Set Point Generation Research Articles

Robust Short-Term Operation of AC Power Network With Injection Uncertainties

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) for conventional non-linear ACOPF results in computationally intractable Robust Counterpart (RC), which is undesirable as ACOPF is an operational problem. Hence, this paper proposes a single-stage non-integer non-recursive RC of ACOPF, using a dual transformation, for short-term injection uncertainties. The proposed RC is convex, tractable, and provides base-point active power generations and terminal voltage magnitudes (setpoints) of conventional generators that satisfy all constraints for all realizations of defined injection uncertainties. The non-linear impact of uncertainties on other variables is inherently modeled without using any affine policy. The proposed approach also includes the budget of uncertainty constraints for low conservatism of the obtained setpoints. Monte-Carlo Simulation (MCS) based participation factored AC power flows validate the robustness of the obtained setpoints on NESTA and case9241pegase systems for different injection uncertainties. Comparison with previous approaches indicates the efficacy of the proposed approach in terms of low operational cost and computation time.

Motion Control of Wafer Scanners in Lithography Systems: From Setpoint Generation to Multi-Stage Coordination

Motion Control of Wafer Scanners in Lithography Systems: From Setpoint Generation to Multi-Stage Coordination

Reagent Addition Control for Zinc First Rougher With a Dual FP Tree-Based Feature Setpoint Generator and Knowledge Core-Based Reagent Fine Presetting

Reagent Addition Control for Zinc First Rougher With a Dual FP Tree-Based Feature Setpoint Generator and Knowledge Core-Based Reagent Fine Presetting

State machine-based architecture to control system processes in a hybrid fuel cell electric vehicle

This paper presents the development and implementation of a system supervisory controller in a hydrogen-based fuel cell electric vehicle. The controller's primary function is to ensure the safe control of the fuel cell system processes while facilitating coordination among various subsystems, including the balance of plant subsystems, vehicle control unit, diagnosis unit, and powertrain. The supervisory controller comprises of three primary parts: a State Machine, an Optimal Setpoint Generator, and a Power Limit Calculator. The State Machine, which serves as the central part of the supervisory controller, coordinates the fuel cell system's different operational states, including the complex processes of start-up and shutdown. To maximize the fuel cell system's efficiency and minimize the stack's degradation, the Optimal Setpoint Generator produces the subsystem's setpoints by solving an optimization problem and considering the manufacturer's requirements. The Power Limit Calculator assesses the stack's power output capability and calculates the current setpoint for the DC/DC converter. It then provides this data to the Energy Management System (EMS), which oversees the distribution of power between the fuel cell system and the batteries. The proposed fuel cell system supervisory controller is verified using the Worldwide Harmonized Light Vehicles Test Cycles (WLTC) in a real-world car. The designed control structure is implemented in a prototype hydrogen-based electric car at both PowerCell and CEVT facilities under the framework of the INN-BALANCE Horizon 2020 European project.

Optimal Operation of a Benchmark Simulation Model for Sewer Networks Using a Qualitative Distributed Model Predictive Control Algorithm

This article presents a distributed model predictive control algorithm including fuzzy negotiation among subsystems and a dynamic setpoint generation method, applied to a simulated sewerage network. The methodology considers WWTP as an additional objective of control. To improve the performance of a DMPC using a hydraulic model for prediction, a more detailed model has been considered including suspended solids concentration (TSS). The results obtained with the proposed methodology have been validated on a benchmark simulation model for sewer systems developed to test and compare methodologies, showing good performance.

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.

Frequency dynamics-aware real-time marginal pricing of electricity

This paper presents a frequency dynamics-aware marginal pricing method for real-time electricity markets. The proposed method captures the impact of system frequency dynamics on the marginal price of electricity in the presence of aggressive net-load variations made more likely by greater renewable integration. We formulate a dynamics-aware economic dispatch (ED) by augmenting the traditional single-snapshot ED with constraints pertaining to system frequency dynamics, and frequency deviations from the synchronous speed are penalized in the modified objective function. The resulting ED embeds two distinct time steps to optimize over fast system dynamics and slower decisions on generator set-points. We prove that the dynamics-aware marginal price is the (suitably scaled) Lagrange multiplier of the power balance constraint and that it converges in steady state to the marginal price obtained from traditional ED solved with comparable system load. Numerical case studies involving the Western System Coordinating Council test system validate our findings and confirm added revenue opportunities for generators contributing to frequency regulation.

