This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen’s quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.

In unmanned aerial vehicle (UAV) systems, achieving extended flight autonomy remains a significant challenge, even in hybrid systems utilizing both fuel and battery as energy sources. To extend the flight time, our paper introduces a novel application of online composite control that achieves fuel savings through disturbance awareness. Our contributions include the derivation of a nonlinear model of the energy-conversion dynamics and its connection with lateral dynamics. We discuss how these nonlinearities are linearized through timescale separation based on the operational rates of drone energy sources. The effectiveness of our composite control method is validated through real-world drone flight data. Numerical results show a reduction in fuel usage of approximately 4.5% through a disturbance-aware policy. This paper not only advances the fundamental understanding of composite control for bilinear time-scale separated systems but also opens new directions for research in trajectory optimization for hybrid powertrain systems.

This paper addresses the problem of designing optimal distributed control in the changing electric energy systems. We aim to improve the aggregated reactive power inefficiency of inverter-controlled and power-coupled electrical systems. To achieve this, we formulate an optimal control problem aimed at optimizing reactive power and interface mismatch using the energy state space model. Despite the general complexity of solving an optimal control problem, we demonstrate that our proposed model in energy state space admits a linear optimality condition in the variational form. We demonstrate that this condition can be decomposed into local state variables and measurements from adjacent connected components, facilitating a distributed approach to optimal control design. The variables exchanged with the neighboring components are technology agnostic and can be interpreted as costate variables with intuitive physical meaning. We illustrate the conditions and results of the distributed optimal control using a small system with controllable source serving a time-varying power load. Additionally, we explore voltage regulation in the proposed control design, which can be implemented using fast switching power electronics.

This special issue brings together recent research on the efficient use of energy in commercial, residential, and other types of buildings and facilities.

In this paper, we propose an information exchange framework to incorporate smart building demands into a microgrid operation. We especially focus on scheduling smart building demand along with other possibly highly uncertain microgrid generation resources a day ahead or shorter ahead of operation. Since every building demand has different energy consumption profiles and dynamics, it is inefficient for the grid operator to directly control a potentially large number of buildings. On the other hand, if the grid operator does not have sufficient information on the buildings and their resource potentials, smart buildings cannot be deployed to the full extent. Our proposed framework attempts to align these conflicting objectives in a reasonable way, taking into consideration the realistic limitations of communication time, various physical characteristics and dynamics of the buildings, and the building managers' economic objectives. This framework is in line with the concept of dynamic monitoring and decision systems (DYMONDS), where the economic objectives and physical dynamics of any flexible generation and loads on the grid are aggregated by load aggregators and incorporated in the optimal system operation.

This paper addresses the problem of electricity cost management for Internet service providers with a collection of spatially distributed data centers. As the demand on Internet services and cloud computing has kept increasing in recent years, the power usage associated with IDC operations has been uprising significantly. The cyber and physical aspects of IDCs interact with each other, and bring unprecedented challenges in power management. While most existing research focuses on reducing power consumptions of IDCs at one specific location, the problem of reducing the total electricity cost has been overlooked. This is an important problem faced by service providers, especially in the present multielectricity-market environment, where the price of electricity may exhibit temporal and spatial diversities. Further, for these service providers, guaranteeing the quality of service (QoS; i.e., service level objectives) such as service delay guarantees to the end users is of critical importance. This paper studies the problem of minimizing the total electricity cost geared to QoS constraint as well as the location diversity and time diversity of electricity price under multiregional electricity markets. We jointly consider both the cyber and physical management capabilities of IDCs, and exploit both the center-level load balancing and the server-level power control in a unified scheme. We model the problem as a constrained mixed integer programming based on generalized benders decomposition (GBD) technique. Extensive evaluations based on real-life electricity price data for multiple IDC locations demonstrate the effectiveness of our scheme.

This paper concerns the role of smart grids comprising man-made electric power networks and their supporting information communications technology (ICT) as enablers of sustainable energy services. A proposed socio-ecological energy system (SEES) framework used to characterize the core-level subsystems (resources, users, and governance) in terms of second-level and deeper level variables is motivated by the more general multilevel nested socio-ecological system (SES) framework. Second-level and deeper level variables are introduced as a means of characterizing how sustainable a SEES is likely to be by assessing their characteristics. Given vast spatial and temporal complexities in a typical SEES, it becomes necessary to enhance the physical power network with just-in-time (JIT), just-in-place (JIP), and just-in-context (JIC) functionalities. These, in turn, support sustainable energy services by aligning temporal, spatial, and contextual characteristics of resources and users. We refer to the ICT implementation in support of these functionalities as dynamic monitoring and decision systems (DYMONDS). The tradeoffs between the ability of the SEES to ensure sustainable services and the complexity of the supporting smart grid are discussed for several representative energy system architectures.

This paper surveys major technical challenges for power system operations in support of large-scale wind energy integration. The fundamental difficulties of integrating wind power arise from its high inter-temporal variation and limited predictability. The impact of wind power integration is manifested in, but not limited to, scheduling, frequency regulations, and system stabilization requirements. Possible alternatives are suggested for a more reliable and cost-effective power system operation. New computationally efficient methods for improving system performances by using prediction and operational interdependencies over different time horizons remain critical open research problems.

