Minh Ha-Duong
email: minh.haduong@gmail.com

Sven Teske

Dimitri Pescia

Mentari Pujantoro

Planning, policy and integration for sustainable development of offshore wind energy in Vietnam 2022 -2030

Keywords: Wind energy, Vietnam, scenarios

The wind power sector took off in Vietnam after the feed-in tariff was raised to 8.5 UScents / kWh for onshore projects in 2018. As of March 2021, 113 wind projects with total capacity 6,038MW have signed a power purchase agreement. Most are expected to enter commercial operation before December 2021. We explore here three scenarios for wind power development in Vietnam through 2030. It argues that by 2030 the wind power installed capacity in the New Normal could be around 17 GW onshore and 10 GW offshore. In a Factor Three scenario, offshore wind reaches 21 GW by 2030. This has three policy implications. First, Vietnam's next power development plan provides an important opportunity to increase at low costs the level of ambition of wind power development. Second, flexibility should be the guiding principle of the plan. Third, to realize the large potential of offshore wind power, infrastructure planning has to start soon.

Introduction

Early 2021, Vietnam's 2025 targets for installed wind power capacity in the Power Development Plan VIII (PDP 8) draft were: 11.3 GW for onshore and nearshore, zero offshore. For 2030, these numbers increased respectively to 16.0 GW onshore / nearshore, and 2 GW offshore [START_REF] Ha-Duong | Quy hoạch điện 8: Ưu tiên gì để không phải chịu hệ quả đắt giá?[END_REF]. These numbers are not final, as the administration is still revising the draft. This manuscript perspective argues that Vietnam rely more on its excellent wind resources, which potential has been overlooked in the PDP 8 draft. Considering the pipeline of projects in the country, the upcoming Power Development Plan 8 (PDP8) still has the opportunity to increase the target to have several GW of installed offshore wind capacity by 2030.

Our argument follows the scenario analysis method. The next section exposes the natural and technical potential of wind energy in Vietnam. Section 3 presents an original project-level inventory of the wind power sector in Vietnam. The pipeline is large enough to meet the government targets by far. Still using our inventory dataset, section 4 reviews the stated costs of wind power projects in the country, and why they are expected to decline. Section 5 defines three scenario narratives to explore the range of possible futures. We quantified our scenarios with a top-down energy system modeling study led by the Institute for Sustainable Future, hereafter 'ISF' [START_REF] Teske | Renewable Energy for Viet Nam -A proposal for an economically and environmentally sustainable 8th Power Development Plan for the Viet Nam Government[END_REF]. Sections 6 and 7 discuss, respectively, two implications of the expected surge in wind power: the need for flexibility in the whole system, and the urgency to plan the offshore transmission infrastructure. Section 8 summarizes our results and concludes by reviewing the specific Vietnamese policy changes called for by the wind power opportunities.

Vietnam wind resource is excellent

Vietnam's wind resources mostly lie along its coastline of more than 3000 km, and in the hills and highlands of the northern and central regions. The Global Wind Atlas estimates that over 39% of Vietnam's area has annual average wind speed over 6 m/s at a height of 65 m, and over 8% of Vietnam's land area has annual average wind speed over 7 m/s (see Figure 1 from [START_REF]The Global Wind Atlas 3.1 is released in partnership with the World Bank Group, utilizing data[END_REF]). This corresponds to wind resources physical potential of 512 GW and 110 GW. In 2006 already, [START_REF] Nguyen | Wind energy in Vietnam: Resource assessment, development status and future implications[END_REF] found 3.5 GW economic potential at less than 6 US cents/kWh for onshore wind power. Figure 1 shows that Vietnam's the offshore wind potential is much larger than the onshore wind potential. Winds over sea are stronger, more stable. Land use constraints to sit turbines and transmission infrastructure are also lower.

Sean and Whittaker [START_REF] Whittaker | Global picture of offshore wind," presented at the Consultation workshop on Developing offshore wind in Vietnam[END_REF] presented estimates of the wind offshore potential for Vietnam, accounting only for wind speed greater than 7 m/s and less than 200 km from the coast. they found a potential of 261 GW in waters less than 50 m deep, and an additional potential of 214 GW in waters 50 to 1000 m deep.

Using the Weather Research and Forecasting (WRF) model (10-km resolution for 10 years, from 2006 to 2015), Doan et al., [START_REF] Doan | Usability and Challenges of Offshore Wind Energy in Vietnam Revealed by the Regional Climate Model Simulation[END_REF], [START_REF] Van Toan | The Zoning of Offshore Wind Energy Resources in the Vietnam Sea[END_REF] estimated the energy potential in the offshore area around the Phú Quý island (Bình Thuận province). This area alone can provide 38.2 GW of offshore wind power generation capacity. [START_REF] Quang | Evaluation of resource spatial-temporal variation, dataset validity, infrastructures and zones for Vietnam offshore wind energy[END_REF] precised the analysis, estimating that the five-year capacity factor over 2007-2011 of a LW 8 MW wind turbine over Phu Quy was over 54%.

The Institute of Energy based its study for the official Power Development Plan VIII on technical potentials 47 GW for onshore wind, 162 GW for offshore ( [START_REF]Draft National Energy Development Plan for 2021-2030 period, with vision to 2050 -Power sector[END_REF], ch. 8, tables 8.14 and 8.15 p 332, see annexes 8.4 and 8.5 pp 32-35 for the distribution by province). This is at 80m hub height, for wind speed over 5.5 m/s onshore, 6 m/s offshore, filtering out unsuitable land use.

The present manuscript is based on our own resource analysis [START_REF] Teske | Renewable Energy for Viet Nam -A proposal for an economically and environmentally sustainable 8th Power Development Plan for the Viet Nam Government[END_REF]. Taking into account land-use restriction -excluding mountain areas with slopes of more than 30%, fractured spaces with a size of less than 1 km² and areas more than 10 km away from existing power lines -it found 42 GW of technical onshore wind energy potential for utility scale wind farms. It estimates the offshore wind resource to 609 GW, spreading over a total of 3000 km coastline and area of 150 000 km². This takes into account only coastal areas with a maximum water depth of 50 m and a maximum distance to the shore of 70 km, and uses 2015 meteorological data. The ISF study further estimated the regional wind energy potential and capacity factors (for details, see [START_REF] Dương | Options for wind power in Vietnam by 2030[END_REF] annexes). While being a variable energy source, the estimated capacity factor of wind energy in Vietnam is up to 36% for onshore and 54% for offshore wind.

3

Towards 5 GW of wind power operational at the end of 2021.

As we exposed in [START_REF] Dương | Options for wind power in Vietnam by 2030[END_REF], the history of the sector shows fifteen years of growing pains. In 2019, Vietnam's wind power industry began to take off, although not as spectacularly as the solar power industry. By 31 May 2019, seven wind power plants were in operation, for a total capacity of 331 MW. By February 2020, we count eleven projects in operation, for a total capacity of 425 MW. To take one example: the Trung Nam project in Ninh Thuận province inaugurated in April 2019 an hybrid facility, colocating a 40 MW wind farm with a 204 MW solar PV plant. This plant was being extended by 112 MW in phase two, by installing 28 turbines of 4MW, and after phase 3 added another 48 MW by mid April 2021, it became the largest in Vietnam. Installing this class of machines demonstrates that the local market has now reached the technological frontier.

We maintain a database of wind projects in Vietnam. To the best of our knowledge, this is the most complete and up to date list, even including commercial offers. The full database is available online [START_REF] Ha-Duong | List of wind power projects in Vietnam[END_REF].. Table 1 summarizes the pipeline of projects in Vietnam by mid 2021. Projects are classified by phase. The Operation phase is after the commercial operation date. The Implementation phase is from the groundbreaking ceremony to the commercial operation date. The Development phase is before building but after the administrative Decision of Investment, which usually given by provincial authorities soon after the project is added to the national Power Development Plan. The preliminary phase is from exclusivity MoU to administrative Decision of Investment.

Our database includes has all projects authorized by the Government. They are in Operation/Implementation/Development phases. Between these three rows, our num-bers are only indicative. They are based solely on publicly available sources, we are only able to record projects advances when they are reported.

Row four adds up projects at the Preliminary phase. This is a wide category. It includes projects already proposed for inclusion in the Power Development Plan, but also projects that have not yet started the pre-feasiblity study, or projects seeking an active investor. The 20 525 MW of Onshore wind projects at the Preliminary phase include 14 422 MW of capacity signed in the Gia Lai province investment projects [START_REF] Võ | Decision 68/QD-UBND on approving the list of investment of the province in the period 2020-2021[END_REF]. The 31 812 MW of offshore wind projects at the Preliminary phase is less than the 59 720 MW capacity of potential projects listed by Institute of Energy in Table PL9.3 in [9, pp. 52-53]. The power development master plan PDP 7 revised, published in 2016, has set targets of 0.8 GW of wind power capacity by 2020, 2 GW by 2025 and 6 GW by 2030. Capacity in operation reached 538 MW at the end of 2020, so the 2020 target was missed [START_REF] Đình | Letter 196/2021/TTg-CN[END_REF]. However, EVN has signed PPA for more than 6 GW already, out of the 11 262 MW authorized by the government. It is certain that the 2025 objectives will be exceeded by the end of 2021.

