DC microgrids (MGs) have been gaining a continually increasing interest over the past couple of years both in academia and industry. The advantages of dc distribution when compared to its ac counterpart are well known. The most important ones include higher reliability and efficiency, simpler control and natural interface with renewable energy sources, and electronic loads and energy storage systems. With rapid emergence of these components in modern power systems, the importance of dc in today's society is gradually being brought to a whole new level. A broad class of traditional dc distribution applications, such as traction, telecom, vehicular, and distributed power systems can be classified under dc MG framework and ongoing development, and expansion of the field is largely influenced by concepts used over there. This paper aims first to shed light on the practical design aspects of dc MG technology concerning typical power hardware topologies and their suitability for different emerging smart grid applications. Then, an overview of the state of the art in dc MG protection and grounding is provided. Owing to the fact that there is no zero-current crossing, an arc that appears upon breaking dc current cannot be extinguished naturally, making the protection of dc MGs a challenging problem. In relation with this, a comprehensive overview of protection schemes, which discusses both design of practical protective devices and their integration into overall protection systems, is provided. Closely coupled with protection, conflicting grounding objectives, e.g., minimization of stray current and common-mode voltage, are explained and several practical solutions are presented. Also, standardization efforts for dc systems are addressed. Finally, concluding remarks and important future research directions are pointed out.

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DC Microgrids—Part II: A Review of Power Architectures, Applications, and Standardization Issues

Computer Controlled Systems: Theory and Design

This paper proposes a novel comprehensive operation and self-healing strategy for a distribution system with both dispatchable and nondispatchable distributed generators (DGs). In the normal operation mode, the control objective of the system is to minimize the operation costs and maximize the revenues. A rolling-horizon optimization method is used to schedule the outputs of dispatchable DGs based on forecasts. In the self-healing mode, the on-outage portion of the distribution system will be optimally sectionalized into networked self-supplied microgrids (MGs) so as to provide reliable power supply to the maximum loads continuously. The outputs of the dispatchable DGs will be rescheduled accordingly too. In order to take into account the uncertainties of DG outputs and load consumptions, we formulate the problems as a stochastic program. A scenario reduction method is applied to achieve a tradeoff between the accuracy of the solution and the computational burden. A modified IEEE 123-node distribution system is used as a test system. The results of case studies demonstrate the effectiveness of the proposed methodology.

Self-Healing Resilient Distribution Systems Based on Sectionalization Into Microgrids

within the context of an international framework that shapes related actions and decisions at all levels.1 This framework is defined here as the spectrum of agreements, mechanisms, instruments and actors governing and driving climate change action globally. The overall structure of this framework is complex and multidimensional in that it is comprised of elements that are quite different and distinct in many of their functions and approaches, constituencies, scope and focus.2 While international agreements negotiated by national governments such as the United Nations Framework Convention on Climate Change (UNFCCC) and its Kyoto Protocol remain crucial aspects of the framework, they are not the only mechanisms governing climate change action. Other layers of intervention have become equally important in implementing innovative climate change responses and policies, including those at the regional, subnational and local levels. Cities have a vital role to play in the implementation and achievement of commitments within the international climate change framework. They also stand to benefit from the opportunities created by this framework for local responses to climate change. Yet, local-level actors and authorities often lack an understanding of the nature and functioning of the various components of the international climate change framework and how they could utilize these to enhance their mitigation and adaptation strategies. For instance, many decision-makers operating at the city level lack a working knowledge of the opportunities and constraints associated with international financing options, including those established as part of the UNFCCC.3 In view of this, the aim of this chapter is to highlight the key elements of the international climate change framework and its effects on interventions at the local level. It is also intended to frame discussions of climate change conditions, trends and policies in the rest of this Global Report. The chapter starts by briefly describing the process by which climate change emerged as an issue of international concern culminating in the establishment of the UNFCCC as the key element of the international regime governing climate change issues. The core mechanisms, instruments and financing strategies of this Convention are then outlined. The Kyoto Protocol is also reviewed as the main international treaty with legally binding emission reduction commitments. Subsequently, the key actors, components and actions of climate governance at the international, regional, national and sub-national levels are considered. Finally, the implications of the international climate change framework for local action at the city level are outlined.

United nations framework convention on climate change

In this paper we provide a broad survey of developments in active vision in robotic applications over the last 15 years. With increasing demand for robotic automation, research in this area has received much attention. Among the many factors that can be attributed to a high-performance robotic system, the planned sensing or acquisition of perceptions on the operating environment is a crucial component. The aim of sensor planning is to determine the pose and settings of vision sensors for undertaking a vision-based task that usually requires obtaining multiple views of the object to be manipulated. Planning for robot vision is a complex problem for an active system due to its sensing uncertainty and environmental uncertainty. This paper describes such problems arising from many applications, e.g. object recognition and modeling, site reconstruction and inspection, surveillance, tracking and search, as well as robotic manipulation and assembly, localization and mapping, navigation and exploration. A bundle of solutions and methods have been proposed to solve these problems in the past. They are summarized in this review while enabling readers to easily refer solution methods for practical applications. Representative contributions, their evaluations, analyses, and future research trends are also addressed in an abstract level.

