Biblio

The NIST Transactive Energy (TE) Modeling and Simulation Challenge for the Smart Grid (Challenge) spanned from 2015 to 2018. The TE Challenge was initiated to identify simulation tools and expertise that might be developed or combined in co-simulation platforms to enable the evaluation of transactive energy approaches. Phase I of the Challenge spanned 2015 to 2016, with team efforts that improved understanding of TE concepts, identified relevant simulation tools and co-simulation platforms, and inspired the development of a TE co-simulation abstract component model that paved the way for Phase II. The Phase II effort spanned Spring 2017 through Spring 2018, where the teams collaboratively developed a specific TE problem scenario, a common grid topology, and common reporting metrics to enable direct comparison of results from simulation of each team's TE approach for the defined scenario. This report presents an overview of the TE Challenge, the TE abstract component model, and the common scenario. It also compiles the individual Challenge participants' research reports from Phase II. The common scenario involves a weather event impacting a distribution grid with very high penetration of photovoltaics, leading to voltage regulation challenges that are to be mitigated by TE methods. Four teams worked with this common scenario and different TE models to incentivize distributed resource response to voltage deviations, performing these simulations on different simulation platforms. A fifth team focused on a co-simulation platform that can be used for online TE simulations with existing co-simulation components. The TE Challenge Phase II has advanced co-simulation modeling tools and platforms for TE system performance analysis, developed a referenceable TE scenario that can support ongoing comparative simulations, and demonstrated various TE approaches for managing voltage on a distribution grid with high penetration of photovoltaics.

With the advent of remarkable development of solar power panel and inverter technology and focus on reducing greenhouse emissions, there is increased migration from fossil fuels to carbon-free energy sources (e.g., solar, wind, and geothermal). A new paradigm called Transactive Energy (TE) has emerged that utilizes economic and control techniques to effectively manage Distributed Energy Resources (DERs). Another goal of TE is to improve grid reliability and efficiency. However, to evaluate various TE approaches, a comprehensive simulation tool is needed that is easy to use and capable of simulating the power-grid along with various grid operational scenarios that occur in the transactive energy paradigm. In this research, we present a web-based design and simulation platform (called a design studio) targeted toward evaluation of power-grid distribution system and transactive energy approaches. The design studio allows to edit and visualize existing power-grid models graphically, create new power-grid network models, simulate those networks, and inject various scenario-specific perturbations to evaluate specific configurations of transactive energy simulations. The design studio provides (i) a novel Domain-Specific Modeling Language (DSML) using the Web-based Generic Modeling Environment (WebGME) for the graphical modeling of power-grid, cyber-physical attacks, and TE scenarios, and (ii) a reusable cloud-hosted simulation backend using the Gridlab-D power-grid distribution system simulation tool.

Today's smart-grids have seen a clear rise in new ways of energy generation, transmission, and storage. This has not only introduced a huge degree of variability, but also a continual shift away from traditionally centralized generation and storage to distributed energy resources (DERs). In addition, the distributed sensors, energy generators and storage devices, and networking have led to a huge increase in attack vectors that make the grid vulnerable to a variety of attacks. The interconnection between computational and physical components through a largely open, IP-based communication network enables an attacker to cause physical damage through remote cyber-attacks or attack on software-controlled grid operations via physical- or cyber-attacks. Transactive Energy (TE) is an emerging approach for managing increasing DERs in the smart-grids through economic and control techniques. Transactive Smart-Grids use the TE approach to improve grid reliability and efficiency. However, skepticism remains in their full-scale viability for ensuring grid reliability. In addition, different TE approaches, in specific situations, can lead to very different outcomes in grid operations. In this paper, we present a comprehensive web-based platform for evaluating resilience of smart-grids against a variety of cyber- and physical-attacks and evaluating impact of various TE approaches on grid performance. We also provide several case-studies demonstrating evaluation of TE approaches as well as grid resilience against cyber and physical attacks. 

