Optimal Control Strategy for Mixed Fuel Use in a Renewable

Polygeneration System

Luca Cimmino

, Jimmy Barco Burgos

and Ursula Eicker

Department of Industrial Engineering University of Naples Federico II, Naples, Italy

Department of Buildings, Civil and Environmental Engineering, Concordia University, Montréal, Canada

Keywords: Polygeneration Systems, Cogeneration, Renewable Energy Systems, Feedback Control Strategies, Energy

Storage.

Abstract: The proposed work aims to analyse an optimal control strategy applied to a polygeneration system based on

a gasification unit, an anaerobic digester and an alkaline electrolyzer to meet the fuel demand of an internal

combustion engine. The purpose of the studied system is to meet the electricity and thermal energy demand

of several end-users. The focus of this paper is on the fuel management strategy for a 2 MW cogenerator used

to meet both these demands for a hospital. In the proposed model, the fuel injected into the internal combustion

engine is a mixture of gaseous flows produced by renewable energy systems. The mixture is first composed

of biogas, produced by an anaerobic digester fed by organic urban wastes. Secondarily, hydrogen obtained

from the electrolysis of water through a 2 MW alkaline cell is considered. In addition, syngas produced by a

1.7 MW allothermal downstream gasification unit is adopted with different gasifying agents considered,

including oxygen produced by the alkaline electrolyzer. Results show that with the adopted strategy, the fuel

energy demand is met by 15% by biogas, 3% by hydrogen, 45% by syngas using oxygen as gasifying agent

and 37% by syngas using steam as gasifying agent.

1 INTRODUCTION

The adoption of energy management strategies to

reduce the energy consumption in several sectors is

becoming an increasingly relevant issue (Nassar,

2023). Residential, industrial, commercial buildings

as well as the transport sector require energy in form

of power, heat, cool, and fuels (Papadis and

Tsatsaronis, 2020). In most cases, these demands are

still met through fossil fuels technologies that result

in greenhouse gases emissions (Zhang, 2022). The

integration of renewable energy based technologies is

pivotal to prevent the energy systems from being

increasingly climate affecting (Lima, 2020).

Renewable technologies are, however, characterized

by low energy density income and require optimal

control strategies (Dalala, 2022). Studying different

strategies for managing the energy flows produced by

renewable sources is therefore crucial (Khan, 2022).

Mostly, is important to adopt energy storage

https://orcid.org/0000-0001-6382-3619

https://orcid.org/0000-0002-2779-6614

https://orcid.org/0000-0002-6851-250X

strategies to adequately exploit the fluctuating energy

vectors incoming from solar energy (Calise, 2019).

The need for optimum management is especially

significant for polygeneration systems, i.e. systems

based on renewables and capable of meeting more

than three different energy demands (Khoshgoftar

Manesh, 2022). In case of residential applications, the

production of domestic hot water is considered as

additional demand (Gesteira, 2023). Studies on

hybrid polygeneration systems are increasingly

studied (Pipicelli, 2023). For residential applications,

renewable energy systems are commonly based on

solar energy (Kasaeian, 2020). In particular, for

meeting the demand for domestic hot water, solar

thermal collectors (STC) and photovoltaic-thermal

collectors (PVT) are often investigated (Calise,

2022). In many cases, these studies contain energy

and thermoeconomic analyses (Ceglia, 2020). In

(Calise, 2020) the authors proposed a thermo-

economic analysis of three water-energy-nexus

Cimmino, L., Burgos, J. and Eicker, U.

Optimal Control Strategy for Mixed Fuel Use in a Renewable Polygeneration System.

DOI: 10.5220/0012000200003491

In Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2023), pages 70-78

ISBN: 978-989-758-651-4; ISSN: 2184-4968

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

polygeneration systems for the production of energy

and desalinated water for two Mediterranean islands.

