The thesis deals with control methods for flexible and efficient power consumption in commercial refrigeration systems that possess thermal storage capabilities, and for facilitation of more environmental sustainable power production technologies such as wind power. We apply economic model predictive control as the overriding control strategy and present novel studies on suitable modeling and problem formulations for the industrial applications, means to handle uncertainty in the control problems, and dedicated optimization routines to solve the problems involved. Along the way, we present careful numerical simulations with simple case studies as well as validated models in realistic scenarios. The thesis consists of a summary report and a collection of 13 research papers written during the period Marts 2010 to February 2013. Four are published in international peer-reviewed scientific journals and 9 are published at international peer-reviewed scientific conferences

Hovgaard, Tobias Gybel

I denne afhandling beskriver vi regulering og styring af to forskellige industrielle applikationer, der befinder sig i hver sin ende af elnettet; en elforbruger i form af et kommercielt køleanlæg, og vindmøller til elproduktion. Vores hovedstudier fokuserer på økonomisk modelprædiktiv regulering af et kommercielt multizonekøleanlæg bestående af flere kølenheder forbundet til en fælles kompressor, som har det formål at nedkøle adskillige rum eller områder, f.eks. i et supermarked. Betydelige mængder energi bliver brugt af køleanlæg verden over, og der er derfor et stærk incitament for at introducere mere energieeffektive og omkostningsreducerende reguleringsteknikker. Samtidig undergår elnettet en forandring fra et centralt system med en relativt regulerbar produktion i de konventionelle kraftværker til et langt mere decentraliseret netværk af uafhængige energiproducerende enheder, hvor en stærkt stigende andel af elektriciteten kommer fra vedvarende, fossilfrie energikilder såsom sol og vinenergi. For at muliggøre integrationen af store mængder af disse mere uregelmæssige elproducenter, er det ikke længere tilstrækkeligt (eller muligt) kun at styre elproduktionen, og vi må i langt højere grad også styre elforbruget til at være både effektivt og fleksibelt. Ved at muliggøre brugen af termisk energilagring i supermarkeder vil vi åbne muligheder for et fleksibelt elforbug, som kan reducere driftsomkostningerne for køleanlægget, og vi udvikler og demonstrerer prototype-reguleringsteknologier, som skaber helt nye forretningsmuligheder, hvor fleksibiliteten kan sælges til elnettet som regulerende effekt. Denne fleksibilitet åbner desuden for, at vindenergi kan dække en endnu større del af elproduktionen, og udvider derved markedspotentialet for vindmøller og andre vedvarende energikilder. Med vores arbejde sigter vi altså mod at facilitere brugen af miljømæssigt bæredygtige metoder til energiproduktion og samtidig skabe nye forretningsmuligheder for både elforbrugere og producenter af vedvarende energi. Vores sekundære fokus i denne afhandling, vindenergi, bringer os til produktionssiden af elnettet. Formålet er her at forbedre kvaliteten og integrerbarheden af strøm leveret til elnettet fra store vindmølleparker. Målet med dette studie er at reducere den fluktuerende natur, som typisk præger vindmøllegenereret strøm, og dermed leve op til strengere krav for tilslutning til elnettet. Dette kan opnås ved at introducere en mere intelligent reguleringsstrategi både i den enkelte vindmølle, på parkstyringsniveau og i samarbejdet med eksible elforbrugere eller andre typer energilagring. Denne mulige interaktion og synergi mellem de to systemer, som vi betragter i dette projekt, er i sig selv en grund til, at vi inkluderer begge sider af sagen, og som vi skal se, kan de forskellige reguleringsproblemer formuleres med så store ligheder, at vi kan anvende stort set identiske metoder til at løse dem på trods af den umiddelbare mangel på sammenfald.Vi foreslår en økonomisk optimerende modelprædiktiv regulator (kendt som economic model predictive control eller blot, economic MPC) til at håndtere reguleringen af såvel køle- som vindmølleapplikationen. MPC er en feedback-reguleringsteknik, som er karakteriseret ved dens eksplicitte håndtering af styring til dynamiske systemer med begrænsninger (constraints), hvortil en model, der kan forudsige systemets fremtidige opførsel, benyttes sammen med forudsigelser af fremtidige forstyrrelser. Til hvert tidsskridt beregnes reguleringsvariablene ved at løse et optimalt reguleringsproblem over en given horisont. Resultatet af dette er en optimal åbensløjfesekvens af reguleringsvariable, hvoraf kun det første skridt implementeres. Feedback bliver implementeret ved at løse open-sløjfe problemet igen og igen i en rullende horisont-facon, i takt med at nye forudsigelser bliver tilgængelige.Vores undersøgelser fokuserer primært på: 1) modellering af de virkelige systemer så de er kompatible med den