A unified control strategy for active distribution networks via demand response and distributed energy storage systems

AbstractAs part of the transition to a future power grid, distribution systems are undergoing profound changes evolving into Active Distribution Networks (ADNs). The presence of dispersed generation, local storage systems and responsive loads in these systems incurs severe impacts on planning and operational procedures. This paper focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to voltage regulation and lines congestion management. We identify the main challenges and opportunities related to ADNs control and we discuss recent advances in this area. Finally, we describe a broadcast-based unified control algorithm designed to provide ancillary services to the grid by a seamless control of heterogeneous energy resources such as distributed storage systems and demand-responsive loads

The Effect of an Unconscious Auditory Stimulus on Pilot Performance under Varying Instrument Flying Conditions

Human error remains a significant contributing factor with respect to accidents in civil air transportation. It is therefore crucial to establish avenues by which performance on the flightdeck can be enhanced under conditions of distress. The purpose of this study was to examine whether an unconscious auditory stimulus (UAS) could enhance pilot performance under varying instrument flight (IFR) conditions on the aircraft flightdeck. Forty IFR student pilots underwent two eight-minute simulated flights, whereupon they were presented with different IFR weather conditions. During the trial, the experimental group listened to a UAS, whereas the control group listened to white noise (WN). Performance was measured based on the deviation from the localizer (LOC), the glide slope (GS), and the air speed (AS). It was hypothesized that the UAS would assist in enhancing pilot performance under varying IFR weather conditions, and that overall good weather conditions would degrade performance less than poor weather conditions. The results of this experiment did not support the hypotheses. Possible explanations are presented in the discussion section

AC OPF in Radial Distribution Networks - Parts I,II

The optimal power-flow problem (OPF) has played a key role in the planning and operation of power systems. Due to the non-linear nature of the AC power-flow equations, the OPF problem is known to be non-convex, therefore hard to solve. Most proposed methods for solving the OPF rely on approximations that render the problem convex, but that may yield inexact solutions. Recently, Farivar and Low proposed a method that is claimed to be exact for radial distribution systems, despite no apparent approximations. In our work, we show that it is, in fact, not exact. On one hand, there is a misinterpretation of the physical network model related to the ampacity constraint of the lines' current flows. On the other hand, the proof of the exactness of the proposed relaxation requires unrealistic assumptions related to the unboundedness of specific control variables. We also show that the extension of this approach to account for exact line models might provide physically infeasible solutions. Recently, several contributions have proposed OPF algorithms that rely on the use of the alternating-direction method of multipliers (ADMM). However, as we show in this work, there are cases for which the ADMM-based solution of the non-relaxed OPF problem fails to converge. To overcome the aforementioned limitations, we propose an algorithm for the solution of a non-approximated, non-convex OPF problem in radial distribution systems that is based on the method of multipliers, and on a primal decomposition of the OPF. This work is divided in two parts. In Part I, we specifically discuss the limitations of BFM and ADMM to solve the OPF problem. In Part II, we provide a centralized version and a distributed asynchronous version of the proposed OPF algorithm and we evaluate its performances using both small-scale electrical networks, as well as a modified IEEE 13-node test feeder

Efficient Computation of Sensitivity Coefficients of Node Voltages and Line Currents in Unbalanced Radial Electrical Distribution Networks

The problem of optimal control of power distribution systems is becoming increasingly compelling due to the progressive penetration of distributed energy resources in this specific layer of the electrical infrastructure. Distribution systems are, indeed, experiencing significant changes in terms of operation philosophies that are often based on optimal control strategies relying on the computation of linearized dependencies between controlled (e.g. voltages, frequency in case of islanding operation) and control variables (e.g. power injections, transformers tap positions). As the implementation of these strategies in real-time controllers imposes stringent time constraints, the derivation of analytical dependency between controlled and control variables becomes a non-trivial task to be solved. With reference to optimal voltage and power flow controls, this paper aims at providing an analytical derivation of node voltage and line current flows as a function of the nodal power injections and transformers tap-changers positions. Compared to other approaches presented in the literature, the one proposed here is based on the use of the [Y] compound matrix of a generic multi-phase radial unbalanced network. In order to estimate the computational benefits of the proposed approach, the relevant improvements are also quantified versus traditional methods. The validation of the proposed method is carried out by using both IEEE 13 and 34 node test feeders. The paper finally shows the use of the proposed method for the problem of optimal voltage control applied to the IEEE 34 node test feeder.Comment: accepted for publication to IEEE Transactions on Smart Gri

