Application of quadratically-constrained model predictive control in power systems

We describe the application of model predictive control (MPC) using state mapping to the automatic landing system of a spaceplane. The controller has to be designed to enable the vehicle to follow reference signals with respect to trajectory and velocity, which are expressed as functions of the downrange. To track the reference signals, a control input is generally designed so that one or more ideal transient responses are realized. We also have developed a flight control system based on this concept. However, it was not satisfactory for use as the controller of an actual vehicle due to the lack of consideration of the input and state constraints. A controller that considers these constraints can be designed by applying MPC. On the other hand, it is difficult to predefine the transient responses necessary to track the reference signals, which vary with the current downrange. In this paper, a new approach using state mapping and feedback linearization is proposed to improve the control performance of tracking the reference signals. State mapping is derived to describe a system in which the reference signals can be handled as a constant set point, and the described system is linearized using state feedback. The proposed system is applied to the Japanese Automatic Landing Flight Experiment (ALFLEX) vehicle. A numerical simulation is performed to verify the validity of the proposed method based on MPC.

Summary form only given. This paper introduces an experimental platform (SYSLAB) for the research on advanced control and power system communication in distributed power systems and one of its components-an intelligent office building (PowerFlexHouse), which is used to investigate the technical potential for active load management. It also presents in detail how to implement a thermal Model Predictive Controller (MPC) for the heaters' power consumption prediction in the PowerFlexHouse. It demonstrates that this MPC strategy can realize load shifting, and using good predictions in MPC-based control, a better matching of demand and supply can be achieved. With this demand side control study, it is expected that MPC strategy for active load management can dramatically raise energy efficiency and improve grid reliability, when there is a high penetration of intermittent energy resources in the power system.

Guest Editorial: Situational awareness of integrated energy systems

This paper presents a review of the design and application of model predictive control strategies for Micro Aerial Vehicles and specifically multirotor configurations such as quadrotors. The diverse set of works in the domain is organized based on the control law being optimized over linear or nonlinear dynamics, the integration of state and input constraints, possible fault-tolerant design, if reinforcement learning methods have been utilized and if the controller refers to free-flight or other tasks such as physical interaction or load transportation. A selected set of comparison results are also presented and serve to provide insight for the selection between linear and nonlinear schemes, the tuning of the prediction horizon, the importance of disturbance observer-based offset-free tracking and the intrinsic robustness of such methods to parameter uncertainty. Furthermore, an overview of recent research trends on the combined application of modern deep reinforcement learning techniques and model predictive control for multirotor vehicles is presented. Finally, this review concludes with explicit discussion regarding selected open-source software packages that deliver off-the-shelf model predictive control functionality applicable to a wide variety of Micro Aerial Vehicle configurations.

The electrical power system is becoming more and more complex with the increasing demand of the electricity across the world, as the world is becoming the global village. The electrical power system must be reliable and safe enough to fulfil the energy requirements with the continuous supply of energy. The chances of power system's blackouts and outages are increasing with its increased complexity. So, there is a need of an efficient control system to make the power system more safe, efficient and reliable. Wide Area Monitoring, Protection and Control system (WAMPAC) is becoming an emerging technology for an efficient monitoring, protection and control of power system. Phasor measurement unit (PMU) is an integrated part of wide area monitoring, protection and control (WAMPAC) system, which can be used for monitoring, protection and control of complex electrical power systems. PMU gives the synchronized phasor measurements of voltage and current and can easily monitor and control even small disturbances in power system to protect the power system from any blackouts and outages before any fault occurs. In the proposed paper a PMU model is designed in MATLAB/Simulink and then PMU is used for power system protection and the comparative analysis are done with the conventional protection technique for an interconnected two-area network power system.

Conventional control techniques such as proportional integral (PI) control and proportional integral derivative (PID) control are widely used as the load frequency controller in power system control applications; however, the increase in complexity of the power system is degrading the performance of classical control. Therefore, in this paper a new control approach using fractional calculus has been proposed for the load frequency control problem of two-area hydro-thermal power system. The paper presents an interconnected hydro-thermal power system working under different operating conditions introduced in the form of various nonlinearities. Four case studies have been presented in this paper considering transient analysis with (i) different loading conditions, (ii) parameter variations, (iii) different nonlinearities (governor dead band nonlinearity, time delay and generation rate constraints), and (iv) superconducting magnetic energy storage (SMES) device. A tilt integral derivative (TID) control has been presented as a secondary controller to stabilize the frequency deviations occurring in the two-area power system with the above-mentioned different operating conditions. The parameters of TID controller have been optimized using a differential evolution algorithm which solves an optimization problem formulated using the integral of time-weighted absolute error (ITAE) performance index. In addition, a comparison of dynamic system response obtained using TID control and PID control has been presented, which focusses on the better performance of TID control over PID control in an interconnected power system.

This paper presents an algorithm for load frequency control of multi-area power system. A more realistic models is used to represent the elements of power system. The frequency bias factor (B) is set optimally. The effect of generation rate constraints (GRC) and the dead band characteristics of the governor is taken into consideration. A simultaneous optimization of the fractional order (PIλ Dµ) control parameters, the speed regulation (R) of the governor and the frequency bias factor (B) is proposed for the first time. The objective function to be minimizes is a modified integral time absolute error (MITAE). The tuning of the control parameters is based on using cuckoo search algorithm (CSA) to minimize the objective function. The proposed algorithm is applied to a typical interconnected three-area power systems with a different levels of load step change. Results of simulation of the presented method are compared with those obtained by applying particle swarm optimization technique. The results obtained reveal the robustness of the suggested algorithm in terms of settling time and peak over shoot.

