566 research outputs found
COHORT: Coordination of Heterogeneous Thermostatically Controlled Loads for Demand Flexibility
Demand flexibility is increasingly important for power grids. Careful
coordination of thermostatically controlled loads (TCLs) can modulate energy
demand, decrease operating costs, and increase grid resiliency. We propose a
novel distributed control framework for the Coordination Of HeterOgeneous
Residential Thermostatically controlled loads (COHORT). COHORT is a practical,
scalable, and versatile solution that coordinates a population of TCLs to
jointly optimize a grid-level objective, while satisfying each TCL's end-use
requirements and operational constraints. To achieve that, we decompose the
grid-scale problem into subproblems and coordinate their solutions to find the
global optimum using the alternating direction method of multipliers (ADMM).
The TCLs' local problems are distributed to and computed in parallel at each
TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses
combinatorial and non-convex constraints, we characterize these constraints as
a convex set through relaxation, thereby making COHORT computationally viable
over long planning horizons. After coordination, each TCL is responsible for
its own control and tracks the agreed-upon power trajectory with its preferred
strategy. In this work, we translate continuous power back to discrete on/off
actuation, using pulse width modulation. COHORT is generalizable to a wide
range of grid objectives, which we demonstrate through three distinct use
cases: generation following, minimizing ramping, and peak load curtailment. In
a notable experiment, we validated our approach through a hardware-in-the-loop
simulation, including a real-world air conditioner (AC) controlled via a smart
thermostat, and simulated instances of ACs modeled after real-world data
traces. During the 15-day experimental period, COHORT reduced daily peak loads
by an average of 12.5% and maintained comfortable temperatures.Comment: Accepted to ACM BuildSys 2020; 10 page
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
Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps
Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart
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