Control and Communication Protocols that Enable 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 network of power-consuming devices such as HVAC components, water heaters, and buildings. 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 the 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. In the end we discuss the fundamental trade-off between achieving controllability and endowing flexibility

Delay Performance of MISO Wireless Communications

Ultra-reliable, low latency communications (URLLC) are currently attracting significant attention due to the emergence of mission-critical applications and device-centric communication. URLLC will entail a fundamental paradigm shift from throughput-oriented system design towards holistic designs for guaranteed and reliable end-to-end latency. A deep understanding of the delay performance of wireless networks is essential for efficient URLLC systems. In this paper, we investigate the network layer performance of multiple-input, single-output (MISO) systems under statistical delay constraints. We provide closed-form expressions for MISO diversity-oriented service process and derive probabilistic delay bounds using tools from stochastic network calculus. In particular, we analyze transmit beamforming with perfect and imperfect channel knowledge and compare it with orthogonal space-time codes and antenna selection. The effect of transmit power, number of antennas, and finite blocklength channel coding on the delay distribution is also investigated. Our higher layer performance results reveal key insights of MISO channels and provide useful guidelines for the design of ultra-reliable communication systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Optimization and Integration of Electric Vehicle Charging System in Coupled Transportation and Distribution Networks

With the development of the EV market, the demand for charging facilities is growing rapidly. The rapid increase in Electric Vehicle and different market factors bring challenges to the prediction of the penetration rate of EV number. The estimates of the uptake rate of EVs for light passenger use vary widely with some scenarios gradual and others aggressive. And there have been many effects on EV penetration rate from incentives, tax breaks, and market price. Given this background, this research is devoted to addressing a stochastic joint planning framework for both EV charging system and distribution network where the EV behaviours in both transportation network and electrical system are considered. And the planning issue is formulated as a multi-objective model with both the capital investment cost and service convenience optimized. The optimal planning of EV charging system in the urban area is the target geographical planning area in this work where the service radius and driving distance is relatively limited. The mathematical modelling of EV driving and charging behaviour in the urban area is developed

Stability of Service under Time-of-Use Pricing

We consider "time-of-use" pricing as a technique for matching supply and demand of temporal resources with the goal of maximizing social welfare. Relevant examples include energy, computing resources on a cloud computing platform, and charging stations for electric vehicles, among many others. A client/job in this setting has a window of time during which he needs service, and a particular value for obtaining it. We assume a stochastic model for demand, where each job materializes with some probability via an independent Bernoulli trial. Given a per-time-unit pricing of resources, any realized job will first try to get served by the cheapest available resource in its window and, failing that, will try to find service at the next cheapest available resource, and so on. Thus, the natural stochastic fluctuations in demand have the potential to lead to cascading overload events. Our main result shows that setting prices so as to optimally handle the {\em expected} demand works well: with high probability, when the actual demand is instantiated, the system is stable and the expected value of the jobs served is very close to that of the optimal offline algorithm.Comment: To appear in STOC'1

Co-simulation of a Low-Voltage Utility Grid Controlled over IEC 61850 protocol

International audienceThis paper presents a co-simulation model using MATLAB® toolboxes to illustrate an interaction between the communication system and the energy grid, coherent with the concept of smart grid that employs IEC 61850 communication standard. The MMS (Manufacturing Message Specification) protocol supported by IEC 61850, based on TCP/IP is used for the vertical communication between the Supervisory and Data Acquisition (SCADA) system and Intelligent Electronic Devices (IEDs) embedding the local control of different parts of the smart grid. In this paper an IED supporting the power control of a photovoltaic (PV) plant connected to a low-voltage (LV) utility grid is considered. Communication system consisting of the transport layer and a router placed on the network layer is modeled as an event driven system using SimEvents® toolbox and energy grid is modeled as a time-driven system using SimPowerSystems® toolbox. Co-simulation results are obtained by combining different communication scenarios and time-varying irradiance scenarios for thee PV plant when the PV plant is required to provide a certain power in response to a power reference received from SCADA over the communication network. The analysis aims at illustrating the impact that stochastic behavior and delays due to network communication have on the global system behavior

