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  • 1.
    Armendariz, Mikel
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Wallin, Edel
    Vattenfall R&D.
    Comparative Study of Optimal Controller Placement Considering Uncertainty in PV Growth and Distribution Grid Expansion2018In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 155C, p. 48-57Article in journal (Refereed)
    Abstract [en]

    Distributed generation (DG) and especially grid-connected residential photovoltaic (PV) systems areemerging and high penetration levels of these can have an adverse impact on several low voltage (LV)distribution grids in terms of power quality and reliability. In order to reduce that effect in a cost-effectivemanner, the traditional distribution grid planning process is being reengineered by incorporating the gridcontrol operations and considering the uncertainties e.g., DG power, demand and urban/rural expansionplans. One of the challenges is to determine if the required technology deployment to operate the gridscan provide a better solution in terms of quality and cost than the traditional approach, which is prin-cipally based on cable reinforcement and change of transformers. In addition, if controllers were to bedeployed, it would be important to determine where they should be placed and at what stage of theexpansion planning, especially when the planning is assumed to be non-deterministic.Therefore, following this situation, in this paper we propose an optimal way to deploy and to operateutility’s controllable resources at the distribution grid and additionally we consider the uncertaintiesrelated to PV growth and distribution grid expansion. Thus, we include the non-deterministic multistageperspective to the controller placement problem. Furthermore, we perform a techno-economic analysis ofthe results and we show that an optimal controller placement allows removing the overvoltage problemsarising in the LV grid in a more cost-effective way compared to a typical traditional grid reinforcementapproach.

  • 2.
    Azuatalam, Donald
    et al.
    Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia..
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Ma, Yiju
    Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia..
    Foerstl, Markus
    Tech Univ Munich, Inst Elect Energy Storage Technol, Munich, Germany..
    Chapman, Archie C.
    Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia..
    Verbic, Gregor
    Univ Sydney, Sch Elect & Informat Engn, Sydney, NSW, Australia..
    Energy management of small-scale PV-battery systems: A systematic review considering practical implementation, computational requirements, quality of input data and battery degradation2019In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 112, p. 555-570Article, review/survey (Refereed)
    Abstract [en]

    The home energy management problem has many different facets, including economic viability, data uncertainty and quality of strategy employed. The existing literature in this area focuses on individual aspects of this problem without a detailed, holistic analysis of the results with regards to practicality in implementation. In this paper, we fill this gap by performing a comprehensive comparison of seven different energy management strategies, each with different levels of practicality, sophistication and computational requirements. We analyse the results in the context of these three characteristics, and also critique the modelling assumptions made by each strategy. Our analysis finds that using a more sophisticated energy management strategy may not necessarily improve the performance and economic viability of the PV-battery system due to the effects of modelling assumptions, such as the treatment of uncertainties in the input data and battery degradation effects.

  • 3.
    Brodén, Daniel
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Nordström, Lars
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems. KTH, School of Industrial Engineering and Management (ITM), Production Engineering.
    MATLAB Applications to Generate Synthetic Electricity Load Profiles of Office Buildings and Detached Houses2017In: 2017 IEEE Innovative Smart Grid Technologies - Asia: Smart Grid for Smart Community, ISGT-Asia 2017, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 1-6, 2017Conference paper (Refereed)
    Abstract [en]

    In this paper we present two MATLAB applications that generates synthetic electricity load profiles for office buildings and detached houses down to 1-minute resolution. The applications have been developed using App Designer — a MATLAB environment for application development. The applications are based on consumer load models for office buildings and detached houses published in previous research work. The aim of this paper is to present an overview of the application functionalities, code design, assumptions and limitations, and examples of their potential use in power system education and research. To the author’s knowledge these are the first applications which allow generating synthetic load profiles for office buildings and houses in practical and intuitive manner where building attributes can be easily configured.

