Abstract-In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. We show via numerical simulations that the proposed strategy performs similarly to the standard WLS estimator on a small distribution network. A key advantage of the proposed approach is that it provides explicit off-line computation of the estimation error confidence intervals, which we use to explore the trade-offs between number of PMUs, PMU placement and measurement uncertainty. Since the estimation error in distribution systems tends to be dominated by uncertainty in loads and scarcity of instrumented nodes, the linearized method along with the use of high-precision PMUs may be a suitable way to facilitate on-line state estimation where it was previously impractical.

As known, phasor measurement units (PMUs) greatly enhance smart grid monitoring capabilities with advantageous impacts on power network management. Generally, PMUs accuracy is expressed in terms of total vector error, which comprises the joint effect of both angle and magnitude errors, thus possibly concealing the algorithm ability to measure phase. Some recent research works emphasize the importance of measuring current or voltage phasor angle with high accuracy (in the order of a few milliradians) at the distribution level. Because this issue is seldom considered in the literature, in this paper the phase measurement accuracy of three algorithms, namely the basic DFT, the windowed Taylor-Fourier filter, and the interpolated dynamic DFT (IpD 2 FT) estimator, is extensively analyzed by means of simulations performed in various conditions described in the Standards IEEE C37.118.1:2011 and EN 50160:2010. In addition, some meaningful considerations about the uncertainty contributions due to imperfect synchronization are reported.

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Next-generation Phasor Measurement Units (PMU) are expected to be more accurate than existing ones, especially to address the stricter requirements of future active distribution grids. From this perspective, the influence of acquisition wideband noise (which includes multiple contributions both in amplitude and in phase) has to be carefully evaluated. In this context, the contribution of this paper is twofold. First, it provides a general framework to evaluate the effect of wideband noise on synchrophasor amplitude, phase, frequency and rate of change of frequency (ROCOF) estimation. The results of this analysis show that the influence of wideband noise can become critical for compliance with the requirements reported in the IEEE Standards C37.118.1-2011 and C37.118.1a-2014, especially for frequency and ROCOF estimation. In addition, the paper reports general guidelines to choose the PMU effective resolution and sampling rate for which the impact of wideband noise on both P Class and M Class PMUs is negligible.

As is known, a reduction in CO 2 emissions is closely related to the improvement of energy efficiency and the increasing use of renewable energy sources in building stock due to its high contribution to worldwide energy consumption. The retail sector has become particularly interesting in this sense, because commercial buildings are no longer just places where a variety of services are offered to customers. In fact, they can be beacons of energy efficiency. In this paper, we propose a predictive energy control strategy that, through the combination of production and demand forecasting, can effectively shave and shift the peak consumption of shopping malls equipped with battery energy storage systems (BESS). The adopted optimization strategy takes into account the variability of electricity tariffs over time, as is customary in some European countries. The performed energy and economic simulations based on the experimental data collected in an Italian shopping mall clearly highlight the benefits in terms of energy and economic savings. Moreover, the reported results lead to the conclusion that BESS management, photovoltaic (PV) generation, and peak switch strategies can have a reasonable pay-back investment time even for buildings with a large energy demand.reason, a variety of smart building energy management systems (BEMS), e.g., based on a multi-agent architecture (MAS) have been proposed in the scientific literature [3]. For instance, a specific strategy based on case-reasoning is presented in [4], where it is clearly explained that the reduction of building consumption depends not only on a proper coordination of devices (or agents), but it should take into account human behavior as well. Despite this, it is important to emphasize that energy reduction alone could not be enough to enhance suitability and to reduce the related costs significantly. For this reason, in this work we focus on how BEMS can support smart and flexible renewable-based generation and storage as for example presented in [5]. The large roof and parking lots areas of shopping malls are particularly suitable to install photovoltaic (PV) generators with a significant capacity. Currently, PV systems are the most common type of renewable energy source in the building sector due to their scalability, modularity, low maintenance needs, long effective life (more than 25 years) and fast response. The combination of lower cost and economic incentives in some countries has greatly contributed to a widespread and quick diffusion of PV generation [6]. Particularly interesting is the scenario of smart PV systems in which generation and consumption can be simultaneous, as in the case of shopping malls [7]. This implies that the ratio between the PV energy directly self-consumed on-site and produced (usually defined as self-consumption) could be as high as 100%. The benefits of maximizing the self-consumption impact directly on the amount of power drawn from the grid as well as on the energy-related costs for shopping malls. Many studies are present in ...