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Conference paperPudjianto D, Strbac G, Boyer D, 2017,
Virtual power plant: managing synergies and conflicts between transmission system operator and distribution system operator control objectives
, CIRED 24th International Conference on Electricity Distribution, Publisher: IET, Pages: 2049-2052, ISSN: 2515-0855In this study, the implementation of virtual power plant (VPP) as a means to coordinate the use of distributed resources for different control objectives by transmission system operator and distribution system operator is described. In order to illustrate the concept, a range of illustrative studies demonstrating the application of VPP concept on a real 11 kV system in Brixton will be presented, using data from the Low Carbon London project. The studies demonstrate the changes in the operating characteristics of the VPP area over a range of system operating conditions.
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Conference paperHuyghues-Beaufond N, Jakeman A, Tindemans S, et al., 2017,
Challenges in model and data merging for the implementation of a distribution network contingency analysis tool
, 24th International Conference & Exhibition on Electricity Distribution (CIRED), Publisher: IET, Pages: 1621-1624, ISSN: 2515-0855The electricity network in the South East of England has become more challenging to manage both for the transmission and distribution network operators due to increased distributed generation connection and increased power flows on transmission interconnectors to and from continental Europe. UK Power Networks (UKPN), the distribution network operator (DNO), has trialled for the first time online contingency analysis on a distribution network in Great Britain. The Kent Active System Management project aims to demonstrate the benefits of using a contingency analysis system for both operational and planning time frames. This study describes challenges and the recommended approach to overcome data exchange and data-quality challenges when developing a real-time power flow model from existing datasets. It provides a real-world example of dealing with data exchange and also highlights the need for transmission system operator/DNO coordination.
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Journal articleThammawichai M, Kerrigan EC, 2017,
Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors
, Real-Time Systems, Vol: 54, Pages: 132-165, ISSN: 0922-6443We propose three novel mathematical optimization formulations that solve the same two-type heterogeneous multiprocessor scheduling problem for a real-time taskset with hard constraints. Our formulations are based on a global scheduling scheme and a fluid model. The first formulation is a mixed-integer nonlinear program, since the scheduling problem is intuitively considered as an assignment problem. However, by changing the scheduling problem to first determine a task workload partition and then to find the execution order of all tasks, the computation time can be significantly reduced. Specifically, the workload partitioning problem can be formulated as a continuous nonlinear program for a system with continuous operating frequency, and as a continuous linear program for a practical system with a discrete speed level set. The latter problem can therefore be solved by an interior point method to any accuracy in polynomial time. The task ordering problem can be solved by an algorithm with a complexity that is linear in the total number of tasks. The work is evaluated against existing global energy/feasibility optimal workload allocation formulations. The results illustrate that our algorithms are both feasibility optimal and energy optimal for both implicit and constrained deadline tasksets. Specifically, our algorithm can achieve up to 40% energy saving for some simulated tasksets with constrained deadlines. The benefit of our formulation compared with existing work is that our algorithms can solve a more general class of scheduling problems due to incorporating a scheduling dynamic model in the formulations and allowing for a time-varying speed profile.
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Journal articleXiang X, Zhang X, Chaffey G, et al., 2017,
An Isolated Resonant Mode Modular Converter with Flexible Modulation and Variety of Configurations for MVDC Application
, IEEE Transactions on Power Delivery, Vol: 33, Pages: 508-519, ISSN: 0885-8977The dc tap or dc transformer will play an important role in interfacing different voltages of dc links in dc grids. This paper presents an isolated resonant mode modular converter (RMMC) with flexible modulation and assorted configurations to satisfy a wide variety of interface requirements for medium voltage dc (MVDC) networks. The transformer-less RMMC, as introduced in the literature, implemented a restricted modulation scheme leading to a very limited range of step-ratio and the diode rectifier resulted in unidirectional power flow. Both of these limitations are removed in this proposal and galvanic isolation has also been added. Moreover, this new RMMC approach can serve as a building block for variety of configurations. Two such derived topologies are given, which inherently balance the voltage and current between different constituent circuits and realize the high power rating conversion for very low or very high step-ratio application. The theoretical analysis is validated by a set of full-scale simulations and a down-scaled experimental prototype. The results illustrate that this isolated RMMC and its derivatives have promising features for dc taps or dc transformers in MVDC applications.
