressure and heat release rates with crank angle are scrutinized and compared with base fuel. Sustained research in this direction can convey practical engine technology, concerning fuel combinations in the dual fuel mode, paving the way to alternatives which counter the continued fossil fuel utilization that has detrimental impacts on the climate.

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Generation Units Maintenance in Combined Heat and Power Integrated Systems Using the Mixed Integer Quadratic Programming Approach

Cited by 39 publications

References 51 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

Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Hydrogen Injection in a Dual Fuel Engine Fueled with Low-Pressure Injection of Methyl Ester of Thevetia Peruviana (METP) for Diesel Engine Maintenance Application

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