The layouts of the different configurations are

developed in TRNSYS to perform dynamic

simulations. Details of the electric, thermal, cooling,

and freshwater loads are provided by means of the

TRNBuild add-on which allows to simulate the

building behaviour. The three layouts are based,

respectively, on concentrating PVTs (CPVTs) and

electric chillers, PV modules with heat pumps, and

CPVTs equipped with reverse osmosis units. The one

that gave best results in terms of economic feasibility

is the system based on completely electricity driven

technologies.

For industrial applications, biomass driven

polygeneration systems are paving the way for the

production of clean fuels (Tabriz, 2023). Using

different biomasses allows one to produce several

biofuels (as hydrogen, bioethanol, or biodiesel) which

can be exploited for different applications (Seo,

2022). Gasification and pyrolysis are the most studied

processes for syngas production from biomass

(Daraei, 2021), but anaerobic digestion is getting

increasing interest for the possibility of coupling with

the carbon capture process (Salomoni, 2011).

Optimal control strategies are fundamental in

hybrid polygeneration systems, as revealed by several

studies (Menon, 2013), but the proposed works

mainly focus on the optimal management of the

system coupled with the grid (Rossi, 2016). In (Rejeb,

2022) the authors proposed a hybrid polygeneration

system based on PVT collectors, Organic Rankine

Cycle (ORC), proton exchange membrane

electrolyzer (PEM) and liquefied natural gas (LNG).

The model was implemented in EES software and

optimization was carried out by means of genetic

algorithm, setting as objective function the cost and

the overall exergy efficiency. Results shows that

optimal values of 16.24% for the exergy efficiency

and 4.48 $ for the cost rate are obtained, basing on the

TOPSIS decision-making process.

1.1 Aim and Novelty

As investigated in the literature review, many works

analyse hybrid polygeneration systems for both

residential and industrial end use. Furthermore,

optimization algorithm are used for the optimal

management strategy of hybrid systems coupled to

smart networks. This paper introduces some important

novelties in the scientific framework discussed:

• An innovative layout of a polygeneration

system based on gasification unit and

anaerobic digester, which exploit biomasses

and alkaline electrolyzer fed by photovoltaic

to meet the energy demand of a hospital.

• A novel control strategy proposed to guarantee

an optimal functioning of the technologies

considered. In fact, the configuration

investigated allows the technologies to operate

dynamically with optimal efficiency.

• A dynamic model of renewable technologies

producing a gaseous compound useful for

several applications in polygeneration energy

systems.

The system proposed and discussed is part of a greater

work which will be introduced and discussed in the

following sections.

2 SYSTEM LAYOUT

The layout of the polygeneration system proposed in

this work is shown in Figure 1.

This work is included in a wider project of a

hybrid polygeneration system providing power,

heating, cooling, hydrogen, oxygen, syngas, and

biogas for several applications. The original project

consists in a polygeneration system whose aim is to

meet all the energy demands of the botanical garden

José Celestino Mutis in Bogotá (Colombia). The

internal combustion engine (ICE) and the

photovoltaic (PV) systems are the technologies

already available to meet the power demand.

Moreover, the PV surpluses are used to feed an

alkaline electrolyzer (AEC) which provides hydrogen

that can be used to increase the LHV of the fuel

injected into the ICE. Furthermore, oxygen produced

by the AEC is exploited as a gasifying agent for a

gasification unit which provides syngas to feed the

ICE. Together with these technologies available, a

solar thermal collectors (STC) field is used to meet

the thermal energy demand of the system, together

with the ICE used in cogeneration mode (CHP). The

STC is also used to meet the thermal energy demand

of an anaerobic digester which provides biogas which

is used to feed the ICE too. As a first application of

the control strategy, the energy demand of a hospital,

already available from a previous work of the authors,

is considered. The part of the layout which is of

interest for this paper includes the PV system, the

alkaline electrolyzer, the gasification unit, and the

anaerobic digester (AD), equipped with the tanks for

the storage of the gases. These gaseous flow rates are

then mixed according to a precise control strategy to

meet the fuel demand of the ICE used for

cogeneration (CHP) purpose. The flow chart of the

control strategy applied is proposed in Figure 2.

