ASRAdvances in Science and ResearchASRAdv. Sci. Res.1992-0636Copernicus PublicationsGöttingen, Germany10.5194/asr-14-253-2017The smoothing effect for renewable resources in an Afro-Eurasian power gridKrutovaMariamaria.krutova@uni-oldenburg.deKiesAlexanderSchyskaBruno U.von BremenLuederForWind, Center for Wind Energy Research, University of Oldenburg, Oldenburg, GermanyWind Energy Department, Technical University of Denmark, Lyngby, DenmarkFrankfurt Institute for Advanced Studies, Goethe University Frankfurt, Frankfurt, GermanyMaria Krutova (maria.krutova@uni-oldenburg.de)27July20171425326015January201713June201715June2017This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://asr.copernicus.org/articles/14/253/2017/asr-14-253-2017.htmlThe full text article is available as a PDF file from https://asr.copernicus.org/articles/14/253/2017/asr-14-253-2017.pdf
Renewable power systems have to cope with highly variable
generation. Increasing the spatial extent of an interconnected power
transmission grid smooths the feed-in by exchange of excess energy over long
distances and therefore supports renewable power integration. In this work,
we investigate and quantify the balancing potential of a supergrid covering
Europe, Africa and Asia. We use ten years of historical weather data to model
the interplay of renewable generation and consumption and show that a
pan-continental Afro-Eurasian supergrid can smooth renewable generation to a
large extent and reduce the need for backup energy by around 50 %. In
addition, we show that results for different weather years vary by up to
approximately 50 %.
Introduction
The growing shares of feed-in from renewable sources of wind and
photovoltaics (PV) pose a demanding challenge to stable operation of power
systems . Generation from renewable sources is not
dispatchable, but highly fluctuating and therefore cannot replace
dispatchable generation from conventional sources directly. Instead, power
systems, which rely on renewable generation sources, need additional
balancing means. Possibilities include backup, storage
or
reinforced transmission grid .
Alternatives aim to increase the usage of system-friendly renewables
, which describe, for instance,
the deployment of renewable generation facilities with respect to being part
of a highly renewable power system instead of focusing on meteorological
conditions alone. An example is to optimize the mix of renewables
. Besides taking measures on the generation side, it is
possible to modify the consumption side via demand side management (which
usually refers to load shifting; or
sector coupling ).
In the final consequence, increasing the spatial extent of a power grid leads
to a pan-continental or even global supergrid. A vision of such global grid
was put forward by . Furthermore, Chinese president Xi Jinping
proposed discussions on a “global energy internet” in a keynote speech at the
UN Sustainable Development
Summit
www.sgcc.com.cn/ywlm/mediacenter/corporatenews/
in September 2015. The concept was also discussed in more detail by the State
Grid Corporation of China . The needs for infrastructure of a global supergrid were recently
discussed by .
In this paper, we investigate the potential benefit from the smoothing effect
for renewable feed-in in a pan-continental supergrid connecting large parts
of Europe, Asia and Africa. For this purpose, we quantify the benefit as a
reduction in the need for backup energy in a highly renewable power system
where renewable generation equals demand on average. In addition, we quantify
the year-to-year variability of the need for backup energy. First, we
determine the optimal mix of wind and photovoltaics generation for every node
in the network independently (Sect. 3). Using these optimal mixes, we
investigate the potential reduction of the need for backup in the grid
connecting three continents in Sect. 4. In Sect. 5 we investigate the smoothing
effect further by iteratively connecting nodes to the grid starting with
Europe processing eastwards.
Methodology
We simulate a simplified power network covering large parts of Eurasia and
Africa. Super regions within this network are represented as nodes
(Fig. ), which are connected by long-distance transmission
lines.
The investigated simplified power system consists of super regions
connected by long distance transmission lines.
