Vahid R. Disfani

An SOC-Based Battery Management System for Microgrids

This paper investigates modeling and control of a battery management system used in a microgrid for both grid-connected and autonomous modes. The paper has three salient contributions: 1) An aggregated battery circuit model with the open circuit voltage as a nonlinear function of the state of the charge (SOC) is derived and modeled in PSCAD. 2) Closed-loop feedback control strategies of the battery system are developed for the microgrid under both operation modes. At the grid-connected mode, power control is employed while at the autonomous mode, voltage and frequency control is employed for the battery to act as a synchronous generator by providing voltage and frequency support. 3) An upper level SOC based management system is also developed. Since SOC cannot be directly measured, an estimation scheme is derived based on power and voltage measurements from the battery. The overall management system is demonstrated to be effective by six case studies at different microgrid operation modes.

A Mathematical Model for Urban Traffic and Traffic Optimization Using a Developed ICA Technique

This paper proposes a mathematical model for the traffic of metropolises, which can further be optimized to efficiently control the traffic. This model takes into account all the parameters that can have an effect on the traffic, e.g., escape rates (ERs) of intersections and paths. Moreover, to optimize the proposed model, the imperialist competitive algorithm (ICA) is used. It will be shown that the optimization of the mathematical model proposed in this paper for urban traffic not only reduces the traffic but prevents any kind of traffic standstill in the city as well. In addition, this method uniformly distributes the traffic in the city so that the maximum potential of the citys infrastructure can be used. This model makes it possible to reduce the number of cars in the under-construction streets through a manual change in a coefficient called the ER. Finally, the mathematical model is simulated and analyzed for two cities with nine and 18 intersections using non-real-time and real-time simulations. These simulations are carried out on software developed in accordance with the optimization presented in this paper .(http://tinyurl.com/TSBVK). The results obtained from the simulations demonstrate that the model proposed is appropriate for traffic control and is flexible enough to be expanded for all kinds of infrastructure.

Distributed DC Optimal Power Flow for radial networks through partial Primal Dual algorithm

In a smart grid platform, where more distributed resources are joining everyday and each of them wills to keep its own privacy and shares as less information as possible with the grid management, there is a motivation for transition from the current centralized structures to more distributed ones. In this paper, a distributed method is presented to solve DC Optimal Power Flow (DC-OPF) for radial networks based on partial Primal-Dual algorithm. The algorithm development process starts from an economic dispatch (ED) problem, derives a distributed ED, and finally makes a bridge from the distributed ED formulation to a distributed algorithm for DC-OPF. It is proved in the paper that the optimal solution of the distributed algorithm complies with the necessary and satisfactory KKT conditions of DC-OPF problem. The algorithm is tested on a radial power network with and without network congestion, where the results converge to the optimal solution.

A one-step model predictive control for modular multilevel converters

Pulse Width Modulation (PWM) and Model Predictive Control (MPC) are two switching methods for Modular Mutlilevel Converter (MMC) presented in the literature [1], [2]. PWM switching cannot mitigate circulating currents in the converter bridges. An MPC method proposed in [2] compares all switching sequences and finds the optimal one to minimize the objective function (a weighted sum of the absolute values of the ac current deviation, capacitor voltage deviation, and the circulating current of the next time step). The computation effort is significant when the numbers of submodules increase. In this paper, a one-step model predictive control with minimum computing effort has been proposed to control the ac currents, keep the capacitor voltages nominal, and mitigate the circulating currents. The algorithm determines the ideal values of the lower-level and upper-level voltages on each phase based on the current state. The algorithm also determines which submodules to be switched on/off for the next time step according to the corresponding upper/lower current. The performance of the proposed method is evaluated via simulation in Matlab SimPowerSystems. The PWM and the proposed MPC are compared for their control effort and performance.

Real-time simulation and hardware-in-the-loop tests of a battery system

In this paper, a real time model of the microgrid with an energy storage system has been implemented in RT-Lab. Coordinated control of the battery storage system with a PV system is simulated. To be more realistic, hardware-in-the-loop (HIL) testbed has been implemented with a real battery cell (3.2-V, 40-Ahr) connected to the real-time simulation model of the microgrid. A programmable current source (Magna) and programmable load (Bk 8500) have been used to create charging and discharging paths between the energy storage unit and the RT-LAB.