Optimal Control Strategy for Mixed Fuel Use in a Renewable Polygeneration System

Figure 1: Layout of the system.

Figure 2: Flow chart of the control strategy.

SMARTGREENS 2023 - 12th International Conference on Smart Cities and Green ICT Systems

As can be noticed from the flow chart, the backup

technology which provides fuel to the CHP is the

gasifier operating with steam as gasifying agent. In

fact, when no power is available from the tanks,

meaning that none of them is at least 25%, the gasifier

gets immediately activated. In case the biogas tank is

at least 25% full - Φ

TK,bio

= 0.25 - the fuel power

available from biogas is the first one sent to the

engine. The main reason for the adoption of this

strategy is the constant flow rate of the biogas

produced, which allows one to stabilize the fuel

properties and increase the engine efficiency (Jatana,

2014) more easily.

To increase the LHV of the fuel and fully exploit

the electrolyzer production, the hydrogen flow rate is

immediately considered after biogas. More

specifically, if the biogas flow rate is enough to meet

the fuel demand, no more power is required from the

system. If no biogas is available or the energy is not

sufficient, the hydrogen tank is checked. At this point,

if the pressure level of the H

tank is more than 25%-

TK,H2

≥ 0.25 – the hydrogen flow rate is exploited,

otherwise the gasification unit is exploited.

According to the same strategy, if it is possible to

use oxygen as gasifying agent, it is used, otherwise

the system exploits steam pre-heated with the

outgoing syngas flow rate. In this case, there are two

reasons to adopt this strategy. On the one hand, the

oxygen produced is supposed not to be sold to the

hospital, since it should undergo several purification

steps to be usefully exploited.

On the other hand, the exergy efficiency of the

gasification unit increases when using O

instead of

steam as gasifying agent, and the biomass

consumption is reduced (Kalincı and Dincer, 2018).

Thus, O

is used as gasifying agent as long as it is

available, according to the pressure level in the tank.

If the power generated is not enough or none of the

tanks has sufficient charge, the steam flow rate is

needed to feed the gasifier. The final lower heating

value (LHV) of the fuel is calculated as the sum of the

LHV of each component of the mixture times the

fraction of the gas in the compound (ξ).

3 MODEL

The proposed layout integrates several technologies

whose models were developed in large part by the

authors. Some of these models are developed in EES

software, as the gasifier (Barco-Burgos, 2021), others

are developed in MatLab, as for the case of the

alkaline electrolyzer (Firtina-Ertis, 2022). All the

models are then integrated in the TRNSYS platform

for dynamic simulation purpose (Calise, 2017).

For the sake of brevity, in this section only the main

models are concisely discussed, namely:

 Gasifier

 Alkaline electrolyzer

 Anaerobic Digester

All the models are based on the assumption of ideal

gas behaviour.

3.1 Gasification Unit

This model is a semi-empirical model already

developed and validated by the authors in (Barco-

Burgos, 2021). It simulates a downstream

allothermal gasification unit that produces syngas

from lignocellulosic biomass.

The calculation procedure starts with the following

input: the temperature of the gasifier, the biomass

composition, the gasifying agent/biomass ratio, tar

composition, and heat losses. All these data are

empirically verified. With these values, the

equilibrium constants K1 to K3 are calculated and

then the Newton-Raphson method is adopted to solve

the system of nonlinear equations:

()()

()( )

CO H

CO H O

(1)

()()

()

CH TOT

(2)

()()

()()()

CO H

CH H O TOT

nnn

(3)

The value of the here mentioned equilibrium

constants, as a function of the temperature, are

obtained from the JANAF thermodynamic tables

(Chase,1975).

