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Integrated Modeling and Assessment of the Operational Impact of Power-to-Gas (P2G) on Electrical and Gas Transmission Networks

15 May 2015-IEEE Transactions on Sustainable Energy (IEEE)-Vol. 6, Iss: 4, pp 1234-1244
TL;DR: In this article, the authors introduce an original methodology to analyze different power-to-gas (P2G) processes and assess their operational impacts on both electricity and gas transmission networks, using a novel integrated model specifically developed for the simulation of operational interdependences between the two networks considering P2G.
Abstract: Power-to-gas (P2G) is the process whereby electricity is used to produce hydrogen or synthetic natural gas The electricity for the P2G process could, for instance, come from renewable energy which would otherwise be curtailed due to system or line constraints The existing natural gas network could then potentially be used as a means to store, transport, and reutilize this energy, thus preventing its waste While there are several ongoing discussions on P2G in different countries, these are generally not backed by quantitative studies on its potential network implications and benefits To bridge this gap, this paper introduces an original methodology to analyze different P2G processes and assess their operational impacts on both electricity and gas transmission networks This is carried out by using a novel integrated model specifically developed for the simulation of operational interdependences between the two networks considering P2G To demonstrate the several innovative features of the proposed model, technical, environmental, and economic operational aspects of P2G and its potential benefits are analyzed on the case of the Great Britains system, also providing insights into relief of gas and electrical transmission network constraints

Summary (5 min read)

Introduction

  • The electricity for the P2G process could, for instance, come from renewable energy which would otherwise be curtailed due to system or line constraints.
  • Integrated energy systems; Multi-energy systems; Hydrogen production.
  • This paragraph of the first footnote will contain the date on which you submitted your paper for review.
  • On the above premises, the aim of this paper is to model and assess the possibility of integrating the P2G process into an existing energy system, with focus on modelling the impact on the gas and electrical transmission infrastructure.
  • All these are unique and novel contributions to understanding the implications of P2G considering realistic network operation and constraints.

A. Operation and location of the power-to-gas facilities

  • Different types of P2G facilities can be considered which depend on the end-use of the gas produced as well as its locality and reason for the curtailment of the renewable sources.
  • On the other hand, SNG facilities can be placed away from gas terminals and their utilization as a means of relieving gas network congestion by altering the gas flows will also be assessed as a potential application.
  • Besides for system stability requirements, curtailment may also occur due to transmission line constraints.
  • When this is away from gas terminals, an SNG facility will be considered in the scenarios analyzed; on the other hand, if the curtailment occurs at a gas terminal, then both H2 and SNG will be considered.
  • In the modelling and the studies carried out below, it is assumed that the locations and sizes of the three sets of P2G facilities are assigned based on prior electrical and gas network analysis that identify the relevant requirements.

B. Power-to-gas processes

  • In the first type of P2G process, gaseous hydrogen is formed by the process of electrolysis whereby water is split into hydrogen and oxygen.
  • Facilities of this type are currently employed in P2G for gas distribution networks [17] [18].
  • The methane forming process, methanation, is a secondary process which requires H2 resulting from electrolysis along with carbon dioxide: CO2 + 4H2 → CH4 + 2H2O.
  • This process may be either chemical or biological [9].
  • At a system level the overall process is similar and, for the purposes of this work, no distinction will be made between the two.

C. Modelling of the injection of H2 in the gas network

  • There are technical and legislative restrictions on the quantity of H2 that may be blended into the NG network.
  • The legislative limits vary widely for different regions and gas networks [19].
  • The amount of gas entering the network is considered in terms of its energy content.
  • Since the HHV of H2, by volume, is approximately a third of that of NG, the H2-NG mix will have a smaller HHV than NG.
  • This HHV value will be used to convert energy gas demand into volumetric gas demand.

D. Modelling of the CO2 emission reduction

  • The P2G process, as well as offering cost benefits while it uses otherwise unutilized energy, also offers benefits in the reduction of the system carbon emissions.
  • In fact, combustion of H2 does not produce any of the greenhouse gases associated with the use of fossil fuels.
  • The CO2 benefits from the production of SNG are taken as the quantity of CO2 removed from the atmosphere.

A. Overall network modelling and simulation methodology

  • The overall integrated network modelling process is formed of fours steps (Fig. 1), namely, a two-stage DC OPF alternated with gas network transient analysis.
  • Using the gas demand requirements for electrical generation, a transient gas flow is conducted to determine the modelling parameters of the P2G operation.
  • These are used in the second OPF (Section III.C) which determines the level of power injected into P2G facilities to maximize renewable integration while taking into account the renewable curtailment and the location and type of P2G facilities.
  • The process is then repeated for the next time interval and so on.
  • The integrated network model has been implemented and solved in MATLAB [22].

B. First stage OPF

  • Starting from classical DC OPF formulation [24], the first stage OPF (Step 1 in Fig. 1) determines the dispatch 𝑃CG𝑖 1 of each generating unit CG𝑖.
  • Run transient gas flow to assess impact of P2G on the gas network operation Step 3.
  • Each of the renewable generators, RG𝑟, are assumed at zero marginal cost 𝑐RG𝑟 in the rest of the paper.
  • 𝐽𝑙 are observed by limiting the magnitude of the real power flows 𝑌𝐽𝑙 1(𝑡) in (8).
  • Reserve is fulfilled by conventional generation and characterized by the generator’s upward ramp capability 𝑅CG𝑖(𝑡), which is determined by the generator’s maximal ramp 𝑅CG𝑖 and the availability of upward generation, as in (13).

C. Gas network transient analysis and second stage OPF

  • More specifically, results from the first OPF are used to determine the level and location of the curtailed wind.
  • This allows for power from the renewable generation sources to be transported to the areas of gas network congestion, instead of being allocated to nearby P2G facilities.
  • The results of the preliminary OPF define the generation levels 𝑃CG𝑖 1 (𝑡) and 𝑃RG𝑟 1 (𝑡) in (20).
  • The power flows 𝑌𝐽𝑙(𝑡) along the lines must continue to satisfy the line constraints 𝑌𝐽𝑙 as in (21).

