This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

Taxonomy research of artificial intelligence for deterministic solar power forecasting

The system frequency response (SFR) model describes the average network frequency response after a disturbance and has been applied to a wide variety of dynamic studies. However, the traditional literature does not provide a generic, analytical method for obtaining the SFR model parameters when the system contains multiple generators; instead, a numerical simulation-based approach or the operators’ experience is the common practice to obtain an aggregated model. In this paper, an analytical method is proposed for aggregating the multi-machine SFR model into a single-machine model. The verification study indicates that the proposed aggregated SFR model can accurately represent the multi-machine SFR model. Furthermore, the detailed system simulation illustrates that the SFR model can also accurately represent the average frequency response of large systems for power system dynamic studies. Finally, three applications of the proposed method are explored, with system frequency control, frequency stability, and dynamic model reduction. The results show the method is promising with broad potential applications.

Analytical Method to Aggregate Multi-Machine SFR Model With Applications in Power System Dynamic Studies

An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting

Integration of more renewable energy resources introduces a challenge in frequency control of future power systems This paper reviews and evaluates the possible challenges and the new control methods of frequency in future power systems Different types of loads and distributed energy resources (DERs) are reviewed A model representation of a population of the water heater devices for the demand side frequency response is considered A model representation of a population of battery energy storage system (BESS)-based DERs such as smart electric vehicles (EVs) charging, large-scale BESSs, and residential and non-residential BESSs, are highlighted The simplified Great Britain power system and the 14-machine South-East Australian power system were used to demonstrate the effectiveness of the new methods in controlling power system frequency following a disturbance These new methods are effective in recovering the fallen frequency response and present a great potential in controlling the frequency in future power systems

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Frequency control of future power systems: reviewing and evaluating challenges and new control methods

Frequency performance has been a crucial issue for islanded microgrids. On the one hand, most distributed energy resources are converter-interfaced and do not inherently respond to frequency variations. On the other hand, current inertia emulation approach cannot provide guaranteed response. In this paper, a model reference control based inertia emulation strategy is proposed for diesel-wind systems. Desired inertia can be precisely emulated through the proposed strategy. A typical frequency response model with parametric inertia is set to be the reference model. A measurement at a specific location delivers the information about the disturbance acting on the diesel-wind system to the reference model. The objective is for the speed of the diesel generator to track the reference so that the desired inertial response is realized. In addition, polytopic parameter uncertainty will be considered. The control strategy is configured in different ways according to different operating points. The parameters of the reference model are scheduled to ensure adequate frequency response under a predefined worst case. The controller is implemented in a nonlinear three-phase diesel-wind system fed microgrid using the Simulink software platform. The results show that exact synthetic inertia can be emulated and adequate frequency response is achieved.

Provision for Guaranteed Inertial Response in Diesel-Wind Systems via Model Reference Control

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Converter-interfaced power sources (CIPS) are hybrid control systems as they may switch between multiple operating modes. Due to increasing penetration, the hybrid behavior of CIPS, such as wind turbine generators (WTGs), may have significant impact on power system dynamics. In this paper, the frequency dynamics under inertia emulation and primary support from WTG is studied. A mode switching for WTG to ensure adequate frequency response is proposed. The switching instants are determined by our proposed concept of a region of safety (ROS), which is the initial set of safe trajectories. The barrier certificate methodology is employed to derive a new algorithm to obtain and enlarge the ROS for the given desired safe limits and the worst case disturbance scenarios. Then, critical switching instants and a safe recovery procedure are found. In addition, the emulated inertia and load-damping effect are derived in the time frame of inertia and primary frequency response, respectively. The theoretical results under critical cases are consistent with simulations and can be used as guidance for a practical control design.

Hybrid Controller for Wind Turbine Generators to Ensure Adequate Frequency Response in Power Networks

Converter-interfaced power sources (CIPS) are hybrid control systems as they may switch between multiple operating modes. Due to increasing penetration, the hybrid behavior of CIPS, such as, wind turbine generators (WTG), may have significant impact on power system dynamics. In this paper, the frequency dynamics under inertia emulation and primary support from WTG is studied. A mode switching for WTG to ensure adequate frequency response is proposed. The switching instants are determined by our proposed concept of a region of safety (ROS), which is the initial set of safe trajectories. The barrier certificate methodology is employed to derive a new algorithm to obtain and enlarge the ROS for the given desired safe limits and the worst-case disturbance scenarios. Then critical switching instants and a safe recovery procedure are found. In addition, the emulated inertia and load-damping effect is derived in the time frame of inertia and primary frequency response, respectively. The theoretical results under critical cases are consistent with simulations and can be used as guidance for practical control design.

In this paper, we introduce a deep learning aided constraint encoding method to tackle the frequency-constraint microgrid scheduling problem. The nonlinear function between system operating condition and frequency nadir is approximated by using a neural network, which admits an exact mixed-integer formulation (MIP). This formulation is then integrated with the scheduling problem to encode the frequency constraint. With the stronger representation power of the neural network, the resulting commands can ensure adequate frequency response in a realistic setting in addition to islanding success. The proposed method is validated on a modified 33-node system. Successful islanding with a secure response is simulated under the scheduled commands using a detailed three-phase model in Simulink. The advantages of our model are particularly remarkable when the inertia emulation functions from wind turbine generators are considered.

Yichen Zhang

Papers

Provision for Guaranteed Inertial Response in Diesel-Wind Systems via Model Reference Control

Novel stochastic methods to predict short-term solar radiation and photovoltaic power

Hybrid Controller for Wind Turbine Generators to Ensure Adequate Frequency Response in Power Networks

Hybrid Controller for Wind Turbine Generators to Ensure Adequate Frequency Response in Power Networks

Approximating Trajectory Constraints With Machine Learning – Microgrid Islanding With Frequency Constraints