valgte reguleringsarkitektur, 2) formulering af MPCreguleringslove, som løser de særlige udfordringer der, følger med de industrielle applikationer og fastlæggelse af økonomiske mål, således at disse afspejler både fysikken bag systemerne såvel som vores denerede reguleringsformål, 3) effektiv realtidsberegning af de implicerede, ikke-trivielle optimeringsproblemer, og 4) demonstration af gennemførligheden og potentialet ved vores foreslåede metoder gennem udførlige simuleringer og sammenligninger med de eksisterende reguleringsmetoder samt data fra systemer i drift.Vi præsenterer vores bidrag på følgende områder:Økonomisk MPC til kommercielle køleanlæg, herunder-Lineære økonomisk optimerende MPC-formuleringer der udnytter fleksibiliteten i køleanlæg til at modvirke ubalancer mellem elforbrug og -produktion.-Økonomisk MPC med probabilistiske begrænsninger, der sikrer robustydelse og robust overholdelse af begrænsningerne på trods af upræcisesystemmodeller og forudsigelser.-Ulineær økonomisk MPC, der afspejler de ikke-konvekse elementer i de temperaturafhængige eektiviteter i køleprocessen.-Ulineær økonomisk MPC med usikre forudsigelser samt implementeringer af simple prædiktionsmetoder, som udelukkende baseres på historiske data af fx. elpriser og udendørstemperaturer.Økonomisk MPC til vindmøller, herunder-Beregning af optimale statiske driftspunkter for vindmølleparker.-Ulineær økonomisk MPC for individuelle møller.-Skift af variable og konvekse formuleringer af økonomisk MPC for individuellemøller.Implementerbare optimeringsmetoder for MPC-problemerne, herunder-Sekventiel konveks programmering (SCP) for de specifikke ikke-konvekse problemer, der opstår i vores studier af kommercielle køleanlæg såvel som i vores studier af vindenergi.-Succesfuld demonstration af SCP-tilgangen på tre forskellige problemer det kommercielle kølesystem med lineær dynamik og begrænsninger, men med en ikke-konveks objektfunktion, den individuelle vindmølle med ulineær dynamik, begrænsninger, og objektfunktion, og den statiske optimering af vindmølleparkdriftspunkter, hvor kun en black-box-model er tilgængelig.Vores hovedbidrag er formuleringen af disse reguleringsproblemer samt demonstrationer, der viser hvordan vores SCP-metode kan bruges til løsning af disse. Vi demonstrerer bl.a. betydelige omkostningsbesparelser i størrelsesordenen 30 %, sammenlignet med standard termostatstyrede supermarkedskøleanlæg og viser desuden, hvordan vores metode udviser sostikeret priselastisk forbrug overfor realtids variationer i elpriserne. På trods af usikkerheder i systemet kan overskridelser af temperaturgrænserne begrænses til meget lave forekomstrater. For vindmøller kan effektændringer i strømmen eektivt begrænses til 3 % af møllens nominelle effekt pr. minut, når vi udnytter tilknyttet energilagring, og vi demonstrerer, hvordan den nødvendige lagerkapacitet kan reduceres med op til 30 % uden at reducere den producerede mængde energi ved en aktiv udnyttelse af rotorens inerti som ekstra energilager.In this thesis, we consider the control of two different industrial applications that belong at either end of the electricity grid; a power consumer in the form of a commercial refrigeration system, and wind turbines for power production. Our primary studies deal with economic model predictive control of a commercial multi-zone refrigeration system, consisting of several cooling units that share a common compressor, and is used to cool multiple areas or rooms, e.g., in supermarkets. Substantial amounts of energy are consumed in refrigeration systems worldwide and there is a strong motivation for introducing more energy ecient as well as cost reducing control techniques. At the same time, the power grid is evolving from a centralized system with rather controllable production in the conventional power plants to a much more decentralized network of many independent power generators and a large penetration of renewable, fossil-free energy sources such as solar and wind power. To facilitate such intermittent power producers, we must not only control the production of electricity, but also the consumption, in an ecient and exible manner. By enabling the use of thermal energy storage in supermarkets, we open up for exible power consumption schemes with the possibility of reducing operational costs and we develop and demonstrate prototype control technology that creates completely new business opportunities for selling regulating power to the grid. Moreover, this enables a larger penetration of wind energy in the power production and increases the potential market size for wind power generators and other renewable energy sources. Thus, we aim at promoting the use of environmentally sustainable power production technologies while creating new business opportunities for both power consumers and producers of renewable energy. The second application, wind turbines, takes us to the production side of the power grid. The key concern here is to improve the