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Journal articleCantoni M, Farokhi F, Kerrigan EC, et al., 2020,
Structured computation of optimal controls for constrained cascade systems
, International Journal of Control, Vol: 93, Pages: 30-39, ISSN: 0020-7179Constrained finite-horizon linear-quadratic optimal control problems are studied within the context of discrete-time dynamics that arise from the series interconnec- tion of subsystems. A structured algorithm is devised for computing the Newton-like steps of primal-dual interior-point methods for solving a particular re-formulation of the problem as a quadratic program. This algorithm has the following properties: (i) the computation cost scales linearly in the number of subsystems along the cascade; and (ii) the computations can be distributed across a linear proces- sor network, with localized problem data dependencies between the processor nodes and low communication overhead. The computation cost of the approach, which is based on a fixed permutation of the primal and dual variables, scales cubically in the time horizon of the original optimal control problem. Limitations in these terms are explored as part of a numerical example. This example involves application of the main results to model data for the cascade dynamics of an automated irrigation channel in particular.
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Journal articleJudge P, Chaffey G, Merlin MMC, et al., 2017,
Dimensioning and modulation index selection for the hybrid modular multilevel converter
, IEEE Transactions on Power Electronics, Vol: 33, Pages: 3837-3851, ISSN: 1941-0107The Hybrid MMC, comprising a mixture of fullbridgeand half-bridge sub-modules, provides tolerance to DCfaults without compromising the efficiency of the converter to alarge extent. The inclusion of full-bridges creates a new freedomover the choice of ratio of AC to DC voltage at which theconverter is operated, with resulting impact on the converter’sinternal voltage, current and energy deviation waveforms, allof which impact the design of the converter. A design methodaccounting for this, and allowing the required level of deratingof nominal sub-module voltage and up-rating of stackvoltage capability to ensure correct operation at the extremes ofthe operating envelope is presented. A mechanism is identifiedfor balancing the peak voltage that the full-bridge and halfbridgesub-modules experience over a cycle. Comparisons aremade between converters designed to block DC side faultsand converters that also add STATCOM capability. Resultsindicate that operating at a modulation index of 1.2 gives agood compromise between reduced power losses and additionalrequired sub-modules and semiconductor devices in the converter.The design method is verified against simulation results and theoperation of the converter at the proposed modulation index isdemonstrated at laboratory-scale.
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Conference paperXiang X, Zhang X, Chaffey G, et al., 2017,
The isolated resonant modular multilevel converters with extreme step-ratio for MVDC application
, 2017 IEEE 18TH WORKSHOP ON CONTROL AND MODELING FOR POWER ELECTRONICS (COMPEL), Publisher: IEEEThe dc-dc conversion will play an important role in multi-terminal dc networks and dc grids. This paper presents two isolated resonant modular multilevel converters (IRMMCs) to fulfill the large step-ratio conversion for medium voltage dc (MVDC) networks. The conventional resonant modular multilevel converters (RMMCs) suffer the common problems of non-isolation and high current stress, which are solved in the proposed IRMMCs. They not only inherit the beneficial features of inherent sub-module (SM) voltage-balancing and soft-switching operation from RMMCs, but also develop multi-module configurations to neutralize the current ripples on both sides of the dc-links. The theoretical analysis is verified by a set of full-scaled simulations for different application examples in MVDC collection and distribution. The results demonstrate the proposed IRMMCs and its derived configurations have good potential for operation as large step-ratio MVDC transformers.
