Bowen Zhang

A novel packet switching framework with binary information in demand side management

Inspired by the success of packet switched data transfer that improves the fairness and efficiency in communication networks, this paper proposes a novel concept of packetized energy that is capable of improving the performance of the smart grid in providing demand response. Here the term packetized refers to a temporal quantization into fixed length intervals of energy authorization based on binary information - energy is either requested by an appliance if it wishes to consume or withdrawn if the desired consumption level is reached. We model the energy request and withdrawal process as a queuing system with multiple servers and probabilistic returns. We derive an analytical expression for the mean waiting time (MWT) per authorization as a function of the packet length. We show that with short packet duration the MWT of the packet switching framework is smaller than the system without packet switching, and that the total waiting time (TWT) to complete the service remains the same. Consequently, the packet switched framework guarantees fairness in energy delivery when limited resources are available. We also provide a sensitivity analysis of the MWT and the TWT in terms of the packet length. Results show that the TWT is more robust to the change of packet length than the MWT. Simulations are provided to verify theoretical results.

A two level feedback system design to provide regulation reserve

Demand side management has gained increasing importance as the penetration of renewable energy grows. Based on a Markov jump process model of a group of thermostatic loads, this paper proposes a two level feedback system to govern the interactions between an independent system operator (ISO) and a number of regulation reserve providers such that two objectives are achieved: 1) the ISO can optimally dispatch regulation signals to multiple providers in real time in order to reduce the requirement for expensive spinning reserves, and 2) each reserve provider can control its thermostatic loads to respond to the ISO signal. It is shown that the amount of regulation reserve that can be provided is implicitly restricted by a few fundamental parameters of the provider itself, such as the allowable set point choice and its thermal constant. An interesting finding is that there is a trade-off between an appliances capacity for providing a large sustained reserve and its capacity of rapid reserve ramping. Simulations are presented to verify theoretical results.

Control and Communication Protocols Based on Packetized Direct Load Control in Smart Building Microgrids

Recent communication, computation, and technology advances coupled with climate change concerns have transformed the near future prospects of electricity transmission, and, more notably, distribution systems and microgrids. Distributed resources (wind and solar generation, combined heat and power) and flexible loads (storage, computing, EV, HVAC) make it imperative to increase investment and improve operational efficiency. Commercial and residential buildings, being the largest energy consumption group among flexible loads in microgrids, have the largest potential and flexibility to provide demand-side management. Recent advances in networked systems and the anticipated breakthroughs of the Internet of Things will enable significant advances in demand-response capabilities of intelligent load networks of power-consuming devices such as HVAC components, water heaters, EV charging stations, and many more. In this paper, a new operating framework, called packetized direct load control (PDLC), is proposed based on the notion of quantization of energy demand. This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of appliances. We discuss the optimal demand-side operation for both protocols and analytically derive the performance differences between the protocols. We propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both day-ahead and real-time markets. At the end of the paper, we discuss the fundamental tradeoff between achieving controllability and endowing consumer choice and flexibility.

The Kirchhoff-Braess paradox and its implications for smart microgrids

Well known in the theory of network flows, Braess paradox states that in a congested network, it may happen that adding a new path between destinations can increase the level of congestion. In transportation networks the phenomenon results from the decisions of network participants who selfishly seek to optimize their own performance metrics. In an electric power distribution network, an analogous increase in congestion can arise as a consequence Kirchhoffs laws. Even for the simplest linear network of resistors and voltage sources, the sudden appearance of congestion due to an additional conductive line is a nonlinear phenomenon that results in a discontinuous change in the network state. It is argued that the phenomenon can occur in almost any grid in which they are loops, and with the increasing penetration of small-scale distributed generation, it suggests challenges ahead in the operation of microgrids.

Data center optimal regulation service reserve provision with explicit modeling of quality of service dynamics

Data centers have shown great opportunities to participate in extensive demand response programs in recently years. This paper specifically focuses on data centers as participants in regulation service reserves (RSR) power market. We propose a novel approach to model the dynamics of the job processing Quality of Service (QoS) in data centers that offer RSR, and use stochastic dynamic programming (DP) to solve for the optimal reserve deployment policies. We show that the job QoS degradation can be modeled as a time varying probability distribution function (PDF) whose mean and variance evolve as functions of recent control statistics. The mean and variance are in fact additional state variables or sufficient statistics of the stochastic DP whose solution provides the data center operator (DCO) decision supports to minimize the average operating costs associated with RSR signal tracking error and job processing QoS degradation. Simulation results show that the feedback control policy obtained from the stochastic DP solution can reduce the DCOs operating costs compared to heuristic operating protocols reported in the literature. In addition, the DP value function can assist the DCO to bid optimally into the hour-ahead joint energy and reserve market.