The role of hydro power, storage and transmission in the decarbonization of the Chinese power system

The role of hydro power, storage and transmission in the decarbonization of the Chinese power system

Hailiang Liu HLL@eng.au.dk Tom Brown Gorm Bruun Andresen David P. Schlachtberger Martin Greiner Department of Engineering, Aarhus University, Inge Lehmanns Gade 10, 8000 Aarhus C, Denmark Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
Abstract

Deep decarbonization of power sectors requires high penetration of renewable sources such as wind and solar PV. Hydro power and storage complement their high temporal volatility, and inter-connecting transmission infrastructure helps the power balancing by moving electricity in the space dimension. We study cost optimal Chinese power systems under ambitious emission reduction targets, by deploying a 31-node hourly-resolved techno-economic optimization model supported by a consistent validated 38-year-long dataset. With a new, more realistic reservoir hydro model, we find that, in 2050, if emission reduction goes beyond 70%, storage facilities such as hydro, battery and hydrogen become necessary to a moderate system cost. Numerical results show that these flexibility components can lower renewable curtailment by two thirds, allow higher solar PV share by a factor of two and contribute to covering summer cooling demand. We show that expanding unidirectional high-voltage DC lines on top of the regional inter-connections is technically sufficient and more economical than ulta-high-voltage-AC-connected "One-Net" grid. Finally, constraining transmission volume by up to 25% does not push total cost higher, while the significant need for battery storage is binding even with abundant interconnectivity.

keywords:
hydro power, storage, wind power, China, decarbonization, large-scale integration of renewables
\@input@ hydro_paper.nls

1 Introduction

The Chinese power sector accounts for almost half of the country’s annual emission, which is expected to reach 12.0 Gt in 2020. To honor its own pledge as part of the global effort in curbing climate change and also improve regional air quality, it is in the process of decarbonization by transforming the electricity supply to rely on more renewables. In 2017, 26.4% of the electricity is supplied by non-fossil fuel sources.

Electricity generated from renewable sources, such as solar, wind and hydro is characterized by high diurnal, synoptic or seasonal variability, respectively. In systems of high penetration levels, their intermittent fluctuations can be smoothed out by geographical aggregation, inter-connecting transmission, storage or local short-term conventional power balancing. Storage units are charged when excess electricity present, and can be discharged at a later time within a limited period with a round trip efficiency. Reservoir hydro can be considered as storage facilities with an uncontrollable natural inflow, which supplies annually approximately 20% of the power demand. In contrast to moving electricity in the time dimension in this way, inter-connecting transmission balances the fluctuations by transporting electrical energy geographically from sources to sinks.

The cost of such complex systems, together with temporal availability of renewable generators, operational constraints of transmission lines, hydro reservoir cascades and storage charge/discharge and their emission intensities, calls for a model which sees the system on a micro level. Furthermore, to secure the optimal system configuration, long term validated continuous high-resolution weather data, which both renewable supply and power demand rely on, is a necessity.

This study aims to improve the understanding of future highly renewable Chinese power systems, in which the role hydro power, storage and transmission may play, under ambitious emission reduction scenarios. Methodologically, this study takes a linear programming approach to minimize the total annualized investment and operation cost of generation, storage and transmission. We consider the range of weather conditions that affect wind, solar and hydro power generation as well as electricity demand with a single, consistent 38-year-long dataset. The model optimizes on a hourly scale, whose numerical results then allow insights into the spatial-temporal patterns and correlations of the system components.

There have been numerous studies on future transformations of the Chinese power sector, from various perspectives. Multi-region planning models looked into the chronological transition of the supply side based on today’s composition and policy targets, and consequently the transmission infrastructure needed cheng2015multi (); zhang2018multi (); zhang2018system (); yi2016inter (); guo2016multi (). Ref. zhang2017integrated () co-optimized the system by considering the power supply and transmission components at the same time. Ref. chen2014assessing () assessed the low-carbon effects of inter-connections, and ref. yang2018regional () looked into the reduction benefits of integrating renewables to district heating. Ref. he2016switch () was the first which studied the Chinese power sector decarbonization by including renewables, storage, hydro, and transmission in a detailed dispatch model. It gave a fine overview of an 80% carbon reduction scenario in 2050 supported by extensive data and operation considerations.

Building on these, this study includes—to our knowledge—the first calibrated high resolution reservoir hydro time series, compares costs under two distinct grid expansion strategies, examines hydro and storage operations under magnifier glasses and explores the solution space in case that transmission volume is constrained.

In this paper, following the model description and presenting the data in Sections 2 and 3, in Section 4 we first show an overview of cost optimal scenarios under the emission reduction sweep and their cost allocation. Then, hydro and storage are explored in the temporal dimension in Section 5, and Section 6 considers possible transmission volume constraints. Section 7 concludes the paper.

2 Model

We study scenarios of a future Chinese electricity system using the one-node-per-province network presented in our previous paper LIU2018534 (), but here we apply a linear techno-economic optimization brown2018pypsa (); HORSC$H_2$018207 () of total annualized cost:

(1)

The indices label the nodes of the system, which represent the provinces in China without Hong Kong, Macau and Taiwan, but including the direct-controlled municipalities, Beijing, Shanghai, Tianjin and Chongqing. The system costs are composed of fixed annualised costs for generation and storage capacity , variable costs for generation , and fixed annualised costs for transmission capacity . The indices  label the generation and storage technologies comprising onshore wind, offshore wind, solar PV, open cycle gas turbines (OCGT), hydrogen storage (electrolysis and fuel cells for conversion, steel tanks for storage), central batteries (lithium ion), reservoir hydro generation.

The optimization has to satisfy a number of constraints described in the following.

2.1 Power balance

To ensure a stable operation of the network, energy demand and generation have to match in every hour in each node. If the inelastic demand at node and time is given by then

(2)

where is the incidence matrix of the network bollobas1998modern (). This means the mismatch at node between local power supply and demand is balanced by importing and exporting through the transmission network.

2.2 Generators

The dispatch of conventional fuel powered generators is constrained by the capacity

(3)

The maximum producible energy per hour in each installed unit of the renewable generators depends on the their local weather conditions, which is expressed as an availability per unit of its capacity:

(4)

Note that excess energy can always be curtailed, e.g., by pitch angle control of wind turbines or disconnecting PV plants. Only reservoir hydro power plants can delay the dispatch of the natural inflow to some extent by utilizing the storage reservoir, which is explained in detail in Section 3.3.

