110 research outputs found
Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination
Aggregators have emerged as crucial tools for the coordination of distributed, controllable loads. However, to be used effectively, aggregators must be able to communicate the available flexibility of the loads they control to the system operator in a manner that is both (i) concise enough to be scalable to aggregators governing hundreds or even thousands of loads and (ii) informative enough to allow the system operator to send control signals to the aggregator that lead to optimization of system-level objectives, such as cost minimization, and do not violate private constraints of the loads, such as satisfying specific load demands. In this paper, we present the design of a real-time flexibility feedback signal based on maximization of entropy. The design provides a concise and informative signal that can be used by the system operator to perform online cost minimization and real-time capacity estimation, while provably satisfying the private constraints of the loads. In addition to deriving analytic properties of the design, we illustrate the effectiveness of the design using a dataset from an adaptive electric vehicle charging network
Real-time Flexibility Feedback for Closed-loop Aggregator and System Operator Coordination
Aggregators have emerged as crucial tools for the coordination of
distributed, controllable loads. However, to be used effectively, aggregators
must be able to communicate the available flexibility of the loads they control
to the system operator in a manner that is both (i) concise enough to be
scalable to aggregators governing hundreds or even thousands of loads and (ii)
informative enough to allow the system operator to send control signals to the
aggregator that lead to optimization of system-level objectives, such as cost
minimization, and do not violate private constraints of the loads, such as
satisfying specific load demands. In this paper, we present the design of a
real-time flexibility feedback signal based on maximization of entropy. The
design provides a concise and informative signal that can be used by the system
operator to perform online cost minimization and real-time capacity estimation,
while provably satisfying the private constraints of the loads. In addition to
deriving analytic properties of the design, we illustrate the effectiveness of
the design using a dataset from an adaptive electric vehicle charging network.Comment: The Eleventh ACM International Conference on Future Energy Systems
(e-Energy'20
Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments
The increased penetration of uncertain and variable renewable energy presents
various resource and operational electric grid challenges. Micro-level
(household and small commercial) demand-side grid flexibility could be a
cost-effective strategy to integrate high penetrations of wind and solar
energy, but literature and field deployments exploring the necessary
information and communication technologies (ICTs) are scant. This paper
presents an exploratory framework for enabling information driven grid
flexibility through the Internet of Things (IoT), and a proof-of-concept
wireless sensor gateway (FlexBox) to collect the necessary parameters for
adequately monitoring and actuating the micro-level demand-side. In the summer
of 2015, thirty sensor gateways were deployed in the city of Managua
(Nicaragua) to develop a baseline for a near future small-scale demand response
pilot implementation. FlexBox field data has begun shedding light on
relationships between ambient temperature and load energy consumption, load and
building envelope energy efficiency challenges, latency communication network
challenges, and opportunities to engage existing demand-side user behavioral
patterns. Information driven grid flexibility strategies present great
opportunity to develop new technologies, system architectures, and
implementation approaches that can easily scale across regions, incomes, and
levels of development
An Efficient Method for Quantifying the Aggregate Flexibility of Plug-in Electric Vehicle Populations
Plug-in electric vehicles (EVs) are widely recognized as being highly
flexible electric loads that can be pooled and controlled via aggregators to
provide low-cost energy and ancillary services to wholesale electricity
markets. To participate in these markets, an EV aggregator must encode the
aggregate flexibility of the population of EVs under their command as a single
polytope that is compliant with existing market rules. To this end, we
investigate the problem of characterizing the aggregate flexibility set of a
heterogeneous population of EVs whose individual flexibility sets are given as
convex polytopes in half-space representation. As the exact computation of the
aggregate flexibility set -- the Minkowski sum of the individual flexibility
sets -- is known to be intractable, we study the problems of computing
maximum-volume inner approximations and minimum-volume outer approximations to
the aggregate flexibility set by optimizing over affine transformations of a
given convex polytope in half-space representation. We show how to
conservatively approximate the pair of maximum-volume and minimum-volume set
containment problems as linear programs that scale polynomially with the number
and dimension of the individual flexibility sets. The class of approximations
methods provided in this paper generalizes existing methods from the
literature. We illustrate the improvement in approximation accuracy achievable
by our methods with numerical experiments.Comment: 10 pages, 4 figure
Learning-based Predictive Control via Real-time Aggregate Flexibility
Aggregators have emerged as crucial tools for the coordination of
distributed, controllable loads. To be used effectively, an aggregator must be
able to communicate the available flexibility of the loads they control, as
known as the aggregate flexibility to a system operator. However, most of
existing aggregate flexibility measures often are slow-timescale estimations
and much less attention has been paid to real-time coordination between an
aggregator and an operator. In this paper, we consider solving an online
optimization in a closed-loop system and present a design of real-time
aggregate flexibility feedback, termed the maximum entropy feedback (MEF). In
addition to deriving analytic properties of the MEF, combining learning and
control, we show that it can be approximated using reinforcement learning and
used as a penalty term in a novel control algorithm -- the penalized predictive
control (PPC), which modifies vanilla model predictive control (MPC). The
benefits of our scheme are (1). Efficient Communication. An operator running
PPC does not need to know the exact states and constraints of the loads, but
only the MEF. (2). Fast Computation. The PPC often has much less number of
variables than an MPC formulation. (3). Lower Costs. We show that under certain
regularity assumptions, the PPC is optimal. We illustrate the efficacy of the
PPC using a dataset from an adaptive electric vehicle charging network and show
that PPC outperforms classical MPC.Comment: 13 pages, 5 figures, extension of arXiv:2006.1381
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