Under the smart grid paradigm, distribution systems with large penetrations of photovoltaic–based power generation are called to optimize their operational resources to achieve a more efficient and reliable performance. In this context, this paper proposes a multiperiod mixed integer second order cone formulation to optimize distribution feeders operation. The model takes into account the feeder physical behavior; discrete control equipment (tap changers and capacitors banks) with a maximum allowable daily switching operation number; photovoltaic inverters operation; and the uncertain nature of solar energy and loads. A two–stage robust optimization framework is used to include the uncertainty into the model, where discrete and continuous control actions are assumed to be part of the first and second stage of this model, respectively. The conservativeness level of the robust model is controlled by an polyhedral uncertainty set whose vertexes are adaptively adjusted in a data–driven fashion in order to better capture complex spatiotemporal dependencies among uncertain parameters. Extensive computational experiments are performed utilizing modified versions of various IEEE test feeders. The performance of the proposed data–driven model is contrasted against traditional deterministic and robust budget–constrained models, using a rolling horizon out–of–sample evaluation methodology. When compared to the deterministic model, the data–driven approach yields a reduction in power losses of approximately 15% and a reduction up to 98% in hourly voltage violations. Results also suggests that the proposed approach exhibits better performance in terms of both average and conditional–value–at–risk metrics in comparison to budget–constrained models.

Determination of solar irradiance is a critical asset to ensure efficient working conditions for a photovoltaic (PV) system. This work analyze the feasibility of assessing solar irradiance on a PV device assuming the knowledge of the device temperature and the voltage/current operating point. This work proposes an approach based on a manipulation of the analytic expressions found in the reduced form of the “single diode” circuit model for a silicon PV device. The approach was validated through different practical experiments, and the results obtained are comparable to the ones of a commercial instrument for irradiance sensing. The ease of construction and the reduced costs involved make a device based on the proposed approach suitable for large-scale integration in a PV plant.

This article presents a stochastic dynamic programming model for a condition-based maintenance application. The approach seeks to determine the most opportune moment to inspect and execute preventive maintenance over each component of a nonredundant system, where the number of inspections to be performed simultaneously during each period is limited due to resource constraints. The model minimizes the total maintenance cost per unit of time, considering failure, maintenance, and inspection costs. Unlike most related literature, the model proposed herein allows nonperiodic inspections; it does not require to predefine a maintenance threshold and does not necessarily connect inspections to maintenance actions. Also, the criticality of each component is not static through time, or defined beforehand, but dynamically determined according to the available resources and the risk of failure. A numerical example illustrates the performance of the proposed model in comparison to three traditional maintenance models, namely corrective maintenance, age-based maintenance, and condition-based maintenance with periodic inspections. Results suggest that the proposed model yields the best solution among the studied policies and is more efficient, with a significant reduction of 90% in inspection resources.

The increasing number of megawatt-scale photovoltaic (PV) power plants and other large inverter-based power stations that are being added to the power system are leading to changes in the way the power grid is operated. In response to these changes, new grid code requirements establish that inverter-based power stations should not only remain connected to the grid during faulty conditions but, also provide dynamic support. This feature is referred in the literature to as momentary cessation operation. The few published studies about momentary cessation operation for PV power plants have not shed much light on the impact of these systems on the overall power system stability problem. As an attempt to address this issue, this paper proposes a control scheme for PV inverters that improves the transient stability of a synchronous generator connected to the grid. It is shown through the paper that the proposed control scheme makes the PV inverter's dc link capacitors absorb some of the kinetic energy stored in the synchronous machine during momentary cessation. Besides that, the proposed solution is also able to improve voltage stability through the injection of reactive power. Experimental and simulation results are presented in order to demonstrate the effectiveness of the proposed control scheme.

Legacy distribution systems need to adapt quickly to upcoming operating scenarios. The expected growth of electromobility and distributed generation will bring forth many challenges regarding efficient operation. Additionally, the limited communication infrastructure of these networks hinders the applicability of heavily centralized control strategies. In this context, this paper proposes a distributed control scheme for photovoltaic generators. The scheme comprises a local real–time control layer for the synthesis of active and reactive power references, using traditional voltage droop control, along with affine policies to handle the uncertainty associated to solar irradiance and demand levels. The characterization and periodic adaptation of such control scheme is achieved via a global, computationally tractable robust model able to co–optimize both droop and affine policy parameters. Performance is benchmarked via extensive computational experiments against other state–of–the–art distributed and local control strategies. Results suggest the proposed scheme is able to outperform all competitive frameworks. Its performance is also evaluated as a strictly local control scheme obtaining superior voltage regulation as compared to the recommendations in the Standard IEEE 1547.8.