Pub Date : 2024-08-09DOI: 10.3389/fenrg.2024.1413910
Pan Lei, Jinquan Zhao
The short-circuit ratio index (SCR) can effectively quantify the voltage support strength in traditional DC grid-connected scenarios, yet it cannot reasonably describe the voltage support strength in diverse device grid connection scenarios. This paper introduces a new calculation method of the complex short-circuit ratio index (SCR∼) and derives the threshold value of the complex short-circuit ratio index to enable a comprehensive quantitative assessment of grid voltage support strength across diverse device grid connection scenarios. Firstly, critical short-circuit ratio (CSCR) under different assumed conditions were derived based on the short-circuit ratio index. Secondly, the calculation method of the complex short-circuit ratio index was introduced, considering both the equivalent impedance angle of the device and the Thevenin equivalent impedance angle. This was followed by the determination of the threshold value of the complex short-circuit ratio (CSCR∼), enabling a precise quantitative evaluation of power grid voltage support strength in diverse device grid connection scenarios. Finally, the example analysis proves the accuracy and efficacy of the complex short-circuit ratio index in assessing the voltage support strength of diverse devices in grid-connected scenarios.
{"title":"The analysis of the threshold value of the complex short-circuit ratio index and its significance in the context of static voltage stability","authors":"Pan Lei, Jinquan Zhao","doi":"10.3389/fenrg.2024.1413910","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1413910","url":null,"abstract":"The short-circuit ratio index (SCR) can effectively quantify the voltage support strength in traditional DC grid-connected scenarios, yet it cannot reasonably describe the voltage support strength in diverse device grid connection scenarios. This paper introduces a new calculation method of the complex short-circuit ratio index (SCR∼) and derives the threshold value of the complex short-circuit ratio index to enable a comprehensive quantitative assessment of grid voltage support strength across diverse device grid connection scenarios. Firstly, critical short-circuit ratio (CSCR) under different assumed conditions were derived based on the short-circuit ratio index. Secondly, the calculation method of the complex short-circuit ratio index was introduced, considering both the equivalent impedance angle of the device and the Thevenin equivalent impedance angle. This was followed by the determination of the threshold value of the complex short-circuit ratio (CSCR∼), enabling a precise quantitative evaluation of power grid voltage support strength in diverse device grid connection scenarios. Finally, the example analysis proves the accuracy and efficacy of the complex short-circuit ratio index in assessing the voltage support strength of diverse devices in grid-connected scenarios.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fenrg.2024.1435455
Belachew Desalegn, Bimrew Tamrat
Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment.
最近的研究普遍报告称，可持续能源的间歇性特点对可持续能源采集技术的效率和成本竞争力提出了巨大挑战。因此，现代可持续能源系统需要实施严格的电源管理策略，在降低成本的同时实现最大可能的绿色电力生产。鉴于可持续能源的上述特点，如今的电力管理系统已变得越来越复杂。在解决未来可持续电力系统的分析、调度和控制问题时，传统的基于模型的方法因无法处理可再生能源发电中的不规则电力干扰而效率低下。因此，随着近年来智能电网的出现，电力系统运营商开始依赖智能计量和先进的传感设备来收集更广泛的数据。这反过来又促进了先进机器学习算法的应用，通过从海量数据中学习，最终生成有用的信息，而无需假设和简化处理电力系统最不规则的运行行为。本文旨在探讨一些主要适用于风能转换系统（WECS）的机器学习算法的各种应用目标。此外，本文还特别介绍了一种增强型比例积分（PI）（2DoF）算法，并在基于双馈异步发电机（DFIG）的风能转换系统中加以应用，以提高电力生产的可靠性。本文的主要贡献在于利用 PI (2DoF) 算法的优越性来提高 WECS 在不确定情况下的性能、稳定性和鲁棒性。最后，通过在 MATLAB-Simulink 环境中开发虚拟现实，证明了该研究的有效性。
{"title":"Overview of the PI (2DoF) algorithm in wind power system optimization and control","authors":"Belachew Desalegn, Bimrew Tamrat","doi":"10.3389/fenrg.2024.1435455","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1435455","url":null,"abstract":"Recent research generally reports that the intermittent characteristics of sustainable energy sources pose great challenges to the efficiency and cost competitiveness of sustainable energy harvesting technologies. Hence, modern sustainable energy systems need to implement a stringent power management strategy to achieve the maximum possible green electricity production while reducing costs. Due to the above-mentioned characteristics of sustainable energy sources, power management systems have become increasingly sophisticated nowadays. For addressing the analysis, scheduling, and control problems of future sustainable power systems, conventional model-based methods are completely inefficient as they fail to handle irregular electric power disturbances in renewable energy generations. Consequently, with the advent of smart grids in recent years, power system operators have come to rely on smart metering and advanced sensing devices for collecting more extensive data. This, in turn, facilitates the application of advanced machine learning algorithms, which can ultimately cause the generation of useful information by learning from massive data without assumptions and simplifications in handling the most irregular operating behaviors of the power systems. This paper aims to explore various application objectives of some machine learning algorithms that primarily apply to wind energy conversion systems (WECSs). In addition, an enhanced proportional integral (PI) (2DoF) algorithm is particularly introduced and implemented in a doubly fed induction generator (DFIG)-based WECS to enhance the reliability of power production. The main contribution of this article is to leverage the superior qualities of the PI (2DoF) algorithm for enhanced performance, stability, and robustness of the WECS under uncertainties. Finally, the effectiveness of the study is demonstrated by developing a virtual reality in a MATLAB-Simulink environment.