Sardar Muhammad Aneeq Khan, A. Badar, M. S. Siddiqui, Muhammad Zeeshan Siddique, Muhammad Saad Ul Haq, Fahad Sarfraz Butt
This work pertains to the transient modeling and comparative study of active solar thermal space and water heating systems using liquid and air-type solar thermal collectors as the main energy source. The study utilizes TRNSYS to simulate the two systems in the context of Taxila's weather data (located at 33.74°N, 72.83°E), with the goal of meeting peak space and domestic water heating demands of 20 kW and 200 lit/day, respectively. The liquid water-based system (S-1) is primarily composed of a liquid solar collector, thermal storage, an auxiliary heater, connections to the hot water supply, and the space heating load through a water–air heat exchanger. In contrast, the air-based system (S-2), employs a pebble bed storage to store heat extracted from the solar thermal air collector. The heated air is subsequently used directly for space heating and passed through an air–water heat exchanger for water heating. Dynamic simulations of both systems span the entire winter season, and various performance metrics, including solar fraction, primary energy savings, and solar collector thermal efficiency, are computed. The results revealed that at the same collector area, the liquid water-based system (S-1) shows a higher solar fraction than the air-based systems (S-2) while the primary energy savings of the S-1 resulted in lower values than S-2 at smaller collector areas (< ∼30 m2) but surpasses the S-2 with increasing collector size. The optimal collector tilt for both systems is determined to be 50°, while specific storage volumes corresponding to maximum primary energy savings are estimated to be 100 and 40 L/m2 for S-1 and S-2, respectively.
{"title":"Modeling and comparative assessment of solar thermal systems for space and water heating: Liquid water versus air-based systems","authors":"Sardar Muhammad Aneeq Khan, A. Badar, M. S. Siddiqui, Muhammad Zeeshan Siddique, Muhammad Saad Ul Haq, Fahad Sarfraz Butt","doi":"10.1063/5.0175130","DOIUrl":"https://doi.org/10.1063/5.0175130","url":null,"abstract":"This work pertains to the transient modeling and comparative study of active solar thermal space and water heating systems using liquid and air-type solar thermal collectors as the main energy source. The study utilizes TRNSYS to simulate the two systems in the context of Taxila's weather data (located at 33.74°N, 72.83°E), with the goal of meeting peak space and domestic water heating demands of 20 kW and 200 lit/day, respectively. The liquid water-based system (S-1) is primarily composed of a liquid solar collector, thermal storage, an auxiliary heater, connections to the hot water supply, and the space heating load through a water–air heat exchanger. In contrast, the air-based system (S-2), employs a pebble bed storage to store heat extracted from the solar thermal air collector. The heated air is subsequently used directly for space heating and passed through an air–water heat exchanger for water heating. Dynamic simulations of both systems span the entire winter season, and various performance metrics, including solar fraction, primary energy savings, and solar collector thermal efficiency, are computed. The results revealed that at the same collector area, the liquid water-based system (S-1) shows a higher solar fraction than the air-based systems (S-2) while the primary energy savings of the S-1 resulted in lower values than S-2 at smaller collector areas (< ∼30 m2) but surpasses the S-2 with increasing collector size. The optimal collector tilt for both systems is determined to be 50°, while specific storage volumes corresponding to maximum primary energy savings are estimated to be 100 and 40 L/m2 for S-1 and S-2, respectively.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"4 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139304670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Controlling wind power plants is a challenging issue, however. This is due to its highly nonlinear dynamics, unknown disturbances, parameter uncertainties, and quick variations in the wind speed profiles. So robust controllers are needed to overcome these challenges. This paper suggests two novel control approaches for doubly fed induction generator-based wind turbines. Its key objective is to regulate the generator speed and rotor currents. A radial basis function (RBF) neural network disturbance observer based fractional order backstepping sliding mode control (SMC) is presented to control the rotor currents. This RBF neural network-based disturbance observer estimates unknown disturbances. Also, a new adaptive fractional order terminal SMC is suggested for the control of the generator speed. This robust chattering-free controller that does not require any information about the bound of uncertainties fractional calculus is adopted in the SMC design to eliminate undesired chattering phenomena. The controller parameters are optimally tuned utilizing the ant colony optimization algorithm. The proposed approach was validated using a simulation study entailing various conditions. Its performance was also compared to that of the conventional backstepping and conventional backstepping sliding mode controller. The simulations results verified the approach's ability to maximize power extraction from the wind and properly regulate the rotor currents. The proposed method has about 20% less tracking error than the other two methods, which means 20% higher efficiency.