Active vision in robotic systems: A survey of recent developments

This paper presents an online optimal energy/power control method for the operation of energy storage in grid-connected electricity microgrids. The approach is based on a mixed-integer-linear-program optimization formulated over a rolling horizon window, considering predicted future electricity usage and renewable energy generation. Performance objectives include electricity usage cost, battery operation costs, and utility oriented goals related to the peak demand and load smoothing. A robust counterpart formulation of the optimization problem is also proposed to handle uncertainty in energy demand/generation prediction in a computationally efficient way. Further reduction in the computations is achieved by employing variable time steps and relaxing binary constraints. A series of simulations demonstrate the effectiveness of various features of the proposed energy/power management methodology in different scenarios.

An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

This paper presents a multilateral control architecture for teleoperation in multimaster/multislave environments. The proposed framework incorporates flow of position and force information between all master and slave robots, rather than merely between corresponding units. Within this architecture, cooperative performance measures are defined to enhance coordination among the operators and the robots for achieving the task objectives. A /spl mu/-synthesis-based methodology for cooperative teleoperation control is also introduced. This approach guarantees robust stability of cooperative teleoperation in the presence of dynamic interaction between slave robots, as well as unknown passive operators and environment dynamics. It also improves task coordination by optimizing relevant performance objectives. Experiments carried out with a two-master/two-slave single-axis system demonstrate the effectiveness of the proposed approach.

Modeling and control of cooperative teleoperation systems

The control of time-delay bilateral teleoperation systems involves a delicate tradeoff between the conflicting requirements of transparency and robust stability. The control design is complicated by latency in data communication between the master and slave sites, as well as uncertainties in the dynamics of operator, master, slave, and environment. This paper proposes a systematic design procedure for improving teleoperation fidelity while maintaining its stability in the presence of dynamic uncertainty and a constant time delay. In a two-step control approach, first local Lyapunov-based adaptive/nonlinear controllers are applied to linearize the system dynamics and eliminate dependency on the master and slave parameters. Teleoperation coordination, subject to parametric uncertainty in the user and environment dynamics, is then achieved by formulating an I/O time-delay Hinfin robust control synthesis that is solved via its decomposition to the so-called adobe problems. The transparency and robust stability properties of the proposed method is examined via numerical analysis. Furthermore, the results are successfully validated in experiments.

Adaptive/Robust Control for Time-Delay Teleoperation

Prior efforts in bilateral teleoperation under communication delay have mainly yielded control algorithms that sacrifice performance in order to guarantee robust stability. In contrast, this paper proposes a multimodel predictive controller that can enhance the teleoperation transparency in the presence of a known constant delay. Separate controllers are designed for free motion/soft contact and contact with rigid environments, with switching between these mode-based control laws occurring according to the identified contact mode. Performance objectives such as position tracking and tool impedance shaping for free motion/soft contact, as well as position and force tracking for contact with rigid environments, are incorporated into a multi-input/multi-output state-space representation of the system dynamics. New Artstein-type state and measurement transformations are proposed to generate delay-free dynamics suitable for output-feedback control, based on the original dynamics with delays in various input and output channels. The application of the continuous-time linear quadratic Gaussian control synthesis to the resulting mode-based delay-free dynamics yields control laws that guarantee closed-loop stability and enhanced performance in each phase of teleoperation. The robustness of the mode-based controllers with respect to parametric uncertainty is analyzed. Experimental results with a single-axis teleoperation setup demonstrate the effectiveness of the proposed approach

Model Predictive Control for Transparent Teleoperation Under Communication Time Delay

An online optimal control strategy for power flow management in microgrids with on-site battery, renewable energy sources, and integrated electric vehicles (EVs) is presented in this paper. An optimization problem in the form of a mixed integer linear program is formulated. It is executed over a rolling time horizon using predicted values of the microgrid electricity demand, renewable energy generation, EV connection and disconnection times, and the EV state of charge at time of connection. The solution to this optimization problem provides the on-site storage and EV charge/discharge powers. Both bidirectional and unidirectional charging scenarios are considered for EVs. The proposed optimal controller maximizes economic benefits and ensures user-specified charge levels are reached at the time of EV disconnection from the microgrid. By formulating the problem as a stochastic chance constraints optimization, significant improvement is shown in the system robustness over conventional rolling horizon controller, while dealing with uncertainties in the predictions of demand/generation, and EV state of charge and connection/disconnection times. Results of Monte Carlo simulations show that the proposed chance constraints-based controller is highly effective in reducing cost and meeting the user desired EV charge level at time of disconnection from the microgrid, even in the presence of uncertainty.

Shahin Sirouspour

Papers

An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

Modeling and control of cooperative teleoperation systems

Adaptive/Robust Control for Time-Delay Teleoperation

Model Predictive Control for Transparent Teleoperation Under Communication Time Delay

A Chance-Constraints-Based Control Strategy for Microgrids With Energy Storage and Integrated Electric Vehicles