21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
1
PLANNING OF A SELF-SUFFICIENT ENERGY SYSTEM WITH INTERNAL
COMBUSTION ENGINE
Andrej PIRC, Boštjan DROBNIČ, Mitja MORI, Mihael SEKAVČNIK
POVZETEK
V tem prispevku je obravnavano optimalno konfiguriranje samozadostnega energetskega
sistema. Na začetku je predstavljena sama metoda naprednega načrtovanja energetskih
proizvodnih enot. V nadaljevanju je bilo uporabljeno programsko okolje Mathwork Simulink,
s katerim so modelirani porabnik energije, vir energije (motor z notranjim zgorevanjem z
generatorjem), hranilne kapacitete (baterija) in regulacija. Na koncu so bile izvedene
simulacije obratovanja večih testnih primerov, s katerimi smo poiskali optimalno rešitev.
Slednja je bila določena na podlagi racionalne rabe energije, pravil obratovanja, velikosti
posameznih enot in ekonomskega vidika. Povsem na koncu so predstavljeni rezultati v obliki
diagramov in tabel.
Ključne besede: energetsko samozadosten sistem, napredno načrtovanje, Matlab Simulink.
ABSTRACT
The paper presents method of optimization of a self-sufficient energy system
configuration. At the beginning an optimisation method for advanced planning of energy
supply systems is presented. Secondly, Mathwork’s Simulink was used to describe dynamic
mathematical model consisting of energy user, energy production unit (internal combustion
engine - ICE), energy saving capacities (battery) and regulation. At the end optimal system
was found through a series of simulations which ensures stable and rational energy supply
with respect to different rules of operation, particular sub-system’s sizes and economical
aspects. At the end, results, appropriate diagrams and future guidelines are shown.
Keywords: self-sufficient energy system, advanced planning, Matlab Simulink.
1.
INTRODUCTION
Stable, secure and sustainable energy supply is undoubtedly very important for energy
dependant modern societies. While traditional power supply and distribution systems
provided stability with large scale power production units and also large and passive
distribution network [1], future development of energy supply systems favours distributed
generation [2] for environmental, commercial and social reasons. These facts require optimal
design (Figure 1) of future energy network [3], which has to take into consideration: location
2
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
and availability (time and energy) of primary energy source, energy conversion systems,
location of consumers and their dynamics of use of energy, operating experience with
advanced technologies, environmental impacts and economic background.
Figure 1: Influential parameters by energy system planning
Complex energy network such as an energy supply, storage, distribution and consumption
system requires careful planning using advanced numerical tools. After taking into
consideration the review of computer tools [4] and the research topics that were planned,
Homer code was used for numerical simulations. Several different energy systems’ operations
were examined with simulations [5], [6]. During these case studies several limitations of the
employed software were found and described in [7]. Therefore, decision on development of a
custom made model has been accepted. The model should be able to simulate operation of
various combinations of energy systems with emphasis on accurate regulation of system’s
elements. Based on experiences of other researchers, MathWork’s Simulink was used as a
solver [8].
2.
SYSTEM DESCRIPTION
Energy self-sufficient islanded system was set up, consisting of a consumer of electricity
(industrial plant), an energy supplier (internal combustion engine with generator), energy
storage capacities (batteries) and regulation as seen in Figure 2. Each of these systems is
mathematically described below.
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
3
Figure 2: Observed energy supply/storage/consumption system
2.1
Electricity consumer
An electricity consumer with dynamically varying load; in the presented example the
consumer is an industrial plant [9] with typical daily operation and minimal power
consumption during night time and weekends. A weekly load of the observed plant is shown
in Figure 3.
Figure 3: Diagram of energy consumption
2.2
Internal combustion engine
An internal combustion engine (ICE) with power generator is the conventional energy
source in the observed system to provide stable energy supply. It has a limited capacity that is
considerably smaller than the peak loads of the consumer. Also the lowest output capacity is
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
4
limited due to decreased efficiency of the engine. Produced electrical power depends on
supplied heat flow Qsupply and electricity production efficiency ηel.
 fuel H fuel ηel
PICE  Q fuel ηel  m
(1)
parameter Hfuel is caloric value of used fuel.
Additional parameter that can significantly affect engine’s performance is the number of
start-ups and shut-downs and the time intervals between them. As no accurate data were
available on the influence of start-ups on engine’s lifetime only the actual number of start-ups
is shown in analysis for comparison of various systems.