{"title":"Adaptive fractional backstepping intelligent controller for maximum power extraction of a wind turbine system","authors":"A. Veisi, H. Delavari","doi":"10.1063/5.0161571","DOIUrl":"https://doi.org/10.1063/5.0161571","url":null,"abstract":"Controlling wind power plants is a challenging issue, however. This is due to its highly nonlinear dynamics, unknown disturbances, parameter uncertainties, and quick variations in the wind speed profiles. So robust controllers are needed to overcome these challenges. This paper suggests two novel control approaches for doubly fed induction generator-based wind turbines. Its key objective is to regulate the generator speed and rotor currents. A radial basis function (RBF) neural network disturbance observer based fractional order backstepping sliding mode control (SMC) is presented to control the rotor currents. This RBF neural network-based disturbance observer estimates unknown disturbances. Also, a new adaptive fractional order terminal SMC is suggested for the control of the generator speed. This robust chattering-free controller that does not require any information about the bound of uncertainties fractional calculus is adopted in the SMC design to eliminate undesired chattering phenomena. The controller parameters are optimally tuned utilizing the ant colony optimization algorithm. The proposed approach was validated using a simulation study entailing various conditions. Its performance was also compared to that of the conventional backstepping and conventional backstepping sliding mode controller. The simulations results verified the approach's ability to maximize power extraction from the wind and properly regulate the rotor currents. The proposed method has about 20% less tracking error than the other two methods, which means 20% higher efficiency.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"8 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139304883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cu2O-based solar cells offer a promising solution to address future energy challenges due to their affordability, eco-friendliness, and impressive power conversion efficiency (PCE). With the development of thin film deposition technology, the maximum PCE of single-junction solar cells fabricated based on Cu2O is 9.5%. Because the spectral sensitivity overlaps between Cu2O and crystalline silicon (c-Si) is small, Cu2O thin-film solar cells can be made into tandem solar cells with Si-based solar cells to achieve higher PCE. The Cu2O–Si tandem solar cell has been delivered 24.2% PCE in 2020, a time when the PCE of stand-alone silicon solar cells was 17.6%. The purpose of this paper is to summarize the development of Cu2O-based heterojunction, homojunction. The Cu2O material properties, n and p-type doping, the role of defects and impurities in bulk of films or at the interface of the p–n-junction and n-type buffer layer on the performance of Cu2O-based heterojunction like ZnO–Cu2O, and the difficulty in decreasing the interface state and doping in Cu2O homojunction solar cells are discussed. This review discusses the Cu2O film material preparation method, the history of Cu2O based solar cells, the essential factors required to enhance the performance of various types of Cu2O-based solar cells, and the potential future research opportunities for as a top subcells in Cu2O–Si tandem solar cells.