The installed capacity itself is also subject to optimization, with a maximum limit set by the geographic potential:

(5)

The capacity and the final dispatch of each generator are determined in the optimization such that they respect the physical constraints, while minimising the total costs summed in the objective function Equation (1).

2.3 Storage operation

The state-of-charge of all storage units has to be consistent with the charging and discharging in each hour, and less than the energy capacity

(6)
(7)

The efficiencies determine the losses during charging and discharging, respectively. These losses also imply that the storage is only charged when there is oversupply of power available in the system, and discharged when the generators can not produce enough power and the import options are not sufficient. The state-of-charge is limited by the energy capacity . Here, is the fixed amount of time in which the storage unit can be fully charged or discharged at maximum power.

The state-of-charge is assumed to be cyclic, i.e., it is required to be equal in the first and the last hour of the simulation: . This is reasonable when modelling a full year, due to the annual periodicity of demand and seasonal generation patterns, and allows efficient usage of the storage at the beginning of the modelled time range.

Here, we chose two of the popular energy storage technologies: lithium-ion battery and hydrogen storage. The former is regarded as the oldest storage device which stores electricity as chemical energy. Batteries can be built in different sizes with capacity ranging from less than 100 W to several megawatts. Their is set at 6 hours, charge/discharge efficiency is assumed to be 0.9/0.9, making the round trip efficiency of 0.81. Hydrogen storage’s efficiency, on the other hand, is on the low side, assumed at 0.75 (electrolysis) / 0.58 (fuel cells), overall 0.435. This is partially compensated by the high storage energy density and its low loss over time. is assumed to be one week, i.e. 168 hours. Hydrogen, furthermore, is looked upon as the next generation clean energy carrier, and we expect it would soon be deployed in large scale. The value assumptions here are based on data provided in the literature aneke2016energy (); hasan2013review (); arabkoohsar2017subcooled (); ZHANG2017397 (); MCPHERSON2018649 (); CORGNALE2018426 (); REU2017290 (); VELUSWAMY2014112 (); KAVADIAS2017 ().

2.4 Inter-connecting transmission

The transmission lines between provinces are simplified as a transport model with controllable dispatch (a coupled source and sink), constrained by energy conservation at each node.

The absolute flows on these transmission lines cannot exceed the line capacities due to thermal limits:

(8)

The line capacities can be expanded by the model if it is cost-effective to do so. To satisfy n-1 security requirements, a safety margin of 33% of the installed capacity can be used brown2016optimising (). This can be emulated a posteriori by increasing the optimized NTCs by a factor of .

The lengths of the interconnecting transmission lines are set by the distance between the geometric centers of the provinces, so that some of the transmission within each province is also reflected in the optimization. A factor of 25% is added to the line lengths to account for the fact that transmission lines may not be placed completely straight due to land use restriction.

2.5 Transmission and CO emission constraints

The sum of transmission line capacities multiplied by their lengths is restricted by a cap , in the unit of MWkm, which is varied in different simulations:

(9)

Line capacities are weighted by their lengths because the length increases the cost as well. Please note, the cap used in the simulations are total transmission volume, meaning the distribution of this volume is not restricted here. Its distribution in the network follows the power balance optimization, and at the same time respects this total volume cap.

CO emissions are also limited by a cap , implemented using the specific emissions in CO-tonne-per-MWh of the fuel of generator type and the efficiency of the generator:

(10)

This cap is varied under different simulations to satisfy emission reductions goals, compared to today’s level of approximately 6 billion ton of CO annually from the power sector he2016switch (). The KKT multiplier indicates, in an unconstrained market, the carbon dioxide price zhu2018impact () necessary to obtain this reduction in emissions, i.e. shadow prices.

3 Data

3.1 Renewable potential

The expansion of renewable capacities of solar, onshore and offshore wind is limited by local geography. In the simulations, nodal renewable capacities are optimized to scale wind and solar generation up and down, and this expansion is capped with its local geographical potential .

Here, we use a simple installation density to calculate the potential limits. Specifically, onshore wind turbine spacing is assumed to be 2.5 and 20% of the provincial territory is available for installation due to land use considerations. The values for offshore turbines are 5 and 20%, respectively. And a 50 sea depth is used to calculate suitable offshore sites. This assumption is justified because even though offshore farms today are mostly built within a depth of 20, offshore foundation engineering has been showing huge improvements in recent years and floating turbines have shown high viability. PV panel farms, however, are more restricted by land use limits, so we set a territory fraction of mere 1% and a spacing of 30. To simplify the calculations, we do not take into account distributed PV on house rooftops, high-rise windows, and so on.

3.2 Wind and solar time series

Hourly wind and solar power time series are modeled based on the Renewable Energy Atlas andresen2015validation (), which was validated for Denmark, Germany, China and has been used to produce time series for Europe victoria2018using (), US becker2015renewable () and Australia. In a nutshell, we combine gridded reanalysis weather data from CFSR cisl_rda_ds094.1 (), such as 10 above ground wind speed, surface solar radiation, temperature, and wind turbine, solar panel installations to calculate wind/solar power generation time series for selected areas, by interpolating turbine power curves or simulating radiation diffusion on inclined PV panels andresen2015validation (); staffell2016using (); pfenninger2016long (). The time series calculation is explained in detail in our previous studies LIU2018534 (); liu2017howto ().

These time series are calculated per unit capacity, meaning represents the maximum renewable power available at time . Also, we recognize that different geographical layout of wind and solar capacities would give different power generation due to weather condition variations. Here, we simplify their distribution by assuming a uniform spreading of the capacities over the top 40% of raster cells in each province, i.e. stay the same regardless of capacity expansion.

3.3 Hydroelectricity time series

Figure 1: a. Forty-one largest hydro stations spanning major rivers in the southwest. Most dams are part of a cascade on the same river. b. Upstream basins of hydro reservoirs are determined using the HydroBASINS dataset lehner2013global () and the Pfafstetter Coding System verdin1999topological (), the algorithm of which is described in detail in the appendix. Thick solid lines represent higher level basin boundaries, and thin dashed lines enclose lower level basins. Hydro station Xiluodu collects surface runoff from the colored areas, and different colors denote various basin levels. c&d. Daily reservoir inflow time series in 2015 in terms of both water volume (left) and potential power production (right) for the 6 largest hydro stations.

Here, we focused on 41 large scale reservoir-based hydro stations in China, determined their corresponding basin areas, estimated their inflow based on gridded surface runoff data from CFSR cisl_rda_ds094.1 () and calculated their daily inflow time series in terms of both flow volume and potential power generation. To our knowledge, no high resolution hydroelectricity generation time series have been modeled or validated before.

