Hongyu Wu

Chance-Constrained Day-Ahead Scheduling in Stochastic Power System Operation

This paper proposes a day-ahead stochastic scheduling model in electricity markets. The model considers hourly forecast errors of system loads and variable renewable sources as well as random outages of power system components. A chance-constrained stochastic programming formulation with economic and reliability metrics is presented for the day-ahead scheduling. Reserve requirements and line flow limits are formulated as chance constraints in which power system reliability requirements are to be satisfied with a presumed level of high probability. The chance-constrained stochastic programming formulation is converted into a linear deterministic problem and a decomposition-based method is utilized to solve the day-ahead scheduling problem. Numerical tests are performed and the results are analyzed for a modified 31-bus system and an IEEE 118-bus system. The results show the viability of the proposed formulation for the day-ahead stochastic scheduling. Comparative evaluations of the proposed chance-constrained method and the Monte Carlo simulation (MCS) method are presented in the paper.

Fast Identification of Inactive Security Constraints in SCUC Problems

Security constrained unit commitment (SCUC) is one of the most important daily tasks that independent system operators (ISOs) or regional transmission organizations (RTOs) must accomplish in daily electric power market. Security constraints have long been regarded as difficult constraints for unit commitment problems. If the inactive security constraints can be identified and eliminated, the SCUC problem can be greatly simplified. In this paper, a necessary and sufficient condition for a security constraint to be inactive is established. It is proved that all inactive constraints can be identified by solving a series of small-scale mixed integer linear programming (MILP) problems. More importantly, an analytical sufficient condition is established and most of the inactive constraints can be quickly identified without solving MILP or linear programming (LP) problems. A very important feature of the conditions obtained is that they are only related to the load demands and parameters of the transmission network. Numerical testing is performed for three power grids and the results are impressive. Over 85% of the security constraints are identified as inactive and the crucial transmission lines affecting the total operating cost are among those associated with the remaining security constraints, providing useful information for transmission planning.

Hourly Demand Response in Day-Ahead Scheduling Considering Generating Unit Ramping Cost

This paper proposes a day-ahead scheduling model in which the hourly demand response (DR) is considered to reduce the system operation cost and incremental changes in generation dispatch when the ramping cost of thermal generating units is considered as penalty in day-ahead scheduling problem. The power output trajectory of a thermal generating unit is modeled as a piecewise linear function. The day-ahead scheduling is formulated as a mixed-integer quadratically constrained programming (MIQCP) problem with quadratic energy balance constraint, ramping cost, and DR constraints. A Lagrangian relaxation (LR) based method is applied to solve this problem. Numerical tests are conducted on a 6-bus system and the modified IEEE 118-bus system. The results demonstrate the merits of the proposed scheduling model as well as the impact of introducing ramping costs as penalty and DR as incentives in the day-ahead scheduling of power systems.

Thermal Generation Flexibility With Ramping Costs and Hourly Demand Response in Stochastic Security-Constrained Scheduling of Variable Energy Sources

This paper proposes a stochastic day-ahead scheduling of electric power systems with flexible resources for managing the variability of renewable energy sources (RES). The flexible resources include thermal units with up/down ramping capability, energy storage, and hourly demand response (DR). The Monte Carlo simulation (MCS) is used in this paper for simulating random outages of generation units and transmission lines as well as representing hourly forecast errors of loads and RES. Numerical tests are conducted for a 6-bus system and a modified IEEE 118-bus system and the results demonstrate the benefits of applying demand response as a viable option for managing the RES variability in the least-cost stochastic power system operations.

A Systematic Method for Constructing Feasible Solution to SCUC Problem With Analytical Feasibility Conditions

Obtaining high-quality feasible solution is the core and the major difficulty in solving security-constrained unit commitment (SCUC) problems. This paper presents a systematic method for constructing feasible solutions to SCUC problem based on a group of analytical feasibility conditions. The feasibility check is performed based on the analytical necessary conditions such that most of infeasible UC states can be identified without solving LP problem. If a UC state is infeasible, it is adjusted with the possibly minimal operating cost increase based on the cost information. This UC adjusting issue is formulated as a zero-one programming problem and a branch and bound (B&B) method is established based on these feasibility conditions. Numerical testing is performed for a 31-bus system, an IEEE 24-bus system, and an IEEE 118-bus system. The testing results suggest that over 95% of infeasible UC states are identified by the analytical necessary conditions. The near-optimal feasible schedules for SCUC problem can be obtained efficiently by the proposed method. The feasible schedules obtained are compared with those obtained from mixed integer programming-based method in the IEEE 118-bus system. It is shown that the new method can produce competitive results in terms of solution quality and computational efficiency.

