Hashem Nehrir

Direct Energy Transfer for High Efficiency Photovoltaic Energy Systems Part I: Concepts and Hypothesis

This two-part paper presents a comprehensive comparative study on parallel power processing (PPP) and standard schemes in dc/dc converters for photovoltaic (PV) energy systems. It is demonstrated how PPP can improve direct energy transfer (DET), which results in PV systems operating at higher voltage and efficiency. Discussions of the concepts, hypotheses and computer simulations are presented in Part I. Part II provides the experimental results, which confirm the validity of the analysis and simulations.

From hybrid energy systems to microgrids: Hybridization techniques, configuration, and control

This paper reviews the hybridization techniques and system configurations for hybrid alternative energy systems. The topic is extended to the control and energy management of microgrids, the cornerstones of smart grids. A framework of distributed, hierarchical control for microgrids is also discussed in the paper.

Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization

Energy sustainability of hybrid energy systems is essentially a multi-objective, multi-constraint problem, where the energy system requires the capability to make rapid and robust decisions regarding the dispatch of electrical power produced by generation assets. This process of control for energy system components is known as energy management. In this paper, the application of particle swarm optimization (PSO), which is a biologically-inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind–microturbine energy system is presented. Results demonstrate that the proposed PSO-based energy management algorithm can solve an extensive solution space while incorporating many objectives such as: minimizing the cost of generated electricity, maximizing microturbine operational efficiency, and reducing environmental emissions. Actual wind and end-use load data were used for simulation studies and the well-established sequential quadratic programming (SQP) optimization technique was used to validate the results obtained from PSO. Promising simulation results indicate the suitability of PSO for real-time energy management of hybrid energy systems.

Real-Time Multiobjective Microgrid Power Management Using Distributed Optimization in an Agent-Based Bargaining Framework

In this paper, we propose a multi-objective power management procedure for microgrids (MGs). Through this procedure the power management problem is modeled as a bargaining game among different agents with different sets of objective functions. Nash bargaining solution (NBS) is employed to find the solution of the bargaining game. NBS lies on the Pareto-front of the power management problem. Moreover, it introduces a unique and fair balance among the objective functions of different agents and removes the need to track the whole Pareto-front in real-time. Distributed gradient algorithm is applied to find the NBS through a modular distributed decision framework without using a central control unit. In this way, the problem of data privacy of different parties within the MG is addressed. The proposed methodology has been tested through simulations on islanded and grid-connected MGs under different pricing scenarios (fixed versus time-of-use pricing).

A Hybrid Intelligent Framework for Wind Power Forecasting Engine

Following the growing wind energy integration into the grid, several wind power forecasting technique have been reported in the literature in recent years. This paper presents an advanced hybrid wind power forecasting methodology based on the combination of three different techniques: signal processing, artificial intelligence, and data mining. The signal processing component primarily filters out stochastic wind power time series data. An Adaptive Neuro Fuzzy Inference system (ANFIS), a neuro-fuzzy tool, is applied as a forecasting engine. A data mining tool, support vector machine (SVM) classifier is used to reduce the wind power forecasting error. Finally, grid search (GS) algorithm is applied to optimize the SVM parameters for improving the wind power forecasting performance. The key feature of this paper is to find out a simplified way to forecast wind power without taking into account various input parameters. The proposed wind power forecasting strategy is applied to real-life data from wind power producers in Alberta, Canada. The presented numerical results demonstrate the efficiency of the proposed strategy compared to some other existing wind power forecasting methods.

Design and implementation of a low-cost solar photovoltaic experimental station for education enhancement

Throughout the years, Montana State Universitys (MSUs) Energy Conversion Laboratory (ECL) has been a valuable educational asset for students understanding of the basics of electrical power and energy conversion. With increased interest in the deployment of solar photovoltaic (PV) systems, a need for the development of an experiment on solar PV was recognized so that the undergraduate students taking the required course, “Energy Conversion Devices,” become familiar with the characteristics of the solar-electric energy conversion system. This paper presents the design and implementation of a low-cost 50-W solar PV experimental station for in-door use. Students obtain in the laboratory the solar PV panel I-V and P-V characteristics, the effect of tilt angle, light intensity and temperature on the panel characteristics, and the function of a charge controller and battery in providing a desired steady voltage at the output of the system to the extent possible. The system is fully computerized and portable, sitting on a rolling cart. In addition to the above, a brief description of the capabilities of the ECL and the typical experiments undergraduate students perform in the Electrical Engineering departments undergraduate energy conversion course is given.

