31,625 research outputs found
Risk-limiting Load Restoration for Resilience Enhancement with Intermittent Energy Resources
Microgrids are resources that can be used to restore critical loads after a
natural disaster, enhancing resilience of a distribution network. To deal with
the stochastic nature of intermittent energy resources, such as wind turbines
(WTs) and photovoltaics (PVs), many methods rely on forecast information.
However, some microgrids may not be equipped with power forecasting tools. To
fill this gap, a risk-limiting strategy based on measurements is proposed.
Gaussian mixture model (GMM) is used to represent a prior joint probability
density function (PDF) of power outputs of WTs and PVs over multiple periods.
As time rolls forward, the distribution of WT/PV generation is updated based
the latest measurement data in a recursive manner. The updated distribution is
used as an input for the risk-limiting load restoration problem, enabling an
equivalent transformation of the original chance constrained problem into a
mixed integer linear programming (MILP). Simulation cases on a distribution
system with three microgrids demonstrate the effectiveness of the proposed
method. Results also indicate that networked microgrids have better uncertainty
management capabilities than stand-alone microgrids
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Data-driven Decision Making with Probabilistic Guarantees (Part 2): Applications of Chance-constrained Optimization in Power Systems
Uncertainties from deepening penetration of renewable energy resources have
posed critical challenges to the secure and reliable operations of future
electric grids. Among various approaches for decision making in uncertain
environments, this paper focuses on chance-constrained optimization, which
provides explicit probabilistic guarantees on the feasibility of optimal
solutions. Although quite a few methods have been proposed to solve
chance-constrained optimization problems, there is a lack of comprehensive
review and comparative analysis of the proposed methods. Part I of this
two-part paper reviews three categories of existing methods to
chance-constrained optimization: (1) scenario approach; (2) sample average
approximation; and (3) robust optimization based methods. Data-driven methods,
which are not constrained by any particular distributions of the underlying
uncertainties, are of particular interest. Part II of this two-part paper
provides a literature review on the applications of chance-constrained
optimization in power systems. Part II also provides a critical comparison of
existing methods based on numerical simulations, which are conducted on
standard power system test cases.Comment: (under review) to be update
Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: a bi-level programming approach via real-time pricing
In order to coordinate the scheduling problem between an isolated microgrid
(IMG) and electric vehicle battery swapping stations (BSSs) in
multi-stakeholder scenarios, a new bi-level optimal scheduling model is
proposed for promoting the participation of BSSs in regulating the IMG economic
operation. In this model, the upper-level sub-problem is formulated to minimize
the IMG net costs, while the lower-level aims to maximize the profits of the
BSS under real-time pricing environments determined by demand responses in the
upper-level decision. To solve the model, a hybrid algorithm, called JAYA-BBA,
is put forward by combining a real/integer-coded JAYA algorithm and the branch
and bound algorithm (BBA), in which the JAYA and BBA are respectively employed
to address the upper- and lower- level sub-problems, and the bi-level model is
eventually solved through alternate iterations between the two levels. The
simulation results on a microgrid test system verify the effectiveness and
superiority of the presented approach.Comment: Accepted by Applied Energ
A Robust Approach to Chance Constrained Optimal Power Flow with Renewable Generation
Optimal Power Flow (OPF) dispatches controllable generation at minimum cost
subject to operational constraints on generation and transmission assets. The
uncertainty and variability of intermittent renewable generation is challenging
current deterministic OPF approaches. Recent formulations of OPF use chance
constraints to limit the risk from renewable generation uncertainty, however,
these new approaches typically assume the probability distributions which
characterize the uncertainty and variability are known exactly. We formulate a
Robust Chance Constrained (RCC) OPF that accounts for uncertainty in the
parameters of these probability distributions by allowing them to be within an
uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that
scales to large power systems. We demonstrate the RRC OPF on a modified model
of the Bonneville Power Administration network, which includes 2209 buses and
176 controllable generators. Deterministic, chance constrained (CC), and RCC
OPF formulations are compared using several metrics including cost of
generation, area control error, ramping of controllable generators, and
occurrence of transmission line overloads as well as the respective
computational performance
Reliable Dispatch of Renewable Generation via Charging of Dynamic PEV Populations
The inherent storage of plug-in electric vehicles is likely to foster the
