M. Hashem Nehrir

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 multiobjective, multiconstraint 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 (MT) 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 MT 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 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.

A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting

With rapid increase in wind power penetration into the power grid, wind power forecasting is becoming increasingly important to power system operators and electricity market participants. The majority of the wind forecasting tools available in the literature provide deterministic prediction, but given the variability and uncertainty of wind, such predictions limit the use of the existing tools for decision-making under uncertain conditions. As a result, probabilistic forecasting, which provides information on uncertainty associated with wind power forecasting, is gaining increased attention. This paper presents a novel hybrid intelligent algorithm for deterministic wind power forecasting that utilizes a combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network, which is optimized by using firefly (FF) optimization algorithm. In addition, support vector machine (SVM) classifier is used to minimize the wind power forecast error obtained from WT+FA+FF. The paper also presents a probabilistic wind power forecasting algorithm using quantile regression method. It uses the wind power forecast results obtained from the proposed hybrid deterministic WT+FA+FF+SVM model to evaluate the probabilistic forecasting performance. The performance of the proposed forecasting model is assessed utilizing wind power data from the Cedar Creek wind farm in Colorado.

Real-Time Demand Response Through Aggregate Electric Water Heaters for Load Shifting and Balancing Wind Generation

Demand response (DR) has shown to be a promising tool for balancing generation and demand in the future power grid, specifically with high penetration of variable renewable generation, such as wind. This paper evaluates thermostat setpoint control of aggregate electric water heaters (EWHs) for load shifting, and providing desired balancing reserve for the utility. It also assesses the economic benefits of DR for the customers through time-of-use pricing. Simulation results reveal the achievement of the economic benefits to the customers while maintaining their comfort level and providing a large percentage of desired balancing reserve at the presence of wind generation.

Introducing Dynamic Demand Response in the LFC Model

Demand response (DR) has proved to be an inevitable part of the future grid. Much research works have been reported in the literature on the benefits and implementation of DR. However, little works have been reported on the impacts of DR on dynamic performance of power systems, specifically on the load frequency control (LFC) problem. This paper makes an attempt to fill this gap by introducing a DR control loop in the traditional LFC model (called LFC-DR) for a single-area power system. The model has the feature of optimal operation through optimal power sharing between DR and supplementary control. The effect of DR communication delay in the controller design is also considered. It is shown that the addition of the DR control loop increases the stability margin of the system and DR effectively improves the system dynamic performance. Simulation studies are carried out for single-area power systems to verify the effectiveness of the proposed method.

Multi-Timescale Power Management for Islanded Microgrids Including Storage and Demand Response

Power management is an essential tool for microgrid (MG) safe and economic operation, particularly in the islanded operation mode. In this paper, a multi-timescale cost-effective power management algorithm (PMA) is proposed for islanded MG operation targeting generation, storage, and demand management. Comprehensive modeling, cost, and emission calculations of the MG components are developed in this paper to facilitate high accuracy management. While the MGs overall power management and operation is carried out every several minutes to hours, depending on the availability of the required data, simulation for highly dynamic devices, such as batteries and electric water heaters (EWHs) used for demand response (DR), are performed every minute. This structure allows accurate, scalable, and practical power management taking into consideration the intrainterval dynamics of battery and EWHs. Two different on/off strategies for EWH control are also proposed for DR application. Then, the PMA is implemented using the two different DR strategies and the results are compared with the no-DR case. Actual solar irradiation, ambient temperature, nonEWH load demand, and hot water consumption data are employed in the simulation studies. The simulation results for the MG studied show the effectiveness of the proposed algorithm to reduce both MGs cost and emission.

