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... annual load duration curve of a control area is calculated by combining all average (hourly, quarterly, etc.) loads generated within a calendar year. The load duration curves for Greece and Belgium for 2005 are shown in Figure 3 Projecting load duration curves in the future is another very complex task. A simple technique is to use the latest known normalised load duration curve and combine this with the projected peak load for each corresponding period [14]. Examples of normalised load duration curves are shown in Figure 3.6. The vertical axis shows the ratio of load over the annual peak load; and the horizontal axis the ratio of time (in hours) over the total number of hours within a year, i.e. 8 760. This method assumes that the pattern of electricity use will not change over time and that the load change will be distributed consistently over all types of ...
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... annual load duration curve of a control area is calculated by combining all average (hourly, quarterly, etc.) loads generated within a calendar year. The load duration curves for Greece and Belgium for 2005 are shown in Figure 3 Projecting load duration curves in the future is another very complex task. A simple technique is to use the latest known normalised load duration curve and combine this with the projected peak load for each corresponding period [14]. Examples of normalised load duration curves are shown in Figure 3.6. The vertical axis shows the ratio of load over the annual peak load; and the horizontal axis the ratio of time (in hours) over the total number of hours within a year, i.e. 8 760. This method assumes that the pattern of electricity use will not change over time and that the load change will be distributed consistently over all types of ...
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... type of information is extracted from a load duration curve. The load duration curve is a graphic representation of the distribution of loads in the electrical system; in other words a rearrangement of loads within a time period from the highest to the lowest. Figure 3.4 shows the daily load duration curves of the load curves of the Greek electrical system shown in Figure 3.3. In this example, on 5 February 2006, the load in the Greek electrical system was greater than 6 000 MW for 12 ...
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... type of information is extracted from a load duration curve. The load duration curve is a graphic representation of the distribution of loads in the electrical system; in other words a rearrangement of loads within a time period from the highest to the lowest. Figure 3.4 shows the daily load duration curves of the load curves of the Greek electrical system shown in Figure 3.3. In this example, on 5 February 2006, the load in the Greek electrical system was greater than 6 000 MW for 12 ...
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... share of these groups of power plants in the electrical generation system depends on the requirements for peak and base load, which is refl ected in the shape of the load duration curve. This is schematically shown in Figure 3.7. The share of base load plants is high when the load duration curve is fl attened, while on the contrary, a prominent peak will necessitate a large peak plant capacity. However, the shape of the load duration curve is not the only determinant of the technology mix. This is infl uenced by the shape of the load duration curve in combination with the cost attributes of the different power plant ...
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... planning for new electricity generation capacity is usually performed in three stages (see Figure ...
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... variation of load is typically shown using load curves, plots of temporal average loads (hourly, half-hourly, etc.) ranked by the actual time of occurrence. Figure 3.3 shows the load curves for four days in the Greek and the Belgian electrical systems: a summer and a winter Sunday, and a summer and a winter weekday. Although an overall pattern of load variation is identifi able, the load depended on the time of day and the period of the year as a result of the associated activity and the weather conditions that were prevailing in each control ...
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... relationship between annual hours of operation and total annual costs for any plant is graphically shown in Figure 3.8. This is the cost curve of a power plant. The intercept of the y-axis represents the annual fi xed costs (F j ) and the slope of the line the variable costs (V j ...
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... with electricity demand, the electrical load, i.e. the amount of electrical power generated by the electrical system and delivered to consumers, varies with time. Although some patterns of load variation are predictable, for example the electrical load is low at night and high during the day, absolute load values vary between hours or days. An example of the variation of the average hourly load during a calendar day in two control areas, Greece and Belgium, countries with comparable populations, is shown in Figure 3.2. In this fi gure, load data from the electrical system of continental Greece and the islands connected to this, and from that of Belgium (excluding the AIESH grid but including the Sotel Luxemburgish grid), as reported by the corresponding transmission system operators, HTSO S.A. [18] and Elia [19] respectively, are plotted against the hours of the day. The pattern of electrical load in these two control areas is similar: the load reaches a minimum around 8 a.m. and peaks around 1 p.m. and 8 ...
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... the second step of the screening curve method the optimal temporal range of operation of each power plant type (step 1) is translated into a capacity mix. This is achieved by combining the temporal ranges resulting from Figure 3.9 with a load duration curve. This is shown in Figure 3.10. The thick lines at the top part of Figure 3.10 show the lowest- cost power plant type as a function of operating time through a calendar year. The times t 1 and t 2 , that mark the ranges for the economic operation of each type of power plant are transposed using the load duration curve (lower part of Figure 3.10) into capacities for each type of power plant. More information on the screening curve method can be found in the relevant literature, e.g. in ...
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... the second step of the screening curve method the optimal temporal range of operation of each power plant type (step 1) is translated into a capacity mix. This is achieved by combining the temporal ranges resulting from Figure 3.9 with a load duration curve. This is shown in Figure 3.10. The thick lines at the top part of Figure 3.10 show the lowest- cost power plant type as a function of operating time through a calendar year. The times t 1 and t 2 , that mark the ranges for the economic operation of each type of power plant are transposed using the load duration curve (lower part of Figure 3.10) into capacities for each type of power plant. More information on the screening curve method can be found in the relevant literature, e.g. in ...