Each node produces energy from the renewable sources of wind and
photovoltaics (PV). The time series of renewable production of node n is
given by Gn(t)=GnW(t)+GnS(t), where GnW and GnS refer to
the partial generation time series from wind and PV, respectively. In
addition, each node has a load time series Ln(t). The mismatch between
generation from renewable sources and load, Δn(t)=Gn(t)-Ln(t)
needs to be balanced by the system, i.e.,
Δn(t)=Cn(t)-Bn(t)+Pn(t).Cn(t) is curtailment time series, Bn(t) is backup generation from
perfectly flexible power plants and Pn(t) is the injection pattern
(exports – imports), which describes the usage of the transmission grid.
Backup time series are calculated via
Bn(t)=-min0,Δn(t)-Pn(t)
We are interested in two major quantities concerning the need for backup:
the need for backup energy BnE of a node, defined viaBnE=∫Bn(t)dt
the need for backup capacity BnC, i.e., the generation capacity of dispatchable generation that needs to be kept at hand, defined via0.99=∫0BnCp(Bn(t))dp,
p(.) describes the distribution function of backup events. The 99th
percentile is chosen to reduce the sensitivity towards the weather database.
The same assumption is made for instance by .
Transmission bottlenecks within single regions are not considered in these
calculations.
Generation and load data
For Europe, we model generation and load data with a temporal resolution of
one hour and a spatial resolution of 7 km covering years 2003–2012. Load data
is taken from ENTSO-E and modified according to expected changes due to
electrification of transport and heating within the R&D project RESTORE
2050. Wind speeds are taken from statistically downscaled MERRA reanalysis
data . Details on generation data are given by . For all other regions, we use data from
MERRA reanalysis without downscaling.
Since real time-resolved load data is only for Europe available to us, the
load time series of node n≠ EU is modelled as
Ln(t)=〈Ln〉1-14cosπt4380-14sinπ(t+τ+4)12+εt,
where τ is the GMT time zone offset and 〈Ln〉 is
proportional to the projected GDP by 2050 . Table 1 shows
average load per node,
〈Ln〉=〈LEU〉GDPn2050GDPEU2050.εt is a random normally distributed value N(0,0.04〈Ln〉) representing short term fluctuations.
Use of projected GDP determines the nodes which we consider in our model.
GDP, and therefore load, of the countries not listed is assumed negligible.
Russia is split into two parts. However, all of its demand is assumed to be
concentrated in the European part (RU), which is more densely populated and
industrialized. Asian part of Russia (SB) is considered solely a renewable
energy provider.
Transmission
We use power flow equations in a common linear approximation.
Fl denotes the flow over link l and can be either positive or negative (the sign indicates the direction of the flow).
Flows and the injection pattern P are related via
F=PTDF⋅P,
PTDF is the Power Transfer Distribution Factor matrix, which we calculate via
PTDF=KTL+,
where K is the incidence matrix and L+ the Moore-Penrose pseudoinverse of the Laplacian matrix of the network defined as L=KTK.
The transmission system is operated to minimize the need for backup energy and modelled as a two-step optimization problem.
The first step reads
minimizeP(t)∑nBn(t)=:Bmin(t)subjectto∑nPn(t)=0Fl-<Fl(t)≤Fl+.
However, the solution Fl of this optimization problem is usually degenerate.
The second step ensures the uniqueness of the solution by minimizing the squared flows.
This can be understood as minimizing dissipation losses (which are ∝F2) and reads
minimizeP(t)∑lFl2subjectto∑nPn(t)=0Fl-<Fl(t)≤Fl+∑nBn(t)=Bmin(t).
Once the time series of flows via a certain link l are known, its required transmission capacity TlC can be computed via
0.99=∫0TlCp(|Fl(t)|)dp,
An equivalent transmission model is, for instance, used by .
Backup energy need in dependency of the corresponding wind/PV mix. Markers are
placed at the position of the optimal power mix.
Growth of the backup energy need in dependency of wind/PV mix.
Backup energy is normalized to the corresponding minimum values.
Wind/PV mix
Before we investigate benefits resulting from smoothing in the following sections, we neglect transmission and optimize the mix of wind/PV for
every node independently.
