|Abstract (english)|| |
Increase in greenhouse gas emissions is becoming a global concern and many countries are taking effort in dealing with the emission reduction. The first thing to do on that path is the introduction of laws and directives which will lead to goal achievement. The Paris agreement is one of them. Its goal is to limit global warming to well below 2°C, preferably to 1,5°C, compared to pre-industrial levels. It means the reduction of fossil fuel use which causes the emission increase and therefore increase in global temperature. Europe is aiming to reduce the use of fossil fuels by 55% until 2030, as it is prescribed by directives REPowerEU and Fit for 55. The goal can be achieved by replacing the fossil fuels with the renewable energy source (RES) in different sectors. Energy production, transport and thermal sectors are responsible for most of the air pollution. Their transition to electricity and mutual integration could enhance emission reduction. Replacing fossil fuels in energy production with RES could cause instability and uncertainty of energy supply due to intermitted RES, such as wind and solar power. Although, the combination of wind and solar power in electricity production has shown benefits over the stand-alone production systems, it still requires additional efforts in order to achieve stability and security of electricity supply, especially in the systems with a high share of RES in electricity production. Transport and thermal sector have a potential to utilize a high amount of electricity produced by RES if they convert to electricity. In transport sector, it can be done by replacing conventional vehicles with electric (EV) ones. Heat pumps are given as the best solution for thermal sector in order to replace heating and cooling systems based on fossil fuels.
Electrical power systems with a high share of RES, even by 100% share, can be achieved but they require additional flexibility in the system in order to enhance stabile and sustainable system. Flexibility needs are derived from intermitted RES electricity production, especially wind and solar power, as well as uncertain electricity demand and unexpected events. On the other hand, there are different sources of flexibility that can be integrated into the system in order to enhance utilization of high share of RES and provide stability to the system. The flexible resource exists in different parts of the power system: in the portfolio of dispatchable power plants; in the ability of the demand side to respond or to be managed; in storage facilities; in interconnections to adjacent power systems to allow trade; and in integration of different sectors. Wind and solar power plants, together with hydropower plants, can be defined as flexible resources as well, since they belong to dispatchable power plants, with the ability to ramp output up and down on demand.
Smaller electrical power systems, such as city, region and municipality, have higher needs for flexibility due to their lower number and possibly lower diversity of electric power plants as well as flexible resources. This thesis analyses such a smaller electrical power system of a wider urban area. Dubrovnik wider urban area is selected and energy plan is done until the year 2050. in order to achieve 100% renewable and self-sufficient energy system.
Hypothesis and research description
The aim of the thesis is the comparison between the hourly energy plan model and a new model based on 10-minute time step. A new model is build according to the EnergyPLAN model tool based on hourly data input and used for energy planning. New model is developed for 10-minute and hourly data input base. It is compared with the EnergyPLAN model tool in order to demonstrate the validity of a new model. New algorithm is upgraded using the Calliope computer program in order to achieve self-sufficient 100% renewable energy system of the selected wider urban area using smart energy system approach.
The boundaries of the Dubrovnik wider urban area are defined. They considered urban city and its surrounding which is spatially, demographically, functionally and traffic-wise closely connected to the city. The selected area covers the area from Slano in the northwest to Pločice in the southeast. The aim is to develop the energy plan model of 100% renewable energy system of the selected area until 2050 with 100% share of RES electricity production and transition of thermal and transport sector to electricity. All of the personal conventional vehicles are considered to be replaced by the electric ones and all of the fossil fuels used in thermal sector will be replace with seawater heat pumps (SWHP) until 2050. Year 2014 is selected as a base scenario.
The first step was to collect the data on electricity demand, electricity production, thermal demand and transport, as well as the potential on renewable electricity production capacities, storage capacities and capacities of interconnection lines to the adjacent energy systems of the selected area based on 2014. Collected data are arranged in hourly and 10-minute time step.
Collected data on electricity demand, outside temperature, solar radiation and wind speed are selected to analyse linear correlation and regression in-between the data based on a short-term scale of 10 minutes. The analyses are done in STATISTICA program tool for the data selected for 2012, 2013 and 2014 for four selected scenarios. First group of data considered the analyses of the relationship in between three consecutive years for each of the selected data based on a 10-minute time scale. Other three groups of data took into account analyses of the relationship in between the data for all selected years based on a mean monthly values and 10-minute time step. These analyses present a novelty according to previous literature and give an insight in a relationship between the selected data based on a short-term scale, which are used for further calculations in a new model.
