5,515 research outputs found
Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts
We develop a stochastic equilibrium model for an electricity market with
asymmetric renewable energy forecasts. In our setting, market participants
optimize their profits using public information about a conditional expectation
of energy production but use private information about the forecast error
distribution. This information is given in the form of samples and incorporated
into profit-maximizing optimizations of market participants through chance
constraints. We model information asymmetry by varying the sample size of
participants' private information. We show that with more information
available, the equilibrium gradually converges to the ideal solution provided
by the perfect information scenario. Under information scarcity, however, we
show that the market converges to the ideal equilibrium if participants are to
infer the forecast error distribution from the statistical properties of the
data at hand or share their private forecasts
Optimal Net-Load Balancing in Smart Grids with High PV Penetration
Mitigating Supply-Demand mismatch is critical for smooth power grid
operation. Traditionally, load curtailment techniques such as Demand Response
(DR) have been used for this purpose. However, these cannot be the only
component of a net-load balancing framework for Smart Grids with high PV
penetration. These grids can sometimes exhibit supply surplus causing
over-voltages. Supply curtailment techniques such as Volt-Var Optimizations are
complex and computationally expensive. This increases the complexity of
net-load balancing systems used by the grid operator and limits their
scalability. Recently new technologies have been developed that enable the
rapid and selective connection of PV modules of an installation to the grid.
Taking advantage of these advancements, we develop a unified optimal net-load
balancing framework which performs both load and solar curtailment. We show
that when the available curtailment values are discrete, this problem is
NP-hard and develop bounded approximation algorithms for minimizing the
curtailment cost. Our algorithms produce fast solutions, given the tight timing
constraints required for grid operation. We also incorporate the notion of
fairness to ensure that curtailment is evenly distributed among all the nodes.
Finally, we develop an online algorithm which performs net-load balancing using
only data available for the current interval. Using both theoretical analysis
and practical evaluations, we show that our net-load balancing algorithms
provide solutions which are close to optimal in a small amount of time.Comment: 11 pages. To be published in the 4th ACM International Conference on
Systems for Energy-Efficient Built Environments (BuildSys 17) Changes from
previous version: Fixed a bug in Algorithm 1 which was causing some min cost
solutions to be misse
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