D. P2G operation modelling

  • As mentioned in Section II.A, there are three sets of P2G facilities that have been considered in the model: P2G facilities at congested electrical nodes: this first set is composed of P2G units P2G𝑧 that could generate SNG from curtailed wind due to electrical line constraints.
  • When there is low pressure and curtailed wind, a P2G facility may introduce gas into the network to alleviate the congestion.
  • It is assumed that this rule as to the operational conditions for congestion relief as well as suitable facilities’ placement are obtained from prior gas network operational analysis.
  • While for a H2 facility P2G𝑇𝑘,H2 with associated H2 storage of capacity 𝐶𝑇𝑘, the quantity of H2 which may be injected depends on the current level of storage 𝑉𝑇𝑘(𝑡), which defines its spare capacity 𝐶𝑇𝑘 − 𝑉𝑇𝑘(𝑡).

E. Gas network transient flow analysis model

  • Gas network studies at Step 2 and Step 4 of Fig. 1 are conducted via a transient gas flow analysis model.
  • Transient gas flow in a section of pipeline is characterized by three relations, namely, the equation of state and the continuity and motion equations (see equations (34), (35) and (36).
  • The point 𝑘 may include a terminal or storage facility, 𝑇𝑘 , with supply/injection rate 𝑄𝑇𝑘, a gas demand 𝐺𝐷𝑘, a compressor station with inlet flow 𝐶𝐼𝑘 or outlet flow 𝐶𝑂𝑘, or a point of P2G injection 𝑄𝑃2𝐺,𝑘 1 .These are combined with the pipe flows so that if at node 𝑘 there are 𝑁𝑘 adjacent pipe sections.
  • As a further study element, the power requirements of the compressor stations to overcome transportation pressure drops are modelled as in [26].

A. Case study description

  • The model developed has been applied to the GB gas and electricity transmission networks in five case studies: - Case 1. P2G operational cost and environmental benefits.
  • Before analyzing the case study results below, there is a description of the electrical, gas, and P2G facility data used.

B. Electrical network data

  • The installed generation are those predicted by National Grid’s ‘Gone Green’ scenario in 2030 [16], where wind generation accounts for 40% (48GW) of the total installed capacity of 120GW, and there might therefore be large amounts of curtailment (peak demand is 63GW [16]).
  • A A B C E F l (a) (b) St. Fergus D IEEE Transactions on Sustainable Energy – 2015 7 TABLE I GENERATION TECHNOLOGY AND RESPECTIVE INSTALLED CAPACITY, COST, MSG AND 30-MIN RAMP RATES [28].
  • The system reserve considers the capacity of the largest generator, taken as 𝑅Gen = 1.8 GW for 2030 [29], plus reserves for uncertainty in load and wind generation forecast, as in [25].
  • The resulting levels of wind generation and wind curtailment over a month as from the 30-min OPF are shown in Fig.

C. Gas network data

  • A simplified version of the GB Gas National Transmission System with 79 nodes has been used to conduct the analysis of the gas flows and pressures across the network, as shown in Fig. 2(b).
  • All gas terminals and compressor stations have been preserved.
  • In fact, the flow characteristics of the pipe define the relation between the flows and pressures in the network.
  • The ability of this simplified network to realistically model the pipe flows, pressures and linepack has been verified by comparison with historical gas flows provided by National Grid Gas.
  • Historical daily gas demands from December 2012 have been used for network offtakes excluding the large industrial and interconnector demands, taken as 7.8GW and 5.0GW, respectively.

D. Power-to-gas facilities data

  • The following P2G facilities have been considered.
  • The efficiencies for the P2G producing process is taken as 73% for H2 production and 64% for SNG production [32].
  • These efficiencies also include the energy required to compress the gas to 80bar, a pressure suitable for the gases’ consequent introduction into the gas network.
  • This level is under review with industry petitioning for it to be raised to 3%vol. [19].
  • System gas demand for non-power and power generation.

E. Case 1. P2G operational cost and environmental benefits

  • The cost benefit of the P2G process, for each half-hour, is shown in Fig.
  • The systems emission reduction is measured at the time at which the H2 or SNG enters the gas network (also shown in Fig. 5).
  • The total CO2 emission reduction for the month is 250 kilotonnes.
  • Fig. 5. System cost benefit and emission reduction from power-to-gas.

F. Case 2. Benefits of using hydrogen storage facilities

  • When the level of wind curtailment is greater than that sufficient to produce the maximum levels of H2 permissible to be blended into the gas network, H2 may be put into storage, if available.
  • For illustrative purposes, wind curtailment is compared against that required to meet the maximal level of H2 content of the gas network for an average winter day.
  • This is shown in Fig. 6 where it can be seen that, without H2 storage, there is a large unutilized H2 content capacity of the gas network as well as curtailed wind which could have been converted into H2 for successive injection into the gas network.
  • Therefore, storage facilities could allow for greater and safe use of the H2 capacity of the gas network.
  • The energy saved by the introduction of storage facilities, over the monthly time frame considered, is 18GWh, corresponding to £430,000 at the considered gas price.

G. Case 3. Effects of P2G on the gas network

  • The P2G process will have a number of effects on the gas network.
  • The addition of large SNG facilities away from terminals will alter the network flow patterns while the introduction of H2 will reduce the HHV of the gas and increase the volume of gas necessary to satisfy the demand.
  • The energy transportation capability of the GB gas transmission network far exceeds that of the installed wind generation.
  • And, if the terminals are used for the large scale P2G facilities, then the inputted H2 and SNG act to displace NG supplied through the terminals.
  • There is minimal effect on the gas network flow characteristics.

H. Case 4. Use of P2G to reduce electrical congestions

  • Line constraints due to excessive wind generation occur on the electrical transmission line a (Fig. 2(a)).
  • The P2G process can be used to relieve these line constraints by increasing the load at certain nodes by means of a P2G facility.
  • Again, the value of the SNG produced can be considered based on the gas price.
  • Fig. 8. SNG production following electrical transmission line constraints.
  • IEEE Transactions on Sustainable Energy – 2015 9.

