International Business Machines Corporation

Utopus Insights, Inc.

Other

Globalfoundries Inc.

Soumyadip Ghosh

Dung Tien Phan

Xiaoxuan Zhang

Jayant R Kalagnanam

Ramesh Natarajan

Jonathan R Hosking

Markus Ettl

Sugato Bagchi

Shivaram Subramanian

Stacey C Ramos

Young Min Lee

Mark Steven Squillante

Shivaram Subramaniam

Dmitriy A Katz-Rogozhnikov

Chai Wah Wu

Pawan Raghunath Chowdhary

Jinjun Xiong

Yu Tang

Jie Xu

Ali Koc

Amit Dhurandhar

Dharmashankar Subramanian

Pu Huang

Mayank Sharma

Karthik Sourirajan

Jonathan R M Hosking

Xu Sun

Marek Petrik

Bruce C Graves

Antonius B Dieker

William Stephen Schaefer

James R Luedtke

A method for operating a power generating facility connected to a power distribution grid having an uncertain power generation condition includes predicting a probabilistic power flow forecast in a transmission line of the power distribution grid for a period of time, wherein the transmission line is electrically coupled to the power generating facility, predicting, using the probabilistic power flow forecast, a probability of congestion over the transmission line of the power distribution grid during the period of time, generating a mitigation plan, including a load adjustment on the transmission line, using the probability of congestion predicted over the transmission line and a thermal limit constraint of the transmission line, wherein the mitigation plan balances load adjustment and an overlimit line capacity on the transmission line, and controlling the power generating facility, using the mitigation plan, to achieve load modification and mitigate the probability of congestion predicted in the transmission line.

Congestion control in electric power system under load and uncertainty

A method for determining power output levels of a plurality of nodes in an electric power system includes receiving, at a first node of the plurality of nodes, voltage information and multipliers of all neighboring nodes of the first node within the electric power system, determining, by the first node, a local power generation and a local voltage using the voltage information and the multipliers of the neighboring nodes and distributing the local power generation and the local voltage to the neighboring nodes, determining, by the first node, an estimated voltage of each of the neighboring nodes and distributing the estimated voltage to each of the neighboring nodes, and updating, by the first node, a local multiplier using the voltage information received from the neighboring nodes and the estimated voltage of each of the neighboring nodes determined by the node.

Distributed control of electric power grids

A predictive-control approach allows an electricity provider to monitor and proactively manage peak and off-peak residential intra-day electricity usage in an emerging smart energy grid using time-dependent dynamic pricing incentives. The daily load is modeled as time-shifted, but cost-differentiated and substitutable, copies of the continuously-consumed electricity resource, and a consumer-choice prediction model is constructed to forecast the corresponding intra-day shares of total daily load according to this model. This is embedded within an optimization framework for managing the daily electricity usage. A series of transformations are employed, including the reformulation-linearization technique (RLT) to obtain a Mixed-Integer Programming (MIP) model representation of the resulting nonlinear optimization problem. In addition, various regulatory and pricing constraints are incorporated in conjunction with the specified profit and capacity utilization objectives.

Managing time-substitutable electricity usage using dynamic controls

Systems and methods for modeling and forecasting cyclical demand systems in the presence of dynamic control or dynamic incentives. A method for modeling a cyclical demand system comprises obtaining historical data on one or more demand measurements over a plurality of demand cycles, obtaining historical data on incentive signals over the plurality of demand cycles, constructing a model using the obtained historical data on the one or more demand measurements and the incentive signals, wherein constructing the model comprises specifying a state-space model, specifying variance parameters in the model, and estimating unknown variance parameters.

System and method for modeling and forecasting cyclical demand systems with dynamic controls and dynamic incentives

A method for operating a power generating facility connected to a power distribution grid having an uncertain power generation condition includes predicting a probabilistic power flow forecast in a transmission line of the power distribution grid for a period of time, wherein the transmission line is electrically coupled to the power generating facility, predicting, using the probabilistic power flow forecast, a probability of congestion over the transmission line of the power distribution grid during the period of time, generating a mitigation plan, including a load adjustment on the transmission line, using the probability of congestion predicted over the transmission line and a thermal limit constraint of the transmission line, wherein the mitigation plan balances the load adjustment and an overlimit line capacity on the transmission line, and controlling the power generating facility, using the mitigation plan, to achieve the load modification and mitigate the probability of congestion predicted in the transmission line.