The table confirms the potential estimates from the previous section: there is about 67.7 GW of wind power capacity in identified sites in Vietnam. Most of this potential comes from projects at the Preliminary stage. May will never be realized, but these numbers provide the best frame of reference to discuss 2030 scenarios for the sector.

The early 2021 draft of the Power Development Plan VIII proposed the revise the targets for wind power in 2030 as follows: In the low demand scenario, 16 GW onshore and 2 GW offshore. In the high demand scenario, increase the offshore target to 3 GW. ( [START_REF]Draft National Energy Development Plan for 2021-2030 period, with vision to 2050 -Power sector[END_REF], ch 9, tables 1 and 2 pp 353-353). The analysis (figure 9.10, p 389) further examines several scenarios in which new wind power generation capacity substitutes for new imported coal generation capacities at the 2030 horizon.

Overall, the 16 to 19 GW target is realistic considering the amount of projects already accepted or proposed at the end of 2020. However, this draft of the plan does not include most large offshore projects under discussion at that moment.

In the offshore sector, the Ca Mau 1 (350 MW) project is under construction, while HBRE Vung Tau (500 MW) and Phu Cuong 1A, 1B (200 MW) are approved. But there are more than 31 GW of offshore projects at the preliminary stage. Offshore projects Phu Cuong 1C, 1D (200 MW) and Binh Dai Ben Tre (400 MW) are to be considered for PDP VIII. AIT and Mainstream's project in Bến Tre (500MW) is also proposed to the Plan. Phase 1 (600 MW) of the Thang Long project by Entreprize Energy (UK) has obtained a full year of wind data and is continuing its site survey. The La Gàn project by Copenhaguen Infrastructure Partners (Denmark) started strong, as is the Binh Dinh project by PNE AG (Germany). History will judge the 600MW offshore project in Bình Thuận between PV Power and IMPSA , signed in April 2011 with Argentina, was only a diplomatic gesture.

4

Cost of wind power is rapidly falling, but FIT is uncertain.

Offshore wind becomes more and more economically viable due to its higher yields, larger turbines and clusters. The auctions conducted to lease the rights to build wind farms offshore Massachusetts illustrate the cost decline in the industry. In 2014, they did not receive any bids. When the auction ran again in December 2018, the winning developers paid 405 million USD for the right to build wind farms [START_REF] Gerdes | Record-Breaking Massachusetts Offshore Wind Auction Reaps $405 Million in Winning Bids[END_REF], [START_REF] Asimov | In Race for Offshore Wind, Three New Bids[END_REF].

Costs will also improve because the average rated capacity size of offshore turbine units is increasing. Though it is around 7 MW per turbine today, the first 12 MW turbines are already being field tested, and the capacity is expected to reach 15 MW per turbine in 2025. The average capacity factor of current projects also ranges from 50-57% [START_REF] Noonan | IEA Wind TCP Task 26: Offshore Wind Energy International Comparative Analysis[END_REF]. From 2020 to 2022, the cost of electricity from newly commissioned offshore wind power projects could range from 60 USD / MWh to 100 USD / MWh based on current trends and the winning bids from 2018 auctions -a significant decline compared to 140 USD / MWh in 2017.

For example, France aims to have 10 GW of offshore wind power by 2028, and recently conducted an auction for a 600 MW offshore wind farm near Dunkerque, scheduled to open in 2025-2026. The eight candidates proposed to sell electricity for 20 years between 44 € / MWh to 60,9 € / MWh (equivalent to 48 to 67 USD / MWh), with an average at 51 € / MWh (equivalent to 56 USD / MWh). Germany has held just two offshore wind auctions, one in 2017 and one in 2018. In both tenders, capacity was won by investors who offered to build parks without subsidies.

There is no auction for offshore wind electricity delivered in Vietnam around 2025. But if the Government held one, there would be important differences with France and Germany. The bidders would factor in the risks of being the first to develop the technology in the country. And the bidders would also have to factor in the costs of the transmission infrastructure is required from them. According to experts surveyed in [START_REF] Li | Vietnam's future transition to offshore wind auctions[END_REF], "With regards to support mechanisms for offshore wind, the current FiT level was seen as appropriate." which means that the industry expects to be able to deliver •under 98 USD/MWh.

Since 2011, the Feed in tariff (FiT) for wind power project in Vietnam was 78 USD / MWh and that was not commercially viable for developers. In November 2019, the FiT was raised to 85 USD / MWh for onshore wind power projects and 98 USD / MWh for offshore wind power projects [START_REF] Xuân | Decision 39/2018/QD-TTg -Amending several articles of decision[END_REF]. The new electricity tariffs are applied to a part or whole of the grid connected wind power projects with commercial operation date before 1 November 2021 for 20 years from the date of commercial operation. Already operating projects will benefit from the tariffs retroactively from 1 November 2018 for the remaining period of the signed PPA.

Future tariffs for wind power projects completed after 1/11/2021 remain uncertain. In 2020, the Ministry of Industry and Trade (MOIT) proposed to extend the current FIT scheme to the end of 2021, effectively giving a two months extra time for developers to finish their projects [START_REF] Nangluongvietnam | Bộ trả lời nghị VEA về 'giá FIT cho điện gió, mặt trời[END_REF]. Later that year, MOIT proposed to extend the FIT scheme for already registerd projects by two more years (Letter 8159/2020/BCT-DL cited in [START_REF] Công | Đề xuất kéo dài cơ chế giá FIT điện gió đến hết năm 2023[END_REF]), reducing the rates as described in Table 2 below. From our perspective, extending by two years seem preferable to rushing all projects to the end of 2021. This can be justified considering the administrati•ve delays in 2019 and the COVID crisis. More time allows a better execution of projects. Time is also needed to alleviate grid congestion issues. Variable renewable projects do not contribute to the supply when they are curtailed. And the auction mechanism, which is prepared to follow the FIT scheme, has not started to be deployed by June 2021.

Scenarios for wind power in Vietnam.

The previous sections have shown that in mid 2021, the pipeline of wind power projects in Vietnam is much larger than the government targets. In [START_REF] Teske | Renewable Energy for Viet Nam -A proposal for an economically and environmentally sustainable 8th Power Development Plan for the Viet Nam Government[END_REF], [START_REF] Dương | Options for wind power in Vietnam by 2030[END_REF] we examined three visions for the development of the power sector in Vietnam. We estimated future demand trajectories and looked at how various energy mixes could satisfy it. In these visions, the role of wind power is defined by Table 3:

The first scenario corresponds to the PDP7 revised, which planned for 6 GW of installed wind capacity in 2030, accounting for 2,1% of the power production. The other two scenarios respectively achieve that year a total installed wind capacity of 26 GW and 42 GW. In the most ambitious scenario, annual markets increase about 2 GW a year for both offshore and onshore wind during the next decade. While such annual market figures may seem ambitious, the country is expecting a high energy demand growth apparent from the PDP7 revised which planned to install 70 GW of additional generation capacity between 2020 and 2030 -mostly baseload plants. Indeed, high growth rates in the power supply infrastructure are required to lift a 100 million people from lower to higher middle income in ten years. Technological progress is rapidly reducing costs, leading to considerable shifts in investment patterns. For example the PDP7 solar PV objectives were 4 GW for 2025, but the installed capacity achieved more than 5 GW at the end of 2019. As explained in section 3 above, a similar-sized wave of wind energy projects will arrive in 2021.

The following narratives flesh out the three scenarios, explaining various development rhythms:

In the Old Plan scenario, a wave of new wind farms connects to the grid in time to get the FIT before November 2021. After that, the government does not renew the FIT, and legal issues delay the first pilot auction to 2022. By then a global economic crisis affects Vietnam, reducing economic growth and, by extension, domestic electricity demand. International fossil fuels prices hit historical lows as global demand dips, and producing countries try to sell their remaining reserves before they are made useless. Vietnam policy priority is on GDP growth by international trade rather than climate protection. Natural gas plays the first role in the 2020-2030 decade. A rapid In the New Normal scenario, after the initial wave of wind projects in 2021 there is no second FIT period due to concerns on the part of policymakers that the market will overheat. The market is driven by direct power purchase agreements -multinational companies in Vietnam procuring green electricity directly from wind project developers -and by government auctions. The government credibly commits to an auction program for 1 GW of offshore wind per year. During the 2021-2025 period, the technology develops as a progressive extension of the near-shore wind farms, while the "true offshore" projects scale up. The expected market size leads many industries to choose Vietnam as their Southeast Asia base for manufacturing equipment and operating wind projects. The offshore wind industry is organized around two hubs: the port of Vũng Tàu serves farms in the zone facing Bình Thuận and Cà Mau coasts; and the port of Hải Phòng serves zones facing Quảng Ninh coasts.

