The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10-min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well-established benchmarks. The proposed approach forms a new artificial neural network-based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision-making activities in power systems.

Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation

Over the past few decades, many publications have been made in the area of Load frequency control (LFC) of interconnected power systems. Load frequency control is necessary to develop better control in order to achieve less effect on the frequency and tie line power deviations after a load perturbation. However, number of control strategies has been employed in the design of load frequency controllers in order to achieve a better dynamic response and the exact choice of the LFC controller in a particular case requires sufficient expertise because each controller has its own merits and demerits. Due to this, an appropriate review of load frequency control (LFC) mechanism is essential and a few attempts have been made in this concern. This paper presents a detailed survey on load frequency control (LFC) mechanism. The overall study explores the depth study issues related to LFC mechanism based on different sources of power system models. This paper focused on different control techniques of LFC, which also includes all the recent application of FACTS devices. This review reveals the investigation of soft computing based optimization technique and application of Energy Storage System (ESS) and HVDC-link in LFC. These studies also illustrates conventional power system, deregulated of power environment as well as distributed generation and micro grids. This paper is designed in order to highlight the major traits of Load forecasting and some critical case studies on LFC.

A comprehensive state of the art literature survey on LFC mechanism for power system

Frequency control is one of the most important issues in a power system due to increasing size, changing structure and the complexity of interconnected power systems. Increasing economic constraints for power system quality and reliability and high operational costs of generation side controllers have inclined researchers to consider demand response as an alternative for preserving system frequency during off-normal conditions. However, the main obstacle is calculating the accurate amount of load related to the value of disturbances to be manipulated, specifically in a multi-area power system. Dealing with this challenge, this paper makes an attempt to find a solution via monitoring the deviations of tie-line flows. The proposed solution calculates the magnitude of disturbances and simultaneously determines the area where disturbances occurred, to apply demand response exactly to the involved area. To address communication limitations, the impact of demand response delay on the frequency stability is investigated. Furthermore, this paper introduces a fuzzy-PI-based supervisory controller as a coordinator between the demand response and secondary frequency control avoiding large frequency overshoots/undershoots caused by the communication delays. To evaluate the proposed control scheme, simulation studies are carried out on the 10-machine New England test power system.

Intelligent Demand Response Contribution in Frequency Control of Multi-Area Power Systems

This paper describes a fully distributed power dispatch method that can achieve fast frequency recovery and minimal generation cost for autonomous microgrids. The method is comprised of two stages. In the first stage, a subgradient-based consensus algorithm is used to recover frequency. The equal increment rate criteria is incorporated into this algorithm to achieve a minimal regulating cost, obtained by economically distributing power among distributed energy resources. Control signals updated with latest local measurements are executed in each iteration step to speed up the frequency recovery procedure. In the second stage, an average consensus algorithm is applied to resolve frequency oscillations caused by measurement errors. Numerical tests are described to demonstrate the validity of the proposed method and its applicability in the presence of communication time delay is discussed.

A Fully Distributed Power Dispatch Method for Fast Frequency Recovery and Minimal Generation Cost in Autonomous Microgrids

This paper presents a fuzzy adaptive model predictive approach for load frequency control of an isolated micro grid. A generalized state space model of a typical isolated micro grid having controllable and uncontrollable generating power sources is derived, and the same has been utilized to predict the future output and control inputs for the micro grid frequency control. A centralized model predictive control (MPC) is implemented with a single input multi-output system model based on the controllable distributed energy resources in the micro grid. The parameter-driven MPC is made adaptable by dynamically adjusting its parameter $R_{w}$ using fuzzy controller. The proposed fuzzy MPC employs a rule-based fuzzy controller to fuzzify the tuning parameter $R_{w}$ present in the cost function of MPC, which plays an important role in minimizing the frequency deviation in the system. The closed loop system response obtained by the proposed fuzzy MPC has been found faster and adaptable for different scenarios in the system. The effectiveness has been evaluated with performance index integral time square error (ITSE) value and has been compared with MPC with constant tuning parameter value also with the proportional–integral controller response. Thus, the efficacy of using proposed fuzzy MPC in secondary load frequency control has been validated thereof.

Load Frequency Control of an Isolated Micro Grid Using Fuzzy Adaptive Model Predictive Control

Reducing the fossil fuel consumption of aluminum smelting production, which consumes around 8% of electricity annually, is of great significance for China's energy sector. One possible solution is to integrate wind power as supply for aluminum smelter loads. Based on this background, this paper studies an actual industrial case-an isolated power system for aluminum production with integrated wind penetration levels as high as 30%. However, due to the integration of wind power, frequency stability issues become critical in such a system. Therefore, a demand-side frequency control scheme responding to frequency deviations of the system is proposed in this paper to control the load power of aluminum smelters. The scheme is designed based on the relationship between the DC voltage supply for aluminum smelting loads and the commutation voltage drop which can be adjusted by changing the inductance value of the saturable reactors. Finally, simulations under both N-1 and N-2 contingency scenarios demonstrate the effectiveness of the proposed control scheme considering various wind power outputs.

Demand Side Frequency Control Scheme in an Isolated Wind Power System for Industrial Aluminum Smelting Production

Aluminum production relies on the smelting process which consumes huge amounts of electrical energy One possible solution to reduce the fossil fuel consumption by aluminum production is the integration of wind power Based on this background, this paper studies an isolated power system for aluminum smelting production with a high penetration of wind power A dynamic model of the aluminum smelter load (ASL) is introduced in this paper using the field experiment data Based on this model, the dynamic model of the isolated system considering demand side participation is proposed The MPC-based frequency controller is proposed to keep the frequency deviation within a proper range ( $\pm$ 01 Hz) under wind power fluctuation as well as to recover the system frequency after a large disturbance (such as one generator trip) Finally, simulation results demonstrate the effectiveness of the proposed MPC based frequency controller

MPC-Based Frequency Control With Demand-Side Participation: A Case Study in an Isolated Wind-Aluminum Power System

Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law cannot be evaluated before the MPC controller is put into service. Therefore, system operators may not validate its performances in advance. To overcome this drawback, the explicit MPC (EMPC) method is introduced and applied to obtain an explicit control law. In addition, another major contribution is that an improved partition algorithm of EMPC is studied which enables the EMPC method to be extended to a system of large number of state variables and more constraints. A simple single generator single load case is used to illustrate the whole procedure of EMPC and then the EMPC is applied to an actual isolated power system for frequency control. Simulation results show that the explicit control law of EMPC is able to restore system frequency to its nominal value under large disturbance. Moreover, the physical meaning of the explicit control law given by EMPC can be clearly explained for the studied system.

Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

Demand side frequency control scheme in an isolated wind power system for industrial aluminum smelting production

Considering wind farm integrations, a robust optimization model for security-constrained unit commitment (SCUC) is built to deal with the errors on wind power predictions. In the proposed formulation, spinning reserve requirement is specially considered to support possible wind power change between two successive time iterations. Network security constraints are considered by DC power flow equations. Moreover, an iterative method is proposed to solve the SCUC problem considering network losses by using loss factors and loss distribution factors. Case studies on the IEEE 30-bus system demonstrate the effectiveness of the proposed method. The results under different wind integration scenarios are also compared.

Hao Jiang

Papers

Demand Side Frequency Control Scheme in an Isolated Wind Power System for Industrial Aluminum Smelting Production

MPC-Based Frequency Control With Demand-Side Participation: A Case Study in an Isolated Wind-Aluminum Power System

Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

Demand side frequency control scheme in an isolated wind power system for industrial aluminum smelting production

Robust optimization method for unit commitment with network losses considering wind uncertainties