Hybrid Plant of Renewable Stochastic Source and

Hybrid Plant of Renewable Stochastic Source and Multi-Level Storage for Emission-Free Deterministic Power Generation Kai Strunz, E. Kristina Brock where is the density of the air; is the length of the rotor blades; is the wind speed; is the performance coefficient; is the tip speed ratio; is the angular velocity of the rotor blades. The performance coefficient can be modeled in closed form by non-linear functions [1]. Using (1) with modeled, a maximum power output can be found for each wind speed. An example curve for the maximal power output of a 2 MW wind turbine is depicted in Fig. 1. Here, the cutout wind speed is 25 m/s, i. e. the wind turbine is turned off due to excessive winds above 25 m/s. 2 power (MW)

Abstract— The market for distributed energy resources expands rapidly. Two distributed energy sources that are at the center of interest in this context are wind energy converters and fuel cells. While renewable energy based and environmentally benign, a major problem of wind energy conversion with regard to large-scale network integration is the direct dependence of the power generation capability on the given wind speed. Similar problems of controllability exist for other renewable energy sources with intermittent output. This conflicts with the need to schedule power output in a deterministic manner. A concept to overcome the stochastic nature of the source is proposed. The overall solution consists of a plant where the stochastic source is coupled over a DC bus with diverse storage modules and a versatile grid interface. As storage solutions hydrogen and in addition a second form of storage for quasi-instantaneous power delivery is considered. The intermittent source is so transformed into an emission-free deterministic generation plant with controlled power output to the grid. Index Terms— Distributed generation, energy storage, flywheels, fuel cells, hydrogen, power electronics, power system simulation, renewable energy, solar energy, wind energy.

(1) (2)

The authors are with the Department of Electrical Engineering, University of Washington, Seattle, WA 98195-2500.

0.5

0 Fig. 1.

5 10 15 20 wind speed (m/s)

25

Example maximum power output curve of 2 MW wind turbine

In Fig. 2, measurements of the wind speed are depicted in the form of a time series over one month. Given the changing wind speeds and the dependence of the power output on the wind speed as shown in Fig. 1, it is obvious that the stochastic form of power output poses problems in terms of the largescale network integration. Customers expect electric power to be available at their convenience rather than being a function of the wind speed.

II. BACKGROUND A. Stochastic Energy Sources The mechanical output power of a wind turbine is calculated as follows:

1

0

wind speed (m/s)

I. I NTRODUCTION According to a study of the Gas Research Institute of the USA, distributed energy resources are expected to capture about 30 % of the energy market by 2030. At present, electric power generation through wind energy conversion enjoys particularly strong growth. No fuel is required for wind energy conversion and no pollutants occur. The widespread exploitation of wind energy conversion is complicated by the fact that the maximum possible power output is a function of the given wind speed. Similar observations apply to other renewable energy sources with stochastic output. In the following sections, the concept of Stochastic Energy Source Access Management (SESAM) is introduced and discussed as a solution to the problem of stochastic power output in the context of network integration. Through the concept, a hybrid configuration with storage of multiple levels is established. In section II, basics and prior work are reviewed. In section III, the proposed plant is discussed. In section IV, the role of transients simulation is considered. Conclusions are drawn in section V.

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Fig. 2.

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Example wind speed measurements over one month

Despite of the intermittent availability, certain stochastic energy sources such as wind and solar energy sources are very attractive. The reason is that they have key advantages

PROCEEDINGS OF THE CIGRE/IEEE PES INTERNATIONAL SYMPOSIUM OF ELECTRIC POWER DELIVERY SYSTEMS, MONTREAL, CANADA, OCTOBER 2003.

in common. They are renewable, which means that they are continuously replenished. Renewable energy sources are clean and do not rely on the combustion of fossil fuels. Potential political and economic crises related to the availability of fuel could boost the necessity for electricity generation based on renewable sources further. Research into the large-scale integration of renewable energy sources into the network is therefore actively pursued. B. Hydrogen Economy Hydrogen is the simplest and also the most widely available element. Fuel cells turn hydrogen in the presence of oxygen and an electrolyte into electricity and are considered for mobile as well as stationary applications. As opposed to the power generation processes based on the combustion of fossil fuels, the process is very clean. Hydrogen is widely seen as key to present and future energy solutions. A largescale implementation will involve the shift from the fossil fuel economy to the hydrogen economy. While hydrogen is widely available, it is so in the form of compounds. For the purpose of generating electricity in fuel cells, however, hydrogen must be made available in pure form. This is possible through electrolysis where water is split into its component parts of hydrogen and oxygen. In order to run the electrolysis, energy must be supplied. C. Storage Storage can be combined with stochastic energy sources to smooth the power output. The use of hydrogen as storage provides here a startling alternative [2] to other means of storage including flywheels for kinetic energy, superconductors for magnetic energy, supercapacitors for electric energy, and batteries [3]. Investigations have shown that combining a stochastic energy source with hydrogen storage is very beneficial since the power output of the hybrid combination can then be better scheduled [4], [5]. Storage elements such as flywheels, superconductors, and supercapacitors have shown to be very useful for the smoothing of short-term fluctuations of the output of stochastic sources [6]–[9].

