Integration of Wind Power into the British System in 2020

Ngoc Anh Le†

and Subhes C. Bhattacharyy1‡

*

This is the pre-publication version of the paper which appeared in Energy (vol. 36, Issue 20, pp.

5975-83, 2011).

† CEPMLP, University of Dundee, United Kingdom. Email: lnaecon@gmail.com.

*‡ Corresponding author. Previously with CEPMLP, University of Dundee, Dundee, UK. At present, Professor,

Energy Economics and Policy, De Montfort University.

Email: subhesb@dmu.ac.uk / subhes_bhattacharyya@yahoo.com

Abstract

This paper investigates the integration of renewable electricity into the UK system in

2020. The purpose is to find the optimal wind generation that can be integrated based on total

cost of supply. Using EnergyPLAN model and the Department of Energy and Climate Change

(DECC) energy projections as inputs, this paper simulates the total cost of electricity supply with

various levels of wind generation considering two systems: a reference and an alternative system.

The results show that 80 TWh of wind electricity is most preferable in both systems, saving up to

0.9% of total cost when compared to a conventional system without wind electricity production.

The alternative system, with decentralized generation and active demand management, brings

relatively more cost saving, and higher wind utilisation, compared to the reference case. The

sensitivity analysis with alternative fuel and capital costs again confirms the superiority of the

alternative over the reference system.

Keywords: wind power, EnergyPLAN, British system, cost minimization.

1. Introduction

Electricity from wind while clean and sustainable is variable in nature. The ability to

generate power depends on the availability of wind at an acceptable speed1 . However, wind

speed is difficult to forecast accurately, which in turn raises concerns about large-scale

integration of wind electricity into the power system due to technical and economic challenges

[1]. Technically, the availability of wind power will be lower than expected during low wind

speed conditions, which in turn will cause supply imbalance (and can cause power shortages),

thereby requiring a greater reliance on back-up capacity. On the other hand, high wind speed can

lead to network bottlenecks and low capacity utilisation for conventional power plants. These

technical problems are directly linked to commercial issues, as they affect the system operating

costs. Given this trade-off between conventional power and wind power in terms of costs and

environmental considerations, a careful analysis is required to determine how much wind

generation can be integrated at the lowest social cost, or at an acceptable electricity price to the

consumers.

The UK government has put renewable energies as a top priority in its energy policy.

According to the UK Renewable Energy Strategy [2], wind will be the major source for

renewable electricity in 2050. While the issues related to wind power integration have been

studied extensively for the UK, such as in Refs. [3], [4], [5], [6], [7], and [8] using a variety of

cost-benefit approaches to analyze the benefits to the electricity sector or to the whole energy

system; there is no general consensus from these studies. For example, Boyle [7] suggested that

95% of electricity needs can be supplied from wind power based on a technical acceptability

analysis, while another study [8] indicated that 40% of wind integration is desirable. This study

applies a modelling framework, EnergyPLAN that has been widely used for other countries in

Europe [as can be seen from Refs. [9] and [10]], except, to the best of our knowledge, the UK.

The EnergyPLAN model has also been used for the case of Denmark [11], [12], [13] and [20]

and focused on the interaction between three sectors: heat, electricity and transport, with a

special emphasis on the district heating subsector. Other applications of the model include Ref.

[21].

This paper examines the integration of wind power into the British system in 2020.

Specifically, the optimal wind capacity for the UK will be identified based on total social cost.

Two scenarios for the UK energy systems will be considered:

- Reference system: 2020 UK energy system with conventional transmission and

distribution.

- Alternative system: where a modified energy system is considered in 2020 with more

distributed generation and advanced transmission and distribution.

The paper is organized as follows: section 2 presents the methodology, data used and the

details of the alternative scenarios. Section 3 presents the technical results while section 4

contains the economic results. Some concluding remarks are presented in the last section.

1 There is a minimum (cut-in) and a maximum (cut-out) speed for any wind turbine. Power is generated when the

speed lies within this range. The cut-in speed generally lies between 7 and 10 miles per hour (mph) while the cut-out

speed can range between 45 and 80 mph.

2. Methodology

As indicated earlier, this paper uses the EnergyPLAN model to analyse the issue of wind

power integration into the British system quantitatively. This section outlines the analytical

approach, input data used in the model and the simulation strategy used in the study.

