Cost Efficient Distributed Load Frequency Control in Power Systems

The introduction of new technologies and increased penetration of renewable resources is altering the power distribution landscape which now includes a larger numbers of micro-generators. The centralized strategies currently employed for performing frequency control in a cost efficient way need to be revisited and decentralized to conform with the increase of distributed generation in the grid. In this paper, the use of Multi-Agent and Multi-Objective Reinforcement Learning techniques to train models to perform cost efficient frequency control through decentralized decision making is proposed. More specifically, we cast the frequency control problem as a Markov Decision Process and propose the use of reward composition and action composition multi-objective techniques and compare the results between the two. Reward composition is achieved by increasing the dimensionality of the reward function, while action composition is achieved through linear combination of actions produced by multiple single objective models. The proposed framework is validated through comparing the observed dynamics with the acceptable limits enforced in the industry and the cost optimal setups

Comparison of Two Optimal Control Strategies for a Grid Independent Photovoltaic System

This paper presents two optimal control strategies for a grid independent photovoltaic system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The first strategy is based on Action Dependent Heuristic Dynamic Programming (ADHDP), a model-free adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. ADHDP critic network is used in a PV system simulation study to train an action neural network (optimal neurocontroller) to provide optimal control for varying PV system output energy and loadings. The second optimal control strategy is based on a fuzzy logic controller with its membership functions optimized using the particle swarm optimization. The emphasis of the optimal controllers is primarily to supply the critical base load at all times, thus requiring sufficient stored energy during times of less or no solar insolation. Simulation results are presented to compare the performance of the proposed optimal controllers with the conventional priority control scheme. Results show that the ADHDP based controller performs better than the optimized fuzzy controller, and the optimized fuzzy controller performs better than the standard PV-priority controller

Real Time Predictive and Adaptive Hybrid Powertrain Control Development via Neuroevolution

The real-time application of powertrain-based predictive energy management (PrEM) brings the prospect of additional energy savings for hybrid powertrains. Torque split optimal control methodologies have been a focus in the automotive industry and academia for many years. Their real-time application in modern vehicles is, however, still lagging behind. While conventional exact and non-exact optimal control techniques such as Dynamic Programming and Model Predictive Control have been demonstrated, they suffer from the curse of dimensionality and quickly display limitations with high system complexity and highly stochastic environment operation. This paper demonstrates that Neuroevolution associated drive cycle classification algorithms can infer optimal control strategies for any system complexity and environment, hence streamlining and speeding up the control development process. Neuroevolution also circumvents the integration of low fidelity online plant models, further avoiding prohibitive embedded computing requirements and fidelity loss. This brings the prospect of optimal control to complex multi-physics system applications. The methodology presented here covers the development of the drive cycles used to train and validate the neurocontrollers and classifiers, as well as the application of the Neuroevolution process

Energy-efficient driving strategies for rail vehicles

The majority of railways internationally are striving to improve their financial performance while meeting competition from other modes of transport. A key factor in achieving this is by reducing energy consumption, which accounts for a significant proportion of all operating costs. The research undertaken for this Thesis addresses this challenge by applying optimization approaches to develop energy-efficient train operation strategies. It does this by developing a hybrid optimization approach, which combines global optimization techniques, for their "global" optimality properties, with local ones, for their faster convergence rate. Due to the number of control constraints and the number of decision stages involved for the control of a typical running train, a ruled-based quasi-global optimal control strategy is developed. This means that instead of first optimizing the control strategy for each particular running scenario, the Thesis shows how to develop optimized parameterized train operational control policies from empirical experience. The second step to develop the control sequence/strategy is using the control strategy generated from the optimized train operational control policies as initial searching point(s), then necessary optimality conditions are applied to locate the sub-optimal strategy for the vehicle in the particular running scenario. The proposed hybrid optimization method has been assessed and validated with the use of examples. The method shows good potential for significantly improving the fuel economy of running trains. The method has also shown significant numerical advantages over other conventional optimization methods in solving the optimization problem of the optimal/sub-optimal operation of a general running train with a long control horizon

Design and optimization of a semi-active suspension system for railway applications

