Carlos A. Hernandez-Aramburo

Energy Management in Autonomous Microgrid Using Stability-Constrained Droop Control of Inverters

This paper presents an energy management system (EMS) for a stand-alone droop-controlled microgrid, which adjusts generators output power to minimize fuel consumption and also ensures stable operation. It has previously been shown that frequency-droop gains have a significant effect on stability in such microgrids. Relationship between these parameters and stability margins are therefore identified, using qualitative analysis and small-signal techniques. This allows them to be selected to ensure stability. Optimized generator outputs are then implemented in real-time by the EMS, through adjustments to droop characteristics within this constraint. Experimental results from a laboratory-sized microgrid confirm the EMS function.

Fuel consumption minimization of a microgrid

A cost optimization scheme for a microgrid is presented. Prior to the optimization of the microgrid itself, several schemes for sharing power between two generators are compared. The minimization of fuel use in a microgrid with a variety of power sources is then discussed. The optimization of a small power system has important differences from the case of a large system and its traditional economic dispatch problem. Among the most important differences is the presence of a local heat demand which adds another dimension to the optimization problem. The microgrid considered in this paper consists of two reciprocating gas engines, a combined heat and power plant, a photovoltaic array and a wind generator. The optimization is aimed at reducing the fuel consumption rate of the system while constraining it to fulfil the local energy demand (both electrical and thermal) and provide a certain minimum reserve power. A penalty is applied for any heat produced in excess of demand. The solution of the optimization problem strongly supports the idea of having a communication infrastructure operating between the power sources.

Estimating rotational iron losses in an induction machine

We present an assessment of the effect of rotational losses on an induction machine. The assessment provides an estimate of the iron losses in an induction machine by three methods, all of which rely on the output data of a two-dimensional finite-element method: 1) calculating iron losses as if they were produced by a purely alternating field; 2) calculating the iron losses by adding the losses produced by the orthogonal components of the flux density, as if the losses produced by these components were independent phenomena; 3) applying a correction factor based on experimental data to improve the rotational loss calculation. The correction factor is a function of the peak flux density value and the ratio of the major to the minor axis of the flux density loci. The third method represents the main contribution of this paper to the field and is explained in detail. Finally, a discussion of the results addresses two aspects: the location where rotational fields occur, and their impact on the total loss calculation.

An Improved Maximum Power Point Tracking Algorithm with Current-Mode Control for Photovoltaic Applications

An improved perturb-and-observe maximum power point tracking algorithm is presented that incorporates a current compensated converter. In order to achieve fast response and accurate holding of the maximum photovoltaic (PV) power under changing environmental conditions, a variable perturbation step size is adopted. In addition, a control parameter alpha, is introduced to enhance the tracking sensitivity during abrupt changes of environmental conditions. Instead of directly perturbing the switch duty-cycle, the system operates on the current reference of an inner controller that regulates the PV current. The effects of different values of fixed and variable step sizes are assessed through simulation and the results described. The performance of the new MPPT controller was simulated using the PLECS extension to Simulink

Analysis of perturb and observe maximum power point tracking algorithm for photovoltaic applications

This paper highlights the compromises between good steady-state accuracy and the speed of convergence in Perturb and Observe (P&O) maximum power point tracking (MPPT) algorithm. Three fixed step-sizes and a variable step-size have been defined as the perturbation step-sizes and the effect of applying different step-sizes for P&O algorithm are discussed. The perturbation step-sizes have been simulated using Matlab/Simulink and the maximum power tracking efficiency for each step-size is analysed. These step-sizes have been experimentally tested using a PV illumination test rig to emulate rapid changes in shadow effect on a PV panel. The fixed step-sizes are tested using a direct duty-cycle control boost converter while the variable step-size is examined by applying current-mode controlled boost converter. It is concluded that the application of fixed perturbation step-size has a limitation in performing MPPT while a variable step-size is necessary to balance the competing aims of speed and accuracy.

Energy Management System with Stability Constraints for Stand-alone Autonomous Microgrid

This paper presents an energy management system (EMS) for a stand-alone droop-controlled microgrid, which adjusts generator outputs to minimize fuel consumption and also ensures stable operation. It has previously been shown that droop gains have a significant effect on stability in such microgrids. Approximate relationships between these parameters and stability margins are therefore identified, using qualitative analysis and small-signal techniques. This allows them to be selected to ensure stability. Optimized generator outputs are then implemented in real-time by the EMS, through adjustments to droop characteristics within this constraint. Experimental results from a laboratory-sized microgrid confirm the EMS function.

A current-mode controlled maximum power point tracking converter for building integrated photovoltaics

This paper presents a current-mode controlled maximum power point tracking (MPPT) converter and BIPV design considerations covering the photovoltaic orientation and tilt angle. An MPPT algorithm featuring rapid convergence and steady-state accuracy is described. The power tracking performance of the proposed MPPT converter has been experimentally verified. The experimental test rig consists of a 65 W multi-crystalline PV panel with controllable illumination to emulate the changes of solar irradiance. The test rig is fitted with a FPGA-DSP controlled rapid prototyping platform for power conditioning. The current-mode control method provides better transient performance than directly perturb the duty-cycle of the converter. A step-size varies in response to the former approach and previous error ensures rapid accurate tracking following shadow effects of clouds or obstructions. The experimental results show that the proposed MPPT converter is capable of reaching the steady-state condition quickly during a transient with negligible oscillations and tracking under dynamic changes of solar irradiance efficiently.

Assessment of power losses of an inverter-driven induction machine with its experimental validation

Detailed power loss distribution in induction machines is assessed using time-stepped finite element analysis coupled to circuit equations of an inverter. Losses are examined for various load conditions. Iron losses are largely unexplored and so particular attention is paid to them here. The simulation has revealed the division between ohmic, hysteresis, classic eddy current and anomalous losses; and the distribution in the frequency spectrum between fundamental, slotting, and multiples of the switching frequency. Insight is gained into the spatial location of the loss. Experimental validation is provided for several fundamental frequencies from full-load to light-load conditions.

A Stochastic Simulation of Battery Sizing for Demand Shifting and Uninterruptible Power Supply Facility

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak tariff times and outage protection (an uninterruptible power supply function) for commercial buildings. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, outage and demand peaks are not of deterministic nature or location. A given battery size can only offer a particular probability of surviving an outage or completing a demand shift, this offers an opportunity to battery sizing using stochastic methods. In this work, the Monte Carlo method takes into account the historical outage statistics and building load profiles. Further, it describes the life-cycle cost of the system and the ratio of saving and investment for ten cases of demand shifting. It also reports on the customer outage costs which were calculated based on a weekday load profile for different outage durations. By considering the statistics of both problems together, customers can assess the risk of not being able to survive in an outage and the success rate of meeting the demand shift capability based on the results of the proposed method.

Wind farm output smoothing through co-ordinated control and short-term wind speed prediction

This paper presents a new method of using co-ordinated control and short term wind speed prediction to smooth the output of an array of wind turbines. The proposed controller uses knowledge of the kinetic energy storage potential in the rotor disc to allow production of piecewise static power outputs from variable wind inputs. Previous studies have shown that there are large peaks in wind power variability in the minutes timescale which is within the ability of a control system to reduce. This variation will cause issues with balancing systems within the local grid, as such it is beneficial to remove it. Results of tests to the system are presented which show a significant reduction in wind farm output power variability without a significant drop in output power.