Green Scheduling of Control Systems for Peak Demand Reduction

Building systems such as heating, air quality control and refrigeration operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. We present an approach to fine-grained coordination of energy demand by scheduling the control systems within a constrained peak while ensuring custom climate environments are facilitated. The peak constraint is minimized for energy efficiency, while we provide feasibility conditions for the constraint to be realizable by a scheduling policy for the control systems. The physical systems are then coordinated by the scheduling controller so as both the peak constraint and the climate/safety constraint are satisfied. We also introduce a simple scheduling approach called lazy scheduling. The proposed control and scheduling strategy is implemented in simulation examples from small to large scales, which show that it can achieve significant peak demand reduction while being efficient and scalable

Time programmable smart devices for peak demand reduction of smart homes in a microgrid

Abstract: Increasing electricity access through Microgrids for rural areas is often faced with the challenge of increased peak demand through increased electricity demand as more electronic devices will be acquired by the consumers and more small businesses will spring up in the community. If not taken care of, this leads to additional cost of incurring higher peaker plants to meet the peak demand, and the burden of the cost of peaker plants are consequentially transferred to the consumers. Since this load is generated by the consumers, it is most desirable to control the peak demand from the consumers’ side. Therefore, a method of Time Programmable Smart Devices (TPSD) with an efficient Electricity Use Plan (EUP) is proposed in this paper by introducing appliance working knowledge and improving load shifting technique of Demand Side Management for peak demand reduction in a rural Microgrid. This method yielded lower morning and evening peaks, a lower peak-to-peak difference than those available in literature, and a peak period shift from the traditional peak period to traditional off-peak period. These lead to financial savings, reduced cost of peaker plants and a safer environment from less greenhouse gases emissions

Safe Schedulability of Bounded-Rate Multi-Mode Systems

Bounded-rate multi-mode systems (BMMS) are hybrid systems that can switch freely among a finite set of modes, and whose dynamics is specified by a finite number of real-valued variables with mode-dependent rates that can vary within given bounded sets. The schedulability problem for BMMS is defined as an infinite-round game between two players---the scheduler and the environment---where in each round the scheduler proposes a time and a mode while the environment chooses an allowable rate for that mode, and the state of the system changes linearly in the direction of the rate vector. The goal of the scheduler is to keep the state of the system within a pre-specified safe set using a non-Zeno schedule, while the goal of the environment is the opposite. Green scheduling under uncertainty is a paradigmatic example of BMMS where a winning strategy of the scheduler corresponds to a robust energy-optimal policy. We present an algorithm to decide whether the scheduler has a winning strategy from an arbitrary starting state, and give an algorithm to compute such a winning strategy, if it exists. We show that the schedulability problem for BMMS is co-NP complete in general, but for two variables it is in PTIME. We also study the discrete schedulability problem where the environment has only finitely many choices of rate vectors in each mode and the scheduler can make decisions only at multiples of a given clock period, and show it to be EXPTIME-complete.Comment: Technical report for a paper presented at HSCC 201

Game-theoretic decentralized model predictive control of thermal appliances in discrete-event systems framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is P-observable

