Large-scale integration of optimal combinations of PV, wind and wave power into the electricity supply

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1 Renewable Energy 31 (26) Large-scale integration of optimal combinations of PV, wind and wave power into the electricity supply H. Lund* Department of Development and Planning, Aalborg University, Fibigerstraede 13, 922 Aalborg, Denmark Received 31 May 24; accepted 18 April 25 Available online 1 June 25 Abstract This article presents the results of analyses of large-scale integration of wind power, photo voltaic (PV) and wave power into a Danish reference energy system. The possibility of integrating Renewable Energy Sources (RES) into the electricity supply is expressed in terms of the ability to avoid excess electricity production. The different sources are analysed in the range of an electricity production from to 1% of the electricity demand. The excess production is found from detailed energy system analyses on the computer model EnergyPLAN. The analyses have taken into account that certain ancillary services are needed in order to secure the electricity supply system. The idea is to benefit from the different patterns in the fluctuations of different renewable sources. And the purpose is to identify optimal mixtures from a technical point of view. The optimal mixture seems to be when onshore wind power produces approximately 5% of the total electricity production from RES. Meanwhile, the mixture between PV and wave power seems to depend on the total amount of electricity production from RES. When the total RES input is below 2% of demand, PV should cover 4% and wave power only 1%. When the total input is above 8% of demand, PV should cover 2% and wave power 3%. Meanwhile the combination of different sources is alone far from a solution to large-scale integration of fluctuating resources. This measure is to be seen in combination with other measures such as investment in flexible energy supply and demand systems and the integration of the transport sector. q 25 Elsevier Ltd. All rights reserved. Keywords: Wind power; Wave power; Photo voltaic; Energy system analysis; Distributed generation; Renewable energy; Energy modelling * Tel.: C ; fax: C address: lund@plan.aau.dk /$ - see front matter q 25 Elsevier Ltd. All rights reserved. doi:1.116/j.renene

2 54 H. Lund / Renewable Energy 31 (26) Introduction Renewable energy together with energy conservation and combined heat and power production (CHP) are essential factors for the implementation of European climate change response objectives. And such technologies are intended for further expansion in the near future. The EU strategy is that 22.1% of the total EU electricity consumption in 21 should stem from RES [1 4]. Consequently, there is a growing trend towards distributed generation in many European countries [5 1]. Denmark is one of the leading countries in terms of implementing CHP, energy conservation and renewable energy into the energy supply system [11 14]. Large-scale integration of electricity production from fluctuating renewable energy sources into the electricity system must address the challenge of designing integrated regulation strategies of a complex system of distributed power producers. The renewable energy sources must interact with the rest of the production units in the system to make it possible for the system to secure a balance between supply and demand. This article presents the results of analyses of large-scale integration of wind power, photo voltaic and wave power into a Danish reference energy system. A number of studies on the integration of wind power and photo voltaic have been carried out with focus on stand-alone systems including new technologies such as fuel cells and hydrogen [15 22]. Previous studies have analysed the possible solutions of large-scale integration of wind power into the Danish system [23 27]. Meanwhile also wave power is an emerging technology [28 32] and here the studies of large-scale integration are expanded to include both photo voltaic and wave power. 2. Methodology The analyses have been made on the Danish energy system, which traditionally is based on the burning of fossil fuels. Denmark has very little hydro power and during the 6s and 7s, the electricity supply has been coming solely from large steam turbines located near the big cities. Denmark has a long tradition of district heating. And during the 6s and 7s the total energy system were based on the imports of oil. Thus, 92% out of a total of 833 PJ of primary energy supply in 1972 was oil. Since after the first oil crisis Denmark has become a leading country in terms of implementing CHP, energy conservation and renewable energy. Hence, by means of energy conservation and expansion of CHP and district heating Denmark has been able to maintain the same primary fuel consumption for a period of more than 3 years. And by means of increasing the share of renewable energy Denmark has been able to replace 14% of fossil fuels. At the same time oil has been replaced by coal and natural gas, and consequently the Danish energy system has been changed from at situation in 1972, in which 769 PJ out of totally 833 PJ was oil, to a situation of today in which only 343 PJ out of 828 PJ is oil. In the same period both the transportation, the electricity consumption as well as the area of heated space has increased substantially. Today, the share of electricity production from CHP is as high as 5%, and approximately 2% of the electricity demand is supplied from wind power. Until recently,

