Fast and Reliable Primary Frequency Reserves From Refrigerators with Decentralized Stochastic Control

Due to increasing shares of renewable energy sources, more frequency reserves are required to maintain power system stability. In this paper, we present a decentralized control scheme that allows a large aggregation of refrigerators to provide Primary Frequency Control (PFC) reserves to the grid based on local frequency measurements and without communication. The control is based on stochastic switching of refrigerators depending on the frequency deviation. We develop methods to account for typical lockout constraints of compressors and increased power consumption during the startup phase. In addition, we propose a procedure to dynamically reset the thermostat temperature limits in order to provide reliable PFC reserves, as well as a corrective temperature feedback loop to build robustness to biased frequency deviations. Furthermore, we introduce an additional randomization layer in the controller to account for thermostat resolution limitations, and finally, we modify the control design to account for refrigerator door openings. Extensive simulations with actual frequency signal data and with different aggregation sizes, load characteristics, and control parameters, demonstrate that the proposed controller outperforms a relevant state-of-the-art controller.Comment: 44 pages, 17 figures, 9 Tables, submitted to IEEE Transactions on Power System

Aggregation and Control of Populations of Thermostatically Controlled Loads by Formal Abstractions

This work discusses a two-step procedure, based on formal abstractions, to generate a finite-space stochastic dynamical model as an aggregation of the continuous temperature dynamics of a homogeneous population of Thermostatically Controlled Loads (TCL). The temperature of a single TCL is described by a stochastic difference equation and the TCL status (ON, OFF) by a deterministic switching mechanism. The procedure is formal as it allows the exact quantification of the error introduced by the abstraction -- as such it builds and improves on a known, earlier approximation technique in the literature. Further, the contribution discusses the extension to the case of a heterogeneous population of TCL by means of two approaches resulting in the notion of approximate abstractions. It moreover investigates the problem of global (population-level) regulation and load balancing for the case of TCL that are dependent on a control input. The procedure is tested on a case study and benchmarked against the mentioned alternative approach in the literature.Comment: 40 pages, 21 figures; the paper generalizes the result of conference publication: S. Esmaeil Zadeh Soudjani and A. Abate, "Aggregation of Thermostatically Controlled Loads by Formal Abstractions," Proceedings of the European Control Conference 2013, pp. 4232-4237. version 2: added references for section

Breaker to Control Center Integration & Automation: Protection, Control, Operation & Optimization

Recent technological advances in protection, control and optimization are enabling a more automated power system. This paper proposes the use of these technologies towards an integrated and seamless infrastructure for protection, control and operation. This infrastructure is the basis for accommodating and providing robust solutions to new problems arising from the integration of renewables, namely more uncertainty and steeper ramp rates. At the lower level we propose a dynamic state estimation of a protection zone (EBP) for the purpose of providing protection for the zone. The estimation based protection (EBP) provides the real time dynamic model of the zone as well as the real time operating conditions. Since protection is ubiquitous, it can cover the full system. We assume that GPS synchronization of the EBP is available providing accurate time tags for the real time model and operating conditions. The real time model and operating conditions can extent from the “turbine to the toaster”. We propose a methodology for automatically constructing the power system state locally and centrally at the control center with distributed controls as well as centralized controls depending on the application. For example, the centralized \ system wide real time model is used to perform system optimization functions, and then send commands back through the same communication structure to specific power system components. Since protection is ubiquitous and the modern power system has several layers of communication infrastructure, the proposed approach is realizable with very small investment. The availability of the real time dynamic model and state locally and centrally enables the seamless integration of applications. Three applications are discussed in the paper: (a) setting-less protection, (b) voltage/var control and (c) feeder load flexibility scheduling. The proposed approach and infrastructure can form the basis for the next generation of Energy Management Systems.

