A review of deep learning for renewable energy forecasting

The Benders decomposition algorithm: A literature review

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Voltage Stability Of Electric Power Systems

This paper presents an adjustable robust optimization approach to account for the uncertainty of renewable energy sources (RESs) in optimal power flow (OPF). It proposes an affinely adjustable robust OPF formulation where the base-point generation is calculated to serve the forecast load which is not balanced by RESs, and the generation control through participation factors ensures a feasible solution for all realizations of RES output within a prescribed uncertainty set. The adjustable robust OPF framework is solved using quadratic programming with successive constraint enforcement and can coordinate the computation of both the base-point generation and participation factors. Numerical results on standard test networks reveal a relatively small increase in the expected operational cost as the uncertainty level increases. In addition, solutions of networks that include both uncertain wind generation and Gaussian distributed demand are shown to have less cost and a higher level of robustness as compared to those from a recent robust scheduling method.

Adjustable Robust OPF With Renewable Energy Sources

Critical review of recent advances and further developments needed in AC optimal power flow

With the increased variability and uncertainty of net load induced from high penetrations of renewable energy resources and more flexible interchange schedules, power systems are facing great operational challenges in maintaining balance. Among these, the scarcity of ramp capability is an important culprit of power balance violations and high scarcity prices. To address this issue, market-based flexible ramping products (FRPs) have been proposed in the industry to improve the availability of ramp capacity. This paper presents an in-depth review of the modeling and implementation of FRPs. The major motivation is that although FRPs are widely discussed in the literature, it is still unclear to many that how they can be incorporated into a co-optimization framework that includes energy and ancillary services. The concept and a definition of power system operational flexibility as well as the needs for FRPs are introduced. The industrial practices of implementing FRPs under different market structures are presented. Market operation issues and future research topics are also discussed. This paper can provide researchers and power engineers with further insights into the state of the art, technical barriers, and potential directions for FRPs.

Enhancing Power System Operational Flexibility With Flexible Ramping Products: A Review

Achieving high renewable energy penetrated power systems requires considerable operational flexibility to hedge the variability and uncertainty of variable renewable energy (VRE) generation. Compared with VRE sources, concentrating solar power (CSP) is an emerging controllable renewable generation technique that utilizes solar thermal power to generate electricity. The operational dispatchability of CSP would contribute to the power system transition toward high renewable penetration. In this paper, we explore how the generation portfolio will change toward high renewable energy penetrations, how much cost is involved, and what role CSP will play in realizing a high renewable energy penetrated power system. This study relies on a stochastic two-stage generation and transmission expansion planning model with CSP plants. The model captures the uncertainty and variability of renewable generation and the flexibility limits of thermal plants. With the target of achieving a renewable-dominated minimum-cost system with an expected renewable energy penetration level, the investments of both generation and transmission facilities are optimized. A case study on IEEE test systems with renewable technology cost data in 2050 is performed to analyze the value of CSP toward high renewable energy penetrated power systems.

The Role of Concentrating Solar Power Toward High Renewable Energy Penetrated Power Systems

The benefit of risk-based (RB) security-constrained optimal power flow (SCOPF) model lies in its ability to improve the economic performance of a power system while enhancing the system's overall security level. However, the RB-SCOPF model is difficult to solve due to the following two characteristics: 1) the overload severity of a circuit changes with the loading condition on it, thus is hard to express with a deterministic function, and 2) the risk index is a function of the state variables in both normal and contingency states, which greatly increases the scale of optimization. To handle the first issue, a new expression of severity function is proposed so that it is possible to decompose the model into a SCOPF subproblem and a risk subproblem. To deal with the second issue, a nested Benders decomposition with multi-layer linear programming method is proposed. Illustrations use the ISO New England bulk system is provided to demonstrate the feasibility of the proposed method. Analysis is presented to demonstrate the merits of the RB-SCOPF over the traditional SCOPF model.

A Computational Strategy to Solve Preventive Risk-Based Security-Constrained OPF

With increasing wind penetration, wind power ramps (WPRs) are currently drawing great attention to balancing authorities, since these wind ramps largely affect power system operations. To help better manage and dispatch the wind power, this paper develops a data-driven probabilistic WPR forecasting (p-WPRF) method based on a large number of simulated scenarios. A machine learning technique is first adopted to forecast the basic wind power forecasting scenario and produce the historical forecasting errors. To accurately model the distribution of wind power forecasting errors, a generalized Gaussian mixture model is developed and the cumulative distribution function (CDF) is also analytically deduced. The inverse transform method based on the CDF is used to generate a large number of forecasting error scenarios. An optimized swinging door algorithm is adopted to extract all the WPRs from the complete set of wind power forecasting scenarios. The p-WPRF is generated based on all generated scenarios under different weather and time conditions. Numerical simulations on publicly available wind power data show that the developed p-WPRF method can predict WPRs with a high level of reliability and accuracy.

A Data-Driven Methodology for Probabilistic Wind Power Ramp 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 system's 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.

Qin Wang

Papers

Enhancing Power System Operational Flexibility With Flexible Ramping Products: A Review

The Role of Concentrating Solar Power Toward High Renewable Energy Penetrated Power Systems

A Computational Strategy to Solve Preventive Risk-Based Security-Constrained OPF

A Data-Driven Methodology for Probabilistic Wind Power Ramp Forecasting

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