Modelling and Forecasting of Photovoltaic Generation for Microgrid Applications: from Theory to Validation

The penetration of stochastic renewable generation in modern power systems requires to reconsider conventional practices to ensure the reliable functioning of the electrical network. Decentralized control schemes for distributed energy resources (DERs) have gained attention to support the grid operation. In order to cope with the uncertainties of the DERs, predictive schemes that leverage on forecast of renewable generation recently came into prominence. The period of the control action typically depends on the availability of the reserve in the grid. For the case of microgrids, their limited physical extension and the lack of spatial smoothing imply fast power fluctuations and the necessity of coupling energy management strategies with real-time control. Among the DERs, small-scale photovoltaic (PV) systems are expected to represent most of the future available capacity, and consequently, solar resource assessment and power forecasting are of fundamental importance. This thesis focuses on developing forecasting methods and generation models to support the integration of photovoltaic systems in microgrids, considering short-term temporal horizons (below one hour) and fine spatial resolution (single site installations). In particular, we aim at computing probabilistic prediction intervals (PIs), i.e. we include information accounting for the intrinsic uncertainty of the prediction. In this respect, nonparametric tools to deliver PIs from sub-second to intra-hour forecasting horizons are proposed and benchmarked. They forecast the AC power and/or the global horizontal irradiance (GHI) by extracting selected endogenous influential variables from historical time series. The methods are shown to outperform available state-of-the-art techniques, and are able to capture the fastest fluctuations of small-scale PV plants. Then, we investigate how the inclusion of features from ground all-sky images can be used to improve time-series-based forecasting tools, thanks to identifying clouds movement. In this respect, we define a toolchain that allows predicting the cloud cover of the sun disk, through image processing and cloud motion identification. Furthermore, a methodology to estimate the irradiance from all-sky images is proposed, investigating the possibility of using an all-sky camera as an irradiance sensor. Next, we consider the problem of having power measurements that are corrupted by exogenous control actions (e.g. curtailment) and, therefore, not representative of the true potential of the PV plant. We propose a model-based strategy to reconstruct the maximum power production of a PV power plant thanks to integrating measurements of the PV cell temperature, system DC voltage and current. The strategy can improve time series-based direct power forecasting techniques when the production of the PV system is curtailed and thus the measured power does not correspond to the maximum available. The proposed methods to model and forecast the PV generation are then integrated in a single chain that allows to deliver power PIs that are able to account for the overall uncertainty of a PV system at a predefined confidence level. In the last part of the thesis, the proposed methods are experimentally validated in a real microgrid by considering possible applications in modern power systems

Cloud Motion Identification Algorithms Based on All-Sky Images to Support Solar Irradiance Forecast

International audienceCloud motion is a cause of direct irradiance variations at ground level and determines significant fluctuations of PV generation. In this work, we investigate on how integrating information on clouds motion extracted from all-sky images into a time series-based forecasting tool for global horizontal irradiance (GHI) to enhance its prediction performance. We consider three different cloud motion algorithms: heuristic motion detection (HMD), particle image velocimetry (PIV), and a persistent model. The HMD method is originally proposed in this paper. It consists in choosing the cloud motion vector leading to the best cloud map prediction considering the most recent sky images. Results show that integrating the information of the predicted cloud coverage in the circumsolar area leads to a decrease of the width of the GHI prediction intervals up to 2% for prediction horizons in the range 1 to 10 minutes

Increasing the PV hosting Capacity of Distribution Grids with Distributed Storage: Siting, Sizing and Costs