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141925337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fenrg.2024.1459667
Yanyu Yan, Shiwei Xie, Jianlin Tang, Bin Qian, Xiaoming Lin, Fan Zhang
A virtual power plant (VPP) has the ability to aggregate numerous decentralized distributed energy resources using advanced control technology, offering a promising approach for low-carbon development. In order to enhance the VPP’s contribution to reducing carbon emissions, a bi-level framework is proposed that incorporates an integrated energy-carbon price response mechanism. This model allows VPPs to participate in a multi-energy system through a multi-agent Stackelberg game framework. Initially, a transaction model is established where the power distribution system operator and the gas distribution system operator act as leaders, while the virtual power plant operator acts as a follower in the multi-energy system. Subsequently, an integrated energy-carbon pricing method, rooted in carbon emission flow theory, is introduced to encourage VPPs to proactively adjust their energy-use and trading strategies within multi-energy systems, thereby promoting multi-principal interactive trading. To achieve a distributed solution among multiple entities while maintaining the privacy of each entity’s information, the adaptive step-size alternating direction multiplier method is employed. The feasibility and effectiveness of the proposed model and method are then demonstrated through case studies.
{"title":"Transaction strategy of virtual power plants and multi-energy systems with multi-agent Stackelberg game based on integrated energy-carbon pricing","authors":"Yanyu Yan, Shiwei Xie, Jianlin Tang, Bin Qian, Xiaoming Lin, Fan Zhang","doi":"10.3389/fenrg.2024.1459667","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1459667","url":null,"abstract":"A virtual power plant (VPP) has the ability to aggregate numerous decentralized distributed energy resources using advanced control technology, offering a promising approach for low-carbon development. In order to enhance the VPP’s contribution to reducing carbon emissions, a bi-level framework is proposed that incorporates an integrated energy-carbon price response mechanism. This model allows VPPs to participate in a multi-energy system through a multi-agent Stackelberg game framework. Initially, a transaction model is established where the power distribution system operator and the gas distribution system operator act as leaders, while the virtual power plant operator acts as a follower in the multi-energy system. Subsequently, an integrated energy-carbon pricing method, rooted in carbon emission flow theory, is introduced to encourage VPPs to proactively adjust their energy-use and trading strategies within multi-energy systems, thereby promoting multi-principal interactive trading. To achieve a distributed solution among multiple entities while maintaining the privacy of each entity’s information, the adaptive step-size alternating direction multiplier method is employed. The feasibility and effectiveness of the proposed model and method are then demonstrated through case studies.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.3389/fenrg.2024.1419427
Xianwei Zhou, Chuncheng Liu, Yi Qian, Zhenyu Bi, Mo Yang
At present, energy storage in industrial and commercial scenarios has problems such as poor protection levels, flexible deployment, and poor battery performance. Aiming at the pain points and storage application scenarios of industrial and commercial energy, this paper proposes liquid cooling solutions. In this paper, the box structure was first studied to optimize the structure, and based on the liquid cooling technology route, the realization of an industrial and commercial energy storage thermal management scheme for the integrated cabinet was studied to ensure that the temperature between the cabinets was consistent and reduce the system capacity loss caused by the liquid-cooled battery module was inconsistent. The industrial and commercial energy storage integrated cabinet comprehensively considers the flexible deployment of the system, enhances the protection level of the cabinet, and the structural strength of the cabinet, and improves the temperature balance characteristics of the battery module in the cabinet.