3 H. Lund / Renewable Energy 31 (26) the CHP plants have not been operated to balance fluctuations in the wind power and consequently Denmark has had problems of excess electricity production. The CHP plants have to operate when the heat demand is high in the winter and the wind power has to operate when the wind is blowing. So far the excess production has been exported and sold on the international electricity market. Meanwhile the price has been low especially in hours of high wind productions. In the future, limitations in the transmission capacity may set limits to the export, especially if the share of RES is due to further expansions. As reference system for the analyses were chosen the western part of Denmark, in which the share of CHP and wind is the highest. The region is identical to the area of the transmission system operator, Eltra. And the reference is based on the Eltra system plan 21 which was used in the work of an expert group, who in 21 on request of the Danish Parliament, investigated the problem of large-scale integration of wind and analysed possible means and strategies for managing the problem [33]. As part of the work, Aalborg University made some long-termed energy system analyses for 22 on the EnergyPLAN model of investments in more flexible energy systems in Denmark [26,34]. As reference year has been chosen year 22, which is constituted by the following development: the electricity demand is expected to be TWh with a peak demand of 44 MW. Existing large coal-fired CHP steam turbines are replaced by new natural gas fired combined cycle CHP units when the old CHP plants expire. The electricity production from CHP is as high as 5% of the demand. The reference is described in detail in [26]. The possibility of integrating fluctuating RES into the electricity supply is expressed in terms of the ability to avoid excess electricity production. Previously wind power has been analysed to avoid excess production. But also to find its potential of reducing domestic CO2 emissions and possible trade value on the international electricity market. Here the analyses have been made solely from a technical point of view, investments are not included. At the present development stage of photo voltaic and wave power these new technologies will be excluded when identifying an economically optimal solution. The different sources are analysed in the range of an electricity production from to 1% of the electricity demand. The excess production is found from detailed energy system analyses on the computer model EnergyPLAN (see Fig. 1). The model is an input/ output model making annual analyses in steps of 1 h. General inputs are demands, capacities and the choice of a number of different regulation strategies, putting emphasis on import/export and excess electricity production. Outputs are energy balances and resulting annual productions, fuel consumption and import/exports. The energy system in the EnergyPLAN model includes heat production from solar thermal, industrial CHP, CHP units, heat pumps and heat storage and boilers. District heating supply is divided into three groups of boiler systems and decentralised and centralised CHP systems. In addition to the CHP units the systems include electricity production from renewable energy as well as traditional power plants (condensation plants). The model emphasises the analysis of different regulation strategies, including ancillary service restrictions of different power production units in order to secure grid stability in the electricity supply. For a detailed description of the model, please consult [35].