integration of intermittent generation from renewable energy sources into
existing power systems. In the present paper, we propose a three-stage scheme
to the end of achieving dispatchability of a system composed of plug-in
electric vehicles and intermittent generation. The main difficulties in
dispatching such a system are the uncertainties inherent to intermittent
generation and the time-varying aggregation of vehicles. We propose to address
the former by means of probabilistic forecasts and we approach the latter with
separate stage-specific models. Specifically, we first compute a dispatch
schedule, using probabilistic forecasts together with an aggregated dynamic
model of the system. The power output of the single devices are set
subsequently, using deterministic forecasts and device-specific models. We draw
upon a simulation study based on real data of generation and vehicle traffic to
validate our findings.Comment: Submitted to the IEEE Transactions on Power System
Chance-Constrained AC Optimal Power Flow: Reformulations and Efficient Algorithms
Higher levels of renewable electricity generation increase uncertainty in
power system operation. To ensure secure system operation, new tools that
account for this uncertainty are required. In this paper, we formulate a
chance-constrained AC optimal power flow problem, which guarantees that
generation, power flows and voltages remain within their bounds with a
pre-defined probability. We then propose an accurate, yet tractable analytical
reformulation of the chance constraints. The reformulation maintains the full,
non-linear AC power flow equations for the forecasted operating point, and
models the impact of uncertainty through a linearization around this point. We
discuss different solution algorithms, including one-shot optimization with and
without recourse, and an iterative algorithm which enables scalable
implementations. We further discuss how more general chance constraint
reformulations can be incorporated within the iterative solution algorithm. In
a case study based on four different IEEE systems, we compare the performance
of the solution algorithms, and demonstrate scalability of the iterative
scheme. We further show that the analytical reformulation accurately and
efficiently enforces chance constraints in both in- and out-of-sample tests,
and that the analytical approach outperforms two alternative, sample based
chance constraint reformulations
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
A Novel Probabilistic Framework to Study the Impact of PV-battery Systems on Low-Voltage Distribution Networks
Battery storage, particularly residential battery storage coupled with
rooftop PV, is emerging as an essential component of the smart grid technology
mix. However, including battery storage and other flexible resources like
electric vehicles and loads with thermal inertia into a probabilistic analysis
based on Monte Carlo (MC) simulation is challenging, because their operational
profiles are determined by computationally intensive optimization.
Additionally, MC analysis requires a large pool of statistically-representative
demand profiles to sample from. As a result, the analysis of the network impact
of PV-battery systems has attracted little attention in the existing
literature. To fill these knowledge gaps, this paper proposes a novel
probabilistic framework to study the impact of PV-battery systems on
low-voltage distribution networks. Specifically, the framework incorporates
home energy management(HEM) operational decisions within the MC time series
power flow analysis. First, using available smart meter data, we use a Bayesian
nonparametric model to generate statistically-representative synthetic demand
and PV profiles. Second, a policy function approximation that emulates battery
scheduling decisions is used to make the simulation of optimization-based HEM
feasible within the MC framework. The efficacy of our method is demonstrated on
three representative low-voltage feeders, where the computation time to execute
our MC framework is 5% of that when using explicit optimization methods in each
MC sample. The assessment results show that uncoordinated battery scheduling
has a limited beneficial impact, which is against the conjecture that batteries
will serendipitously mitigate the technical problems induced by PV generation.Comment: 10 pages, 7 figure
Whither probabilistic security management for real-time operation of power systems ?
This paper investigates the stakes of introducing probabilistic approaches
for the management of power system's security. In real-time operation, the aim
is to arbitrate in a rational way between preventive and corrective control,
while taking into account i) the prior probabilities of contingencies, ii) the
possible failure modes of corrective control actions, iii) the socio-economic
consequences of service interruptions. This work is a first step towards the
construction of a globally coherent decision making framework for security
management from long-term system expansion, via mid-term asset management,
towards short-term operation planning and real-time operation.Comment: Presented at the 2013 IREP Symposium-Bulk Power Systems Dynamics and
Control-IX (IREP), August 25-30,2013, Rethymnon, Crete, Greec
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