{"title":"A review of Cu2O solar cell","authors":"Sinuo Chen, Lichun Wang, Chunlan Zhou, Jinli Yang","doi":"10.1063/5.0167383","DOIUrl":"https://doi.org/10.1063/5.0167383","url":null,"abstract":"Cu2O-based solar cells offer a promising solution to address future energy challenges due to their affordability, eco-friendliness, and impressive power conversion efficiency (PCE). With the development of thin film deposition technology, the maximum PCE of single-junction solar cells fabricated based on Cu2O is 9.5%. Because the spectral sensitivity overlaps between Cu2O and crystalline silicon (c-Si) is small, Cu2O thin-film solar cells can be made into tandem solar cells with Si-based solar cells to achieve higher PCE. The Cu2O–Si tandem solar cell has been delivered 24.2% PCE in 2020, a time when the PCE of stand-alone silicon solar cells was 17.6%. The purpose of this paper is to summarize the development of Cu2O-based heterojunction, homojunction. The Cu2O material properties, n and p-type doping, the role of defects and impurities in bulk of films or at the interface of the p–n-junction and n-type buffer layer on the performance of Cu2O-based heterojunction like ZnO–Cu2O, and the difficulty in decreasing the interface state and doping in Cu2O homojunction solar cells are discussed. This review discusses the Cu2O film material preparation method, the history of Cu2O based solar cells, the essential factors required to enhance the performance of various types of Cu2O-based solar cells, and the potential future research opportunities for as a top subcells in Cu2O–Si tandem solar cells.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"4 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135609556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jessa A. Ibañez, I. Benitez, Jayson M. Cañete, J. Magadia, J. Principe
Forecasting models are often constrained by data availability, and in forecasting solar photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to solar PV output. The objective of this study is to identify which between the satellite-based and reanalysis solar irradiance data, namely, short wave radiation (SWR) and surface solar radiation downward (SSRD), respectively, is a better alternative to in situ solar irradiance in forecasting solar PV output should the latter become unavailable. Nine seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were presented in this study to assess the forecasting performance of each solar irradiance data together with weather parameters. Using only historical data to forecast solar PV output, three seasonal autoregressive integrated moving average (SARIMA) models were run to forecast solar PV output and to compare and validate the efficacy of the SARIMAX models. The analysis was divided into seasons as defined by the Philippine Atmospheric, Geophysical and Astronomical Services Administration: hot dry, rainy, and cool dry. Results show that the use of SSRD is a better alternative than SWR when forecasting solar PV output for the hot dry season and cool dry season. For the hot dry season, SSRD has an root mean square error (RMSE) value of 0.411 kW while SWR has 0.416 kW. For the cool dry season, SSRD has an RMSE value of 0.457 kW while SWR has 0.471 kW. Meanwhile, SWR outperforms SSRD when forecasting solar PV output during the rainy season, with RMSE values at 0.375 and 0.401 kW, respectively.
{"title":"Accuracy assessment of satellite-based and reanalysis solar irradiance data for solar PV output forecasting using SARIMAX","authors":"Jessa A. Ibañez, I. Benitez, Jayson M. Cañete, J. Magadia, J. Principe","doi":"10.1063/5.0160488","DOIUrl":"https://doi.org/10.1063/5.0160488","url":null,"abstract":"Forecasting models are often constrained by data availability, and in forecasting solar photovoltaic (PV) output, the literature suggests that solar irradiance contributes the most to solar PV output. The objective of this study is to identify which between the satellite-based and reanalysis solar irradiance data, namely, short wave radiation (SWR) and surface solar radiation downward (SSRD), respectively, is a better alternative to in situ solar irradiance in forecasting solar PV output should the latter become unavailable. Nine seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) models were presented in this study to assess the forecasting performance of each solar irradiance data together with weather parameters. Using only historical data to forecast solar PV output, three seasonal autoregressive integrated moving average (SARIMA) models were run to forecast solar PV output and to compare and validate the efficacy of the SARIMAX models. The analysis was divided into seasons as defined by the Philippine Atmospheric, Geophysical and Astronomical Services Administration: hot dry, rainy, and cool dry. Results show that the use of SSRD is a better alternative than SWR when forecasting solar PV output for the hot dry season and cool dry season. For the hot dry season, SSRD has an root mean square error (RMSE) value of 0.411 kW while SWR has 0.416 kW. For the cool dry season, SSRD has an RMSE value of 0.457 kW while SWR has 0.471 kW. Meanwhile, SWR outperforms SSRD when forecasting solar PV output during the rainy season, with RMSE values at 0.375 and 0.401 kW, respectively.