I. Case 5. Use of P2G to reduce gas congestions

  • The P2G concept can be used to reduce congestion in the gas network by introducing SNG production units at vulnerable areas in the gas network, for example, at network extremities, where the pressures will be least.
  • An alternative to pipeline reinforcements may be the installation of a P2G facility at a nearby location which would be able to inject gas into the network at times when flow problems may occur, for example, due to high demand or network failures.
  • Each scenario is modelled with the P2G installations of Section IV.D with the first scenario also including the additional 2GW SNG facility at node B. Fig.
  • More specifically, when there is spare electrical generation and transmission capacity then this can be used to transport energy and fulfil gas demand, whilst previously this energy would have to be transported via the gas network and would have contributed to compression costs.

V. CONCLUDING REMARKS

  • IEEE Transactions on Sustainable Energy – 2015 10 operational implications that P2G programs could have, including impact on both transmission networks.
  • The benefits of including H2 storage facilities as a means of capturing the curtailed winds spikes in the P2G process and reducing conversion losses have also been quantified, together with potential use of P2G facilities as a substitute to electrical and gas transmission line reinforcement.
  • It has also been shown how strategically placed SNG facilities can be used as an alternative to compressor usage.
  • Work in progress aims at performing cost benefit analyses that take into account planning aspects of P2G as a measure to increase system flexibility and a substitute for network reinforcement.
  • In addition, a full multi-energy system [34] model that integrates the electricity, heat and gas systems is under development.

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IEEE Transactions on Sustainable Energy 2015
1
Abstract Power-to-gas (P2G) is the process whereby
electricity is used to produce hydrogen or synthetic natural gas.
The electricity for the P2G process could, for instance, come from
renewable energy which would otherwise be curtailed due to
system or line constraints. The existing natural gas network could
then potentially be used as a means to store, transport and
reutilize this energy, thus preventing its waste. While there are
several ongoing discussions on P2G in different countries, these
are generally not backed by quantitative studies on its potential
network implications and benefits. To bridge this gap, this paper
introduces an original methodology to analyze different P2G
processes and assess their operational impacts on both electricity
and gas transmission networks. This is carried out by using a
novel integrated model specifically developed for the simulation
of operational interdependences between the two networks
considering P2G. To demonstrate the several innovative features
of the proposed model, technical, environmental, and economic
operational aspects of P2G and its potential benefits are analyzed
on the case of the Great Britain’s system, also providing insights
into relief of gas and electrical transmission network constraints.
Index TermsPower-to-gas; Natural gas networks; Optimal
power flow; Integrated energy systems; Multi-energy systems;
Hydrogen production.
I. INTRODUCTION
ITH the continuing increase in the installed capacity of
renewable energy sources, it is likely that more and
more generation will have to be curtailed to maintain certain
levels of system reliability [1]. Much research is therefore
being carried out as to what practical means there are to make
beneficial usage of this potentially unutilized energy. In this
context, there has been widespread discussion of the power-to-
gas (P2G) process whereby electrical energy is converted to
hydrogen (H
2
) or synthetic natural gas (SNG), stored and
recovered at a later time through combustion to generate low-
carbon electricity and/or heat [2] [3]. An additional benefit of
this is the practical possibility of using existing natural gas
(NG) networks for storing and transporting this energy. This is
also very attractive to maintain high gas asset utilization even
This paragraph of the first footnote will contain the date on which you
submitted your paper for review.
This work was supported by EPSRC and National Grid Gas plc.
The authors are with the School of Electrical and Electronic Engineering,
University of Manchester, Manchester, M13 9PL UK (e-mail:
stephen.clegg@postgrad.manchester.ac.uk, p.mancarella@manchester.ac.uk).
in future scenarios with reduced gas based energy supply [4].
Academic studies into the P2G process have primarily been
considered in combination with other hydrogen producing
technologies as part of a greater hydrogen economy (e.g.,
[3][5][6]). In this line, references [7] and [8] consider
introducing alternative gases as part of a program ultimately
aiming at the conversion of the NG network into a hydrogen
network. On the other hand, industrial reports on P2G have
focused on technological development and safety implications.
For example, in [9] a comprehensive review is given of the
characteristics of the technologies involved for both H
2
and
SNG production. In [8] and [10] the authors elaborate on the
factors limiting the amount of H
2
that may be blended with
NG, including H
2
embrittlement of steel pipes and change in
the gas flame characteristics on combustion.
From a system perspective, previous studies that have
discussed the use of P2G as a mechanism to reduce the levels
of curtailment in renewable energy sources [4] do not model
the levels of gas production with consideration of power
system requirements through the use of an optimal power flow
(OPF). On the other hand, studies which integrate the
electrical and gas transmission network models, for example,
[11][12][13], do not consider the effects of P2G. To the
authors’ knowledge, this work is the first to model P2G with
power system requirements and integration with gas network
modelling.
On the above premises, the aim of this paper is to model
and assess the possibility of integrating the P2G process into
an existing energy system, with focus on modelling the impact
on the gas and electrical transmission infrastructure. Specific
stress is put on P2G where the electricity curtailed from
variable energy sources (in particular wind, in the application
studies performed here) is converted into H
2
or SNG which
are consequently injected into the gas transmission network.
The primary novelty of this work lies in addressing the gas
and electrical network implications of the P2G process, for
which an integrated electricity-and-gas network analysis
model has been specifically developed. In particular, an
original two-stage DC OPF model coupled to a gas network
transient analysis model has been specifically developed to
quantify the levels of wind curtailment, the P2G
transformation of electricity into different forms of gas, the
utilization of gas from power generators, and changes in
power flows caused by transfer of power due to P2G facilities.
Integrated modelling and assessment of the
operational impact of power-to-gas (P2G) on
electrical and gas transmission networks
Stephen Clegg and Pierluigi Mancarella, Senior Member, IEEE
W