We define the mix to be optimal, if the resulting need for backup energy is minimized. The corresponding optimization problem for node n reads
minimizeβnBnEsubjectto0≤βn≤1αn=1Fl=0∀l,
where βn is the share of PV power in the renewable generation mix and αn is the renewable penetration at the node n.
βn=〈GnS(t)〉〈Gn(t)〉αn=〈Gn(t)〉〈Ln(t)〉.
The need for backup energy in dependency of the renewable mix is shown for all nodes in Fig. .
The optimal share values are provided in Table .
It can be observed that the optimal solar shares for all nodes lie between
0.11 and 0.31. Similar investigations have shown that in Europe a solar share
of ca. 0.4 minimizes monthly standard deviation .
An obvious outlier is the IP node, containing India. It has the highest value
as optimal PV share and also an interesting property of being the node, where
the relative need for backup energy is lowest in a PV-dominated scenario
(Fig. ). This seems to be caused by a lower
correlation of PV feed-in. However, further investigation is required to
quantify the reason for this effect. Optimal shares of African nodes are
grouped in range 0.2–0.3 with West Africa node (WA) having the highest
βnopt of them.
The total renewable generation of each region in the following sections is
calculated with power mixes resulting from optimal βnopt.
Afro-Eurasia
Yearly backup energy need and its deviation from 10-year average ΔBnC=〈BnC〉-BnC
for the isolated regions with α=1.
Yearly backup energy need and its deviation from 10-year average ΔBnC=〈BnC〉-BnC
for Afro-Eurasian grid with α=1.
The next step is to investigate the impact of the smoothing effect on the
need for backup energy in the supergrid. The yearly variation of the backup
energy need of the single regions is shown in Fig. for
isolated regions and Fig. , if all of them are connected.
While yearly patterns of the deviation from average are similar in both
cases, relative differences increase in case of the supergrid. The main
reason for this is overall decrease of the backup need in the grid, and
therefore – the average backup, by around 50 %. Absolute deviations stay
within the same range as in the case of isolated nodes, while relative
deviation increases because of the lower average.
Yet not all nodes see backup deviation change proportional to overall backup
reduction. This is the result of linking nodes through the transmission grid.
In case of isolated regions the excess renewable power is not shared with
other nodes. Therefore, nodes with comparably high demand have to balance it
with their local resources. When nodes participate in power exchange, excess
renewable power is used to balance the demand throughout the grid.
In addition, the reduction for backup energy need is higher, if the year is
favourable for wind and solar power generation. Hence, relative yearly
differences are additionally emphasized in the grid due to different weather
years. For some nodes, backup energy requirements of consecutive years vary
by 40–50 %. This comparison emphasizes the importance of considering more
than a single weather year to obtain reliable results in renewable power
system analysis.
The total 10-year need for backup energy in dependency of the renewable
penetration α for several transmission grid setups
(Table ) is investigated by linking different groups of
Eurasian and African nodes. The global renewable penetration is defined
similarly to Eq. () through global generation and load
averages, where distribution is assumed to be homogeneous, i.e.
αn=α for all nodes.
Transmission grid setups. While all regions are always present in the simulation, some of them are not
connected to a grid. Node to node links correspond to topology suggested in Fig.
The different setups allow to explore, how the need for backup energy is
effected as more regions are connected through the transmission grid. If all
Afro-Eurasian nodes are connected, a reduction by around 50 % is observed
compared to isolated regions for α=1 (Fig. ).
The decrease is mainly caused by the interconnection between Europe and Asia,
whereas adding the connection between Europe and Africa has only a small
beneficial effect, as seen by comparing setups 2–3 and 4–5. The maximum
backup reduction is reached when all regions are linked into the
intercontinental Afro-Eurasian grid (setup 5). The much smaller benefits from
linking Europe and Africa compared to Europe and Asia are, besides the lesser
spatial extent, most likely caused by high correlation of solar feed-in in
Europe and Africa as they belong to similar longitudes.
Need for backup energy for different grid toplogies in dependency of the renewable penetration.
