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

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

DeMon++: A framework for designing and implementing Distributed Monitoring Systems based on Hierarchical Finite State Machines

In today’s interconnected world, the proliferation of diverse and numerous devices has become increasingly common. This phenomenon is particularly evident in the field of industrial computing, which has experienced rapid growth. With this rapid expansion, monitoring an industrial control system (ICS) consisting of a large num- ber of devices becomes a critical activity. To evaluate our approach, we chose the CERN ICS as a suitable case study for our research. The CERN ICS is a complex network of thousands of heterogeneous control devices, including PLCs, front-end computers, supervisory control and data acquisition systems. Our approach resulted in DeMon++, a framework for designing and implementing distributed monitoring systems. DeMon++ uses the concept of hierarchical finite state machines to model the system, capturing the hierarchical relationship between devices. In particular, DeMon++ aims to be a flexible, scalable and maintainable monitoring framework to abstract, aggregate and summarise the health state of industrial control sys- tems composed of a heterogeneous set of devices. As part of the CERN OpenLab programme, this thesis provides a flexible and maintainable approach to monitoring complex and distributed ICS, with a particular focus on the demanding environment of CERN

On optimal coordinated dispatch for heterogeneous storage fleets with partial availability

This paper addresses the problem of optimal scheduling of an aggregated power profile (during a coordinated discharging or charging operation) by means of a heterogeneous fleet of storage devices subject to availability constraints. Devices have heterogeneous initial levels of energy, power ratings and efficiency; moreover, the fleet operates without cross-charging of the units. An explicit feedback policy is proposed to compute a feasible schedule whenever one exists and scalable design procedures to achieve maximum time to failure or minimal unserved energy in the case of unfeasible aggregated demand profiles. Finally, a time-domain characterization of the set of feasible demand profiles using aggregate constraints is proposed, suitable for optimization problems where the aggregate population behaviour is of interest