Smart Grid State Estimation with PMUs Time Synchronization Errors

We consider the problem of PMU-based state estimation combining information coming from ubiquitous power demand time series and only a limited number of PMUs. Conversely to recent literature in which synchrophasor devices are often assumed perfectly synchronized with the Coordinated Universal Time (UTC), we explicitly consider the presence of time-synchronization errors in the measurements due to different non-ideal causes such as imperfect satellite localization and internal clock inaccuracy. We propose a recursive Kalman-based algorithm which allows for the explicit offline computation of the expected performance and for the real-time compensation of possible frequency mismatches among different PMUs. Based on the IEEE C37.118.1 standard on PMUs, we test the proposed solution and compare it with alternative approaches on both synthetic data from the IEEE 123 node standard distribution feeder and real-field data from a small medium voltage distribution feeder located inside the EPFL campus in Lausanne.Comment: 10 page, 7 figure

Distribution network topology detection with time-series measurements

This paper proposes a novel approach to detecting the topology of distribution networks based on the analysis of time series measurements. The analysis approach draws on data from high-precision phasor measurement units (PMUs or synchrophasors) for distribution systems. A key fact is that time-series data taken from a dynamic system show specific patterns regarding state transitions such as opening or closing switches, as a kind of signature from each topology change. The algorithm proposed here is based on the comparison of the actual signature of a recent state transition against a library of signatures derived from topology simulations. The IEEE 33-bus model is used for initial algorithm validation

Algorithms and Performance Analysis for Synchrophasor and Grid State Estimation

The electrical quantities of future power networks are expected to exhibit strong fluctuations caused by dynamic bidirectional energy flows transferred from/to a multitude of "prosumers”. Such variations have to be accurately measured in real-time either for efficient power distribution or for safety and protection purposes. This task can be accomplished by the Phasor Measurement Units (PMUs), which measure the phasor of voltage or current waveforms synchronized to the Coordinated Universal Time (UTC). Accuracy of synchrophasor measurements is one of the many open challenges that need to be addressed in order to guarantee smart grid reliability and availability. Synchrophasor measurement has gained an undisputed relevance in the research community working on power delivery issues for various reasons. Among them, state estimation (SE) of both transmission and distribution networks is one of the most important. Within this general context, this dissertation covers two complementary topics. In the first part, starting from the concept of synchrophasor and from the definition of the parameters to evaluate PMU performances, useful guidelines to design a filter-based synchrophasor estimator are provided. Afterwards, an extensive performance comparison of some state-of-the-art synchrophasor estimation algorithms is reported in most of the static and dynamic conditions described in the IEEE Standards C37.118.1-2011. Also, a novel technique able to address both static and dynamic disturbances is presented and analyzed in depth. In this respect, special attention is devoted to phasor angle estimation accuracy, which is particularly important for active distribution networks. The second part of the dissertation is focused on the role and the impact of PMUs for grid state estimation. After recalling the state estimation problem and the traditional Weighted Least Square (WLS) technique to solve it, a general uncertainty sensitivity analysis to different types of measurements is introduced and justified both theoretically and through simulations. Afterwards, the effect of a growing number of PMUs on WLS-based state estimation uncertainty is evaluated as a function of instrumental accuracy and line parameters tolerance. Finally, a Bayesian linear state estimator (BLSE) based on a linear approximation of power flow equations for distribution networks is presented. The main advantage of BLSE is that in most cases it is so accurate as the WLS technique, but it is computationally lighter, faster and more stable from the numerical point of view

A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses

In the context of smart grids, Distribution Systems State Estimation (DSSE) is notoriously problematic because of the scarcity of available measurement points and the lack of real-time information on loads. The scarcity of measurement data influences on the effectiveness and applicability of dynamic estimators like the Kalman filters. However, if an Extended Kalman Filter (EKF) resulting from the linearization of the power flow equations is complemented by an ancillary prior least-squares estimation of the weekly active and reactive power injection variations at all buses, significant performance improvements can be achieved. Extensive simulation results obtained assuming to deploy an increasing number of next-generation smart meters and Phasor Measurement Units (PMUs) show that not only the proposed approach is generally more accurate and precise than the classic Weighted Least Squares (WLS) estimator (chosen as a benchmark algorithm), but it is also less sensitive to both the number and the metrological features of the PMUs. Thus, low-uncertainty state estimates can be obtained even though fewer and cheaper measurement devices are used

Predictive Energy Control Strategy for Peak Switch and Shifting Using BESS and PV Generation Applied to the Retail Sector

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

Time-Series PV Hosting Capacity Assessment with Storage Deployment

Europe aims to diversify energy sources and reduce greenhouse gas emissions. On this field, large PV power growth is observed that may cause problems in existing networks. This paper examines the impact of distributed PV systems on voltage quality in a low voltage feeder in terms of the European standard EN 50160. As the standard defines allowable percentage of violation during one week period, time-series analyses are done to assess PV hosting capacity. The simulations are conducted with 10-minute step and comprise variable load profiles based on Gaussian Mixture Model and PV profiles based on a distribution with experimentally obtained parameters. In addition, the outcomes are compared with “snapshot” simulations. Next, it is examined how energy storage utilization affects the hosting capacity. Several deployments of energy storages are presented with different number and capacity. In particular, a greedy algorithm is proposed to determine the sub-optimal energy storage deployment based on the voltage deviation minimization. The simulations show that time-series analyses in comparison with snapshot analyses give completely different results and change the level of PV hosting capacity. Moreover, incorrect energy storage capacity selection and location may cause even deterioration of power quality in electrical systems with high RES penetration

The Role of Flexibility in Photovoltaic and Battery Optimal Sizing towards a Decarbonized Residential Sector

The ambitious environmental goals set by the 2030 Climate Target Plan can be reached with a strong contribution coming from the residential sector and the exploitation of its flexibility, intended as the capacity of a building to shift its consumption to maximize the use of renewable energy. In the literature, the impact of flexibility has been mainly studied for the optimization of the control logic, assuming that the photovoltaic system and the electric storage have already been installed. Conversely, in this work, we adopt a different perspective that analyses the system from the designer point of view. Different scenarios with a variable degree of flexibility have been created and tested in a residential district considering various demand profiles (i.e., home appliances, heat pumps, and electric vehicles consumption). The profiles have been then used as input for an optimization tool that can design the optimal system according to a specific target function. Firstly, the system has been optimized according to economic indicators. However, results suggested that adopting only an economic perspective in the design phase could lead to results that are not in line with the European environmental targets. Thus, the system has been optimized also considering energy indicators to design a system that could give a relevant contribution to the energy transition of the residential sector. Results suggest that demand flexibility coupled with storage can boost the installation of photovoltaic systems due to the improved economic profitability and at the same time guarantee a relevant contribution to the decarbonization of the sector

Distribution network topology detection with time-series measurements

This paper proposes a novel approach to detecting the topology of distribution networks based on the analysis of time series measurements. The analysis approach draws on data from high-precision phasor measurement units (PMUs or synchrophasors) for distribution systems. A key fact is that time-series data taken from a dynamic system show specific patterns regarding state transitions such as opening or closing switches, as a kind of signature from each topology change. The algorithm proposed here is based on the comparison of the actual signature of a recent state transition against a library of signatures derived from topology simulations. The IEEE 33-bus model is used for initial algorithm validation