Stochastic SCUC Solution With Variable Wind Energy Using Constrained Ordinal Optimization

This paper proposes a constrained ordinal optimization (COO) based method for solving the scenario-based stochastic security constrained unit commitment problem. The basic idea is to sample a large number of candidate unit commitment (UC) solutions by a crude model and then use an accurate model on a small selected subset to find good enough UC solutions over all scenarios. To facilitate the proposed method, a feasibility model is utilized that applies analytical conditions for identifying the feasibility of UCs. A blind picking approach based on the feasibility model is incorporated in the COO-based method for seeking good enough solutions. Numerical tests are performed on a modified IEEE 118-bus system with a high penetration of wind energy, in which hourly forecast errors of wind speed and loads and random outages of system components are considered. The simulation results show the validity and the effectiveness of the proposed method. Comparative evaluations of the proposed COO-based method with mixed-integer linear programming solvers are considered in this paper.

Quantifying the Economic and Grid Reliability Impacts of Improved Wind Power Forecasting

Wind power forecasting is an important tool in power system operations to address variability and uncertainty. Accurately doing so is important to reduce the occurrence and length of curtailment, enhancing market efficiency, and improving the operational reliability of the bulk power system. This research quantifies the value of wind power forecasting improvements in the IEEE 118-bus test system as modified to emulate the generation mixes of Midcontinent, California, and New England independent system operator balancing authority areas. To measure the economic value, a commercially available production cost modeling tool was used to simulate the multitimescale unit commitment (UC) and economic dispatch process for calculating the cost savings and curtailment reductions. To measure the reliability improvements, an in-house tool, Flexible energy scheduling tool for integrating variable generation, was used to calculate the systems area control error and the North American Electric Reliability Corporation Control Performance Standard 2. The approach allowed scientific reproducibility of results and cross validation of the tools. A total of 270 scenarios were evaluated to accommodate the variation of three factors: generation mix, wind penetration level, and wind forecasting improvements. The modified IEEE 118-bus systems utilized 1 year of data at multiple time scales, including the day-ahead UC, 4-h-ahead UC, and real-time dispatch. The value of improved wind power forecasting was found to be strongly tied to the conventional generation mix, existence of energy storage devices, and the penetration level of wind energy. The simulation results demonstrate that wind power forecasting brings clear benefits to power system operations.

Transactive Home Energy Management Systems: The Impact of Their Proliferation on the Electric Grid

Approximately 100 million singlefamily homes in the United States account for 36% of the electricity load, and often they determine the peak system load, especially on hot summer days when residential air-conditioning use is high. Traditional building power profiles are changing. Currently, there is an increased use of energy-efficient building materials and designs, which decreases building loads. In addition, there is an increased adoption of rooftop solar photovoltaic (PV), which leads to bidirectional power flow and significant power ramps as PV output decreases in the late afternoon. Building power profiles are likely to change even more as residential energy storage products proliferate. Therefore, a better understanding of residential electricity demand is key to addressing the envisioned transition of the electric power system from its traditional structure to one that is transactive.

Wind-Friendly Flexible Ramping Product Design in Multi-Timescale Power System Operations

With increasing wind power penetration in the electricity grid, system operators are recognizing the need for additional flexibility, and some are implementing new ramping products as a type of ancillary service. However, wind is generally thought of as causing the need for ramping services, not as being a potential source for the service. In this paper, a multi-timescale unit commitment and economic dispatch model is developed to consider the wind power ramping product (WPRP). An optimized swinging door algorithm with dynamic programming is applied to identify and forecast wind power ramps (WPRs). Designed as positive characteristics of WPRs, the WPRP is then integrated into the multi-timescale dispatch model that considers new objective functions, ramping capacity limits, active power limits, and flexible ramping requirements. Numerical simulations on the modified IEEE 118-bus system show the potential effectiveness of WPRP in increasing the economic efficiency of power system operations with high levels of wind power penetration. It is found that WPRP not only reduces the production cost by using less ramping reserves scheduled by conventional generators, but also possibly enhances the reliability of power system operations. Moreover, wind power forecasts play an important role in providing high-quality WPRP service.

IGMS: An Integrated ISO-to-Appliance Scale Grid Modeling System

This paper describes the integrated grid modeling system (IGMS), a novel electric power system modeling platform for integrated transmission-distribution analysis that co-simulates off-the-shelf tools on high performance computing platforms to offer unprecedented resolution from independent system operator (ISO) markets down to appliances and other end uses. Specifically, the system simultaneously models hundreds or thousands of distribution systems in co-simulation with detailed ISO markets and automatic generator control-level reserve deployment. IGMS uses a new message passing interface-based hierarchical co-simulation framework to connect existing sub-domain models. Our initial efforts integrate open-source tools for wholesale markets, bulk ac power flow, and full-featured distribution systems including physics-based end-use and distributed generation models (many instances of GridLAB-D). The modular IGMS framework enables tool substitution and additions for multi-domain analyses. This paper describes the IGMS tool, characterizes its performance, and demonstrates the impacts of the coupled simulations for analyzing high-penetration solar photovoltaic and price responsive load scenarios.