In recent years, the household microgrids with photovoltaic (PV) and energy storage have experienced rapid development. The large-scale integration of household microgrids has provided power grid with vast regulation resources, giving rise to the emergence of household microgrid aggregators (HMAs). For HMAs, the key challenge lies in meeting the grid regulation requirements while ensuring the optimality and real-time performance of power disaggregation. Therefore, this paper proposes a distributed power disaggregation model based on optimal sensitivity. First, the optimal sensitivity is employed to calculate the marginal cost curves for heterogeneous household microgrids. Second, based on the different marginal cost curves, a distributed power disaggregation model is established. Finally, using real-world data from the UK, comparative studies with existing centralized optimization and proportional disaggregation methods demonstrate the superiority of the proposed model in terms of optimality and real-time performance.

Batteries play a critical role in microgrid energy management by ensuring power balance, enhancing renewable utilization, and reducing operational costs. However, battery degradation poses a significant challenge, particularly under extreme temperatures. This paper investigates the optimal trade-off between battery degradation and operational costs in microgrid dispatch to find a robust cost-effective strategy from a full life-cycle perspective. A key challenge arises from the endogenous uncertainty (or decision-dependent uncertainty, DDU) of battery degradation: Dispatch decisions influence the probability distribution of battery degradation, while in turn degradation changes battery operation model and thus affects dispatch. In this paper, we first develop an XGBoost-based probabilistic degradation model trained on experimental data across varying temperature conditions. We then formulate a parametric model predictive control (MPC) framework for microgrid dispatch, where the weight parameters of the battery degradation penalty terms are tuned through long-term simulation of degradation and dispatch interactions. Case studies validate the effectiveness of the proposed approach.

Generating critical scenarios of transmission interfaces with low safety margins is essential for developing power systems operation mode and contingency preparation. This paper proposes a data synthesis framework based on denoising diffusion model (DDM) to improve the efficiency of scenarios generation. First, the total transmission capacity (TTC) and safety margin of transmission interface are solved with the objective of maximizing its power flow, which accumulates scenarios with different features for training DDM. Furthermore, DDM is pre-trained on all training scenarios and subsequently fine-tuned on critical scenarios. Case studies in modified IEEE 39-bus test system demonstrate that the proposed method can generate batches of desired scenarios, and it outperforms baselines in terms of scenario quality and synthesis speed.

The number of electric vehicles (EVs) has been growing rapidly in recent years, and if their potential as mobile energy storage is fully utilized, they could provide substantial support to the power grid. However, effective aggregation regulation faces significant challenges due to the heterogeneity of individual EVs. To address this issue, this paper proposes two Vehicle-to-Grid (V2G) power flexibility quantification strategies based on the approximate inner-box approach: conservative and aggressive strategies to assess the flexibility capability of EVs in future time periods. To address the long computation times required for solving the power flexibility of large-scale EVs, this paper develop a deep learning-based surrogate model to replace traditional optimization methods, significantly enhancing computational efficiency. Finally, the proposed method is validated using real data, with experimental results demonstrating its significant effectiveness and superiority.

Virtual power plant (VPP) play a crucial role in the market-based operation of power systems, contributing to enhanced electricity supply security and the local consumption of renewable energy. To improve the economic performance of VPPs in market operations, this paper proposes a market-based operation method for VPP that considers the active recovery of remaining available regulation capacity. First, an equivalent energy storage regulation model for VPP is developed, accounting for the regulation and rebound characteristics of VPP and incorporating the active recovery of remaining available regulation capacity. Next, based on this equivalent energy storage regulation model, a joint optimization model for the VPP in the electricity spot market, peak-shaving, and frequency regulation markets are established. Finally, case studies demonstrate the effectiveness of the proposed method in reducing VPP operating costs in market environments.

