Many energy supply systems around the world are currently undergoing a phase of transition characterized by a continuing increase in installed renewable power generation capacities. The inherent volatility and limited predictability of renewable power generation pose various challenges for an efficient system integration of these capacities. One approach to manage these challenges is the deployment of small-scale dispatchable power generation and storage units on a local level. In this context, gas turbine cogeneration units, which are primarily tasked with the provision of power and heat for industrial consumers, can play a significant role, if they are equipped with a sufficient energy storage capacity allowing for a more flexible operation. The present study investigates a system configuration, which incorporates a heat-driven industrial gas turbine interacting with a wind farm providing volatile renewable power generation. The required energy storage capacity is represented by an electrolyzer and a pressure vessel for intermediate hydrogen storage. The generated hydrogen can be reconverted to electricity and process heat by the gas turbine. The corresponding operational strategy for the overall system aims at an optimal integration of the volatile wind farm power generation on a local level. The study quantifies the impact of selected system design parameters on the quality of local wind power system integration, that can be achieved with a specific set of parameters. In addition, the impact of these parameters on the reduction of CO2 emissions due to the use of hydrogen as gas turbine fuel is quantified. In order to conduct these investigations, detailed steady-state models of all required system components were developed. These models enable accurate simulations of the operation of each component in the complete load range. The calculation of the optimal operational strategy is based on an application of the dynamic programming algorithm. Based on this model setup, the operation of the overall system configuration is simulated for each investigated set of design parameters for a one-year period. The simulation results show that the investigated system configuration has the ability to significantly increase the level of local wind power integration. The parameter variation reveals distinct correlations between the main design parameters of the storage system and the achievable level of local wind power integration. Regarding the installed electrolyzer power consumption capacity, smaller additional benefits of capacity increases can be identified at higher levels of power consumption capacity. Regarding the geometrical volume of the hydrogen storage, it can be determined that the storage volume loses its limiting character on the operation of the electrolyzer at a characteristic level. The additional investigation of the CO2 emission reduction reveals a direct correlation between the level of local wind power integration and the achievable level of CO2 emission reduction.

References

1.
Hohymeyer
,
O. H.
, and
Bohm
,
S.
,
2015
, “
Trends Towards 100% Renewable Electricity Supply in Germany and Europe: A Paradigm Shift in Energy Policy
,”
Wiley Interdiscip. Rev.: Energy Environ.
,
4
(
1
), pp.
74
97
.
2.
ISE
,
2018
, “
Energy-Charts
,” Fraunhofer-Institut für Solare Energiesysteme, Freiburg, Germany, accessed Jan. 24, 2018, https://www.energy-charts.de/
3.
IEA
,
2016
, “
World Energy Outlook 2016—Executive Summary
,” International Energy Agency, Paris, France.
4.
Holttinen
,
H.
, “
Impact of Hourly Wind Power Variations on the System Operation in the Nordic Countries
,”
Wind Energy
,
8
(
2
), pp.
197
218
.
5.
Xia
,
J.
,
Pan
,
Z.
, and
Dai
,
Y.
,
2012
, “
Operation and Simulation of Hybrid Wind and Gas Turbine Power System Employing Wind Power Forecasting
,”
ASME J. Eng. Gas Turbines Power
,
134
(
12
), p.
124501
.
6.
Branchini
,
L.
,
Bianchi
,
M.
,
Cavina
,
N.
,
Cerofolini
,
A.
,
Pascale
,
A.
, and
Melino
,
F.
,
2014
, “
Wind-Hydro-Gas Turbine Unit Commitment to Guarantee Firm Dispatchable Power
,”
ASME
Paper No. GT2014-25049
.
7.
Guandalini
,
G.
,
Campanari
,
S.
, and
Romano
,
M. C.
,
2014
, “
Comparison of Gas Turbines and Power-to-Gas Plants for Improved Wind Park Energy Dispatchability
,”
ASME
Paper No. GT2014-26838
.
8.