In the Factor Three scenario, the Party decides to steer Vietnam into green development. The national oil and gas company PVN is redefined as a sustainable energy provider that mobilizes its offshore work capacities and the complementarity between gas and variable renewables. All projects meet their initially announced targets, which are extremely unlikely considering the situation today. The Thang Long Wind power project by Enterprize Energy, which intends to develop large-scale offshore near the Kê Gà area in Bình Thuận province, starts operating its first 600 MW phase at the end of 2022, and extends by 600MW every year. The three other projects -in Bac Lieu by KOSY group, in Vung Tau by HBRE group and in Phu Cuong by Mainstream -add 1.7 GW of offshore wind power capacity by 2024. By end of that year, the total offshore capacity is 2.9 GW, and the sector is competitive. This convinces the government to adopt a regional leadership strategy in the wind energy sector. A national scale offshore transmission infrastructure -power centers hubs and under sea linesstarts by connecting the existing projects. Starting in 2025, the backbone strategy to meet the power demand expansion in Vietnam is to auction 3 GW of offshore wind every year.

The key early signpost indicating which scenario Vietnam is heading towards will be the government's next decision about for wind power incentives. Feed In Tariff (FIT) is by far the proven policy mechanism, it worked in Germany between 1991 and 2018, and it is working in Vietnam now. Recent experience in Germany is also an example for the Old Plan scenario: auction design problems can cool down the wind market. Alternatively, a decision to offer FIT over 90 USD / MWh for offshore wind after 2022 would steer towards the third scenario. Costs would be high but tendering the rights to use offshore wind development zones could recover a portion of these costs.

Most previously published Vietnam power system development scenarios are close to the Old Plan scenario when it comes to wind power. The Renewable Energy scenario [22, p. 39] sees 8.5 GW of wind installed capacity in 2030. The Danish Energy Agency [23, p. 45] scenarios project wind power capacity to grow to 6.2 -6.4 GW in 2030, which is close to the PDP7 revised. The 2019 update to this Vietnam Energy Outlook [START_REF] Hưng | pháp mô phỏng và các kịch bản dẫn tới kết quả[END_REF] sees more wind power generation than solar in 2030 in all cases; offshore wind enters at 2.5 GW in 2030. The ECA Made in Vietnam Energy Plan 2.0 [25, p. 11] assumes that a doubling of the PDP7A target is possible, for a 12 GW of installed wind power by 2030, not mentioning offshore wind. The low-carbon development pathway report for Vietnam by the Asian Development Bank [26, p. 35] targets 16 GW of wind capacity by 2050, giving a preference to solar, nuclear and biomass energy. To sum up, up to 2018 even the most progressive previously published scenario for Vietnam targeted a maximum of 12 GW in 2030, way below the new trajectories assessed by ISF, while still overlooking the offshore wind potential. Only the Renewables-Led Pathway [START_REF] Breu | Exploring an alternative pathway for Vietnam's energy future[END_REF] foresees 39 GW of wind capacity installed in 2030.

World Bank published an Offshore Wind Roadmap for Vietnam [START_REF] Tran | Offshore wind roadmap for Vietnam[END_REF] to argue that building 10 GW in 2030 is much cheaper in the long run than building only 5GW. This provocative result arise because the cost of electricity in the high growth scenario reach grid parity earlier. It drops to 7 UScent/kWh in 2030 as opposed to 8 UScent/kWh in the low growth scenario. Two reasons explain the economies of scale. One is capital expenditure reduction with learning by doing and increased competition. The other is the cost of capital reduction with increased market confidence and less risk.

GWEC [START_REF] Lee | Global Offshore Wind Report 2021[END_REF] reports that "2020 saw global new wind power installations surpass 90 GW, a 53% growth compared to 2019, bringing total installed capacity to 743 GW, a growth of 14% compared to last year. The world's top five markets in 2020 for new installations were China, the US, Brazil, Netherlands and Germany." The share of offshore installations is growing [29, p. 51]. Offshore wind installation reached its first GW in 2007. According to REN21 [START_REF]Renewables 2021 Global Status Report[END_REF] globally about 6 GW of offshore wind capacity was connected in 2020, half of it in China. In this growing market, there are opportunities to develop domestic manufacturing and engineering capacities. Altough the result is surprising, the World Bank arguments that the economic policy objectives are as important as energy policy objectives has some value. The question is not just how to produce the cheapest possible electricity, but also how to integrate Vietnam in the global value chain of a growing sector. Now that Taiwan and Japan have both announced that they will install more than 5 GW of offshore wind power, manufacturers are already redesigning their machines and platforms to resist the typhoons and earthquakes prevalent in the Pacific area.

The Old Plan scenario (and previously published studies) neglects the role that offshore wind could play in 2030 in Vietnam, while the Factor Three scenario might seem extremely ambitious today: the commission of 42 new offshore wind farms in ten years. Both scenarios are extreme and unlikely. The most plausible future scenario is something in between. Connecting the realm of possible with extreme cases is useful for analysis.

Party resolution 55 orienting the national energy development strategy of Vietnam [START_REF]Vietnam's Politburo[END_REF] prioritize wind and solar energy for electricity generation, and mentions building 8 billion m3 of LNG import capacity by 2030. This suggests that the middle scenario is the most plausible of the three. Given the worldwide trends, the abundance of wind in Vietnam, the full pipeline of projects and the urgent need to increase electricity supply, we believe that the government will revise upwards the wind targets in the next PDP and commission several 500 MW offshore wind farms over the next ten years.

6

Variability and flexibility needs.

Wind power is a variable energy source: wind speed varies by day, region and season. The high wind speed season is during the north-eastern monsoon months (December, January, February) and low wind speed seasons is during the inter-monsoon months (March, April, and May). [START_REF] Doan | Usability and Challenges of Offshore Wind Energy in Vietnam Revealed by the Regional Climate Model Simulation[END_REF] observed that the seasonal variability 1 in Vietnam associated with monsoon onsets and the daily variability 2 associated with the wind diurnal cycles is in the 30-50% range, and the inter-annual variability 3 can reach up to 10%. The wind regime in the North differs from the wind regimes in the South and Center. The central and southern regions have high correlation (<80%), while the northern region shows weak correlation with the south (<5%). These fairly low correlations suggest that the wind generation would not peak at the same time within the country, as Figure 2 illustrates. Distributing wind generation deployment across the region and across intra-regional exchanges creates balancing effects that considerably reduces the variation of wind power output. The maximum change of wind generation within the central region from one hour to the next is 30%, but the Vietnam-wide maximum change is 2%.

To illustrate how the power system copes with variability, we compared simulations of the power supply under the Factor Three scenario in 2030 for the Red River Delta region during a windy and a windless week. By choosing this area, we want to stress that while the southern and central provinces attracted most solar and wind energy projects to date, the power grid must be balanced, and increasing power generation from renewable sources in the northern regions is an option.

In a windy week in May 2030 (Figure 3), wind and solar provide a major share of electricity generation, while fossil fuels make up only a minor share. In this case, wind power gets shunted from the windy Northeast and/or North Central Coast to the Red River Delta and displaced coal generation in three evenings.

Figure 4 shows the windless week simulation, in August 2030. Most of the power supply during this week comes from coal and gas, while solar photovoltaic and wind have minor contributions. The Red River Delta region is self-sufficient for almost the entire week, importing only minor amounts of electricity from other regions during evening hours. The variability of the two situations described in these figures is already being experienced by countries that have a much larger share of wind power in their energy mix than Vietnam does. In 2019, wind power represented 55% of the electricity produced in Denmark and 20% in Germany, according to IEA statistics. Because of intraannual variability, the maximum share of wind at monthly and weekly time scale is higher in some months. For example, wind satisfied 30% of Germany's electricity demand in January 2019 and only 18% in June 2019.

These simulations only aim to showcase the potential magnitude of the variability issue, in the hope that solutions will be found. The major flexibility solutions include pumped storage, battery storage, more flexible thermal and interconnection. Experience from Europe shows that anti-correlation of the seasonal weather patterns of monthly onshore wind and PV generation yields a more stable total variable renewable energy output. There is also a seasonal-scale balancing effect between variable renewables in Vietnam. Statistically, from November to February wind speed is high, from February to June solar radiation reaches a peak, and during the wet season from July to November the water inflows are high [32].

Researchers have observed that offshore wind has more stable output throughout the year than onshore. This alleviates the variability issue and contributes to reliable capacity in the medium and long term.

Another lesson from these countries is that variable renewable energy capacity is faster to install than flexibility solutions, in particular those involving additional grid infrastructure. That can lead to system congestion, high level of curtailment and suboptimal period of negative electricity prices.