Optimal Power Flow in DC Networks With Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in dc networks such that stability and operational constraint satisfaction are of concern. Most existing dc network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. In contrast, this article studies a DN-OPF formulation, which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this article’s robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the feasible region using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator setpoints are obtained by optimizing over the proposed convex stability set. The validity and value of the proposed algorithm are demonstrated through various dc networks adapted from IEEE test cases.

Deep learning feature-based setpoint generation and optimal control for flotation processes

Computer vision-based control is a nonintrusive, cost-effective, and reliable technique for flotation process control. It is known that deep learning features can depict the complex behavior of the froth surface more comprehensively and accurately than handcrafted features. However, few studies have tried to use additional information to improve flotation performance through optimal control. To this end, we have attempted to develop a novel deep learning feature-based two-layer optimal control scheme. The first layer is proposed for setpoint generation of high-dimensional features using improved fuzzy association rule reasoning. Then, an offline conservative double Q-learning control layer that can learn from historical industrial records by mitigating bootstrapping error in action value functions is developed. The proposed method can adapt the setpoint to the change in process feeds. Meanwhile, in contrast to traditional approximate dynamic programming methods that need to interact with real/simulated process systems, this controller can work without any further interactions, which makes it possible to transfer the success of reinforcement learning algorithms to complex industrial process control where opportunities to explore are missing. Experiments demonstrate that the proposed method is effective and promising for practical flotation process control.

Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems

This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.

Learning Feedforward Control Using Multiagent Control Approach for Motion Control Systems

Multiagent control system (MACS) has become a promising solution for solving complex control problems. Using the advantages of MACS-based design approaches, a novel solution for advanced control of mechatronic systems has been developed in this paper. The study has aimed at integrating learning control into MACS. Specifically, learning feedforward control (LFFC) is implemented as a pattern for incorporation in MACS. The major novelty of this work is that the feedback control part is realized in a real-time periodic MACS, while the LFFC algorithm is done on-line, asynchronously, and in a separate non-real-time aperiodic MACS. As a result, a MACS-based LFFC design method has been developed. A second-order B-spline neural network (BSN) is used as a function approximator for LFFC whose input-output mapping can be adapted during control and is intended to become equal to the inverse model of the plant. To provide real-time features for the MACS-based LFFC system, the open robot control software (OROCOS) has been employed as development and runtime environment. A case study using a simulated linear motor in the presence of nonlinear cogging and friction force as well as mass variations is used to illustrate the proposed method. A MACS-based LFFC system has been designed and implemented for the simulated plant. The system consists of a setpoint generator, a feedback controller, and a time-index LFFC that can learn on-line. Simulation results have demonstrated the applicability of the design method.

Distributed Model Predictive Control Applied to a Sewer System

In this work, a Distributed Model Predictive Control (MPC) methodology with fuzzy negotiation among subsystems has been developed and applied to a simulated sewer network. The wastewater treatment plant (WWTP) receiving this wastewater has also been considered in the methodology by means of an additional objective for the problem. In order to decompose the system into interconnected local subsystems, sectorization techniques have been applied based on structural analysis. In addition, a dynamic setpoint generation method has been added to improve system performance. The results obtained with the proposed methodology are compared to those obtained with standard centralized and decentralized model predictive controllers.