In this paper, a centralized model predictive control (MPC) is applied on a group of interconnected microgrids (MGs) with the main grid. The objective is to maximize the benefits for all the elements constituting the MGs in addition to the benefits of the main grid. The application of MPC in our study needs a forecasting information about energy prices, production power, and loads. The algorithm is tested on five interconnected MGs connected to the main grid. Results have shown the performance of the proposed algorithm, especially for the benefits of MG owners, the coordination between MGs while respecting of the constraints related to each one of them.

Model Predictive Control In Solar Trough Plants: A Review

Lithium-ion battery cell-level control using constrained model predictive control and equivalent circuit models

In this paper, a review of some recent results on power system control (PSC) is given. Some important methods on power system analysis and control are summarized. In addition, the theoretical challenges encountered in the structure preserving power systems are discussed. Furthermore, a detailed structure preserving power system model with flux linkages dynamics is proposed, which is a basic of power system analysis and transient stability improvement.

Model-based network-wide optimizations in real-time emergency control of power systems are largely bottlenecked by the fast computational requirements. Furthermore, the operational aspects of modern power and energy systems are going through transformative changes to tackle global renewable energy expansion-related issues. Decentralized operations in power systems are becoming essential with the influx of distributed energy resources (DERs), dynamic loads, and electric vehicles (EVs). This research considers a well-known model-based strategy, Model Predictive Control (MPC), for real-time operation and controls of power systems and proposes novel methods utilizing machine learning, data-driven methods, and operator theory to solve computational challenges by making those real-time for the first time. Additionally, we develop a distributed MPC framework to address the recent needs of power systems' operation and control. In Part I, we first present a machine learning (ML)-accelerated MPC implementation in emergency voltage control problems. This method computes an optimal control strategy for the nominal system model offline and performs successive online control corrections at each control instant to adapt the offline computed controls for real-time scenarios. In the online phase, the required voltage trajectory prediction and its sensitivity computation to control inputs are achieved by machine learning-based approaches, thereby accelerating the overall control computation multiple times. Next, we present an alternative data-learned linear Koopman embedding approach for model predictive emergency voltage control in a power grid. This approach involves a "basis-dictionary free" method to lift the nonlinear power system dynamics to a higher dimensional linear space where MPC is exercised in a real-time fashion. We propose a novel data-driven encoder-decoder architecture, Koopman-inspired deep neural network (KDNN), which supports end-to-end learning from trajectory data. The MPC optimization in linear space makes the problem scalable and rapid for real-time implementations. Part II of this research focuses on distributed MPC design for quasi-static power flow-based voltage control problems under short-term (15-minute) variations of load and generation profiles. To realize the proposed distributed control, first, a centralized voltage control problem assuming grid observability is formulated and next transferred to the distributed versions based on both bus-wise and area-wise decomposition of the network. Distributed versions are solved via the alternating direction method of multipliers (ADMM), utilizing only neighbor-to-neighbor communications to attain optimal solutions. The implementation, efficacy, performance, and robustness of proposed research approaches are validated with IEEE test system benchmarks.

In this study, a self‐tuning control scheme is proposed and applied to automatic generation control (AGC) in power system with superconducting magnetic energy storage (SMES). The system is assumed consisting of two areas. The proposed self‐tuning control scheme is used to implement the automatic generation control adding to conventional control configuration. The performance of the system for load changes in the areas in the interconnected power system is studied. A comprehensive performance evaluation of the system is presented. The computer simulation of the interconnected power system with two areas shows that the proposed self‐tuning control scheme is very effective in damp out of oscillations caused by load disturbances.

With the large-scale integration of renewable energy sources, the deployment of series-compensated transmission lines (SCTLs) is increasing globally to enhance power transfer capability and improve system stability. Accurate estimation of SCTL parameters is critical to improve the performance of power system protection, control, and monitoring applications. Parameter estimation of SCTL is quite challenging due to presence of series capacitors. Most of the existing methods involve solving nonlinear equations, require multiple sets of measurements, or demand high sampling rates, thereby increasing the computational burden and convergence issues. This paper proposes a closed-loop parameter estimation method that uses a single set of positive-sequence voltage and current phasors from both line terminals, along with the series capacitor value, to estimate parameters of the SCTL. The method is verified using EMTDC simulations for various compensation levels and field data. Results confirm that the proposed solution achieves parameter estimation accuracy within industry-accepted limits while minimizing computational requirements. The performance of the proposed solution is independent of the location series capacitor. The estimated parameters enable applications in power system control, monitoring and protection settings.

Power system automation is heavily dependent on the reliable interconnection of power system field equipment and centralised control applications. Particularly important to achieve reliability in automated power systems is the redundant connection of field equipment controllers. Today, the fundamental redundancy management and switch-over processes are handled by the power system control applications itself. This, however, increases the complexity of the control systems and leads to an intersection of concerns. Consequently, the design and the implementation of field equipment controller redundancy is time-consuming and cost-intensive.In this paper, we propose the implementation of a redundancy virtualisation layer for power system controllers based on software-defined networking (SDN). The goal is to relieve the control applications from managing field level redundancy. Thus, our SDN approach allows to detect gateway failures and to perform transparent switch-overs. This significantly simplifies the configuration of the control application with redundant components and finally leads to more flexible and simplified redundancy design, deployment and operation. Arbitrary redundancy topologies, such as triple-controller-redundancy can be implemented without modifying the control applications.