Because of the problems of low operation efficiency and poor energy management of multienergy input and output system with complex load demand and energy supply, this paper uses the double-layer nondominated sorting genetic algorithm to optimize the multienergy complementary microgrid system in real-time, allocating reasonably the output of each energy supply end and reducing the energy consumption of the system on the premise of meeting the demand of cooling, thermal and power load, so as to improve the economy of the whole system. According to the system load demand and operation mode, the first layer of this double-layer operation strategy calculates the power required by each node of the microgrid system to reduce the system loss. The second layer calculates the output of each equipment by using nondominated sorting genetic algorithm with various energy values calculated in the first layer as constraint conditions, considering the operation characteristics of various equipment and aiming at economy and environmental protection. In this paper, a typical model of energy input-output is established. This model combines with the operation control strategy suitable for multienergy complementary microgrid system, considers the operation mode and equipment characteristics of the system, and uses a double-layer nondominated sorting genetic algorithm to optimize the operation of each equipment in the multienergy complementary system in real time, so as to reduce the operation cost of the system.
Aiming at the problems of complex structures, variable loads, and fluctuation of power outputs of multienergy networks, this paper proposes an optimal allocation strategy of multienergy networks based on the double-layer nondominated sorting genetic algorithm, which can optimize the allocation of distributed generation (DG) and then improve the system economy. In this strategy, the multiobjective nondominated sorting genetic algorithm is adopted in both layers, and the second-layer optimization is based on the optimization result of the first layer. The first layer is based on the structure and load of the multienergy network. With the purpose of minimizing the active power loss and the node voltage offset, an optimization model of the multienergy network is established, which uses the multiobjective nondominated sorting genetic algorithm to solve the installation location and the capacity of DGs in multienergy networks. In the second layer, according to the optimization results of the first layer and the characteristics of different DGs (wind power generator, photovoltaic panel, microturbine, and storage battery), the optimization model of the multienergy network is established to improve the economy, reliability, and environmental benefits of multienergy networks. It uses the multiobjective nondominated sorting genetic algorithm to solve the allocation capacity of different DGs so as to solve the optimal allocation problem of node capacity in multienergy networks. The double-layer optimization strategy proposed in this paper greatly promotes the development of multienergy networks and provides effective guidance for the optimal allocation and reliable operation of multienergy networks.
Background White matter hyperintensity (WMH) is associated with risk of acute ischemic stroke (AIS) and poor outcomes after AIS. The purpose of this prospective study was to evaluate the association between serum YKL-40 levels and WMH burden in patients with AIS. Methods From February 2020 to March 2021, a total of 672 consecutive AIS patients with magnetic resonance imaging data were prospectively recruited form two centers. Serum YKL-40 levels were quantified using enzyme-linked immunosorbent assay. The burden of WMH was semiquantitatively measured by the Fazekas visual grading scale. According to severity of overall WMH, patients were dichotomized into none–mild WMH group (Fazekas score 0–2) or moderate–severe WMH group (Fazekas score 3–6). Besides, based on severity of periventricular WMH (PV-WMH) and deep WMH (D-WMH), patients were categorized as none–mild (Fazekas score 0–1) or moderate–severe (Fazekas score 2–3). Results Among the 672 patients, 335 (49.9%) participants were identified with moderate–severe overall WMH, 326 (48.5%) with moderate–severe PV-WMH and 262 (39.0%) with moderate–severe D-WMH. Compared with the first quartile of serum YKL-40, the adjusted odds ratio (OR) of the fourth quartile for moderate–severe PV-WMH was 2.473 (95% confidence interval [CI] 1.316–4.646; P =0.005). No significant association was observed between YKL-40 and overall WMH (OR 0.762; 95% CI 0.434–1.336; P =0.343) or D-WMH (OR 0.695; 95% CI 0.413–1.171; P =0.172). Conclusion Our results suggested that higher YKL-40 levels appeared to be associated with PV-WMH, but not with overall WMH or D-WMH in patients with AIS.
Energy consumption of the data center is one of the main constraints for the development of data centers in developed regions. Shanghai has clearly defined the conditions and requirements for the construction of the data center. Through the scheme demonstration and economic analysis of gas distributed energy stations for the data center in Shanghai, the energy economy is the main factor restricting the gas distribution energy supply for the data center. Under the current background, the gas distributed energy station for the data center in Shanghai is more suitable for the construction of third-party investment operation and maintenance entities, which can significantly reduce the power usage effectiveness of the data center, although the annual energy cost of the data center increase significantly. At the same time, gas distributed energy stations need to pay attention to expand winter heat users and improve the equivalent utilization hours of energy stations to achieve greater economic benefits.
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