2.1. EnergyPLAN model.

EnergyPLAN is an integrated-energy-system model developed by the Sustainable Energy

Planning Research Group at Aalborg University, Denmark2. It is designed to analyse the national

or regional energy system planning strategies considering the technical and economic aspects of

energy system planning and investment decisions. It can capture renewable energy production in

detail and pays greater attention to heat and electricity demand and supply, as well as

consumption of energy in the industrial and the transport sectors.

The model requires input data for demand and production of heat and electricity, cost

data, capacity data for the energy plants, and renewable energy generation information. Users

can choose how to regulate and optimize the energy system. Regulation strategies determine how

the electricity grid is stabilised via conventional plants’ production share; or how excess

electricity is reduced and avoided. Regulations also decide how the model is simulated. There are

two simulation options available to the users:

a) The technical optimization decides the heat and electricity generating plant operating

strategy so as to minimize import/export of electricity and fuel consumption. This

option is suitable to study technical problems, such as excess electricity or CO2

emission.

b) The economic optimization aims at total cost minimization where all units, except

renewable plants, operate according to their marginal costs. This option is useful to

study economic outcomes, such as total annual cost or electricity price.

Detailed consumption, production and investment results are obtained as outputs of the

model. These outputs are analysed to identify the effects of initial assumptions about energy

systems and regulation strategies on the simulation. Based on these, conclusions can be made.

2.2. The paper’s analytical approach

Ostergaard [22] suggests that a number of optimisation criteria can be considered for

renewable energy integration to identify the optimal energy mix. Even the economic and techno-

operational objectives can be analysed using different criteria and the optimal design will vary

depending on the objective chosen. In this study, the optimal wind level for the UK in 2020 will

be determined by looking at total social cost. Specifically, wind level will be increased gradually,

and the total annual cost will be examined to indentify the total wind capacity that results in the

lowest annual cost for the society. Total annual cost covers capital and fuel cost, as well as

2 For further information, please see [14]. A review of tools is available in [15].

capacity cost, but other system costs like balancing and net-work related costs are excluded. This

will be shown in the economic analysis.

In addition, a technical analysis will be performed beforehand to address two problems

for wind integration: Critical Excess Electricity Production (CEEP) and CO2 emissions. The

avoidance of CEEP is required since CEEP can cause technical problems for the system

operators and affect the generators’ income. Therefore, the highest wind penetration level that

produces no CEEP will be indentified and used as a pre-requisite for the economic analysis.

Besides, three regulation strategies will be investigated, and the most effective strategy to reduce

CEEP and CO2 emissions will be chosen for the economic analysis.

The optimal wind level will be found for two different energy systems. The purpose is to

check the impact of several configurations within the system on wind integration. These

configurations, including flexible electricity demand, district heating size, and conventional

plants’ minimum grid share will be discussed further in the next section (2.4 Scenarios). The

decision to choose these three criteria and then construct an alternative energy system derives

from a qualitative analysis of the commercial, technical and regulatory challenges for wind

integration in the UK. The basic framework of this paper is shown in Fig. 1.

[Please insert Fig. 1 here]

2.3. Data

To analyse the issue of wind power integration into the British energy system in 2020, the

energy balance for 2020 is required as an input. This is constructed based on DECC Updated

Energy and Emissions Projections [16]. Some inputs not available from the above source were

forecast using a simple growth rate compared to 2008 data. In this paper, total electricity demand

includes energy industry use, pumped storage and net import. Conventional power plant’s

capacity, including coal, oil, gas, are from [8], which takes into account plant closures and new

plants in 2020. Wind capacity, including mix of onshore and offshore, is based on [8]. Detailed

generation capacity is presented in Table 1.

[Please insert Table 1 here]

Fuel and CO2 price scenarios are taken from [17], and are shown in Table 2. Capital

costs of conventional and renewable power plants are as in [18], using a mix of first-of-a-kind

and n-of-a-kind estimates. Capital costs are expected to fall from 2009 level, so an index from

0.85 to 0.9 is applied to various technologies. Energy data are in gross caloric value (GCV),

while costs are in 2009 GBP (£).

[Please insert Table 2 here]

2.4. Scenarios

Scenarios were developed to capture alternative configurations of the energy system and

to analyse regulation strategies. Each scenario includes ten cases, where wind power production

increases from 0 to 180 TWh (0% to 50% of total electricity generation). Comparing these cases

with the “base” case of no wind power shows how renewable integration affects outcomes in that

scenario. Each energy system will be combined with one regulation strategy to create a full

dataset for EnergyPLAN model.