Between 2008 and 2018, China increased its renewable power generation capacity from 15 GW to 370 GW [START_REF] Richard | Curtailment reduction 'could double Chinese wind fleet[END_REF], but failed to keep the same pace in transmission capacities. As a result, wind farms across China had an average curtailment rate of 17% in 2011 and 20% in 2012 [START_REF] Liu | The Economics of Wind Power in China and Policy Implications[END_REF]. Increased investment in transmission in recent years succeeded to bring down curtailment rates significantly, to 17.1% in 2016 and further in 2018 to only 7% [START_REF] Richard | Curtailment reduction 'could double Chinese wind fleet[END_REF].

This challenge also occurs in the Vietnam energy system. Over 30% curtailement rates are frequent in provinces where installed solar capacity exceed grid capacity [START_REF] Kenning | Vietnam leapfrogs Australia in solar deadline boom[END_REF]. Looking at the ability to release power from wind and solar sources up to 2022, [START_REF] Le | Ability to release capacity for wind power and solar power up to 2022[END_REF] found that in the Central Highlands area, only 40% of the total available capacity of wind power plants could be used, and there are also acute curtailment issues in the South Central area.

The increasing penetration of solar and wind power requires the adoption of technical and economic flexibility solutions. The flexibility solutions are not a distant hypothetical; they are a clear and near-term need. It is time to adapt the system now. Flexibility should be the guiding principle for Vietnam's 2020-2030 power development strategy.

Economic flexibility solutions aim to level the playing field for all technologies, replacing must-run and priority dispatch rules with competitive electricity markets. Mature markets include liquid intra-day trading, the balancing and ancillary services market, and the future and derivative markets. It is paramount that curtailment decisions remain transparent.

Technological flexibility solutions include: the flexible operation of thermal power plants; smart demand management; stronger interconnections with neighboring provinces and countries; hydro or chemical storage; and switching from coal to gas.

All the solutions cited above are or should be considered the PDP8. In preparation for 2050, Vietnam must also consider more innovative technical solutions such as power-to-gas; power-to-hydrogen; or vehicle to grid storage. Floating gas power plants, although not a part of the Masterplan, offer high flexibility at multiple timescales and complement offshore wind well at low investment cost.

7

Infrastructure requirements.

The rapid development of Vietnam electricity grid needs national-scale infrastructure investment. The next power development plan is an opportunity to optimize the conceptual design and prepare the financing of this infrastructure. The plan serves to coordinate the anticipations of the government, the State owned enterprises EVN and PVN and the private sector, towards the prospect of having many GW of offshore wind generation capacity installed in the next decade.

According to the IEA Wind, the 2017 baseline infrastructure cost for an existing offshore project was around 638 € / kW, with total capital expenditure of 3500 € / kW. At the scale of a 500 MW farm, this translates into 320 million euros for infrastructure and 1.75 billion euros capital expenditure. At the national scale, 10 GW capacity by 2030 means to invest 6.4 billion euros in offshore infrastructure. It is thus highly profitable to find ways to optimize these costs. Besides link capacity, distance from the shore and technological progress, a key driver of these costs is the network topology.

The naive approach is to connect each offshore wind farm to the inland grid independently. The ISF study (Teske, Morris, and Nagrath 2019) argues that the new infrastructure should not only be seen as East-West transmission lines from wind farms offshore to the land. As an alternative topology, consider the offshore busbar approach. An undersea cable runs parallel to the coast line, and wind farms connect to it. In any case, Vietnam needs to reinforce its electricity transmission network integrating the North, Center and South electric systems to accommodate its expansion. Part of the new network North-South capacity could be submarine. This National Offshore Power Link would transport the power to the two load centers in the north and south and -with lower capacity interconnects -to the other coastal provinces in between.

Figure 5 shows a possible position of this offshore wind cable which has interconnections with six currently existing grid knots to each of the six coastal regions. The total length of this cable would be 1775 km, including land connections.

Existing undersea projects confirm that such infrastructure is technically possible. To date, Ultra High Voltage cables have the largest transmission capacity of around 1 GW with voltage levels of typically 1000 kV per cable. The NordLink between Norway and Germany is over 620 km long, has a 1400 MW capacity, and is estimated to cost 1.5 to 2 billion euros (Skopljak 2019). The North Sea Link between Norway and UK at 730 km long and a 1400 MW capacity is estimated to cost 2 billion euros (North Sea Link 2019). The EuroAsia Interconnector cable will have 2000 MW capacity and a submarine segment of 1208 km. The construction cost of stage 1, with half capacity, is expected to cost 2.5 billion euros (EuroAsia interconnector 2019).

Having a strategic infrastructure, not only may decrease the offshore projects cost efficiency, it would also send signal that Vietnam is committed to generate electricity from offshore and near shore wind farms, and increase the' confidence of investors to build up a local industry. Diminishing returns due to the scarcity of the good onshore site will be felt in the long term, which increases the attractiveness of offshore wind. An undersea cable to reinforce the North-Center-South power system interconnection does not require sensitive discussions about land-use. The undersea interconnectors may feed discussions about the regional ASEAN super grid, which stands to be an important source of system flexibility and reliability at the 2050 planning horizon.

Conclusion and policy implications.

Vietnam is in the early stage of forming a marine economic development strategy in general and an offshore wind development plan in particular. Near-shore and offshore wind development is one of the key levers to boost the marine economy and protect the sovereignty of Vietnamese seas and islands. The following national plans define the role and orientation of wind offshore development strategies:  The National Marine Spatial Plan for 2021-2030 with a view to 2050. This plan provides the legal basis for the management and exploitation of offshore wind potential, for many aspects (i) the development and selection of marine spatial exploitation scenarios, to co-construct the near-shore and offshore power generation policies; (ii) maps for resource exploitation and maps for infrastructure development within marine space, to allocate zones for nearshore and offshore wind farms, for undersea cables and for seaports that serve the energy sector.  The National Power Development Plan for 2030 with vision to 2045 should specify annual targets for offshore wind development, consider the needs and opportunities of having submarine segments in the power transmission network, and coordinate the new offshore infrastructure with the new LNG power centers to maximize their complementarity.  The National Seaports Plan for 2030 with a vision to 2045 should improve the capacity of the seaport systems to support the energy sector. The plan should have specific goals for the development of seaports that provide services for the energy sector such as offshore wind combined with logistics and business services for other energy types. There is economic and technical potential to build dozens of GW of wind power generation capacity in Vietnam. After fifteen years of growing pains, the sector is taking off decisively. By mid 2021, only 538 MWp of wind power are operating, but 6 GW have signed PPA. And the total potential capacity based on projects is over 67.7 GW, of which 32.9 GW offshore. EVN expect to have about 5 GW of wind power connected by the end of 2021, before November to benefit from the preferential FIT of 85 USD/MWh.

Previously published studies, the Old plan scenario and the third draft of the Power Development Plan VIII underestimate the role of offshore wind power. Offshore wind energy can contribute significantly to the mid-to long-term Vietnamese energy mix. We believe that the government will auction several 500 MW offshore wind farms during the next decade. The most plausible of the three scenarios discussed in this paper is the New Normal: an installed capacity in 2030 of 15 GW for wind onshore and 10 GW for wind offshore. Wind offshore in the scenario includes two actions: a phase of progressive seaward extension of existing near-shore projects, and the construction of new large-scale "true offshore" projects delivering from 2025.

While offshore has more potential in the long run, in the next few years the onshore and near-shore will dominate installations because the technology is more mature and smaller scale projects are easier to conduct and integrate in the grid.

These numbers are orders of magnitude, more detailed analysis is needed to better understand how they will fit into Vietnam's fast moving energy supply and demand landscape. Moreover, one must identify the policy mechanisms for implementing the scenario at a reasonable cost to consumers [START_REF] Nguyễn | Cơ hội và thách thức trong phát triển điện gió ở Việt Nam -Opportunities and challenges to wind energy development in Vietnam[END_REF]. The two actions in the New normal scenarios may require two different mechanisms. For "true offshore projects" the industry [START_REF] Li | Vietnam's future transition to offshore wind auctions[END_REF] proposes a FIT with a 2026 deadline and a 4-5GW capacity cap, before moving into an auction system. This negociated tariff would compare to the negotiated PPAs terms given to thermal power projects. The land-use constraints for onshore wind require innovative solutions that respect agricultural needs. Finally, the reliability of offshore wind turbines in East Asian conditions must be demonstrated.

When it comes to policy discussion for 2030, while a scenario can be a useful anchor, the indication of a plausible range instead of a point estimate allows for more robust planning. To propose such ranges, we have to keep in mind a few points. First, today's pipeline recognizes 67.7 GW of potential wind power projects. Second, offshore has more potential to scale up than onshore. Third, 2030 is in ten years, while it takes 2 to 5 years to realize a projects. Considering these, we propose that the target for wind power in Vietnam disregards the old objectives in the PDP7, and focuses on 2030 installed capacity ranges of: 12-15 GW for onshore, 10-12 GW for offshore.