An integrated energy system (IES) contributes to improving energy efficiency and promoting sustainable energy development. For different dynamic characteristics of the system, such as demand/response schemes and complex coupling characteristics among energy sources, siting and sizing of multitype energy storage (MES) are very important for the economic operation of the IES. Considering the effect of the diversity of the IES on system reserve based on electricity, gas and heat systems in different scenarios, a two-stage MES optimal configuration model, considering the system reserve value, is proposed. In the first stage, to determine the location and charging/discharging strategies, a location choice model that minimizes the operating cost, considering the system reserve value, is proposed. In the second stage, a capacity choice model, to minimize the investment and maintenance cost of the MES, is proposed. Finally, an example is provided to verify the effectiveness of the MES configuration method in this paper in handling operational diversity and ensuring system reserve. Compared with the configuration method that disregards the system reserve value, the results show that the MES configuration method proposed in this paper can reduce the annual investment cost and operating cost and improve the system reserve value.

Under the goals of carbon peaking and carbon neutrality, the adoption of clean energy for power generation has become an essential choice for the power industry. The distribution system plays an essential role in clean energy consumption and user-side emission reduction, however, it also faces new challenges. Firstly, we propose a framework of energy storage systems on the urban distribution network side taking the coordinated operation of generation, grid, and load into account. Secondly, we establish a capacity optimization model for energy storage systems by considering the various costs of energy storage systems throughout their entire lifecycle. Furthermore, we establish an optimization dispatch model that incorporates the limitations of both energy storage systems and distribution network flow to minimize the overall operational expenses. Finally, a planning-operation double-layer optimization model is constructed, considering both the capacity optimization configuration and the optimization dispatch of the coordinated operation of generation, grid, and load, and the effectiveness of the proposed optimization configuration scheme is verified through case studies.

The optimal energy consumption is one of sustainable development issues in many countries to the appraisal of the economic and technical indices in the energy sector. The consumption of multi-carrier energy hub systems like electricity, natural gas, and heat has been expanding in recent years. Due to technical signs of progress in heat and electrical energy networks, multi-carrier energies are employed in parallel modes to meet the energy required on the demand side. In this article, the operation of the multi-carrier energy system is studied based on local energy price and penetration of the renewable energies. The optimization of the energy system is performed with attention to maximization of the reliability and minimization of the costs subject to peak demand pricing. The modeling optimization is implemented in GAMS software with numerical simulation and several case studies. The proposed optimization algorithm is implemented to peak demand reshape at a high energy price. The obtained results in case studies show the cost-effectiveness of the multi-carrier energy system with acceptable reliability in peak demand. With implementing the peak demand pricing, costs, reliability index, and penetration of the renewable energies are improved by 16.38

Industrial load is a key part of the new load management system. With the increase of industrial users, the number of user-side equipment with adjustable capacity is gradually increasing. Using industrial equipment to participate in the optimal dispatching of power grid is of great significance to alleviate the contradiction between supply and demand of power grid, improve the consumption rate of new energy and realize the low-carbon operation of power grid. Taking the cement manufacturing and electrolytic aluminum industry as an example, this paper proposes an optimal scheduling method for low-carbon power grids considering the priority of equipment response. Firstly, the production principle and power supply mode of cement load and electrolytic aluminum load are analyzed. Considering the operation characteristics of different enterprise equipment, the response priority strategy is formulated according to the equipment response capacity and response flexibility. Then, considering the cost of new energy consumption and carbon emission, a power grid dispatching model is established with the optimization goal of power grid dispatching operation cost. The rationality and effectiveness of the proposed model and method are verified by an example analysis.

To avoid the phenomenon that the initial clustering center point is selected to the outlier or the same cluster class point, this paper proposes an improved k-medoids clustering algorithm based on the load feature set. Firstly, for the problem of insufficient mining of the degree of influence of meteorological factors on the load, the dominant meteorological factor characteristics are screened out by Pearson correlation coefficient and principal component analysis; then, load characteristics and meteorological factor characteristics are considered to establish load characteristic indexes and meteorological factor indexes, and then construct the load feature set that considers meteorological influences; at the same time, the original k-medoids clustering algorithm has the following advantages in initial At the same time, the original k-medoids clustering algorithm has randomness in the selection of the initial clustering center, based on which the density optimization coefficient is used to realize the selection of the initial clustering center; finally, the clustering results of the load under different algorithms are obtained by selecting the load data of industrial production in a certain region and the meteorological data in the same region, which verifies that the proposed algorithm in this paper is more effective in clustering.