2.4.1. Energy system

Reference system (Ref Sys): This is the UK energy system in 2020 that assumes no significant

change in transmission and distribution. Energy demand of major sectors is presented in Table 3.

[Please insert Table 3 here]

Alternative system (Alt Sys): The alternative system differs from the reference case as indicated

below:

- Flexible electricity demand: It is assumed that demand can be actively managed

according to the system operator’s request: it is cut but stays positive during tight supply,

and raised but remains below given capacity during excess supply. This is the result of

advanced features in future appliances, such as deferred freezers and fridges, deferred

heat pumps, and electric vehicles. Estimates of annual flexible-demand and maximum

effect of capacity for the UK are made using expert judgement. Intra-day flexible demand

is taken as 5 TWh with a maximum capacity of 3,000 MW, whereas intra-week flexible

demand is considered to be 3 TWh with a maximum capacity of 2,000 MW.

- District heating: total district heating size is raised from 10 TWh to 30 TWh. This

represents moving from current policy to “pure” policy with de-risked interest rate for

investors as suggested in [19]. Individual heating demand is reduced by 20 TWh

accordingly.

- Regulation: conventional plant’s minimum grid share is reduced from 35% to 30%,

assuming more distributed generation, smoother transmission and more efficient

balancing services.

2.4.2. Regulation strategy

Three alternative regulation strategies are considered in this study as presented below.

Reference Regulation (Ref Reg): This case is based on the following assumptions:

- At least 35% of conventional plant capacity is used for grid stabilisation.

- Interconnector capacity is 4,200 MW

- Wind turbines run according to availability.

- There are no measures to reduce excess electricity.

Alternative Regulation 1 (Alt Reg 1): This assumes the following:

- Excess electricity is first reduced by replacing CHP with boilers, so that electricity

generated by CHP is minimized.

- If further cut is needed, electric heating will replace boilers to meet heat demand, so that

there is more electricity demand to absorb excess supply.

Alternative Regulation 2 (Alt Reg 2): This is a variation of the Alt Reg 1 where in addition to the

measures of Alt Reg 1 further regulation is considered for excess electricity regulation by

stopping wind turbines (wind curtailment).

The model was run considering the above system configurations, scenarios and energy

regulation strategies, and the quantitative results were obtained for technical optimisation and

economic optimisation. These results are presented in two subsequent sections. First, the

technical results are presented, followed by economic results. Optimal wind level is chosen

based on economic cost, but it must satisfy technical requirements.

3. Technical Results

In technical terms, two main aspects are analysed for the reference and alternative

systems under different regulation strategies. These are Critical Excess Electricity Production

(CEEP) from wind sources, and CO2 emission from the system. CEEP is the critical amount left

after using and even exporting power through the interconnector. This situation arises due to

supply-demand mismatches: if high wind power generation takes place during low electricity

demand conditions, CEEP is likely to occur. Similarly, one of the main objectives of wind power

integration into electricity system is to reduce CO2 emission. A comparison of emission

reduction in various cases can therefore produce interesting insights. We discuss the results for

three scenarios below.

3.1. Reference system (Ref Sys).

Reference Regulation in Reference System: Fig. 2 shows changes in CEEP for the

Reference Regulation when wind is integrated into the system. CEEP only appears when 60

TWh of wind power is integrated into the system. At lower levels, no excess power appears due

to the existence of the 4,200MW interconnector. But CEEP rises sharply after that and reaches

54 TWh at 180-TWh wind power. This means 30% of wind production (54 out of 180 TWh) is

not utilised in such a situation.

[Please insert Fig. 2 here]

Fig. 3 shows the changes in CO2 emissions subsequent to wind power integration into the

system. CO2 emission falls from 490 Mt in base case (no-wind) to 438 Mt at 160 TWh of wind

power integration. This lowest level corresponds to a reduction rate at 10.6%. However, CO2

reduction slows down after that, due to increasing production from conventional plants to

stabilise the grid.

[Please insert Fig. 3 here]

Alternative Regulations in Reference System: The quantitative results show that two

alternative regulations (Alt Reg 1 and 2) do not make any improvements for the Reference

System, both in terms of CEEP and CO2 emissions. This is because actions to address critical

excess production will be taken within the district heating subsector (CHP and electric heating),

which is small in size (10 TWh) compared to total heat demand (372 TWh). This is a weakness

of the Reference System that will be improved in the Alternative System below.