IEEE Transactions on Sustainable Energy 2015
2
The developed transient analysis model, [14] [15], is able to
highlight gas network flexibility characteristics and possible
shortcomings. In particular, by explicitly considering gas
pipeline storage (“linepack”) characteristics throughout the
network, the model is capable of showing the impact P2G
facilities will have on the gas flows from both a temporal and
geographical point of view. The physical interface between the
two networks takes place through P2G facilities and gas-fired
power plants. In this respect, P2G facilities are specifically
modelled to account for the constraints in the capability to
integrate the produced gas, e.g., due to network storage
limitations or the limits to use the existing gas infrastructure
with H
2
. Allowance for H
2
storage facilities, is also
considered. The benefits of P2G are investigated in terms of
wind curtailment and carbon emissions displacement,
economic cost saving associated with natural gas production,
and congestion relief in both the gas and electrical networks.
All these are unique and novel contributions to understanding
the implications of P2G considering realistic network
operation and constraints.
In the rest of the paper, Section II discusses the
fundamentals of the P2G process. Section III outlines the
integrated gas and electricity network model proposed,
including the modelling of the P2G facilities. Section IV
exemplifies the application of the model through case studies
on the Great Britain (GB)’s gas and electricity transmission
networks in the Gone Green scenario put forward by National
Grid [16]. Section V contains the concluding remarks. Details
on the gas network analysis model are in the Appendix.
II. POWER-TO-GAS MODELLING
A. Operation and location of the power-to-gas facilities
Different types of P2G facilities can be considered which
depend on the end-use of the gas produced as well as its
locality and reason for the curtailment of the renewable
sources. More specifically, there are three sets of P2G
facilities considered here at three respective sets of locations,
namely, at gas terminals, at congested gas nodes, and at
congested electrical nodes. Without loss of generality, so as to
prevent an overconcentration of H
2
in the gas network (see
Section II.C) the placement of facilities which produce H
2
will
be associated to the gas terminals. On the other hand, SNG
facilities can be placed away from gas terminals and their
utilization as a means of relieving gas network congestion by
altering the gas flows will also be assessed as a potential
application. Besides for system stability requirements,
curtailment may also occur due to transmission line
constraints. In this situation, the P2G process is required to
occur at the locality of curtailment. When this is away from
gas terminals, an SNG facility will be considered in the
scenarios analyzed; on the other hand, if the curtailment
occurs at a gas terminal, then both H
2
and SNG will be
considered. In the modelling and the studies carried out below,
it is assumed that the locations and sizes of the three sets of
P2G facilities are assigned based on prior electrical and gas
network analysis that identify the relevant requirements. More
details are given in Section III.D.
B. Power-to-gas processes
In the first type of P2G process, gaseous hydrogen is
formed by the process of electrolysis whereby water is split
into hydrogen and oxygen. This process is described by



󰇒
󰇏

. The technology may be alkaline
electrolysis or proton exchange membrane (PEM). Owing to
its potential faster adaptation to wind fluctuations thanks to its
quicker ramp rates, PEM appears to be the favorable
technology for the P2G process [9]. Facilities of this type are
currently employed in P2G for gas distribution networks [17]
[18].
As a second P2G process, the production of the SNG gas
methane is considered. The methane forming process,
methanation, is a secondary process which requires H
2
resulting from electrolysis along with carbon dioxide:



󰇒


. This process may be either
chemical or biological [9]. Although the technologies in each
are different, at a system level the overall process is similar
and, for the purposes of this work, no distinction will be made
between the two. As methanation is a secondary process using
the H
2
, its efficiency will always be less than that of the
hydrogen forming process.
C. Modelling of the injection of H
2
in the gas network
There are technical and legislative restrictions on the
quantity of H
2
that may be blended into the NG network. The
legislative limits vary widely for different regions and gas
networks [19]. For example, the UK has, historically, set a
comparatively small limit of 0.1% by volume (%vol.) on the
content of the H
2
in the NG network. In this work, the amount
of gas entering the network is considered in terms of its energy
content. Hence, if

is the maximum volume level of H
2
allowed in the network, then its maximum energy level is









󰇛󰇜
where 
and 

(in MJ/m
3
) are the higher heating
values (HHV) of H
2
and NG, respectively. As mentioned
earlier, it is assumed that the H
2
will be blended with NG
and/or SNG at the gas terminals up to the predefined limit.
When doing so, it is also realistically assumed that the gas
energy demand levels for each node are not affected by the H
2
content of the gas. Since the HHV of H
2
, by volume, is
approximately a third of that of NG, the H
2
-NG mix will have
a smaller HHV than NG. Therefore, the volumetric gas
demand requirements will change with the introduction of H
2
.
The change in the HHV of the network gas will be determined
at a system level. As such, it is assumed that, on introduction
to the network, H
2
is fully dissipated throughout the network.
Then, if H
2
makes up
(measured in per unit) of the total
energy which enters the gas network, then the HHV of the
resulting blended gas is given by







󰇛󰇜

IEEE Transactions on Sustainable Energy 2015
3
This HHV value will be used to convert energy gas demand
into volumetric gas demand. More specifically, if
󰇛󰇜 is the
energy content of the gas from P2G which is introduced at gas
node at the time , from either SNG installations or a H
2
-NG
blend, then the volumetric quantity of gas is


󰇛󰇜

(t)  (3)
D. Modelling of the CO
2
emission reduction
The P2G process, as well as offering cost benefits while it
uses otherwise unutilized energy, also offers benefits in the
reduction of the system carbon emissions. In fact, combustion
of H
2
does not produce any of the greenhouse gases associated
with the use of fossil fuels. In this work, the CO
2
emission
reduction due to the introduction of H
2
is calculated in terms
of the NG displaced (CO
2
emission factor of 185 kg/MWh,
[20]). The SNG production process also allows for carbon
benefits as atmospheric CO
2
can be used in its production. The
CO
2
benefits from the production of SNG are taken as the
quantity of CO
2
removed from the atmosphere. With respect
to the energy content of the SNG produced, the CO
2
saving in
kg/MWh is given by