A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Real-time forecasting of electricity demand in a streams-based architecture with applications

A method for solving a two-stage non-linear stochastic formulation for the economic dispatch problem under renewable-generation uncertainty. Certain generation decisions are made only in the first stage and fixed for the subsequent (second) stage, where the actual renewable generation is realized. The uncertainty in renewable output is captured by a finite number of scenarios. Any resulting supply-demand mis-match must then be alleviated using high marginal-cost power sources that can be tapped in short time frames. The solution implements two outer approximation algorithms to solve this nonconvex optimization problem to optimality including the application of a decomposition approach derived from the Alternating Direction Method of Multipliers (ADMM) algorithm.

Planning economic energy dispatch in electrical grid under uncertainty

Analyzing one or more physical infrastructure elements of a data center by generating a plurality of outputs from a stochastic process for a set of inputs to the stochastic process. The inputs comprise one or more inputs of certainty and one or more inputs of uncertainty. A statistical output analysis of the outputs is utilized to generate one or more threshold reaching projections for each of the physical infrastructure elements of the data center.

Data center physical infrastructure threshold analysis

The present disclosure relates generally to the field of distributed charging of electrical assets. In various examples, distributed charging of electrical assets may be implemented in the form of systems, methods and/or algorithms.

Distributed charging of electrical assets

A system and computer program product for solving a two-stage non-linear stochastic formulation for the economic dispatch problem under renewable-generation uncertainty. Certain generation decisions are made only in the first stage and fixed for the subsequent (second) stage, where the actual renewable generation is realized. The uncertainty in renewable output is captured by a finite number of scenarios. Any resulting supply-demand mis-match must then be alleviated using high marginal-cost power sources that can be tapped in short time frames. The solution implements two outer approximation algorithms to solve this nonconvex optimization problem to optimality. Under certain conditions the sequence of optimal solutions obtained under both alternatives has a limit point that is a globally-optimal solution to the original two-stage nonconvex program. A further decomposition approach derived from the Alternating Direction Method of Multipliers algorithm is implemented.

A method for solving a two-stage non-linear stochastic formulation for the economic dispatch problem under renewable-generation uncertainty. Certain generation decisions are made only in the first stage and fixed for the subsequent (second) stage, where the actual renewable generation is realized. The uncertainty in renewable output is captured by a finite number of scenarios. Any resulting supply-demand mis-match must then be alleviated using high marginal-cost power sources that can be tapped in short time frames. The solution implements two outer approximation algorithms to solve this nonconvex optimization problem to optimality. Under certain conditions the sequence of optimal solutions obtained under both alternatives has a limit point that is a globally-optimal solution to the original two-stage nonconvex program. A further decomposition approach derived from the Alternating Direction Method of Multipliers algorithm is implemented.

The present disclosure relates generally to systematic algorithms (and associated systems and methods) that take a forecast model as input and produce a discrete probability distribution as output, using scenario reduction ideas from stochastic programming. In one example, an algorithm (and associated system and method) creates scenarios sequentially for each time period, leading to a scenario tree.

Systems and methods for generating wind power scenarios for wind-power-integrated stochastic unit commitment problems

Assessing resiliency of facility configuration strategies, in one aspect, may employ a mathematical model representation of facility configuration. Organization environment factors and one or more desired performance metrics associated with the organization may be determined. A technical model specification that defines the configuration of facilities of the organization may be received, and a mathematical model representation of the configuration may be created. The mathematical model representation is solved for steady-state characteristics, and one or more values may be estimated respectively of the desired performance metrics based on the steady-state characteristics. The resiliency may be measured based on the estimated one or more values.

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Peekskill, NY, United States of America

Soumyadip Ghosh