Guido Carpinelli

A Bayesian-Based Approach for a Short-Term Steady-State Forecast of a Smart Grid

Future distribution networks are undergoing radical changes, due to the high level of penetration of dispersed generation and information/communication technologies, evolving into the new concept of the Smart Grid. Dispersed generation systems, such as wind farms and photovoltaic power plants, require particular attention due to their incorporation of uncertain energy sources. Further and significant well-known uncertainties are introduced by the load demands. In this case, many new technical considerations must be addressed to take into account the impacts of these uncertainties on the planning and operation of distribution networks. This paper proposes novel Bayesian-based approaches to forecast the power production of wind and photovoltaic generators and phase load demands. These approaches are used in a probabilistic short-term steady-state analysis of a Smart Grid obtained by means of a probabilistic load flow performed using the Point Estimate Method. Numerical applications on a 30-busbar, low-voltage distribution test system with wind farms and photovoltaic power plants connected at different busbars are presented and discussed.

Demand response and energy storage systems: An industrial application for reducing electricity costs. Part II: Numerical application

This is the companion paper to “Part I: theoretical aspects”. In Part I a new procedure was proposed in order to control an energy storage system installed in an industrial facility. The control strategy, based on the combined use of day ahead scheduling and very short time predictive control, aims at minimizing the cost suffered by the industrial owner for energy consumption and satisfying constraints related to the efficiency and lifecycle of the storage system. In this paper, numerical applications based on the control procedure proposed in Part I are presented and the results are reported and discussed. Aim of this study is to quantify the benefits that can be derived from the installation of an energy storage system in an industrial facility. The results of the simulation demonstrated the potential advantages of adopting such devices in manufacturing facilities with benefits achievable by both system operators and users, even in presence of constraints aimed at prolonging the battery lifetime.

Demand response and energy storage systems: An industrial application for reducing electricity costs. Part I: Theoretical aspects

In this paper, the benefits that can be derived from the installation of an energy storage system in an industrial facility are analyzed, and an optimal control strategy of the storage system is proposed. The proposed control strategy is based on a two-step procedure and aims at (i) reducing the electricity costs sustained by an industrial customer that provides demand response and (ii) satisfying technical constraints for the maximization of the efficiency and lifecycle of the storage system. The procedure was performed based on two different time periods, i.e., day ahead and very short time. The day-ahead scheduling evaluates the peak value of the power that the facility must request from the grid and the periods in which the battery is allowed to charge and discharge; the very short-time control evaluates the batterys charging/discharging power to minimize costs. This paper reports the theoretical aspects of the optimization models for the two-step procedure, whereas the companion paper “Part II: numerical applications” proposes the results of numerical applications of the proposed approach on an actual industrial facility.

Advanced methods for the assessment of time varying waveform distortions caused by wind turbine systems. Part II: Numerical applications

This is a companion paper to a paper of the same title, Part I, in which the theoretical aspects of some spectrum estimation methods for the analysis of non-stationary waveform distortions in power systems due to Wind Turbine Systems (WTSs) are analysed. In particular, Part I dealt with sliding window (SW) DFT, some parametric methods (SW Prony and ESPRIT) and some hybrid methods (SW ESPRIT-DFT and Prony-DFT) that conjointly use DFT and parametric methods. In this paper, numerical applications of the methods presented in Part I are discussed in order to highlight the implementation problems and practical applications. Several experiments were performed using non-stationary test waveforms and voltage and current measured on actual WTSs. The comparisons were realized on the basis of result accuracy and computational efforts. Conclusions and recommendations are made concerning the framework of each method.

On the comparison between Ensemble Kalman Filter and Kalman Filter for the dynamic harmonic state estimation in a hybrid micro grid

The management of micro grids requires the dynamic harmonic state estimation of the system in order to perform control strategies that optimize waveform distortions. In industrial context, AC and DC devices and sources coexist and, therefore, micro grids can be a hybrid combination of AC and DC power sections. Also for such hybrid AC/DC micro grids optimal control strategies that optimize waveform distortions are mandatory. In this paper, the comparison of two different methods for the dynamic harmonic state estimation of hybrid AC/DC micro grids is performed analyzing both theoretical and numerical aspects. In particular, the Kalman Filter- and Ensemble Kalman Filter-based dynamic harmonic state estimations are compared in terms of accuracy and computational efforts. The numerical applications were performed on a hybrid AC/DC μG proposed for an actual industrial facility in southern Italy.