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Journal articleScarciotti G, Astolfi A, 2017,
Nonlinear Model Reduction by Moment Matching
, Foundations and Trends in Systems and Control, Vol: 4, Pages: 224-409, ISSN: 2325-6818Mathematical models are at the core of modern science and technology. An accurate description of behaviors, systems and processes often requires the use of complex models which are difficult to analyze and control. To facilitate analysis of and design for complex systems, model reduction theory and tools allow determining “simpler” models which preserve some of the features of the underlying complex description. A large variety of techniques, which can be distinguished depending on the features which are preserved in the reduction process, has been proposed to achieve this goal. One such a method is the moment matching approach.This monograph focuses on the problem of model reduction by moment matching for nonlinear systems. The central idea of the method is the preservation, for a prescribed class of inputs and under some technical assumptions, of the steady-state output response of the system to be reduced. We present the moment matching approach from this vantage point, covering the problems of model reduction for nonlinear systems, nonlinear time-delay systems, data-driven model reduction for nonlinear systems and model reduction for “discontinuous” input signals. Throughout the monograph linear systems, with their simple structure and strong properties, are used as a paradigm to facilitate understanding of the theory and provide foundation of the terminology and notation. The text is enriched by several numerical examples, physically motivated examples and with connections to well-established notions and tools, such as the phasor transform.
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Journal articleMerlin MMC, Soto-Sanchez D, Judge PD, et al., 2017,
The extended overlap alternate arm converter: a voltage source converter with DC fault ride-through capability and a compact design
, IEEE Transactions on Power Electronics, Vol: 33, Pages: 3898-3910, ISSN: 1941-0107The Alternate Arm Converter (AAC) was one ofthe first modular converter topologies to feature DC-side faultride-through capability with only a small penalty in powerefficiency. However, the simple alternation of its arm conductionperiods (with an additional short overlap period) resulted in(i) substantial 6-pulse ripples in the DC current waveform,(ii) large DC-side filter requirements, and (iii) limited operatingarea close to an energy sweet-spot. This paper presents a newmode of operation called Extended Overlap (EO) based onthe extension of the overlap period to 60◦which facilitates afundamental redefinition of the working principles of the AAC.The EO-AAC has its DC current path decoupled from the ACcurrent paths, a fact allowing (i) smooth DC current waveforms,(ii) elimination of DC filters, and (iii) restriction lifting on thefeasible operating point. Analysis of this new mode and EO-AAC design criteria are presented and subsequently verifiedwith tests on an experimental prototype. Finally, a comparisonwith other modular converters demonstrates that the EO-AACis at least as power efficient as a hybrid MMC (i.e. a DC faultride-through capable MMC) while offering a smaller converterfootprint because of a reduced requirement for energy storagein the submodules and a reduced inductor volume.
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Journal articleSingh AK, Pal BC, 2017,
Decentralized nonlinear control for power systems using normal forms and detailed models
, IEEE Transactions on Power Systems, Vol: 33, Pages: 1160-1172, ISSN: 1558-0679This paper proposes a decentralized method fornonlinear control of oscillatory dynamics in power systems. Themethod is applicable for ensuring both transient stability as wellas small-signal stability. The method uses an optimal control lawwhich has been derived in the general framework of nonlinearcontrol using normal forms. The model used to derive the controllaw is the detailed subtransient model of synchronous machinesas recommended by IEEE. Minimal approximations have beenmade in either the derivation or the application of the controllaw. The developed method also requires the application ofdynamic state estimation technique. As the employed control andestimation schemes only need local measurements, the methodremains completely decentralized. The method has been demon-strated as an effective tool to prevent blackouts by simulating amajor disturbance in a benchmark power system model and itssubsequent control using the proposed method.
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Journal articleMylvaganam T, Sassano M, Astolfi A, 2017,
A differential game approach to multi-agent collision avoidance
, IEEE Transactions on Automatic Control, Vol: 62, Pages: 4229-4235, ISSN: 0018-9286A multi-agent system consisting of N agents is considered. The problem of steering each agent from its initial position to a desired goal while avoiding collisions with obstacles and other agents is studied. This problem, referred to as the multi-agent collision avoidance problem, is formulated as a differential game. Dynamic feedback strategies that approximate the feedback Nash equilibrium solutions of the differential game are constructed and it is shown that, provided certain assumptions are satisfied, these guarantee that the agents reach their targets while avoiding collisions.
