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Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used. The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied.

Currently, the microgrid operators try to operate this special type of the electrical grid in an optimal way due to the energy and cost saving and enhancing the other technical, environmental and economic aspects. Two of the most important tasks of operators to improve the efficiency of the microgrid are the optimal unit commitment and the distribution feeder reconfiguration. If these tasks are individually carried out, it may not lead to the optimal operation. Simultaneously, performing these two tasks in an unbalanced microgrid is a challenging multi-objective problem that this paper is faced with it. The assumed unbalanced microgrid has been equipped by two hybrid energy systems which include the dispatchable distributed generations that are fuel cell units and the non-dispatchable ones that are wind turbines and photovoltaic cells. The stochastic behavior of the non-dispatchable generation units and electrical demand is modeled by a stochastic copula scenario-based framework. The objective functions are minimization of the operational cost of the microgrid, minimization of active power loss, maximization of voltage stability index, minimization of emissions, and minimization of the voltage and current unbalance indices subject to diverse technical constraints. The proposed multi-objective problem is optimized by multi-objective covariance matrix adaption-evolution strategy (MOCMA-ES) algorithm, and a set of Pareto solutions is achieved. The best compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on an unbalanced 25-bus microgrid. The simulation results show the efficacy of the proposed model to optimize objective functions, while the constraints are satisfied. [ABSTRACT FROM AUTHOR]

Using distributed generations (DGs) with optimal scheduling and optimal distribution feeder reconfiguration (DFR) are two aspects that can improve efficiency as well as technical and economic features of microgrids (MGs). This work presents a stochastic copula scenario-based framework to jointly carry out optimal scheduling of DGs and DFR. This framework takes into account non-dispatchable and dispatchable DGs. In this paper, the dispatchable DG is a fuel cell unit and the non-dispatchable DGs with stochastic generation are wind turbines and photovoltaic cells. The uncertainties of wind turbine and photovoltaic generations, as well as electrical demand, are formulated by a copula-based method. The generation of scenarios is carried out by the scenario tree method and representative scenarios are nominated with scenario reduction techniques. To obtain a weighted solution among the various solutions made by several scenarios, the average stochastic output (ASO) index is used. The objective functions are minimization of the operational cost of the MG, minimization of active power loss, maximization of voltage stability index, and minimization of emissions. The best-compromised solution is then chosen by using the fuzzy technique. The capability of the proposed model is investigated on a 33-bus MG. The simulation results show the efficiency of the proposed model to optimize objective functions, while the constraints are satisfied. [ABSTRACT FROM AUTHOR]

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities (“Grain For Green” program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems.

It is important to identify the non-stationarity in the relation between runoff and sediment load under the backdrop of the changing environment. This relation helps to further understand the mechanisms of runoff and sediment yield. A copula-based method was used to detect possible change points in the relation between runoff and sediment load in the Wei River Basin (WRB), China, where soil erosion is a very severe issue. The modified Mann-Kendall trend test method was applied to obtain the trends of runoff and sediment load spanning 1960–2010 at monthly and annual timescales. Finally, the causes of the identified non-stationarity of the relation between runoff and sediment load were roughly analyzed from the perspective of climate change and human activities. Results indicated that: (1) the runoff and sediment load in the Jinghe and Wei rivers were generally characterized by noticeably decreasing trends at both monthly and annual timescales; (2) both the Jinghe and Wei rivers had a common change point (2002), implying that the stationarity of the relation between runoff and sediment load in the Jinghe and Wei River was invalid; (3) human activities including increasing water consumption and growing application of soil conservation practices are dominant factors resulting in non-stationarity in the relation between runoff and sediment load in the WRB. This study provides a new idea for identifying the non-stationarity of multivariate relation in the hydro-meteorological field under the background of the changing environment. [ABSTRACT FROM AUTHOR]

Understanding the changing relationships between vegetation coverage and precipitation/temperature (P/T) and then exploring their potential drivers are highly necessary for ecosystem management under the backdrop of a changing environment. The Jing River Basin (JRB), a typical eco-environmentally vulnerable region of the Loess Plateau, was chosen to identify abrupt variations of the relationships between seasonal Normalized Difference Vegetation Index (NDVI) and P/T through a copula-based method. By considering the climatic/large-scale atmospheric circulation patterns and human activities, the potential causes of the non-stationarity of the relationship between NDVI and P/T were revealed. Results indicated that (1) the copula-based framework introduced in this study is more reasonable and reliable than the traditional double-mass curves method in detecting change points of vegetation and climate relationships; (2) generally, no significant change points were identified during 1982–2010 at the 95% confidence level, implying the overall stationary relationship still exists, while the relationships between spring NDVI and P/T, autumn NDVI and P have slightly changed; (3) teleconnection factors (including Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO), Niño 3.4, and sunspots) have a more significant influence on the relationship between seasonal NDVI and P/T than local climatic factors (including potential evapotranspiration and soil moisture); (4) negative human activities (expansion of farmland and urban areas) and positive human activities ("Grain For Green" program) were also potential factors affecting the relationship between NDVI and P/T. This study provides a new and reliable insight into detecting the non-stationarity of the relationship between NDVI and P/T, which will be beneficial for further revealing the connection between the atmosphere and ecosystems. [ABSTRACT FROM AUTHOR]