Applying model predictive control to power system frequency control

Model predictive control (MPC) is investigated as a control method which may offer advantages in frequency control of power systems than the control methods applied today, especially in presence of increased renewable energy penetration. The MPC includes constraints on both generation amount and generation rate of change, and it is tested on a one-area system. The proposed MPC is tested against a conventional proportional-integral (PI) controller, and simulations show that the MPC improves frequency deviation and control performance. © 2013 IEEE

A Proposed Energy Management System to Overcome Intermittence of Hybrid Systems Based on Wind, Solar, and Fuel Cells

Distributed resource (DR) impacts voltage and frequency, and deviations out of tolerance limits are financial damage to the customers. This chapter presents an energy management system (EMS) with several approaches to overcome intermittency and create a semi-dispatchable generation supply. The EMS will work as a prosumer considering its level of dispatchability, without disturbing the frequency of the network. The power generation model is based on a small wind turbine, solar panels, PEMFC, and a hydrogen storage system. Probabilistic information concerning short-term forecast applied to wind speed and radiation is provided by individual stochastic models. The prosumer is modeled by applying time series analysis through the root mean square algorithm with forgetting factor and by using model predictive control to integrate the system. A case is presented using historic wind speed and radiation data from Mexico City and loads curves based on average households and mini-store on a daily basis

Model predictive control for power system frequency control taking into account imbalance uncertainty

© IFAC.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

A predictive control with flying capacitor balancing of a multicell active power filter

Unlike traditional inverters, multicell inverters have the following advantages: lower switching frequency, high number of output levels, and less voltage constraints on the insulated-gate bipolar transistors. Significant performances are provided with this structure which is constituted with flying capacitors. This paper deals with a predictive and direct control applied to the multicell inverter for an original application of this converter: a three-phase active filter. To take advantage of the capabilities of the multicell converter in terms of redundant control states, a voltage control method of flying capacitor is added, based on the use of a switching table. Flying capacitor voltages are kept on a fixed interval, and precise voltage sensors are not necessary. The association of predictive control and voltage balancing increases considerably the bandwidth of the active filter

Guidelines for Weighting Factors Adjustment in Finite State Model Predictive Control of Power Converters and Drives

INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY () (.2009.VICTORIA, AUSTRALIA)Model Predictive Control with a finite control set has emerged as a promising control tool for power converters and drives. One of the major advantages is the possibility to control several system variables with a single control law, by including them with appropriate weighting factors. However, at the present state of the art, these coefficients are determined empirically. There is no analytical or numerical method proposed yet to obtain an optimal solution. In addition, the empirical method is not always straightforward, and no procedures have been reported. This paper presents a first approach to a set of guidelines that reduce the uncertainty of this process. First a classification of different types of cost functions and weighting factors is presented. Then the different steps of the empirical process are explained. Finally, results for several power converters and drives applications are analyzed, which show the effectiveness of the proposed guidelines to reach appropriate weighting factors and control performance

A cascade MPC control structure for PMSM with speed ripple minimization

This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a PMSM. The new results are based on a cascade model predictive control scheme with embedded disturbance model, where the per unit model is utilized to improve the numerical condition of the scheme. Results from an experimental application are given to support the design

Cooling load forecasting-based predictive optimisation for chiller plants

Extensive electric power is required to maintain indoor thermal comfort using heating, ventilation and air conditioning (HVAC) systems, of which, water-cooled chiller plants consume more than 50% of the total electric power. To improve energy efficiency, supervisory optimisation control can be adopted. The controlled variables are usually optimised according to instant building cooling load and ambient wet bulb air temperature at regular time intervals. In this way, the energy efficiency of chiller plants has been improved. However, with an inherent assumption that the instant building cooling load and ambient wet bulb temperature remain constant in the coming time interval, the energy efficiency potential has not been fully realised, especially when cooling loads vary suddenly and extremely. To solve this problem, a cooling load forecasting-based predictive optimisation method is proposed. Instead of minimising the instant system power according to the instant building cooling load and ambient wet bulb temperature, the controlled variables are derived to minimise the sum of the instant system power and one-time-step-ahead future system power according to both instant and forecasted future building cooling loads. With this method, the energy efficiency potential of a chiller plant can be further improved without shortening the operation time interval. 80% redundant energy consumption has been reduced for the sample chiller plant; energy can be saved for chiller plants that work for years. The evaluation on the effect of cooling load forecasting accuracy turns out that the more accurate the forecasts are, the more redundant energy consumption can be reduced