Capacity usage determination of a Capacitor-less D-STATCOM considering Power System Uncertainties

The increasing adoption of distributed energy resources (DERs), particularly solar generation and the use of unconventional loads such as plug-in electric vehicles (PHEVs), has a profound impact on the planning and operation of electric distribution systems. In particular, PHEV charging introduces stochastic peaks in energy consumption, while solar generation is fraught with variability during intermittent clouds. The stochastic nature of such DERs renders the operation of mechanical assets such as on-load tap changers and switched capacitor banks ineffective. A possible solution to mitigate the undesirable effects of DERs is using solid-state-based devices such as a distribution static synchronous compensator (D-STATCOM). This paper examines the capacity usage of a capacitor-less D-STATCOM in distribution systems while considering the uncertainties associated with using the aforementioned DERs. We propose a Monte Carlo simulation to study the capacity usage problem with DER inputs sampled from the proposed underlying distributions

Role of control, communication, and markets in smart building operation

This thesis explores the role of control, communication, and markets in the operation of smart buildings and microgrids. It develops models to study demand response (DR) alternatives in smart buildings using different communication and control protocols in building management systems. Moreover, it aims at understanding the extent to which smart buildings can provide regulation service reserves (RSR) by real time direct load control (DLC) or price-based indirect control approaches. In conducting a formal study of these problems, we first investigate the optimal operational performance of smart buildings using a control protocol called packetized direct load control (PDLC). This is based on the notion of the energy packet which is a temporal quantization of energy supplied to an appliance or appliance pool by a smart building operator (SBO). This control protocol is built on top of two communication protocols that carry either complete or binary information regarding the operation status of the appliances in the pool. We discuss the optimal demand side operation for both protocols and analytically derive the performance differences between them. We analyze the costs of renewable penetration to the system's real time operation. In order to strike a balance between excessive day-ahead energy reservation costs and stochastic real time operation costs, we propose an optimal reservation strategy for traditional and renewable energy for the PDLC in both the day-ahead and the real time markets to hedge the uncertainty of real time energy prices and renewable energy realization. The second part of the thesis proposes systematic approaches for smart buildings to reliably participate in power reserve markets. The problem is decomposed into two parts in the first of which the SBO starts by estimating its prior capacity of reserve provision based on characteristics of the building, the loads, and consumer preferences. We show that the building's reserve capacity is governed by a few parameters and that there is a trade off for smart buildings to provide either sustained reserve or ramping reserve. Based on the estimated capacity, we propose two real time control mechanisms to provide reliable RSR. The first is a DLC framework wherein consumers allow the SBO to directly modulate their appliances' set points within allowable ranges. We develop a feedback controller to guarantee asymptotic tracking performance of the smart building's aggregated response to the RSR signal. The second is a price controlled framework that allows consumers to voluntarily connect and consume electricity based on their instantaneous utility needs. Consumers' time varying dynamic preferences in providing RSR are studied by Monte Carlo simulation, in which such preferences are characterized by sufficient statistics that can be used in a stochastic dynamic programming (DP) formulation to solve for the optimal pricing policy

