• Energy Research

The smart-grid approach undergoes many difficulties regarding the strategy that will enable its actual implementation. In this paper, an overview of real-time simulation technologies and their applicability to the smart-grid approach are presented as enabling steps toward the smart-grid’s actual implementation. The objective of this work is to contribute with an introductory text for interested readers of real-time systems in the context of modern electric needs and trends. In addition, a comprehensive review of current applications of real-time simulation in electric systems is provided, together with the basis to understand real-time simulation and the topologies and hardware used to implement it. Furthermore, an overview of the evolution of real-time simulators in the industrial and academic background and its current challenges are introduced.

The interconnection of new generating and storing devices to the power grid imposes the necessity of synchronizing, so the power flow can be manipulated and distributed. In the presence of an increasingly perturbed electric grid, many proposals of novel and modified synchronization techniques attained enough robustness to deal with known perturbations. However, such proposals exhibit drawbacks on their own, leaving open enhancement opportunities, mostly over their discrete implementation—e.g., sampling issues and not-considered inter/harmonics—and their inherent complexity—e.g., the need for frequency adaptability. In this work, three traditional synchronous reference frame (SRF) phase-locked loops (PLL) are modified to implement discrete filtering, such as the well-known proposals based on moving average filters (MAFs), to avoid the problems mentioned above, known for affecting the MAF’s performance. This proposal makes use of discrete, efficient units modularly assembled to yield a signal’s average, based on elliptic half-band filters. The proposed PLLs were tested and exhibited clear advantages—robustness against frequency disturbances—over MAF-based equivalents at standardized tests over a typical simulation environment, setting through this work an initial milestone for its verification and further incorporation in more complex synchronization topologies.

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes a straightforward, distributed island detection technique only relying on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A “stiffness change” effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.

Abstract Distribution networks are currently the main focus of modernization in electric power systems. For instance, to deal with emerging and increasingly challenging scenarios (e.g., high penetration of renewables, distributed generation, electric vehicle proliferation, etc.), new smart grid technologies are being deployed over distribution networks. This is the case of micro phasor measurement units ( μ PMUs), which can be seen as a modern high-precision version of the legacy transmission system PMU. Motivated by the benefits, as well as the costs associated with μ PMU deployment, in this work we introduce a new methodology to determine optimal μ PMU placement, considering grid topology changes. Firstly, a technique to minimize power losses across the grid by changing its topology is presented. Afterward, a technique to estimate the grid states – full numerical observability – with the minimum number of μ PMUs is disclosed, introducing the concept of bus “virtualization” through pseudo-measurements. This work is based on a multi-objective approach, acting on genetic algorithms, and validated over a distribution network with a variant structure, which is optimally instrumented. It is shown that the proposed methodology can maintain low approximation errors despite topology variations. Also, the virtualization approach enables full numerical observability even when using fewer measurement units, i.e., the observability constraint is eliminated and the number of μ PMUs can be solely related to the estimation error.

Nowadays, there are many technological-intensive applications that claim to be “smart”. From smartphones to the smart grid, people relate the word smart with technical novelty, automation, enabled communication, and service integration. There is indeed a gap between those smart technologies and their intended “intelligence”; this has arisen an indirect debate between works focusing on automation and mechatronics design and others pursuing a conceptual approach based on fulfilling determinate objectives. One last approach relates the said smartness to deep learning methodologies. In this work, it is attempted to explore both perspectives by providing an overview of recent works around energy usage toward smart cities and the smart grid, pointing out the main conceptual pillars upon which both approaches stand. Certainly, there are enabling technologies supporting the smart concept overall; thus, this work addresses them to characterize “smart” not from technological or conceptual one-sided viewpoints but from their common backbone. Therefore, the interested reader can find in this work an integrative conceptualization of the smart context, a literature review of recent advances, and a deep discussion of how enabling technologies and current technological trends based on energy consumption are shaping the ongoing efforts toward a sustainable future. More importantly, a new approach to define smart in the said context is elaborated far from the typical misunderstanding of technological nesting or mere usage of “advanced” digital technologies. Rather, smartness is addressed by the integrative objectives the application pursues, the objectives set by its users’ intent, and the attained results in terms of public benefit.