2.3
Battery
Batteries are used for excess energy storage and supply of peak load energy. Whenever the
ICE is unable to cover the power demand, the consumer is supplied with additional energy
from the batteries. On the other hand, when power demand is small, production systems can
store the excess energy in the batteries. Re-used power is decreased due to charging and
discharging efficiency and equals
P  ηcharge ηdischarge P0
2.4
(2)
Regulation
The basic purpose of the system regulator is to ensure that the system’s operation
complies with the independent requirements from the system’s environment. To achieve
these, various commands need to be sent to individual elements of the system. However, the
regulator must also take into consideration the limitations each of the elements might have.
Thus both, requirements and the limitations have to be considered when making operating
rules and appropriate actions.
Regulation of the ICE operation is based on the following parameters: consumer’s energy
demand and the battery charge level. Any particular ICE has limited maximum power output
and to avoid low efficiency operation a lower power limit is also set. If the required power is
lower than the limit the engine is shut down. It is also advisable to avoid frequent start ups
and shut downs of the engine so the operating rules should consider the current state of the
engine operation.
Capacity of the battery is also limited once an actual battery is chosen for the system. Both
overcharging and complete discharging of the battery should be avoided. Appropriate actions
should be taken when certain charge levels are reached.
Regulation of an energy system is done using system state matrix approach. Based on
values of certain observed parameters the regulation identifies current state of the system from
predefined matrix of all possible states. For each state appropriate actions are defined that
provide the optimal response and operation of the system. The matrix and operating rules that
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
5
should be followed in particular situations are shown in Table 1 and Table 2. As the only
controllable element of the system is the ICE, all the actions within rules apply only to the
engine.
Table 1: System state matrix for the observed system
Battery charge
< Cmin
≥ Cmin & < Cmax
≥ Cmax
R1
R3
R2
R1
R1
R4
R1
R1
R2
≥ P0 & < PICEmin
Consumer’s energy demand
≥ PICEmin & < PICEmax
≥ PICEmax
Table 2: List of operating rules and associated actions
Rule
Action
R1
Run engine with maximum power output.
R2
Shut down engine.
R3
Do not change engine operation.
R4
Follow consumption.
3.
NUMERICAL SIMULATION AND RESULTS
To test the observed system’s operation under the predefined parameters and rules, a
numerical model of the system was set up using MathWork’s Simulink [10] software while
Matlab was used to import data and set up operating parameters and rules.
Design of system’s components
3.1
Sizing of particular components was made through the optimisation process. Primary goal
of the optimisation process is to achieve stable, secure and sustainable energy supply for
given energy demand. Three system’s components (Table 3) were set up for the given power
consumer with taking into consideration the following facts:
 Stable and secure energy supply through the whole operating time.
 Minimal sizes of particular elements with respect to investment costs.
 Minimal operating costs and cost price of levelized cost of energy.
Table 3: List of particular element’s size and its investment costs
Element of the system
Consumer
ICE
Battery
Size/capacity/consumption
Specific investment cost
170 kWh
/
[40, 60, 80] kW
800 €/kW
[400, 350, 200] kWh
100 €/kWh
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
6
The levelized cost of produced electrical energy is the information that predicts the
viability of individual power-plant and depends on the fixed and variable costs. Fixed costs
include investment costs, whereas variable costs include fuel and other operating costs.
Total costs are sum of annual fixed costs CA, operating costs COP and fuel costs CF. Finally
levelized cost, cLCOE, equals
cLCOE 
C A  COP  C F
E prod
(3)
where Eprod is total produced energy.
3.2
Operating simulation
From all the various cases shown in Table 3 three proved to be able to provide user with
reliable source of energy. The three systems were analysed and compared considering the
following results:
 Operating diagrams [one week scale].
 Levelized cost of electricity production, [€/kWh].
 Investment cost, [€].
 Fuel use, [kg/a].
 Operating hours (only operating), [h/a].
 Number of cold starts, [-/a].
The compared cases are
1. 40 kW ICE and 400 kWh battery
2. 60 kW ICE and 350 kWh battery
3. 80 kW ICE and 200 kWh battery
Results for cases 1., 2. and 3. are shown in Figures 4, 5 and 6, respectively. A comparison
of integral parameters of the presented cases is shown in Table 4.
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
Figure 4: Operating diagram for system with ICE 40 kW
Figure 5: Operating diagram for system with ICE 60 kW
7
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
8
Figure 6: Operating diagram for system with ICE 80 kW
3.3
Validation of results
Comparison of the operating results of analysed particular systems is shown in Table 4.