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

How to effectively use the multi-energy demand elasticity of users to bid in the multi-energy market and formulate multi-energy retail packages is an urgent problem which needs to be solved by integrated energy service providers (IESPs) to attract more users and reduce operating costs. This paper presents a unified clearing of electricity and natural gas based on a bi-level bidding and multi-energy retail price formulation method for IESPs considering multi-energy demand elasticity. First, we propose an operating structure of IESPs in the wholesale and retail energy markets. The multi-energy demand elasticity model of retail-side users and a retail price model for electricity, gas, heat and cooling are established. Secondly, a bi-level bidding model for IESPs considering multi-energy demand elasticity is established to provide IESPs with wholesale-side bidding decisions and retail-side energy retail price decisions. Finally, an example is given to verify the proposed method. The results show that the method improves the total social welfare of the electricity and natural gas markets by 7.99% and the profit of IESPs by 1.40%. It can reduce the variance of the electricity, gas, and cooling load curves, especially the reduction of the variance of the electricity load curve can which reach 79.90%. It can be seen that the research in this paper has a positive effect on repairing the limitations of integrated energy trading research and improving the economics of the operation of IESPs.

BackgroundIntravenous immunoglobulin (IVIG) has been reported to exert a beneficial effect on severe fever with thrombocytopenia syndrome (SFTS) patients with neurological complications. However, in clinical practice, the standard regime is unclear and there is a lack of evidence from large-scale studies.MethodsA single-center retrospective study was conducted to determine the influence of IVIG dosage and duration on SFTS patients with neurological complications. The primary outcome was 28-day mortality, and laboratory parameters before and after IVIG treatment were measured. Survival curves were generated using the Kaplan–Meier method and analyzed with the log-rank test according to the median IVIG dosage and IVIG duration. Besides, multivariate Cox regression analysis was performed to examine the association between the independent factors and 28-day mortality in SFTS patients.ResultsOverall, 36 patients (58.06%) survived, while 26 (41.9%) patients died. The median age of the included patients was 70 (55–75) years, and 46.8% (29/62) were male. A significantly higher clinical presentation of dizziness and headache was observed in the survival group. The IVIG duration in the survival group was longer than in the death group (P <0.05). Additionally, the IVIG dosage was higher in the survival group than in the death group, but there was not a statistically significant difference between the two groups (P = 0.066). The mediating effect of IVIG duration was verified through the relationship between IVIG dosage and prognosis using the Sobel test. Univariate analysis revealed that IVIG dosage (HR: 0.98; 95% CI: 0.97–1.00; P = 0.007) and IVIG duration (HR: 0.54; 95% CI: 0.41–0.72; P <0.001) were significantly associated with risk of death. The multivariate analysis generated an adjusted HR value of 0.98 (95% CI: 0.96–1.00; P = 0.012) for IVIG dosage and 0.26 (95% CI: 0.09–0.78; P = 0.016) for dizziness and headache.ConclusionProlonged high-dose IVIG is beneficial to the 28-day prognosis in SFTS patients with neurological complications.

With increasing focus on sustainability and efficiency, Integrated Energy Systems (IES) have gained more attention in the provision of electricity and thermal energy. However, the inherent complexity and uncertainty of IES pose challenges to their optimization and management. This paper proposes a Proximal Policy Optimization (PPO) algorithm for the operation of IES, based on an adaptive learning rate decay strategy, aimed at enhancing the operational efficiency and stability of IES. Initially, a Markov decision process is established to simulate the operation of IES. Subsequently, a PPO algorithm with an adaptive learning rate decay strategy is proposed. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional PPO algorithms in reducing overall operational costs and improving the balance of electricity and thermal energy supply and demand. This research provides an effective optimization method for the efficient and sustainable operation of IES.

