Abstract-Due to its reduced communication overhead and robustness to failures, distributed energy management is of paramount importance in smart grids, especially in microgrids, which feature distributed generation (DG) and distributed storage (DS). Distributed economic dispatch for a microgrid with high renewable energy penetration and demand-side management operating in grid-connected mode is considered in this paper. To address the intrinsically stochastic availability of renewable energy sources (RES), a novel power scheduling approach is introduced. The approach involves the actual renewable energy as well as the energy traded with the main grid, so that the supplydemand balance is maintained. The optimal scheduling strategy minimizes the microgrid net cost, which includes DG and DS costs, utility of dispatchable loads, and worst-case transaction cost stemming from the uncertainty in RES. Leveraging the dual decomposition, the optimization problem formulated is solved in a distributed fashion by the local controllers of DG, DS, and dispatchable loads. Numerical results are reported to corroborate the effectiveness of the novel approach.Index Terms-Demand side management, distributed algorithms, distributed energy resources, economic dispatch, energy management, microgrids, renewable energy, robust optimization. NOMENCLATURE A. Indices, numbers, and setsT , t Number of scheduling periods, period index. M , m Number of conventional distributed generation (DG) units, and their index. N , n Number of dispatchable (class-1) loads, load index. Q, q Number of energy (class-2) loads, load index. J, jNumber of distributed storage (DS) units, and their index. I, iNumber of power production facilities with renewable energy source (RES), and facility index. S, sNumber of sub-horizons, and sub-horizon index. Parameter of utility function of load q.Depth of discharge specification of DS unit j; and parameters of storage cost. C. Uncertain quantities W t iPower output from RES facility i in period t. D. Decision variables P t GmPower output of DG unit m in period t.Power consumption of load n in period t. P t EqPower consumption of load q in period t. P Power production from all RES facilities in t yielding the worst-case transaction cost. E. Functions C t m (·)Cost of conventional DG unit m in period t. U

Abstract-Wireless Local Area Network (WLAN) has become a promising choice for indoor positioning as the only existing and established infrastructure, to localize the mobile and stationary users indoors. However, since WLAN has been initially designed for wireless networking and not positioning, the localization task based on WLAN signals has several challenges. Amongst the WLAN positioning methods, WLAN fingerprinting localization has recently achieved great attention due to its promising results. WLAN fingerprinting faces several challenges and hence, in this paper, our goal is to overview these challenges and the state-of-the-art solutions. This paper consists of three main parts: 1) Conventional localization schemes; 2) State-of-the-art approaches; 3) Practical deployment challenges. Since all the proposed methods in WLAN literature have been conducted and tested in different settings, the reported results are not equally comparable. So, we compare some of the main localization schemes in a single real environment and assess their localization accuracy, positioning error statistics, and complexity. Our results depict illustrative evaluation of WLAN localization systems and guide to future improvement opportunities.

I. INTRODUCTIONAlbeit the North American power grid has been recognized as the most important engineering achievement of the 20th century, the modern power grid faces major challenges [87]. Increasingly complex interconnections even at the continent size render prevention of the rare yet catastrophic cascade failures a strenuous concern. Environmental incentives require carefully revisiting how electrical power is generated, transmitted, and consumed, with particular emphasis on the integration of renewable energy resources. Pervasive use of digital technology in grid operation demands resiliency against physical and cyber attacks on the power infrastructure. Enhancing grid efficiency without compromising stability and quality in the face of deregulation is imperative. Soliciting consumer participation and exploring new business opportunities facilitated by the intelligent grid infrastructure hold a great economic potential.The smart grid vision aspires to address such challenges by capitalizing on state-of-the-art information technologies in sensing, control, communication, and machine learning [2], [24]. The resultant grid is envisioned to have an unprecedented level of situational awareness and controllability over its services and infrastructure to provide fast and accurate diagnosis/prognosis, operation resiliency upon contingencies and malicious attacks, as well as seamless integration of distributed energy resources. A. Basic Elements of the Smart GridA cornerstone of the smart grid is the advanced monitorability on its assets and operations. Increasingly pervasive installation of the phasor measurement units (PMUs) allows the so-termed synchrophasor measurements to be taken roughly 100 times faster than the legacy supervisory control and data acquisition (SCADA) measurements, time-stamped using the global positioning system (GPS) signals to capture the grid dynamics. In addition, the availability of low-latency two-way communication networks will pave the way to high-precision real-time grid state estimation and detection, remedial actions

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The theme of this paper is three-phase distribution system modeling suitable for the Z-Bus load-flow. Detailed models of wye and delta constant-power, constant-current, and constantimpedance loads are presented. Models of transmission lines, step-voltage regulators, and transformers that build the bus admittance matrix (Y-Bus) are laid out. The Z-Bus load-flow is then reviewed and the singularity of the Y-Bus in case of certain transformer connections is rigorously discussed. Based on realistic assumptions and conventional modifications, the invertibility of the Y-Bus is proved. Last but not least, MATLAB scripts that model the components of the IEEE 37-bus, the IEEE 123-bus, the 8500-node feeders, and the European 906-bus lowvoltage feeder are provided.