3.2 Alkaline Electrolyzer

This model is a model developed by the authors

which simulates the operating voltage of the cell at

different current input. The model is validated by

experimental data provided in ref. (Firtina-Ertis,

2022), using the same approach of the overvoltage

calculations. First the reversible voltage of the cell is

Optimal Control Strategy for Mixed Fuel Use in a Renewable Polygeneration System

calculated. Then, the activation, concentration, and

ohmic overvoltages are calculated as well. The

nonlinear system of equation integrated in the model

is the following:

cell rev act conc ohm

VVVV V=++ +

(4)

1/ 2

(, ) ( , ) ln

rev rev

VTp VTp

nF p







(5)

0, 0,

ln ln

act

ccaa

RT i RT i

nF i nF i

αα

 

 

 

(6)

ln 1

conc

nF i



=− −





(7)

()

ohm lectrode membr lectrol

VRi=++⋅

(8)

The molar production of hydrogen and oxygen is

calculated according to the stoichiometry of the

electrolysis reaction. Given that, the massic flow rates

are known.

3.3 Anaerobic Digester

The model of the anaerobic digestion adopted is the

ADM1, mainly used for biomasses with low total

solid content (Ashraf, 2022). Details of this model

can be found in ref. (Calise, 2023).

The system of equations used for the calculation

of the biogas production from the input biomass has

the following structure:

n, in,

liq i

jij

liq liq

in i out i

dS q

dt V V



(9)

Where the term S,i represents the concentration of

the species “i” in the substrate, q is the flow rate, and

V is the volume occupied by the biomass in the

digester, supposed to be constant for a continuously

stirred tank reactor (CSTR). The term φ

i,j

is the kinetic

term and α

i,j

is the biochemical coefficient per each

process “j”. The first order Monod kinetics for the

biochemical reactions are considered. Arrhenius

kinetics are used for the dependence from the reactor

temperature. An accurate thermal model is also

developed to iteratively calculate the influence of the

evolving temperature on the AD process (Calise,

2023).

For the gas storage, the ideal gas behaviour is

supposed for the compression of the compounds in

the tanks.

4 CASE STUDY

In this section, the main data regarding the

technologies modelled for the dynamic simulation are

provided. As already mentioned, this case study is

part of a larger project involving more renewable

technologies and end users. For this control strategy,

in fact, only the electric energy demand of a large

hospital complex was considered for the internal

combustion engine operation. The interest is indeed

on the analysis of the control strategy applied to the

technologies in charge of feeding the engine. More

specifically, the engine is a JMS-612-GS-

N.L.Jenbacher CHP engine of 2 MW of rated electric

power, whose details are shown in Table 1. In this

table, the most important data regarding the 2 MW

alkaline electrolyzer (AEC) and the 1.7 MW

gasification unit (GAS) are shown. The biogas flow

rate is constant tanks to the use of a buffer

downstream the digester, the volumetric flow rate is

equal to 83.97 Nm

/h.

The data regarding the hospital load and the CHP

are provided with detail in (Cappiello and Erhart

2021). The hospital complex consists of several

buildings, the total heated volume is 214´000 m

and

the floor heated area is 18´640 m

As will be discussed in the “Results” section, the

power demand of the complex is quite stable over the

year because of the intensive usage of electrical

devices and hospital machineries (Cappiello and

Erhart 2021).

5 RESULTS

This section shows and discusses the main results

obtained from the dynamic simulation of the hybrid

polygeneration system with the control strategy

applied for the tanks.

Figure 3 (left) shows the power flow rates for the

different gases considered. The results are perfectly

consistent with the expected behaviour of the system.

In fact, the energy provided by the biogas flow rate is

constant and represent the baseload sent to the CHP

during its operation. Only in the first moments of the

SMARTGREENS 2023 - 12th International Conference on Smart Cities and Green ICT Systems

Table 1: Data of the main components.

Component Parameter Description Value Unit

CHP

- Model name JMS-612-GS-N.L -

- Manufacturer GE Jenbacher GmbH & Co OHG -

el,CH

P Rated power 2002 kW

th,fuel

Rated fuel input 4424 kW

el,CHP

Rated electric efficiency 0.452 -

AEC

cell

Number of cells in series 49 -

stack

Number of parallel stacks 100 -

el,AEC

Rated electrolyzer power 2200 kW

T Operating temperature 333 K

p Operating pressure 8 bar

GAS

th,GAS

Rated input power 1730 kW

biomass

Rated biomass input 386 kg/h

agent

Gasifying agent inlet

temperature

700 K

gasifier

Gasfication temperature 1123 K

O/bio Steam/biomass ratio 0.85 -

/bio Oxygen/biomass ratio 0.26 -

Table 2: Energy usage ratio.