Керування енергоспоживанням в системі MicroGrid

Метою роботи є розробка алгоритмів керування навантаженнями та генераторами на базі прогнозу електроспоживання для усунення пікових навантажень та забезпечення надійності роботи системи. Поставлені у роботі задачі вирішувалися шляхом проведення теоретичних досліджень та моделювання. Використано апарат математичного аналізу. Також застосовувались сучасні методи та програмні засоби комп’ютерного моделювання. На базі проведеного огляду методів прогнозування енергоспоживання розроблено класифікацію моделей прогнозування та визначено їх переваги при застосуванні в MicroGrid. Для визначення ефективності всіх моделей проведено порівняння погодинного прогнозу для трьох діапазонів. Розроблена технічна реалізація прогнозування енергоспоживання в системі MicroGrid, що забезпечують координоване управління розподіленими енергоресурсами, засобами управління режимом і конфігурацією мережі. Визначено сценарії використання таких систем. Дані результати можуть бути використані для застосування в локальних мережах MicroGrid а також в SmartGrid. Основні наукові положення дисертації представлено в двох наукових публікаціях, одна з яких опублікована в міжнародній базі SCOPUS, друга – в матеріалах конференції «Електроніка 2018».The purpose of work is development of load management algorithms and generators on the basis of the forecast of electricity consumption to eliminate peak loads and ensure the reliability of the system. The tasks put in the work were solved by conducting theoretical studies and modeling. Used mathematical analysis device. Also, modern methods and software tools for computer simulation were used. On the basis of the review of methods for forecasting energy consumption, the classification of forecasting models has been developed and their advantages in application in MicroGrid have been determined. To determine the effectiveness of all models, a comparison of the hourly forecast for the three ranges was made. The technical implementation of forecasting of energy consumption in the system of MicroGrid, which provides coordinated management of distributed energy resources, means of regime control and network configuration, is developed. Scenarios for using such systems are defined. These results can be used for use on local networks of MicroGrid as well as in SmartGrid. The main scientific provisions of the dissertation are presented in two scientific papers publications, one of which is published in the international base of SCOPUS, the second one - in the materials of the conference "Electronics 2018".Целью работы является разработка алгоритмов управления нагрузками и генераторами на базе прогноза электропотребления для устранения пиковых нагрузок и обеспечения надежности работы системы. Поставленные в работе задачи решались путем проведения теоретических исследований и моделирования. Использован аппарат математического анализа. Также применялись современные методы и программные средства компьютерного моделирования. На основе проведенного обзора методов прогнозирования энергопотребления разработана классификация моделей прогнозирования и определены их преимущества при применении в MicroGrid. Для определения эффективности всех моделей проведено сравнение почасового прогноза для трех диапазонов. Разработана техническая реализация прогнозирования энергопотребления в системе MicroGrid, обеспечивающих координированное управление распределенными энергоресурсами, средствами управления режимом и конфигурацией сети. Определены сценарии использования таких систем. Данные результаты могут быть использованы для применения в локальных сетях MicroGrid а также в SmartGrid. Основные научные положения диссертации представлены в двух научных публикациях, одна из которых опубликована в международной базе SCOPUS, вторая - в материалах конференции «Электроника 2018»

Determining Key Parameters and Guidelines for the Design of an Electrically Activated Concrete Slab for Peak Shifting in a Light-Weight Residential Building in a Northern Climate

A thermal storage system for residential buildings in a Northern climate is developed for electrical peak shifting and shaving. To facilitate implementation, only commercially available products are used for the system in conjunction with common construction methods. A thermal model is created with the TRNSYS simulation software and validated using data from a two-year monitoring campaign. The thermal model is used to identify key system parameters and propose system design guidelines. It is determined that, for residential buildings with a footprint varying between 80 m2 to 200 m2, the basement floor slab can be used for thermal storage with electrical heating cables and that the entire basement heating load can, during the peaks, be shifted to off peak periods. The optimal assembly for the basement floor is composed of 102 mm of extruded polystyrene insulation followed by 152 mm of concrete. The electric heating cables are positioned at the bottom of the concrete layer. This assembly can be controlled with the air set point temperature. The air setpoint temperature of basement rooms during charging needs to be 2 degC higher than the air setpoint temperature during normal operating conditions. The required charging time for building footprints of 80, 120 160 and 200 m2 corresponds to 6.00, 5.51, 5.05 and 4.66 hours, respectively

Commande prédictive désynchronisée pour le contrôle d'une grande population de systèmes thermostatiques