  • 4.
    Emamian, Sepehr
    et al.
    Sharif University of Technology.
    Mohsen, Hamzeh
    Sharif University of Technology.
    Paridari, Kaveh
    Sharif University of Technology, Tehran, Iran.
    Karimi, Hamid Reza
    Sharif University of Technology.
    Bakhshai, Alireza
    Queen's university.
    Robust decentralized voltage control of an islanded microgrid under unbalanced and nonlinear load conditions2013Conference paper (Refereed)
    Abstract [en]

    This paper presents a new decentralized control strategy for the islanded operation of a microgrid under unknown load conditions. In the islanded mode of operation, the microgrid should provide the load with a set of regulated balanced three-phase voltages. The load which is parametrically and topologically uncertain can also be unbalanced and/or nonlinear. Thus, the use of conventional control strategies results in the poor performance and even instability of the microgrid system. The proposed method assumes that the load current is a measurable disturbance signal. The robust optimal controlapproaches are used to design a controller to overcome the disturbances resulting from the unknownloads dynamics. The optimization problem is converted to a convex problem and is solved using the linear matrix inequalities (LMIs). The performance of the designed controller is verified using time-domain simulations carried out in PSCAD/EMTDC software.

  • 5.
    Herre, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Tomasini, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Optimal Day-Ahead Bidding of a Risk-Averse Pulp and Paper Mill in the Energy and Reserve Market2019In: 16th International Conference on European Energy Market / [ed] IEEE, IEEE, 2019, p. 1-5, article id 8916364Conference paper (Refereed)
    Abstract [en]

    Due to increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill (PPM) is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints of the PPM. The bids in the energy and reserve markets are optimized subject to price uncertainty. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of PPM. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk adversity of the PPM is investigated. We show that reserve offers can significantly improve the profitability of the PPM.

    Download full text (pdf)
    Herre et al - 2019 - Optimal Day-Ahead Bidding of a Risk-Averse Pulp and Paper Mill in the Energy and Reserve Market
  • 6.
    Herre, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Tomasini, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Industrial Engineering and Management (ITM), Production Engineering. KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Simplified model of integrated paper mill for optimal bidding in energy and reserve markets2020In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 279, article id 115857Article in journal (Refereed)
    Abstract [en]

    Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets. In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill.

  • 7.
    Herre, Lars
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Tomasini, Federica
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Söder, Lennart
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Simplified Model of Integrated Paper Mill for Optimal Bidding in Energy and Reserve MarketsManuscript (preprint) (Other academic)
    Abstract [en]

    Due to the increased use of variable renewable energy sources, more capacity for reserves is required. Non-generating resources such as large industrial consumers can arbitrage energy prices and provide reserve capacity by exploiting the inherent flexibility in selected industrial processes. A large enough industrial consumer can capitalize on this flexibility through optimized bidding in electricity markets.In this work, the day-ahead cost minimization of a risk-averse pulp and paper mill is formulated as a two-stage stochastic problem, considering thermodynamic and electrical constraints. The bids in the energy and reserve markets are jointly optimized subject to price uncertainty as well as uncertainty of frequency realization. The results of a case study in Sweden display a significant economic benefit in exploiting the flexibility of integrated pulp and paper mills with electric boilers. The expected cost of the pulp and paper mill resulting from different strategies are compared and the risk-aversion of the pulp and paper mill is investigated. Reserve offers are mainly facilitated by fast-acting electric boilers and supported by flexibility in the steam network. We show that reserve offers can significantly improve the profitability of the pulp and paper mill.

    Download full text (pdf)
    fulltext
  • 8.
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Hierarchical energy management in smart grids: Flexibility prediction, scheduling and resilient control2019Doctoral 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.

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  • 9.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Optimal and Resilient Control with Applications in Smart Distribution Grids2016Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The electric power industry and society are facing the challenges and opportunities of transforming the present power grid into a smart grid. To meet

    these challenges, new types of control systems are connected over IT infrastructures. While this is done to meet highly set economical and environmental goals, it also introduces new sources of uncertainty in the control loops. In this thesis, we consider control design taking some of these uncertainties into account.

    In Part I of the thesis, some economical and environmental concerns in smart grids are taken into account, and a scheduling framework for static loads (e.g., smart appliances in residential areas) and dynamic loads (e.g., energy storage systems) in the distribution level is investigated. A robust formulation is proposed taking the user behavior uncertainty into account, so that the optimal scheduling cost is less sensitive to unpredictable changes in user preferences. In addition, a novel distributed algorithm for the studied scheduling framework is proposed, which aims at minimizing the aggregated electricity cost of a network of apartments sharing an energy storage system. We point out that the proposed scheduling framework is applicable to various uncertainty sources, storage technologies, and programmable electrical loads.