4 56 H. Lund / Renewable Energy 31 (26) EnergyPLAN Model 6. Input Demands Fixed electricity Flexible electricity District Heating RES Wind and PV Capacities (MW) Distribution Factor Solar Thermal and CSHP (TWh/year) Capacities & Efficiencies CHP, Power plant, Heat Pump, Boiler Heat Storage Regulation Market prises Multiplication factor Addition factor Depend factor Marginal production Cost (Import, export) Stabilisation demands Distribution Data: Electricity District H. Wind Market Prices Solar Industrial CHP Photo Voltaic Regulation strategy: 1. Meeting heat demand 2. Meeting both heat and electricity demand Electricity Market Strategy: Import/export optimisation Critical surplus production: reducing wind, replacing CHP with boiler or heat pump Electric heating and/or Bypass Fuel Types of fuel CO2 emission factors Fuel prices Output Results: (Annual, monthly and hour by hour values) Heat productions Electricity production Electricity import export Forced electricity surplus production Fuel consumption Payments from import/export CO2 emissions Share of RES Fig. 1. The EnergyPLAN energy system analysis model. The ability of integrating renewable energy depends not only on the fluctuations in the renewable source but also of the fluctuations in the demand and the flexibility of the rest of the supply system. Consequently, the result differs from one system to another and from one country to another. The results of wind power integration into different systems have been analysed in [36]. Here the analyses are all done in relation to a Danish reference system, which differs from most other systems by having a high percentage of combined heat and power production (CHP). For such reference energy system, the integration ability of a fluctuating renewable energy source can be shown as in Fig. 2 for Danish onshore wind power. The x-axis gives the wind power production between and 25 TWh equal to a variation from to 1% of Excess Electricity Production Excess production (TWh) With and without ancillery service restrictions Wind production (TWh) With Without Fig. 2. The integration of onshore wind power into the Danish reference electricity supply system.

5 H. Lund / Renewable Energy 31 (26) the demand (24.87 TWh). And the y-axis gives the excess production in TWh. The less the curve raises, the better the integration of the renewable energy sources. The analysis has been made with the following restrictions in ancillary services in order to achieve grid stability: At least 3% of the power (at any hour) must come from power production units capable of supplying ancillary services. At least 35 MW running capacity in big power stations must be available at any moment. Distributed generation from CHP and RES is not capable of supplying ancillary services. To show the influence of including ancillary services in the analysis the results in Fig. 2 are shown both in the cases with and without such restrictions. The inclusion of ancillary service restrictions in the analysis sometimes makes the excess production even higher than the RES input itself. In the case of very large inputs of RES, the system in some cases needs to increase the production from steam-turbines in order to fulfil such requirement. Previous studies have analysed how wind power can be better integrated into the electricity supply (laying down the curve in Fig. 2) by investing in flexible energy systems such as heat pumps and heat storage capacities and including small CHP plants in the balancing of supply and demand and in the supply of ancillary services [27,37]. Also flexible demand and the integration of energy supply for transportation via electrical vehicles and hydrogen has been included in the analyses. Such analyses show that a high percentage of RES can be integrated into the electricity supply without excess production if the system around the RES production units is properly designed. Meanwhile, the influence from investments in such flexible systems is not included in the following analysis, in which all RES are measured against the reference system shown in Fig. 2. The purpose in this article is to identify an eventual optimal combination of different RES productions units. In the case of large-scale integration of RES the optimal combination needs to be seen together with the implementation of other measures such as flexible systems. 3. Distribution data The hour by hour distribution of the different RES has been based on actual measurements whenever possible. For onshore wind power, which has existed in Denmark for many years, the distribution is based on actual production from wind turbines located in the reference area (i.e. the western part of Denmark). The same thing has been possible for photo voltaic, since measurements form a pilot project of 3 small installations has been ongoing since 2. Meanwhile such data do not exist for wave power. Consequently, data have been generated from wave data with 21 as reference year Onshore wind power The distribution of electricity production from onshore wind power is found on the basis of actual productions in the western part of Denmark, namely the area of the TSO

6 58 H. Lund / Renewable Energy 31 (26) Wind production Eltra 21 (1964 MWh pr MW) 2 15 MWh/h Hours PV production Sol3 21 (1 MWh/MW) kwh/h Hours Wave Power estimated year 21 (41 MWh/MW) kwh/h pr. m wave front Hours Fig. 3. Distribution of electricity production from wind, PV and wave power in Denmark 21. company, Eltra. Data for 21 have been downloaded from the homepage of Eltra [38]. The total wind turbine electricity production is shown in Fig. 3 (top) together with the duration curve. The analysis has ruled out offshore wind power. Small offshore wind farms between 5 and 25 MW have existed in Denmark since 1991, and by the end of 23 seven offshore wind farms were in operation representing approximately 85% of the total global offshore wind power capacity. The 16 MW wind farm on Horns Rev off the west coast in the North Sea went into operation in 22. The offshore production has had much better