Eurasia
It is shown in the previous section that major benefits stem from the
extension of the European power grid to the east, i.e., Asia. Therefore, we
investigate this option further and compute a different set of grid
configurations. We start with isolated regions and then link regions one by
one from west to east. The first simulated grid contains only the link
between EU + RU, the second comprises a triangle EU + RU + MS, and so on
until all Eurasian regions are connected. Figure shows the
need for backup energy, backup capacity and transmission capacity in
dependency of the grid extension. The transmission capacity is summed up for
all links containing a corresponding node.
It can be observed that connecting RU, IP and CN causes the biggest decrease
in the need for backup energy and backup capacity for the European Union
(EU). This is likely caused by the fact that they are modelled as having a
similar average load and generation due to the projected size of their
economies in 2050.
However, extending the transmission network to the east comes at the price of
a large need for transmission capacities. Comparing EU + RU to the case of
entire Eurasia shows transmission capacities to grow for Europe by a factor
of 7. It is notable, that transmission needs increase for all nodes with the
grid extension, except for MS, which sees a small decrease after the node CN
is connected. Possessing a very high demand, the CN node redirects power
flows to itself, thus slightly relieving the power exchange through MS.
Need for backup energy, backup capacity and transmission capacity of the Eurasian regions in dependency
of the grid expansion from west to east, α=1. The node labels are listed in the order of connection to a grid.
Discussion, summary and conclusions
In this work, we have investigated a pan-continental supergrid.
Technologically, such a supergrid would most likely depend on high-voltage
direct-current (HVDC) transmission systems. Components of such systems mostly
already exist today. However, because converters, cables, and other
components for HVDC are more expensive than those for AC as of today, they
are economically inefficient for short connections, but HVDC systems become
advantageous over long distances because of favourable features, such as easy
transfer of power between grids that are operating at different frequencies.
The greatest obstacle for a global supergrid seems to be a fair
cost-allocation mechanism. Even on the country level, costs for different
system components and their allocation to consumers and generators is the
subject of regular dispute. However, the advantages of such global grid,
including the ones presented in this paper, can make future research and
efforts promoting global cooperation on renewable energy generation and
exchange appear meaningful.
A straightforward extension of this work would be to include storage options
in the calculations. have found for a
similar Eurasian grid that HVDC transmission leads to a cut-off of storage
utilization and decrease the need for primary generation capacities.
In our calculations it was shown that such a grid has the potential to reduce
the need for backup energy by up to one half by transmitting energy surpluses
via long distances to cover energy deficits. This is a comparable number as
found by other studies. found the need for
backup energy decrease from 18 to 10 % of the total consumption if
Europe, Russia, North-Africa and the Middle East are connected.
Besides, the importance of using multiple weather years to obtain reliable
results in the modelling of renewable energy systems was substantiated by the
observed yearly variation within the 10-year period.
It can be concluded that a pan-continental Eurasian power grid has the
potential to reduce fluctuations on the generation side and consequently the
need for dispatchable backup power to a large degree.
Data on generation and load per node is publicly available (Krutova et al., 2017).
The only exception is the load time series for the EU node, which was produced within the project
RESTORE 2050. The copyright for this data is owned by the Wuppertal Institute.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “16th EMS Annual Meeting & 11th European Conference on Applied Climatology (ECAC)”.
It is a result of the 16th EMS Annual Meeting & 11th European Conference on Applied Climatology (ECAC), Trieste,
Italy, 12–16 September 2016.
Acknowledgements
The work is part of the RESTORE 2050 project (Wuppertal Institute, Next Energy, University of Oldenburg) that
is financed by the Federal Ministry of Education and Research (BMBF, Fkz. 03SFF0439A, 2013–2016). We would like to thank our
project partners from Wuppertal Institute and Next Energy for helpful discussions.
We thank two reviewers for helpful comments and suggestions, which helped to improve this manuscript.
Edited by: Sven-Erik Gryning
Reviewed by: Guido Plessmann and one anonymous referee
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