Real-Time Optimal Controls for Active Distribution Networks:From Concepts to Applications

Decentralized generation, distributed energy storage systems and active participation of end-users in the lower level of the electrical infrastructure, intelligently managed to provide grid support, define the notion of Active Distribution Networks (ADNs). The presence of distributed generation in ADNs incurs severe impacts on planning and operational procedures and calls for intelligent control techniques. This thesis focuses on the compelling problem of optimal operation and control of ADNs, with particular reference to the design of real-time voltage control and lines congestion management algorithms. In the first part of the thesis, we adopt a centralized architecture for voltage control and lines congestion management in ADNs. The goal of the proposed controller is to schedule the active and reactive power injections of a set of controllable resources, in coordination with traditional resources, in order to achieve an optimal grid operation. The controller relies on a linearized approach that links control variables and controlled quantities using sensitivity coefficients. Once the proposed algorithm is validated, as a further step, we relax the assumption that the DNO has an accurate knowledge of the system model, i.e., a correct admittance matrix and we adapt the proposed control architecture to such a scenario. When the controllable resources are heterogeneous and numerous, control schemes that rely on two-way communication between the controllable entity and the DNO cannot scale in the number of network buses and controllable resources. In this direction, in the second part of this thesis, we propose the use of broadcast-based control schemes that rely on state estimation for the feedback channel. We propose a low-overhead broadcast-based control mechanism, called Grid Explicit Congestion Notification (GECN), intended for provision of grid ancillary services by a seamless control of large populations of distributed, heterogeneous energy resources. Two promising candidates in terms of controllable resources are energy storage systems and elastic loads. Therefore, we choose to validate GECN in the case of aggregations of thermostatically controlled loads, as well as of distributed electrochemical-based storage systems. In the last part of the thesis, we formulate the control problem of interest as a non-approximated AC optimal power flow problem (OPF). The AC-OPF problem is non-convex, thus difficult to solve efficiently. A recent approach that focuses on the branch-flow convexification of the problem is claimed to be exact for radial networks under specific assumptions. We show that this claim, does not hold, as it leads to an incorrect system model. Therefore, there is a need to develop algorithms for the solution of the non-approximated, inherently non-convex OPF problem. We propose an algorithm for the AC-OPF problem in radial networks that uses an augmented Lagrangian approach, relies on the method of multipliers and does not require convexity. We design a centralized algorithm that converges to a local minimum of the original problem. When controlling multiple dispersed energy resources, it is of interest to define also a distributed method. We investigate the alternating direction method of multipliers (ADMM) for the distributed solution of the OPF problem and we show cases for which it fails to converge. As a solution we present a distributed version of the proposed OPF algorithm that is based on a primal decomposition

Multimodal evidence for delayed threat extinction learning in adolescence and young adulthood

Previous research in rodents and humans points to an evolutionarily conserved profle of blunted threat extinction learning during adolescence, underpinned by brain structures such as the amygdala and medial prefrontal cortex (mPFC). In this study, we examine age-related efects on the function and structural connectivity of this system in threat extinction learning in adolescence and young adulthood. Younger age was associated with greater amygdala activity and later engagement of the mPFC to learned threat cues as compared to safety cues. Furthermore, greater structural integrity of the uncinate fasciculus, a white matter tract that connects the amygdala and mPFC, mediated the relationship between age and mPFC engagement during extinction learning. These fndings suggest that age-related changes in the structure and function of amygdala-mPFC circuitry may underlie the protracted maturation of threat regulatory precesses

Thinking about others and the future: neural correlates of perspective taking relate to preferences for delayed rewards