7 H. Lund / Renewable Energy 31 (26) full load hours than most onshore turbines, which stresses the importance of location. The best production is achieved on the west coast, in which full load hours of up to 44 h/ year are expected. Consequently, offshore wind power would result in less excess production than onshore wind power Photo voltaic The distribution of electricity production from photo voltaic has been derived from the Danish Sol3 project. The project involves 267 PV installations from 2 on typical one family houses in eight locations in Denmark. The 267 installations add up to 663 kw peak on the PV units. Meanwhile, not all DC/AC converters are capable of converting peak loads. Consequently, the total capacity is a little bit smaller. Measurements from each installation have been collected in the period July 2 till February 23 by the utility company EnergiMidt. The data from EnergiMidt consist of electricity production in Wh for each 15 min for each of the eight locations. The data is generated form a data-logger on each installation. However, some data are missing. For 21, 1 month is missing for one location, and for 22, 1 month is missing for four locations. And the number of installations included in the data varies from month to month and do not include all installations for any of the locations. Typically, between 1 and 15 of the 267 installations are included. Each data set states the peak capacity of the installations, which are included in the electricity production. For 21 and 22, the monthly peak capacity of the installations included in the data sets varies between 227 and 382 kw. The total production for 21 is shown in Fig. 3 (middle). Again the duration curve is shown in the same diagram. The Sol3 data have been transformed into annual distribution data for the EnergyPLAN model on the following basis: the values for each 15 min have been added into hour values (Wh pr h); for each month the hour values of the eight locations have been weighted according to the peak capacity of the represented installations before being added to a total hourly electricity production; for each month the total values have been weighted according to the capacity of the represented installations before being linked in an annual distribution curve; the distribution curve is adjusted according to the maximum 1 h production leading to annual full load production of 12 h in 21 and 738 h in 22. A load factor in year 21 of approximately, 1 h of full load production may seam a little bit high for Danish weather conditions. Meanwhile the figure is influences by the correlation when being adjusted to the maximum 1 h production Wave power So far wave power plants in Denmark have existed only as small test facilities, and consequently, production data and distribution data do not exist. The distribution of wave

8 51 H. Lund / Renewable Energy 31 (26) power derives from wave-measurements in the North Sea off the west coast of Denmark. The measurements consist of the following data: the significant wave height H (meter), defined as the average height of the highest 1/3 of the waves within one measurement; the mean wave period T 2 (second) and; the peak wave period T P (second). The energy of the waves P W (W/m) has been calculated according to the following relation [28,39] between the height and the length of the period in which k is a constant: P W Z kh 2 ðt 2 CT P Þ=2 Data exist from 1999 to 24, but due to failures in the measurement equipment some of the data are missing. Some measurements are given on an hourly basis and some are given for shorter periods. The data have been transformed into annual distribution data in the following way: values shorter than 1 h have been added to average hour values; missing hour values have been replaced with the same hour in the day before; missing monthly periods have been replaced by monthly data from other years; the distribution curve is adjusted according to the maximum one hour production leading to annual full load production of 442 h in 1999 and 41 h in 21; electricity production has been calculated as wave energy multiplied with a plant efficiency of 5%. The resulting distribution data of electricity production together with the duration curve are shown in Fig. 3 (bottom). 4. Results Results are generated in the same form as Fig. 2, i.e. a curve of excess electricity production for increasing RES inputs expressing the rate of integration of the RES into the electricity supply. Results have been generated for each of the different RES and for relevant combinations in order to identify an optimal mixture. It should be added that electricity productions equal to 1% of the demand is hard to imagine in practice especially for such technologies as PV and wave power. For example an annual production of 25 TWh from PV requires approximately 25. MWp installed capacity, which for both financial as well as practical reasons is not likely. And the same goes for wave power. At the same time it is not likely that such amount of RES will be added to the reference system without improving the system in order be to able to integrate more RES and thereby decreasing the excess production. Meanwhile the analysis is valid to illustrate the differences between the three different sources and to identify optimal combinations.

