Jack Brouwer

A new maximum power point tracking controller for photovoltaic power generation

This paper proposes a new maximum power point tracking (MPPT) method in combination with one-cycle control for photovoltaic power generation. This control scheme is based on the automatic current-adjusting feature of one-cycle control. The output current of the inverter can be adjusted according to the voltage of the photovoltaic (PV) array so as to extract the maximum power from it. In the mean time, one-cycle control guarantees that the output current is in the same shape of and in phase with the grid voltage. All these are accomplished in one power stage and a simple control circuit. No detection and calculation of power are needed. Compared with previously proposed approaches, this method is much more efficient and more cost-effective and yet exhibits excellent performance. The principle is explained qualitatively and extensive experiments have been carried out to verify and validate the proposed method.

Active control for gas turbine combustors

Closed-loop feedback control is implemented in two model combustors as a demonstration of the application of feedback control to gas turbine combustion. The first combustor is an axi-symmetric, swirl-stabilized, spray-fired combustor, while the second combustor incorporates discrete wall injection of primary and dilution air, representative of an actual gas turbine combustor. In both combustors, the emission of carbon monoxide and carbon dioxide, the radiative heat flux to the liner associated with soot and combustor stability are monitored in real time and controlled as a function of combustor load. The control input to the system is the nozzle atomizing air flow rate. The emission of carbon monoxide and carbon dioxide, the radiative flux to the liner, and the combustor stability are obtained through non-intrusive radiometric sensors mounted near the combustor exit plane. This information is conveyed to a control computer which invokes an optimization algorithm to minimize the CO and soot radiative flux, while maximizing the CO2 radiative flux. The index of combustion instability (onset of elevated acoustic emission) is, in the present case, a characteristic frequency in the power spectral density of the CO signal. The identical control methodology is applied to the two combustors with satisfactory and promising results that demonstrate the potential of active control to practical systems.

Model Predictive Control of Central Chiller Plant With Thermal Energy Storage Via Dynamic Programming and Mixed-Integer Linear Programming

This work considers the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a thermal energy storage (TES). Typically, the chillers are operated in ON/OFF modes to charge TES and supply chilled water to satisfy the campus cooling demands. A bilinear model is established to describe the system dynamics of the central plant. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity cost. At each time step, the MPC problem is represented as a large-scale mixed-integer nonlinear programming problem. We propose a heuristic algorithm to obtain suboptimal solutions for it via dynamic programming (DP) and mixed integer linear programming (MILP). The system dynamics is linearized along the simulated trajectories of the system. The optimal TES operation profile is obtained by solving a DP problem at every horizon, and the optimal chiller operations are obtained by solving an MILP problem at every time step with a fixed TES operation profile. Simulation results show desired performance and computational tractability of the proposed algorithm. This work was motivated by the supervisory control need for a campus central plant. Plant operators have to decide a scheduling strategy to mix and match various chillers with a thermal energy storage to satisfy the campus cooling demands, while minimizing the operation cost. This work mathematically characterizes the system dynamics of a campus central plant and establishes a linear model to predict campus cooling load. It proposes a model predictive control (MPC) strategy to optimally schedule the campus central plant based on plant system dynamics and predicted campus cooling load. A heuristic algorithm is proposed to obtain suboptimal solutions for the MPC problem. The effectiveness and efficiency of the proposed approach are well demonstrated for the central plant at the University of California, Irvine.

A Thermodynamic Analysis of Electricity and Hydrogen Co-Production Using a Solid Oxide Fuel Cell

This paper presents the electricity and hydrogen co-production concept, a methodology for the study of SOFC hydrogen co-production, and simulation results that address the impact of reformer placement in the cycle on system performance. The methodology is based on detailed thermodynamic and electrochemical analyses of the systems. A comparison is made between six specific cycle configurations, which use fuel cell heat to drive hydrogen production in a reformer using both external and internal reforming options. SOFC plant performance has been evaluated on the basis of methane fuel utilization efficiency and each component of the plant has been evaluated on the basis of second law efficiency. The analyses show that in all cases the exergy losses (irreversibilities) in the combustion chamber are the most significant losses in the cycle. Furthermore, for the same power output, the internal reformation option has the higher electrical efficiency and produces more hydrogen per unit of natural gas supplied. Electrical efficiency of the proposed cycles ranges from 41 to 44%, while overall efficiency (based on combined electricity and hydrogen products) ranges from 45 to 80%. The internal reforming case (steam-to-carbon ratio of 3.0) had the highest overall and electrical efficiency (80 and 45% respectively), but lower second law efficiency (61%), indicating potential for cycle improvements. Copyright

Development of controls for dynamic operation of carbonate fuel cell-gas turbine hybrid systems

Hybrid fuel cell/gas turbine (FC/GT) systems have been shown through experiment and simulation to be highly efficient technologies with low emissions. Maintaining efficient, low emission, and safe operation, whether during disturbances or regular operational transients, is a challenge to both understand and address. Some likely disturbances can arise from changes in ambient temperature, fuel flow variations induced by supply pressure disturbances, fuel composition variability, and power demand fluctuations. To gain insight into the dynamic operation of such cycles and address operating challenges, dynamic modeling tools have been developed at two different laboratories. In this paper these models are used to simulate the dynamic operation of an integrated MCFC/GT hybrid system and to subsequently develop and test control strategies for the hybrid power plant. Two control strategies are developed and tested for their ability to control the system during various perturbations. Predicted fuel cell operating temperature, fuel utilization, fuel cell and GT power, shaft speed, compressor mass flow and temperatures throughout the FC/GT system are presented for the controlled response to a fuel cell voltage increase in order to show the effect of a load decrease.Copyright

