Model Predictive Control for Power System Frequency Control Taking into Account Imbalance Uncertainty

نویسندگان

  • Anne Mai Ersdal
  • Davide Fabozzi
  • Lars Imsland
  • Nina F. Thornhill
چکیده

Model predictive control (MPC) is investigated as a control method for frequency control of power systems which are exposed to increasing wind power penetration. For such power systems, the unpredicted power imbalance can be assumed to be dominated by the fluctuations in produced wind power. An MPC is designed for controlling the frequency of wind-penetrated power systems, which uses the knowledge of the estimated worst-case power imbalance to make the MPC more robust. This is done by considering three different disturbances in the MPC: one towards the positive worst-case, one towards the negative worst-case, and one neutral in the middle. The robustified MPC is designed so that it finds an input which makes sure that the constraints of the system are fulfilled in case of all three disturbances. Through simulations on a network with concentrated wind power, it is shown that in certain cases where the state-of-the-art frequency control (PI control) and nominal MPC violate the system constraints, the robustified MPC fulfills them due to the inclusion of the worst-case estimates of the power imbalance.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving the stability of the power system based on static synchronous series compensation equipped with robust model predictive control

Low-frequency oscillations (LFO) imperil the stability of the power system and reduce the Capacity of transmission lines. In the power systems, FACTS devices and Power System stabilizers are used to improve the stability. Static synchronous series compensators is one of the most important FACTS devices. This paper investigates the damping of LFO with static synchronous series compensator (SSSC)...

متن کامل

Application of ANN Technique for Interconnected Power System Load Frequency Control (RESEARCH NOTE)

This paper describes an application of Artificial Neural Networks (ANN) to Load Frequency Control (LFC) of nonlinear power systems. Power systems, such as other industrial processes, have parametric uncertainties that for controller design had to take the uncertainties in to account. For this reason, in the design of LFC controller the idea of robust control theories are being used. To improve ...

متن کامل

Model Predictive Control of Distributed Energy Resources with Predictive Set-Points for Grid-Connected Operation

This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

متن کامل

Designing a Fractional Order PID Controller for a Two-Area Micro-Grid under Uncertainty of Parameters

Increasing the number of microgrids has raised the complexity and nonlinearity of the power system and conventional controllers do not present acceptable efficiency in a wide range of operation points. In this study, a fractional order proportional–integral–derivative controller optimized with hybrid grey wolf-pattern search algorithm is used to control the frequency of each area of the microgr...

متن کامل

Power injection of renewable energy sources using modified model predictive control

This paper presents a simple model predictive control (MPC) approach to control the power injection system (PIS) for renewable energy applications. A DC voltage source and a single-phase inverter that is connected to the grid by an LCL filter form the PIS. Grid voltage is considered a disturbance for the system. For eliminating this disturbance, a modified model is proposed. It is usual to ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014