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Hierarchical energy management in smart grids: Flexibility prediction, scheduling and resilient control
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.ORCID iD: 0000-0002-4210-8672
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The electric power industry and society are facing challenges and opportunitiesof transforming the present power grid into a smart grid. Energymanagement systems (EMSs) play an important role in smart grids. A generalhierarchical structure for EMSs is considered here, which is composed ofa lower layer and an upper layer.

The first research objective of the thesis is detailed modeling, schedulingand control of flexible loads at the lower layer of EMSs. To do this, a wellstudiedframework has been extended, which focuses on scheduling of staticloads and dynamic loads for home energy management systems (HEMSs).Then, a robust formulation of the framework is proposed, which takes theuser behavior uncertainty into account so that the cost of optimal schedulingof appliances is less sensitive to unpredictable changes in user preferences.Considering that the optimization algorithms in the proposed framework canbe computationally intensive, an efficient plug-and-play policy is proposedand validated through several simulation studies.

The second research objective is to predict, plan, and control the aggregatedflexible load at the upper layer. Here, an iterative distributed approachamong aggregator and HEMSs is designed, to maximize the aggregated profitmade out of the shared energy storage system, while technical and operationalconstraints are satisfied. In addition, a strategy is proposed for flexibilityprediction of aggregated heterogeneous thermostatically controlled loads ina single micro-community of households. Then, algorithms are designed forplanning and control of aggregated flexibility in several micro-communities,to be used for bidding in energy and reserve markets.

To meet these research objectives, the control systems in the hierarchicalEMSs are connected over IT infrastructures and are in interaction with endusers.While this is done to achieve economical and environmental goals,it also introduces new sources of uncertainty in the control loops. Thus,the third research objective is to design policies to make the EMSs resilientagainst uncertainties and cyber attacks. Here, the user behavior uncertaintyhas been modeled, and a robust formulation is designed so that the optimalsolution for scheduling of appliances is more resilient to the uncertainties. Inaddition, fault-tolerant control techniques have been applied to a hierarchicalEMS to mitigate cyber-physical attacks, with no need for major re-designof the local control loops in already existing EMSs. Moreover, stability andoptimal performance of the proposed attack-resilient control policy have been proven.

Abstract [sv]

I samband med den pågående omvandlingen av nuvarande elsystem tillsmarta elnät finns både utmaningar och möjligheter för elkraftindustrin. Såkallade energihanteringssystem (EMS) spelar en viktig roll i smarta elnät. Härbehandlas en generell hierarkisk struktur för EMS, bestående av två lager, ettlägre och ett övre lager.

Det främsta målet i avhandlingen är detaljerad modellering, schemaläggningoch styrning av flexibla laster i det lägre lagret av EMS. Ett tidigarestuderat ramverk som fokuserar på schemaläggning av statiska och dynamiskalaster för hushållens energihanteringssystem (HEMS) har därför vidareutvecklats.Vidare föreslås en robust formulering av ramverket som tarhänsyn till användarens beteendeosäkerhet så att kostnaden för optimal schemaläggningav apparater blir mindre känslig för oförutsägbara förändringar ianvändarpreferenser. Eftersom att optimeringsalgoritmerna kan vara beräkningsintensivaföreslås och valideras en effektiv plug-and-play-metod genomflera simuleringsstudier.

Ett annat syfte har varit att förutsäga, planera och styra den aggregeradeflexibla lasten i det övre lagret i EMS. Därför har ett iterativt distribuerattillvägagångssätt för aggregat och HEMS utformats för att maximera vinstenfrån det delade energilagringssystemet, samtidigt som tekniska och operativabegränsningar uppfylls. Dessutom föreslås en strategi för att förutsägaflexibiliteten hos aggregerade heterogena termostatstyrda belastningar i ettmikrosamhälle bestående av flera hushåll. Vidare utformas algoritmer för planeringoch kontroll av aggregerad flexibilitet i flera mikrosamhällen, som kananvändas för att delta på energi- och reservmarknader.