Optimal scheduling of chiller plant with thermal energy storage using mixed integer linear programming

In this paper, we consider the optimal scheduling problem for a campus central plant equipped with a bank of multiple electrical chillers and a Thermal Energy Storage (TES). Typically, the chillers are operated in ON/OFF modes to charge the TES and supply chilled water to the campus. A bilinear model is established to describe the system dynamics. A model predictive control (MPC) problem is formulated to obtain optimal set-points to satisfy the campus cooling demands and minimize daily electricity costs. At each time step, the MPC problem is represented as a large-scale mixed integer nonlinear programming (MINLP) problem. We propose a heuristic algorithm to search for suboptimal solutions to the MINLP problem based on mixed integer linear programming (MILP), where the system dynamics is linearized along the simulated trajectories of the system. Simulation results show good performance and computational tractability of the proposed algorithm.

Gas turbine assessment for air management of pressurized SOFC/GT hybrid systems

This paper analyzes and compares transient and steady-state performance characteristics of different types of single-shaft turbo-machinery for controlling the air through a pressurized solid oxide fuel cell (SOFC) stack that is integrated into a SOFC/GT pressurized hybrid system. Analyses are focused on the bottoming part of the cycle, where the gas turbine (GT) has the role of properly managing airfiow to the SOFC stack for various loads and at different ambient conditions. Analyses were accomplished using two disparate computer programs, which each modeled a similar SOFC/GT cycle using identical generic gas turbine performance maps. The models are shown to provide consistent results, and they are used to assess: (1) the influence of SOFC exhaust composition on expander behavior for on-design conditions, (2) the off-design performance of the bypass, bleed, and variable speed controls for various part-load conditions and for different ambient conditions; (3) the features of such controls during abrupt transients such as load trip and bypass/bleed valve failure. The results show that a variable speed micro-turbine is the best option for off-design operation of a SOFC/GT hybrid system. For safety measures a bleed valve provides adequate control of the system during load trip. General specifications for a radial GT engine for integration with a 550 kW pressurized SOFC stack are identified, which allow operation under a wide range of ambient conditions as well as several different cycle configurations. Copyright

Hybrid Fuel Cell Gas Turbine System Design and Optimization

Ultrahigh efficiency, ultralow emission fuel cell gas turbine (FC/GT) hybrid technology represents a significant breakthrough in electric power generation. FC/GT hybrid designs are potentially fuel flexible, dynamically responsive, scalable, low-emission generators. The current work develops a library of dynamic component models and system design tools that are used to conceptualize and evaluate hybrid cycle configurations. The physical models developed for the design analysis are capable of off-design simulation, perturbation analysis, dispatch evaluation, and control development. A parametric variation of seven fundamental design parameters provides insights into design and development requirements of FC/GT hybrids. As the primary generator in most configurations, the FC design choices dominate the system performance, but the optimal design space may be substantially different from a stand-alone FC system. FC operating voltage, fuel utilization, and balance of plant component sizing has large impacts on cost, performance, and functionality. Analysis shows that hybridization of existing fuel cell and gas turbine technology can approach 75% fuel-to-electricity conversion efficiency. [DOI: 10.1115/1.4024569]

A methodology for developing Distributed Generation scenarios in urban areas using geographical information systems

The implementation of Distributed Generation (DG) may lead to increased pollutant emissions that adversely affect air quality. This work presents a systematic methodology to characterise DG installation in urban basins. First, a set of parameters that characterise a DG implementation scenario is described. Second, a general approach using Geographic Information Systems (GIS) data is presented. Third, the methodology is demonstrated by application to the South Coast Air Basin (SoCAB) of California. Results show that realistic scenarios in the SoCAB concentrate DG technologies nearby industrial zones and introduce pollutant mass increments no larger than 0.43% with respect to baseline emissions.

A Cost-effective Three-phase Grid-connected Inverter with Maximum Power Point Tracking

Solar energy is desirable due to its renewable and pollution-free properties. In order to utilize the present utility grid infrastructure for power transmission and distribution, grid connected dc-to-ac inverters are needed for solar power generation. However, previously proposed voltage source inverters with a two-power-stage structure or a cascaded structure increase the circuit complexity, power losses, and system cost. In addition, conventional maximum power point tracking (MPPT) methods usually need power calculation and complex logic judgment, so expensive multipliers and/or microprocessors are needed. This leads to high inverter capital cost, which becomes a major barrier for the wide use of solar power generation. A cost-effective MPPT method integrated within the one-cycle control (OCC) core is proposed in this paper. When integrated with a three-phase boost type inverter, the proposed method tracks MPP with good precision, and solar power is converted into three-phase ac power with a single power stage. There is no power calculation in the controller, which yields a simple and cost-effective solution. Experiments have been carried out with a photovoltaic source to verify good performance