quality and integrability of power delivered to the grid from large parks of wind turbines. Our goal is to reduce the fluctuating nature of the power output and to meet tightened demands from the grid by enabling a more intelligent control at both the individual turbine level, at the park controller level, and in cooperation with exible power consumers or other means of energy storage. The possible interaction and synergies of the two applications are obvious reasons to consider both in this thesis, and as we will see, the similarities in our formulations of the dierent control problems allow us to apply almost identical techniques despite the lack of immediate similarity. For control of the commercial refrigeration application as well as the wind turbine application, we propose an economic optimizing model predictive controller, economic MPC. MPC is a feedback control technique that is characterized by its explicit handling of constrained control problems in which a model is used to predict the future behavior of a system along with forecasts of future disturbances. At each time step the values of the control inputs are computed by solving an open-loop nite time optimal control problem over a dened prediction horizon. Only the rst step in this optimal open-loop sequence is implemented as a control command. Feedback is obtained by solving the open-loop problem repeatedly, in a receding horizon fashion, as new predictions become available. Our investigations are primarily concerned with: 1) modeling of the applications to suit the chosen control framework; 2) formulating the MPC controller laws to overcome challenges introduced by the industrial applications, and defining economic objectives that reect the real physics of the systems as well as our control objectives; 3) solving the involved, non-trivial optimization problems eciently in real-time; 4) demonstrating the feasibility and potential of the proposed methods by extensive simulation and comparison with existing control methods and evaluation of data from systems in actual operation. We present contributions on: Economic MPC for commercial refrigeration systems, including  -Linear economic MPC formulations that utilize the exibility in refrigeration systems to counteract uctuations in the balance between power consumption and production. -Economic MPC with probabilistic constraints, ensuring a robust performance and constraint satisfaction in spite of inaccurate system models and forecasts. -Nonlinear economic MPC, reflecting the nonconvexity in the realistic description of temperature dependent efficiencies in the refrigeration cycle. -Nonlinear economic MPC with uncertain predictions and the implementation of very simple predictors that use entirely historical data of, e.g., electricity prices and outdoor temperatures. Economic MPC for wind turbines, including -Optimal steady-state calculation for wind farms. -Nonlinear economic MPC for individual turbines. -Change of variables and convex formulations of economic MPC for individual turbines. Tractable optimization methods for the MPC problems, including -Sequential convex programming (SCP) for specific nonconvex problems originating from our studies of commercial refrigeration as well as from our studies concerning wind power. -Successful demonstration of the SCP approach on three different problems the commercial refrigeration system with linear dynamics and constraints and a nonconvex objective, the individual wind turbine with nonlinear dynamics and constraints, and the static optimization of the wind farm with a black-box model. The major contribution is the formulation of these problems and the demonstrations to show that the SCP method can be used for their solution. We demonstrate, i.a., substantial cost savings, on the order of 30 %, compared to a standard thermostat-based supermarket refrigeration system and show how our methods exhibit sophisticated demand response to real-time variations in electricity prices. Violations of the temperature ranges can be kept at a very low frequency of occurence inspite of the presence of uncertainty. For the power output from wind turbines, ramp rates, as low a 3 % of the rated power per minute, can be effectively ensured with the use of energy storage and we show how the active use of rotor inertia as an additional energy storage can reduce the needed storage capacity by up to 30 % without reducing the power output.</p

Online Research Database In Technology

Power Management for Energy Systems