För att möta dessa forskningsmål kopplas styrsystemen i de hierarkiskaEMS-systemen ihop över IT-infrastruktur och samverkar med slutanvändare. Detta görs för att uppnå ekonomiska och miljömässiga mål, men kan ocksåskapa nya källor till osäkerhet i kontrollslingorna. Det tredje forskningsmåletär således att utforma metoder för att göra EMS motståndskraftiga motosäkerheter och cyberattacker. Här har osäkerheter i användarbeteenden modelleratsoch en robust formulering utformats för att göra schemaläggningav apparater mer motståndskraftig mot osäkerhet. Dessutom har feltolerantakontrolltekniker applicerats på en hierarkisk EMS för att mildra cyber-fysiskaattacker, utan att det behövs någon större förändring av de lokala kontrollslingornai redan befintliga EMS. Vidare har stabilitet och optimal prestandaför den föreslagna attackmotståndskraftiga kontrolltekniken bevisats.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2019. , p. 55
Series
TRITA-EECS-AVL ; 2019:20
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Energy Technology
Identifiers
URN: urn:nbn:se:kth:diva-244843ISBN: 978-91-7873-123-7 (print)OAI: oai:DiVA.org:kth-244843DiVA, id: diva2:1292660
Public defence
2019-03-22, K1, Teknikringen 56, Stockholm, 10:00 (English)
Opponent
Supervisors
Note

QC 20190301

Available from: 2019-03-01 Created: 2019-02-28 Last updated: 2019-03-01Bibliographically approved
List of papers
1. Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries
Open this publication in new window or tab >>Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries
2014 (English)In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, p. 632-639Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we present a novel method for scheduling smart appliances and batteries, in order to reduce both the electricity bill and the CO2 emissions. Mathematically, the scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which can be solved by using standard algorithms. A case study is performed to assess the performance of the proposed scheduling framework. Numerical results show that the new formulation can decrease both the CO2 emissions and the electricity bill. Furthermore, a survey of studies that deal with scheduling of smart appliances is provided. These papers use methods based on MILP, Dynamic Programming (DP), and Minimum Cut Algorithm (MCA) for solving the scheduling problem. We discuss their performance in terms of computation time and optimality versus time discretization and number of appliances.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2014
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-164081 (URN)10.1109/CoASE.2014.6899394 (DOI)
Conference
2014 IEEE International Conference on Automation Science and Engineering (CASE)18-22 Aug. 2014, Taipei
Note

This paper was one of the Best student paper award finalist.

QC 20150427

Available from: 2015-04-13 Created: 2015-04-13 Last updated: 2019-02-28Bibliographically approved
2. Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty
Open this publication in new window or tab >>Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty
2015 (English)In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 1, p. 247-259Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a robust approach for scheduling of smart appliances and electrical energy storages (EESs) in active apartments with the aim of reducing both the electricity bill and the CO2 emissions. The proposed robust formulation takes the user behavior uncertainty into account so that the optimal appliances schedule is less sensitive to unpredictable changes in user preferences. The user behavior uncertainty is modeled as uncertainty in the cost function coefficients. In order to reduce the level of conservativeness of the robust solution, we introduce a parameter allowing to achieve a trade-off between the price of robustness and the protection against uncertainty. Mathematically, the robust scheduling problem is posed as a multi-objective Mixed Integer Linear Programming (MILP), which is solved by using standard algorithms. The numerical results show effectiveness of the proposed approach to increase both the electricity bill and CO2 emissions savings, in the presence of user behavior uncertainties. Mathematical insights into the robust formulation are illustrated and the sensitivity of the optimum cost in the presence of uncertainties is investigated. Although home appliances and EESs are considered in this work, we point out that the proposed scheduling framework is generally applicable to many use cases, e.g., charging and discharging of electrical vehicles in an effective way. In addition, it is applicable to various scenarios considering different uncertainty sources, different storage technologies and generic programmable electrical loads, as well as different optimization criteria.

Place, publisher, year, edition, pages
IEEE Press, 2015
Keywords
Demand response, mixed-integer linear programming, multi-objective robust optimization, robust scheduling of smart appliances, user behavior uncertainty
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-182340 (URN)10.1109/TASE.2015.2497300 (DOI)000374443300025 ()2-s2.0-84949883456 (Scopus ID)
Funder
Swedish Energy AgencyKnut and Alice Wallenberg FoundationVINNOVA
Note

QC 20160226

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2019-02-28Bibliographically approved
3. Demand response for aggregated residential consumers with energy storage sharing
Open this publication in new window or tab >>Demand response for aggregated residential consumers with energy storage sharing
2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