9 H. Lund / Renewable Energy 31 (26) Excess production (TWh) Individual renewable energy sources Wind Power, Photo Voltaic and Optimal Mixture Excess Electricity Production RES production (TWh) The results of the different RES are shown in Fig. 4. In general, photovoltaic is the RES with the highest excess production followed by wave power and onshore wind power. In another study the same curve has been generated for more than 1 year for each renewable source [4]. Even though there are variations from 1 year to another, the results in terms of excess production are almost exactly the same for each of the individual renewable sources. Also the actual electricity production from various installations of PV has been compared to an estimate based on the sunshine during a test reference year. Such synthetic photo voltaic results in an excess production a little bit higher than the actual measurements. This is most likely due to the fact that the actual measurements include an element of correlation between the many locations, while the synthetically generated distribution in principle assumes all installations to be located in the same spot. This emphasises the importance of using measurements and not synthetic data. Consequently, it also indicates that the result of the synthetic wave power data is likely to slightly overestimate the excess production Optimal mix of different renewable energy sources The result of combinations of different RES has been calculated as shown in Fig. 5 for the combination of onshore wind and photo voltaic. The figure shows the excess production for different inputs of electricity from RES illustrated by the curves starting with a total of 5 TWh rising in steps of 5 TWh to a total of 25 TWh. Each curve shows the resulting excess production for different shares of photovoltaic and wind power. To the left the share of PV is cero, and to the right the share of PV is 1%. All the curves show an optimal combination in which the excess production is minimal. The optimal combination is generated when the PV is between 2 and 4%. In the same way optimal combinations of PV, onshore wind and wave power have been identified Optimal mixture and resulting excess production Photo Voltaic Wave Power Wind Power Optimix Fig. 4. The integration of individual renewable sources compared to the optimal mixture. Fig. 6 shows the optimal combination of the three renewable sources, i.e. the combination of the minimum excess electricity production. Fig. 4 compares excess

10 512 H. Lund / Renewable Energy 31 (26) Photo Voltaic and Onshore Wind Power Excess Electricity Production Excess production (TWh) Share of PV production (Percent) 25 TWh 2 TWh 15 TWh 1 TWh 5 TWh Optimum Fig. 5. The analysis of identifying optimal combinations of photo voltaic and onshore wind power. production from PV, onshore wind power and wave power with such optimal mix of the three renewable energy sources. The diagram shows that the optimal mixture results in less excess production. And consequently, the diagram illustrates that the combination of different sources reduces the integration problem, however, it is still considerable. It should be emphasised that a very high percentage of RES can be integrated into the electricity supply with out any excess production if other measures such as flexible energy systems are implemented. Fig. 4 shows how a certain combination of RES units will help such implementation strategies. 5. Conclusion This paper has analysed the problems of integration of electricity production from fluctuating renewable energy sources into the electricity supply. The magnitude of the problem has been illustrated in terms of excess electricity production when different RES are integrated into a Danish reference system with a high degree of CHP. The idea has been to take benefit of the different patterns in the fluctuations of different renewable sources. And the purpose has been to identify optimal mixtures from a technical 3 Optimal combination of RES TWh/year TWh/year Wave PV Wind Fig. 6. Optimal combination of the renewable sources, i.e. the combination of minimum excess production.