Energy plan model of the Dubrovnik wider urban area was done until 2050 for all system participants located within the boundaries of the selected area. Electricity demand data are provided by Elektrojug d.o.o. for substation Komolac and projections are done until 2050. Electricity production consists of the production from hydropower plant (HPP) Dubrovnik and the projected production capacities of wind (WPP) and solar power plants (SPP). Data on transport sector are collected for 2014 and projections are done until 2050 with the assumption that all of the personal conventional vehicles will be replaced with electric ones. Characteristics of the EV batteries (BES) are defined and EV electricity demand until 2050 is gained based on the traffic data. Data on thermal demand are collected and projected until 2050, with the assumption that all of the fossil fuel based heating and cooling system will be replaced with SWHP systems. Energy storage facilities are also considered in the system in order to provide additional flexibility. These are additional thermal energy storages (TES) for heating and cooling purposes, battery second use (B2U) and hydrogen energy storage with electrolyser and fuel cell. B2U model is used for BEV when they end their life in EV and lose 20% of their initial capacity. Hydrogen is used to store electricity by converting it to hydrogen gas using electrolyser, storing hydrogen in a tank and converting it again to electricity using fuel cell.
The ability of SWHP system to utilize high share of RES in electricity production is analysed for one part of the selected area, the old city of Dubrovnik. District heating and cooling SWHP system is analysed for three scenarios and three cases. First scenario took into account SWHP as a stand-alone system and the other two scenarios took into a consideration battery energy storage (BES) and TES. First scenario is done on the hourly and 10-minute data calculation base, while the other two are done on the hourly basis. First two cases considered WPP and SPP as stand-alone systems while the third case considered combination of wind and solar resources in electricity production. BES and TES are compared according to their required capacity, volume and costs.
Transport sector is in more detail analysed developing a new model. New model is built on hourly and 10-minute data base input according to EnergyPLAN model tool. Analyses done using new model took into account electricity production from RES, electricity demand and EV electricity demand of the selected area and projections until 2050. Two scenarios are observed, for 2030 and 2050, doing calculation on hourly and 10-minute time step. Comparison is done according to two different time step calculations and regulation of EV charging and discharging in the system. Unregulated system considered unregulated EV charging which is based on consumer needs and habits due to their driving needs. Regulated system took into account the implementation of ‘vehicle-to-grid’ (V2G) model and a regulation of EV charging and discharging according to the production from RES. V2G vehicles are charged when there is excess in electricity production and discharged in the time of a lack of electricity production in the system in order to reduce import and export capacities. All of the EV are considered as one integrated battery. It is assumed that all of V2G vehicles are available for charging and discharging in every time step, if they are not driving or charging in order to meet the user's driving needs. V2G are charged and discharged if there is enough battery capacity available in each time step.
Hourly time step calculations took into account EV standard charging and discharging that requires 5 hours to recharge the BEV up to 100% SOC. 10-minute time step calculations took into account EV extremely fast charging and discharging that requires 10 minutes to recharge the BEV up to 100% SOC. Four cases are done for two scenarios and comparison is done according to unregulated and regulated system on hourly and 10-minute time base. Case 1 took into account production from WPP and SPP, electricity demand and EV electricity demand with the assumption that all of the EV are available for charging and discharging in each time step. In case 2, defined system in case 1 is upgraded with HPP Dubrovnik. Case 3 analysed minimal BEV connection capacity required in order to meet the results gained in case 1. Case 4 analysed cost and saving of EV consumers after the implementation of V2G model and the replacement of present two-tariff model (P2T) in electricity prices with variable two-tariff model (V2T). Results of a new model are compared to the results gained in EnergyPLAN according to the scenario 1 and case 1.
New model is upgraded in Calliope energy plan model tool in hourly and 10-minute time base in order to achieve self-sufficient and 100% renewable smart energy system of a wider urban area until 2050. The upgrade contained the integration and electrification of transport and thermal sector through implementation of EV and SWHP systems, additional ES and the integration through the open electricity market. Transport sector included 100% share of personal EV with regulated model V2G. V2G model is developed in Calliope and the results are compare to the results of a new model gained according to the scenario 2 and case 1.
Thermal sector included the implementation of SWHP systems with the additional TES for heating and cooling purposes. Other additional storage facilities are also considered within the upgrade, such as B2U and hydrogen energy storage with electrolyser and fuel cell. Input data of each of the selected technologies, with their main characteristics, are defined within the model together with their operation, maintenance and installation costs. Data on technology costs are taken from the Danish Energy Agency. Energy system is considered to be connected to the adjacent system through interconnection lines, which will enable electricity trade according to the variable electricity prices at the open market. Market electricity prices are defined according to CROPEX electricity prices from 2018. Calliope model operation is focused on RES integration and minimization of costs. Of all of the provided and defined technologies of the developed model, optimization analyses done in Calliope will result in optimal solution with minimum cost for the developed system. The aim of Calliope model is to achieve 100% renewable, self-sufficient and stable energy system.