A novel distributed algorithm is proposed in this paper for a network of consumers coupled by energy resource sharing constraints, which aims at minimizing the aggregated electricity costs. Each consumers is equipped with an energy management system that schedules the shiftable loads accounting for user preferences, while an aggregator entity coordinates the consumers demand and manages the interaction with the grid and the shared energy storage system (ESS) via a distributed strategy. The proposed distributed coordination algorithm requires the computation of Mixed Integer Linear Programs (MILPs) at each iteration. The proposed approach guarantees constraints satisfaction, cooperation among consumers, and fairness in the use of the shared resources among consumers. The strategy requires limited message exchange between each consumer and the aggregator, and no messaging among the consumers, which protects consumers privacy. Performance of the proposed distributed algorithm in comparison with a centralized one is illustrated using numerical experiments.

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-182382 (URN)10.1109/CDC.2015.7402504 (DOI)000381554502030 ()2-s2.0-84962003798 (Scopus ID)978-1-4799-7884-7 (ISBN)
Conference
IEEE 54th Annual Conference on Decision and Control (CDC), 2015
Note

QC 20160419

Available from: 2016-02-18 Created: 2016-02-18 Last updated: 2019-02-28Bibliographically approved
4. A plug-and-play home energy management algorithm using optimization and machine learning techniques
Open this publication in new window or tab >>A plug-and-play home energy management algorithm using optimization and machine learning techniques
Show others...
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CONTROL, AND COMPUTING TECHNOLOGIES FOR SMART GRIDS (SMARTGRIDCOMM), 2018Conference paper, Published paper (Refereed)
Abstract [en]

A smart home is considered as an automated residential house that is provided with distributed energy resources and a home energy management system (HEMS). The distributed energy resources comprise PV solar panels and battery storage unit, in the smart homes in this study. In the literature, HEMSs apply optimization algorithms to efficiently plan and control the PV-storage, for the day ahead, to minimize daily electricity cost. This is a sequential stochastic decision making problem, which is computationally intensive. Thus, it is required to develop a computationally efficient approach. Here, we apply a recurrent neural network (RNN) to deal with the sequential decision-making problem. The RNN is trained offline, on the historical data of end-users’ demand, PV generation, time of use tariff and optimal state of charge of the battery storage. Here, optimal state of charge trace is generated by solving a mixed integer linear program, generated from the historical demand and PV traces and tariffs, with the aim of minimizing daily electricity cost. The trained RNN is called policy function approximation (PFA), and its output is filtered by a control policy, to derive efficient and feasible day-ahead state of charge. Furthermore, knowing that there are always new end-users installing PV-storage systems, that don’t have historical data of their own, we propose a computationally efficient and close-to-optimal plug-and-play planning and control algorithm for their HEMSs. Performance of the proposed algorithm is then evaluated in comparison with the optimal strategies, through numerical studies.

National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-240663 (URN)10.1109/SmartGridComm.2018.8587418 (DOI)000458801500004 ()978-1-5386-7954-8 (ISBN)
Conference
EEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), OCT 29-31, 2018, Aalborg, DENMARK
Note

QC 20180121

Available from: 2018-12-30 Created: 2018-12-30 Last updated: 2019-03-08Bibliographically approved
5. Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling
Open this publication in new window or tab >>Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling
2018 (English)In: 2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 338-343, article id 8340694Conference paper, Published paper (Refereed)
Abstract [en]

Nowadays, end-users can participate in demand response (DR) programs, and even slight load reductions from many houses can add up to major load shifts in the power system. Aggregators, which act as mediators between end-users and distribution system operator (DSO), play an important role here. The aggregator contracts the end-users for DR programs, plans ahead for times when customers should shift their load, and controls the load shifts in the running time. In this paper, our main focus is on planning the end-users for load shifting. Here, we first define and formulate the flexibilities (e.g., Stamina, repetition, and capacity) related to the dynamic loads such as space heating systems (SHSs) in detached houses. Assuming some end-users being contracted for DR program, based on estimation of their house characteristics and load flexibilities, an algorithm is then proposed to plan the SHSs for load shifting. In this algorithm the states in which a flexible load can be planned, kept in backup, or unavailable are considered by the aggregator. Another algorithm has been proposed here to deal with the different sources of uncertainties (which cause some of the planned SHSs to become unavailable). Numerical results are presented at the end, which discuss performance of the proposed strategy in terms of load flexibilities, load shifts in response to DR signals, and sensitivity analysis. Here, how to estimate the houses characteristics is a difficult issue, and we approximate them based on available models in the literature.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Engineering and Technology Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-226678 (URN)10.1109/SmartGridComm.2017.8340694 (DOI)2-s2.0-85051029519 (Scopus ID)9781538640555 (ISBN)
Conference
2017 IEEE International Conference on Smart Grid Communications, SmartGridComm 2017, Dresden, Germany, 23 October 2017 through 26 October 2017
Note

