10,966 research outputs found
Joint Frequency Regulation and Economic Dispatch Using Limited Communication
We study the performance of a decentralized integral control scheme for joint
power grid frequency regulation and economic dispatch. We show that by properly
designing the controller gains, after a power flow perturbation, the control
achieves near-optimal economic dispatch while recovering the nominal frequency,
without requiring any communication. We quantify the gap between the
controllable power generation cost under the decentralized control scheme and
the optimal cost, based on the DC power flow model. Moreover, we study the
tradeoff between the cost and the convergence time, by adjusting parameters of
the control scheme.
Communication between generators reduces the convergence time. We identify
key communication links whose failures have more significant impacts on the
performance of a distributed power grid control scheme that requires
information exchange between neighbors
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
Distributed Lagrangian Methods for Network Resource Allocation
Motivated by a variety of applications in control engineering and information
sciences, we study network resource allocation problems where the goal is to
optimally allocate a fixed amount of resource over a network of nodes. In these
problems, due to the large scale of the network and complicated
inter-connections between nodes, any solution must be implemented in parallel
and based only on local data resulting in a need for distributed algorithms. In
this paper, we propose a novel distributed Lagrangian method, which requires
only local computation and communication. Our focus is to understand the
performance of this algorithm on the underlying network topology. Specifically,
we obtain an upper bound on the rate of convergence of the algorithm as a
function of the size and the topology of the underlying network. The
effectiveness and applicability of the proposed method is demonstrated by its
use in solving the important economic dispatch problem in power systems,
specifically on the benchmark IEEE-14 and IEEE-118 bus systems
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The role of large-scale energy storage design and dispatch in the power grid: A study of very high grid penetration of variable renewable resources
We present a result of hourly simulation performed using hourly load data and the corresponding simulated output of wind and solar technologies distributed throughout the state of California. We examined how we could achieve very high-energy penetration from intermittent renewable system into the electricity grid. This study shows that the maximum threshold for the storage need is significantly less than the daily average demand. In the present study, we found that the approximate network energy storage is of the order of 186. GW. h/22. GW (approximately 22% of the average daily demands of California). Allowing energy dumping was shown to increase storage use, and by that way, increases grid penetration and reduces the required backup conventional capacity requirements. Using the 186. GW. h/22. GW storage and at 20% total energy loss, grid penetration was increased to approximately 85% of the annual demand of the year while also reducing the conventional backup capacity requirement to 35. GW. This capacity was sufficient to supply the year round hourly demand, including 59 GW peak demand, plus a distribution loss of about 5.3%. We conclude that designing an efficient and least cost grid may require the capability to capture diverse physical and operational policy scenarios of the future grid. © 2014 Elsevier Ltd
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
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