A central problem for transition studies is how to accelerate or decelerate them with policy guidance. Incumbent-led transitions with government support can generate substantial public support for deceleration. Civil society organizations (CSOs) lead and formulate public opinion in this type of industrial transition. Analysis of CSO strategy can contribute to a better understanding of transition acceleration and deceleration. Four main elements of political strategy are identified for how CSOs attempt to affect an industrial transition. The transition to connected and autonomous (or automated) vehicles (CAVs) in the United States is used to explore the role of civil society in the acceleration and deceleration of sociotechnical transitions. This is an “incumbent-led transition,” which occurs when large industrial corporations in one or more industries lead a systemic technological change. This type of transition may generate public concerns about risk and uncertainty, which can be expressed and mobilized by civil society organizations (CSOs). In turn, CSOs may also attempt to decelerate the transition process in order to develop better regulation and to change technology design. Based on an analysis of CSO statements in the public sphere and media reports on CAVs in the U.S., the political strategy of CSOs is examined to improve understanding of the role of civil society in incumbent-led transitions. The analysis indicates that the strategy includes four main aspects: articulating an alternative political goal (slower introduction of advanced autonomous vehicles and more rapid introduction of existing driver-assisted technology), engaging multiple targets or venues of action (different government units and the private sector), forming and expanding a broad coalition, and selecting effective tactics of influence (lobbying, media outreach, and research involving public opinion polls).

Transactive Energy (TE) is an emerging discipline that utilizes economic and control techniques for operating and managing the power grid effectively. Distributed Energy Resources (DERs) represent a fundamental shift away from traditionally centrally managed energy generation and storage to one that is rather distributed. However, integrating and managing DERs into the power grid is highly challenging owing to the TE implementation issues such as privacy, equity, efficiency, reliability, and security. The TE market structures allow utilities to transact (i.e., buy and sell) power services (production, distribution, and storage) from/to DER providers integrated as part of the grid. Flexible power pricing in TE enables power services transactions to dynamically adjust power generation and storage in a way that continuously balances power supply and demand as well as minimize cost of grid operations. Therefore, it has become important to analyze various market models utilized in different TE applications for their impact on above implementation issues.In this demo, we show-case the Transactive Energy Simulation and Analysis Toolsuite (TE-SAT) with its three publicly available design studios for experimenting with TE markets. All three design studios are built using metamodeling tool called the Web-based Graphical Modeling Environment (WebGME). Using a Git-like storage and tracking backend server, WebGME enables multi-user editing on models and experiments using simply a web-browser. This directly facilitates collaboration among different TE stakeholders for developing and analyzing grid operations and market models. Additionally, these design studios provide an integrated and scalable cloud backend for running corresponding simulation experiments.

This study reviews the development of shared (community) solar and community choice aggregation in the U.S. states of California and New York. Both states are leaders in energy-transition policy in the U.S., but they have different trajectories for the two forms of energy decentralization. Shared solar is more advanced in New York, but community choice is more advanced in California. Using a field theory framework, the comparative review of the trajectories of energy decentralization shows how differences in restructuring and regulatory rules affect outcomes. Differences in the rules for retail competition and authority for utilities to own distributed generation assets, plus the role of civil society and the attention from elected officials, shape the intensity of conflict and outcomes. They also contribute to the development of different types of community choice in the two states. In addition to showing how institutional conditions associated with different types of restructured markets shape the opportunities for decentralized energy, the study also examines how the efforts of actors to gain support for and to legitimate their policy preferences involve reference to broad social values.

Although there is great media attention to connected and automated vehicles (CAVs) and strong public interest in the technology, it is still under development. Their deployment to the broader public will require new regulations and road traffic rules that are also under development, and there is not yet a globally harmonized approach. This paper reviews the main safety and liability issues for CAVs with a focus on the rules developed for on-road testing to date in Australia, the United States, and Germany. It also reviews government policies from Victoria, Australia, and California, the United States, and it provides an appendix on European Union (E.U.) regulations. After a review of similarities and differences regarding safety and liability provisions, the study suggests how the current provisions can be brought together toward a globally harmonized approach to safety issues that builds on best practices in the three countries.