3.2. Alternative System (Alt Sys)

Reference Regulation: Figure 4 shows CEEP for Reference Regulation (Ref Reg) in both

systems’ configuration. In the Alternative System (Alt Sys - Ref Reg), CEEP only appears from

80-TWh wind power, compared to 60-TWh wind power integration in the Reference System

(Ref Sys - Ref Reg). This is obtained as a consequence of higher level of district heating in the

system. In addition, the highest CEEP at 180-TWh wind power integration stands at 41 TWh,

which is 14 TWh less than that in the Reference System. This therefore suggests that for an

effective wind power integration into the British system, more focus on district heating is

required. CO2 reduction also sees considerable improvement. As in Fig. 5, maximum reduction

rate reaches 12% in the Alternative System, 2% higher than that in the Reference System.

[Please insert Fig. 4 and 5 here]

Alternative Regulations in the Alternative System: Figures 6 and 7 present the results of

alternative regulations in the Alternative System. As can be seen, the performance improves

slightly under these alternative regulations. For example, Fig. 6 indicates that Alternative

Regulation 1 can control CEEP more effectively: at 180-TWh wind power integration, CEEP is

only 31 TWh (10 TWh lower than that in the Reference System). Alternative Regulation 2 again

eliminates CEEP by stopping wind when required. Regarding CO2 reduction, Fig. 7 shows that

there is not much difference among three regulations. However, the Alternative System can still

reduce CO2 slightly more (maximum 12%) than the Reference System (10%) when wind is

integrated into the system.

[Please insert Figs. 6 and 7 here.]

Summing up, the technical analysis shows that the Alternative System performs better

than the Reference System, in reducing CEEP and CO2 emissions. In such a case, 180 TWh of

wind power generation can be integrated into the system (zero CEEP and highest CO2 reduction

rate) by 2020. If however the district heating system does not develop as is considered in this

scenario, the British system could only integrate 160 TWh of wind power.

4. Economic results

As before, the economic results are provided first for the Reference System with a

regulation strategy. Subsequently, the results for the Alternative System are presented.

4.1. Reference System

Our optimal integration of wind power is based on the assumption that CEEP must be

eliminated. Accordingly, Alternative Regulation 2 is chosen for the economic analysis, where

optimal wind level is chosen based on total cost. The energy system’s total cost consists of

annualized investment cost, fuel and CO2 cost, and other expenses (like net revenue from

electricity trade). First, Fig. 8 shows the annualized capital investment for the UK Reference

System in 2020.

As wind power is integrated into the system, capital investment in the electricity sector

changes. This is reflected by decreasing investment for conventional power plants, though by

limited amount; and increasing investment for onshore and offshore wind turbines. Because

other capital costs (other renewable, nuclear and other capital costs in heat sector) remain

unchanged, total capital investment increases significantly from £11 billions (no-wind) to more

than £18 billions (180-TWh wind integration).

[Please insert Fig. 8 here.]

Besides capital investment, changes in variable costs also take place mostly in the

electricity sector, and therefore variable costs depend on the level of electricity generation.

Figure 9 shows that the output from conventional power plants falls gradually with wind power

integration. This however slows down from 140-TWh of wind power integration, due to

increasing grid-stabilisation requirements.

[Please insert Fig. 9 here]

As a result, total fuel and CO2 costs (in electricity, heat and transport sectors), show a

pattern as shown in Fig. 10. Wind power integration brings down fuel and CO2 cost from £99

billions (no-wind) to low of £93.8 billions (140-TWh wind power). But the system’s variable

cost starts rising at higher integration levels.

[Please insert Fig. 10 here.]

Changes in the total cost of the energy system with wind integration are presented in

Figure 11. Total cost falls from £111.5 billions (no-wind), to lowest at £110.5 billions (80-TWh

wind), before shooting up again. It is concluded that 80 TWh of wind production is the optimal

level based on the economic analysis, with saving up to 0.85% of the energy system’s total cost.

This arises due to faster increase in the capital investment costs compared to the variable cost

saving achieved at higher levels of wind power integration.

[Please insert Fig. 11 here.]