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Journal articleDjapic P, Strbac G, McKenna R, et al., 2018,
Assessing the implications of socioeconomic diversity for low carbon technology uptake in electrical distribution networks
, Applied Energy -
Conference paperFatouros P, Konstantelos I, Papadaskalopoulos D, et al., 2017,
A stochastic dual dynamic programming approach for optimal operation of DER aggregators
, IEEE PowerTech 2017, Publisher: IEEEThe operation of aggregators of distributed energy resources (DER) is a highly complex task that is affected by numerous factors of uncertainty such as renewables injections, load levels and market conditions. However, traditional stochastic programming approaches neglect information around temporal dependency of the uncertain variables due to computational tractability limitations. This paper proposes a novel stochastic dual dynamic programming (SDDP) approach for the optimal operation of a DER aggregator. The traditional SDDP framework is extended to capture temporal dependency of the uncertain wind power output, through the integration of an n-order autoregressive (AR) model. This method is demonstrated to achieve a better trade-off between solution efficiency and computational time requirements compared to traditional stochastic programming approaches based on the use of scenario trees.
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Conference paperYe Y, Papadaskalopoulos, Moreira, et al., 2017,
Strategic Capacity Withholding by Energy Storage in Electricity Markets
, 12th IEEE PES PowerTech Conference, Publisher: IEEEAbstract:Although previous work has demonstrated the ability of large energy storage (ES) units to exercise market power by withholding their capacity, it has adopted modeling approaches exhibiting certain limitations and has not analyzed the dependency of the extent of exercised market power on ES operating properties. In this paper, the decision making process of strategic ES is modeled through a bi-level optimization problem; the upper level determines the optimal extent of capacity withholding at different time periods, maximizing the ES profit, while the lower level represents endogenously the market clearing process. This problem is solved after converting it to a Mathematical Program with Equilibrium Constraints (MPEC) and linearizing the latter through suitable techniques. Case studies on a test market quantitatively analyze the extent of capacity withholding and its impact on ES profit and social welfare for different scenarios regarding the power and energy capacity of ES.
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Conference paperTrovato V, Tindemans S, Strbac G, 2017,
Understanding aggregate flexibility of thermostatically controlled loads
, 12th IEEE Power and Energy Society PowerTech Conference 2017, Publisher: IEEEThermostatically controlled loads (TCLs) are an attractive source of responsive demand. This paper aims to provides a better understanding of the relation between thermal properties of TCLs and their suitability to provide energy arbitrage and frequency services. An approximate analysis on the basis of dimensionless parameters is used to visualise the relative abilities of eight classes of TCLs. The results are compared to those obtained from a formal optimisation approach, in the context of a GB case study. Additional studies are performed to investigate the impact of increasingly flexible frequency services and physical variations of TCL thermal models (thermal conductance and temperature deadband).
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Conference paperScarciotti G, Teel AR, 2017,
Model Order Reduction for Stochastic Nonlinear Systems
, 56th IEEE Conference on Decision and Control, Publisher: IEEE -
SoftwareGu Y, Bottrell, Green, 2017,
Reduced-Order Models for Representing Converters in Power System Studies
Matlab codes of reduced-order models for representing power electronic converters in power system analyses.
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Conference paperPadoan A, Astolfi A, 2017,
Eigenvalues and Poles of a Nonlinear System: a Geometric Approach
, 56th IEEE Conference on Decision and Control, Publisher: IEEE -
Journal articleTindemans S, Strbac G, 2017,
Robust estimation of risks from small samples
, Philosophical Transactions A: Mathematical, Physical and Engineering Sciences, Vol: 375, ISSN: 1471-2962Data-driven risk analysis involves the inference of probability distributions from measured or simulated data. In the case of a highly reliable system, such as the electricity grid, the amount of relevant data is often exceedingly limited, but the impact of estimation errors may be very large. This paper presents a robust non-parametric Bayesian method to infer possible underlying distributions. The method obtains rigorous error bounds even for small samples taken from ill-behaved distributions. The approach taken has a natural interpretation in terms of the intervals between ordered observations, where allocation of probability mass across intervals is well specified, but the location of that mass within each interval is unconstrained. This formulation gives rise to a straightforward computational resampling method: Bayesian interval sampling. In a comparison with common alternative approaches, it is shown to satisfy strict error bounds even for ill-behaved distributions.