Identification of the leakage of hazardous gases plays an important role in the environment protection, human health and safety of industry production. However, lots of current optimization algorithms, such as particle swarm optimization (PSO) and Grey Wolf Optimizer (GWO), suffer from poor global optimization capability and estimation accuracy. In this work, a hybrid differential evolutionary and GWO (DE-GWO) algorithm is proposed. Tested by simulation cases and Prairie Grass emission experimental data, DE-GWO shows higher estimation accuracy than GWO. Compared with the other four optimization algorithms, DE-GWO exhibits finer robust stability under different population sizes, fewer iterations, as well as higher estimation accuracy with fewer search agents. Importantly, simulation results demonstrate that DE-GWO is more suitable to apply in the scene with a small number of sensors. Therefore, the proposed in this paper outperforms other optimization algorithms for the gas emission inverse problem. DE-GWO can provide reliable estimation towards gas emission identification and positioning, which shows huge potential as the data analysis module of real-time monitoring and early warning system.

Under the background of the steady development of the new power system and the continuous construction of the “double carbon” target, the application scenarios of power load forecasting are also showing an increasingly complex and diversified trend. Accurate power load forecasting response plays an important role in the safety, stability and economy of power system operation. Therefore, this paper proposes an adjustable potential analysis method based on SA-TCN (Self-Attention Temporal Convolutional Network). Firstly, based on the performance requirements of adjustable load proposed by power system supply and demand balance, the index system of adjustable load characteristics is constructed. Secondly, based on the Canopy and Kmeans two-level clustering method, the user data are classified according to the daily load rate and daily peak-valley difference rate, and the SA-TCN prediction model is used to predict the user's load. Finally, based on the historical load data of industrial users in a typical area and the index system of adjustable load characteristics, the advantages of the proposed model (SA-TCN) in the stability of prediction accuracy are verified by an example analysis, and the adjustable potential of load is analyzed from multiple perspectives.

The large-scale development of new energy vehicles will realize the clean replacement of fossil fuels from the demand side, which is a crucial step towards achieving the "dual carbon" goal. As a key supporting facility, the operation optimization and pricing strategy of new energy vehicle energy stations are one of the keys to building a sustainable business model and improving operational efficiency. This article addresses the operation optimization problem of new energy vehicle energy stations and proposes an operation optimization mode and dynamic pricing strategy for new energy vehicle energy stations. Firstly, based on the basic structure of the new energy vehicle energy station, an operation model of the new energy vehicle energy station considering load control in a market environment is constructed. Secondly, an optimized operation model of new energy vehicle energy stations considering dynamic pricing is proposed. Finally, the effectiveness of the proposed method is verified through examples.

针对不同的配电网运行情况,考虑微电网群的运行策略以及子微网的协作运行对配电网的影响,建立了配电网储能配置的选址和定容两阶段优化模型.其中,第一阶段的选址模型在日运行时间尺度上,以配电网系统运行成本最小为目标,利用GAMS软件求解储能的位置、个数以及充放电策略;第二阶段的容量优化模型以年作为时间尺度,以配电网中储能投资运行维护年成本最小为目标,利用嵌套储能寿命计算的粒子群算法在MATLAB软件中求解.算例证明在不同的运行场景下,考虑微电网群和系统经济运行的配电网储能优化配置方法可以有效减少配电网的运行成本和储能的投资维护成本,提高系统经济性.

Hydrogen-integrated transportation and power systems (HTPS) will become an important way to achieve the goal of carbon neutrality. As an important coupling unit of HTPS, the business mechanism of hydrogen fueling stations (HFS) is an important starting point for improving system economy and promotion value. This article proposed an interaction mechanism between HFS and HTPS and hydrogen and fuel cell EVs (HFEVs) in smart cities for HFS pricing strategies. First, we construct the framework of the HTPS interaction mechanism based on the basic combination of HTPS. Then, the interactive model of HTPS is constructed, including the scheduling model of HTPS, the pricing model of HFS and the response model of HFEVs. Finally, an HTPS system is constructed based on the improved IEEE 33-node power distribution system for simulation and analysis. The results show that the interaction mechanism and pricing strategy can improve the economics of HTPS and HFS, the operating cost of HTPS has been reduced by approximately 5.57%, the operating income of HFS has increased by approximately 4.17%.