Figure 12 provides the implication of wind power integration for conventional power

plants. Required capacity of conventional power plant (the maximum) reduces when more wind

power is integrated into the system, but the reduction is not significant amount due to limited

wind capacity credit. On the other hand, actual generation from conventional plants (on average)

can be reduced considerably at high-levels of wind power integration. This leads to a fall in the

utilisation rate of the conventional plants, from 54% (no-wind) to lowest of 37.5% (140-TWh

wind power). Certainly, low plant load factor can pose adverse economic-problems for owners of

conventional power plants and reduce overall system efficiency.

[Please insert Fig. 12 here.]

4.2. Alternative system

The capital investment in the Alternative System is not that different from the Reference

System (apart from some more CHP in district heating, and some less gas boilers for individual

heating). So changes in capital investment for wind power integration look almost similar to that

in the Reference System. This is not elaborated any further here.

But there are differences in electricity generation, which will lead to differences in fuel

and CO2 cost. As Fig. 13 shows, the Alternative System allows better utilisation of onshore and

offshore wind generation: in particular, offshore electricity supply is 2,300 MW higher than that

in the Reference System. As a result, conventional power plant’s generation can be reduced up to

2,400 MW (21 TWh less) at 180-wind. This leads to a fall in fuel and CO2 cost for the

Alternative System as shown in Fig. 14. Fuel cost can be reduced by up to £1.6 billions (180-

TWh wind power case), compared to the Reference System.

[Please insert Figs. 13 and 14 here.]

Figure 15 shows the total cost of the Alternative System as wind power production goes

up. Total cost falls from £111 billions (no-wind) to lowest at £110 billions (80-TWh wind), equal

to a 0.91% saving rate. Although the optimal wind level is still 80 TWh, the Alternative System

shows two improvements in economic results over the Reference System. First, as can be seen

from Table 4, the highest cost reduction rate (0.91%) is better than that in the Reference System

(0.85%). Second, further increase in wind power production does not push the rate down

significantly – as happens in the Reference System. This proves that the Alternative System is

better in integrating wind generation.

[Please insert Fig. 15 here.]

[Please insert Table 4 here.]

Specific implication for conventional power plants is again mentioned here. As shown in

Fig. 16, the utilisation rate drops even further in the Alternative System, to as low as 35% as

wind power production approaches 140 TWh. Because the conventional capacity remained

unchanged, and wind electricity is better utilised, it is no surprise that plant load factor is reduced

more in the Alternative System. Plant owners will be more negatively affected as a consequence.

[Please insert Fig. 16 here.]

To sum up, the optimal wind power integration level remains at 80 TWh in the

Alternative System as was found in the Reference System, but the Alternative System shows

better capability to integrate wind, although the conventional plant utilisation rate drops here.

Whether this result holds in other contexts of fuel price and capital cost, will be revealed in the

sensitivity analysis.

4.3 Sensitivity analysis

The model results depend on a variety of input assumptions, among which commodity

price and power plant’s capital cost are subject to highest uncertainty. Accordingly, the

sensitivity of the reported results to changes in these assumptions is explored in this section. Two

additional fuel price and capital cost assumptions (low and high) have been considered here

outside the central estimates used in the previous cases. This will show how the optimal wind

generation changes when a factor from the external environment changes. Moreover, comparing

the results from two energy systems will reveal which one is better at coping with the uncertainty

of fuel and capital expenses.

Fuel Price: As shown in Fig. 17, both the Reference and Alternative Systems are very

sensitive to fuel price. For both systems, optimal wind power integration level drops from 80

TWh to 20 TWh in the low fuel price case, which in turn yields limited cost savings. In contrast,

for high commodity prices, the Alternative System outperforms when optimal wind power

integration level increases to 120 TWh, yielding a cost saving of 2%; compared with 100 TWh

wind power integration at a cost saving of 1.5% in the Reference System.

[Please insert Fig. 17 here.]

Capital Cost: Capital costs of conventional power plants, onshore and offshore wind

power plants are subjected to 5%, 10%, 20% changes respectively. As shown in Fig. 18, higher

capex reduces the optimal wind power integration level to 40 TWh in both systems. But with

lower capex, again the Alternative System performs better: optimal wind level jumps to 120

TWh, saving 1.8% of the total cost; compared with respective figures of 100 TWh and 1.4% in

the Reference System.

[Please insert Fig. 18 here.]

