Mohammad B. Shadmand

Multi-Objective Optimization and Design of Photovoltaic-Wind Hybrid System for Community Smart DC Microgrid

Renewable energy sources continues to gain popularity. However, two major limitations exist that prevent widespread adoption: availability of the electricity generated and the cost of the equipment. Distributed generation, (DG) grid-tied photovoltaic-wind hybrid systems with centralized battery back-up, can help mitigate the variability of the renewable energy resource. The downside, however, is the cost of the equipment needed to create such a system. Thus, optimization of generation and storage in light of capital cost and variability mitigation is imperative to the financial feasibility of DC microgrid systems. PV and wind generation are both time dependent and variable but are highly correlated, which make them ideal for a dual-sourced hybrid system. This paper presents an optimization technique base on a Multi-Objective Genetic Algorithm (MOGA) which uses high temporal resolution insolation data taken at 10 seconds data rate instead of more commonly used hourly data rate. The proposed methodology employs a techno-economic approach to determine the system design optimized by considering multiple criteria including size, cost, and availability. The result is the baseline system cost necessary to meet the load requirements and which can also be used to monetize ancillary services that the smart DC microgrid can provide to the utility at the point of common coupling (PCC) such as voltage regulation. The hybrid smart DC microgrid community system optimized using high-temporal resolution data is compared to a system optimized using lower-rate temporal data to examine the effect of the temporal sampling of the renewable energy resource.

Model Predictive Control of PV Sources in a Smart DC Distribution System: Maximum Power Point Tracking and Droop Control

In a dc distribution system, where multiple power sources supply a common bus, current sharing is an important issue. When renewable energy resources are considered, such as photovoltaic (PV), dc/dc converters are needed to decouple the source voltage, which can vary due to operating conditions and maximum power point tracking (MPPT), from the dc bus voltage. Since different sources may have different power delivery capacities that may vary with time, coordination of the interface to the bus is of paramount importance to ensure reliable system operation. Further, since these sources are most likely distributed throughout the system, distributed controls are needed to ensure a robust and fault tolerant control system. This paper presents a model predictive control-based MPPT and model predictive control-based droop current regulator to interface PV in smart dc distribution systems. Back-to-back dc/dc converters control both the input current from the PV module and the droop characteristic of the output current injected into the distribution bus. The predictive controller speeds up both of the control loops, since it predicts and corrects error before the switching signal is applied to the respective converter.

An improved MPPT technique for high gain DC-DC converter using model predictive control for photovoltaic applications

This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.

Maximum Power Point Tracking using Model Predictive Control of a flyback converter for photovoltaic applications

Due to the variable, stochastic behavior of the solar energy resource, Maximum Power Point Tracking (MPPT) of photovoltaic (PV) is required to ensure continuous operation at the maximum power point to generate the most electrical energy. This paper presents a Model Predictive Control (MPC) MPPT technique. Extracting the maximum power from PV systems has been widely investigated within the literature; the main contribution of this paper is improvement of the Perturb and Observe (P&O) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the flyback DC/DC converter. Comparing the developed technique to the conventional P&O method indicates significant improvement in PV system performance. The proposed MPC-MPPT technique for a flyback converter is implemented using the dSpace CP 1103.

Optimization of photovoltaic-wind hybrid system for apartment complexes and other community living environments by minimizing excess capacity

This paper presents an approach for the optimization of photovoltaic-wind hybrid systems with battery back-up to meet the load requirements. The proposed sizing methodology is based on 10 seconds insolation data rate instead of the more commonly used hourly data rate. When analyzed for an entire year, the higher resolution of data identifies excess PV and storage capacity when combined with wind turbine, which can be removed to optimize the system cost. The case study is done on a gird-tied apartment complex in college station, Texas to meet 50% of the load by optimized hybrid system. The methodology employs a techno-economic approach to determine the system that would guarantee a reliable energy supply with lowest investment. The obtained results demonstrate a cost effective and reliable hybrid system is that in which 95% of load is provided by photovoltaic panels and the other 5% by wind turbines. The optimized hybrid system, based on accurate and enhanced 10 seconds insolation data rate of photovoltaic system, is compared to conventional PV-Wind optimized systems based on hourly and daily insolation data. Federal incentives such as Investment Tax Credits, MACRS (Modified Accelerated Cost Recovery System) and Bonus Depreciation, and Renewable Energy Grants were taken into consideration to assess initial and recurring costs for owners. The Life Cycle Costing with payback time and Levelized Cost of Energy (LCOE) with Net Metering are provided as part of the economics in this paper.