Electricity generation of both wind turbines and solar PV depends on local, instantaneous weather conditions, wind speed and solar radiation do not necessarily affect the energy output at a location afar. For hydroelectricity, this is not the case. In fact, the vast majority of the water, whose potential energy is converted to electricity at the hydro station, is not from the raster cell it is in. Here, we only consider the 41 largest hydro stations with a reservoir (Figure 1), i.e. run-of-river, whose generation varies upon instantaneous inflow is not included here.

Spanning over major rivers, hydro reservoirs’ inflow is highly seasonal, and they depend on the precipitation in the upstream areas. Usually, river basins are well-defined and documented from source to mouth. However, only basin areas which lie upstream of the hydro stations affect the reservoir inflows.

The HydroBASINS dataset lehner2013global (), is a series of polygon layers that depict basin boundaries and sub-basin delineations at a global scale. It provides a seamless global coverage of consistently sized and hierarchically nested sub-basins at different scales (from tens to millions of square kilometers), supported by the Pfafstetter coding scheme verdin1999topological () that allows for analysis such as up- and downstream connectivity. Basins, or watersheds were delineated in a consistent manner at different scales, and a hierarchical sub-basin breakdown was created following the topological concept of the Pfafstetter coding system.

Here, considering the fine spatial resolution in our model, we used basin levels 5, 6 and 7 of the HydroBASINS dataset. Larger the number, finer the resolution. Shown in Algorithm LABEL:code_basin_determination, we used the three most important features of the Pfafstetter scheme and determined the hydro stations upstream basins: Odd digits denote basin segments on the main stem, even digits denote tributaries of the main stem; at each level, higher digits denote upstream segments; a basin’s Pfafstetter code is exactly the same as its finer scale basin taken out the last digit verdin1999topological (). An example is shown in Figure 1b, basins being separated by solid or doted lines, upstream of hydro station Xiluodu colored in green, teal and blue. Note that the rivers are only drawn to verify the basin delineations and they are not used in the upstream determination.

Then, surface runoff in the upstream basins are aggregated and calibrated against historical yearly reservoir inflow measurements Almanac (), to account for evaporation, transpiration, irrigation, groundwater infiltration or runoff movement. This time series is also made to account for the delay of runoff from upstream raster cells to the reservoirs, with an assumed flow speed of 1 yamazaki2009deriving (). The delays turn out to be ranging from 1 day to 2 weeks.

Finally, hydro stations’ power production per unit water depends on their head heights. They are calculated by dividing the annual power generation by annual water inflow, and averaged over 7 years Almanac (). Shown in Figure 1c&d, two pairs of hydro stations have identical inflow time series, due to their proximity over the same river, but their potential power generation are different from each other, for they are distinct in head heights. confirm One important character of hydro dams in China is that, they are usually part of a hydro station cascade, such as Three Gorges / Gezhouba, Xiluodu / Xiangjiaba, Longyangxia / Laxiwa / Lijiaxia / Gongboxia / Qingtongxia SHANG201814 (); LU201556 (). In such cascades, the dams are chained by the same river, and downstream dams’ inflow largely depends on their upstream stations’ turbine control or spillage. This is also accounted for in the system power balance optimization. A number of time series for Three Gorges (Sanxia) and Gezhouba for weather year 2016 is shown in Figure 11.

The 41 selected hydro stations’ annual generation explains over 80% of the national hydroelectricity production, and in the model, their capacities are assumed to be fixed, even though a long list of major dams are currently in plan or under construction. Indeed, the technically exploitable hydro power capacity is estimated to be 542 hess-20-3343-2016 (); huertas2017hydro (); gernaat2017high (); liu2018hydro (), and only 341 were put to use until 2017. The location of planned hydro stations may be known, but we are not able to validate the inflow or approximate the head heights until dam discharge measurement recorded. Therefore, the reader should bear in mind that hydro reservoir capacity in the future is expected to be significantly larger.

This dataset is available publicly online https://doi.org/10.5281/zenodo.1471322 liu_hailiang_2018_1471322 ().

3.4 Grid topology

Figure 2: Grid networks: Regional Grids (RG), Regional Grids with unidirectional DC lines (RGDC) and Fully Connected national Grid (FCG).

Historically, the Chinese power grid has been run by seven regional grids independently, each covers several geographically contagious provinces, as shown in Figure 2a. The grid companies were responsible for the power balance in their own region, supplied by coal primarily, and inter-regional power transfer was scarce bogdanov2016north (). In the model, the grids are represented as follows. Provinces are simplified as single nodes, located at their geometric center and connected by transmission lines.

From the 1990s, several large-scale hydro power stations have been built in the provinces marked in Figure 1a and subsequently long-range high-voltage direct current transmission lines are erected to transport hydroelectricity to the eastern regions. Comes the new century, especially in the 2010s, wind and solar power installations grew astronomically in the northwest. But their generation intermittency and low local demand caused high curtailment problems. To stem this, long-range transmission to the central-eastern provinces was the evident option. The majority of these new long-range transmission are built as ultra High-voltage (800 ) point-to-point unidirectional DC lines, as their primary purpose is to export renewable electricity. This way, the regional grid networks are linked by the DC lines, and at both ends, using converter stations direct current flows are converted from and to AC flows. This so-called RGDC topology (Regional Grids with unidirectional DC), shown in Figure 2b, is simplified by combining current DC lines and taking into account future construction plans lin2017cost (). The unidirectional DC added on top of the regional grids are, Gansu-Hubei, Gansu-Jiangsu, Inner Mongolia-Shandong, Inner Mongolia-Jiangsu, Sichuan-Shanghai and Hubei-Guangdong.

Another grid expansion strategy, that is heavily supported by the State Grid Corporation, is synchronizing the whole country by bidirectional Ultra High-Voltage (1000 ) AC networks, namely fully connecting the provinces as one national grid (FCG). Although its high cost—almost twice of HVDC per —and technical security are criticized by many, several UHVAC lines have been approved and under construction ming2016trans (). Drawn in Figure 2c, the simplified UHVAC lines connect the regional grids by multiple links, and a close to meshed grid in the east is formed.

The readers should note that the three grid networks are highly simplified, and does not represent reality to every detail. For instance, Inner Mongolia is actually covered by two separate grid companies in the eastern and western part of the province. And several HVDC lines are aggregated into one when they connect the same regional grids, for example, from Sichuan hydro stations to Shanghai or Jiangsu. Furthermore, the grid networks here only takes the topology, but not their current transmission capacity into account, as in the future heavy capacity expansion is expected.

