The effect of a smart meter on congestion and stability in a power market

Kiani, Arman; Annaswamy, Anuradha M.

doi:10.1109/cdc.2010.5717141

Cited by 20 publications

(20 citation statements)

References 9 publications

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“…See e.g. also Kiani and Annaswamy (2010) and Roozbehani et al (2010) for more recent papers on realtime dynamic pricing, which mainly focus on optimal supply-demand matching. However, the coupling between the solution of the optimization problem and the physical dynamics of the network should not be ignored as this could result in instability of the grid.…”

Section: Introductionmentioning

confidence: 99%

Port-Hamiltonian Formulation of the Gradient Method Applied to Smart Grids

Persis

Schaft

2015

IFAC-PapersOnLine

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The gradient method is a well-known tool for solving convex optimization problems. This paper shows that the gradient method admits a Brayton-Moser and a port-Hamiltonian representation. In fact, its dynamics can be interpreted as a interconnection of multiple (portHamiltonian) passive systems, which plays a key role in proving asymptotic stability of the method. As an application to smart grids, this paper studies the problem of frequency regulation in power grids, while maximizing the social welfare. By applying the gradient method, we obtain a real-time dynamic pricing model in port-Hamiltonian form. By coupling with the port-Hamiltonian description of the physical network we obtain a closed-loop port-Hamiltonian system, which properties are exploited to prove asymptotic stability to the set of optimal points.

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Section: Introductionmentioning

confidence: 99%

Port-Hamiltonian Formulation of the Gradient Method Applied to Smart Grids

Persis

Schaft

2015

IFAC-PapersOnLine

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“…Latencies are another challenge associated with AMI as they can affect both grid stability and performance [41], [42]. In [41], the effect of DR in the presence of delayed price responsiveness of consumers was investigated.…”

Section: A Grid Structurementioning

confidence: 99%

“…It was shown that with a lag in response of 30 minutes, DR is still a viable and useful tool to reduce peak demand and mitigate the volatile behavior of the RERs, despite the fact that about 70% of the benefits of DR was lost due to the latency. In [42], delay in AMI communication and its effects on stability and robustness of the electricity grid was investigated.…”

Section: A Grid Structurementioning

confidence: 99%

Demand response in smart grids: Participants, challenges, and a taxonomy

Hansen

Knudsen

Annaswamy

2014

53rd IEEE Conference on Decision and Control

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In recent decades, moves toward higher integration of Renewable Energy Resources have called for fundamental changes in both the planning and operation of the overall power grid. One such change is the incorporation of Demand Response (DR), the process by which consumers can adjust their demand in a flexible manner. This paper presents a survey of various aspects of DR including the different types of participants, as well as the underlying challenges and the overall potential of DR when it comes to large-scale implementations. Benefits of DR as reported in the literature for performance metrics such as frequency control and price control, as well as methods for ensuring privacy are discussed. A quantitative taxonomy of DR recently proposed in the literature based on the inherent magnitude, run-time, and integral constraints is discussed and its integration with economic dispatch is explored.

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“…In (9), ℓ is a function of time as its value changes according to v(t), and the error ||v(t)−v (ℓ) || can be reduced by adopting a large L. In control theory, ℓ is referred to as an operating point, and thus we have L operating points according to (8). The gain scheduler performs the operation in (9) so that the MO can use the correct gains k …”

Section: Proposed Dno and Mo Designsmentioning

confidence: 99%

“…In summary, the gain scheduler is operated according to (9), and the power distribution operator performs the power distribution according to (10) with h n = h (ℓ) n obtained by solving (15). The next subsection presents our MO design based on the established DNO.…”

Section: ) Power Distribution Operatormentioning

confidence: 99%