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"186 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139294268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flow conditions in an urban environment are complex, featuring varying levels of turbulence intensity and shear. The influence of these flow characteristics on the performance of a roof-mounted vertical axis wind turbine of the Savonius (drag) type is investigated at lab scale. Five different inflow conditions are generated with an active grid in a wind tunnel, covering turbulence intensities from 0.9% to 11.5% and relative vertical shear from 0% to 17%. The flow field is captured using particle image velocimetry, and the power output of the turbine is assessed through measurements of the converted power. The set-up consists of two-surface mounted cubes aligned with each other in the main flow direction, spaced apart by two cube heights. The turbine is placed on top of these model buildings at six different streamwise positions along the centerline and at two different heights. It was observed that the turbulence intensity in the inflow has a significant impact on the flow field and also on the power output of the turbine. The increasing turbulence intensity leads to smaller regions of recirculating flow. Thus, the turbine experiences higher flow velocities, which is reflected in the measured power. The influence of shear is comparably small on both the flow field and the turbine performance. The higher of the two turbine positions yields higher power output overall. Furthermore, it was shown that the impact of the turbine on the flow field is significant for all inflow conditions and can vary substantially depending on the inflow.
{"title":"Influence of incoming turbulence and shear on the flow field and performance of a lab-scale roof-mounted vertical axis wind turbine","authors":"Y. Jooss, R. J. Hearst, T. Bracchi","doi":"10.1063/5.0170059","DOIUrl":"https://doi.org/10.1063/5.0170059","url":null,"abstract":"Flow conditions in an urban environment are complex, featuring varying levels of turbulence intensity and shear. The influence of these flow characteristics on the performance of a roof-mounted vertical axis wind turbine of the Savonius (drag) type is investigated at lab scale. Five different inflow conditions are generated with an active grid in a wind tunnel, covering turbulence intensities from 0.9% to 11.5% and relative vertical shear from 0% to 17%. The flow field is captured using particle image velocimetry, and the power output of the turbine is assessed through measurements of the converted power. The set-up consists of two-surface mounted cubes aligned with each other in the main flow direction, spaced apart by two cube heights. The turbine is placed on top of these model buildings at six different streamwise positions along the centerline and at two different heights. It was observed that the turbulence intensity in the inflow has a significant impact on the flow field and also on the power output of the turbine. The increasing turbulence intensity leads to smaller regions of recirculating flow. Thus, the turbine experiences higher flow velocities, which is reflected in the measured power. The influence of shear is comparably small on both the flow field and the turbine performance. The higher of the two turbine positions yields higher power output overall. Furthermore, it was shown that the impact of the turbine on the flow field is significant for all inflow conditions and can vary substantially depending on the inflow.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"15 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135509574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Charge-while-driving technology is a promising application in the future. A routing approach suitable for hybrid stationary and dynamic wireless charging networks is thus worth investigating. This paper aims to determine the optimal path with minimum generalized travel cost as well as provides charging policy recommendations for electric vehicle (EV) users. A hybrid charging network, including charging stations and wireless lanes, is constructed first. The generalized travel cost is then investigated to help EV users understand the complicated cost components. A dynamic programming algorithm is developed as the solution measure. Numerical experiments show that a higher level of wireless charging lane penetration can significantly reduce generalized travel costs, especially implicit costs such as travel time cost or stopping cost. EVs are more likely to prefer wireless charging modes when the value of the user's time and the cost of stopping is high. The methodology proposed in this study not only provides services to EV owners, such as navigation, but is also a useful tool for administrations wishing to direct incentives to facilitate the transition to more sustainable energy sources, as it quantifies the benefits of wireless charging for different network attributes.