Model Predictive Control for Demand Response of Thermostatically Controlled Loads

Charakteristickým rysem moderní energetiky je narůstající podíl výroby elektřiny z obnovitelných zdrojů. To přináší řadu výhod z pohledu kvality životního prostředí. Výroba elektřiny z obnovitelných zdrojů má však výrazně stochastický charakter a integrace většího množství takto vyrobené elektřiny do elektrizační sítě není možná, pokud nebudou vytvořeny nové metody řízení spotřeby elektřiny, nové technologie pro skladování elektrické energie a vyspělá řídicí a komunikační infrastruktura. Na straně spotřeby elektrické energie připadá významný podíl termostaticky řízeným spotřebičům. Ty jsou navíc obvykle těsně propojeny s velkými tepelně akumulačními kapacitami. Jsou proto zvláště vhodné pro řízení spotřeby elektřiny a nákladově efektivní akumulaci energie. Z této motivace vychází zaměření této disertační práce na pokročilé algoritmy pro řízení termostatických spotřebičů.Jakékoliv řízení nutně předpokládá, že existuje vhodný řídicí signál, kterým můžeme chování řízené soustavy ovlivňovat. V této práci pracujeme s nepřímým řídicím signálem: cenou elektřiny proměnnou v reálném čase. Tento koncept je používán v řadě pilotních projektů v USA i v EU. Z řady hledisek je tento koncept výhodný: zákazníci si mohou sami rozhodnout, jak na proměnnou cenu budou reagovat bez toho, že by jejich komfort byl ohrožen. Rovněž tak není nutné instalovat složitá rozhraní pro přímé ovládání spotřebičů a monitorování jejich stavu. Návrh vhodných algoritmů pro to, jak reagovat na proměnné ceny však zůstává stále do značné míry otevřeným problémem. Tato práce je zaměřena na dva aspekty tohoto problému.První část práce se zabývá problematikou řízení jednotlivých velkých termostatických spotřebičů, které reaguje na proměnnou cenu elektřiny. Tyto spotřebiče jsou zde popsány obecně jako lineární časově proměnné systémy a jejich řízení je navrženo jako lokální ekonomické prediktivní řízení. Tento ekonomický prediktivní regulátor musí vzít v úvahu časově proměnný charakter řízené soustavy. Tím, že provádí lokální ekonomickou optimalizaci, napomáhá tento regulátor udržet rovnováhu výroby a spotřeby v elektrizační soustavě. Tato část práce vznikla v rámci projektu H2020 SmartNet a jako případovou studii používá jedno z pilotních experimentálních zařízení tohoto projektu: vyhřívaný plavecký bazén. Časová proměnnost matematického modelu tohoto bazénu pramení ze změn součinitele přestupu tepla mezi vodou a vzduchem v závislosti na rychlosti větru.Druhá část práce je zaměřena na menší termostatické spotřebiče, které sice mají jednotlivě zanedbatelný příkon, mohou však hrát významnou roli, pokud je jejich větší počet sdružen dohromady. Struktura navrhovaného řídicího systému je hierarchická. Ekonomický prediktivní regulátor na vyšší rovině řízení reaguje na proměnnou cenu elektřiny a mění žádané hodnoty termostatů na nižší rovině. Cíl řízení je stejný jako v první části práce: cena provozu celé skupiny spotřebičů je minimalizována a to napomáhá udržení rovnováhy v síti. Vzhledem k velkému počtu spotřebičů však není možné, aby prediktivní regulátor pracoval s modely všech jednotlivých spotřebičů, ale bylo nutné vyvinout a ověřit sdružený model dynamiky celé skupiny. Tento model je nelineární a ekonomický prediktivní regulátor musí řešit úlohu nelineárního smíšeného celočíselného programování. Efektivita navržené strategie řízení byla prokázána pomocí simulačních experimentů.Increasing the share of renewable electricity generation is a characteristic feature of modern energy systems. Renewable electricity generation has important environmental benefits, however, it is also marked by significant stochasticity and its large scale integration into power grid is not possible without new methods for control of electricity consumption, new energy storage technologies and communication infrastructure. Thermostatically controlled loads represent a significant share of total electricity consumption and they are often tightly connected with large thermal storage capacities. For these reasons they can be used for controlling electricity consumption and cost effective energy storage. This motivates the focus of this thesis on advanced control algorithms for thermostatically controlled loads.Any control requires a suitable control signal. In this thesis, an indirect control signal is used - the role of the control signal is played by variable electricity price. This concept is considered in many pilot projects both in the USA and in the EU. It has certain advantages: the customers can choose the preferred strategy for responding to the needs of the grid, so their comfort is not compromised; also there is no need to install significantly more complex interfaces for direct control of the loads and monitoring of their states. However, the design of suitable control algorithms for responding to variable prices is still a largely open problem. The thesis focuses on two aspects of this problem.The first part of the thesis considers the control of a single large thermostatically controlled load that responds to the price signal. This load is described by a linear time varying system and a local economic model predictive controller is designed for it. This controller must account for the time varying dynamics of the controlled load. By performing local economic optimization this controller helps to balance supply and demand in the electricity grid. This part of the thesis was created within the framework of H2020 SmartNet project and it considers one of the project pilot demonstrations: heated swimming pool. The time varying character of the model of this pool is due to the changes of the heat transfer coefficient between water and air depending on the wind speed.The second part of the thesis focuses on smaller thermostatically controlled loads. They are negligible individually, but they can play an important role if a larger population is aggregated. The structure of the proposed control system is hierarchical. Economic model predictive controller in the upper level responds to varying electricity price and changes the temperature setpoints of the thermostats in the lower level. The objective of the control system is the same as in the first part of the thesis: the cost of the operation of this population is minimized and this helps to keep the balance in the grid. However, the high number of the loads does not allow individual modelling of each load in the model predictive controller and an aggregate model had to be developed and tested. This model is non-linear and economic model predictive controller has to solve mixed integer non-linear optimization problem. The effectiveness of the proposed control strategy was demonstrated by simulation

