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In this paper we study dynamical distortion problems in future electrical energy systems with high renewable penetration. We introduce a new time-domain modeling of electrical energy systems comprising inverter-controlled distributed energy resources (DERs). This modeling is first used to quantify the relations between distortions and real/reactive power dynamics. Next, to ensure acceptable Quality of Service (QoS), a novel nonlinear distributed inverter control is introduced. Sufficient conditions are established for the guaranteed performance of the proposed control. These conditions further support the practical implementation of the derived controller. The effectiveness of this enhanced control is illustrated using simulations for the case of avoiding system instability during sudden grid reconfigurations. Simulations also show that distortions can be suppressed in systems with parallel-connected solar photovoltaics (PVs). [ABSTRACT FROM AUTHOR]

This paper is motivated by the need to ensure fast microgrid stability. Modeling for purposes of establishing stability criterion and possible implementations are described. In particular, this paper proposes that highly heterogeneous microgrids comprising both conventional equipment and equipment based on rapidly emerging new technologies can be modeled as purely electric networks in order to provide intuitive insight into the issues of network stability. It is shown that the proposed model is valid for representing fast primary dynamics of diverse components (gensets, loads, and PVs), assuming that slower variables are regulated by the higher level controllers. Based on this modeling approach, an intuitively appealing criterion is introduced requiring that components or their combined representations must behave as closed-loop passive electrical circuits. Implementing this criterion is illustrated using a typical commercial feeder microgrid. Notably, these set the basis for standards that should be required for groups of components (subgrids) to ensure no fast instabilities in complex microgrids. Building the need for incrementally passive and monotonic characteristics into standards for network components may clarify the system level analysis and integration of microgrids. [ABSTRACT FROM AUTHOR]

Today, the state-of-the-art wind generators (WGs) are double-fed induction generators that integrate storage devices into their systems. These WGs are expected to be among the largest producers of renewable energy worldwide in the coming years. In this paper, we propose a consensus approach to the distributed control of the energy storage systems (ESS) for carrying out real-time wind farm power output regulation with power-sharing among these storage devices. These two objectives can be attained when the controllers of the ESS and the grid-side converter (GSC) within a single WG are coordinated and the controllers of the GSCs of all WGs are coordinated with each other as well. Our approach is comprehensive, including a coordinated control architecture for the ESS and GSCs to attain the former objective and a consensus-based control architecture for coordinating all GSCs to attain the latter. Calling upon results from singular perturbation and Lyapunov’s theories, we establish stability of the coupled closed-loop dynamics of the ESS and the consensus protocol. Lastly, we construct the control algorithms for the GSC and the DC-DC converter which yield accomplishment of the desired control objectives. Effectiveness of the proposed approach is demonstrated through numerical simulations on the IEEE 24-bus power system under critical scenarios. [ABSTRACT FROM AUTHOR]

Due to a high penetration of renewable energy, power systems operational planning today needs to capture unprecedented uncertainties in a short period. Fast probabilistic state estimation (SE), which creates probabilistic load flow estimates, represents one such planning tool. This paper describes a graphical model for probabilistic SE modeling that captures both the uncertainties and the power grid via embedding physical laws, i.e., KCL and KVL. With such a modeling, the resulting maximum a posteriori (MAP) SE problem is formulated by measuring state variables and their interactions. To resolve the computational difficulty in calculating the marginal distribution for interested quantities, a distributed message passing method is proposed to compute MAP estimates using increasingly available cyber resources, i.e., computational and communication intelligence. A modified message passing algorithm is then introduced to improve the convergence and optimality. Simulation results illustrate the probabilistic SE and demonstrate the improved performance over traditional deterministic approaches via: 1) the more accuracy mean estimate; 2) the confidence interval covering the true state; and 3) the reduced computational time. [ABSTRACT FROM AUTHOR]

A grand challenge for state estimation in newly built smart grid lies in how to deal with the increasing uncertainties. To solve the problem, we propose a data-driven state estimation approach based on recent targeted investment on sensors, data storage, and computing devices. An architecture is proposed to use power system physics and pattern to systematically clean historical data and conduct supervised learning, where historical similar measurements and their states are used to learn the relationship between the current measurement and the state. In order to deal with nonlinearity, kernel trick is used to produce linear mapping in a carefully selected higher dimensional space. To speed up the data-driven approach for online services, we analyze power system data set and discover its clustering property due to the periodic pattern of power systems. This leads to significant dimension reduction and the idea of preorganizing data points in a tree structure for inquiry, leading to 1000 times speedup. Numerical results show that the proposed data-driven approach works well in a smart grid setting with increasing uncertainties and it produces an online state estimate excelling current industrial approach. [ABSTRACT FROM PUBLISHER]

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

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. [ABSTRACT FROM AUTHOR]