The sensitivity analysis therefore suggests that the results are influenced by the key price

and cost variables but the results obtained are generally quite robust in the sense that rankings of

the scenarios or regulation strategies do not change, although the absolute value of the wind

power integration will vary depending on the price or cost assumptions.

5. Conclusion

Using EnergyPLAN model, this paper finds that 80 TWh of wind power production can

be optimally integrated into the UK energy system in 2020. At this level of integration, the total

cost of supply is minimised, therefore, lower or higher wind integration level will incur more

cost to the system. Under current and projected developments in commercial, technical and

regulatory framework till 2020, such wind generation level is achievable, though it is associated

with certain difficulties. For example, bottlenecks in the wind turbine supply chain are biggest

commercial challenges. These include limited numbers of turbine manufacturers and specialised

vessels for setting up offshore facilities, which result in low build rate for wind farms. Besides,

there are technical requirements for grid expansion and reinforcement to connect offshore wind

to onshore transmissions, and properly move power to load centres over the UK. Lastly, in order

to incentivise much more wind power generation, the current Renewable Obligation scheme will

need to be modified, in addition to other measures like the Feed-in Tariff.

Some of those difficulties can be addressed by changing the energy system

configurations: moving from centralized only to more decentralized generation, and from passive

to active demand management. This is what we have considered in our two scenarios: a shift

from the Reference to the Alternative System will improve wind integration into the British

system. Quantitative results show that the Alternative System is more effective than the

Reference in accommodating wind power: with optimality still at 80 TWh, but at higher total

cost saving rate. Sensitivity analysis results with fuel and capital costs, again show the

Alternative System can utilise more renewable generation: with higher optimal-level, and at

higher saving rate.

The fact that optimal wind capacity remains at 80 TWh, and total cost saving only

improves slightly when moving from the reference to the alternative system (with baseline fuel

cost), can come from several reasons. Firstly, this is because of the small size of district heating

(30 TWh in the alternative system) compared to total heat demand (372 TWh). This leads to

limited impacts of district-heating CHP, electric heating on the overall system. Secondly, the

small size of flexible electricity demand (8 TWh) relative to total electricity demand (382 TWh),

means there is less room to cut electricity demand in low wind and push up consumption in high

wind situation. This again constrains the benefits of high-level wind integration for the

alternative system.

Outcomes of two energy systems lead to some policy recommendations. First, supporting

the Alternative System with decentralized generation, the UK should create more incentives for

small generators. New FIT scheme will be the main tool, but other solutions to attract non-

energy professionals are also important, such as credit support or simplified installation and

operation for micro generators at the household level. Second, long-term planning for grid

development and pricing should be carefully (but also timely) designed. Moreover, the regulator

should actively negotiate with offshore wind developers about onshore connection points. This is

because while the shortest connection line is best for developers, another (and probably longer)

route can relieve onshore bottlenecks and reduce extra deep-reinforcement. Balancing

developers’ objectives with that of the system operator will be tricky but beneficial. Third,

financial incentives to promote active demand management are necessary. Cost saving of the

Alternative over the Reference System, suggests how much should be paid to users engaged in

the scheme. On the other hand, effective mechanism regarding capacity payment will be crucial

to support conventional generators, given declining load factor.

References

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List of Figures

Fig. 1. Analytical framework.

Fig. 2. CEEP – Reference Regulation in Reference System.

Fig. 3. CO2 emissions – Reference Regulation in Reference System.

Fig. 4. CEEP – Reference and Alternative Systems with Reference Regulation.

Fig. 5. CO2 reduction – Reference and Alternative Systems with Reference Regulation.

Fig. 6. CEEP – Reference and Alternative Regulations in Alternative System.

Fig. 7. CO2 emissions – Reference and Alternative Regulations in Alternative System.

Fig. 8. Annual capital investment – Reference System.

Fig. 9. Electricity generation – Reference System.

Fig. 10. Annual variable cost – Reference System.

Fig. 11. Total cost and saving rate – Reference System.

Fig. 12. Conventional power plant’s capacity and utilisation rate.

Fig. 13. Wind generation difference – Alternative vs. Reference System.

Fig. 14. Variable cost difference – Alternative vs. Reference System.

Fig. 15. Total cost and saving rate – Alternative System.

Fig. 16. Convention power plant’s utilisation rate.

Fig. 17. Fuel price sensitivity analysis.

Fig. 18. Capital cost sensitivity analysis.

Fig. 1. Analytical framework.