Wind power forecasting: Comparing two statistical signal processing algorithms

Wind power forecasting (WPF) has turned into a substantial tool for limiting the negative impact of wind power intermittency on power system. In this paper, we study and compare two different WPF algorithms: classical autoregressive model (AR), as a base case method, and kernel density estimation (KDE) with minimum mean square error estimator (MMSE). Using the data history of a wind farm in Colorado, these two algorithms are implemented in MATLAB and used to produce 24 hours ahead predictions of wind power time series of the said wind farm. The results obtained from the two methods are then compared from various perspectives (precision, applicability, etc.). The comparisons show that although AR-based wind power prediction has slightly less error than KDE, AR-based prediction does not produce probability density function (PDF) of wind speed/power, while KDE does. PDF of wind speed/power is an important parameter for estimating the required reserve allocation in economic dispatch studies.

Ensuring stability in a multi-zone MVDC shipboard power system

Naval ship power systems are evolving towards more electronics, more “vital” electronic systems, and a larger percentage of the overall energy load needed in the form of electricity. This is due to larger load demands from sensors, future weapons systems, and electrified propulsion. New technologies, such as All-Electric Ships, Integrated Propulsion Systems, and DC-Microgrids, the combination of which is referred to as MVDC, bring the promise of greater flexibility in design and operation, improved system resiliency, and increased energy efficiency. These advancements, however, depend on power electronic devices to control power flow and ensure voltage stability. At points in the power network, power electronic devices can behave as constant power loads (CPLs), which exhibit negative impedance, and at other times can demand large, instantaneous pulses of power. An improperly designed MVDC will become unstable amid large swings in power demand, and the voltage will collapse bringing the entire system down. Through a hierarchical control structure, where ensuring that system stability is maintained at a different level of control than the optimization of the power flow, these challenges can be understood and overcome separately. Different system goals are accomplished at each level in the control hierarchy, and each higher level of control is executed on a successively slower time scale. At the lowest level are current and voltage control for each of the above mentioned devices. The next level involves droop control on the current and voltage set points to ensure stability and up to the moment power balance, and ensures proper voltage regulation at each bus, and the third level focuses on optimization of resources. This paper focuses on the middle level, establishes stability criteria using non-linear techniques, and demonstrates the feasibility using numerical simulation.

An Agent-Based Hierarchical Bargaining Framework for Power Management of Multiple Cooperative Microgrids

In this paper, we propose an agent-based hierarchical power management model in a power distribution system composed of several microgrids (MGs). At the lower level of the model, multiple MGs bargain with each other to cooperatively obtain a fair, and Pareto-optimal solution to their power management problem, employing the concept of Nash bargaining solution and using a distributed optimization framework. At the highest level of the model, a distribution system power supplier, e.g., a utility company, interacts with both the cluster of the MGs and the wholesale market. The goal of the utility company is to facilitate power exchange between the regional distribution network consisting of multiple MGs and the wholesale market to achieve its own private goals. The power exchange is controlled through dynamic energy pricing at the distribution level, at the day-ahead and real-time stages. To implement energy pricing at the utility company level, an iterative machine learning mechanism is employed, where the utility company develops a price-sensitivity model of the aggregate response of the MGs to the retail price signal through a learning process. This learned model is then used to perform optimal energy pricing. To verify its applicability, the proposed decision model is tested on a system with multiple MGs, with each MG having different load/generation data.

Primary frequency regulation in islandec microgrids through droop-based generation and demand control

Conventionally, droop control has been used for primary frequency control, allowing generators to share imbalances in generation and load. This paper proposes the use of different types of droop-based control logics for load regulation to turn on or off groups of loads to help restore the system power balance. The proposed droop-based controllers are used by an aggregator to identify individual loads that can be used for demand response (DR) and control them according to their assigned priority. This procedure incorporates an incentive provided by the utility to the customer to allow control of their loads. Also, we will show that droop control can be used on a variety of resources in an MG at the same time, including energy storage system (ESS), generator, and loads to cooperatively contribute to frequency stabilization. Numerical experiments presented show that the proposed method is an effective way to prevent large frequency deviations due to variations in renewable generation and power contingencies in islanded MGs.