BIOGAS

SYN,O2

SYN,STEAM

0.15 0.03 0.45 0.37

day, when the biogas tank is less than 25% filled, the

energy is entirely provided from the gasification unit.

In this case, since no solar radiation is still available,

the H

and O

tanks are not fed by the electrolyzer,

then the steam is used as gasifying agent, see Figure

3 (right). In the first hours of the morning the biogas

tank continuously sends fuel to the CHP but the

energy mismatch is always met by the syngas

produced with the steam. The reason is that the H

and

tanks are still charging up to the desired state of

charge, 25%.

Around 11 AM., the O

tank has reached enough

charge to allow the oxygen to feed the gasifier. In this

case, the syngas provided by the gasification unit has

a higher LHV than the one produced with steam, and

the effect is immediately beneficial. In fact, the higher

the LHV of the syngas, the higher is the LHV of the

mixture sent to the engine, Figure 4 (left). Moreover,

the higher the LHV, the lower is the mass flow rate of

fuel that is necessary to send to the CHP, Figure 4

(right).

The effect of the H

flow rate is the most relevant,

as expected, on the increasing of the LHV of the fuel.

In fact, despite a small amount of H

is sent during the

allowed activation hours of the tank, the LHV of the

compound increases from 70 to roughly 75 MJ/kg,

see figure 5. This means that the flexibility in the

energy sent to the ICE from the several tanks would

be easily increased by simply applying a different

strategy.

For the sake of completeness, Table 2 shows the

values of the ratio of the energy share from the three

different fuels during the year of functioning. As it is

possible to observe, with this strategy the larger

fraction, 0.45, is due to the syngas obtained from O

which is the first target for the control developed.

Large share is also due to the syngas operating with

steam, which means that the H

could be exploited

more to increase the flexibility of the gaseous

moisture.

Optimal Control Strategy for Mixed Fuel Use in a Renewable Polygeneration System

Figure 3: Fuel energy provided by the unit (left) and tank pressure level (right).

Figure 4: LHV of the fuel (left) and mass flow rate injected (right).

6 CONCLUSIONS

In conclusion, the control strategy proposed allows

one to easily manage the several fuels available to

exploit the technologies in the most convenient way,

according to the different targets that could be set for

the hybrid polygeneration system. In this case study

the hospital complex had an almost constant energy

demand and the first aim was to let the gasifier

operate with oxygen as gasifying agent. In a different

scenario, the first aim could be producing oxygen to

be purified and used by the hospital complex and the

system could be easily managed changing the control

strategy.

SMARTGREENS 2023 - 12th International Conference on Smart Cities and Green ICT Systems

The final aim of this work was to provide a suitable

control strategy for the optimal dynamic operating

conditions of a polygeneration system based on

biofuels production which could be use for whatever

application.

NOMENCLATURE

AD anaerobic digestion

AEC alkaline electrolyzer

CHP cogeneration of heat and power

CPVT concentrating photovoltaic-thermal

CSTR continuously stirred tank reactor

GAS gasification unit

ICE internal combustion engine

LHV lower heating value [MJ/kg]

LNG liquefied natural gas

M maintenance [euro/year]

m mass flow rate [kg/h]

P power [kJ/h or kW]

PEM proton exchange membrane

PV photovoltaic

PVT photovoltaic-thermal

STC solar thermal collectors

T temperature [°C]

Subscript

bio biogas

fuel fuel

H2 hydrogen

Need demand

O2 oxygen

Steam steam

Syn syngas

TK tank

Greek symbol

ɳ efficiency [-]

ξ gas fraction [-]

Φ fill factor [-]

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SMARTGREENS 2023 - 12th International Conference on Smart Cities and Green ICT Systems