QC 20180515

Available from: 2018-04-24 Created: 2018-04-24 Last updated: 2019-02-28Bibliographically approved
6. Flexibility prediction, scheduling and control of aggregated TCLs
Open this publication in new window or tab >>Flexibility prediction, scheduling and control of aggregated TCLs
2020 (English)In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 178, article id 106004Article in journal (Refereed) Published
Abstract [en]

There should be a constant balance between the demand and supply of electrical power. In Nordic countries, electricity markets have been formulated in such a way so as to facilitate this balance. These markets enable purchases, through bids, for buying and selling the energy (e.g., the day-ahead market) and the reserves (e.g., the frequency containment reserve for normal operation (FCR-N)). Demand response (DR) has received increased attention in recent years since it can efficiently support bidding in these markets. Aggregators, which act as mediators between end-users and the system operator, play an important role here. The aggregator contracts a large number of end-users for DR programs, and plans and controls their heterogeneous thermostatically controlled loads (TCLs), and offers their load flexibility to the markets. Taking into account the small market value of each contributing unit, the cost for the communication and control system enabling the DR service must be kept at a minimum. In this paper, we propose a framework which is adaptable to pre-existing and newly emerging TCLs, with no need for major re-design of the local control loops. We then design a strategy for the aggregator, to predict, schedule and control the aggregated flexibility of the contracted heterogeneous TCLs, in response to the DR signals and in the presence of end-users’ behavior uncertainties. In this strategy, we have applied a recurrent neural network (RNN) which learns the aggregated consumption of end-users and predict their aggregated load flexibility. The scheduling and control algorithms are then designed with the aim of participation in FCR-N market. We show that uncertainties in the prediction and scheduling are compensated in the control stage by activating back-up resources. A numerical study on 2000 number of detached houses has been conducted, which shows available 500 kW capacity for participation in the FCR-N market.

Place, publisher, year, edition, pages
Elsevier, 2020
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Energy Technology; Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-244849 (URN)10.1016/j.epsr.2019.106004 (DOI)000501654500002 ()2-s2.0-85071689991 (Scopus ID)
Note

QC 20190301

Available from: 2019-03-01 Created: 2019-03-01 Last updated: 2020-01-14Bibliographically approved
7. A Framework for Attack-Resilient Industrial Control Systems: Attack Detection and Controller Reconfiguration
Open this publication in new window or tab >>A Framework for Attack-Resilient Industrial Control Systems: Attack Detection and Controller Reconfiguration
Show others...
2017 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 106, no 1, p. 113-128Article in journal (Refereed) Published
Abstract [en]

Most existing industrial control systems (ICSs), such as building energy management systems (EMSs), were installed when potential security threats were only physical. With advances in connectivity, ICSs are now, typically, connected to communications networks and, as a result, can be accessed remotely. This extends the attack surface to include the potential for sophisticated cyber attacks, which can adversely impact ICS operation, resulting in service interruption, equipment damage, safety concerns, and associated financial implications. In this work, a novel cyber-physical security framework for ICSs is proposed, which incorporates an analytics tool for attack detection and executes a reliable estimation-based attack-resilient control policy, whenever an attack is detected. The proposed framework is adaptable to already implemented ICS and the stability and optimal performance of the controlled system under attack has been proved. The performance of the proposed framework is evaluated using a reduced order model of a real EMS site and simulated attacks.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017
Keywords
Artifical intelligence, building management systems, cyber-physical security, energy management, industrial control, knowledge-based systems, resilient control, SCADA systems, security analytics, stability, virtual sensor
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-213737 (URN)10.1109/JPROC.2017.2725482 (DOI)000418768700009 ()
Projects
CERCES
Funder
EU, FP7, Seventh Framework Programme, 608224Swedish Research Council, 2013-5523; 2016-0861Swedish Civil Contingencies Agency
Note

QC 20170906

Available from: 2017-09-06 Created: 2017-09-06 Last updated: 2019-08-20Bibliographically approved

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