820 research outputs found
Chance-Constrained AC Optimal Power Flow Integrating HVDC Lines and Controllability
The integration of large-scale renewable generation has major implications on
the operation of power systems, two of which we address in this work. First,
system operators have to deal with higher degrees of uncertainty due to
forecast errors and variability in renewable energy production. Second, with
abundant potential of renewable generation in remote locations, there is an
increasing interest in the use of High Voltage Direct Current lines (HVDC) to
increase transmission capacity. These HVDC transmission lines and the
flexibility and controllability they offer must be incorporated effectively and
safely into the system. In this work, we introduce an optimization tool that
addresses both challenges by incorporating the full AC power flow equations,
chance constraints to address the uncertainty of renewable infeed, modelling of
point-to-point HVDC lines, and optimized corrective control policies to model
the generator and HVDC response to uncertainty. The main contributions are
twofold. First, we introduce a HVDC line model and the corresponding HVDC
participation factors in a chance-constrained AC-OPF framework. Second, we
modify an existing algorithm for solving the chance-constrained AC-OPF to allow
for optimization of the generation and HVDC participation factors. Using
realistic wind forecast data, for 10 and IEEE 39 bus systems with HVDC lines
and wind farms, we show that our proposed OPF formulation achieves good in- and
out-of-sample performance whereas not considering uncertainty leads to high
constraint violation probabilities. In addition, we find that optimizing the
participation factors reduces the cost of uncertainty significantly
Stochastic Optimal Power Flow Based on Data-Driven Distributionally Robust Optimization
We propose a data-driven method to solve a stochastic optimal power flow
(OPF) problem based on limited information about forecast error distributions.
The objective is to determine power schedules for controllable devices in a
power network to balance operation cost and conditional value-at-risk (CVaR) of
device and network constraint violations. These decisions include scheduled
power output adjustments and reserve policies, which specify planned reactions
to forecast errors in order to accommodate fluctuating renewable energy
sources. Instead of assuming the uncertainties across the networks follow
prescribed probability distributions, we assume the distributions are only
observable through a finite training dataset. By utilizing the Wasserstein
metric to quantify differences between the empirical data-based distribution
and the real data-generating distribution, we formulate a distributionally
robust optimization OPF problem to search for power schedules and reserve
policies that are robust to sampling errors inherent in the dataset. A simple
numerical example illustrates inherent tradeoffs between operation cost and
risk of constraint violation, and we show how our proposed method offers a
data-driven framework to balance these objectives
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Chance-constrained optimization (CCO) has been widely used for uncertainty
management in power system operation. With the prevalence of wind energy, it
becomes possible to consider the wind curtailment as a dispatch variable in
CCO. However, the wind curtailment will cause impulse for the uncertainty
distribution, yielding challenges for the chance constraints modeling. To deal
with that, a data-driven framework is developed. By modeling the wind
curtailment as a cap enforced on the wind power output, the proposed framework
constructs a Gaussian process (GP) surrogate to describe the relationship
between wind curtailment and the chance constraints. This allows us to
reformulate the CCO with wind curtailment as a mixed-integer second-order cone
programming (MI-SOCP) problem. An error correction strategy is developed by
solving a convex linear programming (LP) to improve the modeling accuracy. Case
studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that
the proposed method is capable of accurately accounting the influence of wind
curtailment dispatch in CCO
Demand flexibility management for buildings-to-grid integration with uncertain generation
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