Learning and Control Applied to Demand Response and Electricity Distribution Networks

Balancing the supply and demand of electrical energy in real-time is a core task in power system operation. Traditionally, this balance has been achieved by controlling power plants, but increasing amounts of renewable energy generation increases the variability in generation and requires additional energy balancing capacity. An alternative to providing this additional capacity via power plants is to provide signals to loads that induce changes in their demand, which is referred to as demand response. There exists a large potential capacity for demand response using residential loads, but enabling these loads to participate in demand response requires communication and sensing capabilities. Thermostatically controlled loads (TCLs) are ubiquitous in residences and have inherent flexibility as they cycle on and off during normal operation. Coordinating on/off switching of TCL aggregations can provide energy balancing. However, TCLs are a spatially distributed resource that require sensing and communication infrastructure to enable demand response capabilities. A key to realizing cost effective residential demand response is minimizing infrastructure costs while maximizing the accuracy of the provided energy balancing, which results in increased revenue while improving reliability in the power system. The main contribution of this dissertation is to show that advanced algorithms can leverage existing infrastructure to make energy balancing with loads feasible in the near-term, which improves the reliability, economics, and environmental impact of the power grid. The dissertation first presents control algorithms, estimation algorithms, and models for residential demand response on fast timescales, i.e., on the order of seconds. Following this, the dissertation presents online learning algorithms for real-time feeder-level energy disaggregation within an electricity distribution network, which can be used to estimate the demand-responsive load in real-time. Methods for both topics are developed to operate within the capabilities of existing communication and sensing infrastructure, which reduces the implementation costs of the methods. Control and estimation algorithms are developed that address communication delays while taking into account realistic measurement availability. Results indicate that incorporating delay information into the algorithms can mitigate the effects of communication delays, allowing demand response providers to reduce infrastructure costs by using less expensive, lower quality communication networks. Additional work adapts three existing residential demand response models for a more detailed simulation environment, modifies each model to be more accurate in this environment, and benchmarks the model variations against each other. Results indicate that the model modifications produce more accurate predictions versus the unmodified models. Improving modeling accuracy can improve the reliability of the system and increase revenues for a demand response provider by improving the performance of model-based control and estimation algorithms. The energy disaggregation algorithms seek to separate the measured demand of a distribution feeder into components (e.g., the demand-responsive load and the remaining load) as feeder-level measurements become available. An online learning algorithm is adapted to perform real-time energy disaggregation using active power measurements of the total demand on the distribution feeder. Results indicate that the algorithm is able to effectively separate the air conditioning demand from the remaining demand connected to a distribution feeder. This algorithm is then extended to include reactive power, voltage, and smart meter measurements. Results indicate that the availability of additional real-time measurements leads to more accurate disaggregation of the demand components. Additional work in state estimation establishes connections between the online learning methods used and Kalman filtering algorithms.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149905/1/gsledv_1.pd

A Framework for Flexible Loads Aggregation

L'abstract è presente nell'allegato / the abstract is in the attachmen