3.5 Cost assumptions

All cost assumptions are summarized in Table 1. The given overnight capital costs were converted to net present costs with a discount rate of over the economic lifetime zhao2016effectiveness ().

The transmission investment per line is calculated as: with converter pair costs , if DC lines, and n-1 security factor . The unit cost is interpolated with respect to length, from State Grid Corporation’s budget reports wangweb () for AC, HVDC and UHVAC respectively.

The fixed operation and maintenance costs for transmission lines are 2% of the investment cost liu2015survey ().

Technology capital fixed marginal lifetime efficiency cost per
O&M cost energy stored
Units €/kW €/kW/y €/MWh years fraction €/kWh hour
onshore wind 1182 35 0.015a 25 1
offshore wind 2506 80 0.02a 25 1
solar PV 600 25 0.01a 25 1
OCGTb 400 15 58.4c 30 0.39
coalb 1400 43 24.7c 30 0.45
storaged 737 12.2 0 20 e 11.2 168
batteryd 411 12.3 0 20 e 192 6
transmissionf 2% 0 40 1
hydro 2000g 20 0 80 1 N/Ag 2400h
  • The order of curtailment is determined by assuming small marginal costs for renewables.

  • Open-cycle gas turbines have a CO emission intensity of 0.19 t/MW, and supercritical coal-fired power plants 0.9 t/MW.

  • This includes fuel costs of 21.6 (gas) / 8.4 (coal) €/MWh.

  • Budischak et al. budischak2013cost ().

  • The storage round-trip efficiency consists of charging and discharging efficiencies .

  • Interpolated from wangweb () Unit: €/MW/km.

  • The installed facilities are not expanded in this model and are considered to be amortized.

  • Assumed according to guntner2004simple ().

Table 1: Cost assumptions based on Schroder2013Current () unless stated otherwise.

4 Results: A highly renewable China 2050

4.1 Towards 100% renewable electricity

Figure 3: Component-wise average system costs on the pathway to 100% emission reduction under the three grid topologies. The dashed lines indicate the average cost of systems with only storage, only hydro and with neither.
Figure 4: A selection of component costs of RGDC (with hydro and storage) on the pathway to 100% emission reduction. The dashed lines indicate the average component costs RGDC with hydro but no storage.

The first question we want to answer is the feasibility of a Chinese power system with zero carbon emission. The answer is yes, with the right transmission network.

The power sector pathway to 100% emission reduction, compared to today’s value 6.0 billion metric ton per year he2016switch (), with the three grid topologies are shown in Figure 3. On the left, the traditional regional grids are able to reduce emission up to 67.5% with hydro and storage. At this threshold, none of the regions with high demand, namely North, East, Central and South are able to supply enough renewable power. The bottleneck lies at renewable generator installations, which are capped by their geographical potential. On the other hand, in low power demand regions, Northeast, Northwest and West, installations only count up to 46.9%, 20.3% and 18.4% of their potential, respectively. It is clear that, absence of inter-connections restricts renewable energy exploitation in provinces of high capacity factors—equivalent to full load hours. This problem is similar to what we are facing today. Provinces with high wind/solar resources attracted major investments for installations, but the belated affiliated transmission infrastructure causes high curtailment up to 30% zhang2016reducing (); XU2018585 (); wang2018short (). Furthermore, in terms of average system costs, at 67.5% emission reduction RG costs 93.6 €/, which is 24.5% and 19.0% higher than RGDC and FCG, respectively. This can be attributed to the fact that, more renewable generators are required to supply the same amount of electricity if installed in provinces with lower capacity factors LIU2018534 ().

As for RGDC and FCG systems, when emission reduction is smaller than 40%, the total average cost is almost flat. Restricted gas combustion is replaced mainly by increased capacity of onshore wind and solar PV. Neither transmission or battery/ storage plays important role under such scenarios. This indicates that, 40% emission reduction can be reached even with minimal interconnection and storage balancing units. This can also be deducted from the small differences among the dashed lines in Figure 3. In Figure 4 for RGDC scenarios, we can see the interaction between gas-powered OCGT and supercritical coal power plants. Emission reduction targets below 10% allow small amount of coal-fired plants for their cheaper fuel costs, while OCGT prevails with increasing reduction limits. The almost flat OCGT capital costs up to 40% emission reduction also implies that beyond this limit, gas-fired OCGT starts to play the role of balancing residual load. This can be seen as well, from the emergence of battery units in the system.

Another landmark reduction limit is around 70%. In a RGDC network without storage, slightly increased OCGT capital cost (Figure 4) and renewable generators (not shown) can counter the effect of storage units, if reduction limit is below 70%. Above this, significantly more transmission expansion—83.4% more at 90% emission reduction—is required, because increasing renewable generation alone is no longer the cost-optimal solution.

4.2 Cost allocation

Figure 5 shows geographical distribution of power generation as well as transmission volume, while in Figure 6 we map the component-wise storage costs, both for cost-optimal systems under RGDC and FCG, respectively. An immediate observation would be, renewable generators are assigned predominately in the northwestern regions, and consequently provinces Inner Mongolia, Qinghai, Xinjiang, Gansu and Tibet supply 51.1% / 45.4% (RGDC / FCG) of the national load on average. On the contrary, the geographically small provinces in the center and the east are mostly commissioned with insignificant amount of solar PV and OCGT, with the exception of coastal provinces of offshore wind installation (12.9% / 14.6% of the national load). This is in good agreement to our previous study LIU2018534 (), where we showed that in a cost-optimal system design, higher renewable capacity factor in the northwest shifts generator installations away from the demand center in the east.

Under the two grid networks, almost identical dipole structure is observed for storage units. Battery and (Figure 6) follows the solar and wind installations, for their charge/discharge cycles match the respective generators’ diurnal and synoptic fluctuations. In terms of cost, the five northwestern provinces takes up 71.4% / 71.8% of the national storage infrastructure.

This situation is quite different from that of Europe tranberg2018flow (), where major countries, such as Germany, Britain, Spain, France and Italy with overwhelmingly high power demand, are also blessed with decent wind or solar capacity factors as well as large installation potentials. Their geographically central presence on the continent makes it preferable to inter-connect them and the small countries nearby, forming an European transmission network. With these bidirectional links, the cooperation among the countries help reduce each other’s power mismatch and backup infrastructure, with moderate transmission expansion rodriguez2015localized (); schlachtberger2017benefits ().