Charging infrastructure planning and resource allocation for electric vehicles

With the increasing uptake of electric vehicles (EVs) and relative lag in the development of charging facilities, how to plan charging infrastructure and effectively use existing charging resources have become the top priority for governments, related industry and research communities. This study aims to address two key issues related to EV charging - charging station planning and charging resource allocation. The major contributions of the study are: (1) Introduced a model for charging infrastructure planning based on origin-destination data of EV traffic flows. I first showed how to use the gravity model to calculate point-to-point traffic flows from traffic data at each intersection and further induce the origin-to-destination flow data. Then, I introduced an optimization model for charging allocation based on origin-destination traffic flow data and extended it into a formal model for charging station planning by minimizing the total waiting time of EVs. (2) Applied the charging infrastructure planning model to Sydney Metropolitan charging station planning. I selected a set of representative areas from Sydney metropolitan and collected traffic data for these areas. I then used the gravity model to calculate the EV flow for each route based on possible portions of EVs among all traffic. The optimisation constraints under consideration include charging station locations, total budget and feasibility of charging allocations. Optimisation for chargers at each intersection for different scenarios is solved using the least squares method. (3) Designed an algorithm for charging facility allocation to balance the load of charging stations. By considering the maximum driving range, the number of chargers at charging stations, and waiting time and queue length at each charging station, a queue balancing algorithm is proposed. Numerical experiments were conducted to validate the algorithm based on a linear road scenario. I believe that the outcomes of this research have a great potential to be used for government/industry planning of charging stations and improvement of utilization of charging stations resources

Performance Evaluation of Wireless Medium Access Control Protocols for Internet of Things

The Internet of Things makes the residents in Smart Cities enjoy a more efficient and high-quality lifestyle by wirelessly interconnecting the physical and visual world. However, the performance of wireless networks is challenged by the ever-growing wireless traffic data, the complexity of the network structures, and various requirements of Quality of Service (QoS), especially on the Internet of Vehicle and wireless sensor networks. Consequently, the IEEE 802.11p and 802.11ah standards were designed to support effective inter-vehicle communications and large-scale sensor networks, respectively. Although their Medium Access Control protocols have attracted much research interest, they have yet to fully consider the influences of channel errors and buffer sizes on the performance evaluation of these Medium Access Control (MAC) protocols. Therefore, this thesis first proposed a new analytical model based on a Markov chain and Queuing analysis to evaluate the performance of IEEE 802.11p under imperfect channels with both saturated and unsaturated traffic. All influential factors of the Enhanced Distributed Channel Access (EDCA) mechanism in IEEE 802.11p are considered, including the backoff counter freezing, Arbitration Inter-Frame Spacing (AIFS) defers, the internal collision, and finite MAC buffer sizes. Furthermore, this proposed model considers more common and actual conditions with the influence of channel errors and finite MAC buffer sizes. The effectiveness and accuracy of the developed model have been validated through extensive ns-3 simulation experiments. Second, this thesis proposes a developed analytical model based on Advanced Queuing Analysis and the Gilbert-Elliot model to analyse the performance of IEEE 802.11p with burst error transmissions. This proposed analytical model simultaneously describes transmission queues for all four Access Categories (AC) queues with the influence of burst errors. Similarly, this presented model can analyse QoS performance, including throughputs and end-to-end delays with the unsaturated or saturated load traffics. Furthermore, this model operates under more actual bursty error channels in vehicular environments. In addition, a series of simulation experiments with a natural urban environment is designed to validate the efficiency and accuracy of the presented model. The simulation results reflect the reliability and effectiveness of the presented model in terms of throughput and end-to-end delays under various channel conditions. Third, this thesis designed and implemented a simulation experiment to analyse the performance of IEEE 802.11ah. These simulation experiments are based on ns-3 and an extension. These simulation experiments' results indicate the Restricted Access Window (RAW) mechanism's influence on the throughputs, end-to-end delays, and packet loss rates. Furthermore, the influences of channel errors and bursty errors are considered in the simulations. The results also show the strong impact of channel errors on the performance of IEEE 802.11ah due to urban environments. Finally, the potential future work based on the proposed models and simulations is analysed in this thesis. The proposed models of IEEE 802.11p can be an excellent fundamental to optimise the QoS due to the precise evaluation of the influence of factors on the performance of IEEE 802.11p. Moreover, it is possible to migrate the analytical models of IEEE 802.11p to evaluate the performance of IEEE 802.11ah