{"title":"Optimal routing for electric vehicles in hybrid charging networks","authors":"Kun Jin, Wei Wang, Xinran Li, Xuedong Hua","doi":"10.1063/5.0178669","DOIUrl":"https://doi.org/10.1063/5.0178669","url":null,"abstract":"Charge-while-driving technology is a promising application in the future. A routing approach suitable for hybrid stationary and dynamic wireless charging networks is thus worth investigating. This paper aims to determine the optimal path with minimum generalized travel cost as well as provides charging policy recommendations for electric vehicle (EV) users. A hybrid charging network, including charging stations and wireless lanes, is constructed first. The generalized travel cost is then investigated to help EV users understand the complicated cost components. A dynamic programming algorithm is developed as the solution measure. Numerical experiments show that a higher level of wireless charging lane penetration can significantly reduce generalized travel costs, especially implicit costs such as travel time cost or stopping cost. EVs are more likely to prefer wireless charging modes when the value of the user's time and the cost of stopping is high. The methodology proposed in this study not only provides services to EV owners, such as navigation, but is also a useful tool for administrations wishing to direct incentives to facilitate the transition to more sustainable energy sources, as it quantifies the benefits of wireless charging for different network attributes.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"24 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135608998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingwei Zhang, Yisheng Su, Yongjie Liu, Zenan Yang, Kun Ding, Yuanliang Li, Xihui Chen, Xiang Chen
Harsh outdoor operations may cause various abnormalities or faults of photovoltaic (PV) array, decrease the energy yield and lifespan, and even cause catastrophic events. Recently, many approaches have been successfully applied to the fault diagnosis for PV arrays. However, few studies investigate the evaluation and quantification of fault severity. The quantified fault severity can facilitate the fault severity-dependent maintenance of PV system. In this paper, a fault severity quantification approach based on pre-estimation and fine-tuning of fault parameters is proposed. The key features of the I–V characteristics under different faults are determined to train a backpropagation neural network for estimating the preliminary diagnosis and quantification results. Then, the particle swarm optimizer is further used to locally optimize the estimated results to improve the accuracy of quantified fault severity. Compared with other diagnosis approaches, the experimental results verify that the proposed fault diagnosis and quantification approach obtains higher accuracy with decent computational speed. The proposed method is suitable for the fault severity-dependent maintenance of the PV systems.
{"title":"A fault severity quantification approach of photovoltaic array based on pre-estimation and fine-tuning of fault parameters","authors":"Jingwei Zhang, Yisheng Su, Yongjie Liu, Zenan Yang, Kun Ding, Yuanliang Li, Xihui Chen, Xiang Chen","doi":"10.1063/5.0152868","DOIUrl":"https://doi.org/10.1063/5.0152868","url":null,"abstract":"Harsh outdoor operations may cause various abnormalities or faults of photovoltaic (PV) array, decrease the energy yield and lifespan, and even cause catastrophic events. Recently, many approaches have been successfully applied to the fault diagnosis for PV arrays. However, few studies investigate the evaluation and quantification of fault severity. The quantified fault severity can facilitate the fault severity-dependent maintenance of PV system. In this paper, a fault severity quantification approach based on pre-estimation and fine-tuning of fault parameters is proposed. The key features of the I–V characteristics under different faults are determined to train a backpropagation neural network for estimating the preliminary diagnosis and quantification results. Then, the particle swarm optimizer is further used to locally optimize the estimated results to improve the accuracy of quantified fault severity. Compared with other diagnosis approaches, the experimental results verify that the proposed fault diagnosis and quantification approach obtains higher accuracy with decent computational speed. The proposed method is suitable for the fault severity-dependent maintenance of the PV systems.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"30 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139291657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids. High peak-to-valley differences on the load side also affect the stable operation of the microgrid. To improve the accuracy of capacity configuration of ES and the stability of microgrids, this study proposes a capacity configuration optimization model of ES for the microgrid, considering source–load prediction uncertainty and demand response (DR). First, a microgrid, including electric vehicles, is constructed. Second, the uncertainty of renewable energy resources and electric demand is handled by Monte Carlo scenario generation techniques and K-means-based scenario reduction techniques. Then, a DR model combining price-based demand response and incentive-based demand response is constructed to achieve a better match between electricity demand and supply. Finally, the results of the ES capacity configuration are determined with the objective of minimizing the total daily cost of the microgrid. The simulation results show that the optimal configuration of ES capacity and DR promotes renewable energy consumption and achieves peak shaving and valley filling, which reduces the total daily cost of the microgrid by 22%. Meanwhile, the DR model proposed in this paper has the best optimization results compared with a single type of the DR model. It is verified through comparative analysis that under a certain proportion of flexible loads, the total daily cost of the microgrid is the lowest when the time-shiftable loads and the curtailable loads are both 10%.