(4)
where 


is the HHV of methane with respect to its
mass, taken as 0.0153 MWh/kg, and the molecular mass of
CO
2
and CH
4
is 44 and 16, respectively. The resulting CO
2
emission reduction from SNG production is 180 kg/MWh.
III. INTEGRATED NETWORK MODELLING AND SIMULATION
A. Overall network modelling and simulation methodology
The overall integrated network modelling process is formed
of fours steps (Fig. 1), namely, a two-stage DC OPF alternated
with gas network transient analysis. The first OPF (Section
III.B) determines the dispatch levels of conventional
generators and the renewable energy to be potentially curtailed
due to system and line constraints. Using the gas demand
requirements for electrical generation, a transient gas flow (the
general model is described in Section III.E and in the
Appendix) is conducted to determine the modelling
parameters of the P2G operation. These are used in the second
OPF (Section III.C) which determines the level of power
injected into P2G facilities to maximize renewable integration
while taking into account the renewable curtailment and the
location and type of P2G facilities. The power to each P2G
facility is then transformed into H
2
(to be blended with NG) or
SNG, as discussed in Section II, and injected into the gas
network. All gas supplies and demands are considered in
terms of the energy content of the gas, and are then
transformed into volumetric units (Eq. (3)) for the purposes of
a second transient gas flow analysis to assess the impact on the
gas network at the considered time interval, which is equal to
30 minutes. The process is then repeated for the next time
interval and so on. In addition, in order to realistically
represent the within-day linepack variations and the demand-
supply mismatch, a gas supply-demand balancing across 24
hours has been considered, as currently done by the GB
system operator [21]. The gas system imbalance introduced by
the P2G facilities in a 24-h balancing period is resolved in
terms of a reduction of conventional gas supply in the next
balancing period. The integrated network model has been
implemented and solved in MATLAB [22]. The OPFs have
been implemented with the support of MATPOWER [23].
Details of the gas flow equation solutions are given later.
Fig. 1. Outline of the overall integrated network analysis methodology.
B. First stage OPF
Starting from classical DC OPF formulation [24], the first
stage OPF (Step 1 in Fig. 1) determines the dispatch

of
each generating unit 
. This OPF is formulated as follows:

󰇛


󰇜




󰇛
󰇜





󰇛
󰇜
󰇛󰇜

󰇛
󰇜


󰇛
󰇜
󰇛󰇜
󰇛
󰇜

󰇛
󰇜





󰇛
󰇜


󰇛󰇜

󰇛
󰇜
󰇛󰇜


󰇛
󰇜
󰇝

󰇞
󰇛󰇜



󰇛
󰇜



󰇛
󰇜
󰇛󰇜


󰇛
󰇜


󰇛
󰇜
󰇛󰇜


󰇛
󰇜

󰇛󰇜󰇛󰇜



󰇛
󰇜
󰇛󰇜󰇛󰇜


󰇛
󰇜

󰇛
󰇜





󰇛
󰇜
󰇛󰇜

󰇛
󰇜

󰇛
󰇜

󰇛
󰇜



󰇛󰇜

󰇛
󰇜

󰇛
󰇜

󰇛
󰇜



󰇛󰇜
In (5), the vectors

and

of conventional and renewable
generation’s power outputs are determined so as to minimize
the cost of generation. For a conventional generator 
a
constant marginal generation cost, 

, is used for the non-gas
generators (must-run generators are modelled with cost zero),
Step 4. Run transient gas flow to assess impact of P2G on the gas network
operation
Step 3. Run second OPF to determine the power supplied to each P2G
facility and energy supplied to gas network
Step 2. Run transient gas flow to determine modelling parameters of the
P2G processes
Step 1. Run first OPF to determine power generated by different
generators and wind curtailment due to system and line constraints

IEEE Transactions on Sustainable Energy 2015
4
while the cost of gas technologies (Combined Cycle Gas
Turbines CCGT, Combined Heat and Power CHP, and
Open Cycle Gas Turbines OCGT) depends on their
efficiency and the price of NG. Each of the renewable
generators, 
, are assumed at zero marginal cost 

in the
rest of the paper. The vector
of the power flows
is
described by (6), in which
is the vector of power injections
󰇛󰇜 at each bus , the admittance matrix, the line-bus
incidence matrix, and the diagonal matrix with the line
impedances as entries [24]. The power injections
󰇛󰇜 are
described in (7) by the demand
and the sum of the real
power generation

(resp.

) for each conventional (resp.
renewable) generator 
(resp. 
) in the set of generators

(resp. 
) at each bus . The power output

is restricted to a range defined by the minimum stable
generation (MSG)

and maximum generation

when
the generator is online, as from (10a), or is equal to 0, as from,
(10b), while the binary variable

󰇛󰇜 defined in (9) denotes
whether the generator is online at time . The renewable
generators 
have power output

󰇛󰇜 between a lower
bound of zero and upper bound

󰇛󰇜 in (11). The line
constraints
for each line
are observed by limiting the
magnitude of the real power flows
󰇛󰇜 in (8). The system’s
reserve requirement 󰇛󰇜 in (12) accounts for uncertainty in
demand, outage of conventional generators, and wind forecast
uncertainty [25]. Reserve is fulfilled by conventional
generation and characterized by the generator’s upward ramp
capability

󰇛
󰇜
, which is determined by the generator’s
maximal ramp

and the availability of upward generation,
as in (13). Finally, relations (14) and (15) describe the
minimum up and down times 

and 

requirements of generator 
.
C. Gas network transient analysis and second stage OPF
The modelling of the operation of the P2G facility at a
given time depends on a number of parameters determined
by the gas and electrical system states. More specifically,
results from the first OPF are used to determine the level and
location of the curtailed wind. The gas generation fuel
requirements are used to conduct a preliminary gas flow
analysis (as generally described in Section III.E) to determine,
at each time areas of gas congestion, the gas throughput at
each terminal (Step 2 in Fig. 1).
A second OPF is then run to model the operation of P2G
facilities starting from the dispatch levels of the conventional
generating units resulting from (5)(15) (which are now fixed)
and the gas network parameters as from Step 2. The objective
is to maximize the system benefit of the otherwise curtailed
renewables as determined in the first OPF. At this stage, the
P2G facilities are modelled as equivalent generating units

