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Abstract-Electric vehicles (EVs) can be considered as flexible mobile battery storages in microgrids. For multiple microgrids in an area, coordinated scheduling on charging and discharging are required to avoid power exchange spikes between the multimicrogrid system and the main grid. In this paper, a two-stage integrated energy exchange scheduling strategy for multimicrogrid system is presented, which considers EVs as storage devices. Then several dual variables, which are representative of the marginal cost of proper constraints, are utilized to form an updated price, thereby being a modification on the original electricity price. With this updated price signal, a price-based decentralized scheduling strategy is presented for the Microgrid Central Controller (MGCC). Simulation results show that the two-stage scheduling strategy reduces the electricity cost and avoids frequent transitions between battery charging/discharging states. With the proposed decentralized scheduling strategy, each microgrid only needs to solve its local problem and limits the total power exchange within the safe range.Index Terms--Microgrid, electric vehicle, energy exchange, dual variable, updated price signal, decentralized scheduling strategy. I. NOMENCLATURE Index:i Index for microgrid,, ( ) i s B t Lower bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh).Upper bound of the aggregate batteries' remaining energy in microgrid i during t in scenario s (kWh). ( ) C tOriginal electricity price during t (CNY/kWh).

The correlation information is very important for system operations with geographically distributed wind farms, and necessary for optimization-based generation scheduling methods such as the robust optimization (RO). The purpose of this paper is to provide the dynamic spatial correlations between the geographically distributed wind farms and apply them to model the ellipsoidal uncertainty sets for the robust unit commitment model. A stochastic dynamic system is established for the distributed wind farms based on a mesoscale numerical weather prediction (NWP) model, wind speed downscaling, and wind power curve models. By combining the observed wind generation measurements, a dynamic backtracking framework based on the extended Kalman filter is applied to predict the wind generation and the dynamic spatial correlations for the wind farms. In case studies, the new method is tested on actual wind farms and compared with the Gaussian copula method. The testing results validate the effectiveness of the new method. It is shown that the new method can provide more favorable interval forecasts for the aggregate wind generation than the Gaussian copula method in the entire forecast horizon, and by using the predicted spatial correlations, we can obtain more accurate ellipsoidal uncertainty sets than the Gaussian copula method and the frequently used budget uncertainty set (BUS).Index Terms-Dynamic backtracking, ellipsoidal uncertainty set, extended Kalman filter, mesoscale numerical weather prediction (NWP) model, spatial correlation, wind power. n, τIndexes of time. g m (n)Wind power generation of wind farm m at time t n . g m (n + τ |n) τ -step-ahead forecasted wind power generation of wind farm m at time t n . g(n + τ |n)Vector of τ -step-ahead forecasted wind power generation of M wind farm at time t n . e m (n)Prediction error of wind power generation of wind farm m at time t n . S(n + τ |n)Wind power prediction error covariance of overall wind farms. g(n)Vector of wind power generation of M wind farm at time t n . σ l,m,n+τ Covariance between the prediction error of wind farm l and wind farm m at time t n+τ . Ω Uncertainty set. γRobust parameter in the ellipsoidal and budget uncertainty sets (BUS). x(n)Vector of system state at time t n . χ(n)Vector of boundary conditions at time t n . ζ(n)Process noise. Q(n)Covariance of the process noise. η(n)Measurement noise. R(n)Covariance of the measurement noise. f (·)Vector-valued nonlinear function in the state equation. h(·)Vector-valued nonlinear function in the measurement equation. U m (n) Atmospheric wind velocity at 925 hPa pressure level over wind farm m at time t n . w sf m (n) Surface wind velocity of wind farm m at time t n . w sf m (n) Surface wind speed of wind farm m at time t n . a m , b m Regression coefficients in the linear regression model. ε m Residual error of the linear regression model. 1949-3029

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*These authors made equal contribution to this studyBackground and purpose: Kv1.5 channels conduct the ultra-rapid delayed rectifier potassium current (IKur), and in humans, Kv1.5 channels are highly expressed in cardiac atria but are scarce in ventricles. Pharmacological blockade of human Kv1.5 (hKv1.5) has been regarded as effective for prevention and treatment of re-entry-based atrial tachyarrhythmias. Here we examined blockade of hKv1.5 channels by LY294002, a well-known inhibitor of phosphatidylinositol 3-kinase (PI3K). Experimental approach: hKv1.5 channels were heterologously expressed in Chinese hamster ovary cells. Effects of LY294002 on wild-type and mutant (T462C, H463C, T480A, R487V, A501V, I502A, I508A, L510A and V516A) hKv1.5 channels were examined by using the whole-cell patch-clamp method. Key results: LY294002 rapidly and reversibly inhibited hKv1.5 current in a concentration-dependent manner (IC50 of 7.9 mmol·L -1 ). In contrast, wortmannin, a structurally distinct inhibitor of PI3K, had little inhibitory effect on hKv1.5 current. LY294002 block of hKv1.5 current developed with time during depolarizing voltage-clamp steps, and this blockade was also voltage-dependent with a steep increase over the voltage range for channel openings. The apparent binding (k+1) and unbinding (k-1) rate constants were calculated to be 1.6 mmol·L -1-1 ·s -1 and 5.7 s -1 respectively. Inhibition by LY294002 was significantly reduced in several hKv1.5 mutant channels: T480A, R487V, I502A, I508A, L510A and V516A. Conclusions and implications: LY294002 acts directly on hKv1.5 currents as an open channel blocker, independently of its effects on PI3K activity. Amino acid residues located in the pore region (Thr480, Arg487) and the S6 segment (Ile502, Ile508, Leu510, Val516) appear to constitute potential binding sites for LY294002.