5,069 research outputs found
Taming Instabilities in Power Grid Networks by Decentralized Control
Renewables will soon dominate energy production in our electric power system.
And yet, how to integrate renewable energy into the grid and the market is
still a subject of major debate. Decentral Smart Grid Control (DSGC) was
recently proposed as a robust and decentralized approach to balance supply and
demand and to guarantee a grid operation that is both economically and
dynamically feasible. Here, we analyze the impact of network topology by
assessing the stability of essential network motifs using both linear stability
analysis and basin volume for delay systems. Our results indicate that if
frequency measurements are averaged over sufficiently large time intervals,
DSGC enhances the stability of extended power grid systems. We further
investigate whether DSGC supports centralized and/or decentralized power
production and find it to be applicable to both. However, our results on
cycle-like systems suggest that DSGC favors systems with decentralized
production. Here, lower line capacities and lower averaging times are required
compared to those with centralized production.Comment: 21 pages, 6 figures This is a pre-print of a manuscript submitted to
The European Physical Journal. The final publication is available at Springer
via http://dx.doi.org/10.1140/epjst/e2015-50136-
Synchronization-Aware and Algorithm-Efficient Chance Constrained Optimal Power Flow
One of the most common control decisions faced by power system operators is
the question of how to dispatch generation to meet demand for power. This is a
complex optimization problem that includes many nonlinear, non convex
constraints as well as inherent uncertainties about future demand for power and
available generation. In this paper we develop convex formulations to
appropriately model crucial classes of nonlinearities and stochastic effects.
We focus on solving a nonlinear optimal power flow (OPF) problem that includes
loss of synchrony constraints and models wind-farm caused fluctuations. In
particular, we develop (a) a convex formulation of the deterministic
phase-difference nonlinear Optimum Power Flow (OPF) problem; and (b) a
probabilistic chance constrained OPF for angular stability, thermal overloads
and generation limits that is computationally tractable.Comment: 11 pages, 3 figure
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow
(OPF) problem based on limited information about forecast error distributions.
The objective is to determine power schedules for controllable devices in a
power network to balance operation cost and conditional value-at-risk (CVaR) of
device and network constraint violations. These decisions include scheduled
power output adjustments and reserve policies, which specify planned reactions
to forecast errors in order to accommodate fluctuating renewable energy
sources. Instead of assuming the uncertainties across the networks follow
prescribed probability distributions, we assume the distributions are only
observable through a finite training dataset. By utilizing the Wasserstein
metric to quantify differences between the empirical data-based distribution
and the real data-generating distribution, we formulate a distributionally
robust optimization OPF problem to search for power schedules and reserve
policies that are robust to sampling errors inherent in the dataset. A simple
numerical example illustrates inherent tradeoffs between operation cost and
risk of constraint violation, and we show how our proposed method offers a
data-driven framework to balance these objectives
Optimal distribution in smart grids with volatile renewable sources using a message passing algorithm
The design of future electricity grids will allow for renewable energy generators to be effectively incorporated into the network. Current methods of economic dispatch were not designed to accommodate the level of volatility and uncertain nature of sources such as wind and solar; here we demonstrate how an optimisation algorithm called message passing, which is based on principled statistical physics methodologies and is inherently probabilistic, could be an alternative way of considering source volatility efficiently and reliably. The algorithm iteratively passes probabilistic messages in order to find an approximate global optimal solution with moderate computational complexity and inherently consider source volatility. We demonstrate the capabilities of message passing as a distribution algorithm in the presence of uncertainty on synthetic benchmark IEEE networks and show how the volatility increase effects distribution cost
Microgrids: Planning, Protection and Control
This Special Issue will include papers related to the planning, protection, and control of smart grids and microgrids, and their applications in the industry, transportation, water, waste, and urban and residential infrastructures. Authors are encouraged to present their latest research; reviews on topics including methods, approaches, systems, and technology; and interfaces to other domains such as big data, cybersecurity, human–machine, sustainability, and smart cities. The planning side of microgrids might include technology selection, scheduling, interconnected microgrids, and their integration with regional energy infrastructures. The protection side of microgrids might include topics related to protection strategies, risk management, protection technologies, abnormal scenario assessments, equipment and system protection layers, fault diagnosis, validation and verification, and intelligent safety systems. The control side of smart grids and microgrids might include control strategies, intelligent control algorithms and systems, control architectures, technologies, embedded systems, monitoring, and deployment and implementation
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