{"title":"Research and design for a storage liquid refrigerator considering the characteristics of energy storage batteries","authors":"Xianwei Zhou, Chuncheng Liu, Yi Qian, Zhenyu Bi, Mo Yang","doi":"10.3389/fenrg.2024.1419427","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1419427","url":null,"abstract":"At present, energy storage in industrial and commercial scenarios has problems such as poor protection levels, flexible deployment, and poor battery performance. Aiming at the pain points and storage application scenarios of industrial and commercial energy, this paper proposes liquid cooling solutions. In this paper, the box structure was first studied to optimize the structure, and based on the liquid cooling technology route, the realization of an industrial and commercial energy storage thermal management scheme for the integrated cabinet was studied to ensure that the temperature between the cabinets was consistent and reduce the system capacity loss caused by the liquid-cooled battery module was inconsistent. The industrial and commercial energy storage integrated cabinet comprehensively considers the flexible deployment of the system, enhances the protection level of the cabinet, and the structural strength of the cabinet, and improves the temperature balance characteristics of the battery module in the cabinet.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3389/fenrg.2024.1413576
Yajuan Cao, Chenchen Tao
A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker’s accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent’s decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction’s result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator’s defense, demonstrating the overall performance benefit of such framework designs.
{"title":"Reinforcement learning and game theory based cyber-physical security framework for the humans interacting over societal control systems","authors":"Yajuan Cao, Chenchen Tao","doi":"10.3389/fenrg.2024.1413576","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1413576","url":null,"abstract":"A lot of infrastructure upgrade and algorithms have been developed for the information technology driven smart grids over the past twenty years, especially with increasing interest in their system design and real-world implementation. Meanwhile, the study of detecting and preventing intruders in ubiquitous smart grids environment is spurred significantly by the possibility of access points on various communication equipment. As a result, there are no comprehensive security protocols in place preventing from a malicious attacker’s accessing to smart grids components, which would enable the interaction of attackers and system operators through the power grid control system. Recently, dynamics of time-extended interactions are believed to be predicted and solved by reinforcement learning technology. As a descriptive advantage of the approach compared with other methods, it provides the opportunities of simultaneously modeling several human continuous interactions features for decision-making process, rather than specifying an individual agent’s decision dynamics and requiring others to follow specific kinematic and dynamic limitations. In this way, a machine-mediated human-human interaction’s result is determined by how control and physical systems are designed. Technically, it is possible to design dedicated human-in-the-loop societal control systems that are attack-resistant by using simulations that predict such results with preventive assessment and acceptable accuracy. It is important to have a reliable model of both the control and physical systems, as well as of human decision-making, to make reliable assumptions. This study presents such a method to develop these tools, which includes a model that simulates the attacks of a cyber-physical intruder on the system and the operator’s defense, demonstrating the overall performance benefit of such framework designs.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3389/fenrg.2024.1429160
Yuyue Zhang, Xin Tian, Lina Zhang
With the gradual increase in the grid-connected capacity of renewable energy sources, the uncertainty in the operation of power systems has increased, posing challenges to static security assessment considering N-1 contingency scanning. To address this, this article first establishes a static security calculation model based on stochastic power flow. Then, it proposes stochastic component-level safety indexes and system-level safety indexes. Finally, using the analytic hierarchy process to analyze the obtained weighting coefficients, the article establishes a system of static security assessment indexes for power systems. A data-driven simulation method based on extreme gradient boosting (XGBoost) is proposed to tackle the high time consumption of multi-scenario static security assessment, which brings difficulties in model debugging and application. Case studies based on the IEEE 39-bus system demonstrate the effectiveness of the proposed model and the rapidity of the data-driven approach.
{"title":"A stochastic power flow-based static security assessment under uncertain scenarios","authors":"Yuyue Zhang, Xin Tian, Lina Zhang","doi":"10.3389/fenrg.2024.1429160","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1429160","url":null,"abstract":"With the gradual increase in the grid-connected capacity of renewable energy sources, the uncertainty in the operation of power systems has increased, posing challenges to static security assessment considering N-1 contingency scanning. To address this, this article first establishes a static security calculation model based on stochastic power flow. Then, it proposes stochastic component-level safety indexes and system-level safety indexes. Finally, using the analytic hierarchy process to analyze the obtained weighting coefficients, the article establishes a system of static security assessment indexes for power systems. A data-driven simulation method based on extreme gradient boosting (XGBoost) is proposed to tackle the high time consumption of multi-scenario static security assessment, which brings difficulties in model debugging and application. Case studies based on the IEEE 39-bus system demonstrate the effectiveness of the proposed model and the rapidity of the data-driven approach.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery’s electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series–parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric–thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric–thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station.