, 
, 

, 

, with output

󰇛
󰇜
,

󰇛
󰇜
,


󰇛
󰇜
,


󰇛
󰇜
and “negative” costs


, 

, 


, 


, respectively. These costs
depend on the relevant conversion efficiencies of the different
facilities so that higher efficiency conversion is favored.
Moreover, when there is curtailed wind generation, and there
is demand for gas network congestion relief, these P2G
facilities are given operational priority over other P2G
installations. This allows for power from the renewable
generation sources to be transported to the areas of gas
network congestion, instead of being allocated to nearby P2G
facilities. Details are given in Section III.D.
Denoting the curtailed power (from the first OPF) at the
renewable generator 
by 

󰇛󰇜

󰇛󰇜

󰇛
󰇜
,
then, at a given time , the second OPF is described by:

󰇛

󰇜




󰇛
󰇜




󰇛
󰇜





󰇛
󰇜



 




󰇛
󰇜


󰇛󰇜


󰇛󰇜

󰇛󰇜󰇛󰇜


󰇛󰇜

󰇛󰇜󰇛󰇜


󰇛󰇜

󰇛
󰇜
󰇛󰇜



󰇛󰇜


󰇛
󰇜
󰇛󰇜



󰇛󰇜


󰇛
󰇜
󰇛󰇜
󰇛󰇜


󰇛󰇜󰇛󰇜

󰇛
󰇜



󰇛
󰇜

󰇛

󰇛
󰇜

󰇛
󰇜
󰇜







󰇛
󰇜



󰇛
󰇜






󰇛
󰇜







󰇛
󰇜
󰇛󰇜

󰇛󰇜
󰇛󰇜
The results of the preliminary OPF define the generation
levels

󰇛
󰇜
and

󰇛
󰇜
in (20). The introduction of P2G
facilities will change the power injections
in (20) which
now includes the otherwise curtailed generation

󰇛
󰇜
as
well as the powers

,

,


, and


respectively supplied to the P2G facilities 
, 
,

, 
of each type at the bus . Upper power bounds
to the different P2G facilities are applied in (18a)(18d) (see
Section III.D). Equation (19), describes the relation between
the changed vector of power injections 󰇛󰇜 and the vector of
transmission line real power flows 󰇛󰇜 using the same
matrices , , and as in (6) since the characteristics of the
network remain unchanged. The power flows
󰇛󰇜 along the
lines must continue to satisfy the line constraints
as in
(21). Other relevant constraints and relations are explained
next.

IEEE Transactions on Sustainable Energy 2015
5
D. P2G operation modelling
As mentioned in Section II.A, there are three sets of P2G
facilities that have been considered in the model:
P2G facilities at congested electrical nodes: this first set is
composed of P2G units 
that could generate SNG from
(otherwise) curtailed wind due to electrical line constraints.
They are thus located at buses at which curtailment may
occur due to line constraints. If the level of curtailment due to
line constraints returned by the first OPF is given by 
󰇛
󰇜

󰇛
󰇜


, then the P2G facility at the relevant bus
will operate to relieve this constraint by utilizing this power up
to its capacity. The constraints to its power output

󰇛󰇜 is
thus modelled in (18a) as


󰇛󰇜󰇛
󰇛
󰇜

󰇜󰇛󰇜
The cost associated to the facilities is 




where 

is the cost of natural gas and
is the efficiency of
SNG production.
If we consider the gas nodes
associated with the locality
of these P2G units, the quantity of SNG injected at these
nodes is given by
󰇛󰇜

󰇛󰇜.
P2G facilities at congested gas nodes: the second set of
P2G units 
to be considered are those which can be used
as a means of relieving congestions in the gas network due to
excessive gas load. The preliminary gas flow highlights areas
in the gas network where there are reduced pressures. When
there is low pressure and curtailed wind, a P2G facility may
introduce gas into the network to alleviate the congestion. If
the results of the preliminary gas flow indicate network
vulnerability at an extremity node , where the pressure
is
less than a threshold

which is caused by a gas demand
󰇛󰇜 in excess of a threshold

, then the congestion
will be relieved by a P2G facility 
with power

󰇛󰇜
whose operation is limited in (18b) as

󰇛󰇜󰇱


󰇛
󰇛
󰇜

󰇜


󰇛
󰇜

󰇛󰇜

It is assumed that this rule as to the operational conditions for
congestion relief as well as suitable facilities’ placement are
obtained from prior gas network operational analysis.
As mentioned earlier, it is desirable that these P2G facilities
have operational priority over other P2G installations. In the
modelling, this is achieved by reducing the cost so that







where 

is an additional cost
benefit employed to assign this priority.
As above, if we take the gas nodes associated with the
locality of these P2G units, the energy quantity of SNG
injected at these nodes is given by

󰇛󰇜

󰇛󰇜.
P2G facilities at gas terminals: At the gas terminals the
model considers two types of P2G facilitates. The first are H
2
producing facilities which have an associated capability for H
2
storage; the second are SNG production facilitates. On
introduction to the network, H
2
should be blended with NG.
When its production is above that which can be blended with
the NG, this excess may be placed into storage; on the other
hand, when there is reduced H
2
production, then H
2
from
storage can be used to supplement the quantity blended. For a
gas terminal
, the H
2
which may be produced is limited by
that which may be introduced into the network and that which
may be placed into storage. If 
󰇛󰇜 is the natural gas
throughput, the maximum level of H
2
which may be blended is
󰇛󰇜


󰇛󰇜. While for a H
2
facility


with associated H
2
storage of capacity
, the
quantity of H
2
which may be injected depends on the current
level of storage
󰇛󰇜, which defines its spare capacity
󰇛󰇜. As such, in (18c) the facility’s power is limited
by