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Conference paperZhou Y, Boem F, Parisini T, 2017,
Partition-based Pareto-optimal state prediction method for interconnected systems using sensor networks
, 2017 American Control Conference, Publisher: IEEE, Pages: 1886-1891In this paper a novel partition-based state prediction method is proposed for interconnected stochastic systems using sensor networks. Each sensor locally computes a prediction of the state of the monitored subsystem based on the knowledge of the local model and the communication with neighboring nodes of the sensor network. The prediction is performed in a distributed way, not requiring a centralized coordination or the knowledge of the global model. Weights and parameters of the state prediction are locally optimized in order to minimise at each time-step bias and variance of the prediction error by means of a multi-objective Pareto optimization framework. Individual correlations between the state, the measurements, and the noise components are considered, thus assuming to have in general unequal weights and parameters for each different state component. No probability distribution knowledge is required for the noise variables. Simulation results show the effectiveness of the proposed method.
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Conference paperScarciotti G, Teel AR, Astolfi A, 2017,
Model reduction for linear differential inclusions: robustness and time-variance
, 2017 American Control Conference, Publisher: IEEE, ISSN: 2378-5861This paper deals with the problem of modelreduction by moment matching for linear differential inclusions.The problem is formally formulated and the notions of moment-set, perturbed moment trajectory, approximate reduced ordermodel and robust reduced order model are introduced. Twosets of results are presented. The first part of the paper dealswith robustness of the reduced order models with respect toinput perturbations. Exploiting this result an enhanced modelreduction scheme for linear differential equations is presented.In the second part of the paper we focus on the problem ofmodel reduction by moment matching for time-varying systemsdriven by time-varying signal generators. Finally, these two setsof results are used to solve the problem of model reductionfor linear differential inclusions. The results are illustrated bymeans of numerical examples.
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Conference paperPadoan A, Astolfi A, 2017,
Moments of random variables: a system-theoretic interpretation
, 2017 American Control Conference (ACC), Publisher: IEEEMoments of continuous random variables with aprobability density function which can be represented as theimpulse response of a linear time-invariant system are studied.Under some assumptions, the moments of the random variableare characterised in terms of the solution of a Sylvester equationand of the steady-state output response of an interconnectedsystem. This allows to interpret well-known notions and resultsof probability theory and statistics in the language of systemtheory, including the notion of moment generating function, thesum of independent random variables and the notion of mixturedistribution.
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Conference paperPadoan A, Astolfi A, 2017,
Model reduction by moment matching at isolated singularities for linear systems: a complex analytic approach
, 20th IFAC 2017 World Congress, Publisher: ElsevierThe model reduction problem by moment matching for continuous-time, single-input, single-output, linear, time-invariant systems is studied at isolated singularities (in particular, at poles). The notion of moment at a pole of the transfer function is defined. Exploiting this notion a one-to-one correspondence between moments at a pole of the transfer function and the “limit solution” of a family of Sylvester equations is established. Finally, a family of reduced order models is defined. A simple example illustrates the theory.
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Conference paperMylvaganam T, Astolfi A, 2017,
Zero finding via feedback stabilisation
, IFAC 2017 World Congress, Publisher: Elsevier, Pages: 8133-8138, ISSN: 1474-6670Two iterative algorithms for solving systems of linear and nonlinear equations are proposed. For linear problems the algorithm is based on a control theoretic approach and it is guaranteed to yield a converging sequence for any initial condition provided a solution exists. Systems of nonlinear equations are then considered and a generalised algorithm, again taking inspiration from control theory, is proposed. Local convergence is guaranteed in the nonlinear setting. Both the linear and the nonlinear algorithms are demonstrated on a series of numerical examples.
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Conference paperScarciotti G, Teel AR, 2017,
Model order reduction of stochastic linear systems by moment matching
, 20th IFAC World Congress, Publisher: IFAC Secretariat, Pages: 6332-6337, ISSN: 2405-8963In this paper we characterize the moments of stochastic linear systems by means of the solution of a stochastic matrix equation which generalizes the classical Sylvester equation. The solution of the matrix equation is used to define the steady-state response of the system which is then exploited to define a family of stochastic reduced order models. In addition, the notions of stochastic reduced order model in the mean and stochastic reduced order model in the variance are introduced. While the determination of a reduced order model based on the stochastic notion of moment has high computational complexity, stochastic reduced order models in the mean and variance can be determined more easily, yet they preserve some of the stochastic properties of the system to be reduced. The differences between these three families of models are illustrated by means of numerical simulations.