Empathic vehicles are a promising concept to increase the safety and acceptance of automated vehicles. However, on the way towards empathic vehicles a lot of research in the area of automated emotion recognition is necessary. Successful methods to detect emotions need to be trained on realistic data that contain the target emotion and come from a setting close to the final application. At the moment, data sets fulfilling these requirements are lacking. Therefore, the goal of this work is to present an experimental paradigm that induces four different emotional states (neutral, positive, frustration and mild anxiety) in a real-world driving setting using a combination of secondary tasks and conversation-based emotional recall. An evaluation of the paradigm using self-report data, annotation of speech data and peripheral physiology indicates that the methods to induce the target emotions were successful. Based on the insights of the experiment, finally a list of recommendations for the induction of emotions in real world driving settings is given.

Autonomous, shared, and electric - this is the vision for future transport services that enable both efficient and climate-friendly mobility. The success of such services will crucially depend on their actual use by the population, which is in turn determined by perceptions of their usefulness, ease of use, safety, and attractiveness. The new features even entail some new challenges to users. The authors present methods to identify user needs and potential use barriers early in the process of designing autonomous vehicles systems for public transport, and give examples from their user-centered research methods which can be used to incorporate user needs in the development of advanced systems for public transport.

The automotive domain currently experiences a radical transition towards automation, connectivity and digitalization. This is a cause for major change in human-machine interaction. The research presented here examines 1) company visions of future mobility 2) user's reaction to the first trials of these visions. The data analyses reveal that implementing companies' visions for 2040 requires improvement concerning user acceptance. One way of improving user acceptance is to integrate emotion recognition in manual and automated vehicles. By reacting to users' positive and negative emotions, vehicles can learn to improve driving behavior, communication and to adjust driver assistance accordingly. Therefore, a roadmap for future research in emotion recognition has been developed by interviews with twelve experts in the field. Emotions that they judged to be most relevant to detect include anger, stress and fear, amongst others. Furthermore, ideas on sensors for emotion recognition, potential countermeasures for the negative effects of emotions and additional challenges were collected. The research presented is designed to shape further research directions of in-car emotion recognition.

In this paper, we propose a convex programming based method to address a long-standing problem of inner-approximating backward reachable sets of state-constrained polynomial systems subject to time-varying uncertainties. The backward reachable set is a set of states, from which all trajectories starting will surely enter a target region at the end of a given time horizon without violating a set of state constraints in spite of the actions of uncertainties. It is equal to the zero sublevel set of the unique Lipschitz viscosity solution to a Hamilton-Jacobi partial differential equation (HJE). We show that inner approximations of the backward reachable set can be formed by zero sublevel sets of its viscosity supersolutions. Consequently, we reduce the inner-approximation problem to a problem of synthesizing polynomial viscosity supersolutions to this HJE. Such a polynomial solution in our method is synthesized by solving a single semidefinite program. We also prove that polynomial solutions to the formulated semidefinite program exist and can produce a convergent sequence of inner approximations to the interior of the backward reachable set in measure under appropriate assumptions. This is the main contribution of this paper. Several illustrative examples demonstrate the merits of our approach.

DATE is a leading international event providing unique networking opportunities, bringing together designers and design automation users, researchers and vendors, as well as specialists in hardware and software design, test and manufacturing of electronic circuits and systems.