If the government sets an ambitious goal for 2030, we propose a few elements for the roadmap to get there, see [START_REF] Van Toan | The Zoning of Offshore Wind Energy Resources in the Vietnam Sea[END_REF], [18, p.], [START_REF] Tran | Offshore wind roadmap for Vietnam[END_REF], [START_REF] Nguyen | O shore wind integration study -Re ff view international experience on o shore wind integration[END_REF] for more detailed studies. To help the pioneer true offshore wind projects in Vietnam, they should be located in priority in the most windy area shown on Figure 1, the South Central coast. However the Nortern region also has attractive conditions for offshore wind power generation, they should be considered as soon as the technology is demonstrated.

To conclude, we offer three recommendations for the PDP8. First, wind energy targets for 2030 should be increased considerably over those in previous plans. Second, considering the amount of variable power generation sources already under construction and the number already in planning, increased flexibility of the electric system must be paramount in PDP8. Third, an infrastructure development plan for deploying several additional GW of offshore wind energy per year should start immediately.

Figure 1 :

 1 Figure 1: Mean wind power density for Vietnam at 100m, color scale from 0 to 1000+ W/m2. Source: Global Wind Atlas 3.1.

1 2 Normalized

 2 Normalized standard deviation of the monthly mean values of wind power density at 105 m height. standard deviation of hourly data.

3

  Normalized standard deviation of yearly mean from 10-year mean.

Figure 2 :

 2 Figure 2: Wind generation profile in 3 regions, first week of January. Generation profile from Renewable Ninja.

Figure 3 :

 3 Figure 3: Power mix simulation for the Red river delta in a very windy week (16th to 23th May 2030), Factor Three scenario.

Figure 4 :

 4 Figure 4: Power mix simulation for the Red river delta in a very low wind week (6th to 13th August 2030), Factor Three scenario. Imported electricity can be seen as white field between the red load curve and the black marked coal generation.

Figure 5 :

 5 Figure 5: How an offshore busbar could fit in the national power grid topology..

  The Bạch Long Vĩ island hybrid diesel + wind project inaugurated on October 30th, 2004 was a false start, not appropriate to the local capacities at the time. Vietnam's first high-capacity wind farm, 30 MW, was inaugurated by REVN, in the central province of Bình Thuận on April 18th, 2012. This is where the first turbine fire event in Vietnam happened on January 5th, 2020. The next two projects -the Phú Quý island hybrid grid with 6 MW and the near-shore Bạc Liêu phase 1 with 16 MW -both completed in 2013. These three pioneer projects had their lot of technical trouble: one turbine of the Phú Quý system has been broken for years; one turbine in the REVN wind farm caught fired on January 5th, 2020; and the construction of Bac Liêu phase 2 took was much slower than the norm today.No new capacity was added in 2014 or 2015. Provincial wind power development plans for eight provinces -Bạc Liêu, Bến Tre, Bình Thuận, Cà Mau, Ninh Thuận, Quảng Trị, Sóc Trăng and Trà Vinh -were published in 2016. That year the 24 MW Phú Lạc project in Bình Thuậnh province and the 83 MW Bạc Liêu phase 2 were completed. The following year, the 30 MW Hướng Linh 2 project in the Quảng Trị province was connected. By the end of 2018, the total installed wind power capacity in Vietnam had reached about 228 MW.

Table 1 :

 1 The wind power sector in Vietnam, july 2021. Capacities by project type and stage. Source: Author, public sources.

		Onshore	Intertidal	Offshore	Total
	Operation	399 MW	99 MW	0	498 MW
	Implementation	2 516 MW	1 139 MW	350 MW	4 004 MW
	Development	4 265 MW	2 015 MW	700 MW	7 880 MW
	Preliminary	20 525 MW*	2 366 MW	31 812 MW	54 703 MW
	Total	27 704 MW	6 519 MW	32 862 MW	67 085 MW
	According to [[13]], in March 2021, Electricity of Vietnam (EVN) informed the Min-
	istry of Industry and Trade that, up to now, they signed power purchase agreements
	(PPAs) with 113 wind power projects, with a total capacity of 6,038 MW. Based on
	reports of the investors, progress of construction investment and commercial opera-
	tion of the wind power projects as follows:
	1. The number of projects that have already put into commercial operation as
	12 projects with a total capacity of 582 MW.
	2. The number of projects that expect to be put into commercial operation be-
	fore October 31, 2021 as 87 projects with a total capacity of 4,432 MW.
	3. The number of projects that cannot be put into commercial operation before
	December 31, 2021 as 14 projects with a total capacity of 1,024 MW
	The numbers from

EVN confirms the main finding of Table 1, based only on public sources: over 4 GW of wind power projects are actively building by mid 2021.

Table 2 :

 2 Current and proposed FIT for wind projects in Vietnam (updated 2021-06-212).

	Project type	Onshore	At sea
	COD until Oct 2021	8.5 UScent/kWh	9.8 UScent/kWh
	COD from Nov 2021 to Dec 2022 7.02 UScent/kWh	8.47 UScent/kWh
	COD in 2023	6.81 UScent/kWh	8.21 UScent/kWh

Table 3 :

 3 Visions for Vietnam onshore and offshore wind energy installed capacity in 2030. Source: Authors.

	Scenario	Onshore wind	Annual market	Offshore	Annual market
		Capacity			wind capacity
		in 2030			in 2030
			2020-2025	2026-2030
	Old Plan	6.1 GW	300 MW/yr	1 000 MW/yr	0.15 GW	negligible
	New Normal	16.6 GW	730 MW/yr	3 200 MW/yr	9.5 GW	1000 MW/yr in 2023-2025, then
						ramp up
						to 1400 MW/yr in 203
	Factor Three	21.6 GW	1 530 MW/yr	3 625 MW/yr	20.9 GW	1000 MW in 2023
						1900 MW in 2024, then 3000
						MW/yr from 2025 on

Acknowledgements

Lead author HDM is responsible for the framing, data collection and the scenario narratives. The ISF study team is responsible for modeling simulations. All authors contributed to the reviewing. Authors declare no conflict of interest. We acknowledge financial support from the European Climate Foundation grants. We thank two anonymous reviewers.


Paul Ecoffet
email: paul.ecoffet@sorbonne-universite.fr

Nicolas Bredeche

Jean-Baptiste André

Nothing better to do? Environment quality and the evolution of cooperation by partner choice

Keywords: Simulation, Evolution of co-operation, Partner choice, Biological markets

Partner choice enables individuals to avoid defectors, but is seldomly observed in nonhuman animals. We show that the availability of

Introduction

Among the diversity of mechanisms put forward to explain the evolution of cooperation among non-kin, partner choice has been considered over the last twenty years as having probably played a particularly important role [START_REF] Baumard | A mutualistic approach to morality: The evolution of fairness by partner choice[END_REF][START_REF] Bull | Distinguishing mechanisms for the evolution of co-operation[END_REF][START_REF] Eshel | Assortment of encounters and evolution of cooperativeness[END_REF][START_REF] Noë | Biological markets: supply and demand determine the effect of partner choice in cooperation, mutualism and mating[END_REF][START_REF] Schino | Reciprocity in group-living animals: Partner control versus partner choice[END_REF]2 West, Griffin, & Gardner, 2007). When individuals can choose among several different partners, which they can compare and compete against each other as in an economic market, this generates a selection pressure to cooperate more, in order to appear as a good partner, and attract others' cooperation [START_REF] Noë | Biological markets: supply and demand determine the effect of partner choice in cooperation, mutualism and mating[END_REF].

The effects of partner choice have been well described in a large number of biological systems. [START_REF] Noë | Economics in nature: social dilemmas, mate choice and biological markets[END_REF]. For example, in the interaction between cleaner fishes and their clients the law of supply and demand determines the way in which the added value of the interaction is shared, in accordance with market principles [START_REF] Bshary | Image scoring and cooperation in a cleaner fish mutualism[END_REF]. When cleaners are rare, clients tolerate cheating on their part, while they become more picky when cleaners are numerous. The effects of partner choice have been documented in primate grooming, in meta-analyses showing that females groom preferentially those that groom them most and that a positive relation exists between grooming and agonistic support [START_REF] Schino | Grooming and agonistic support: a meta-analysis of primate reciprocal altruism[END_REF][START_REF] Schino | Grooming reciprocation among female primates: a metaanalysis[END_REF]. In vervet monkeys, experiments have shown that individuals groom others in exchange for access to food, and do so for longer periods when fewer partners are available [START_REF] Fruteau | Supply and demand determine the market value of food providers in wild vervet monkeys[END_REF]. The effects of partner choice have also been documented in humans where it has been shown that the need to attract social partners is a major driver of cooperation [START_REF] Barclay | Biological markets and the effects of partner choice on cooperation and friendship[END_REF][START_REF] Barclay | The Evolutionary Psychology of Human Pro-sociality: Adaptations, Byproducts, and Mistakes[END_REF][START_REF] Barclay | Partner choice creates competitive altruism in humans[END_REF][START_REF] Baumard | A mutualistic approach to morality: The evolution of fairness by partner choice[END_REF] Stephane Debove et al., 2015). Besides, beyond cooperation partner choice also plays a decisive role in mating, leading to the evolution of secondary sexual characteristics, nuptial gifts, and/or to assortative matching [START_REF] Andersson | Sexual selection and mate choice[END_REF][START_REF] Hammerstein | Biological trade and markets[END_REF][START_REF] Zahavi | Mate selection-A selection for a handicap[END_REF].