    In Part II of the thesis, we study smart grid uncertainty resulting from possible security threats. Smart grids are one of the most complex cyber-physical systems considered, and are vulnerable to various cyber and physical attacks. The attack scenarios consider cyber adversaries that may corrupt a few measurements and reference signals, which may degrade the system’s reliability and even destabilize the voltage magnitudes. In addition, a practical attack-resilient framework for networked control systems is proposed. This framework includes security information analytics to detect attacks and a resiliency policy to improve the performance of the system running under the attack. Stability and optimal performance of the networked control system under attack and by applying the proposed framework, is proved here. The framework has been applied to an energy management system and its efficiency is demonstrated on a critical attack scenario.

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  • 10.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Azuatalam, Donald
    Chapman, Archie C.
    Verbič, Gregor
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    A plug-and-play home energy management algorithm using optimization and machine learning techniques2018In: 2018 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS, CONTROL, AND COMPUTING TECHNOLOGIES FOR SMART GRIDS (SMARTGRIDCOMM), 2018Conference 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.

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  • 11.
    Paridari, Kaveh
    et al.
    Sharif University of Technology, Tehran, Iran.
    Hamzeh, Mohsen
    Sharif University of Technology, Tehran, Iran.
    Emamian, Sepehr
    Sharif University of Technology.
    Karimi, Hamid Reza
    Sharif University of Technology, Tehran, Iran.
    Bakhshai, Alireza
    Queen's university.
    A new decentralized voltage control scheme of an autonomous microgrid under unbalanced and nonlinear load conditions2013In: Proceedings of the IEEE International Conference on Industrial Technology, IEEE , 2013, p. 1812-1817Conference paper (Refereed)
    Abstract [en]

    This paper presents an effective voltage control strategy for the autonomous operation of a medium voltage (MV) microgrid under nonlinear and unbalanced load conditions. The main objectives of this strategy are to effectively compensate the harmonic and negative-sequence currents of nonlinear and unbalanced loads using distributed generation (DG) units. The proposed control strategy consists of a multi-proportional resonant controller (MPRC) whose parameters are assigned with particle swarm optimization (PSO) algorithm. The optimization function is defined to minimize the tracking error at the specific harmonics considering the stability limitations. In this paper the performance of the proposed controller is investigated for a single DG unit. Due to the fact that DG units can be decentralized, this strategy generalizes for multi-DG unit networks. The performance of the proposed control scheme is verified by using digital time-domain simulation studies in the PSCAD/EMTDC software environment.

  • 12.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Mady, Alie El-Din
    La Porta, Silvio
    Chabukswar, Rohan
    Blanco, Jacobo
    Teixeira, André
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Boubekeur, Menouer
    Cyber-Physical-Security Framework for Building Energy Management System2016In: 2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems, ICCPS 2016 - Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2016, article id 7479072Conference paper (Refereed)
    Abstract [en]

    Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

  • 13.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Flexibility prediction, scheduling and control of aggregated TCLs2020In: Electric power systems research, ISSN 0378-7796, E-ISSN 1873-2046, Vol. 178, article id 106004Article in journal (Refereed)
    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.

  • 14.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Sandels, Claes
    Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling2018In: 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 (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.

  • 15.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    O'Mahony, Niamh
    Mady, Alie El-Din
    Chabukswar, Rohan
    Boubekeur, Menouer
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    A Framework for Attack-Resilient Industrial Control Systems: Attack Detection and Controller Reconfiguration2017In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 106, no 1, p. 113-128Article in journal (Refereed)
    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.

  • 16.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Demand response for aggregated residential consumers with energy storage sharing2015Conference 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.

  • 17.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Energy and CO2 efficient scheduling of smart appliances in active houses equipped with batteries2014In: Automation Science and Engineering (CASE), 2014 IEEE International Conference on, IEEE conference proceedings, 2014, p. 632-639Conference 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.

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  • 18.
    Paridari, Kaveh
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Parisio, Alessandra
    KTH, School of Electrical Engineering (EES), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty2015In: IEEE Transactions on Automation Science and Engineering, ISSN 1545-5955, E-ISSN 1558-3783, Vol. 13, no 1, p. 247-259Article in journal (Refereed)
    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.