The FCG grid expansion strategy seems to resemble the European case. However, our cost optimal analysis suggests UHVAC networks are not necessary, and unidirectional DC lines on top of the regional grids are sufficient economically as well. Marked as vertical dash-dot lines in Figure 3, at 90% emission reduction, RGDC’s average component-wise costs of renewable generators (onshore wind 24.8 €/, offshore wind 23.8 €/, solar PV 14.3 €/) are all within 10% variation of those for FCG. Same goes for storage units. The only significant difference is transmission cost 4.5 €/ and 10.5 €/ respectively. This can be attributed to the unit cost of UHVAC and FCG’s high number of lines. However, if not provided any balancing units such as hydro or storage, FCG takes precedence when emission reduction goes higher than 90% or transmission volume expansion is limited, which is shown in Figure 12.

As for RGDC, its addition of DC connections are characterized by long length and going only in one direction. The diametric distribution of demand and renewable resources makes this unidirectional, long-range grid expansion more economically preferable than a fully connected national grid. The DC links also allows more insight into the cooperation among the regions. In Figure 7, with respect to their average load, Northeast and Northwest export 116% and 186% to other regions, while Central and East have to rely on imports for 70.7% and 52.2% respectively.

Figure 5: Average power production at each node from the generation components onshore/offshore wind turbines, solar PV, reservoir hydro and OCGT, with 90% emission reduction.
Figure 6: Average costs of storage components battery and , with 90% emission reduction.
Figure 7: Hourly average local load, power export and import of the 7 regional grids under the 90% emission reduction scenario, calculated based on the power flows on the unidirectional HVDC lines shown in red.

5 Results: Hydro and storage

In the optimal systems storage components cost 1.11, 1.55 and 11.3 €/ for , hydro and batteries, respectively, which almost count up to offshore sector’s cost, with 90% emission reduction. Their role in the power systems is explored in this section.

Figure 8: Time series on the left show the total load, OCGT and hydroelectricty production; wind and solar power availability/production, based on which curtailment rate; state of charge of storage units and batteries. The solid lines represent weather year 2016, and shaded lines for years 1979-2015. Bars on the right illustrate the mean wind and solar installed capacities, curtailment and levelized transmission costs, for the scenarios RGDC, RGDC hydro, RGDC storage, RGDC hydro&storage, FCG, FCG hydro, FCG storage and FCG hydro&storage, respectively. The error bars indicate the standard deviations around the mean values in 1979-2016.

Spatio-temporal variations of wind speed, solar radiation, surface runoff and ambient temperature are all coupled together, consequently wind/solar/hydro generation as well as electricity load time series should not be analyzed separately kozarcanin2018climate (). The latter is especially important for China, for its heavy use of air-conditioner in hot summer spanning primarily in July and August. Considering the complexity of these systems, the results may strongly depend on the input weather data. Here, we employed 38 years of reanalysis dataset from 1979 to 2016 cisl_rda_ds094.1 (), used weather year 2016 as an example and plotted other years’ results for reference in Figure 8.

One important character of the load for Chinese provinces is that, the fluctuation is flat throughout the year, with generally higher demand during summer, and peaks follow ambient temperature during heatwaves which last several days. Zhejiang, for example, whose load time series shown in the inset in Figure 8 has the highest 3-day average load at 187.2 GWh/h (July 25th), which is 55.6% higher than the annual average and 85.7% higher than the minimum (April 4th).

Renewable power availability, on the other hand, is lowest in summer and strong in spring, shown in Figure 8 (middle left). The time series fluctuation heavily depends on generator distribution evidently, but the seasonal trend is consistent. A simple empirical orthogonal functions analysis, which is common in geophysical sciences, whose aim is to reduce the dimensionality of a spatial-temporal data set by transforming it to a new basis in terms of variance dawson2016eofs (), conducted here for wind speed (shown in Figure 9), proves that the leading principle component which is strongest during summer explains 15.1% of the wind speed covariance patterns, and shows negative wind speed anomalies across the continent. Moreover, the next principle component depicts strong positive wind speed anomalies in springtime. Together with solar PV generation, in the first third of 2016, renewable power availability is 11.9% higher than the second.

Figure 9: 10m above ground/sea level wind speed anomalies () over East Asia 2016. On the left, it shows the first two leading Empirical Orthogonal Functions renormalized by multiplying the square root of associated eigenvalues, and their corresponding Principle Component time series on the right side.

This diametric opposition in the temporal dimension between load and renewable supply, results in high curtailment in spring and heavy use of long cycle storage, shown in Figure 8. The 7.9%/7.7% average curtailment, for RGDC and FCG respectively, is already a reduction from 21.8%/21.4% for both networks with neither hydro power nor storage options, which is beneficial to generator investors and subsequently renewable expansion. Storage units, based on 38 years of optimization simulation, also help introduce more solar PV in the system and cut transmission costs by 54.5%/44.2%. Battery storage’s 6h charge cycle incorporates perfectly with solar PV’s diurnal behavior, and consequently its capacity share overtakes onshore and offshore wind, compared to the scenarios with no storage (Figure 8 top right). Furthermore, halved inter-connection costs, shown in Figure 8 bottom right, indicates that locally storing renewable electricity for later use is more economically preferable than geographically spreading more renewable generators at the expense of higher transmission costs.

Figure 10: Mismatch between national load and wind/solar power availability , shown as 72-h moving average, as well as total hydro reservoir inflow in terms of potential power generation.
Figure 11: Visualization of reservoir inflow, state-of-charge, spillage, and power generation with weather year 2016 at Three Gorges (Sanxia), of which 38 km downstream lies another hydro station Gezhouba. The former is equipped with a 39.3 billion reservoir, 181 m head height and generators totaling 22500 MW, while the latter 1.58 billion , 47 m and 277.7 MW, respectively. For better visualization, we only show 72-hour moving averages.

Hydroelectricity, which covers merely 3.6% of annual load, can reduce average system costs by 6.2% / 8.4% (RGDC/FCG) at 90% emission reduction. Even in systems absent of storage units, renewable generator capacity can be cut by 3.9%/4.7%, curtailment 7.1%/7.6%, transmission cost 18.2%/13.0% (in Figure 8). Its advantage lies in its aligned seasonal inflow with load and reservoir-based storage-like flexibility.