On the other hand, there are a number of other biological situations in which one would typically expect partner choice to also play an important role, but where no such effect has ever been demonstrated. These include most intraspecific collective actions in non-human animals. This is particularly salient in collective hunts such as collobus hunting in chimpanzees, or pack hunting in carnivores. No empirical evidence in these species suggests that individuals cooperate for reasons related to partner choice, either to attract partners or to be accepted by them in their hunts. On the contrary, the majority of available data are consistent with the more parsimonious explanation that individuals are simply 3 doing what is in their immediate best interest at any given time [START_REF] Melis | Do chimpanzees reciprocate received favours?[END_REF][START_REF] Melis | Chimpanzees, Pan troglodytes, share food in the same way after collaborative and individual food acquisition[END_REF][START_REF] Packer | [END_REF][START_REF] Packer | The evolution of cooperative hunting[END_REF]. In particular, if cooperation in collective hunts was driven in part by the need to appear as a good partner, individuals would be expected to willingly share the product of their hunts in a way that depends on everyone's actual engagement, to encourage participation in other hunts in the future. However, such voluntary and conditional sharing has never been documented in animal collective hunts [START_REF] Melis | Chimpanzees, Pan troglodytes, share food in the same way after collaborative and individual food acquisition[END_REF]. In evolutionary terms, therefore, collective hunting in these species is most likely an instance of by-product cooperation, rather than an instance of reciprocal cooperation based on partner choice. This lack of observation is all the more surprising given that, in similar collective actions, human behaviours are demonstrably driven by the need to appear as a good partner [START_REF] Alvard | Rousseau's Whale Hunt?[END_REF][START_REF] Baumard | A mutualistic approach to morality: The evolution of fairness by partner choice[END_REF]. One may therefore wonder why the same effects did not produce the same consequences in other species. Such a lack of observation could always be the consequence of methodological difficulty in empirically proving the existence of partner choice, and more generally of conditional cooperation, outside humans [START_REF] Kaplan | A theory of human life history evolution: diet, intelligence, and longevity[END_REF][START_REF] Raihani | Resolving the iterated prisoner's dilemma: theory and reality[END_REF]. However, we would like to suggest an alternative here, namely that there is in fact a strong constraint impeding partner choice in a large number of situations.

Partner choice requires that individuals can compare and choose among several opportunities for cooperation. In some cases, partners themselves constitute opportunities for cooperation and partner choice then only requires that partners are many and accessible. This is the case, for instance, in mating markets, or in most instances of interspecific mutualism.

In other cases, however, finding an opportunity for cooperation requires more than just finding a partner. This is what happens when cooperation consists of several individuals working together to exploit environmental resources. In this case, a cooperation opportunity requires both a partner(s) and a resource, which imposes an additional constraint limiting the scope of partner choice. When resources are scarce, there are always few options to compare, and partner choice cannot operate. This could explain the lack of 4 cooperation, beyond by-product cooperation, in many instances of collective actions in the wild despite the availability of potential partners.

To our knowledge, all models published so far on the evolution of cooperation by partner choice focus on situations where finding a partner is sufficient to create an opportunity to cooperate. In this case, they show that partner choice is able to drive the evolution of cooperation in a relatively wide range of circumstances [START_REF] Aktipis | Know when to walk away: Contingent movement and the evolution of cooperation[END_REF][START_REF] Aktipis | Is cooperation viable in mobile organisms? Simple Walk Away rule favors the evolution of cooperation in groups[END_REF]J.-B. André & Baumard, 2011;André & Baumard, 2011;[START_REF] Barclay | Competitive helping increases with the size of biological markets and invades defection[END_REF][START_REF] Campennì | Partner choice promotes cooperation: The two faces of testing with agent-based models[END_REF]Stéphane Debove et al., 2015;[START_REF] Debove | On the evolutionary origins of equity[END_REF][START_REF] Geoffroy | Why cooperation is not running away[END_REF][START_REF] Johnstone | Mutualism, market effects and partner control[END_REF][START_REF] Mcnamara | The coevolution of choosiness and cooperation[END_REF][START_REF] Noë | Biological markets: supply and demand determine the effect of partner choice in cooperation, mutualism and mating[END_REF]. In this paper, we wish to examine what happens on the contrary when resource availability constitutes a constraint on the operation of partner choice. To do so, we simulate the evolution of agents placed in an environment containing resources that can be exploited collectively. We show that, in a low-resource environment, and even if there are plenty of partners, partner choice is not able to drive the evolution of cooperation as individuals cannot pit the few cooperation opportunities against each other.

What is more, we also show that the number of potential partners actually has a negative effect on the evolution of cooperation when patches are scarce. When potential partners are numerous relative to the number of patches available, there are always too many individuals on any given resource as individuals have nothing else to do anyway. Hence, there is no point in trying to attract partners but on the contrary there are benefits in trying to limit their number. Partner choice is thus only effective when the number of available partners lies within a precise range of values, all the narrower as the availability of patches is low.

We believe that this constraint plays a central role in explaining that, in many species, although individuals do participate in collective actions, sometimes finely coordinating their behaviour with that of others, they do not actually seek to cooperate beyond what is in their immediate personal interest. In contrast, in the case of the human species, thanks to extensive cognitive skills individuals are able to extract resources from a greater variety of situations. As a result, humans actually live in an environment that is much richer in resources than other species. Hence they can compare and compete over a greater diversity 5 of opportunities for cooperation against one another, and are thus forced to cooperate more intensively to attract partners.

Methods

We consider a population of individuals living in an environment consisting of 𝑁 𝑒 𝜔 different patches on which resources are located. Every generation of the simulations is constituted of time steps during which individuals gather payoff units. At the end of these 𝑇 time steps, individuals reproduce in proportion to their total payoff, and die. During a 𝑇 time step, every individual is considered one by one in a random order. When her turn comes, an individual evaluates each of the patches of the environment, including the 𝜔 patch where she is currently located, assigns each a score (details in section 2.1), and then moves toward the patch with the highest score, or stays on her current patch if that's the one with the highest score. Once every individual has taken this decision, individuals express their cooperation strategy on their local patch, and they collect a payoff that depends on their own and their partners' cooperation strategy. Patches can disappear every time step, with a probability , and are then immediately replaced by an empty patch. 𝑑

In our analyses, we will vary , which represents the number of individuals present 𝑁 𝑒 together in the environment (i.e. the social population size). However, we want to keep constant the genetic population size ( ) so as not to alter the relative strength of drift 𝑁 ≥ 𝑁 𝑒 and selection. To do so, we create parallel environments. The individuals of the ⌈𝑁 𝑒 /𝑁⌉ 𝑁 genetic population are then randomly assigned, so that each environment has exactly 𝑁 𝑒 individuals. For the last environment to be completed, randomly chosen genetic individuals are duplicated, but their payoff in this environment is then not considered for the calculation of their fitnesses.

The decision-making mechanisms

The individuals' strategy in this environment consists of two separate decisions.

6

On the one hand, the individual must evaluate the different patches available and assign a score to each. This decision is made by an artificial neural network, called the "patch ranking" network. For each patch, this neural network has the following input information:

(i) the number of other individuals already present on the patch, (ii) the average level of cooperation expressed by these individuals in the last time step, (iii) the level of cooperation that the focal individual would express should she join this patch, and (iv) a binary that indicates whether or not the individual would have to move in space in order to join this patch (i.e. this binary distinguishes the patch where the individual is currently located from all other patches). For (i), (ii) and (iii), their values are partitioned into a number of decimals and a number of units, each projected to a distinct input of the neural net. This allows the controller to easily distinguish small variations.

On the other hand, the individual must decide on a level of cooperation once she is on a patch. This decision is made by another artificial neuron network called the "cooperation" network (plus some phenotypic variability, see below). As an input, this neural network only has the number of other individuals present on the same patch as the focal. This entails that we assume that the agent cannot modulate her cooperation level in function of others' cooperation level. This assumption is meant to exclude the possibility that partner control strategies may evolve, and allows us to focus only on the effect of partner choice [START_REF] Schino | Reciprocity in group-living animals: Partner control versus partner choice[END_REF].

The connection weights of both networks constitute the genome of each agent. They evolve by natural selection as exposed in the section 2.3.

Phenotypic variability of cooperation

Each individual present on a patch invests a given amount into cooperation -where is 𝑖 𝑥 𝑖 𝑥 𝑖 decided by the individual's cooperation network. However, as is now well established in the literature, selective pressures in favour of any form of conditional cooperation, and therefore in particular in favour of partner choice, stem from the presence of some variability in partners' cooperative behaviour (see [START_REF] Mcnamara | Variation and the response to variation as a basis for successful cooperation[END_REF] for a review of this idea). In order to capture the effect of variability in the simplest possible way, here we consider the effect of phenotypic variance in the expression of individuals' genes.