可再生能源资源的波动和需求方负荷的不确定性会影响微电网中储能（ES）配置的准确性。负荷侧的峰谷差过大也会影响微电网的稳定运行。为了提高储能系统容量配置的准确性和微电网的稳定性，本研究提出了一种微电网储能系统容量配置优化模型，并考虑了源负荷预测的不确定性和需求响应（DR）。首先，构建一个包括电动汽车在内的微电网。其次，通过蒙特卡罗情景生成技术和基于 K-means 的情景还原技术处理可再生能源和电力需求的不确定性。然后，构建了基于价格的需求响应和基于激励的需求响应相结合的 DR 模型，以实现电力需求与供应之间的更好匹配。最后，以微电网每日总成本最小化为目标，确定了 ES 容量配置的结果。仿真结果表明，ES 容量和 DR 的最优配置促进了可再生能源的消纳，实现了削峰填谷，使微电网的日总成本降低了 22%。同时，与单一类型的 DR 模型相比，本文提出的 DR 模型优化效果最佳。通过对比分析验证，在一定的灵活负荷比例下，当可时移负荷和可削减负荷均为 10%时，微电网的日总成本最低。
{"title":"Capacity configuration optimization of energy storage for microgrids considering source–load prediction uncertainty and demand response","authors":"Jinliang Zhang, Zeqing Zhang","doi":"10.1063/5.0174641","DOIUrl":"https://doi.org/10.1063/5.0174641","url":null,"abstract":"The fluctuation of renewable energy resources and the uncertainty of demand-side loads affect the accuracy of the configuration of energy storage (ES) in microgrids. High peak-to-valley differences on the load side also affect the stable operation of the microgrid. To improve the accuracy of capacity configuration of ES and the stability of microgrids, this study proposes a capacity configuration optimization model of ES for the microgrid, considering source–load prediction uncertainty and demand response (DR). First, a microgrid, including electric vehicles, is constructed. Second, the uncertainty of renewable energy resources and electric demand is handled by Monte Carlo scenario generation techniques and K-means-based scenario reduction techniques. Then, a DR model combining price-based demand response and incentive-based demand response is constructed to achieve a better match between electricity demand and supply. Finally, the results of the ES capacity configuration are determined with the objective of minimizing the total daily cost of the microgrid. The simulation results show that the optimal configuration of ES capacity and DR promotes renewable energy consumption and achieves peak shaving and valley filling, which reduces the total daily cost of the microgrid by 22%. Meanwhile, the DR model proposed in this paper has the best optimization results compared with a single type of the DR model. It is verified through comparative analysis that under a certain proportion of flexible loads, the total daily cost of the microgrid is the lowest when the time-shiftable loads and the curtailable loads are both 10%.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"11 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139292840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Overconsuming fossil fuels has worsened global warming and air pollution, requiring us to investigate alternate fuels for compression ignition engines. Biodiesel is a renewable fuel and environmentally favorable. Biodiesel's most significant disadvantage is increased nitrogen oxide (NOx) emissions. The intent of the present study was to examine the impact of antioxidant diphenylamine (DPA) and nanoparticle ceria (CeO2) additive inclusion in a B30 blend on engine performance and exhaust emission characteristics. For this study, diesel, Jatropha biodiesel (B30), 100 ppm of antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA100 and antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA50+CeO250 fuel blends has been used. A hybrid response surface methodology and multi-criteria decision-making techniques (entropy method, TOPSIS, and VIKOR) have been used to develop a sustainable model and find the optimal setting of input parameters in terms of ranking. From experimental findings, the inclusion of antioxidants (DPA) and nanoparticle (CeO2) at 50 ppm to B30 significantly