15,327 research outputs found
Resilient Distributed Energy Management for Systems of Interconnected Microgrids
In this paper, distributed energy management of interconnected microgrids,
which is stated as a dynamic economic dispatch problem, is studied. Since the
distributed approach requires cooperation of all local controllers, when some
of them do not comply with the distributed algorithm that is applied to the
system, the performance of the system might be compromised. Specifically, it is
considered that adversarial agents (microgrids with their controllers) might
implement control inputs that are different than the ones obtained from the
distributed algorithm. By performing such behavior, these agents might have
better performance at the expense of deteriorating the performance of the
regular agents. This paper proposes a methodology to deal with this type of
adversarial agents such that we can still guarantee that the regular agents can
still obtain feasible, though suboptimal, control inputs in the presence of
adversarial behaviors. The methodology consists of two steps: (i) the
robustification of the underlying optimization problem and (ii) the
identification of adversarial agents, which uses hypothesis testing with
Bayesian inference and requires to solve a local mixed-integer optimization
problem. Furthermore, the proposed methodology also prevents the regular agents
to be affected by the adversaries once the adversarial agents are identified.
In addition, we also provide a sub-optimality certificate of the proposed
methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201
A resilient approach for distributed MPC-based economic dispatch in interconnected microgrids
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Economic dispatch of interconnected microgrids that is based on distributed model predictive control (DMPC) requires the cooperation of all agents (microgrids). This paper discusses the case in which some of the agents might not comply with the decisions computed by performing a DMPC algorithm. In this regard, these agents could obtain a better performance at the cost of degrading the performance of the network as a whole. A resilient distributed method that can deal with such issues is proposed and studied in this paper. The method consists of two parts. The first part is to ensure that the decisions obtained from the algorithm are robustly feasible against most of the attacks with high confidence. In this part, we employ a two-step randomization-based approach to obtain a feasible solution with a predefined level of confidence. The second part consists in the identification and mitigation of the adversarial agents, which utilizes hypothesis testing with Bayesian inference and requires each agent to solve a mixed-integer problem to decide the connections with its neighbors. In addition, an analysis of the decisions computed using the stochastic approach and the outcome of the identification and mitigation method is provided. The performance of the proposed approach is also shown through numerical simulations.Peer ReviewedPostprint (author's final draft
Opportunities for Price Manipulation by Aggregators in Electricity Markets
Aggregators are playing an increasingly crucial role in the integration of
renewable generation in power systems. However, the intermittent nature of
renewable generation makes market interactions of aggregators difficult to
monitor and regulate, raising concerns about potential market manipulation by
aggregators. In this paper, we study this issue by quantifying the profit an
aggregator can obtain through strategic curtailment of generation in an
electricity market. We show that, while the problem of maximizing the benefit
from curtailment is hard in general, efficient algorithms exist when the
topology of the network is radial (acyclic). Further, we highlight that
significant increases in profit are possible via strategic curtailment in
practical settings
Decentralized energy management of power networks with distributed generation using periodical self-sufficient repartitioning approach
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a decentralized model predictive control (MPC) method as the energy management strategy for a large-scale electrical power network with distributed generation and storage units. The main idea of the method is to periodically repartition the electrical power network into a group of self-sufficient interconnected microgrids. In this regard, a distributed graph-based partitioning algorithm is proposed. Having a group of self-sufficient microgrids allows the decomposition of the centralized dynamic economic dispatch problem into local economic dispatch problems for the microgrids. In the overall scheme, each microgrid must cooperate with its neighbors to perform repartitioning periodically and solve a decentralized MPC-based optimization problem at each time instant. In comparison to the approaches based on distributed optimization, the proposed scheme requires less intensive communication since the microgrids do not need to communicate at each time instant, at the cost of suboptimality of the solutions. The performance of the proposed scheme is shown by means of numerical simulations with a well-known benchmark case. © 2019 American Automatic Control Council.Peer ReviewedPostprint (author's final draft
On the Impact of Wireless Jamming on the Distributed Secondary Microgrid Control
The secondary control in direct current microgrids (MGs) is used to restore
the voltage deviations caused by the primary droop control, where the latter is
implemented locally in each distributed generator and reacts to load
variations. Numerous recent works propose to implement the secondary control in
a distributed fashion, relying on a communication system to achieve consensus
among MG units. This paper shows that, if the system is not designed to cope
with adversary communication impairments, then a malicious attacker can apply a
simple jamming of a few units of the MG and thus compromise the secondary MG
control. Compared to other denial-of-service attacks that are oriented against
the tertiary control, such as economic dispatch, the attack on the secondary
control presented here can be more severe, as it disrupts the basic
functionality of the MG
Consensus-based approach to peer-to-peer electricity markets with product differentiation
With the sustained deployment of distributed generation capacities and the
more proactive role of consumers, power systems and their operation are
drifting away from a conventional top-down hierarchical structure. Electricity
market structures, however, have not yet embraced that evolution. Respecting
the high-dimensional, distributed and dynamic nature of modern power systems
would translate to designing peer-to-peer markets or, at least, to using such
an underlying decentralized structure to enable a bottom-up approach to future
electricity markets. A peer-to-peer market structure based on a Multi-Bilateral
Economic Dispatch (MBED) formulation is introduced, allowing for
multi-bilateral trading with product differentiation, for instance based on
consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is
described to solve the MBED in fully decentralized manner. A set of realistic
case studies and their analysis allow us showing that such peer-to-peer market
structures can effectively yield market outcomes that are different from
centralized market structures and optimal in terms of respecting consumers
preferences while maximizing social welfare. Additionally, the RCI solving
approach allows for a fully decentralized market clearing which converges with
a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System
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