A. Plant Overview A schematic overview of the proposed plant is shown in Fig. 3 [10]. It consists of four major modules interconnected through a DC bus. Variables , and indicate the number of connections made. The stochastic source module provides power , whereas and are associated with the larger long-term access and smaller short-term access storage modules, respectively. The values of and can be either positive or negative. The active power and reactive power output to the grid, and , are adjusted through the grid interface module. Given that the efficiency of the grid interface module is designated through , then the input power of the grid interface is . For a lossless DC bus network, is obtained as follows:

(3)

While comes from a stochastic source, and are adjusted such that appears to come from a deterministic source. The control system is structured hierarchically. Each module is associated with a local controller. The operation of the local controllers is coordinated by a global control system. The controls make sure that the voltage across the capacitor of the DC bus is maintained at the desired value and that the desired power output is obtained. As reviewed in subsection II-C, combinations of stochastic sources and storage have been considered in different configurations for different applications. The contribution of the design in Fig. 3 is that all the conditions of a model plant as set out above are met. The stochastic source module comprises an environmentally benign renewable source. The long-term and short-term access storage modules provide storage capability at multiple levels. While the long-term access storage is designed to store large amounts of energy, the short-term access storage module has much less storage capability but offers very fast controllability. The grid interface module allows, in addition to the transfer of active power, the generation of reactive power. The decomposition of the overall design into modules with well defined interfaces promotes cost and maintenance efficiency.

III. S TOCHASTIC E NERGY S OURCE ACCESS M ANAGEMENT A model plant for the generation of electric power would comprise the following desirable specifications: a) renewable energy based, b) environmentally benign, c) deterministic generation of active and reactive power, d) fast controllability, e) cost and maintenance efficient. Through items a) and b) the use of fossil fuels and harmful pollution are avoided. Items c) and d) are crucial for large-scale network integration. The possibility of active power generation will enable the operator of the plant to fully participate in electricity markets. Through the generation of reactive power, the plant operator can provide voltage control and contribute to power quality. To do so, fast control schemes are necessary. Item e) is important for economic considerations. The concept of SESAM meets these specifications.

B. Grid Interface Module The grid interface module comprises a power electronic converter and a transformer. The power electronic converter is a switch-mode DC-AC voltage sourced inverter, and a pulse width modulated switching scheme is used [11]. The active power output is primarily dependent on the phase shift between the fundamental frequency component of the AC voltage generated by the inverter and the AC voltage on the grid side. This phase shift is adjusted through feedback control so that can reach its setpoint. The reactive power output is mainly influenced by the magnitude of the AC voltage generated by the inverter. The grid interface module can here provide reactive compensation in the way the Static Synchronous Compensator (STATCOM) [12] does. The plant can therefore contribute to voltage stability.


stochastic source module power electronic converter

stochastic source

grid interface module

DC bus

power electronic converter

stochastic source module local control

grid interface module local control

long-term access storage module long-term access storage


long-term access storage module local control

global SESAM control

short-term access storage module short-term access storage


short-term access storage module local control Fig. 3.

Modular concept of Stochastic Energy Source Access Management

C. Stochastic Source Module

D. Long-Term Access Storage Module

The stochastic source module comprises the renewable energy source with stochastic output. In Fig. 4 an example of a wind energy converter is considered. The system shown has no gearbox, a suitable choice for the three-phase AC generator is a synchronous generator. Through the management and control unit, a maximum power tracker is realized such that for each wind speed the maximum mechanical power is obtained according to the turbine specifications. This leads to changing angular velocities and thus to the generation of AC voltages at different frequencies. The AC waveforms are rectified through an AC-DC diode converter. The boost converter is used to obtain the desired voltage of the DC bus. The power output of the module is which equals minus the inevitable losses.