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Downloaded from orbit.dtu.dk on: Dec 20, 2017Power Management for Energy SystemsHovgaard, Tobias GybelPublication date:2013Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Hovgaard, T. G. (2013). Power Management for Energy Systems [Sound/Visual production (digital)]. DanvakDagen 2013, København, Denmark, 10/04/2013Power Management for Energy SystemsTobias Gybel Hovgaard1,2, Lars F. S. Larsen1, Morten Skovrup3, Stephen Boyd4, and John B. Jørgensen21) Vestas Technology R&D, Aarhus N, Denmark.2) Dept. of Applied Mathematics and Computer Science, Technical University of Denmark.3) IPU Technology Development, Denmark.4) Stanford University*) In collaboration with: Danfoss A/S, Electronic Controls R&D, Denmark.April 2013, Tobias Gybel Hovgaard, Danvak Dagen 2013.Challenge and Motivation• Wind power is the most important renewable energy source today.• Goals for reduced CO2 emission, increased utilization of renewable energy, and phase out of fossil fuels.• E.g. in Denmark: increase the share of wind power to 50% of the electricity consumption by 2020 and fully cover the energy supply by renewable energy by 2050.• Wind power production fluctuates  flexible power consumption is needed (smart grid technologies).Intelligent load-shifting and scheduling by storing “coldness” for: Peak avoidance (foreseeing peaks can reduce dimensioning of the system) Minimal power consumption (Cooling at colder periods is more efficient) Minimal cost (Energy prices may vary over the day) Flexible consumption (More renewable energy calls for flexible power consumption)Example: Drive from A to B with minimal fuel consumption. Utilizing Thermal MassTobias Gybel Hovgaard, April 20133A BPower ManagementExample: Smart Grid (Flexible consumption).Thermal energy storageTobias Gybel Hovgaard, April 20134Power ManagementExample: Smart Grid (Flexible consumption).Economic Model Predictive Control (MPC)demonstrated on power distribution portfolioincluding a cold storage with flexibility.Presented at CDC 2010.Tobias Gybel Hovgaard, April 20135Power ManagementPower consumptionFast variations in power consumption (< 15min) ″Short term load variations″Tobias Gybel Hovgaard, April 20136Challenge and MotivationTobias Gybel Hovgaard, April 2013750% in 2020 !!!Challenge and MotivationTobias Gybel Hovgaard, April 20138Challenge and MotivationTobias Gybel Hovgaard, April 20139A brief introduction: Ancillary service in order to balance production and consumption (stabilize frequency) Up-regulating power: increased production or decreased consumption Down-regulating power: decreased production or increased consumption Different types (amounts, activation times, automatic/manual)Regulating PowerChallenge and MotivationTobias Gybel Hovgaard, April 201310A brief introduction: Ancillary service in order to balance production and consumption (stabilize frequency) Up-regulating power: increased production or decreased consumption Down-regulating power: decreased production or increased consumption Different types (amounts, activation times, automatic/manual)30s 15minMWAutomaticfrequencyregulationAutomaticsecondaryregulationManualregulating powerOrder+15 minOrderI tilfælde af at øvrige reserver udebliverRegulating PowerChallenge and MotivationTobias Gybel Hovgaard, April 201311Regulating PowerPrimary regulation: Automatic frequency dependent activation Uphold activated capacity for 15 min, re-establish in 15 min. No extra payment for activated power Availability payment independent of actual activation (non-symmetric up/down)Time (s)100%Power response as a function of  the frequency deviation- 100%3050%- 50%15Frequency+ 20 mHz- 20 mHz- 200 mHz0%0%+ 200 mHzFlexible consumption with intelligent load-shifting and scheduling by storing energy in the form of “coldness”:1. Utilize thermal mass in e.g. stored goods in supermarkets.2. Food temperatures allowed to vary within defined limits.3. Our studies reveal electricity cost savings up to 30%.But:1. Food temperatures unknown!2. Vast variety of systems!3. Little computational power!Supermarket RefrigerationTobias Gybel Hovgaard, April 201312Supermarket or cold stores with high degree of HVAC & thermal storage capacityThermal energy storageSupermarket RefrigerationTobias Gybel Hovgaard, April 201313DisturbancesControl variablesMethodTobias Gybel Hovgaard, April 201314Economic Model Predictive Control (MPC)Controller cost function: • With e.g. for regulation:MPC Control problem:                                      , s.t.MethodTobias Gybel Hovgaard, April 201315Economic Model Predictive Control (MPC)• Solve an optimization problem at each sample.• Minimize an economic objective related to operation of the system.• Repeat in a receding horizon manner+ Incorporates predictions of future prices, temperatures, etc.+ Handles constraints naturally.+ Intuitive formulation of the cost of operation into a control problem.- Relies on a model of the system and predictions of the disturbances.- Can involve quite complicated numerical optimization problems.Example: Uncertain predictions and models. Example: Drive from A to B with minimal fuel consumption. Stay on the road!UncertaintyA B+/- 2%90 HP +/- 5%Tobias Gybel Hovgaard, April 201316Example: Uncertain predictions and models.Uncertainty+/- 2%90 HP +/- 5%Second Order Cone Programming (SOCP) for uncertainties in Economic MPC problems.Presented at CDC-ECC 2011Tobias Gybel Hovgaard, April 201317Our SolutionTobias Gybel Hovgaard, April 201318Overall setupResultsTobias Gybel Hovgaard, April 201319Simulations:• Covering a full year (2010).