Improving Sustainability of Hybrid Energy Systems Part II: Managing Multiple Objectives With a Multiagent System

Hybrid power systems and microgrids may employ a mixture of dispatchable (conventional) and nondispatchable (renewable) generators alongside storage. Whether in grid-connected or grid-isolated (islanded) modes of operation, these systems may face multiple competing objectives when managing diverse installed assets. Power management of hybrid energy systems, therefore, involves operational tradeoffs amongst Pareto-optimal solutions. These attributes, including the ready implementation of distributed renewable generation and the incorporation of methods to locally manage power-networked assets, make them a unique area of study for pursing better sustainable performance. In part I of this paper, storage system round-trip efficiency and operational cost concepts were formulated for use in real-time dispatch decisions towards yielding improved performance of overall system objectives. In this paper (part II), the concepts of part I are implemented with a decentralized multiagent system (MAS). This MAS is employed for power management of a hybrid (diesel-storage battery) microgrid in grid-connected and islanded modes. This paper highlights the development and implementation of an MAS suitable for hybrid and microgrid system applications, as well as presenting an important discussion about the tradeoffs associated with multiobjective design for power management. The simulation results presented demonstrate improvement in sustainable performance of the hybrid system.

A Physically-Based Dynamic Model for Solid Oxide Fuel Cells

This paper presents a physically based dynamic model for tubular solid oxide fuel cells (SOFCs) based on the electrochemical and thermodynamic characteristics inside SOFC. The diffusion, material conservation, electrochemical, and thermodynamic equations are used to develop the SOFC model. The effect of temperature on the steady-state (V-I and P-I) characteristics of the SOFC model has been studied, and the model responses have been obtained for constant fuel flow as well as for constant fuel utilization operating modes. The dynamic characteristics of the model are investigated in small, medium, and large timescales, from milliseconds to minutes. The model has been implemented in MATLAB/Simulink and used to investigate the distributed generation applications of SOFCs.

Solar PV power generation forecast using a hybrid intelligent approach

A significant role of a smart grid is to substantially increase the penetration of environmentally-friendly renewable energy sources, such as solar photovoltaic (PV) power. One of the major challenges associated with the integration of PV power into the grid is the intermittent and uncontrollable nature of PV power output. Therefore, developing a reliable forecasting algorithm can be extremely beneficial in system planning and market operation of grid-connected PV systems. This paper presents a novel hybrid intelligent algorithm for short-term forecasting of PV-generated power. The algorithm uses a combination of a data filtering technique based on wavelet transform (WT) and a soft computing model based on fuzzy ARTMAP (FA) network, which is optimized using an optimization technique based on firefly (FF) algorithm.

Load Transient Mitigation For Stand-alone Fuel Cell Power Generation Systems

In this paper, a load transient mitigation technique for stand-alone fuel cell (FC)-battery power generation systems is proposed. The technique can be used not only to improve the output power quality of the overall system, but also to mitigate or eliminate the electrical feedback stresses that are caused by load transients upon fuel cells. As a result, the durability of the fuel cell can also be improved. System analysis and controller design procedure for the proposed technique are given in this paper. Simulation studies have been carried out on FC-battery power generation systems using the dynamic models developed for proton exchange membrane fuel cell (PEMFC) and solid-oxide fuel cell (SOFC). Simulation results show the effectiveness of the proposed technique in preventing load transients from affecting the fuel cell performance.

Improving Sustainability of Hybrid Energy Systems Part I: Incorporating Battery Round-Trip Efficiency and Operational Cost Factors

Storage systems are often employed in hybrid systems alongside generation sources. In the most basic configurations, coupling generation and storage in this manner can improve combined performance. Moreover, in advanced applications, such as for microgrids, the employment of storage offers the opportunity to diversify system objectives and pursue multiple performance goals. In this paper (part I), the authors explore formulations of storage system round-trip efficiency and operational cost, along with a model that can be determined from manufacturer data sheets and used in a real-time simulation environment for evaluation of these objectives. The battery model will be used in a real-time power management study for hybrid systems where a decentralized multiagent system (MAS), developed in part II, addresses the multiobjective tradeoff optimization for a hybrid system.