1,795 research outputs found
Distributed Online Modified Greedy Algorithm for Networked Storage Operation under Uncertainty
The integration of intermittent and stochastic renewable energy resources
requires increased flexibility in the operation of the electric grid. Storage,
broadly speaking, provides the flexibility of shifting energy over time;
network, on the other hand, provides the flexibility of shifting energy over
geographical locations. The optimal control of storage networks in stochastic
environments is an important open problem. The key challenge is that, even in
small networks, the corresponding constrained stochastic control problems on
continuous spaces suffer from curses of dimensionality, and are intractable in
general settings. For large networks, no efficient algorithm is known to give
optimal or provably near-optimal performance for this problem. This paper
provides an efficient algorithm to solve this problem with performance
guarantees. We study the operation of storage networks, i.e., a storage system
interconnected via a power network. An online algorithm, termed Online Modified
Greedy algorithm, is developed for the corresponding constrained stochastic
control problem. A sub-optimality bound for the algorithm is derived, and a
semidefinite program is constructed to minimize the bound. In many cases, the
bound approaches zero so that the algorithm is near-optimal. A task-based
distributed implementation of the online algorithm relying only on local
information and neighbor communication is then developed based on the
alternating direction method of multipliers. Numerical examples verify the
established theoretical performance bounds, and demonstrate the scalability of
the algorithm.Comment: arXiv admin note: text overlap with arXiv:1405.778
Online Modified Greedy Algorithm for Storage Control under Uncertainty
This paper studies the general problem of operating energy storage under
uncertainty. Two fundamental sources of uncertainty are considered, namely the
uncertainty in the unexpected fluctuation of the net demand process and the
uncertainty in the locational marginal prices. We propose a very simple
algorithm termed Online Modified Greedy (OMG) algorithm for this problem. A
stylized analysis for the algorithm is performed, which shows that comparing to
the optimal cost of the corresponding stochastic control problem, the
sub-optimality of OMG is bounded and approaches zero in various scenarios. This
suggests that, albeit simple, OMG is guaranteed to have good performance in
some cases; and in other cases, OMG together with the sub-optimality bound can
be used to provide a lower bound for the optimal cost. Such a lower bound can
be valuable in evaluating other heuristic algorithms. For the latter cases, a
semidefinite program is derived to minimize the sub-optimality bound of OMG.
Numerical experiments are conducted to verify our theoretical analysis and to
demonstrate the use of the algorithm.Comment: 14 page version of a paper submitted to IEEE trans on Power System
Wide-Area Time-Synchronized Closed-Loop Control of Power Systems And Decentralized Active Distribution Networks
The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication
Online Energy Storage Management: an Algorithmic Approach
Motivated by the importance of energy storage networks in smart grids, we provide an algorithmic study of the online energy storage management problem in a network setting, the first to the best of our knowledge. Given online power supplies, either entirely renewable supplies or those in combination with traditional supplies, we want to route power from the supplies to demands using storage units subject to a decay factor. Our goal is to maximize the total utility of satisfied demands less the total production cost of routed power. We model renewable supplies with the zero production cost function and traditional supplies with convex production cost functions. For two natural storage unit settings, private and public, we design poly-logarithmic competitive algorithms in the network flow model using the dual fitting and online primal dual methods for convex problems. Furthermore, we show strong hardness results for more general settings of the problem. Our techniques may be of independent interest in other routing and storage management problems
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