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Conference paperKhusainov B, Kerrigan EC, Suardi A, et al., 2017,
Nonlinear predictive control on a heterogeneous computing platform
, IFAC World Congress 2017, Publisher: IFAC / Elsevier, Pages: 11877-11882Nonlinear Model Predictive Control (NMPC) is an advanced control technique that often relies on computationally demanding optimization and integration algorithms. This paper proposes and investigates a heterogeneous hardware implementation of an NMPC controller based on an interior point algorithm. The proposed implementation provides flexibility of splitting the workload between a general-purpose CPU with a fixed architecture and a field-programmable gate array (FPGA) to trade off contradicting design objectives, namely performance and computational resource usage. A new way of exploiting the structure of the Karush-Kuhn-Tucker (KKT) matrix yields significant memory savings, which is crucial for reconfigurable hardware. For the considered case study, a 10x memory savings compared to existing approaches and a 10x speedup over a software implementation are reported. The proposed implementation can be tested from Matlab using a new release of the Protoip software tool, which is another contribution of the paper. Protoip abstracts many low-level details of heterogeneous hardware programming and allows quick prototyping and processor-in-the-loop verification of heterogeneous hardware implementations.
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Conference paperBoem F, Reci R, Cenedese A, et al., 2017,
Distributed clustering-based sensor fault diagnosis for HVAC systems
, 20th IFAC World Congress, Publisher: IFAC / Elsevier, Pages: 4197-4202The paper presents a distributed Sensor Fault Diagnosis architecture for Industrial Wireless Sensor Networks monitoring HVAC systems, by exploiting a recently proposed distributed clustering method. The approach allows the detection and isolation of multiple sensor faults and considers the possible presence of modeling uncertainties and disturbances. Detectability and isolability conditions are provided. Simulation results show the effectiveness of the proposed method for an HVAC system.
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Conference paperShukla H, Khusainov B, Kerrigan EC, et al., 2017,
Software and hardware code generation for predictive control using splitting methods
, IFAC World Congress 2017, Publisher: IFAC / Elsevier, Pages: 14386-14391This paper presents SPLIT, a C code generation tool for Model Predictive Control (MPC) based on operator splitting methods. In contrast to existing code generation packages, SPLIT is capable of generating both software and hardware-oriented C code to allow quick prototyping of optimization algorithms on conventional CPUs and field-programmable gate arrays (FPGAs). A Matlab interface is provided for compatibility with existing commercial and open-source software packages. A numerical study compares software, hardware and heterogeneous implementations of splitting methods and investigates MPC design trade-offs. For the considered testcases the reported speedup of hardware implementations over software realizations is 3x to 11x.
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Journal articleBachtiar V, Manzie C, Kerrigan EC, 2017,
Nonlinear model-predictive integrated missile control and Its multiobjective Tuning
, Journal of Guidance Control and Dynamics, Vol: 40, Pages: 2961-2970, ISSN: 1533-3884 -
Journal articleMajumdar A, Agalgoankar YP, Pal BC, et al., 2017,
Centralized volt-var optimization strategy considering malicious attack on distributed energy resources control
, IEEE Transactions on Sustainable Energy, Vol: 9, Pages: 148-156, ISSN: 1949-3037The adoption of information and communication technology (ICT) based centralized volt-var control (VVC) leads to an optimal operation of a distribution feeder. However, it also poses a challenge that an adversary can tamper with the metered data and thus can render the VVC action ineffective. Distribution system state estimation (DSSE) acts as a backbone of centralized VVC. Distributed energy resources (DER) injection measurements constitute leverage measurements from a DSSE point of view. This paper proposes two solutions as a volt var optimization-distribution system state estimation (VVO-DSSE) malicious attack mitigating strategy when the DER injection measurements are compromised. The first solution is based on local voltage regulation controller set-points. The other solution effectively employs historical data or forecast information. The concept is based on a cumulant based probabilistic optimal power flow with the objective of minimizing the expectation of total power losses. The effectiveness of the approach is performed on the 95-bus UK generic distribution system (UKGDS) and validated against Monte Carlo simulations.
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