Fig. 2. CEEP – Reference Regulation in Reference System.

Fig. 3. CO2 emissions – Reference Regulation in Reference System.

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Wind level

Qualitative Analysis on

Commercial, Technical,

Regulatory Challenges

Sensitivity Analysis

Alternative System

Data

EnergyPLAN

Wind level

Fig. 4. CEEP – Reference and Alternative Systems with Reference Regulation.

Fig. 5. CO2 reduction – Reference and Alternative Systems with Reference Regulation.

Fig. 6. CEEP – Reference and Alternative Regulations in Alternative System.

0

10

20

30

40

50

60

0 20 40 60 80 100 120 140 160 180

Excess P

rod

ucti

on

(T

Wh

)

Wind Production (TWh)

Alt Sys - Ref Reg

Ref Sys - Ref Reg

0

2

4

6

8

10

12

14

0 20 40 60 80 100 120 140 160 180

CO

2 R

ed

ucti

on

(%

)

Wind Production (TWh)

Alt Sys - Ref Reg

Ref Sys - Ref Reg

0

5

10

15

20

25

30

35

40

45

0 20 40 60 80 100 120 140 160 180

Excess P

rod

ucti

on

(T

Wh

)

Wind Production (TWh)

Ref Reg

Alt Reg 1

Alt Reg 2

Fig. 7. CO2 emissions – Reference and Alternative Regulations in Alternative System.

Fig. 8. Annual capital investment – Reference System.

Fig. 9. Electricity generation – Reference System.

390

400

410

420

430

440

450

460

470

480

490

500

0 20 40 60 80 100 120 140 160 180

CO

2 E

mis

sio

ns (

Mt)

Wind Production (TWh)

Ref Reg

Alt Reg 1

Alt Reg 2

-

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

0 20 40 60 80 100 120 140 160 180

Cap

ital

Investm

net

(£ m

illio

ns)

Wind Production (TWh)

Offshore

Onshore

Conventional power plant

Other capital cost

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

0 20 40 60 80 100 120 140 160 180

Gen

era

tio

n (

MW

)

Wind Production (TWh)

Other RES

Offshore

Onshore

ConventionalPP

Fig. 10. Annual variable cost – Reference System.

Fig. 11. Total cost and saving rate – Reference System.

Fig. 12. Conventional power plant’s capacity and utilisation rate.

75,000

80,000

85,000

90,000

95,000

100,000

0 20 40 60 80 100 120 140 160 180

Co

st

(£ m

illio

ns)

Wind Production (TWh)

CO2 cost

Fuel cost

-2

-1.5

-1

-0.5

0

0.5

1

109,000

109,500

110,000

110,500

111,000

111,500

112,000

112,500

113,000

113,500

114,000

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

To

tal

Co

st

(£ m

illio

ns)

Wind Production (TWh)

Total Cost

Saving rate

-

10.0

20.0

30.0

40.0

50.0

60.0

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

0 20 40 60 80 100 120 140 160 180

Uti

lisati

on

Rate

(%

)

Gen

era

tio

n C

ap

acit

y (

MW

)

Wind Production (TWh)

Average Maximum Utilisation rate

Fig. 13. Wind generation difference – Alternative vs. Reference System.

Fig. 14. Variable cost difference – Alternative vs. Reference System.

Fig. 15. Total cost and saving rate – Alternative System.

-3,000

-2,000

-1,000

-

1,000

2,000

3,000

0 20 40 60 80 100 120 140 160 180

Gen

era

tio

n D

iffe

ren

ce (

MW

)

Wind Production (TWh)

Onshore

Offshore

-1,800

-1,600

-1,400

-1,200

-1,000

-800

-600

-400

-200

-

0 20 40 60 80 100 120 140 160 180

Co

st

Ch

an

ges (

£ m

illio

ns)

Wind Production (TWh)

Fuel

CO2

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

109,000

109,500

110,000

110,500

111,000

111,500

112,000

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

To

tal

Co

st

(£ m

illio

ns)

Wind Production (TWh)

Total cost

Saving rate

Fig. 16. Convention power plant’s utilisation rate.

Fig. 17. Fuel price sensitivity analysis.