From the wind speed measurements shown in Fig. 2 it can be recognized that the power output of a wind energy converter changes quite erratically over time. It is the role of the large-size long-term access storage module, shown in Fig. 5, to provide storage so that the power output to the grid can be provided in accordance with commitments and customer demand. The storage needs to be sufficiently large so that the requested power can be supplied in a time frame ranging from several minutes to several months to account for

fuel cell based DC source

DC-DC power electronic converter DC bus

boost converter threephase AC generator

AC-DC diode converter

DC bus

oxygen tank

hydrogen tank

water tank

electrolyser unit Fig. 4. Stochastic source module in form of wind energy conversion conversion unit

DC-DC power electronic converter

Fig. 5. Long-term access storage module in form of hydrogen production, storage and conversion unit


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As indicated in Fig. 6, an induction machine is suitable for the purpose of electromechanical energy conversion. The interface to the DC bus is in this case established via an AC-DC converter. flywheel

DC bus induction machine

AC-DC voltage sourced inverter

Fig. 6. Short-term access storage module in form of flywheel kinetic energy storage unit

Interesting alternatives to the fast accessible fast accessible storage of kinetic energy are the storage of magnetic energy

IV. S IMULATION Simulation is a vital means for the purpose of designing, analyzing and testing the behavior of the individual modules as well as the overall plant. For the detailed modeling of the power electronic circuits, the DC bus, and the interface with the grid, the application of electromagnetic transients simulation is suitable. As an example, the simulation of the grid interface module is considered to test the control of reactive power. Both the circuit and the controller are modeled in MATLAB [15]. The DC bus voltage is set to 3 kV. The AC grid side is represented as a three-phase circuit where each phase consists of an inductor, a resistor, and a voltage source with a root-meansquare voltage of 230 V, measured from phase to ground, and a frequency of 60 Hz. After 20 ms the setpoint of the reactive power is stepped up from 50 kVAr to 75 kVAr. The simulation results are shown in Fig. 7. On the left-hand side the reactive power output and its setpoint are shown. The AC grid phase currents , , and are shown on the right. The steady state is reached very rapidly showing that for the particular network configuration the controller is well adjusted.

reactive power output

grid phase currents

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current / kA

E. Short-Term Access Storage Module Even though the hydrogen-based storage module provides long-term storage capability, its response to step changes of power demand is far from instantaneous. Fuel cells can take tens of seconds to more than a minute to adjust to changes in power demand. It is the role of the short-term access storage module to bridge this time interval to provide power quasi-instantaneously as requested. This ensures fast controllability and can prevent the propagation of short-term power fluctuations from the stochastic source to the grid. It should be noted that the combined kinetic energy stored in the rotating shafts of the synchronous generators connected to the grid is key to maintaining the frequency in today’s power systems. This energy is readily available. Step changes of the load demand can be satisfied from this stored energy and lead, due to the relatively large amount of available kinetic energy, only to small frequency deviations. The frequency is then restored by changing the power input to the generating plants accordingly. One possibility of realizing storage with quasiinstantaneous access for the proposed plant is through a flywheel that stores kinetic energy. For the amount of energy stored through a flywheel with inertia rotating at the angular velocity , the following equation applies:

in superconductors [13] and the storage of electric energy in supercapacitors [14].

reactive power / kVAr

seasonal variations. Principle elements are the electrolyser unit, the fuel cell based DC source, the hydrogen tank, the oxygen tank, and the water tank. The module is interfaced to the DC bus through DC-DC power electronic converters. In a time interval where the short-term access storage is filled to the maximum limit and the power of the stochastic source exceeds the power to be transferred to the grid interface, the surplus of power is supplied to the electrolyser: . Through electrolysis, hydrogen and oxygen are produced from the water and then stored in the tanks. Inversely, in a time period where the power of the stochastic source is lower than the power to be transferred to the grid interface, the management and control unit adjusts the fuel cell operation so that the power difference can be supplied by the module. In this case .

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Simulation of grid interface module

V. C ONCLUSIONS Stochastic Energy Source Access Management has been proposed as a solution to the problem of large-scale network integration of renewable energy sources with stochastic power output. The concept concerns a plant of modular setup consisting of a renewable stochastic energy source, multi-level access storage, and a grid interface. The power transfer between the individual modules is performed over a DC bus. The multilevel storage is decomposed into two modules. First, the longterm access storage module is hydrogen based with large storage capability. Second, less storage capability but shortterm access is obtained via the storage of kinetic, electric or magnetic energy. Through this multi-level approach, deterministic power output to the grid is made possible in different time frames. The modular setup is to facilitate coordination and reduce costs. With its high controllability, environmental friendliness and use of hydrogen as a major storage medium, the proposed concept fits well into the developing distributed energy systems and the hydrogen economy.


