The paper proposes an optimal management strategy for a system composed by a battery and a photovoltaic power plant. This integrated system is called to deliver the photovoltaic power and to simultaneously provide droop-based primary frequency regulation to the main grid. The battery state-of-energy is controlled by power offset signals, which are determined using photovoltaic energy generation forecasts and predictions of the energy required to operate frequency regulation. A two level control architecture is developed. A day-ahead planning algorithm schedules the energy profile which is traded at the day-ahead market and defines the primary control reserve that the integrated system is able to provide in the considered day. During the day operations, a second level algorithm corrects the dispatched plan using updated information, in order to guarantee a continuous and reliable service. Both control algorithms take into account the uncertainties of the photovoltaic generation and of the frequency dynamics using stochastic optimization.

This paper proposes a solution for the day-ahead planning and the real-time control of an integrated system composed of a PhotoVoltaic plant (PV) and a Battery Energy Storage System (BESS). The day-ahead algorithm provides the optimal daily energy delivery profile based on a prediction of the PV production. The objective is to maximize the power delivery, according to the request of specific profile shape. The real- time operation algorithm dynamically regulates the BESS power exchange, in order to realize the scheduled power profile. Both the proposed algorithms exploit chance-constrained stochastic optimization, in order to take into account the uncertainties of the PV power predictions. Simulation results show the effectiveness of the proposed solutions, while their validation is achieved by experimental tests, executed on a LV microgrid controlled by a Distribution Management System (DMS).

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

The integration of Renewable Energy Sources (RES) into the power system requires an upgrade of the adopted management and control solutions. Focusing on frequency regulation, the same RES, as well as loads, both possibly coupled with energy storage systems, are called to provide a contribution through suitable regulation services. In this context, this paper proposes a control strategy for enabling a unit composed by a Wind Farm and Battery Energy Storage Systems (BESSs) to provide a fast frequency regulation service. The control is based on Model Predictive Control technique, whereas the regulation service is defined according to the technical requirements of the Italian Transmission System Operator (TSO). The approach is also tested via Hardware-In-the-Loop simulations. More specifically, tests are carried out by implementing the control algorithm on a Raspberry Pi board that communicates with a real BESS and with a real-time simulator implementing a benchmark power system. The validation results prove the practical effectiveness of the proposed control method since they demonstrate that the designed algorithms can be implemented on a low-cost hardware and applied, without computational and communication issues, in a real-field framework.

Assessment of dynamic stability in a modern power system (PS) is becoming a stringent requirement both in operational planning and in on-line operation, due to the increasingly complex dynamics of a PS. Further, growing uncertainties in forecast state and in the response to disturbances suggests the adoption of risk-based approaches in Dynamic Security Assessment (DSA). The present paper describes a probabilistic risk-based DSA, which provides instability risk indicators by combining an innovative probabilistic hazard/vulnerability analysis with the assessment of contingency impacts via time domain simulation. The tool implementing the method can be applied to both current and forecast PS states, the latter characterized in terms of renewable and load forecast uncertainties, providing valuable results for operation and operational planning contexts. Some results from a real PS model are discussed.

This paper presents a comparison of a variety of control strategies for the primary frequency control of wind power plants. These strategies include inertial as well as pitch angle regulators. The inertial control is sensitive to both the frequency variation through a standard droop control, and the rate of change of frequency. Since these controllers tend to counteract the operation of the Maximum Power Point Tracking (MPPT), the paper also investigates the effect of blocking the MPPT whenever the inertial control is enabled. The pitch angle control allows for a power reserve and prevents the rotor speed from exceeding the nominal value. A modified version of the WSCC 9-bus, 3-machine system with a 30% of wind penetration and a type 3 wind turbine model is considered in the case study. The behavior of these controllers are first analyzed separately. Then a compound controller, which merges together all strategies above, is proposed and discussed.