󰇛󰇜󰇛



󰇛
󰇛󰇜
󰇛󰇜󰇜
󰇜󰇛󰇜
For the SNG producing facility 

, its power


󰇛󰇜 is limited in (18d) by its installed capacity



󰇛
󰇜


󰇛󰇜
The costs associated to the H
2
and SNG producing facilities
are 





and 





,
respectively. As the efficiency of H
2
production is greater than
that of SNG production, the cost benefit of H
2
production will
be greater than that of SNG production and so, at a given
terminal, H
2
production will be prioritized over that of SNG.
The SNG produced is introduced directly into the gas
network; hence, the energy content injected at the terminal
is


󰇛󰇜


󰇛
󰇜
󰇛󰇜
The quantity of H
2
introduced, meanwhile, is also impacted
by the injection/withdrawal operation of the storage facilities.
More specifically, when the H
2
production is less than the
limit for H
2
blending (i.e.,


󰇛
󰇜
󰇛󰇜) then,
where possible, the shortfall will be made up by the gas in
storage so the quantity introduced into the network is

󰇛
󰇜
󰇡


󰇛
󰇜
󰇛
󰇜
󰇛󰇜󰇢󰇛󰇜
While the resulting level of H
2
in storage, which is used as a
parameter in the next iteration of (16)(21) at , is
󰇛
󰇜
󰇛

󰇛
󰇜
󰇛
󰇛
󰇜



󰇛
󰇜
󰇜󰇜󰇛󰇜
Alternatively, if the H
2
production is greater than the limit
for H
2
blending then H
2
is introduced at its maximum capacity


󰇛
󰇜
󰇛
󰇜
󰇛󰇜
and additional H
2
is placed into storage so the resulting level
of storage is
󰇛
󰇜
󰇛
󰇜
󰇛


󰇛
󰇜
󰇛
󰇜
󰇜󰇛󰇜
The total energy content of the gas from P2G introduced at
the terminal
is therefore given by
󰇛
󰇜

󰇛
󰇜

󰇛
󰇜
.

Citations
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Journal ArticleDOI
TL;DR: In this paper, a review of more than 60 studies (plus m4ore than 65 studies on P2G) on power and energy models based on simulation and optimization was done, based on these, for power systems with up to 95% renewables, the electricity storage size is found to be below 1.5% of the annual demand (in energy terms).
Abstract: A review of more than 60 studies (plus m4ore than 65 studies on P2G) on power and energy models based on simulation and optimization was done. Based on these, for power systems with up to 95% renewables, the electricity storage size is found to be below 1.5% of the annual demand (in energy terms). While for 100% renewables energy systems (power, heat, mobility), it can remain below 6% of the annual energy demand. Combination of sectors and diverting the electricity to another sector can play a large role in reducing the storage size. From the potential alternatives to satisfy this demand, pumped hydro storage (PHS) global potential is not enough and new technologies with a higher energy density are needed. Hydrogen, with more than 250 times the energy density of PHS is a potential option to satisfy the storage need. However, changes needed in infrastructure to deal with high hydrogen content and the suitability of salt caverns for its storage can pose limitations for this technology. Power to Gas (P2G) arises as possible alternative overcoming both the facilities and the energy density issues. The global storage requirement would represent only 2% of the global annual natural gas production or 10% of the gas storage facilities (in energy equivalent). The more options considered to deal with intermittent sources, the lower the storage requirement will be. Therefore, future studies aiming to quantify storage needs should focus on the entire energy system including technology vectors (e.g. Power to Heat, Liquid, Gas, Chemicals) to avoid overestimating the amount of storage needed.

420 citations


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  • ...Buchholz 2014 [165] x x Gotz 2016 [11] x x x Connolly 2014 [161] x x x Jentsch 2014 [77] x x DNV 2013 [177] x x x GRTGaz 2014 [179] x x x x x Saint Jean 2014 [167] x x Saint Jean 2015 [239] x x Vartiainen 2016 [186] x x x Klumpp 2015 [162] x x x Clegg 2015 [181] x x x Varone 2015 [168] x x Estermann 2016 [169] x x Dickinson 2010 [164] x x Schiebahn 2015 [178] x x x x Schaaf 2014 [240] x x SGC 2013 [180] x x x x Bailera 2016 [241] x x x Vandewalle 2015 [194] x x x Schneider 2015 [191] x x Giglio 2015a [166] x x Giglio 2015b [242] x x Plessmann 2014 [193] x x x Kotter 2015 [76] x x x Moeller 2014 [108] x x Breyer 2015 [163] x x x Zoss 2016a [170] x x Zoss 2016b [243] x x Ronsch 2016 [244] x x Ahern 2015 [192] x x x Belderbos 2015 [104] x x Henning 2015 [195] x x x DVGW 2013 [189] x x x x Jurgensen 2014 [245] x x Dzene 2015 [246] x x Reiter 2015a [183] x x Meylan 2016 [247] x x EIL 2014 [172] x x x x x x x ENEA 2016 [237] x x x x x Parra 2016 [188] x x x x Budny 2015 [175] x x x de Boer 2014 [99] x x x Palzer 2014 [125] x x x ECN 2013 [190] x x x x x Schaber 2013 [44] x x x x DENA 2016 [171] x x x Fraunhofer 2015 [173] x x x x Schmied 2014 [174] x x Sternberg 2015 [184] x x Sternberg 2016 [185] x x Heinisch 2015 [248] x x Baumann 2013 [249] x x Julch 2016 [220] x x x Gahleitner 2013a [250] x x x Reiter 2015b [187] x x x Zhang 2017 [251] x x Meylan 2017 [252] x x Vo 2017 [253] x x x H....

    [...]

  • ...In [181], the introduction of P2G with an equivalent capacity of one third of the total installed capacity led to a reduction of 3–8% of the seasonal storage, given that part of the gas demand is covered by P2G....

    [...]

  • ...In [181], the focus is on operational costs rather than total (considering investment), but these are reduced by 4–9% depending on the level of penetration (15– 30%)....

    [...]

  • ...There is limited insight on the competition with hydrogen and its use for either mobility or injection in the gas grid ([181] explores injection, but does not include mobility)....