The mismatch, shown in Figure 10, is often negative in spring, and the excess renewable power from wind and solar generators can be stored in batteries or converted to hydrogen, otherwise curtailed. The peaks during summer are aligned with reservoir inflow, which, together with OCGT, act as an important supply and balancing sources. Looking deeper, hydro stations essentially act as storage units with fixed capacity and uncontrollable inflows. The operator may choose to store the water when there’s a heatwave-induced demand peak forecast; or spill it downstream if there’s heavy precipitation in its upstream area and the reservoir is almost full. Figure 11 shows that, as the same time Three Gorges (Sanxia) provides electricity throughout the year, it has to keep the water level between the limits, coordinate with Gezhouba, a smaller reservoir with smaller head downstream, minimize energy waste (spillage) and supply as much power as possible during summer. In fact, all hydro stations are running almost full load between June and September, shown in Figure 8 top left.

However, something none of the hydro and storage options in the study are able to coop is shifting the renewable electricity in spring to summer. This is shown in Figure 8 , frequently up to 20% of renewable power has to be curtailed and the longer cycle storage who is supposed to balance synoptic wind generation, is full during June/July and only discharge to meet a demand peak in August. The nearly zero curtailment also suggests that storage may not be used in summer at all, since all renewable power must supply the load directly to avoid the non-unity efficiency of storage. This implies that longer term energy storage is called for.

6 Results: Constraining transmission

Optimal inter-connection transmission volumes are 2231 and 1851 TWkm for RGDC and FCG, respectively in 90% emission reduction scenarios. These values are 10 times higher than the European case found by similar studies schlachtberger2017benefits (); SCHLACHTBERGER2018100 (), which again can be attributed to the diametric mismatch of renewable power and load in this region. In this section, we will explore the possibility of reducing the transmission volume and consequently its impact on the interaction of the system components.

Figure 12: Component-wise average system costs plotted as a function of constrained transmission volume with respect to their optimal values 2231 / 1851 TWkm for RGDC and FCG, respectively in 90% emission reduction scenarios, both with hydro and storage. The dashed lines indicate total average system cost with only hydro (green), only storage (blue) or neither (red).

One evident observation from Figure 12, shared by both grids, is that the solution space is quite flat while tuning the transmission factor by 25% in both directions. This means, total system costs are to some extent insensitive to transmission infrastructure volume, which usually requires major investment from central TSOs or negotiations among major stakeholders. A 25% transmission reduction can be replaced by an increase of onshore (from 22.9 to 24.4 EUR/MWh) and offshore (from 12.8 to 15.4 EUR/MWh) wind installations and a slight decrease of solar PV of 1.1 EUR/MWh for RGDC. The less volatile offshore wind prevails in this situation, since they are close to the high demand regions. And hydrogen storage also sees a 42.6% sharp increase, for its charge cycle compliments the synoptic behavior of wind generation. The cut in interconnections are primarily the more expensive DC lines, because transmission cost decreases by 41.1% to 3.7 EUR/MWh, compared to the 25% volume reduction. This reduces the country’s energy dependence on the northwest, which could be beneficial from a grid security point of view.

Comparing to the European study schlachtberger2017benefits (), the authors found that even with a 95% emission reduction target, European countries can archive electricity autonomy with zero interconnections at higher cost, solar is favored at lower transmission volume than wind and near zero storage is needed at the optimal solution with 285 TWkm transmission. China is not able to maintain power supply with minimal interconnectivity, even for a fully connected grid (FCG). Furthermore, storage cost, especially battery, remains at relatively high level (10.8 EUR/MWh) despite a 150% transmission volume compared to the optimal solution.

7 Conclusion and outlook

This study continues our previous article, a more detailed power system model is implemented and we furthered the investigation of future highly renewable Chinese power systems by including, among others, reservoir hydro, storage options, emission reduction targets, transmission volume constraints as well as inter-connection network topology discussions.

We first compared the feasibility of going towards zero emission for the three grid networks, showing that unidirectional HVDC lines on top of the regional grids can accommodate a highly renewable Chinese power sector, with the help of reservoir hydro and battery/ storage, and identified that in terms of emission reduction, below 40% the system can sustain without these flexibility components, and above 70% storage units become necessary to maintain reasonable system cost.

Allocating cost to the nodes, we showed that RGDC gave similar generator layout and storage infrastructure as FCG. This indicates that the diametric distribution of demand and renewable resources makes the unidirectionally transport renewable power to the east more economical, than UHVAC-connected "One-Net" national grid. This, however, is merely a simplified techno-economical evaluation of the two, and is not able to account for antecedent system issues or demographical considerations.

We also looked into the role of reservoir hydro and storage, by exploring the temporal dimension of the 90% reduction scenario. We showed that, these flexibility components, can lower renewable curtailment by two thirds, allow higher solar PV share by a factor of two, decrease transmission cost significantly and also contribute to covering the summer peak demand.

Finally, constraining transmission volume by 25% does not push total cost higher, while it demands more longer term storage and slightly increases renewable installation. And the significant need for short term battery storage is binding even with copious transmission, unlike the European case.

When it comes to future work, coupling electricity to heating and transportation sectors is an evident choice to introduce more flexibility to the energy system. Electric vehicles may be a decentralized substitute for the demanding battery storage units, while power-to-gas can offer longer term storage to help shift the seasonal power mismatch.

Acknowledgments

The first author gratefully acknowledges the financial support from China Scholarship Council and Idella Foundation Denmark. T.B., D.P.S., G.B.A. and M.G. are fully or partially funded by the RE-INVEST project (Renewable Energy Investment Strategies – A two-dimensional interconnectivity approach), which is supported by Innovation Fund Denmark (6154-00022B). T.B. also acknowledges funding from the Helmholtz Association, Germany under grant no. VH-NG-1352. The responsibility for the contents lies solely with the authors.