The present work focused on the application of innovative damping technologies in order to improve railway vehicle performances in terms of dynamic stability and comfort. As a benchmark case-study, the secondary suspension stage was selected and different control techniques were investigated, such as skyhook, dynamic compensation, and sliding mode control. The final aim was to investigate which control schemes are suitable for optimal exploitation of the non-linear behavior of the actuators. The performance improvement achieved by adoption of the semi-active dampers on a standard high-speed train was evaluated in terms of passenger comfort. Different control strategies have been investigated by comparing a simple SISO (single input single output) regulator based on the skyhook damper approach with a centralized regulator. The centralized regulator allows for the estimation of a near optimal set of control forces that minimize car-body accelerations with respect to constraints imposed by limited performance of semi-active actuators. Simulation results show that best results is obtained using a mixed approach that considers the simultaneous applications of model based and feedback compensation control terms

A Machine Learning Approach to Two-Stage Adaptive Robust Optimization

We propose an approach based on machine learning to solve two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets. We encode the optimal here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the optimal wait-and-see decisions into what we denote as the strategy. We solve multiple similar ARO instances in advance using the column and constraint generation algorithm and extract the optimal strategies to generate a training set. We train a machine learning model that predicts high-quality strategies for the here-and-now decisions, the worst-case scenarios associated with the optimal here-and-now decisions, and the wait-and-see decisions. We also introduce an algorithm to reduce the number of different target classes the machine learning algorithm needs to be trained on. We apply the proposed approach to the facility location, the multi-item inventory control and the unit commitment problems. Our approach solves ARO problems drastically faster than the state-of-the-art algorithms with high accuracy

Recent results in tilt control design and assessment of high-speed railway vehicles

Active tilt control is a well-established technology in modern railway vehicles, for which currently used control approaches have evolved in an intuitive matter. This paper presents work on a set of novel strategies for achieving local tilt control, i.e. applied independently for each vehicle rather than the whole train precedence approach that is commonly used. A linearized dynamic model is developed for a modern tilting railway vehicle with a tilt mechanism (tilting bolster) providing tilt below the secondary suspension. It addresses the fundamental problems associated with straightforward feedback control, and briefly discusses the current industry norm, which employs command-driven with precedence strategy. Two new advanced schemes are proposed, a model-based estimation approach, and an optimal LQG-based approach, and compared to the command-driven with precedence. The performance of the control schemes is assessed through simulation using a new proposed assessment method

Implementation of Radial Basis Function Artificial Neural Network into an Adaptive Equivalent Consumption Minimization Strategy for Optimized Control of a Hybrid Electric Vehicle

Continued increases in the emission of greenhouse gases by passenger vehicles has accelerated the production of hybrid electric vehicles. With this increase in production, there has been a parallel demand for continuously improving strategies of hybrid electric vehicle control. The goal of an ideal control strategy is to maximize fuel economy while minimizing emissions. The design and implementation of an optimized control strategy is a complex challenge. Methods exist by which the globally optimal control strategy may be found. However, these methods are not applicable in real-world driving applications since these methods require a priori knowledge of the upcoming drive cycle. Real-time control strategies use the global optimal as a benchmark against which performance can be evaluated. Real-time strategies incorporate methods such as drive cycle prediction algorithms, parameter feedback, driving pattern recognition algorithms, etc. The goal of this work is to use a previously defined strategy which has been shown to closely approximate the global optimal and implement a radial basis function (RBF) artificial neural network (ANN) that dynamically adapts the strategy based on past driving conditions. The strategy used is the Equivalent Consumption Minimization Strategy (ECMS) [1], which uses an equivalence factor to define the control strategy. The equivalence factor essentially defines the torque split between the electric motor and internal combustion engine. Consequently, the equivalence factor greatly affects fuel economy. An equivalence factor that is optimal (with respect to fuel economy) for a single drive cycle can be found offline – with a priori knowledge of the drive cycle. The RBF ANN is used to dynamically update the equivalence factor by examining a past time window of driving characteristics. A total of 30 sets of training data are used to train the RBF ANN, each set contains characteristics from a different drive cycle. Each drive cycle is characterized by 9 parameters. For each drive cycle, the optimal equivalence factor is determined and included in the training data. The performance of the RBF ANN is evaluated against the fuel economy obtained with the optimal equivalence factor from the ECMS. For the majority of drive cycles examined, the RBF ANN implementation is shown to produce fuel economy values that are within +/- 2.5% of the fuel economy obtained with the optimal equivalence factor. The advantage of the RBF ANN is that it does not require a priori drive cycle knowledge and is able to be implemented real time while meeting or exceeding the performance of the optimal ECMS. Recommendations are made on how the RBF ANN could be improved to produce better results across a greater array of driving conditions