{"title":"Electro-thermal coupling modeling of energy storage station considering battery physical characteristics","authors":"Mingdian Wang, Peng Jia, Wenqi Wei, Zhihua Xie, Jukui Chen, Haiying Dong","doi":"10.3389/fenrg.2024.1433797","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1433797","url":null,"abstract":"Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery’s electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series–parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric–thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric–thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-08DOI: 10.3389/fenrg.2024.1442144
Weiru Wang, Xueting Cheng, Jing Li, Huiping Zheng, Mengzan Li
To promote the achievement of low-carbon goals in the power industry, rational and effective power system planning is essential. The participation of demand response in power system planning is an important means to reduce carbon emissions. To this end, a dual-layer low-carbon planning model for power systems considering carbon emission flow and demand response was designed. The upper layer investment planning model minimizes investment and operational costs, using an annual 8760-h operation simulation model and unit clustering linearization of the coal-fired units, coordinating the optimized investment and construction capacity of traditional units, new energy, and storage. The lower layer model forms a demand response model based on carbon emission flow theory and a load-side stepped carbon price mechanism, using the unit output and line flow data calculated by the upper layer model. This model reasonably adjusts the load distribution to reduce both the amount and cost of carbon emissions. Finally, the proposed model was analyzed and verified on the improved IEEERTS-24 node system.
{"title":"Role of renewable energy and storage in low-carbon power systems","authors":"Weiru Wang, Xueting Cheng, Jing Li, Huiping Zheng, Mengzan Li","doi":"10.3389/fenrg.2024.1442144","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1442144","url":null,"abstract":"To promote the achievement of low-carbon goals in the power industry, rational and effective power system planning is essential. The participation of demand response in power system planning is an important means to reduce carbon emissions. To this end, a dual-layer low-carbon planning model for power systems considering carbon emission flow and demand response was designed. The upper layer investment planning model minimizes investment and operational costs, using an annual 8760-h operation simulation model and unit clustering linearization of the coal-fired units, coordinating the optimized investment and construction capacity of traditional units, new energy, and storage. The lower layer model forms a demand response model based on carbon emission flow theory and a load-side stepped carbon price mechanism, using the unit output and line flow data calculated by the upper layer model. This model reasonably adjusts the load distribution to reduce both the amount and cost of carbon emissions. Finally, the proposed model was analyzed and verified on the improved IEEERTS-24 node system.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141929169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-17DOI: 10.3389/fenrg.2024.1407183
Wang Rui, Zhengdong Lei, Jia Han, Yang Yang, Yan Yiqun
Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.
储层数值模拟是页岩储层合理高效开发的重要工具和方法。准确描述页岩储层开发中的三维裂缝是提高页岩储层数值模拟精度和鲁棒性的必要条件和充分条件。本文通过建立基于非结构网络的嵌入式离散裂缝模型，实现了对复杂裂缝形态和油气水流的精确表征，在储层复杂形态裂缝的精细表征和储层网格划分方面具有优势。在大矩阵求解方法中，采用牛顿-拉夫逊法对非线性方程进行线性化，构建雅各布矩阵，采用 ILU 法进行预处理，采用共轭梯度法求解线性方程，建立了页岩油准弹性 能源开发数学模型的全隐式求解方法。
{"title":"Complex fracture description and quasi-elastic energy development mathematical model for shale oil reservoirs","authors":"Wang Rui, Zhengdong Lei, Jia Han, Yang Yang, Yan Yiqun","doi":"10.3389/fenrg.2024.1407183","DOIUrl":"https://doi.org/10.3389/fenrg.2024.1407183","url":null,"abstract":"Reservoir numerical simulation is an important tool and method for the reasonable and efficient development of shale reservoirs. Accurate description of three-dimensional fractures in shale reservoir development is a necessary and sufficient condition to improve the accuracy and robustness of shale reservoir numerical simulation. This paper achieves precise characterization of complex fracture shapes and oil, gas and water flow by establishing an embedded discrete fracture model based on a non-structural network, which has advantages in the fine characterization of complex morphological fractures in the reservoir and the grid division of the reservoir. In the large matrix solution method, the Newton-Raphson method is used to linearize the nonlinear equations, the Jacobian matrix is constructed, the ILU method is used for preprocessing, the conjugate gradient method is used to solve the linear equations, and the shale oil quasi-elasticity is established A fully implicit solution method for mathematical models of energy development.","PeriodicalId":503838,"journal":{"name":"Frontiers in Energy Research","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140963742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-16DOI: 10.3389/fenrg.2024.1363378
P. Kepplinger, Gerhard Huber, M. Preißinger
Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization.
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