Teleke
,
S.
,
Mesut
,
E. B.
,
Huang
,
A. Q.
,
Bhattacharya
,
S.
, and
Anderson
,
L.
,
2009
, “
Control Strategies for Battery Energy Storage
,”
IEEE Trans. Energy Convers.
,
24
(
3
), pp.
725
732
.
9.
GasTurb GmbH
,
2016
, “
GasTurb 12
,” GasTurb GmbH, Aachen, Germany, www.gasturb.de
10.
Kurzke
,
J.
,
2005
, “
How to Create a Performance Model of a Gas Turbine From a Limited Amount of Information
,”
ASME
Paper No. GT2005-68536
.
11.
Kawasaki Heavy Industries
,
2012
, “
Kawasaki Gas Turbine Generator Sets
,” Kawasaki Heavy Industries, Tokyo, Japan.
12.
Stolten
,
D.
, and
Emonts
,
B.
,
2016
,
Hydrogen Science and Engineering, Weinheim
,
Wiley-VCH Verlag
,
Germany
.
13.
Kroniger
,
D.
,
Lipperheide
,
M.
, and
Wirsum
,
M.
,
2017
, “
Effects of Hydrogen Fueling on NOx Emissions: A Reactor Model Approach for an Industrial Gas Turbine Combustor
,”
ASME
Paper No. GT2017-64401
.
14.
Cocchi
,
S.
, and
Sigali
,
S.
,
2010
, “
Development of a Low NOx Hydrogen Fuelled Combustor for 10 MW Class Gas
,”
ASME
Paper No. GT2010-23348
.
15.
Kroniger
,
D.
,
Wirsum
,
M.
,
Horikawa
,
A.
,
Okada
,
K.
, and
Kazari
,
M.
,
2016
, “
NOx Correlation for and Industrial 10 MW Non-Premixed Gas Turbine Combutor for High Hydrogen Fuels
,”
ASME
Paper No. GT2016-56189.
16.
Ulleberg
,
O.
,
2003
, “
Modeling of Advanced Alkaline Electrolyzers: A System Simulations Approach
,”
Int. J. Hydrogen Energy
,
28
(
1
), pp. 21–33.
17.
Steeb
,
H.
, and
Aba Qud
,
H.
,
1996
, “
HYSOLAR: German-Saudi Joint Program on Solar Hydrogen Production and Utilization. Phase II, 1992–1995
,” Deutsche Forschungsanstalt fuer Luft- und Raumfahrt e.V., Stuggart, Germany, Report No. DE97721289/XAB.
18.
Buttler
,
A.
,
Kunze
,
C.
, and
Spliethoff
,
H.
,
2013
, “
IGCC-EPI: Decentralized Concept of a Highly Load-Flexible IGCC Power Plant for Excess Power Integration
,”
Appl. Energy
,
104
, pp.
869
879
.
19.
Bexten
,
T.
,
Wirsum
,
M.
,
Roscher
,
B.
,
Schelenz
,
R.
,
Jacobs
,
G.
,
Weintraub
,
D.
, and
Jeschke
,
P.
,
2017
, “
Techno-Economic Study of Wind Farm Forecast Error Compensation by Flexible Heat-Driven CHP Units
,”
ASME
Paper No. GT2017-63557
.
20.
Bellman
,
R.
,
1957
,
Dynamic Programming
,
Princeton University Press
,
Princeton, NJ
.
21.
Sundström
,
O.
, and
Guzella
,
L.
,
2009
, “
A Generic Dynamic Programming Matlab Function
,”
IEEE
Control Applications (CCA) & Intelligent Control (ISIC)
, Saint Petersburg, Russia, July 8–10, pp. 1625–1630.
22.
Sundström
,
O.
,
Ambühl
,
D.
, and
Guzella
,
L.
,
2010
, “
On Implementation of Dynamic Programming for Optimal Control Problems With Final State Constraints
,”
Oil Gas Sci.
,
65
(
1
), pp.
91
102
.
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