11 H. Lund / Renewable Energy 31 (26) point of view. Investments have not been included in the analysis. At the present development stage of photo voltaic and wave power such new technologies will be excluded when identifying an economically optimal solution. The analyses have taken into account that certain ancillary services are needed in order to secure the electricity supply system. And such ancillary services have not been available from the renewable energy sources included in the analysis. So the results are due to limitations in the minimum share of production from conventional power stations. The result illustrates how the excess production increases when the RES input is raised for wind power and photo voltaic as well as wave power. Meanwhile combinations of different RES can slow down the increase in excess production. For example an optimal combination of 2 4% photo voltaic and consequently 6 8% wind power has been found to have less excess production than 1% of either photo voltaic or wind power. The optimal mixture seems to be when onshore wind power produces approximately 5% of the total electricity production from RES. Meanwhile, the mixture between PV and wave power seems to be dependent of the total amount of electricity production from RES. When the total RES input is below 2% of demand, PV should cover 4% and wave power only 1%. When the total input is above 8% of demand, PV should cover 2% and wave power 3%. The combination of different sources is alone far from a solution to the problem of integration. Other measures such as investment in flexible energy supply and demand systems and the integration of the transport sector has a much higher potential of solving the problem. Meanwhile the identification of optimal mixtures of different RES can be seen as a measure to supplement such potential solutions. Together the different measures make possible the integration of a high percent of RES electricity production into the supply without any major excess production. Acknowledgements The author wish to thank Kenn H.B. Frederiksen from EnergiMidt, Denmark, for providing the PV data from the sol3 project, and Peter B. Frigaard, Aalborg University, and Jan Pedersen, ELSAM engineering, for providing wave data and helpful comments. Also the author wish to thank the participants of the ENERGEX 24 conference in Lisbon, 3 6 May 24, were the preliminary results of this article were presented and discussed. References [1] European Council. Directive 21/77/EC of the European Parliament and of the Council of 27 September 21 on the promotion of electricity produced from renewable energy sources in the internal electricity market. Luxembourg: European Parliament; 21. [2] European Council. Directive COM/22/415 of the European Parliament and of the Council on the promotion of cogeneration based on a useful heat demand in the internal energy market. Luxembourg: European Parliament; 22.

12 514 H. Lund / Renewable Energy 31 (26) [3] Hendriks C, Blok K. Regulation for combined heat and power in the European union. Energy Convers Manage 1996;37(6 8): [4] Meyer NI. European schemes for promoting renewables in liberalised markets. Energy Policy 23;31(7): [5] Hvelplund F, Lund H. Rebuilding without restructuring the energy system in east Germany. Energy Policy 1998;26(7): [6] Lund H, Hvelplund F, Kass I, Dukalskis E, Blumberga D. District heating and market economy in Latvia. Energy 1999;24(7): [7] Lund H, Hvelplund F, Ingermann K, Kask U. Estonian energy system proposals for the implementation of a cogeneration strategy. Energy Policy 2;28(1): [8] Mirasgedis S, Georgopoulou E, Sarafidis Y, Balaras C, Gaglia A, Lalas DP. CO2 emission reduction policies in the Greek residential sector: a methodological framework for their economic evaluation. Energy Convers Manage 24;45(4): [9] Rasmussen LH. A sustainable energy-system in Latvia. Appl Energy 23;76(1/3):1 8. [1] Lund H, Siupsinskas G, Martinaitis V. Implementation strategy for small CHP-plants in a competitive market: the case of Lithuania. Appl Energy; in press. [11] Lund H. Implementation of energy-conservation policies: the case of electric heating conversion in Denmark. Appl Energy 1999;64(1/4): [12] Lund H. A green energy plan for Denmark job creation as a strategy to implement both economic growth and a CO2 reduction. Environ Resour Econ 1999;14(3): [13] Maeng H, Lund H, Hvelplund F. Biogas plants in Denmark: technological and economic developments. Appl Energy 1999;64(1/4): [14] Ostergaard PA. Modelling grid losses and the geographic distribution of electricity generation. Renew Energy 25;3(7): [15] Al Hasan AH, Ghoneim AA, Abdullah AH. Optimizing electrical load pattern in Kuwait using grid connected photovoltaic systems. Energy Convers Manage 24;45(4): [16] Badescu V. Dynamic model of a complex system including PV cells, electric battery, electrical motor and water pump. Energy 23;28(12): [17] Iqbal MT. Simulation of a small wind fuel cell hybrid energy system. Renew Energy 23;28(4): [18] Jurado F, Saenz JR. Possibilities for biomass-based power plant and wind system integration. Energy 22; 27(1): [19] Kolhe M, Agbossou K, Hamelin J, Bose TK. Analytical model for predicting the performance of photovoltaic array coupled with a wind turbine in a stand-alone renewable energy system based on hydrogen. Renew Energy 23;28(5): [2] Möller B. Geographically determined interactions of distributed generation, consumption and the transmission network in the case of Denmark. In: Proceedings. Stockholm: second international symposium on distributed generation; October 2 4, 22. [21] Ro K, Rahman S. Control of grid-connected fuel cell plants for enhancement of power system stability. Renew Energy 23;28(3): [22] Duic N, Graca Carvalho M. Increasing renewable energy sources in island energy supply: case study Porto Santo. Renew Sustain Energy Rev 24;8(4): [23] Alberg Ostergaard P. Transmission-grid requirements with scattered and fluctuating renewable electricitysources. Appl Energy 23;76(1/3): [24] Lund H, Ostergaard PA. Electric grid and heat planning scenarios with centralised and distributed sources of conventional, CHP and wind generation. Energy 2;25(4): [25] Lund H, Clark WW. Management of fluctuations in wind power and CHP comparing two possible Danish strategies. Energy 22;27(5): [26] Lund H, Münster E. Management of surplus electricity-production from a fluctuating renewable-energy source. Appl Energy 23;76(1/3): [27] Lund H, Münster E. Modelling of energy systems with a high percentage of CHP and wind power. Renew Energy 23;28(14):

