This paper proposes a distributed multi-energy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multi-carrier energy supplies based on the concept of energy hub, but also exchanges energy with interconnected microgrids and via the transactive market. The multi-microgrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multi-microgrid multi-energy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multi-microgrid system. The proposed method has been fully tested and benchmarked on different scaled multi-microgrid system over a 24-hour scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multi-microgrid problems.

Multi-microgrids (MMGs) provide an effective modality to integrate high-penetration renewable energy as well as other distributed sources. However, the inherent uncertainties from nature and load demand significantly impact the secure operation of the MMGs system. Considering these challenges, this paper proposes a novel intraday robust energy management framework based on the distributed dynamic tube model predictive control (DD-TMPC) method for autonomous multi-microgrids, which has less conservativeness and more economic efficiency. The proposed strategy can dynamically capture the security operation range of the microgrid system based on the set theory and utilize two cooperative MPC controllers to decompose the original uncertainty optimal problem into two different time scale deterministic uncertainty-free scheduling problems, which have lower computation complexity. Furthermore, in order to better balance the robustness and economy of system operation, a game theory-based intraday distributed energy trading mechanism is designed which considers both the transaction expectations of the market participants and the efficiency of the equilibrium solution. Comprehensive simulation studies demonstrate the feasibility of the proposed DD-TMPC strategy.

As an efficient form of energy utilization, an integrated energy system integrates oil, natural gas, coal, and other energy sources and converts them into electric, cooling, and heating for users through energy conversion devices. In this process, integrated energy service providers need to make energy conversion decisions based on users' demand information feedback. Therefore, there is uncertainty and coupling between the electric cooling and heating loads, making it difficult to forecast the loads accurately. Firstly, this paper analyzes the integrated energy system's energy consumption characteristics and the interaction mechanism between the supply and demand sides, which fundamentally explains the coupling relationship between different loads of the integrated energy system. Secondly, REC, DEC, REH, and DEH are constructed from electric cooling and heating loads. The relationship between electric load and cooling and heating load is analyzed by a scatter distribution diagram and maximum information coefficient method. The nonlinear correlation between electric load and cooling and heating loads is proved. Based on this, the integrated energy system's synergetic electric load forecasting formula reflecting the nonlinear synergistic effect between loads is proposed. Finally, based on stacking ensemble learning, an integrated energy system electric load forecasting model considering the nonlinear synergy between loads is established by integrating BP neural network, support vector regression, random forest, and gradient boosting decision tree. Through the experimental analysis of the Arizona State University Tempe campus's integrated energy system project, it is found that the effect of the synergistic quadratic forecasting is better than that of the primary forecasting. Besides, the MAPE of the quadratic synergistic forecasting formula is lower than that of the other two forms, indicating that the proposed synergistic electric load forecasting formula considering the nonlinear synergy between loads can improve the accuracy of electric load forecasting of the integrated energy system.

Distribution network automation is considered by power supply companies as an effective investment strategy to improve reliability and service quality. Switching devices and protective devices play an important role in the distribution automation system (DAS). This paper presents a novel method to optimize placement of fault indicators and sectionalizing switches in distribution networks with branch lines. The objective function of the proposed method includes the total cost of fault indicators and sectionalizing switches as well as interruption cost. Among different automation equipment, this paper considers fault indicators and remote controlled switches. Besides, manual switches are taken into account since their number and location have a significant impact on the optimal placement problem. Mixed-integer linear programming is used to model the problem, and the proposed model can be solved by large-scale commercial solvers. The solution to the problem is composed of the optimal number and location of fault indicators and sectionalizing switches. The validity of the proposed method is demonstrated by relevant case studies and sensitivity analysis. Moreover, the proposed method is applied to a real distribution network to verify its practicability.

Previous studies have shown that Hunan Province has a high incidence of stroke and a high proportion of intracerebral hemorrhage (ICH). Considering the changes over the past three decades, little is known about the current epidemiological characteristics of stroke in Hunan Province. In 2013, a cross-sectional study was conducted at seven national disease surveillance points (DSPs) in Hunan Province. A multistage cluster sampling method was used to select a representative sample. A total of 21,156 participants aged 20 years and older were examined. Among the 21,156 participants, the number of prevalent strokes, incident strokes and deaths was 307, 87, and 36, respectively. The 2010 China census-standardized prevalence, incidence and mortality were 1191.0 per 100,000 people [95% confidence interval (CI) 1044.8–1337.2], 333.6 per 100,000 person-years (95% CI 255.7–411.5) and 129.7 per 100,000 person-years (95% CI 81.1–178.3), respectively. Ischemic stroke (IS), ICH, subarachnoid hemorrhage (SAH), and stroke of undetermined type (UND) constituted 50.6, 41.4, 5.7, and 2.3% of all incident stroke cases, respectively. Tianxin, Liuyang, Wuling, and Hongjiang have high proportions of ICH (61.5, 58.3, 60, and 50%, respectively). Hypertension is the most common risk factor for prevalent stroke (71.34%), followed by smoking (30.62%) and alcohol use (25.73%). In conclusion, Hunan Province has an extremely heavy stroke burden. The high proportion of ICH is not limited to the Changsha community; it represents an important issue for all of Hunan Province.

The quick development of smart grids coupled with IoT devices has opened breaches of security leading to cyberattacks done by attackers with different purposes. In this study, a behavior model is proposed to investigate the risk of cyber attacks on power grids, where the utility value is determined by subjective attack attitude and characteristics of candidate targets firstly, and then the behaviors of attack target selection and attack resource allocation are described by the probability response and utility attenuation model respectively based on data analysis of historical events. The simulation results on RTS79 system indicate that the risk and the vulnerable nodes of the power grid vary with the characteristics of attack behaviors and characteristic attributes of targets, which should be considered in the cyber security defense dynamically.