𝑥 -𝑖 𝑖 𝑃(𝑥 𝑖 ,𝑥 -𝑖 ,𝑛) = 𝐹(𝑛) × [ 𝑎𝑥 𝑖 + 𝑏𝑥 -𝑖 -1 2 𝑥 2 𝑖 ]
where represents the immediate, self-interested, benefit of cooperation, and represents 𝑎 𝑏 the social benefit of cooperation for others. The function is meant to capture the fact 𝐹(𝑛) that there is an optimal number of individuals exploiting a patch and is given by

𝐹(𝑛) = 𝑒 -(𝑛 -𝑛) 2 /(2𝜎 2 )
where is the optimal number of individuals per patch and measures the tolerance to 𝑛 𝜎 variations in the number of individuals per patch (i.e., measures the strength of the 𝜎 -1 penalty that stem from being a suboptimal number of individuals on the same patch).

This payoff function has been chosen in such a way that, in the absence of partner choice, the evolutionarily stable strategy is always to invest the individually optimal investment (i.e.

), whereas the "socially optimal" cooperation, that is the level of cooperation 𝑥 𝐸𝑆𝑆 = 𝑎 that would maximise the average payoff of individuals on the patch, is to invest . 𝑥 = 𝑎 + 𝑏

The evolutionary algorithm

Each individual has a genome composed of the weights of its two neural networks, which makes a total of 84 genes with . We consider a population of 𝑔 = (𝑔

Results

Cooperation cannot evolve when patches are scarce

We simulated the evolution of a population of individuals for 𝑁 𝑒 = 100 𝐺 = 1500 generations, for different values of the number of resource patches , but always in a 𝜔 situation where the optimal number of individuals per patch was . Cooperation only 𝑛 = 2 evolved when patches were more abundant than a threshold (Fig. 1,a). This can be understood as follows. When resource patches are few, precisely when , individuals 𝜔 <

𝑁 𝑒 𝑛

have little cooperation opportunities and there are therefore always more individuals per patch than what would be optimal (in this case, the optimal number of individuals per patch is

). As a result, additional individuals joining a patch are more of a nuisance than a 𝑛 = 2 benefit, and there is therefore no benefit in trying to attract partners by appearing cooperative. We then simulated the evolution of cooperation in situations where the optimal number of individuals per patch, , was larger (Fig. 1, b-c). Overall, the outcome was even less 𝑛 favourable to cooperation. This may seem paradoxical but can be understood as a consequence of the law of large numbers. When the number of individuals per patch is large, whether it is greater or less than , the effect of each individual on the average quality 𝑛 of her patch is very small anyway. There is therefore little value for an individual to invest in cooperation to try and attract partners.

We performed the same simulations in the case where the number of individuals per patch is neutral ( , Fig. 1,d). Cooperation did not evolve either and this can be understood 𝜎→∞ also because there cannot be any benefit in attracting partners when the number of individuals per patch does not matter.

Finally, we run simulations where we vary the coefficient of friction and find that the 𝜎 lower the friction (ie. the higher the ), the less cooperative the agents are. The results are 𝜎 available in the supplementary materials (Fig. S1). We also varied the cost of moving for the agents and find that the higher the cost, the less cooperative the agents are, as expected from the literature on partner choice. These results are available in the supplementary materials (Fig. S2).

Overall, the evolution of cooperation by partner choice can only take place in the restricted conditions where (i) there is an optimal number of individuals per resource patch, (ii) this optimal number is low, and (ii) the number of resource patches in the environment is large.

Cooperation cannot evolve when there are too many partners around

In a second step, we simulated again the evolution of a population of individuals 𝑁 = 100 for generations in a situation where the optimal number of individuals per patch 𝐺 = 1500 was , but this time we held the number of patches constant, , while varying the 𝑛 = 2 𝜔 = 20 actual number of individuals, , present together in the environment.

𝑁 𝑒

In this case, cooperation only evolved when the number of individuals in the environment was intermediate. This can be understood as follows. When the number of individuals in the environment, , is too close to the number of individuals, , that are needed to exploit at 𝑁 𝑒 𝑛 least one patch -or even more so when , then the number of available partners is 𝑁 𝑒 < 𝑛 limiting. As a result, the actual number of cooperation opportunities from which individuals can choose is very low, partner choice is thus a weak force, and the benefit of investing into cooperation is low. On the other hand, when the number of individuals in the environment, is larger than the total number of individuals that can be accommodated on the available 𝑁 𝑒 patches, that is when , the number of available patches is limiting. In this case we 𝑁 𝑒 > 𝑛𝜔 12 find the result described above (Fig. 2,a). The problem is rather that there are always too many individuals on each patch than too few and partner choice is also a weak force. There is, therefore, a range of intermediate population densities, neither too low nor too high, for which cooperation can evolve.

= 2 𝜎 = 1 𝑛 < 𝑁 𝑒 . < 𝜔 × 𝑛
We then performed the same simulations again, but with more patches available in the environment (i.e. for larger , Fig. 2,b,c). We observed that the range of population 𝜔 densities for which cooperation could evolve was then broader. This can again be understood in the above framework. On one hand, the lower boundary of population density, , below which the number of individuals is a limiting factor, is unaffected by 𝑁 𝑒 ≈ 𝑛 the number of patches available. On the other hand, the upper boundary of population density, , above which the number of patches is a limiting factor, increases with the 𝑁 𝑒 > 𝑛𝜔 number of patches, . As a result, the width of the range of population densities where 𝜔 partner choice is effective increases.

Discussion

Partner choice can lead to the evolution of cooperation when individuals can compare several opportunities for social interaction and choose the most advantageous ones. In this article, we have shown that the conditions for this to happen are, however, quite restrictive.

They entail that individuals truly have access to a range of social opportunities. Yet, in many cases, social opportunities are rare because they necessitate the co-occurrence of two things at the same time: (i) at least one available partner, and (ii) an exploitable resource or, more generally, "something to do" with that partner. In this article, we have used individual-centred simulations to study the consequences of this constraint on the evolution of cooperation by partner choice. We have obtained the following results.

First, partner choice cannot lead to the evolution of cooperation when resources are scarce, and therefore opportunities for cooperation are rare. This explains why, in many species, social interactions show no evidence of cooperation beyond immediate self-interest [START_REF] Bullinger | Chimpanzees, Pan troglodytes, prefer individual over collaborative strategies towards goals[END_REF][START_REF] Melis | Chimpanzees, Pan troglodytes, share food in the same way after collaborative and individual food acquisition[END_REF][START_REF] Scheel | Group hunting behaviour of lions: a search for cooperation[END_REF]. Even when individuals engage in collective actions, for example when they hunt collectively, others have so few alternative opportunities anyway that there is no need to seek to draw them into the collective actions. They will come anyway, for want of anything better to do. Even worse than that, as opportunities for cooperation are rare, not only are there always enough partners in each collective action without it being necessary to actively attract them. In fact the opposite is true: There are always too many individuals participating in each cooperation endeavour (see Figure 2). This has been documented for instance in pack hunting in Lions, where Packer showed that lionesses often hunt in groups that are too large compared to what would be optimal [START_REF] Packer | Why lions form groups: food is not enough[END_REF]. In such a case, the average gain per individual in a collective action is reduced and not increased by the participation of others, and there is therefore no selection to attract partners but rather a selection to push them away at the time of sharing.

Second, partner choice can lead to the evolution of cooperation when partners constitute in themselves resources. There is, in this case, no further requirement for a social opportunity, than the need to find a partner. This occurs, for instance, in sexual markets, or in the many instances of interspecific mutualisms, where the other individual alone constitutes an opportunity to cooperate. It is therefore understandable that partner choice plays a particularly important role in these two types of interactions [START_REF] Andersson | Sexual selection and mate choice[END_REF][START_REF] Bshary | Experimental evidence that partner choice is a driving force in the payoff distribution among cooperators or mutualists: The cleaner fish case[END_REF][START_REF] Schino | Grooming reciprocation among female primates: a metaanalysis[END_REF].

14 Third, partner choice can lead to the evolution of cooperation when the environment is rich or, said differently, when individuals are efficient at finding opportunities for cooperation in their environment. Living in an environment rich in opportunities, and/or having skills that increase the effective number of opportunities one can exploit, brings with it the possibility of choosing between different opportunities. This puts greater pressure on individuals, who are then competing to attract partners on their own opportunity, rather than on another, and thus selects for cooperation beyond immediate self-interest.

