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In order to improve the control capability of distributed photovoltaic support, a distributed photovoltaic support consumption method based on energy storage configuration mode and random events is proposed. A networked and constrained parameter analysis model for distributed photovoltaic power supply control was constructed. Based on the direct flexible mode of optical storage, an AC/DC voltage level control model for distributed solar power supply control was constructed. In the operation mode of DC hybrid distribution network, the demand response tracking identification method was used to analyze the uncertain characteristic parameters of distributed solar power supply load, and combined with the planned energy storage capacity parameters, the distributed solar power supply load and photovoltaic output were estimated. By configuring the optimal energy storage capacity, adjusting the power distribution of the microgrid, and integrating the analysis of uncertain factors and random events in the energy storage configuration mode, the design of distributed photovoltaic support consumption has been achieved. The experimental results show that the distributed photovoltaic absorption control using this method has lower load requirements, can effectively reduce the exchange power of the interconnection line, and improve the configuration scale, system reliability, and economy of the photovoltaic energy storage system. [ABSTRACT FROM AUTHOR]

In order to improve the control capability of distributed photovoltaic support, a distributed photovoltaic support consumption method based on energy storage configuration mode and random events is proposed. A networked and constrained parameter analysis model for distributed photovoltaic power supply control was constructed. Based on the direct flexible mode of optical storage, an AC/DC voltage level control model for distributed solar power supply control was constructed. In the operation mode of DC hybrid distribution network, the demand response tracking identification method was used to analyze the uncertain characteristic parameters of distributed solar power supply load, and combined with the planned energy storage capacity parameters, the distributed solar power supply load and photovoltaic output were estimated. By configuring the optimal energy storage capacity, adjusting the power distribution of the microgrid, and integrating the analysis of uncertain factors and random events in the energy storage configuration mode, the design of distributed photovoltaic support consumption has been achieved. The experimental results show that the distributed photovoltaic absorption control using this method has lower load requirements, can effectively reduce the exchange power of the interconnection line, and improve the configuration scale, system reliability, and economy of the photovoltaic energy storage system.

With the high penetration of renewable energy in modern power system, grid-side energy storage can support power system operation as a flexible resource. Evaluating the value of energy storage is a necessary preparation for its capacity sizing. Three value indexes that reflect the value on renewable energy utilization, power system flexibility, and dispatch economy are defined at first. Then, a value evaluation method based on multi-parametric mixed integer linear programming theory is proposed and applied to above indexes. As a result, each index is represented as an analytical piecewise linear function of the power and energy capacity of storage. Finally, the proposed models and method are verified on the Northeast Power Grid data. The simulation results show that proposed method can analytically describe how the value of energy storage is affected by its capacity, along with visualization. The analytical expression contains profound sensitivity information, which reveals the bottleneck of energy storage value and provides insightful reference for energy storage capacity sizing in different application scenarios.

In this work, the one-dimensional (1D) Ni–Co–Se nanowire arrays with rich grain-boundaries were prepared through the solvothermal method and gas-phase selenizaiton. The results showed that the structure and crystallization of the Ni–Co–Se nanowire arrays could be modulated through the optimization of selenizaiton time. The optimal Ni–Co–Se electrode sample displayed an area specific capacitance of 242.6 μ Ah cm−2 at 30 mA cm−2 with a current retention rate of 68.34%. The assembled Ni–Co–Se/Active carbon (AC) electrode-based asymmetric supercapacitor (ASC) showed the area specific capacitances of 329.2 μ Ah cm−2 and 225.8 μ Ah cm−2 at 3 mA cm−2 and 30 mA cm−2, respectively. A 73.33% retention rate of capacitance was observed after 8000 charge/discharge cycles. Besides, the further fabricated all-solid ASC delivered the power densities of 342.94 W kg−1 and 3441.33 W kg−1 at the energy densities of 37.62 Wh kg−1 and 25.81 Wh kg−1, respectively. Those results suggested the potentials of the obtained Ni–Co–Se nanowire arrays as electrode material for the high-performance pseudocapacitors. [ABSTRACT FROM AUTHOR]