Research results and contributions
Results of the linear correlation and regression analyses, for the first group of data, showed that the 10-minute values of solar radiation, air temperature and electricity demand can be pronounced with linear regression line. The relationship between the data for three consecutive years is shown to be significant with correlation coefficient of around 0,8. This means that the data distributions have similar pattern in between years even on 10-minute resolution. Results for the wind speed data showed that the data cannot be pronounced with linear regression line in between years and their relationship is not significant. It confirms the fact that the wind speed, as a renewable resource, is very hard to predict, with a variable pattern on 10-minute resolution in between years. Results of the analyses of the third and fourth group of data, based on 10-minute resolution, in between the selected data for all three years, showed that the relationship between the data cannot be pronounced with linear regression line since it was not significant. This means that none of the data can be used to predict other type of the selected data on 10-minute resolution. 10-minute resolution analyses can provide more details and insight in a real state of the system. These analyses present an upgrade according to the previous literature and give an insight in possible weather cast on 10-minute resolutions.
The analyses of the SWHP district heating and cooling system, in the old city of Dubrovnik, showed the ability of the system to utilize high share of RES electricity production. Results of the scenario 1 showed that the combination of WPP and SPP in electricity production, in case C, provides more opportunities for RES utilization than stand-alone WPP or SPP. RES production, in case C, was able to cover up to 67% of electricity demand of the stand-alone SWHP system. Adding TES and BES to the SWHP system in other two scenarios and their comparison resulted in 60 – 78% of ES cost and volume reduction in case C with combined RES production. It is shown that BES requires 13 times higher cost, but 40 times lower volume in each case in comparison to TES.
New algorithm for energy planning is developed, based on hourly and 10-minute data input, according to EnergyPLAN model. Comparison of the results, gained by a new model and EnergyPLAN, showed that models provide similar results on hourly and 10-minute data base, which approved validity of a new model. Transport sector of the selected area is analysed in a new model for four different cases. Results of a case 1 showed no significant difference between hourly (standard charging and discharging) and 10-minute (extreme fast charging and discharging) based model after the implementation of regulated V2G model. Implementation and regulation of V2G model led to about 60 – 70% decrease in export, while import was still high. Case 1 presented the limiting case when all of V2G, available for charging and discharging at the given moment, are able to be connected to the grid. Adding HPP Dubrovnik to the electricity production, in case 2, led to decrease of import by 97%, while export increased by 15 times. Case 3 analysed the minimum BEV capacity required in order to gain the same results as the one gained by a limiting case, case 1. Comparison of hourly and 10-minute model in case 3 showed that regulated V2G model of extreme fast charging and discharging, requires only 3% V2G vehicles to be connected to the grid in each time step, while standard charging and discharging resulted in 80% of V2G connection. When compared on hourly basis, regulated V2G model of extreme fast charging and discharging can provide 7 times more flexibility to the system, than the standard one. Due to that, regulated V2G model of extremely fast charging and discharging could enhance the integration of high share of RES production in the system. Results of the case 4 showed that, the replacement od P2T model in electricity prices with V2T model and the implementation of regulated V2G model of extreme fast charging and discharging, could provide saving for EV owners. Highest savings are provided by 10-minute model, about 200 EUR/year per EV.
The results of the analyses, done using a new model, showed that the regulated V2G model of extreme fast charging and discharging could provide higher flexibility to the electrical power system. 10-minute based model provided more details on a real state of the system and enabled better integration of high share of intermitted RES production. The implementation of V2T model could enhance participants of the system to take active action and earn savings if they are stimulated with electricity prices.
Upgrade of the new model in Calliope showed that the 100% renewable self-sufficient system of a wider urban area can be achieved through the integration and electrification of transport and thermal sectors, connection through the open electricity market based on variable electricity prices and additional storage facilities. Calliope model was developed on hourly and 10-minute data base. The result showed that 10-minute model can provide more details for electricity market development and more favourable market valuation of flexible sources, since it provides opportunity for the integration of more different sources of flexibility. It enables safer integration of high share of RES, which was manifested in the reduction of cross-border transmission capacities and contributed to the system stability. The total reduction of cross-border transmission capacities was about 60% compared to the hourly model. 10-minute model provided safer system operation by equalizing import and export capacities, which contributed to the reduction of the difference between import and export by 7 times compared to the hourly model.