Highly automated driving will be a novel experience for many users and may cause uncertainty and discomfort for them. An efficient real-time detection of user uncertainty during automated driving may trigger adaptation strategies, which could enhance the driving experience and subsequently the acceptance of highly automated driving. In this study, we compared three different models to classify a user’s uncertainty regarding an automated vehicle’s capabilities and traffic safety during overtaking maneuvers based on experimental data from a driving-simulator study. By combining physiological, contextual and user-specific data, we trained three different deep neural networks to classify user uncertainty during overtaking maneuvers on different sets of input features. We evaluated the models based on metrics like the classification accuracy and F1 Scores. For a purely context-based model, we used features such as the Time-Headway and Time-To-Collision of cars on the opposing lane. We demonstrate how the addition of user heart rate and related physiological features can improve the classification accuracy compared to a purely context-based uncertainty model. The third model included user-specific features to account for inter-user differences regarding uncertainty in highly automated vehicles. We argue that a combination of physiological, contextual and user-specific information is important for an effectual uncertainty detection that accounts for inter-user differences.

Autonomous driving is getting more common and easily accessible with rapid improvements in technology. Prospective buyers of autonomous vehicles need to adapt to this technology equally rapidly to feel comfortable in them. However, this is not always the case, since taking away control from the user often correlates with loss of comfort. Detecting uncomfortable and stressful situations while driving could improve driving quality and overall acceptance of autonomous vehicles through adaption of driving style, interface and other methods. In this paper, we test a range of methods, which measure the discomfort of a user of an autonomous vehicle in real-time. We propose a portable set of sensors that measure heart rate, skin conductance, sitting position, g-forces and subjective discomfort. Preliminary results will be examined and next steps will be discussed.

In this paper, we propose an approach how connected and highly automated vehicles can perform cooperative maneuvers such as lane changes and left-turns at urban intersections where they have to deal with human-operated vehicles and vulnerable road users such as cyclists and pedestrians in so-called mixed traffic. In order to support cooperative maneuvers the urban intersection is equipped with an intelligent controller which has access to different sensors along the intersection to detect and predict the behavior of the traffic participants involved. Since the intersection controller cannot directly control all road users and – not least due to the legal situation – driving decisions must always be made by the vehicle controller itself, we focus on a decentralized control paradigm. In this context, connected and highly automated vehicles use some carefully selected game theory concepts to make the best possible and clear decisions about cooperative maneuvers. The aim is to improve traffic efficiency while maintaining road safety at the same time. Our first results obtained with a prototypical implementation of the approach in a traffic simulation are promising.

The possible interactions between a controller and its environment can naturally be modelled as the arena of a two-player game, and adding an appropriate winning condition permits to specify desirable behavior. The classical model here is the positional game, where both players can (fully or partially) observe the current position in the game graph, which in turn is indicative of their mutual current states. In practice, neither sensing and actuating the environment through physical devices nor data forwarding to and from the controller and signal processing in the controller are instantaneous. The resultant delays force the controller to draw decisions before being aware of the recent history of a play and to submit these decisions well before they can take effect asynchronously. It is known that existence of a winning strategy for the controller in games with such delays is decidable over finite game graphs and with respect to ω-regular objectives. The underlying reduction, however, is impractical for non-trivial delays as it incurs a blow-up of the game graph which is exponential in the magnitude of the delay. For safety objectives, we propose a more practical incremental algorithm successively synthesizing a series of controllers handling increasing delays and reducing the game-graph size in between. It is demonstrated using benchmark examples that even a simplistic explicit-state implementation of this algorithm outperforms state-of-the-art symbolic synthesis algorithms as soon as non-trivial delays have to be handled. We furthermore address the practically relevant cases of non-order-preserving delays and bounded message loss, as arising in actual networked control, thereby considerably extending the scope of regular game theory under delay.

In this paper we study the problem of computing robust invariant sets for state-constrained perturbed polynomial systems within the Hamilton-Jacobi reachability framework. A robust invariant set is a set of states such that every possible trajectory starting from it never violates the given state constraint, irrespective of the actual perturbation. The main contribution of this work is to describe the maximal robust invariant set as the zero level set of the unique Lipschitz-continuous viscosity solution to a Hamilton-Jacobi-Bellman (HJB) equation. The continuity and uniqueness property of the viscosity solution facilitates the use of existing numerical methods to solve the HJB equation for an appropriate number of state variables in order to obtain an approximation of the maximal robust invariant set. We furthermore propose a method based on semi-definite programming to synthesize robust invariant sets. Some illustrative examples demonstrate the performance of our methods.