Optimal Power Flow Design for Enhancing Dynamic Performance: Potentials of Reactive Power

We present a new optimal power flow (OPF) design that not only optimizes fuel cost but also enhances dynamic performance of a power system. Performance is quantified by the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm of the transfer matrix from any disturbance input to a set of performance outputs, which in this case are chosen as the frequencies of the generators. The H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm models the attenuation of the frequency amplitudes following the disturbance, and thereby quantifies the amount of damping torque induced on the tie-line flows. The method, referred to as H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -power flow modification (H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -PFM) is carried out in two steps. First, the regular OPF is solved to obtain the optimal generator setpoints for active and reactive power dispatch. Second, the load setpoints are re-tuned to minimize the aforesaid H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm while keeping the generator setpoints fixed. In particular, manipulating the load reactive power in this way is found to reduce the norm remarkably, improving disturbance attenuation and damping. A gradient descent algorithm is proposed for this minimization. Results are validated using the 68-bus test system with a solar farm.

Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods

The Optimal Power Flow (OPF) problem is a fundamental building block for the optimization of electrical power systems. It is nonlinear and nonconvex and computes the generator setpoints for power and voltage, given a set of load demands. It is often solved repeatedly under various conditions, either in real-time or in large-scale studies. This need is further exacerbated by the increasing stochasticity of power systems due to renewable energy sources in front and behind the meter. To address these challenges, this paper presents a deep learning approach to the OPF. The learning model exploits the information available in the similar states of the system (which is commonly available in practical applications), as well as a dual Lagrangian method to satisfy the physical and engineering constraints present in the OPF. The proposed model is evaluated on a large collection of realistic medium-sized power systems. The experimental results show that its predictions are highly accurate with average errors as low as 0.2%. Additionally, the proposed approach is shown to improve the accuracy of the widely adopted linear DC approximation by at least two orders of magnitude.

Modeling of photovoltaic power uncertainties for impact analysis on generation scheduling and cost of an urban micro grid

In electrical systems, the main objective is to satisfy the load demand at the least cost without having imbalance between generation and consumption. Thus, the uncertainty of photovoltaic (PV) power production must be considered in generation planning of a power system. In this paper, we develop a modeling method of this uncertainty to consider it into the generation scheduling. The optimal generation scheduling in an urban microgrid is made by taking in consideration the operating reserve provision under stochastic characteristics of PV power prediction. By considering a prescribed risk level of unbalancing, a dynamic programming algorithm sets the operational planning of conventional generators by solving a non-convex mixed-integer nonlinear programming model, so that the operational cost and available operating reserve can be calculated. Then, the effect of PV power uncertainty into the unit commitment is analyzed by considering PV forecast intervals with a 95 % confidence level. The unit commitment is then recalculated with new generator set points and the same criteria. Finally, variations of the targeted minimized costs and obtained OR is analyzed according to the uncertainty.

Exergy optimal multi-physics aircraft microgrid control architecture

The more electric aircraft (MEA) concept aims to reduce emissions, fuel costs, and enable incorporation of electric weapon systems and advanced sensor platforms. These systems will further burden the electrical system due to the pulse like loading and require advanced control strategies and distributed energy storage systems to ensure stability. Furthermore, multi-physical coupling of thermal electrical systems are often compartmentalize and analyzed separately, forgoing congruency that could occur if analyzed together. Here, we study how exergy, the amount of useful energy throughout a system, can guide control design and system operation. A multi-physics networked microgrid model was developed of an aircraft with two generation sources, interconnecting power converters, a lumped thermal mass model and pulsed loading. The Hamiltonian Surface Shaping Power Flow Control (HSSPFC) strategy is applied to the electrical grid via idealized and distributed storage elements. The HSSPFC was first developed to solve a general, scalable, form a networked microgrid architecture and then applied to the specific aircraft model. Implementation of the HSSPFC requires an outer loop to balance installed generation and to manage storage. This was accomplished through an exergy optimal set point generation scheme that minimized exergy destruction in the power converters. Bus regulation of within 3% of the desired set point was achieved while servicing a 100 kW pulsed load. A tradeoff between optimization update rate and storage regulation was found to be limited by the algorithm execution speed. Increased optimization update rates were linked to reduced storage use and fewer transients in bus voltage. The thermal model was electrically coupled through pumping loads and by cooling the power electronics. Exergy optimal coolant pump operation was also studied. The minimal exergy and pump energy consumption were obtained by operating the coolant system near the upper temperature limit of the coolant, which minimized cooling electrical loads.

