Maximum Power Point Tracking (MPPT) algorithms are essential in solar photovoltaic (PV) generation. In the last years, a major research effort has been made in order to get more efficient MPPT algorithms. However, in the literature, there is not a unique methodology for testing and comparing these kind of algorithms when facing dynamic irradiance conditions. This paper proposes a systematic way of evaluating and comparing MPPT algorithms for PV systems, at the simulation stage, unifying the following aspects: the PV system configuration, the type of simulation to be performed, and the dynamic irradiance tests to take into account. The MPPT algorithm is implemented into the dc-dc converter of the system for simplification purposes, simulations are carried out through state-space equations in order to reduce the total simulation time, and three irradiance dynamic tests are used systematically to provide full comparison under different environmental conditions. To test the flexibility of the proposed technique, a comparison of three commonly known Perturb and Observe (P&O) algorithms has been performed, giving special attention to the MPPT drift phenomenon. All interested researchers can request a computer application that implements the proposed methodology.

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

Massive integration of non-dispatchable energy into electric power systems is a challenging task. Electric power systems are becoming increasingly vulnerable in terms of frequency stability, as renewable energy displaces conventional synchronous generation from the energy mix. For this reason, grid codes are starting to demand different ancillary services from renewable generators, such as frequency control. In contrast to wind generators, which can deliver to the grid part of the kinetic energy stored in their rotating mass, photovoltaic generators must provide this service using batteries or power curtailment methods. The latter approach is preferable regarding the initial investment and its implementation cost, and several methods have been presented in the literature for this purpose. However, there is no consensus in which is the most appropriate side for operating the photovoltaic system in the curtailed mode. As both possible options have advantages and drawbacks, this paper proposes a novel photovoltaic power curtailment strategy that allows operation on both sides of the power-voltage curve depending on the needs. Moreover, in order to estimate the output characteristic of the photovoltaic system, a real-time nonlinear least squares curve fitting is applied. The proposed methodology has been tested in a simulation environment and the results show that this strategy achieves the requested active power reserves, regardless of the operation side.

As photovoltaic energy increasingly penetrates in power systems, transmission system operators have started to request its participation in providing ancillary services. One of the demanded services is the power ramp-rate control (PRRC), which attempts to limit the power ramps produced by intermittent irradiance conditions. In order to achieve the desired objective, solutions based on storage systems or modifying the maximum power point tracking (MPPT) in perturb and observe (PO) algorithms are commonly adopted. The starting point in PRRC is the determination of the instantaneous power ramp-rate, and different methods have been proposed in the literature for its calculation. However, the accuracy and computational speed of existing procedures can be improved, which may be critical in situations with rapid irradiance fluctuations. In this paper, a decoupled photovoltaic power ramp-rate calculation method is presented, in which the effect of variable irradiance and the PO algorithm are computed separately. The proposed method has been theoretically demonstrated and tested through simulation and experimental tests. Simulation results show that it can improve the previous methods in terms of accuracy and computation time. Experimental validation with hardware-in-the-loop demonstrates the suitability of the proposed method for real-time applications, even in presence of noisy measurements.