RÉSUMÉ Ce mémoire porte sur la modélisation et sur le contrôle d’une grande population de systèmes thermostatiques (TCLs) contrôlés individuellement par une commande prédictive. Le contrôle d’une grande population de systèmes o˙re beaucoup d’opportunités telles que le contrôle en fréquence, le suivi de charges, l’équilibrage énergétique qui peut contribuer à l’amélioration de la stabilité du réseau électrique. Les TCLs sont aussi un moyen d’absorber la production fluctuante d’énergie renouvelable générée par des éoliennes, fermes de panneaux solaires. De plus, la plupart des TCLs, comme les chau˙ages, les climatiseurs, les chau˙e-eaux, les réfrigé-rateurs, ont une consommation d’énergie flexible et élastique en termes de performances. Les TCLs sont considérés comme des éléments importants pour gérer la régulation de la charge, et plus particulièrement peuvent jouer un rôle majeur pour réduire la consommation de pointe et combler les creux de consommation. Ils sont aussi des éléments d’ajustement dans le cadre d’une tarification dynamique de l’énergie dans un réseau électrique intelligent. Le contrôle d’une grande population de systèmes thermostatiques est un problème qui est abordé depuis longtemps et qui continue d’attirer l’attention des chercheurs dans la littérature actuelle. Un des défis majeurs du contrôle d’une grande population de TCLs est la synchronisation des appareils entre eux. Un tel phénomène peut apparaître après une panne de courant, et cela implique des pics de puissance et des oscillations de puissance dans le réseau. Pour aborder ce problème, ce mémoire développe deux méthodes décentralisées qui vont hétérogénéiser individuellement le processus de prise de décision des MPC. Ces deux méthodes consistent à ajouter un délai aléatoire dans la trajectoire de référence et pénaliser aléatoirement la fonction objectif du MPC. Ces méthodes ont été validées dans le contexte du contrôle des ventilateurs de serveurs dans les centres de données. Typiquement, un centre de données est construit à des fins commerciales et abritent des centaines voire des milliers d’étagères pour stocker les serveurs informatiques, qui elles-mêmes peuvent contenir des dizaines de serveurs, ce qui représente une grande population homogène de TCLs. Un modèle thermique dynamique permet de représenter le comportement thermique à l’intérieur des serveurs, et un contrôleur MPC décentralisé permet le contrôle de la température de ceux-ci. Pour ca-ractériser la désynchronisation des TCLs contrôlés par MPC, un modèle composé d’une paire d’équations de transport semi-linéaires couplées est utilisé, en plus des simulations de Monte-Carlo. Les simulations numériques montrent que le comportement global obtenu grâce à cette paire d’équations di˙érentielles correspond aux résultats générés par les simulations de Monte-Carlo. Ceci confirme la validité de l’approche utilisée.----------ABSTRACT This thesis addresses the modeling and control of large populations of thermostatically con-trolled loads (TCLs) operated by model predictive control (MPC) schemes at the level of each TCL. Aggregates of large populations of TCLs can be managed to offer auxiliary services, such as frequency control, load following, and energy balancing, which can contribute to maintaining the overall stability of power networks. TCLs can also provide a means for absorbing the fluctuations of renewable energy generated by, e.g., wind turbines and solar photovoltaic plants. Moreover, due to the fact that most of the TCLs, including space heaters, air conditioners, hot water tanks, and refrigerators, exhibit flexibilities in power demand for their operation and elasticities in terms of performance restrictions, they are considered to be one of the most important Demand Response (DR) resources that can provide such features as power peak shaving and valley filling and enable dynamic pricing schemes in the context of the Smart Grid. Indeed, control of aggregated TCL populations is a long-time standing problem, which continues to attract much attention in the recent literature. A critical issue in the operation of a large population of TCLs is the occurrence of synchronization due to the phenomenon of cold load pickup, which may result in high power demand peaks and load oscillations. To tackle this problem, this thesis developed two fully decentralized schemes that would randomize the decision-making process of the MPC individually by each TCL, namely adding random delays in reference signal and extra penalizations on MPC objective functions. The proposed control schemes are validated in the context of the operation of fans in server enclosures in datacenters. Typically, data centers are built from general purpose commercially available o˙-the-shelf (COTS) processors. A data center may have hundreds or even thousands of server racks; each may host several tens of server enclosures, which represents a large population of homogenous TCLs. The thermal dynamics of the fans has been established, and a decentralized MPC control scheme has been designed for the control of a large population of fans. To characterize desynchronized MPC-based TCLs control schemes, a model governed by a pair of coupled semi-linear transport equations for describing the dynamic behavior of the population has been developed, in addition to Monte-Carlo simulations. Numerical simulation studies show that the aggregate behavior derived from this partial differential equation (PDE) model fits well with the results generated by the Monte-Carlo simulation. This confirmed the validity of the proposed approach