## 1 INTRODUCTION

Driven by increasing prices on fossil fuels and concerns about greenhouse gas emissions, wind power, as a renewable and clean source of energy, is rapidly being introduced into the existing energy supply portfolio throughout the world. The U.S. Department of Energy has analyzed a scenario in which wind power meets 20% of the U.S. electricity demand by 2030, which means that the US wind power capacity would have to reach more than 300 GW. [1] The European Union is pursuing a target of 20/20/20, which aims to reduce greenhouse gas emissions by 20%, increases the amount of renewable energy to 20% of the energy supply, and improve energy efficiency by 20% by 2020 as compared with 1990. [2] China claims it will have 100 GW of wind power capacity by 2020. [3]

Traditionally, power system uncertainty arises from load fluctuation and system contingencies. Additional generation capacity is reserved to hedge against the risk of this uncertainty. However, the large-scale integration of wind power into power systems gives rise to new challenges since the uncertainty from wind power adds to the overall level of uncertainty in the system. Hence, wind power forecasting (WPF) therefore is a critical task for power system operations. There are two main types of WPF methods: point forecasting and uncertainty forecasting. Point forecasting provides single-value estimation of wind power at given points in time. [4-6] The main drawback of point forecasts is that no information is provided about forecast errors magnitude. Uncertainty forecasts [7-12] can estimate the uncertainty of the wind power, in the form of probability density functions (pdfs), scenarios, quantiles, and so forth, which thereby provide more information compared with point forecasts. In terms of the time horizon, there are very short-term (intraday), short-term (next 1–2 days ahead), and long-term WPF. For power system operation, day-ahead and hours-ahead WPF are useful for day-ahead and intraday market operations. For a comprehensive overview of WPF methods, please refer to Monteiro *et al.* [13] and Giebel *et al.* [14]

Misestimating the wind power can make the power system unreliable. For example, if the wind power is underestimated, the system operator could commit more thermal units than necessary and cause wind power curtailment and increased generation costs. If the wind power is overestimated, the system will experience a power supply shortage. [15] Furthermore, misestimating the change of wind power output can also make the system unreliable since there could be insufficient ramping capacity available to account for the fluctuations.

Even if we have perfect forecasting of wind power, because the variability of wind power and constraints on system operation (such as ramping constraints on thermal units or transmission constraints), with a large wind power penetration, there may be situations where wind power cannot be dispatched at its maximum available output although it is free in terms of fuel costs. Therefore, besides WPF techniques, the inherent variability, uncertainty, and limited controllability of wind power also require industrial practitioners to rethink the existing power system modeling techniques, such as unit commitment (UC) and economic dispatch (ED), to account for large amounts of wind power generation. Alternative UC models and reserves strategies for wind power must be designed to guarantee operational reliability and minimize costs. Stochastic UC has been proposed as one approach to better handle the wind power uncertainty in market operations. In Barth *et al.*, [16] the unit commitment problem is modeled as a stochastic linear programming model, and the wind power forecasts are presented by a scenario tree. Bouffard *et al.* [17] propose stochastic secure-economic short-term forward market-based scheduling of generation, load and reserves for power system with uncertainty from wind and load, in which the load and wind power forecasting errors are assumed to be normally distributed. Wang *et al.* [18] present a mixed integer programming unit commitment model with transmission constraints and wind penetration, in which wind power point forecasts are used at the UC stage, and wind power scenarios are used in the dispatch stage. Binlinton *et al.* [19] evaluate the unit commitment risk of a system with wind power. The distribution of wind power is estimated by using autoregressive moving average time series models. Makarov *et al.* [20] evaluate the impact of wind on the load following and regulation requirements of the California Independent System Operator system. The wind is forecasted by adding historical forecast error to expected wind power. Both Pappala *et al.* [21] and Li *et al.* [22] use an adaptive particle swarm optimization (PSO) method to solve the stochastic UC problem for a wind integrated power system, whereas in Li *et al.*, [22] wind power scenario generation and reduction are also performed by the PSO. Tuohy *et al.* [23] examines the effects of stochastic wind and load on the UC and ED of power systems with high levels of wind power by using the WILMAR model. [24] Constantinescu *et al.* [25] present a computational framework for a stochastic UC/ED formulation with the integration of a numerical weather prediction model of wind forecasts. Wang *et al.* [26] propose a two-stage stochastic unit commitment model with a scenario representation of wind power uncertainty to handle the uncertainty in wind power forecasts. Moreover, new approaches to calculating operating reserve requirements considering wind power uncertainty have also been studied. [27-31]

In this paper, by extending our previous work, [13, 26] we take the Illinois power system as a case study and model a power market with a high wind power penetration by a hybrid model of probabilistic WPF and stochastic unit commitment. Specifically, this paper addresses three research questions: (i) how probabilistic wind power forecasts can be used in electricity market operations; (ii) how stochastic UC and its associated commitment strategies impact the power system compared to deterministic UC; and (iii) how different WPFs and operating strategies impact the Illinois power system operations. The paper contributes in the following aspects:

The paper proposes a framework for modeling a two-settlement power market with both day-ahead (DA) and real-time (RT) markets to co-optimize energy and operating reserves, which is close to real-world practice in the USA. The unit commitment decisions in the DA market are adjusted in the RT market by committing/de-committing fast start units based on an updated (more recent) wind power forecast.

This paper applies an advanced probabilistic wind power forecasting model with a quantile-copula estimator (QCE), which can forecast the pdf of hourly wind power for different forecast horizons. This is the first time such a kernel density forecasting (KDF) method has been applied to stochastic scheduling approaches, and this represents and important advance to the current state of the art.

This paper evaluates the impact of a large-scale wind power expansion in the current Illinois power system with the proposed forecasting and operational methods. The comprehensive analysis and observations provide valuable insights to policy makers and market regulators on efficient integration of renewable energy into the electric power grid.

The rest of the paper is organized as follows: Section 2 describes the models used for this study, including the WPF and scenario generation/reduction method, and the two-settlement power market model. Section 3 presents the results and analysis. Section 4 concludes the study and discusses future work.