30.0

35.0

40.0

45.0

50.0

55.0

60.0

0 20 40 60 80 100 120 140 160 180

Uti

lisati

on

Rate

(%

)

Wind Production (TWh)

Alt Sys

Ref Sys

-5

-4

-3

-2

-1

0

1

2

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

Wind Production (TWh)

Reference System

Low

Central

High

-4

-3

-2

-1

0

1

2

3

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

Wind Production (TWh)

Alternative System

Low

Central

High

Fig. 18. Capital cost sensitivity analysis.

-4

-3

-2

-1

0

1

2

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

Wind Production (TWh)

Reference System

Low

Central

High

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0 20 40 60 80 100 120 140 160 180

To

tal

Co

st

Savin

g (

%)

Wind Production (TWh)

Alternative System

Low

Central

High

List of Tables

Table 1: Wind and conventional power plants’ capacity

Table 2: Fuel price assumptions

Table 3: Sectoral energy demand for the UK in 2020 (TWh).

Table 4: Wind integration – total cost (£ millions) and total cost saving (%)

Table 1: Wind and conventional power plants’ capacity

Wind output (TWh) 0 20 40 60 80 100 120 140 160 180

Onshore (TWh) 0 20 28.2 28.2 28.2 28.2 31.7 31.7 36.4 36.4

Offshore (TWh) 0 0 11.8 31.8 51.8 71.8 88.3 108.3 123.6 143.6

Onshore capacity (MW) 0 8,781 11,497 11,497 11,497 11,497 12,697 12,697 14,328 14,328

Offshore capacity (MW) 0 0 3,499 9,404 15,240 21,016 25,200 30,154 33,594 38,571

Conventional power plant (MW) 63,402 61,900 60,600 59,300 58,231 57,700 57,500 57,300 57,100 56,900

* Conventional power plant includes coal, oil, gas and biomas with fixed share of 50.8%, 1%, 40.1% and 8.2%. Nuclear capacity is fixed at 6,022

MW. Other generation (hydro, river hydro, wave) sum up to 2,219 MW. District-heating CHP is 1,200 MW.

Table 2: Fuel price assumptions

Fuel price (£/GJ) Low Central High

Coal 6.94 7.78 8.33

Fuel 9.72 11.94 15.83

Diesel 30.83 33.75 39.55

Petro 34.5 37.31 42.95

Gas 11.11 14.17 17.22

Biomass 4 4 4

CO2 11.24 20.22 25.84

Table 3: Sectoral energy demand for the UK in 2020 (TWh).

2008 System Reference System Alternative System

Electricity 399 390 382

Flexible electricity intra-day 0 0 5

Flexible electricity intra-week 0 0 3

District heating 5 10 30

Individual heating 457 362 342

Industry 520 454 454

Transport 676 691 691

Table 4: Wind integration – total cost (£ millions) and total cost saving (%)

Reference System

Wind production 0 20 40 60 80 100 120 140 160 180

Fuel 89,041 88,172 87,368 86,561 85,872 85,365 85,065 84,948 84,967 85,127

CO2 9,807 9,605 9,419 9,231 9,071 8,954 8,885 8,858 8,863 8,900

Fixed OM 3,503 3,565 3,731 3,980 4,232 4,493 4,701 4,931 5,108 5,340

Capital 7,512 8,044 8,631 9,351 10,075 10,822 11,458 12,113 12,679 13,337

Other 1,627 1,580 1,534 1,456 1,293 1,102 912 780 702 662

Total cost 111,490 110,966 110,683 110,579 110,543 110,736 111,021 111,630 112,319 113,366

Total cost saving (%) 0 0.47 0.72 0.82 0.85 0.68 0.42 -0.13 -0.74 -1.69

Alternative System

Wind production 0 20 40 60 80 100 120 140 160 180

Fuel 88,549 87,680 86,875 86,041 85,254 84,585 84,064 83,725 83,557 83,535

CO2 9,789 9,587 9,400 9,207 9,024 8,872 8,756 8,686 8,654 8,655

Fixed OM 3,587 3,650 3,815 4,065 4,316 4,578 4,785 5,016 5,193 5,424

Capital 7,539 8,071 8,659 9,379 10,103 10,849 11,485 12,140 12,706 13,364

Other 1,629 1,582 1,538 1,488 1,390 1,210 1,023 860 763 713

Total 111,093 110,570 110,287 110,180 110,087 110,094 110,113 110,427 110,873 111,691

Total cost saving (%) 0 0.47 0.73 0.82 0.91 0.9 0.88 0.6 0.2 -0.54