Optimization configuration of energy storage capacity based on the microgrid reliable output power

Nazir, Muhammad Shahzad; Abdalla, Ahmad N.; Wang, Yeqin; Zhang, Chu; Ji, Jie; Tian, Peng; Jiang, Mingxin; Khan, Irfan; Padmanaban, Sanjeevikumar; Tang, Yongfeng

doi:10.1016/j.est.2020.101866

Cited by 46 publications

(24 citation statements)

References 22 publications

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“…For example, the mean wind speed of no more than 5 m/s with the most frequent wind velocity of around 3 m/s is studied for wind energy application in Malaysia [39]. Considering the sunny climate in the region with minor precipitation, our result suggest that a hybrid power system [40,41] with both wind and solar energy can suits the Gulf coast (Qatar) environment.…”

Section: Site Location and View Of The Instrument Towermentioning

confidence: 95%

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Sadr

2021

JSRR

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Climate pattern in the Persian Gulf is of great interest due to the strategic geographical location of the gulf as a waterway of major oil transportation and its increasing regional economic importance. However, long term and continues climate observations in this region are rare, especially for high resolution data. High resolution wind pattern and climate conditions were measured at 3 heights on a 9 m tower on the shoreline north of Qatar (26.08 N, 51.36 E) from August 2015 to September 2016. In this work, the annual wind and climate patterns (wind velocity, temperature, relative humidity, and air pressure) are first presented. Drag coefficient, turbulent kinetic energy and sensible heat flux are calculated using the high speed measured data to explain the observed climate pattern. The results show the wind in the southern part of the gulf is dominant by a northwest stream with a diurnal average speed of 4.7 m/s. During the test year, the diurnal average temperature and relative humidity were 27°C and 70%, respectively. The drag coefficient is much higher for the wind from 270o-330o, corresponding to the wind coming mainly from northwest. The Turbulent Kinetic Energy (TKE) is strong during the daytime, especially around noon when the diurnal value is at its peak, and weak during the night. The result of this analysis may be used for better understanding of the local climate, allowing for further assessment of wind energy and pollution diffusion in the region.

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Section: Site Location and View Of The Instrument Towermentioning

confidence: 95%

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Sadr

2021

JSRR

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“…In grid-connected insulated modes, the characteristics of MG are flexible operating and can improve grid efficiency and safety [24]. If the MG unit can acquire refrigeration power from the grid, the unit can maintain a stable system frequency if it monitors the voltage of the grid.…”

Section: Microgrid Energy System Descriptionmentioning

confidence: 99%

Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm‐Random Forest Regression

et al. 2021

Self Cite

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The hybridization of two or more energy sources into a single power station is one of the widely discussed solutions to address the demand and supply havoc generated by renewable production (wind-solar/photovoltaic (PV), heating power, and cooling power) and its energy storage issues. Hybrid energy sources work based on the complementary existence of renewable sources. The combined cooling, heating, and power (CCHP) is one of the significant systems and shows a profit from its low environmental impact, high energy efficiency, low economic investment, and sustainability in the industry. This paper presents an economic model of a microgrid (MG) system containing the CCHP system and energy storage considering the energy coupling and conversion characteristics, the effective characteristics of each microsource, and energy storage unit is proposed. The random forest regression (RFR) model was optimized by the gravitational search algorithm (GSA). The test results show that the GSA-RFR model improves prediction accuracy and reduces the generalization error. The detail of the MG network and the energy storage architecture connected to the other renewable energy sources is discussed. The mathematical formulation of energy coupling and energy flow of the MG network including wind turbines, photovoltaic (PV), CCHP system, fuel cell, and energy storage devices (batteries, cold storage, hot water tanks, and so on) are presented. The testing system has been analysed under load peak cutting and valley filling of energy utilization index, energy utilization rate, the heat pump, the natural gas consumption of the microgas turbine, and the energy storage unit. The energy efficiency costs were observed as 88.2% and 86.9% with heat pump and energy storage operation comparing with GSA-RFR-based operation costs as 93.2% and 93% in summer and winter season, respectively. The simulation results extended the rationality and economy of the proposed model.

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“…To improve the grid-connected access rate of PV power generation, various technologies, such as demand response [4], [5], microgrid [6]- [8], unit optimization [9], distributed generation [10], energy storage [11]- [14], and PV power prediction, have emerged in the power system. Among them, short-term PV power generation power prediction can better evaluate the short-term operation state and development trend of PV power station.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

Zhang

Zhu

et al. 2021

IEEE Access

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Existing methods in predicting short-term photovoltaic (PV) power have low accuracy and cannot satisfy actual demand. Thus, a prediction model based on similar days and seagull optimization algorithm (SOA) is proposed to optimize a deep belief network (DBN). Fast correlation-based filter (FCBF) method is used to select a meteorological feature set with the best correlation with PV output and avoid redundancy among meteorological factors affecting PV output. In addition, a comprehensive similarity index combining European distance and gray correlation degree is proposed to select the similar day. Then, SOA is used to optimize the number of neurons and the learning rate parameters in DBN. Based on the nonuniform mutation and opposition-based learning method, an improved seagull optimization algorithm (ISOA) with higher optimization accuracy is proposed. Finally, the ISOA-DBN prediction model is established, and the experimental analysis is conducted using the actual data of PV power stations in Australia. Results show that compared with DBN, support vector machine (SVM), extreme learning machine (ELM), radial basis function (RBF), Elman, and back propagation (BP), the mean absolute percentage error indicator of ISOA-DBN is only 1.512% on a sunny day, 5.975 on a rainy day, 3.359 on a cloudy to sunny day, and 1.911% on a sunny to cloudy day. Therefore, the good accuracy of the proposed model is verified. INDEX TERMSPV power generation, prediction model, similar day, seagull optimization algorithm, deep belief network NOMENCLATURE PV photovoltaic SOA seagull optimization algorithm DBN deep belief network FCBF fast correlation-based filter ISOA improved seagull optimization algorithm SVM support vector machine ELM extreme learning machine RBF radial basis function BP back propagation CEEMD complementary ensemble empirical mode decomposition SU symmetric uncertainty GR global radiation DR diffused radiation RBM restricted Boltzmann machines GBRBM Gauss-Bernoulli-restricted Boltzmann machine PSO particle swarm optimization GA genetic algorithm GOA grasshopper optimization algorithm RMSE root mean square error MAPE mean absolute percent error

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Optimization configuration of energy storage capacity based on the microgrid reliable output power

Cited by 46 publications

References 22 publications

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Diurnal Wind Pattern and Climate Condition on the Coastal Region of Qatar

Optimal Economic Modelling of Hybrid Combined Cooling, Heating, and Energy Storage System Based on Gravitational Search Algorithm‐Random Forest Regression

Short-Term Photovoltaic Power Prediction Based on Similar Days and Improved SOA-DBN Model

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