Co-Optimization of Power and Reserves in Dynamic T&amp;D Power Markets With Nondispatchable Renewable Generation and Distributed Energy Resources

Caramanis, Michael C.; Ntakou, Elli; Hogan, William W.; Chakrabortty, Aranya; Schoene, Jens

doi:10.1109/jproc.2016.2520758

Cited by 178 publications

(134 citation statements)

References 115 publications

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“…Centralized clearing at the high-voltage grid level and distributed clearing at the distribution grid level can be integrated in a cooptimization framework, as recently proposed by Caramanis et al [10]. These two applications of distributed computing in power system operations demonstrate the potential to fully harness the flexibility of the grid and smoothly integrate large shares of renewable and other distributed energy resources in power systems without deteriorating the quality of service delivered to consumers.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

Aravena¹,

Papavasiliou²,

Papalexopoulos³

2017

Recent Progress in Parallel and Distributed Computing

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We present a distributed computing architecture for smart grid management, composed of two applications at two different levels of the grid. At the high voltage level, we optimize operations using a stochastic unit commitment (SUC) model with hybrid time resolution. The SUC problem is solved with an asynchronous distributed subgradient method, for which we propose stepsize scaling and fast initialization techniques. The asynchronous algorithm is implemented in a high-performance computing cluster and benchmarked against a deterministic unit commitment model with exogenous reserve targets in an industrial scale test case of the Central Western European system (679 buses, 1037 lines, and 656 generators). At the distribution network level, we manage demand response from small clients through distributed stochastic control, which enables harnessing residential demand response while respecting the desire of consumers for control, privacy, and simplicity. The distributed stochastic control scheme is successfully tested on a test case with 10,000 controllable devices. Both applications demonstrate the potential for efficiently managing flexible resources in smart grids and for systematically coping with the uncertainty and variability introduced by renewable energy.

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Section: Introductionmentioning

confidence: 99%

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

Aravena¹,

Papavasiliou²,

Papalexopoulos³

2017

Recent Progress in Parallel and Distributed Computing

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show abstract

“…In the wholesale market, many ISOs nationwide implement the locational marginal pricing (LMP) strategy either in the form of ex ante LMP, for example New York ISO (NYISO), or ex post LMP, for example ISO-New England (ISO-NE), PJM and Midcontinent-ISO (MISO) [27]. Based on the fact that LMP has been widely adopted to compute electricity prices in the wholesale electricity market [28], some scholars have begun to downscale the LMP schema for distribution networks by proposing its counterpart, distribution locational marginal pricing (DLMP) [29], which can directly work for individual energy end-users without referring to a load serving entity (LSE) or other demand bidding aggregators. It has been applied to several scenarios, such as the congestion management problem and the electric vehicle charging problem [30].…”

Section: Dso With Distribution Level Pricingmentioning

confidence: 99%

“…Stochastic optimization, robust optimization, multi-objective optimization and mathematical programming have been widely adopted for research on the wholesale market for market-clearance, and most of this research takes into consideration various types of uncertainties resulting from variable demand or renewable energy supply [29,35,58]. Retailers in the retail electricity market often refer to these optimization methods to guarantee their revenue through deterministic analysis.…”

Section: Optimization Distributed Optimization and Blockchainmentioning

confidence: 99%

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

et al. 2017

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Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

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“…[4] The DCM model was used to compare the marginal cost of real and reactive power supply to a simulated 800 bus radial feeder with both commercial and residential demand incorporating supply (solar) and storage (electric vehicle) loads along with space conditioning located in the Capital Region of New York State. DCM considers full AC load flow constraints.…”

Section: The Benefits Of Dlmp Over Business As Usual and Only Lmp: Exmentioning

confidence: 99%

Distributed Energy Resources: New Markets and New Products

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Caramanis

Ntakou

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

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Co-Optimization of Power and Reserves in Dynamic T&D Power Markets With Nondispatchable Renewable Generation and Distributed Energy Resources

Cited by 178 publications

References 115 publications

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

A Distributed Computing Architecture for the Large-Scale Integration of Renewable Energy and Distributed Resources in Smart Grids

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

Distributed Energy Resources: New Markets and New Products

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