This entails that the evolution of cooperation is related to the evolution of cognitive abilities, which sheds particular light on the case of the human species. The link between cooperation and cognition is a debated issue and several hypotheses have been put forward in the literature. The social brain hypothesis, in particular, posits that cooperation, and social life more generally, constitutes in itself a selection pressure favouring the evolution of greater cognitive capacities meant to deal with the complexity of social life. More recently, Dos Santos & West [START_REF] Santos | The coevolution of cooperation and cognition in humans[END_REF] have hypothesised that the cognitive ability to cooperate efficiently, and to coordinate with others in particular, could jointly evolve with cooperation itself. Both hypotheses, however, are about the joint evolution of cooperation with cognitive capacities that are specifically dedicated to cooperation itself.

Here we show that cognitive abilities that have nothing to do with cooperation or sociality per se, namely the sheer ability to extract resources from the environment, could also play a role in the evolution of cooperation. This occurs because enhanced cognitive abilities allow to transform and extract high-value resources from the environment [START_REF] Kaplan | A theory of human life history evolution: diet, intelligence, and longevity[END_REF], thereby creating more opportunities for cooperation. As a result, a given environment contains more opportunities for cooperation for individuals with strong cognitive skills, such as human beings, than for the individuals of other species.

This then affects the state of the market for cooperation, increasing the amount of competition between alternative social opportunities, thereby selecting for more investment into cooperation to attract partners.

21

agents cooperate. d-g. When the friction strength is low (ie. ), agents do not cooperate. 𝜎 ≥ 1.5 This is explained by the fact that too many agents (including cheaters) can come on the resource without suffering a friction that has a strong impact on the gains. So there is a dilution effect of responsibility that sets up in the same way as when is big. 𝑛 generations. The reference figure when the cost is 0 is available in Fig. 1,a. The greater the cost is, the less cooperative the population is. Increasing the cost of moving increases the cost of partner choice. When the cost is too high, it is of no interest for the agents to cooperate so as to attract new partners, as if a cheater joins them, it will be too costly for them to leave the opportunity with a defector.

Fig 1 :

 1 Fig 1: Mean investment in simulation for different number of opportunities and a fixed 𝜔 population of individuals. Results after generations. a. When 𝑁 𝑒 = 100 1 500 𝑛 = 2,𝜎 = 1 Cooperation evolves when . b-c. For , cooperative behaviours never evolve. 𝜔 ≥ 50 𝑛 ≥ 3,𝜎 = 1 d. When , there is no pressure for agent to attract partners and cooperative behaviours 𝜎→∞ never evolve.

Fig 2 :

 2 Fig 2: Effect on the population size in the environment with 20, 40 or 80 patches and an optimal number of agents and . Agents have a cooperative behaviour for 𝑛 = 2 𝜎 = 1 𝑛 < 𝑁 𝑒

Fig S2 :

 S2 Fig S2: Mean investment in simulations for different numbers of opportunities , different 𝜔 values of cost of moving and a fixed population of individuals. Results after 1500 𝑁 𝑒 = 100

7

  At each generation of our simulations, each individual is subject to the effect of a phenotypic noise that modifies her cooperation level. If is the cooperation level decided by the 𝑥 𝑔 𝑖 cooperation network of individual i, then the actual cooperation level player by the

	individual is	, where is drawn ramdomly as follows. The interval 𝑖 +𝜖 𝑥 𝑖 = 𝑥 𝑔 𝜖	[ -1,1]	is
	uniformly split in	𝑁 𝑒	values, and every individual gets one value of chosen among these 𝜖	𝑁 𝑒
	values without replacement.
	2.2.	The payoff function
	Individuals present on the same patch play a modified version of the n-player prisoner's
	dilemma. Consider a focal individual playing , in a patch on which there are 𝑖 𝑥 𝑖	𝑛 -1	other
	individuals whose average level of cooperation is	. The payoff of individual is given by

Journal of Evolutionary Biology

Environment quality and partner choice

Funding

This work was supported by the Agence Nationale pour la Recherche under Grants No ANR-18-CE33-0006 MSR, ANR-17-EURE-0017 FrontCog and ANR-10-IDEX-0001-02 PSL. making of this article is available at https://osf.io/p5whz.

data and source code used for the

Conflict of Interest

The authors declare that there is no conflict of interest.

Supplementary Material


Nicolas Bredeche
email: nicolas.bredeche@isir.upmc.fr

Jean-Marc Montanier
email: montanier.jeanmarc@gmail.com

Berend Weel
email: b.weel@vu.nl

Evert Haasdijk
email: e.haasdijk@vu.nl

Roborobo! a Fast Robot Simulator for Swarm and Collective Robotics

Keywords: robot simulator, evolutionary robotics, swarm robotics, collective adaptive systems, C++, SDL graphics library

Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary optimization, as well as online onboard embodied evolution and embodied morphogenesis.

INTRODUCTION

This paper introduces Roborobo!, a light-weight multiplatform simulator for extensive robotics experiment, based on basic robotic hardware setup similar to the famous ePuck [5] or Khepera [START_REF] Mondada | The Development of Khepera[END_REF] robotic platforms. Roborobo! is mostly intended at, but not limited to, researchers and practictionners interested in collective adaptive systems and evolutionary robotics, with a particular emphasis on embodied evolution (ie. online evolution) and swarm robotics (> 100 robots, as illustrated in Figure 1). The underlying idea is to provide a versatile framework for easy development of new ideas and very fast and robust implementation for extensive experiments. Roborobo! is available under the new BSD Licence [18] at https://code.google.com/p/Roborobo/.

With respect to other robotic simulators, Roborobo! takes an intermediate approach to model a robotic setup in order to combine (pseudo-)realistic modelling with fast-paced simulation. As such, it stands inbetween realistic, but slow, robotic simulation framework (such as Player/Stage [START_REF] Gerkey | The player/stage project: Tools for multi-robot and distributed sensor systems[END_REF], Webots [26], V-Rep [25], Gazebo [7] or Microsoft Robotic Developper Studio [17]), and unrealistic, but easy to use, agentbased simulation tools such as Netlogo [START_REF] Tisue | Netlogo: A simple environment for modeling complexity[END_REF] and MASON [12]. It also differs from easy-access and fast robotic agent simu-lators such as Breve [START_REF] Klein | breve: a 3d environment for the simulation of decentralized systems and artificial life[END_REF] and Simbad [START_REF] Hugues | Simbad: An autonomous robot simulation package for education and research[END_REF], by focusing solely on swarm and aggregate of robotic units, focusing on largescale population of robots 1 in 2-dimensional worlds rather than more complex 3-dimensional models.

Roborobo! is written in C + + with the multi-platform SDL graphics library [22] as unique dependency, enabling easy and fast deployment. It has been tested on a large set of platforms (PC, Mac, OpenPandora [20], Raspberry Pi [21]) and operating systems (Linux, MacOS X, MS Windows). It has also been deployed on large clusters, such as the french Grid5000 [START_REF] Cappello | Grid'5000: a large scale and highly reconfigurable grid experimental testbed[END_REF] national clusters. Roborobo! has been initiated in 2009 and is used on a daily basis in several universities for both research and education, including Universite Paris-Sud and Universite Pierre et Marie Curie (France), Vrije Universiteit Amsterdam (NL), and NTNU (Norway).

Roborobo! was originaly developped by Nicolas Bredeche in 2009 in the context of the european Symbrion Integrated Project [23], and has been continuously extended since then. It has been extensively used in various contexts, mostly concerned with evolutionary robotics and swarm robotics, including embodied evolution [START_REF] Huijsman | An on-line on-board distributed algorithm for evolutionary robotics[END_REF][START_REF] García-Sánchez | Testing diversity-enhancing migration policies for hybrid on-line evolution of robot controllers[END_REF], environment-driven evolutionary adaptation [START_REF] Montanier | Embedded evolutionary robotics: the (1+1)-restart-online adaptation algorithm[END_REF]1,[START_REF] Montanier | Surviving the tragedy of commons: Emergence of altruism in a population of evolving autonomous agents[END_REF][START_REF] Montanier | Emergence of altruism in open-ended evolution in a population of autonomous agents[END_REF][START_REF] Bredeche | Environment-driven Distributed Evolutionary Adaptation in a Population of Autonomous Robotic Agents[END_REF][START_REF] Bredeche | Open-ended evolution with a population of autonomous robots[END_REF][START_REF] Noskov | Monee: Multi-objective and open-ended evolution[END_REF] and selfassembly [START_REF] Weel | The emergence of multi-cellular robot organisms through on-line on-board evolution[END_REF][START_REF] Weel | Body building: Hatching robot organisms[END_REF].

Figure 1 :

 1 Figure 1: Classic examples with 1 virtual (e-puck) agent, incl. IR sensor rays (left) ; 100 virtual agents (center) ; 5000 virtual agents (right).

Acknowledgments

The authors wish to thank all people who contributed to this project, including Leo Cazenille. This work was made possible by the European Union FET Proactive Initiative: Pervasive Adaptation funding the Symbrion project under grant agreement 216342. Roborobo! was extensively tested in a cluster environment using the Grid'5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER and several Universities as well as other funding bodies (see https://www.grid5000.fr).