Multiphase Distribution Feeder Reduction

Quasi-static time-series simulations (QSTS) of distribution feeders are a critical element of distributed solar PV integration studies. QSTS are typically carried out through computer simulation tools, such as OpenDSS. Since a typical feeder contains thousands of buses, for long investigation periods or at fine time scales such simulations are computationally costly. Simulation times are reduced in this paper through a reduction of the number of buses in the model. The feeder reduction algorithm considers p-phase distribution feeders with unbalanced loads and generation, unbalanced wire impedance, and mutual coupling, while preserving the spatial variation of load and generation. An extensive Monte Carlo sensitivity analysis was performed on a real feeder from a California utility. All bus voltage differences are found to be less than 1.13% with a root mean square error of 0.21%. Simulation time savings were up to 96% when only one bus is selected to remain in the model. Example applications of the proposed algorithm are interconnection studies of utility-scale photovoltaic system to the distribution grid, siting analyses of other distributed energy resources and dynamic behavior of devices in large systems, such as smart inverters on distribution grids.

Optimal switchable load sizing and scheduling for standalone renewable energy systems

Abstract The variability of solar energy in off-grid systems dictates the sizing of energy storage systems along with the sizing and scheduling of loads present in the off-grid system. Unfortunately, energy storage may be costly, while frequent switching of loads in the absence of an energy storage system causes wear and tear and should be avoided. Yet, the amount of solar energy utilized should be maximized and the problem of finding the optimal static load size of a finite number of discrete electric loads on the basis of a load response optimization is considered in this paper. The objective of the optimization is to maximize solar energy utilization without the need for costly energy storage systems in an off-grid system. Conceptual and real data for solar photovoltaic power production provides the power input to the off-grid system. Given the number of units, the following analytical solutions and computational algorithms are proposed to compute the optimal load size of each unit: mixed-integer linear programming and constrained least squares. Based on the available solar power profile, the algorithms select the optimal on/off switch times and maximize solar energy utilization by computing the optimal static load sizes. The effectiveness of the algorithms is compared using one year of solar power data from San Diego, California and Thuwal, Saudi Arabia. It is shown that the annual system solar energy utilization is optimized to 73% when using two loads and can be boosted up to 98% using a six load configuration.

Quasi-dynamic load and battery sizing and scheduling for stand-alone solar system using mixed-integer linear programming

Considering the intermittency of renewable energy systems, a sizing and scheduling model is proposed for a finite number of static electric loads. The model objective is to maximize solar energy utilization with and without storage. For the application of optimal load size selection, the energy production of a solar photovoltaic is assumed to be consumed by a finite number of discrete loads in an off-grid system using mixed-integer linear programming. Additional constraints are battery charge and discharge limitations and minimum uptime and downtime for each unit. For a certain solar power profile the model outputs optimal unit size as well as the optimal scheduling for both units and battery charge and discharge (if applicable). The impact of different solar power profiles and minimum up and down time constraints on the optimal unit and battery sizes are studied. The battery size required to achieve full solar energy utilization decreases with the number of units and with increased flexibility of the units (shorter on and off-time). A novel formulation is introduced to model quasi-dynamic units that gradually start and stop and the quasi-dynamic units increase solar energy utilization. The model can also be applied to search for the optimal number of units for a given cost function.

Reliability of dynamic load scheduling with solar forecast scenarios

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.

Optimal energy storage sizing and residential load scheduling to improve reliability in islanded operation of distribution grids

Despite the increase of modern residential rooftop solar PhotoVoltaic (PV) installation with smart inverters, islanded operation during grid blackouts is limited for most PV owners. This paper presents an optimization method to construe an resource sharing algorithm for islanded operation during blackouts by using shared PV energy. The optimization methods determine if rooftop PV power is either used directly or distributed to neighbors within a residential subsystem. Residential customers, each with a fixed size rooftop PV system are assumed to be connected by a single point of common coupling to a distribution network. The algorithm derives the optimal power distribution to improve the reliability of electricity supply to each residential customer and the results are benchmarked against the isolated self-consumption only mode. In addition, an energy storage system (ESS) is added to quantify the improvement in reliability, whereas a comparison is made between a distributed and centralized ESS deployment strategy.