Kaveh Paridari

Demand response for aggregated residential consumers with energy storage sharing

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.

Voltage control for interconnected microgrids under adversarial actions

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.

Robust Scheduling of Smart Appliances in Active Apartments With User Behavior Uncertainty

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.

A new decentralized voltage control scheme of an autonomous microgrid under unbalanced and nonlinear load conditions

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.

Cyber-physical-security framework for building energy management system

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.

Energy and CO 2 efficient scheduling of smart appliances in active houses equipped with batteries

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.

Robust decentralized voltage control of an islanded microgrid under unbalanced and nonlinear load conditions

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 control approaches are used to design a controller to overcome the disturbances resulting from the unknown loads 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.

Fractional PI Tuning Satisfying Gain and Phase Margin Constraints

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 der ...

A Framework for Attack-Resilient Industrial Control Systems: Attack Detection and Controller Reconfiguration

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.

Estimation of power system inertia using particle swarm optimization

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.