• Outdoor temperature from Denmark.• Electricity prices from Nordpool.• Uncertain heat load disturbances and thermal masses.• Verified models from supermarket in operation in Denmark.Implementation:• Optimization problem solved iteratively• Ultra fast solvers for real-time implementation.• Soft constraints and back-off for robustness.• Predictors trained on historical data (previous three years).ResultsTobias Gybel Hovgaard, April 201320Temperature profileResultsTobias Gybel Hovgaard, April 201321Demand responseResultsTobias Gybel Hovgaard, April 201322Temperature distributionsResultsTobias Gybel Hovgaard, April 201323Price distributionsResultsTobias Gybel Hovgaard, April 201324Key findings:• Cost savings around 30 %• Potential for additional savings by offering regulating power.• Very simple predictors are sufficient.• Prescient simulation improves total cost with less than 2%.• Closed-loop performance  is quite robust against variations in model parameters.Other resultsTobias Gybel Hovgaard, April 201325Additional results include:• Robustness investigations:• Advanced method with known probability distributions for the uncertainty.• Simpler version with back-off tuned to make constraint violations very infrequent.• Modeling of dynamical systems for optimization and MPC purposes.• Analysis of “active thermal mass” in foodstuffs. • Experiments, identification, and validation on real systems in the lab.• Investigation of optimization methods for industrial applications:• Standard linear and non-linear solvers.• Simplified problem formulations for linear solvers.• Dedicated fast embedded optimization techniques.Connecting to wind powerTobias Gybel Hovgaard, April 201326Co-control of wind power plant and flexible power consumers:• We prove a potential for combining • wind speed forecasts, • control of wind turbines• and control of flexible power consumers (e.g. chains of supermarkets).• Goals:• Improve integrability (grid friendliness) of wind power to the grid.• Obey tight grid codes.• Reject disturbances from wind speed changes with minimal power loss.• Avoid expensive energy storage solutions.Connecting to wind powerTobias Gybel Hovgaard, April 201327Co-control of wind power plant and flexible power consumers:Connecting to wind powerTobias Gybel Hovgaard, April 201328Co-control of wind power plant and flexible power consumers:Nominal controller with real wind scenarioConnecting to wind powerTobias Gybel Hovgaard, April 201329Co-control of wind power plant and flexible power consumers:MPC controller with real wind scenarioConclusionsTobias Gybel Hovgaard, April 201330Findings in project:1. Investigations and proof-of-concept for flexible power consumption in industrial refrigeration by use of Economic MPC.2. Enabling load-shifting strategies and regulating power services with significant cost reductions.3. Challenges in MPC for industrial systems tackled:• Model accuracy, computational load, predictions, etc.4. Synergy and co-control potential with wind energy revealed.Some selected references:1. Hovgaard, T.G., Larsen, L.F.S., Edlund, K. and Jørgensen, J.B. (2012). Model predictive control technologies for efficient and flexible power consumption in refrigeration systems. Energy 44(1), 105-116.2. Hovgaard, T.G., Larsen, L.F.S., Jørgensen, J.B., and Boyd, S. (2013). Nonconvex model predictive control for commercial refrigeration. International Journal of Control. In press. http://www.stanford.edu/~boyd/papers/noncvx_mpc_refr.html3. Hovgaard, T.G., Boyd, S., and Jørgensen, J.B., (2013). Model predictive control for wind power gradients. Submitted to: International Journal of Control. http://www.stanford.edu/~boyd/papers/wind_gradients_cvx.htmlCopyright NoticeThe documents are created by Vestas Wind Systems A/S and contain copyrighted material, trademarks, and other proprietary information. All rights reserved. No part of the documents may be reproduced or copied in any form or by anymeans - such as graphic, electronic, or mechanical, including photocopying, taping, or information storage and retrieval systems without the prior written permission of Vestas Wind Systems A/S. The use of these documents by you, oranyone else authorized by you, is prohibited unless specifically permitted by Vestas Wind Systems A/S. You may not alter or remove any trademark, copyright or other notice from the documents. The documents are provided “as is” andVestas Wind Systems A/S shall not have any responsibility or liability whatsoever for the results of use of the documents by you.Thank you for your attentionQuestions?The authors gratefully thank Danfoss Electronic Controls R&D, Refrigeration and Air-conditioning, Nordborgvej 81, DK-6430 Nordborg, Denmark for their contributions and support.

Tobias Gybel Hovgaard

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