The capacity of electrical distribution systems of hosting photo-voltaic (PV) generation is limited due to the requirements of distribution system operators (DSOs) to respect statutory voltage levels along feeders and not exceed line current limits. Traditional ways to perform voltage regulation in distribution systems are on- load tap changers, voltage series regulators, and coordinated control of the reactive power set-points of PV converters, that however is ineffective in low voltage distribution systems due to the large R/X ratio of the longitudinal parameters of lines. The problem of line current congestions is normally tackled by curtailing PV generation, a practice that is however inefficient because it decreases the capacity factor of PV plants. Thanks to their decreasing cost, battery energy storage systems are gaining of interest as they can provide both voltage control and congestion management, avoiding the use of multiple countermeasures among those listed above. Distributed battery energy storage systems for grid control could be owned and operated by distribution system operators directly, and become a new safeguarding asset for grids. The battery fleet and control infrastructure can be designed to meet industrial-grade operational standards for control accuracy, reliability, and maintenance. This feature would not be possible with behind-the-meter PV self-consumption equipment installed by end customers (which can also relieve grid congestions by promoting the consumption of locally generated electricity) because this asset would not belong to the operator. In this report, we describe a method to plan the deployment of grid-connected batteries in distribution systems with the objective of accommodating a target level of PV generation capacity. The method deter- mines the location, energy capacity and power rating of the batteries with the minimum capital costs such that their injections can restore suitable nodal voltages and line currents in the network. The method is tractable thanks to leveraging an exact convex formulation of the optimal power flow problem and a convex battery model that includes the notion of charging/discharging efficiency. We apply the method to a low voltage (LV) and medium voltage (MV) distribution network, modeled according to the specifications of the CIGRE’s benchmark systems. The analysis is carried out for different levels of installed distributed PV generation capacity, from zero up to 3 times the generation hosting capacity of the grid. Uniform clear-sky conditions are considered as they are conducive to the largest yield from distributed PV generation. In the considered case studies, it is shown that for very large values of PV penetration levels (i.e., twice the PV hosting capacity), the total power rating of the deployed battery systems grows in a 1-to-1 ratio with the installed PV capacity. For less extreme values of installed PV capacity, the growth is generally smaller. The total energy capacity of batteries grows faster than for the power rating due to the typical peak production patterns of PV generation, that typically occur in the middle of the day and last for several hours. In particular, once the injection from a PV plant determines an over-current or over-voltage, it needs to be postponed and stored until it persists (e.g., hours), thus determining large energy capacity requirements. Using distributed battery storage to Research supported by the ”joint activities on scenarios and modelling” program of the Swiss competence center on energy research (SCCER-JASM) 1 mitigate grid congestions caused by distributed PV generation is an energy-intensive application that can be coupled with power-intensive applications, like primary frequency regulation, by leveraging algorithms for the provision of multiple ancillary services

Ultra-short-term prediction intervals of photovoltaic AC active power

The paper describes a heuristic method for the ultra-short-term computation of prediction intervals (PIs) for photovoltaic (PV) power generation. The method allows for directly forecasting the AC active power output of a PV system by simply extracting information from past time series. Two main approaches are investigated. The former relies on experimentally observed correlations between the time derivative of the PV AC active power output and the errors caused by a generic point forecast technique. The latter approach represents an improvement of the first one, where the mentioned correlations are clustered as a function of the value of the AC active power. The work is framed in the context of microgrids and inertialess power systems control, where accounting for the fastest dynamics of the solar irradiance can become extremely valuable. We validate the proposed model using one month of AC active power measurements and for sub-second time horizons: 100, 250 and 500 ms

Photovoltaic-Model-Based Solar Irradiance Estimators: Performance Comparison and Application to Maximum Power Forecasting

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Irradiance prediction intervals for PV stochastic generation in microgrid applications

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Solar irradiance estimations for modeling the variability of photovoltaic generation and assessing violations of grid constraints: A comparison between satellite and pyranometers measurements with load flow simulations

International audienceGlobal horizontal irradiance (GHI) is typically used to model the potential of distributed photovoltaic (PV) generation. On the one hand, satellite estimations are non-pervasive and already available from commercial providers, but they have a limited spatiotemporal resolution. On the other hand, local estimations, e.g., from pyranometers, sky-cameras and monitored PV plants, capture local irradiance patterns and dynamics, but they require in-situ monitoring infrastructure and upgrading the asset of electrical operators. Considering that in most power systems, PV generation is typically the aggregated contribution of many distributed plants, are local GHI estimations necessary to characterize the variability of the power flow at the grid connection point (GCP) and detect violations of the limits of voltages and line currents accurately? To reply, we consider GHI measurements from a dense network of pyranometers (used to model the ground truth GHI potential), satellite estimations for the same area, and information about a medium and low voltage distribution system. We perform load flows at different levels of installed PV capacity and compare the nodal voltages, line currents, and the power at the GCP when the irradiance is from pyranometers and when from satellite estimations, deriving conclusions on the necessity, or not, of highly spatiotemporally resolved irradiance estimations

A Comprehensive Assessment of the Short-Term Uncertainty of Grid-Connected PV Systems

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Experimental Assessment of the Prediction Performance of Dynamic Equivalent Circuit Models of Grid-connected Battery Energy Storage Systems

The paper discusses the model identification, validation and experimental testing of current-to-voltage dynamic circuit models for a grid-connected MW-class battery. The model refers to an utility-scale 720 kVA/560 kWh battery energy storage system (BESS) and is used in a model predictive control framework to forecast the evolution of the battery DC voltage as a function of the current trajectory. The model is identified using measurements from a dedicated experimental session where the BESS is controlled with a pseudo random binary signal (PRBS) to excite the system on a broad spectrum. The identified model relies on the assumption that the battery is a single cell. To test this assumption and assess the quality of predictions, we test the model performance by using a second data set coming from a real-life power system application, where the BESS is used to dispatch the operation of a group of stochastic prosumers (demand and PV generation). Experimental results show that the root mean square voltage prediction error of the best performing model (i.e. two time constant model, TTC) is less than 0.55% for look-ahead times in the range 10 seconds-10 minutes and better than persistence for all considered forecasting horizons