6 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
Competitive Online Peak-Demand Minimization Using Energy Storage
We study the problem of online peak-demand minimization under energy storage
constraints. It is motivated by an increasingly popular scenario where
large-load customers utilize energy storage to reduce the peak procurement from
the grid, which accounts for up to of their electric bills. The problem
is uniquely challenging due to (i) the coupling of online decisions across time
imposed by the inventory constraints and (ii) the noncumulative nature of the
peak procurement. In this paper, we develop an optimal online algorithm for the
problem, attaining the best possible competitive ratio (CR) among all
deterministic and randomized algorithms. We show that the optimal CR can be
computed in polynomial time, by solving a linear number of linear-fractional
problems. More importantly, we generalize our approach to develop an
\emph{anytime-optimal} online algorithm that achieves the best possible CR at
any epoch, given the inputs and online decisions so far. The algorithm retains
the optimal worst-case performance and achieves adaptive average-case
performance. Simulation results based on real-world traces show that, under
typical settings, our algorithms improve peak reduction by over as
compared to baseline alternatives
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