reduced NOx emission. The brake-specific fuel consumption and NOx have been found reduced by 5.67% and 18.87%, respectively, for B30+DPA50+CeO250 as compared to B30. At the same time, brake thermal efficiency increased by 1.01%. The brake mean effective pressure and maximum cylinder pressure) have been found increased by 0.68% and reduced by 4.52%, respectively, for B30+DPA50+CeO250 as compared to B30. The alternative ranking of the input parameters has been found fuel injection pressure (300), compression ratio (17), and load (12) as Rank 1 for TOPSIS and VIKOR. Therefore, the B30+DPA50+CeO250 blend is appropriate for improving diesel engine performance and diminishing exhaust emissions.
{"title":"A sustainable model using RSM and MCDM techniques to evaluate performance and emission characteristics of a diesel engine fueled with diphenylamine antioxidant and CeO2 nanoparticle additive biodiesel blends","authors":"Vijay Kumar, A. Choudhary","doi":"10.1063/5.0168854","DOIUrl":"https://doi.org/10.1063/5.0168854","url":null,"abstract":"Overconsuming fossil fuels has worsened global warming and air pollution, requiring us to investigate alternate fuels for compression ignition engines. Biodiesel is a renewable fuel and environmentally favorable. Biodiesel's most significant disadvantage is increased nitrogen oxide (NOx) emissions. The intent of the present study was to examine the impact of antioxidant diphenylamine (DPA) and nanoparticle ceria (CeO2) additive inclusion in a B30 blend on engine performance and exhaust emission characteristics. For this study, diesel, Jatropha biodiesel (B30), 100 ppm of antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA100 and antioxidant diphenylamine (50 ppm) with ceria nanoparticle (50 ppm) is added to the B30 blend named as B30+DPA50+CeO250 fuel blends has been used. A hybrid response surface methodology and multi-criteria decision-making techniques (entropy method, TOPSIS, and VIKOR) have been used to develop a sustainable model and find the optimal setting of input parameters in terms of ranking. From experimental findings, the inclusion of antioxidants (DPA) and nanoparticle (CeO2) at 50 ppm to B30 significantly reduced NOx emission. The brake-specific fuel consumption and NOx have been found reduced by 5.67% and 18.87%, respectively, for B30+DPA50+CeO250 as compared to B30. At the same time, brake thermal efficiency increased by 1.01%. The brake mean effective pressure and maximum cylinder pressure) have been found increased by 0.68% and reduced by 4.52%, respectively, for B30+DPA50+CeO250 as compared to B30. The alternative ranking of the input parameters has been found fuel injection pressure (300), compression ratio (17), and load (12) as Rank 1 for TOPSIS and VIKOR. Therefore, the B30+DPA50+CeO250 blend is appropriate for improving diesel engine performance and diminishing exhaust emissions.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"33 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139295900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid.
{"title":"Peer-to-peer energy trading in a community based on deep reinforcement learning","authors":"Yiqun Wang, Qingyu Yang, Donghe Li","doi":"10.1063/5.0172713","DOIUrl":"https://doi.org/10.1063/5.0172713","url":null,"abstract":"With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid.","PeriodicalId":16953,"journal":{"name":"Journal of Renewable and Sustainable Energy","volume":"24 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139296195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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