3,481 research outputs found
Gather-and-broadcast frequency control in power systems
We propose a novel frequency control approach in between centralized and
distributed architectures, that is a continuous-time feedback control version
of the dual decomposition optimization method. Specifically, a convex
combination of the frequency measurements is centrally aggregated, followed by
an integral control and a broadcast signal, which is then optimally allocated
at local generation units. We show that our gather-and-broadcast control
architecture comprises many previously proposed strategies as special cases. We
prove local asymptotic stability of the closed-loop equilibria of the
considered power system model, which is a nonlinear differential-algebraic
system that includes traditional generators, frequency-responsive devices, as
well as passive loads, where the sources are already equipped with primary
droop control. Our feedback control is designed such that the closed-loop
equilibria of the power system solve the optimal economic dispatch problem
Robust Decentralized Secondary Frequency Control in Power Systems: Merits and Trade-Offs
Frequency restoration in power systems is conventionally performed by
broadcasting a centralized signal to local controllers. As a result of the
energy transition, technological advances, and the scientific interest in
distributed control and optimization methods, a plethora of distributed
frequency control strategies have been proposed recently that rely on
communication amongst local controllers.
In this paper we propose a fully decentralized leaky integral controller for
frequency restoration that is derived from a classic lag element. We study
steady-state, asymptotic optimality, nominal stability, input-to-state
stability, noise rejection, transient performance, and robustness properties of
this controller in closed loop with a nonlinear and multivariable power system
model. We demonstrate that the leaky integral controller can strike an
acceptable trade-off between performance and robustness as well as between
asymptotic disturbance rejection and transient convergence rate by tuning its
DC gain and time constant. We compare our findings to conventional
decentralized integral control and distributed-averaging-based integral control
in theory and simulations
Review of trends and targets of complex systems for power system optimization
Optimization systems (OSs) allow operators of electrical power systems (PS) to optimally operate PSs and to also create optimal PS development plans. The inclusion of OSs in the PS is a big trend nowadays, and the demand for PS optimization tools and PS-OSs experts is growing. The aim of this review is to define the current dynamics and trends in PS optimization research and to present several papers that clearly and comprehensively describe PS OSs with characteristics corresponding to the identified current main trends in this research area. The current dynamics and trends of the research area were defined on the basis of the results of an analysis of the database of 255 PS-OS-presenting papers published from December 2015 to July 2019. Eleven main characteristics of the current PS OSs were identified. The results of the statistical analyses give four characteristics of PS OSs which are currently the most frequently presented in research papers: OSs for minimizing the price of electricity/OSs reducing PS operation costs, OSs for optimizing the operation of renewable energy sources, OSs for regulating the power consumption during the optimization process, and OSs for regulating the energy storage systems operation during the optimization process. Finally, individual identified characteristics of the current PS OSs are briefly described. In the analysis, all PS OSs presented in the observed time period were analyzed regardless of the part of the PS for which the operation was optimized by the PS OS, the voltage level of the optimized PS part, or the optimization goal of the PS OS.Web of Science135art. no. 107
Optimization of the operation of smart rural grids through a novel rnergy management system
The paper proposes an innovative Energy Management System (EMS) that optimizes the grid operation based on economic and technical criteria. The EMS inputs the demand and renewable generation forecasts, electricity prices and the status of the distributed storages through the network, and solves with an optimal quarter-hourly dispatch for controllable resources. The performance of the EMS is quantified through diverse proposed metrics. The analyses were based on a real rural grid from the European FP7 project Smart Rural Grid. The performance of the EMS has been evaluated through some scenarios varying the penetration of distributed generation. The obtained results demonstrate that the inclusion of the EMS from both a technical point of view and an economic perspective for the adopted grid is justified. At the technical level, the inclusion of the EMS permits us to significantly increase the power quality in weak and radial networks. At the economic level and from a certain threshold value in renewables’ penetration, the EMS reduces the energy costs for the grid participants, minimizing imports from the external grid and compensating the toll to be paid in the form of the losses incurred by including additional equipment in the network (i.e., distributed storage).Postprint (published version
Power Imbalance Detection in Smart Grid via Grid Frequency Deviations: A Hidden Markov Model based Approach
We detect the deviation of the grid frequency from the nominal value (i.e.,
50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch
between power generation and load demand). We first pass the noisy estimates of
grid frequency through a hypothesis test which decides whether there is no
deviation, positive deviation, or negative deviation from the nominal value.
The hypothesis testing incurs miss-classification errors---false alarms (i.e.,
there is no deviation but we declare a positive/negative deviation), and missed
detections (i.e., there is a positive/negative deviation but we declare no
deviation). Therefore, to improve further upon the performance of the
hypothesis test, we represent the grid frequency's fluctuations over time as a
discrete-time hidden Markov model (HMM). We note that the outcomes of the
hypothesis test are actually the emitted symbols, which are related to the true
states via emission probability matrix. We then estimate the hidden Markov
sequence (the true values of the grid frequency) via maximum likelihood method
by passing the observed/emitted symbols through the Viterbi decoder.
Simulations results show that the mean accuracy of Viterbi algorithm is at
least \% greater than that of hypothesis test.Comment: 5 pages, 6 figures, accepted by IEEE VTC conference, Fall 2018
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