ACKNOWLEDGMENT The authors would like to thank the Royalty Research Fund at the University of Washington for supporting this work. R EFERENCES [1] S. Heier. Grid integration of wind energy conversion systems. John Wiley & Sons, London, England, 1998. [2] R. Ramakumar, H. J. Allison, and W. L. Hughes. Solar energy conversion and storage systems for the future. IEEE Transactions on Power Apparatus and Systems, PAS-94(6):1926–1934, March 1975. [3] P. F. Ribeiro, B. K. Johnson, M. L. Crow, A. Arsoy, and Y. Liu. Energy storage systems for advanced power applications. Proceedings of the IEEE, 89(12):1744–1756, December 2001. [4] W.-J. Yang and O. Aydin. Wind energy–hydrogen storage hybrid power generation. International Journal of Energy Research, 25(5):449–463, April 2001. [5] A. Cruden and G. J. W. Dudgeon. The impact of energy storage devices used in conjunction with renewable embedded generators on the protection and control system. In Seventh International Conference on Developments in Power System Protection, pages 230–233, April 2001. [6] I. J. Iglesias, L. García-Tabarés, A. Agudo, I. Cruz, and L. Arribas. Design and simulation of a stand-alone wind-diesel generator with a flywheel energy storage system to supply the required active and reactive power. In IEEE Power Electronics Specialists Conference (PESC), pages 1381–1386, June 2000. [7] H. P. Kan, K. T. Chau, and M. Cheng. Development of doubly salient permanent magnet motor flywheel energy storage for building integrated photovoltaic system. In Sixteenth Annual IEEE Applied Power Electronics Conference and Exposition, pages 314–320, March 2001. [8] R. Cárdenas, R. Pena, G. Asher, and J. Clare. Control strategies for enhanced power smoothing in wind energy systems using a flywheel driven by a vector-controlled induction machine. IEEE Transactions on Industrial Electronics, 48(3):625–635, June 2001. [9] Y. Suzuki, I. Takano, and Y. Sawada. Reduction of PV output fluctuation by modified moving average data processing with EDLC. In ThirtyFourth North American Power Symposium, pages 91–98, October 2002. [10] K. Strunz. Stochastic energy source access management. Technical report, Department of Electrical Engineering, University of Washington, 2003. [11] N. Mohan, T. M. Undeland, and W. P. Robbins. Power Electronics: Converters, Applications and Design. John Wiley & Sons, New York, third edition, 2003. [12] N. G. Hingorani and L. Gyugyi. Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. IEEE Press, Piscataway, USA, 1999. [13] W. V. Hassenzahl. Superconductivity, an enabling technology for 21st century power systems? IEEE Transactions on Applied Superconductivity, 11(1):1447–1453, March 2001. [14] E. Schempp and W. D. Jackson. Sytems consideration in capacitive energy storage. In Thirty-First Intersociety Energy Conversion Engineering Conference (IECEC), pages 666–671, August 1996. [15] A. Biran and M. Breiner. MATLAB for Engineers. Addison-Wesley, Wokingham, England, 1995.

Dr. Kai Strunz graduated with the Dipl.-Ing. degree from the University of Saarland in Saarbrücken, Germany, in 1996, and he was awarded the Dr.Ing. degree with summa cum laude from the same university in 2001. From 1995 to 1997, Dr. Strunz pursued research at Brunel University in London, where he worked in close cooperation with the National Grid Company in the fields of power system stabilization, electromechanical transients, and power system modeling. From 1997 to 2002, he worked at the Division Recherche et Développement of Electricité de France (EDF) in the Paris area. At EDF, his main research work was concerned with the creation of efficient numerical methods for real time digital simulation. In April 2002, he joined the University of Washington at Seattle as assistant professor. He received the National Science Foundation CAREER award in 2003. E. Kristina Brock is from Lee, Massachusetts and is currently a graduate student at the University of Washington. She received her B. A. and B. E. degrees from Dartmouth College in 2001 and 2002, respectively. At Dartmouth, she performed undergraduate research on a power system for DARTSAT, a picosatellite designed by Dartmouth engineering students. During the fall of 2001, she studied at the Royal Institute of Technology in Stockholm, Sweden. Her research interests include power electronics and renewable energy. In 2003, she has been awarded the Grainger Foundation Graduate Fellowship.