Table 4: Comparison of the results
Syste
ICE power
Battery capacity
Investment
ICE
Levelized
Fuel use
ICE
m
/pay-back
/
cost
operating
cost of
[kg/a]
cold
code
time
pay-back time
[€]
hours
electricity
ICE40
[kW/a]
40/4.8
[kWh/a]
400/5
72000
[h/a]
6656
[€/kWh]
0.336
56680
[-/a]
156
ICE60
ICE80
60/5.3
80/5.8
350/5
200/5
83000
84000
4316
3120
0.366
0.368
60840
61464
312
364
starts
Optimal solution of the simulations is case one with ICE of power 40 kW and battery of
capacity 400 kWh. This solution is the best from all points of view, it requires the lowest
investment cost, levelized cost of electricity is also the lowest as well as fuel consumption. It
also requires the least engine start-ups which would allow a longer engine life time.
4.
CONCLUSION
In this paper, optimal operating regulation of self-sufficient energy network is presented.
Based on our research work the following results were obtained:
 Based on available data for one week user’s energy demand, ICE and battery
characteristics, the regulation system was set up to achieve stable and rational energy
supply.
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
9
 Numerical modelling of described system and simulation were done using the
Mathwork’s Simulink code.
 Optimal solution was found through the optimisation process, which has taken into
consideration the following criteria:
o Levelized cost of energy production.
o Investment cost.
o Fuel use.
o Operating hours.
o Number of cold starts.
 Pay-back time of ICE was calculated through the backward process taking into a
consideration a number of cold starts.
 Optimal solution for the presented energy consumer is the one with ICE of power
40 kW and battery of capacity 400 kWh.
 The results show that the system controller actually had the situation under control in
every situation and it managed to provide sufficient amount of energy to the consumer
at any given moment and in any situation.
5.
ACKNOWLEDGEMENT
The part of presented work has been accomplished within the Centre of Excellence for
Low-Carbon Technologies (CO NOT), Hajdrihova 19, 1000 Ljubljana, Slovenia.
6.
REFERENCES
[1]
Tuma, M., Sekavčnik, M.: Energetski sistemi, Fakulteta za strojništvo, Ljubljana, 2004,
(in Slovenian).
[2]
Asmus, P.: Microgrids, virtual power plants and our distributed energy future, The
Electricity Journal 23 (2010), p. 72 – 82.
Cormio, C., Dicorato, M., Minoia, A., Trovato, M.: A regional energy planning
methodology including renewable energy sources and environmental constraints,
Renewable and Sustainable Energy Reviews 7 (2003), p. 99 – 130.
Connolly, D., Lund, H., Mathiesen, B. V., Leahy, M.: A review of computer tools for
analysing the integration of renewable energy into various energy systems, Applied
Energy 87 (2010), p. 1059 – 1082.
NREL: Homer Guide Book, Denver, 2009.
[3]
[4]
[5]
21ST Expert Meeting "KOMUNALNA ENERGETIKA / POWER ENGINEERING", Maribor, 2012
10
[6]
[7]
[8]
Wille-Haussmann, B., Erge, T., Wittwer, C.: Decentralised optimisation of
cogeneration in virtual power plants, Solar Energy 84 (2010), p. 604 – 611.
Pirc, A., Sekavčnik, M., Drobnič, B., Mori, M.: Use of hydrogen technologies for
saving electric energy in combination with renewable energy systems, 6th International
Workshop on Deregulated Electricity Market Issues in South-Eastern Europe, Demsee
2011.
Ishaque, K., Salam, Z., Syafaruddin: A comprehensive MATLAB Simulink PV system
simulator with partial shading capability based on two-diode model, Solar Energy 85
(2011), p. 2217 – 2227.
[9] Elektro Celje: Industrial plant’s weekly consumption of electrical energy, Measuring
Report (2009), (in Slovenian).
[10] Mathworks: Simulink Help (2011).
AUTHORS
Andrej Pirc
Savaprojekt, družba za razvoj, projektiranje, konzalting, inženiring, d. d., Krško, Cesta krških
žrtev 59, 8270 Krško
Dr. Boštjan Drobnič
Dr. Mitja Mori
Assoc. Prof. Dr. Mihael Sekavčnik
University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana.
Centre of Excellence for Low-Carbon Technologies (CO NOT), Hajdrihova 19, 1000
Ljubljana.