  • 19.
    Paridari, Kaveh
    et al.
    Electrical Engineering Department, Sharif University of Technology, Tehran, Iran.
    Tavazoei, Mohammad Saleh
    Sharif University of Technology.
    Fractional PI Tuning Satisfying Gain and Phase Margin Constraints2011Conference paper (Refereed)
    Abstract [en]

    In this paper, an algebraic tuning rule is presented for fractional PI controllers to control first order plus dead-time processes. By using the performance map (PM) method, this tuning rule is derived in order to set the gain margin of the control system close to 3 and the phase margin close to 60 degrees. The robustness and performance of this tuning rule are compared with some well-known PI tuning rules. Simulation results are brought to demonstrate the effectiveness and robustness of this tuning formula against process dynamic uncertainties in comparison with the other tuning methods.

  • 20.
    Teixeira, André
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Sandberg, Henrik
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Voltage control for interconnected microgrids under adversarial actions2015In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, IEEE conference proceedings, 2015Conference paper (Refereed)
    Abstract [en]

    In this paper, we study the impact of adversarial actions on voltage control schemes in interconnected microgrids. Each microgrid is abstracted as a power inverter that can be controlled to regulate its voltage magnitude and phase-angle independently. Moreover, each power inverter is modeled as a single integrator, whose input is given by a voltage droop-control policy that is computed based on voltage magnitude and reactive power injection measurements. Under mild assumptions, we then establish important properties of the nominal linearized closed-loop system, such as stability, positivity, and diagonal dominance. These properties play an important role when characterizing the potential impact of different attack scenarios. In particular, we discuss two attack scenarios where the adversary corrupts measurement data and reference signals received by the voltage droop controllers. The potential impact of instances of each scenario is analyzed using control-theoretic tools, which may be used to develop methodologies for identifying high-risk attack scenarios, as is illustrated by numerical examples.

  • 21.
    Theile, Philipp
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Towle, Anna-Linnea
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Karnataki, Kaustubh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Crosara, Alessandro
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Turk, Graham
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Nordström, Lars
    KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electric Power and Energy Systems.
    Day-ahead electricity consumption prediction of a population of households: analyzing different machine learning techniques based on real data from RTE in France2018In: 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Institute of Electrical and Electronics Engineers (IEEE), 2018, article id 8587591Conference paper (Refereed)
    Abstract [en]

    Forecasting of power consumption has been a topic of great interest for the stakeholders of electricity markets. It has an essential role in decision making, including purchasing and generating electric power, load switching, and demand side management. Different algorithms are tested and used for balancing the demand and supply of energy. This research work focuses on predicting power consumption using time series forecasting methods for the Île-de-France region with publicly available energy data from RTE, France. The two machine learning algorithms Support Vector Machine (SVM) and Recurrent Neural Network (RNN) are implemented and tested for their accuracy in predicting day-ahead half-hourly power consumption data. This paper provides brief insights on the algorithms used and further explains the data handling for its implementation. The Mean Absolute Percentage Error (MAPE) is used as the performance measure. The results indicate a higher accuracy of the RNN at the cost of longer computation times.

  • 22.
    Zografos, Dimitrios
    et al.
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Ghandhari, Mehrdad
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Paridari, Kaveh
    KTH, School of Electrical Engineering (EES), Electric Power and Energy Systems.
    Estimation of power system inertia using particle swarm optimization2017In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, article id 8071383Conference paper (Refereed)
    Abstract [en]

    Power system inertia is being globally reduced, due to the substitution of conventional synchronous power plants by intermittent generation. This threatens the frequency stability of the system and makes the estimation of power system inertia necessary, so that proactive measures can be imposed. A disturbance-based method is proposed in this paper, which estimates the total inertia constant of the power system. The method applies particle swarm optimization (PSO) to minimize a cost function, which is defined based on the swing equation. To do that, data available at the generator buses are employed. The proposed method is applied on the Nordic57 test system under twenty different scenarios, which include generator and load disconnections. Furthermore, a comparison with two methods presented in the literature is performed and demonstrates the higher performance of the proposed method, in the sense of the mean and the variance of the errors.

1 - 22 of 22
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