13 H. Lund / Renewable Energy 31 (26) [28] Kofoed J.P. Wave overtopping of marine structures utilization of wave energy. PhD Thesis, defended January, at Aalborg University. Hydraulics & Coastal Engineering Laboratory, Department of Civil Engineering, Aalborg University; 22. [29] Thakker A, Dhanasekaran TS. Experimental and computational analysis on guide vane losses of impulse turbine for wave energy conversion. Renew Energy 25;3(9): [3] Gross R. Technologies and innovation for system change in the UK: status, prospects and system requirements of some leading renewable energy options. Energy Policy 24;32(17): [31] Sharmila N, Jalihal P, Swamy AK, Ravindran M. Wave powered desalination system. Energy 24;29(11): [32] Kofoed JP, Frigaard P, Friis-Madsen E, Sørensen HC. Prototype testing of the wave energy converter Wave Dragon. In: Proceedings of the eighth. World Renewable Energy Congress, Denver, USA; 24. [33] Danish Energy Agency. Rapport fra arbejdsgruppen om kraftvarme-og VE-elektricitet (Report from the expertgroup on CHP-and RES-electricity). Copenhagen: Danish Energy Agency; 21. [34] Lund H, Münster E. AAU s analyser (Aalborg University Analyses). In: Bilagsrapport fra arbejdsgruppen om kraftvarme-og VE-elektricitet (Attachment report from the expertgroup on CHP-and RES-electricity). Copenhagen: Danish Energy Agency; 21. p. 35. [35] Lund H, Münster E, Tambjerg L. EnergyPLAN, computer model for energy system analysis, version 6.. Division of Technology, Environment and Society, Department of Development and Planning, Aalborg University [36] Lund H. Large-scale integration of wind power into different energy systems. Energy 25;3(13): [37] Lund H, Munster E. Integrated energy systems and local energy markets. Energy Policy; in press. [38] Eltra. Wind Data [39] Falnes J. Theory for extraction of ocean wave energy. Norway: Division of Physics, Norwegian Institute of Technology, University of Trondheim; [4] Lund H. Excess electricity diagrams and the integration of renewable energy. Int J Sustain Energy 23; 23(4):