Bibliography

References

  • [1] Rui Cheng, Zhaofeng Xu, Pei Liu, Zhe Wang, Zheng Li, and Ian Jones. A multi-region optimization planning model for China’s power sector. Applied Energy, 137:413–426, 2015.
  • [2] Yaru Zhang, Tieju Ma, and Fei Guo. A multi-regional energy transport and structure model for China’s electricity system. Energy, 161:907–919, 2018.
  • [3] Yaru Zhang, Yadong Yu, and Tieju Ma. System optimization of long-distance energy transportation in China using ultra-high-voltage power transmission. Journal of Renewable and Sustainable Energy, 10(4):045503, 2018.
  • [4] Bo-Wen Yi, Jin-Hua Xu, and Ying Fan. Inter-regional power grid planning up to 2030 in China considering renewable energy development and regional pollutant control: A multi-region bottom-up optimization model. Applied Energy, 184:641–658, 2016.
  • [5] Zheng Guo, Linwei Ma, Pei Liu, Ian Jones, and Zheng Li. A multi-regional modelling and optimization approach to China’s power generation and transmission planning. Energy, 116:1348–1359, 2016.
  • [6] Ning Zhang, Zhaoguang Hu, Bo Shen, Gang He, and Yanan Zheng. An integrated source-grid-load planning model at the macro level: Case study for China’s power sector. Energy, 126:231–246, 2017.
  • [7] Qixin Chen, Chongqing Kang, Hao Ming, Zeyu Wang, Qing Xia, and Guoxin Xu. Assessing the low-carbon effects of inter-regional energy delivery in China’s electricity sector. Renewable and Sustainable Energy Reviews, 32:671–683, 2014.
  • [8] Yuanzhe Yang, Hongyu Zhang, Weiming Xiong, Da Zhang, and Xiliang Zhang. Regional power system modeling for evaluating renewable energy development and CO2 emissions reduction in China. Environmental Impact Assessment Review, 73:142–151, 2018.
  • [9] Gang He, Anne-Perrine Avrin, James H Nelson, Josiah Johnston, Ana Mileva, Jianwei Tian, and Daniel M Kammen. SWITCH-China: a systems approach to decarbonizing China’s power system. Environmental Science & Technology, 50(11):5467–5473, 2016.
  • [10] Hailiang Liu, Gorm Bruun Andresen, and Martin Greiner. Cost-optimal design of a simplified highly renewable Chinese electricity network. Energy, 147:534–546, 2018.
  • [11] Thomas Brown, Jonas Hörsch, and David Schlachtberger. PyPSA: Python for Power System Analysis. Journal of Open Research Software, 6(1), 2018.
  • [12] Jonas Hörsch, Fabian Hofmann, David Schlachtberger, and Tom Brown. PyPSA-Eur: An open optimisation model of the European transmission system. Energy Strategy Reviews, 22:207–215, 2018.
  • [13] B. Bollobas, S.J. Axler, F.W. Gehring, B.B. s, and P.R. Halmos. Modern Graph Theory. Graduate Texts in Mathematics. Springer New York, 1998.
  • [14] Mathew Aneke and Meihong Wang. Energy storage technologies and real life applications–A state of the art review. Applied Energy, 179:350–377, 2016.
  • [15] Nor Shahida Hasan, Mohammad Yusri Hassan, Md Shah Majid, and Hasimah Abdul Rahman. Review of storage schemes for wind energy systems. Renewable and Sustainable Energy Reviews, 21:237–247, 2013.
  • [16] A Arabkoohsar, M Dremark-Larsen, R Lorentzen, and GB Andresen. Subcooled compressed air energy storage system for coproduction of heat, cooling and electricity. Applied Energy, 205:602–614, 2017.
  • [17] Yang Zhang, Pietro Elia Campana, Anders Lundblad, and Jinyue Yan. Comparative study of hydrogen storage and battery storage in grid connected photovoltaic system: Storage sizing and rule-based operation. Applied Energy, 201:397–411, 2017.
  • [18] Madeleine McPherson, Nils Johnson, and Manfred Strubegger. The role of electricity storage and hydrogen technologies in enabling global low-carbon energy transitions. Applied Energy, 216:649–661, 2018.
  • [19] Claudio Corgnale, Bruce Hardy, Richard Chahine, and Daniel Cossement. Hydrogen desorption using honeycomb finned heat exchangers integrated in adsorbent storage systems. Applied Energy, 213:426–434, 2018.
  • [20] M. Reuß, T. Grube, M. Robinius, P. Preuster, P. Wasserscheid, and D. Stolten. Seasonal storage and alternative carriers: A flexible hydrogen supply chain model. Applied Energy, 200:290–302, 2017.
  • [21] Hari Prakash Veluswamy, Rajnish Kumar, and Praveen Linga. Hydrogen storage in clathrate hydrates: Current state of the art and future directions. Applied Energy, 122:112–132, 2014.
  • [22] K.A. Kavadias, D. Apostolou, and J.K. Kaldellis. Modelling and optimisation of a hydrogen-based energy storage system in an autonomous electrical network. Applied Energy, 2017.
  • [23] Tom Brown, Peter-Philipp Schierhorn, Eckehard Tröster, and Thomas Ackermann. Optimising the European transmission system for 77% renewable electricity by 2030. IET Renewable Power Generation, 10(1):3–9, 2016.
  • [24] K Zhu, M Victoria, T Brown, GB Andresen, and M Greiner. Impact of CO2 prices on the design of a highly decarbonised coupled electricity and heating system in Europe. arXiv preprint arXiv:1809.10369, 2018.
  • [25] Gorm B Andresen, Anders A Søndergaard, and Martin Greiner. Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis. Energy, 93:1074–1088, 2015.
  • [26] Marta Victoria and Gorm B Andresen. Using validated reanalysis data to investigate the impact of the PV system configurations at high penetration levels in European countries. arXiv preprint arXiv:1807.10044, 2018.
  • [27] Sarah Becker, Bethany A Frew, Gorm B Andresen, Mark Z Jacobson, Stefan Schramm, and Martin Greiner. Renewable build-up pathways for the US: Generation costs are not system costs. Energy, 81:437–445, 2015.
  • [28] Suranjana Saha, Shrinivas Moorthi, Xingren Wu, Jiande Wang, Sudhir Nadiga, Patrick Tripp, David Behringer, Yu-Tai Hou, Hui ya Chuang, Mark Iredell, Michael Ek, Jesse Meng, Rongqian Yang, Malaquias Pena Mendez, Huug van den Dool, Qin Zhang, Wanqiu Wang, Mingyue Chen, and Emily Becker. "NCEP Climate Forecast System Version 2 (CFSv2) Selected Hourly Time-Series Products", 2011.
  • [29] Iain Staffell and Stefan Pfenninger. Using bias-corrected reanalysis to simulate current and future wind power output. Energy, 114:1224–1239, 2016.
  • [30] Stefan Pfenninger and Iain Staffell. Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data. Energy, 114:1251–1265, 2016.
