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A plug-and-play home energy management algorithm using optimization and machine learning techniques
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.ORCID iD: 0000-0002-4210-8672
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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.

Place, publisher, year, edition, pages
2018.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-240663DOI: 10.1109/SmartGridComm.2018.8587418ISI: 000458801500004Scopus ID: 2-s2.0-85061059990ISBN: 978-1-5386-7954-8 (print)OAI: oai:DiVA.org:kth-240663DiVA, id: diva2:1274333
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: 2022-06-26Bibliographically approved
In thesis
1. Hierarchical energy management in smart grids: Flexibility prediction, scheduling and resilient control
Open this publication in new window or tab >>Hierarchical energy management in smart grids: Flexibility prediction, scheduling and resilient control
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:nbn:se:kth:diva-244843 (URN)978-91-7873-123-7 (ISBN)
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: 2022-06-26Bibliographically approved

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Paridari, KavehNordström, Lars

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