Increasing complexity in the power system and the transformation towards a smart grid lead to the necessity of new tools and methods for the development and testing of new technologies. One testing method is co-simulation, which allows coupling simulation components from different domains to test their interaction. Because the manual configuration of complex large-scale co-simulation scenarios can be error-prone, we propose an approach for assisting the user in the development of co-simulation scenarios. Our approach uses an information model, a component catalog implemented in a Semantic Media Wiki, and Semantic Web technologies to assist the high-level modeling of co-simulation scenarios, recommend suitable simulation components, and validate co-simulation scenarios. This assistance aims to improve the usability of co-simulation in the development of interdisciplinary, large-scale scenarios.

Global networks like energy grids, transportation networks or financial IT-infrastructure are crucial for the wealth of modern societies. Reliable and resilient control of these infrastructures thus has gained much attention in the last years. Typical approaches to ensure stable operation of these infrastructures follow two contradictory paradigms, i.e. complexity reducing and complexity increasing measures. Whereas the first are supposed to encapsulate interdependencies and decision-making processes and typically reduce transparency as a sideeffect, the latter strengthen the role of the human actor in these systems by increasing transparency to allow for well-informed decision-making. In this paper, we will discuss these two paradigms and show why intra-actor conflicts arise from adding both complexity and reducing transparency at the same time. We will outline a research agenda to model the effect of these conflicts using the example of energy systems and current transparency-enhancing technologies like e.g. distributed ledger technology.

Hybrid automata are an elegant formal model seamlessly integrating differential equations representing continuous dynamics with automata capturing switching behavior. Since the introduction of the computational model more than a quarter of a century ago [Maler et al. 1992], its algorithmic verification has been an area of intense research. Within this note, which is dedicated to Oded Maler (1957--2018) as one of the inventors of the model, we are trying to delineate major lines of attack to the reachability problem for hybrid automata. Due to its relation to system safety, the reachability problem is a prototypical verification problem for hybrid discrete-continuous system dynamics.

We present a method based on the Hamilton-Jacobi framework that is able to compute over- and under-approximations of reachable sets for autonomous dynamical systems beyond polynomial dynamics. The method does not resort to user-supplied candidate polynomials, but rather relies on an expansion of the evolution function whose convergence in compact state space is guaranteed. Over- and under-approximations of the reachable state space up to any designated precision can consequently be obtained based on truncations of that expansion. As the truncations used in computing over- and under-approximations as well as their associated error bounds agree, double-sided enclosures of the true reach-set can be computed in a single sweep. We demonstrate the precision of the enclosures thus obtained by comparison of benchmark results to related simulations.

Vehicular ad-hoc networks (VANETs) offer a diverse set of applications and therefore gain more and more attention from both academic and industrial communities. However, the deployment of VANETs is not very straight-forward. One challenge is highlighted by an uphill task of establishing and subsequently sustaining a robust communication. The need to obviate extra relay infrastructure in dynamically fluctuating topologies plus concurring shielding obstacles only magnifies this arduous task. In this context, information about traffic-density and about its estimated progress are valuable assets to tackle this issue. This paper proposes a novel routing protocol called Traffic Aware Segment-based Routing (TASR) protocol. The proposed protocol comprises two major parts: 1) Real-time vehicular traffic information for route selection allows for calculating the Expected Connectivity Degree (ECD) on different segments, and 2) a new forwarding method based on geographical information transfers packets from source to destination node. The new metric ECD takes vehicle densities into account, estimating the connectivity on each segment and thus the connectivity of nodes and data delivery ratio for transmitting packets. Furthermore, extensive simulations help analyzing the efficiency of TASR, indicating that it outperforms competing routing protocols.