We infer the thoughts and feelings of others by taking their perspectives. Similar processes could be used to understand how we will be affected by future events, by allowing us to take the perspective of our future self. In this paper, we test this idea using a previously presented framework for guiding predictions. The framework proposes that a shared neural mechanism is involved in controlling egocentric bias, both while shifting our perspective away from self and towards others, and while shifting our perspective from immediate to future perspectives. To test this framework, 36 adults performed an intertemporal choice task. They were then scanned using 3T functional magnetic resonance imaging while completing a false-belief “localizer” task, which requires egocentric bias control. A positive correlation was observed between the right temporoparietal junction (rTPJ) response during the false-belief task, and preferences for delayed rewards in intertemporal choices. A subset of participants performed the intertemporal choice task again in the scanner, which revealed that the response of the same rTPJ cluster, individually localized during the false-belief task, was higher during delayed over immediate reward choices. In addition, functional connectivity between the rTPJ and ventromedial prefrontal cortex was found to differ between immediate and delayed choices. The current results indicate an overlap in processes of egocentric bias control and those that determine preferences in intertemporal choices, offering a social cognitive explanation for why rewards are devalued with delay in temporal discounting

Functional neurochemical imaging of the human striatal cholinergic system during reversal learning

Animal studies have shown that acetylcholine (ACh) levels in the dorsal striatum play a role in reversal learning. However, this has not been studied in humans due to a lack of appropriate non-invasive techniques. Proton magnetic resonance spectroscopy (1H-MRS) can be used to measure metabolite levels in humans in vivo. Although it cannot be used to study ACh directly, 1H-MRS can be used to study choline, an ACh precursor which is linked to activity-dependent ACh release. The aim of this study was to use functional-1H-MRS (fMRS) to measure changes in choline levels in the human dorsal striatum during performance of a probabilistic reversal learning task. We demonstrate a task-dependent decrease in choline, specifically during reversal, but not initial, learning. We interpret this to reflect a sustained increase in ACh levels, which is in line with findings from the animal literature. This task-dependent change was specific to choline and was not observed in control metabolites. These findings provide support for the use of fMRS in the in vivo study of the human cholinergic system

Curriculum Learning for Cumulative Return Maximization

Curriculum learning has been successfully used in reinforcement learning to accelerate the learning process, through knowledge transfer between tasks of increasing complexity. Critical tasks, in which suboptimal exploratory actions must be minimized, can benefit from curriculum learning, and its ability to shape exploration through transfer. We propose a task sequencing algorithm maximizing the cumulative return, that is, the return obtained by the agent across all the learning episodes. By maximizing the cumulative return, the agent not only aims at achieving high rewards as fast as possible, but also at doing so while limiting suboptimal actions. We experimentally compare our task sequencing algorithm to several popular metaheuristic algorithms for combinatorial optimization, and show that it achieves significantly better performance on the problem of cumulative return maximization. Furthermore, we validate our algorithm on a critical task, optimizing a home controller for a micro energy grid

Regional striatal cholinergic involvement in human behavioural flexibility

Animal studies have shown that the striatal cholinergic system plays a role in behavioural flexibility but, until recently, this system could not be studied in humans due to a lack of appropriate non-invasive techniques. Using proton magnetic resonance spectroscopy (1H-MRS) we recently showed that the concentration of dorsal striatal choline (an acetylcholine precursor) changes during reversal learning (a measure of behavioural flexibility) in humans. The aim of the present study was to examine whether regional average striatal choline was associated with reversal learning. 36 participants (mean age = 24.8, range = 18-32, 20 female) performed a probabilistic learning task with a reversal component. We measured choline at rest in both the dorsal and ventral striatum using 1H-MRS. Task performance was described using a simple reinforcement learning model that dissociates the contributions of positive and negative prediction errors to learning. Average levels of choline in the dorsal striatum were associated with performance during reversal, but not during initial learning. Specifically, lower levels of choline in the dorsal striatum were associated with a lower number of perseverative trials. Moreover, choline levels explained inter-individual variance in perseveration over and above that explained by learning from negative prediction errors. These findings suggest that the dorsal striatal cholinergic system plays an important role in behavioural flexibility, in line with evidence from the animal literature and our previous work in humans. Additionally, this work provides further support for the idea of measuring choline with 1H-MRS as a non-invasive way of studying human cholinergic neurochemistr