This paper proposes a novel data-driven adaptive robust optimization (ARO) framework for the unit commitment (UC) problem integrating wind power into smart grids. By leveraging a Dirichlet process mixture model, a data-driven uncertainty set for wind power forecast errors is constructed as a union of several basic uncertainty sets. Therefore, the proposed uncertainty set can flexibly capture a compact region of uncertainty in a nonparametric fashion. Based on this uncertainty set and wind power forecasts, a data-driven adaptive robust UC problem is then formulated as a four-level optimization problem. A decomposition-based algorithm is further developed. Compared to conventional robust UC models, the proposed approach does not presume single mode, symmetry, or independence in uncertainty. Moreover, it not only substantially withstands wind power forecast errors, but also significantly mitigates the conservatism issue by reducing operational costs. We also compare the proposed approach with the state-of-the-art data-driven ARO method based on principal component analysis and kernel smoothing to assess its performance. The effectiveness of the proposed approach is demonstrated with the six-bus and IEEE 118-bus systems. Computational results show that the proposed approach scales gracefully with problem size and generates solutions that are more cost effective than the existing data-driven ARO method.

Data-Driven Adaptive Robust Unit Commitment Under Wind Power Uncertainty: A Bayesian Nonparametric Approach

High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

https://www.mdpi.com/1996-1073/11/12/3325/pdf

Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large‐scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis. This article is categorized under: Wind Power > Systems and Infrastructure Energy Infrastructure > Systems and Infrastructure Energy Systems Economics > Systems and Infrastructure

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Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Wind farm power production in the changing wind: Robustness quantification and layout optimization

This study presents the potential role of thermal power generation in a future power system with high shares of variable generation while considering different sources of demand side flexibility such as heat pumps and heat storages in district heating, demand response from industries and electric vehicles. The study was carried out using a generation planning model combined with a unit commitment and economic dispatch model. The results from the planning model show a strong shift away from combined cycle gas turbines to open cycle gas turbines and gas engines as the share of wind power and solar photovoltaic increases. Demand side flexibility measures pushed this trend further. The results from the unit commitment and economic dispatch model demonstrate that the flexibility measures decrease the ramping frequency of thermal units, while the ramp rates of thermal units remain largely unchanged or increased. This indicates that the flexibility measures can cover smaller ramps in the net load more cost-effectively but that thermal power plants are still valuable for larger ramps. Impacts on emissions and electricity prices are also explored.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2017.0107

Long-term impact of variable generation and demand side flexibility on thermal power generation

The article demonstrates the characteristics of wind power variability and net load variability in multiple power systems based on real data from multiple years. Demonstrated characteristics includ ...

/pdf/variability-in-large-scale-wind-power-generation-ct499ygx4u.pdf

Variability in large-scale wind power generation

Growing shares of wind and solar power increase the sub-hourly variability of the net load, leading to higher and more frequent ramping. Sub-hourly unit commitment and economic dispatch modelling may be beneficial in certain regions given that it may capture the increase in short-term ramping requirements. However, sub-hourly scheduling comes at a computational cost. This paper questions the extent to which sub-hourly modelling may be of benefit for regions of different size and and different make-up and dispersion of renewable energy resources. Moreover, the individual impact that wind and solar power may have on sub-hourly net-load ramping is assessed in order to determine whether high-resolution modelling is of equal value in high-wind and high-solar power scenarios. It was found that the short-term net-load variability captured by sub-hourly modelling depends on the geographic dispersion of wind and solar resources, the size of the power system and the make-up of its renewable energy portfolio, and therefore, so may the benefits obtained from sub-hourly modelling for a given power system.

Drivers for sub-hourly scheduling in unit commitment models

The combination of increasing uncertainty and decreasing flexibility, brought about by growing shares of wind and solar power, may require a re-thinking of the way in which power systems are modelled and operated. In such systems, it is no longer certain that running a single day-ahead schedule, followed by a balancing dispatch, will make best use of the available flexibility for the system, nor will it necessarily yield the least-cost solution. In this paper, we compare how dispatch decisions and operating costs differ between day-ahead-only simulations, and those that use rolling planning to implement intraday dispatch. The nature and cost of the corrective actions carried out during the balancing dispatch, in order to rectify forecast errors in the day-ahead and intraday simulations, are discussed. Moreover, the impact that day-ahead-only and intraday dispatches have on the way in which energy storage is scheduled, and on how its value to the system is calculated are presented.

Irene Danti Lopez

Papers

Variability in large-scale wind power generation

Drivers for sub-hourly scheduling in unit commitment models

Intraday dispatch, energy storage and the value of re-scheduling in systems with high wind shares