Arash M. Dizqah

A Multivariable Optimal Energy Management Strategy for Standalone DC Microgrids

Due to substantial generation and demand fluctuations in standalone green microgrids, energy management strategies are becoming essential for the power sharing and voltage regulation purposes. The classical energy management strategies employ the maximum power point tracking (MPPT) algorithms and rely on batteries in case of possible excess or deficit of energy. However, in order to realize constant current-constant voltage (IU) charging regime and increase the life span of batteries, energy management strategies require being more flexible with the power curtailment feature. In this paper, a coordinated and multivariable energy management strategy is proposed that employs a wind turbine and a photovoltaic array of a standalone DC microgrid as controllable generators by adjusting the pitch angle and the switching duty cycles. The proposed strategy is developed as an online nonlinear model predictive control (NMPC) algorithm. Applying to a sample standalone dc microgrid, the developed controller realizes the IU regime for charging the battery bank. The variable load demands are also shared accurately between generators in proportion to their ratings. Moreover, the DC bus voltage is regulated within a predefined range, as a design parameter.

A Fast and Parametric Torque Distribution Strategy for Four-Wheel-Drive Energy-Efficient Electric Vehicles

Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.

Acausal Modelling and Dynamic Simulation of the Standalone Wind-Solar Plant Using Modelica

In order to design model-based controllers applicable to hybrid renewable energy systems (HRES), it is essential to model the HRES mathematically. In this study, a standalone HRES, consisting of a photovoltaic (PV) array, a lead-acid battery bank, a pitch-controlled wind turbine, and a three-phase permanent magnet synchronous generator (PMSG), supplies a variable DC load demand through two boost- and buck-type DC-DC converters. It is shown that the mathematical model of the HRES can be represented by a system of nonlinear hybrid differential algebraic equations (hybrid DAEs). The developed model in this paper employs the Modelica language that allows object-oriented and acausal modelling of the multimode systems. The OpenModelica environment is utilised to compile the model and simulate the system. It is shown that the simulation provides a sufficiently accurate prediction of all the differential and algebraic states including mode transitions. The results of the simulation show a good match with the information available in the components datasheet.

An assessment of solar irradiance stochastic model for the UK

Hybrid Renewable Energy System (HRES) can effectively supply sustainable electrical energy in standalone remote areas. However, in order to design a reliable site and a robust controller, uncertainties in sustainable energy resources need to be modeled properly. This paper proposed a stochastic model of hourly solar irradiance for four locations across the UK. The goodness-of-fit of the proposed model has been evaluated using the Kolmogorov-Smirnov (K-S) test. The proposed model has been employed to simulate the amount of hourly solar irradiance for a location in the UK.

MODELLING AND SIMULATION OF STANDALONE SOLAR POWER SYSTEMS

In the design of the controllers of hybrid renewable energy system (HRES), the system dynamics and constraints need to be modelled and simulated in conjunction with the controller itself. This paper presents mathematical and equivalent electrical models taking into consideration all system dynamics and constraints for the solar branch of HRES. This branch consists of photovoltaic (PV) array, load and battery connected through a boost-type DC–DC converter. The probabilistic behaviour of the solar irradiance, which intrinsically includes the effect of cloud shading, and the dynamics of the battery are also modelled. The platform developed for dynamic simulation of the solar branch of HRES can be employed for design of DC–DC converter controllers as well as design of energy management systems.

Torque Distribution Strategies for Energy-Efficient Electric Vehicles with Multiple Drivetrains

The paper discusses novel computationally efficient torque distribution strategies for electric vehicles with individually controlled drivetrains, aimed at minimizing the overall power losses while providing the required level of wheel torque and yaw moment. Analytical solutions of the torque control allocation problem are derived and effects of load transfers due to driving/braking and cornering are studied and discussed in detail. Influences of different drivetrain characteristics on the front and rear axles are described. The results of an analytically derived algorithm are contrasted with those from two other control allocation strategies, based on the offline numerical solution of more detailed formulations of the control allocation problem (i.e., a multiparametric nonlinear programming (mp-NLP) problem). The control allocation algorithms are experimentally validated with an electric vehicle with four identical drivetrains along multiple driving cycles and in steady-state cornering. The experiments show that the computationally efficient algorithms represent a very good compromise between low energy consumption and controller complexity.

Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation

Abstract In the UK, in 2014 almost fifty thousand motorists made claims about vehicle damages caused by potholes. Pothole damage mitigation has become so important that a number of car manufacturers have officially designated it as one of their priorities. The objective is to improve suspension shock performance without degrading road holding and ride comfort. In this study, it is shown that significant improvement in performance is achieved if a clipped quadratic parameter varying suspension is employed. Optimal design of the proposed system is challenging because of the multiple local minima causing global optimisation algorithms to get trapped at local minima, located far from the optimum solution. To this end an enhanced Fruit Fly Optimisation Algorithm − based on a recent study on how well a fruit fly’s tiny brain finds food − was developed. The new algorithm is first evaluated using standard and nonstandard benchmark tests and then applied to the computationally expensive suspension design problem. The proposed algorithm is simple to use, robust and well suited for the solution of highly nonlinear problems. For the suspension design problem new insight is gained, leading to optimum damping profiles as a function of excitation level and rattle space velocity.

An assessment of wind speed stochastic model for the UK

Hybrid Renewable Energy System (HRES) can effectively supply sustainable electrical energy in standalone remote areas. However, in order to design a reliable site and a robust controller, uncertainties in sustainable energy resources need to be modeled properly. This paper proposes a stochastic model of hourly wind speed for four locations across the UK. The goodness-of-fit of the proposed model has been evaluated using the Kolmogorov-Smirnov (K-S) test. The proposed model has been employed to simulate the amount of hourly wind speed for a location in the UK.