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As a large-scale energy storage technology, compressed air energy storage (CAES) has the advantages of high efficiency and high reliability. This paper analyzed the feasibility of CAES cavern constructed in salt mine with multi-interlayer in Zhangshu, Jiangxi Province. Salt mine in Zhangshu is a typical bedded rock salt, and a method for calculating the cavern volume in Zhangshu was proposed. Using the proposed volume calculation method, it is expected that the volume of Zhangshu salt caverns can reach 27,840,000 m3 by 2060. By modelling the geomechanics used to calculate its stability. Based on the results of the simulation, the recommended operating pressure is 8.5–11.5 MPa. Finally, If this volume is used for CAES, the cycle pressure is assumed to be 8.5–11.5 MPa, energy storage efficiency of 50 %, the daily effective energy storage is 8.062 × 1014 J. This study will guide the development of the Zhangshu Salt Mine, provide lessons for similar areas and have a positive impact on the energy transition in Jiangxi. • A method for calculating the volume of salt caverns in the Zhangshu is proposed. • The recommended pressure range for CAES in Zhangshu is 8.5–11.5 MPa. [ABSTRACT FROM AUTHOR]

The utilization of affordable and cost-effective storage materials is a crucial factor in the development of such systems. In this study, the influence of coil pitch, inlet fluid temperature and hot fluid velocity on sand based thermal energy storage (TES) unit is investigated, using experimental results and theoretical models. The experimental segment of this study focuses on measuring the thermophysical properties of two sand samples obtained from different locations within the United Arab Emirates. A conjugated heat transfer model is developed to predict TES using the experimentally measured sand properties. A regression model utilizing response surface methodology (RSM) approach is developed to represent the energy stored per kilogram of sand as a function of the input factors. Furthermore, an optimization algorithm is employed to determine the optimal values of input factors that maximize the energy storage density. The results reveal that the three factors (fluid inlet temperature, velocity, and number of coil turns) significantly affect the stored thermal energy. The RSM analysis illustrates that maintaining high levels of both inlet temperature and fluid velocity maximizes the energy stored. Similarly, keeping inlet temperature and coil turns at the high level maximizes the energy stored. The optimized sand energy storage unit mass reaches 6.348 kJ/kg after an 8-h charging period, with an associated pressure drop of 71.4 Pa for the currently designed unit. • Elemental and thermal characterization of two common sand types in the UAE are measured. • Conjugate heat transfer model is developed to predict the TES capacity of a sand-based unit. • The RSM is employed to analyze key factors that impact the storage capacity of the proposed sand unit. • Fluid inlet temperature, velocity, and coil turns are significantly affecting the stored thermal energy. [ABSTRACT FROM AUTHOR]

To support long-term energy storage capacity planning, this study proposes a non-linear multi-objective planning model for provincial energy storage capacity (ESC) and technology selection in China. The model aims to minimize the load peak-to-valley difference after peak-shaving and valley-filling. We consider six existing mainstream energy storage technologies: pumped hydro storage (PHS), compressed air energy storage (CAES), super-capacitors (SC), lithium-ion batteries, lead-acid batteries, and vanadium redox flow batteries (VRB). The optimized results show that 1) the accumulated ESC in China could increase at an average annual rate of 14.4–17.6% in 2020–2035, reaching 271.1–409.7 GW in 2035 at a total cost of 34.9–41.6 trillion yuan. 2) With favorable technical development levels and application prospects, lithium-ion batteries will be the dominant technology in 2035 in all scenarios. SC has the fastest growth rate among the six technologies in most scenarios because of its flexible location and long lifetime. 3) In the BAU scenario, Xinjiang, Inner Mongolia, Hebei, Ningxia, and Qinghai are the provinces with the largest ESC in 2035 while the four municipalities of Chongqing, Beijing, Tianjin, and Shanghai, as well as Tibet, have the smallest. This layout depends on the differences in the installed capacity of renewable energy in the provinces. • A multi-objective model for optimizing energy storage capacity and technology selection. • Six energy storage technologies are considered for China's 31 provinces in seven scenarios. • Accumulated energy storage capacity will reach 271.1 GW-409.7 GW in 2035. • Inner Mongolia, Qinghai, and Xinjiang are the provinces with the largest capacity in 2035. • Lithium-ion batteries gradually dominates in all energy storage technologies. [ABSTRACT FROM AUTHOR]