MPPT of Photovoltaic Systems Using Sensorless Current-Based Model Predictive Control

Variability in the solar irradiance level and ambient temperature of photovoltaic (PV) systems necessitates the use of maximum power point tracking (MPPT) of PV systems to ensure continuous harvesting of maximum power. This paper presents a sensorless current (SC) MPPT algorithm using model predictive control (MPC). The main contribution of this paper is the use of model-based predictive control principle to eliminate the current sensor that is usually required for well-known MPPT techniques such as perturb and observe (P&O). By predicting the PV system states in horizon of time, the proposed method becomes an elegant, embedded controller that allows faster response and lower power ripple in steady state than the conventional P&O technique under rapidly changing atmospheric conditions. This becomes possible without requiring expensive sensing and communications equipment and networks for direct measurement of solar irradiation changes. The performance of the proposed SC-MPC-MPPT with reduced load sensitivity is evaluated on the basis of industrial European Efficiency Test, EN 50530, that assesses the performance of PV systems under dynamic environmental conditions. The proposed control technique is implemented experimentally using dSPACE DS1007 platform to verify the simulation results.

A finite-element analysis approach to determine the parasitic capacitances of high-frequency multiwinding transformers for photovoltaic inverters

Magnetic components such as transformers and inductors play a significant role in the efficiency and size/weight of inverter. They are also amongst the most difficult components to design, often requiring numerous design interactions and testing. Understanding and accurate prediction of parasitic winding capacitances of high-frequency multiwinding transformers in PV inverters is fundamental to improve performance, lower cost, and speed time to market. Parasitic capacitances are highly dependent on the winding geometry and the proximity of conducting surfaces. As the geometry of the components becomes more complicated, it is almost impossible to derive analytical equations that describe accurately the behavior of magnetic components. Currently, parasitic capacitances of the multiwinding transformers are only known with certainty once a prototype is built. Therefore a design-build-test cycle needs to be iterated, often at substantial cost and time. This paper presents a technique and method to quantitatively predict the parasitic capacitance of high-frequency multiwinding transformer by means of finite-element analysis (FEA). Comparison of the FEA results with a commercially constructed experimental prototype results shows good agreement.

Photovoltaic-wind hybrid system with battery back-up optimized for apartment complexes and other community living environments

This paper presents an optimization tool which can be used to analyzed the feasibility of a hybrid wind-photovoltaic power system to meet the load requirements. The case study is done on a grid-tied apartment complex where 50% of the load is met by the proposed hybrid energy system. The methodology employs a techno-economic approach to determine the system that would guarantee a reliable energy supply with the lowest investment. The results obtained show that the reliable solution is that in which 95% of load is covered by photovoltaic panels and the other 5% by wind turbines. The Life Cycle Costing with payback time and Levelized Cost of Energy (LCOE) with Net Metering are provided as part of the economics in the project. Federal incentives like Investment Tax Credits, MACRS and Bonus Depreciation, and Renewable Energy Grants were taken into account to evaluate initial and recurring costs for owners.

Predicting Variability of High-Penetration Photovoltaic Systems in a Community Microgrid by Analyzing High-Temporal Rate Data

Interest in renewable energy sources continues to gain popularity. However, a major fundamental limitation exists that prevents widespread adoption: variability of electricity generated. Distributed generation (DG) grid-tied photovoltaic (PV) systems with centralized battery back-up can mitigate the variability of PV systems and be optimized to reduce cost by analyzing high-temporal rate data. Thus, it is an attractive system to meet “go green” mandates, while also providing reliable electricity. The focus of this paper is to analyze the variability of a high-penetration PV scenario when incorporated into the microgrid concept. The proposed system design approach is based on high-temporal rate instead of the more commonly used hourly data rate. The methodology presented in this paper employs a technoeconomic approach to determine the optimal system design to guarantee reliable electricity supply with lowest investment. The proposed methodology is used to demonstrate that the variability of the PV resource can be quantified by determining the number of PV arrays and their corresponding distance in the microgrid and then mitigate with optimized storage.

Model predictive decoupled power control for single-phase grid-tied inverter

Control of ac power in a grid-tied inverter often involves synchronous reference frame transformation, a process which requires phase-angle information typically provided by a Phase-Looked Loop (PLL). This paper presents a decoupled real and reactive power control technique, for a single phase grid-tied inverter, using Model Predictive Control (MPC). The proposed technique does not use a PLL, PWM nor a synchronization transform, which makes the control algorithm well suited for an all-digital implementation. This paper explores the proposed controller performance under distorted grid conditions and variations of system parameters. The results show that the proposed controller keeps good power tracking performance with small error in steady state and the grid side current Total Harmonic Distortion (THD) is within the IEEE-519 standards limits, which allows a much smaller dc-link capacitor to improve systems reliability and power density. The dynamic performance and steady state stability of the proposed predictive controller are evaluated in this paper. The tracking performance of the proposed controller is compared to the conventional PLL-based method, the result demonstrate significant improvement in the steady state power tracking error when using the proposed controller. The simulation result is validated by implementing the control algorithm experimentally using dSPACE 1007.