In this paper, we investigate the protective relays used in electric power systems and their role in large-scale blackouts. After reviewing the state of the art, to mitigate future blackouts, we propose a new machine learning approach for protective relays based on binary hypothesis testing, support vector machines (SVMs), and communications between the protective relays and the supervisory control and data acquisition (SCADA), which we call smart protective relays. The goal of smart relays is to classify and discriminate the normal conditions from fault conditions via local measurements. It is shown that the proposed SVM-based smart relays can detect the location of an initial fault using local current, voltage, real power, and reactive power measurements, and by monitoring these metrics, they can make a correct decision even when the state of the system changes after some equipment failure. We show that by making an intelligent decision on whether and when to trip, and communicating the changes observed to SCADA for fast and intelligent decision making, SVM-based smart relays have the potential to mitigate large-scale blackouts and confine them to much smaller areas. By deploying SVM-based smart relays only at relatively few locations where they have the highest probability to be tripped incorrectly, the probability of cascade of failures and a blackout can be greatly reduced.

This paper proposes modeling the rapidly evolving energy systems as cyber-based physical systems. It introduces a novel cyber-based dynamical model whose mathematical description depends on the cyber technologies supporting the physical system. This paper discusses how such a model can be used to ensure full observability through a cooperative information exchange among its components; this is achieved without requiring local observability of the system components. This paper also shows how this cyber-physical model is used to develop interactive protocols between the controllers embedded within the system layers and the network operator. Our approach leads to a synergistic framework for model-based sensing and control of future energy systems. The newly introduced cyber-physical model has network structure-preserving properties that are key to effective distributed decision making. The aggregate load modeling that we develop using data mining techniques and novel sensing technologies facilitates operations of complex electric power systems.

Micro cogeneration technology (micro-CHP) is expected to be an important novel residential heating technology for the future in certain countries around the world. The interesting aspect of micro-CHP is that it is a controllable distributed generation technology. This paper provides novel conceptual insight into how micro-CHP systems can be controlled aggregatedly in virtual power plants (VPPs). Cost benefits for VPPs can be achieved through 'economies of intelligence' with the help of intelligent control schemes.

This paper addresses potential benefits of applying model predictive control (MPC) to solving the energy dispatch problem in electric energy systems with many intermittent renewable resources. Based on predicting the output from the intermittent resources, this paper introduces a look-ahead optimal control algorithm for dispatching the available generation with the objective of minimizing the total production cost. This method is compared with the static economic dispatch which treats intermittent resources as uncertain negative loads. We suggest that the proposed MPC approach could lower the total generation cost by directly dispatching the output from the renewable resources in order to compensate temporal load variations over pre-defined time horizons. A small 12-bus power system comprising five generators is simulated to illustrate potential benefits from a look-ahead dispatch of both intermittent and more conventional power plants. The proposed method is directly applicable to managing systems with large presence of wind and photovoltaic resources.

In this paper we conjecture that revolutionary advances in future energy services are needed and that these are only possible by means of information technology (IT). To support this claim, we first briefly describe the fundamental needs for changing the ways energy services have been provided, and possible consequences resulting from not adopting qualitatively new paradigms. We next make the case why managing energy services of the future to meet such needs will only be possible when pursuing a systematic deployment of ITbased mechanisms for processing, delivering and consuming energy. We stress open R&D questions to which basic answers are needed in order to rip benefits of IT. Notably, a multidisciplinary approach to modeling and simulating a cyberphysical system (CPS) comprising the physical energy grids, and its support communications, sensing, and computing cyber layers is essential. Designing regulatory policies for facilitating penetration of IT at the value is also viewed as one of the key R&D challenges. Finally, we introduce our vision of an ITframework in support of Dynamic Energy Control Protocols (DECPs) and illustrate potential benefits from implementing DECPs.

In this paper the modeling, monitoring, and control of electric power systems are presented from the point of view of large-scale dynamic systems. First a summary of current hierarchical operations is given, together with an assessment of the underlying assumptions. Next, the challenge of operating electric power systems over very broad ranges of system conditions is presented as an open sensing, estimation, and control problem. The latter part of this paper is motivated by fundamental technological and organizational changes. These are requiring a shift from hierarchical to multilayered open access modeling, monitoring, and control paradigms for large complex electric power systems. A vision of a novel information-based multilayered Dynamic Energy Control Protocols (DECPs) framework for facilitating evolution into open access just-in-time (JIT) and just-in-place (JIP) electricity services of the future is presented.

This paper concerns the critical role enhanced control will play in the operating of future electric power systems reliably and efficiently. The nonstandard control problems are due to a large variety of controllers, presently acting in a multirate mode at various levels of the system. Today's monitoring and control logic is largely effective during normal conditions. This paper concerns its possible enhancements which might enable the system to operate reliably over broader ranges of loading and equipment status. In particular, it is suggested that major benefits could come from providing computer tools to assist human operators with their decision making when the system is under stress. A multilayered approach is introduced to support:1) on-line adjustment of available resources; 2) monitoring the interconnection based on qualitative indices (QIs) essential for deciding the severity of the operating mode; and 3) using the QIs to adjust structure of control as the system evolves from one mode to the next. An equivalenced model of the Northeast Power Coordinating Council (NPCC) interconnection is used to illustrate the potential of enhanced control in scenarios that resemble the blackout of August 2003. Also, the potential for efficient use of the resources during normal conditions is illustrated using this multilayered monitoring and control architecture.