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Jianxiao Wang1, Haiwang Zhong1, Ziming Ma1, Qing Xia1, Chongqing Kang1 
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TL;DR: In this paper, a robust co-optimization scheduling model was proposed to study the coordinated optimal operation of the two energy systems, while considering power system key uncertainties and natural gas system dynamics.
Abstract: The significant growth of gas-fired power plants and emerging power-to-gas (PtG) technology has intensified the interdependency between electricity and natural gas systems. This paper proposes a robust co-optimization scheduling model to study the coordinated optimal operation of the two energy systems. The proposed model minimizes the total costs of the two systems, while considering power system key uncertainties and natural gas system dynamics. Because of the limitation on exchanging private data and the challenge in managing complex models, the proposed co-optimization model is tackled via alternating direction method of multipliers (ADMM) by iteratively solving a power system subproblem and a gas system subproblem. The power system subproblem is solved by column-and-constraint generation (C&CG) and outer approximation (OA), and the nonlinear gas system subproblem is solved by converting into a mixed-integer linear programming model. To overcome nonconvexity of the original problem with binary variables, a tailored ADMM with a relax-round-polish process is developed to obtain high-quality solutions. Numerical case studies illustrate the effectiveness of the proposed model for optimally coordinating electricity and natural gas systems with uncertainties.

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  • ...PtG is a promising technology that can contribute to tackling the issues of increased renewable generations [2], [3], [19]....

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  • ...In addition, PtG as an emerging technology can effectively convert excessive electricity to compatible natural gas, which can be used by gas-fired units and other gas consumers [2], [3]....

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Jiakun Fang1, Qing Zeng1, Xiaomeng Ai, Zhe Chen, Jinyu Wen 
TL;DR: In this article, the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion is studied. But the authors focus on the optimal operating of the system considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated GA and power system.
Abstract: This paper focuses on the optimal operation of the integrated gas and electrical power system with bidirectional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady-state power flow are combined to formulate the dynamic optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single-stage linear programming to obtain the optimal operation strategy for both gas and power systems. Simulation on the test case illustrates the success of the modeling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.

248 citations


Cites background from "Integrated Modeling and Assessment ..."

  • ...In recent years, the emerging power-to-gas (P2G) technologies enable the reverse direction of energy conversion from electricity to natural gas [20], [21]....

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Journal ArticleDOI
TL;DR: In this paper, a multi-stage integrated gas and electrical transmission network model is proposed to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also considering different heating scenarios.
Abstract: In power systems with more and more variable renewable sources, gas generation is playing an increasingly prominent role in providing short-term flexibility to meet net-load requirements. The flexibility provided by the gas turbines in turn relies on the flexibility of the gas network. While there are several discussions on the ability of the gas network in providing this operational flexibility, this has not been clearly modeled or quantified. In addition, the gas network may also be responsible for supplying heating technologies, and low-carbon scenarios see a tighter interaction between the electricity, heating and gas sectors, which calls for a holistic multi-energy system assessment. On these premises, this paper presents an original methodology to quantify the flexibility the gas network can provide to the power system, as well as the constraints it may impose on it, with also consideration of different heating scenarios. This is achieved by a novel multi-stage integrated gas and electrical transmission network model, which uses electrical DC OPF and both steady-state and transient gas analyses. A novel metric that makes use of the concept of zonal linepack is also introduced to assess the integrated gas and electrical flexibility, which is then used to impose gas-related inter-network inter-temporal constraints on the electrical OPF. Case studies are performed for the Great Britain transmission system for different renewables and heating scenarios to demonstrate the proposed integrated flexibility assessment methodology, provide insights into the effects of changes to the heating sector on the multi-energy system’s combined flexibility requirements and capability, and assess how the electrical network can experience local generation and reserve constraints related to the gas network’s lack of flexibility.

236 citations

References
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Book
01 Jan 1984
TL;DR: In this paper, the authors present a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems, including characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security.
Abstract: Topics considered include characteristics of power generation units, transmission losses, generation with limited energy supply, control of generation, and power system security. This book is a graduate-level text in electric power engineering as regards to planning, operating, and controlling large scale power generation and transmission systems. Material used was generated in the post-1966 period. Many (if not most) of the chapter problems require a digital computer. A background in steady-state power circuit analysis is required.

6,344 citations

Journal ArticleDOI
TL;DR: The details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture, are presented, which are used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits.
Abstract: MATPOWER is an open-source Matlab-based power system simulation package that provides a high-level set of power flow, optimal power flow (OPF), and other tools targeted toward researchers, educators, and students. The OPF architecture is designed to be extensible, making it easy to add user-defined variables, costs, and constraints to the standard OPF problem. This paper presents the details of the network modeling and problem formulations used by MATPOWER, including its extensible OPF architecture. This structure is used internally to implement several extensions to the standard OPF problem, including piece-wise linear cost functions, dispatchable loads, generator capability curves, and branch angle difference limits. Simulation results are presented for a number of test cases comparing the performance of several available OPF solvers and demonstrating MATPOWER's ability to solve large-scale AC and DC OPF problems.

5,583 citations


"Integrated Modeling and Assessment ..." refers methods in this paper

  • ...The OPFs have been implemented with the support of MATPOWER [23]....

    [...]

Book
01 Jan 1977

1,937 citations

Journal ArticleDOI
01 Feb 2014-Energy
TL;DR: In this paper, the authors provide a comprehensive and critical overview of the latest models and assessment techniques that are currently available to analyze MES and in particular DMG systems, including for instance energy hubs, microgrids, and VPPs (virtual power plants), as well as various approaches and criteria for energy, environmental, and technoeconomic assessment.

1,060 citations

Journal ArticleDOI
TL;DR: In this article, an international review of numerous power-to-gas pilot plants that have either already been realized or are being planned is presented, which provides information about their installed components and capacities as well as operating experience that has been had with them.

916 citations


"Integrated Modeling and Assessment ..." refers background in this paper

  • ...Facilities of this type are currently employed in P2G for gas distribution networks [17], [18]....

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