Search

Developing photovoltaic (PV) and wind power is one of the most efficient approaches to reduce carbon emissions. Accumulating the PV and wind energy resources at different geographical locations can minimize total power output variance as injected into the power systems. To some extent, a low degree of the variance amplitude of the renewable resources can reduce the requirement of in-depth regulation and dispatch for the fossil fuel-based thermal power plants. Such an issue can alternatively reduce carbon emissions. Thus, the correlation problem by minimizing the variance of total PV and wind power plays a vital role in power system planning and operation. However, the synergistic effect of power output correlation is mainly considered on the generation side, and it is often neglected for the correlation relationship between the power grid components. To address this problem, this paper proposes a correlation coefficient analysis method for the power grid, which can quantify the relationship between energy storage and the probabilistic power flow (PPF) of the grid. Subsequently, to accelerate the mapping efficiency of power correlation coefficients, a novel deep neural network (DNN) optimized by multi-task learning and attention mechanism (MA-DNN) is developed to predict power flow fluctuations. Finally, the simulation results show that in IEEE 9-bus and IEEE14-bus systems, the strong correlation grouping percentage between the power correlation coefficients and power flow fluctuations reached 92% and 51%, respectively. The percentages of groups indicating weak correlation are 4% and 38%. In the modified IEEE 23-bus system, the computational accuracy of MA-DNN is improved by 37.35% compared to the PPF based on Latin hypercube sampling. Additionally, the MA-DNN regression prediction model exhibits a substantial improvement in assessing power flow fluctuations in the power grid, achieving a speed enhancement of 758.85 times compared to the conventional probability power flow algorithms. These findings provide the rapid selection of the grid access point with the minimum power flow fluctuations.

PurposeThis mini-review delves into the realm of Langerhans cell histiocytosis (LCH) in children, focusing on its skeletal involvement. By synthesizing pertinent literature, we sought to provide a comprehensive understanding of LCH’s clinical and radiographic spectrum. Our study then demonstrates the diagnostic prowess of whole-body 99mTc-methyl diphosphonate (MDP) scintigraphy in LCH cases, underscoring its value in tandem with existing knowledge.MethodsOur approach involved an extensive literature review that contextualized LCH within the current medical landscape. Subsequently, we presented a case series featuring five pediatric instances of skeletal LCH, one accompanied by soft tissue infiltration. The principal aim was to illuminate the diagnostic and staging potential of whole-body 99mTc-MDP scintigraphy, augmenting existing insights.ResultsThrough meticulous literature synthesis, we highlighted pediatric LCH’s protean clinical manifestations and radiological variability. Aligning with this spectrum, our case series underscored the role of 99mTc-MDP scintigraphy in diagnosing and staging LCH. Among the five pediatric cases, one demonstrated concurrent soft tissue involvement. This aligns with the multifaceted nature of LCH presentations.ConclusionPediatric LCH can present with a wide range of clinical and radiologic features. By amalgamating our cases with extant literature, we stress the necessity of a multimodal strategy. 99mTc-MDP scintigraphy emerged as an indispensable tool for accurate staging and soft tissue detection. Our findings collectively advocate for a holistic approach to managing LCH, ensuring informed therapeutic decisions for optimal patient outcomes.