  • [31] Hailiang Liu, Gorm Bruun Andresen, and Martin Greiner. How to best exploit wind and solar power in China? In Wind Energy Denmark 2017, 2017.
  • [32] Bernhard Lehner and Günther Grill. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15):2171–2186, 2013.
  • [33] Kristine L Verdin and James P Verdin. A topological system for delineation and codification of the Earth’s river basins. Journal of Hydrology, 218(1-2):1–12, 1999.
  • [34] China Society for Hydropower Engineering. Almanac of hydro power in China. China Power Publishing, 2006-2016.
  • [35] D Yamazaki, T Oki, and S Kanae. Deriving a global river network map and its sub-grid topographic characteristics from a fine-resolution flow direction map. Hydrology and Earth System Sciences, 13(11):2241, 2009.
  • [36] Yizi Shang, Shibao Lu, Yuntao Ye, Ronghua Liu, Ling Shang, Chunna Liu, Xianyong Meng, Xiaofei Li, and Qixiang Fan. China’ energy-water nexus: Hydropower generation potential of joint operation of the Three Gorges and Qingjiang cascade reservoirs. Energy, 142:14–32, 2018.
  • [37] Di Lu, Bende Wang, Yaodong Wang, Huicheng Zhou, Qiuhua Liang, Yong Peng, and Tony Roskilly. Optimal operation of cascade hydropower stations using hydrogen as storage medium. Applied Energy, 137:56–63, 2015.
  • [38] X. Liu, Q. Tang, N. Voisin, and H. Cui. Projected impacts of climate change on hydropower potential in China. Hydrology and Earth System Sciences, 20(8):3343–3359, 2016.
  • [39] Daniel Huertas-Hernando, Hossein Farahmand, Hannele Holttinen, Juha Kiviluoma, Erkka Rinne, Lennart Söder, Michael Milligan, Eduardo Ibanez, Sergio Martín Martínez, Emilio Gomez-Lazaro, et al. Hydro power flexibility for power systems with variable renewable energy sources: An IEA Task 25 collaboration. Wiley Interdisciplinary Reviews: Energy and Environment, 6(1):e220, 2017.
  • [40] David EHJ Gernaat, Patrick W Bogaart, Detlef P van Vuuren, Hester Biemans, and Robin Niessink. High-resolution assessment of global technical and economic hydropower potential. Nature Energy, 2(10):821, 2017.
  • [41] Hailiang Liu, Gorm Bruun Andresen, and Martin Greiner. Hydro power time series modelling using reanalysis data. In 8th Open Energy Modelling Workshop-Zurich 2018, 2018.
  • [42] Hailiang Liu and Gorm Bruun Andresen. Daily hydro power time series (1979-2016) for 43 Chinese reservoir hydro stations, October 2018.
  • [43] Dmitrii Bogdanov and Christian Breyer. North-East Asian Super Grid for 100% Renewable Energy supply: Optimal mix of energy technologies for electricity, gas and heat supply options. Energy Conversion and Management, 112:176–190, 2016.
  • [44] Boqiang Lin and Wei Wu. Cost of long distance electricity transmission in China. Energy Policy, 109:132–140, 2017.
  • [45] Zeng Ming, Peng Lilin, Fan Qiannan, and Zhang Yingjie. Trans-regional electricity transmission in China: Status, issues and strategies. Renewable and Sustainable Energy Reviews, 66:572–583, 2016.
  • [46] Xiaoli Zhao, Shujie Li, Sufang Zhang, Rui Yang, and Suwei Liu. The effectiveness of China’s wind power policy: An empirical analysis. Energy Policy, 95:269–279, 2016.
  • [47] Zhonghong Wang. HVDC and AC in China. http://finance.sina.com.cn/zl/energy/20140327/172518635050.shtml, 2014. [Accessed: 2017-04-18].
  • [48] Zehong Liu, Jun Yu, Xianshan Guo, Tao Sun, and Jin Zhang. Survey of technologies of line commutated converter based high voltage direct current transmission in China. CSEE Journal of Power and Energy Systems, 1(2):1–8, 2015.
  • [49] Andreas Schröder, Friedrich Kunz, Jan Meiss, Roman Mendelevitch, and Christian von Hirschhausen. Current and prospective costs of electricity generation until 2050. DIW Data Documentation 68, Deutsches Institut für Wirtschaftsforschung (DIW), Berlin, 2013.
  • [50] Cory Budischak, DeAnna Sewell, Heather Thomson, Leon Mach, Dana E Veron, and Willett Kempton. Cost-minimized combinations of wind power, solar power and electrochemical storage, powering the grid up to 99.9% of the time. Journal of Power Sources, 225:60–74, 2013.
  • [51] Andreas Güntner, Maarten S Krol, José Carlos De Araújo, and Axel Bronstert. Simple water balance modelling of surface reservoir systems in a large data-scarce semiarid region. Hydrological Sciences Journal, 49(5), 2004.
  • [52] Ning Zhang, Xi Lu, Michael B McElroy, Chris P Nielsen, Xinyu Chen, Yu Deng, and Chongqing Kang. Reducing curtailment of wind electricity in China by employing electric boilers for heat and pumped hydro for energy storage. Applied Energy, 184:987–994, 2016.
  • [53] Fangqiu Xu, Jicheng Liu, Shuaishuai Lin, Qiongjie Dai, and Cunbin Li. A multi-objective optimization model of hybrid energy storage system for non-grid-connected wind power: A case study in China. Energy, 163:585–603, 2018.
  • [54] Xuebin Wang, Jianxia Chang, Xuejiao Meng, and Yimin Wang. Short-term hydro-thermal-wind-photovoltaic complementary operation of interconnected power systems. Applied Energy, 229:945–962, 2018.
  • [55] Bo Tranberg, Leon J Schwenk-Nebbe, Mirko Schäfer, Jonas Hörsch, and Martin Greiner. Flow-based nodal cost allocation in a heterogeneous highly renewable European electricity network. Energy, 150:122–133, 2018.
  • [56] Rolando A Rodriguez, Magnus Dahl, Sarah Becker, and Martin Greiner. Localized vs. synchronized exports across a highly renewable pan-European transmission network. Energy, Sustainability and Society, 5(1):21, 2015.
  • [57] David P. Schlachtberger, Tom Brown, Stefan Schramm, and Martin Greiner. The benefits of cooperation in a highly renewable European electricity network. Energy, 134:469–481, 2017.
  • [58] Smail Kozarcanin, Hailiang Liu, and Gorm Bruun Andresen. Climate change impacts on large-scale electricity system design decisions for the 21st Century. arXiv preprint arXiv:1805.01364, 2018.
  • [59] Andrew Dawson. eofs: A library for eof analysis of meteorological, oceanographic, and climate data. Journal of Open Research Software, 4(1), 2016.
  • [60] David P. Schlachtberger, Tom Brown, Mirko Schäfer, Stefan Schramm, and Martin Greiner. Cost optimal scenarios of a future highly renewable European electricity system: Exploring the influence of weather data, cost parameters and policy constraints. Energy, 163:100–114, 2018.

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