89 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
Electricity Market Equilibrium under Information Asymmetry
We study a competitive electricity market equilibrium with two trading
stages, day-ahead and real-time. The welfare of each market agent is exposed to
uncertainty (here from renewable energy production), while agent information on
the probability distribution of this uncertainty is not identical at the
day-ahead stage. We show a high sensitivity of the equilibrium solution to the
level of information asymmetry and demonstrate economic, operational, and
computational value for the system stemming from potential information sharing
Unit Commitment Predictor With a Performance Guarantee: A Support Vector Machine Classifier
The system operators usually need to solve large-scale unit commitment
problems within limited time frame for computation. This paper provides a
pragmatic solution, showing how by learning and predicting the on/off
commitment decisions of conventional units, there is a potential for system
operators to warm start their solver and speed up their computation
significantly. For the prediction, we train linear and kernelized support
vector machine classifiers, providing an out-of-sample performance guarantee if
properly regularized, converting to distributionally robust classifiers. For
the unit commitment problem, we solve a mixed-integer second-order cone
problem. Our results based on the IEEE 6-bus and 118-bus test systems show that
the kernelized SVM with proper regularization outperforms other classifiers,
reducing the computational time by a factor of 1.7. In addition, if there is a
tight computational limit, while the unit commitment problem without warm start
is far away from the optimal solution, its warmly started version can be solved
to optimality within the time limit
A Consensus-ADMM Approach for Strategic Generation Investment in Electricity Markets
This paper addresses a multi-stage generation investment problem for a
strategic (price-maker) power producer in electricity markets. This problem is
exposed to different sources of uncertainty, including short-term operational
(e.g., rivals' offering strategies) and long-term macro (e.g., demand growth)
uncertainties. This problem is formulated as a stochastic bilevel optimization
problem, which eventually recasts as a large-scale stochastic mixed-integer
linear programming (MILP) problem with limited computational tractability. To
cope with computational issues, we propose a consensus version of alternating
direction method of multipliers (ADMM), which decomposes the original problem
by both short- and long-term scenarios. Although the convergence of ADMM to the
global solution cannot be generally guaranteed for MILP problems, we introduce
two bounds on the optimal solution, allowing for the evaluation of the solution
quality over iterations. Our numerical findings show that there is a trade-off
between computational time and solution quality
Dynamic Pricing in an Energy Community Providing Capacity Limitation Services
This paper proposes a mathematical framework for dynamic pricing in an energy
community to enable the provision of capacity limitation services to the
distribution grid. In this framework, the energy community complies with a
time-variant limit on its maximum power import from the distribution grid in
exchange for grid tariff discounts. A bi-level optimization model is developed
to implicitly coordinate the energy usage of prosumers within the community. In
the upper-level problem, the community manager minimizes the total operational
cost of the community based on reduced grid tariffs and power capacity limits
by setting time-variant and prosumer-specific prices. In the lower-level
problem, each prosumer subsequently adjusts their energy usage over a day to
minimize their individual operational cost. This framework allows the community
manager to maintain central economic market properties such as budget balance
and individual rationality for prosumers. We show how the community benefits
can be allocated to prosumers either in an equal or a proportional manner. The
proposed model is eventually reformulated into a mixed integer second-order
cone program and thereafter applied to a distribution grid case study
Incentivizing Data Sharing for Energy Forecasting: Analytics Markets with Correlated Data
Reliably forecasting uncertain power production is beneficial for the social
welfare of electricity markets by reducing the need for balancing resources.
Describing such forecasting as an analytics task, the current literature
proposes analytics markets as an incentive for data sharing to improve
accuracy, for instance by leveraging spatio-temporal correlations. The
challenge is that, when used as input features for forecasting, correlated data
complicates the market design with respect to the revenue allocation, as the
value of overlapping information is inherently combinatorial. We develop a
correlation-aware analytics market for a wind power forecasting application. To
allocate revenue, we adopt a Shapley value-based attribution policy, framing
the features of agents as players and their interactions as a characteristic
function game. We illustrate that there are multiple options to describe such a
game, each having causal nuances that influence market behavior when features
are correlated. We argue that no option is correct in a general sense, but that
the decision hinges on whether the market should address correlations from a
data-centric or model-centric perspective, a choice that can yield
counter-intuitive allocations if not considered carefully by the market
designer.Comment: 15 pages, 9 figures, 1 tabl
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