Grid-Based Set Point Generation Strategy for Position Control of Dynamic Positioning Assisted Mooring System

Unlike typical a dynamic positioning (DP) system, a DP-assisted mooring system must determine a set point (SP) that can ensure a mooring tension safety range to prevent an excessive increase in mooring tension. In this paper, a new algorithm for determining the SP is suggested in order to reduce the tension on all the mooring lines. To determine the SP, a working area around the vessel is represented by a rectangular grid. Thus, the size of the grid area is limited considering the offset of a vessel with a mooring system. At each grid’s nodes, the resultant tension from all the mooring lines is estimated using the time history of the tension and vessel’s position. The results of static analyses for each grid position are used to estimate the global tension. Consequently, the SP is automatically selected as a position satisfying criterion for minimizing the total tension. In order to validate the suggested algorithm, a motion simulation with the control system in the time domain and a discussion of the results are presented.

Tertiary Controller-Based Optimal Voltage and Frequency Management Technique for Multi-Microgrid Systems of Large Remote Towns

A voltage-frequency management technique is proposed in this paper to retain these quantities within acceptable limits in remote islanded microgrids (MGs). The proposed technique gets activated when existing techniques that control the energy storages or adjust the set-points of generators are not successful. This technique aims to provide external support to an overloaded MG or the one with excessive renewable generation by coupling it to one or more suitable neighboring islanded MGs at the lowest cost. The selected neighboring MGs should have enough unused generation capacity or available loading capacity to support the overloaded or over-generating ones. The technique finds an optimal mixture of demand response (load decrease or increase), energy storage charging and discharging, and renewable curtailment as the other auxiliary options. It is based on a modified version of particle swarm optimization with a suitable convergence for near real-time applications. An objective function which considers different technical, operational, and environmental aspects is formulated, and the required constraints are highlighted. Several simulation studies are also presented to validate its performance.

Hierarchical Force and Positioning Task Specification for Indirect Force Controlled Robots

Indirect force control (IFC) architectures are a common approach for dealing with unknown environments. What all IFC schemes have in common is that the relation between the set point and the actual configuration of the robot is determined by a mechanical relationship (e.g., a mass–spring–damper system). In this paper, we propose a set-point generation method for IFC schemes, enabling intuitive specification of mixed force and positioning tasks on joint and Cartesian levels. In addition, the formulation of equality and inequality tasks is supported and a passivity-based stability proof is formulated using the concept of virtual energy storage. The resulting task programming interface is demonstrated on a 7-degree-of-freedom robot, running a joint space impedance controller. One sample task demonstrates the application of the developed approach and highlights the basic features.

Effect of crack opening velocity on fracture behavior of hyperelastic semi-structural adhesive joints subjected to mode I loading

Rubber-like adhesive joints provide substantial advantages compared to epoxy-based adhesive joints regarding their damping properties, fatigue resistance and energy consumption under impact. Polyurethane-based adhesives with high modulus are even used in structural applications like car body production. The work to be presented describes the dependency of mechanical mode I failure of a hyperelastic semi-structural adhesive on loading rate. Double cantilever beam (DCB) specimens were manufactured with an adhesive layer thickness of 3 mm. In a first test setup, driving velocity of the testing machine was controlled on optically measured crack opening velocity, varying over several orders of magnitude within external setpoint generation. The tests were driven until crack propagation took place and the position of optical measurement was not valid anymore. The J-integral according to Rice (1968) was calculated directly from measured force acting on the DCB specimen and rotation angle of force introduction points, using an analytical approach of Anthony and Paris (1988). In a second test setup, driving velocity of the testing machine was varied over several orders and tests were driven until complete failure of the adhesive joint. Pictures recorded from specimen's edge were used to analyze the rate dependency of crack propagation and fracture behavior.The observed correlation between J and current crack opening velocity showed that fracture energy was significantly and cohesive strength slightly increasing under higher crack opening velocities, while cohesive stiffness remained constant. In a certain range of loading rates, discontinuous crack growth with stick-slip crack propagation was observed. Below and above this range, crack propagation rate was tending to be more stable.