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... the second step of the screening curve method the optimal temporal range of operation of each power plant type (step 1) is translated into a capacity mix. This is achieved by combining the temporal ranges resulting from Figure 3.9 with a load duration curve. This is shown in Figure 3.10. The thick lines at the top part of Figure 3.10 show the lowest- cost power plant type as a function of operating time through a calendar year. The times t 1 and t 2 , that mark the ranges for the economic operation of each type of power plant are transposed using the load duration curve (lower part of Figure 3.10) into capacities for each type of power plant. More information on the screening curve method can be found in the relevant literature, e.g. in ...
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... the second step of the screening curve method the optimal temporal range of operation of each power plant type (step 1) is translated into a capacity mix. This is achieved by combining the temporal ranges resulting from Figure 3.9 with a load duration curve. This is shown in Figure 3.10. The thick lines at the top part of Figure 3.10 show the lowest- cost power plant type as a function of operating time through a calendar year. The times t 1 and t 2 , that mark the ranges for the economic operation of each type of power plant are transposed using the load duration curve (lower part of Figure 3.10) into capacities for each type of power plant. More information on the screening curve method can be found in the relevant literature, e.g. in ...
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... should be stressed that the different control areas within the EU do not reach their maximum and minimum daily loads during the same periods of the year. As can be seen in Figure 3.3, the peak daily load, i.e. the maximum load during a day, was the highest in Greece on the summer Sunday (9.3 GW) and the lowest on the winter Sunday (7.1 GW); furthermore, the ratio of the daily peak to the minimum load was highest during the winter weekday (1.71) and lowest during the summer weekday ...
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... fi rst step of the screening curve method entails constructing and examining the cost curves of all candidate power plant technologies. This is shown graphically in Figure 3.9. For the sake of simplicity, three types of power plant are considered in this example: a peak load (P), a load following (F) and a base load (B) plant. This graph shows that the peak load power plant generates electricity at the lowest cost among the three technologies when it operates for t 1 hours per year or less. Similarly, the base load plant power plant is the most economically attractive option when it operates for t 2 hours per year or ...
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Citations
... Henriot and Glachant (2014) analyze RM in some European countries considering 15% as a comfortable threshold. Tzimas et al. (2009) also explain the importance of having 15-20% RM for reliability. ...
Electricity is a critical commodity for which the demand should be met using reliable, cost-effective, and sustainable resources. The characteristics of supply and demand should be comparatively analyzed for efficient electric power system planning. This study examines Turkey’s current electricity supply and demand and their characteristics for future balance. Turkey’s installed capacity and electricity consumption per capita are analyzed using historical data, and trends for future electricity demand are estimated. Electricity supply based on generation sources is reviewed, and the historical contribution of each source to total generation is shown. Statistical distribution of electricity generation by generation types and their relationships are also investigated. As the supply adequacy should be carefully reviewed for sustainability, finally, the characteristics of supply are analyzed using capacity factors, reserve margins, capacity margins, load duration curves, and peak-to-average demand ratios. It is shown that although the margins are above comfortable levels after 2017, they still need to be increased as the increased use of renewables might require extra availability. The load duration curves and peak-to-average demand ratios have shown that the density of demand is increasing, and capacity needs to be expanded and utilized carefully in the future for the reliability of the power system.Keywords: Electricity Supply-Demand, Electricity Generation Sources, Consumption Trends, Capacity Factor, Reserve Margin, Load Duration CurveJEL Classifications: Q41, O13, L94DOI: https://doi.org/10.32479/ijeep.11412
... The differences in water consumption are caused by different technological levels and conditions for burning fossil fuels. For example, comparing the four regions of Europe classified by [45] (see the orange diamond markers shown in Figure 5), the technological levels (such as average hourly loads) lead to the water footprints ranging with different regions, even when using the same method [46]. Based on the different boundary settings (fuel supply stage, infrastructure stage, and operation stage), the water footprint of nuclear power fluctuates between 0.13 and 4.03 m 3 /MWh. ...
Water use within power supply chains has been frequently investigated. A unified framework to quantify the water use of power supply chains deserves more development. This article provides an overview of the water footprint and virtual water incorporated into power supply chains. A water-use mapping model of the power supply chain is proposed in order to map the analysed research works according to the considered aspects. The distribution of water footprint per power generation technology per region is illustrated, in which Asia is characterised by the largest variation of the water footprint in hydro-, solar, and wind power. A broader consensus on the system boundary for the water footprint evaluation is needed. The review also concludes that the water footprint of power estimated by a top-down approach is usually higher and more accurate. A consistent virtual water accounting framework for power supply chains is still lacking. Water scarcity risks could increase through domestic and global power trade. This review provides policymakers with insights on integrating water and energy resources in order to achieve sustainable development for power supply chains. For future work, it is essential to identify the responsibilities of both the supply and demand sides to alleviate the water stress.
... In the European Union, where electricity demand is growing, 40% of total energy consumption comes from the electricity sector [14,15]. In Europe, there are 450 GW of fossil-fuelled power plants, 200 GW each of coal and gas, and 50 GW of oil. ...
Hydropower electricity generation is considered one of the cheapest technologies regarding electricity generation costs, and it is the most traditional, clean, renewable energy source. However, despite the environmental benefits offered by hydropower, they also can have negative impacts and consequences in the environment affecting water quality and disrupting river ecology. We investigated the environmental effects of four small hydropower plants (SPH) in north-west Spain by looking at the water quality of the four river stretches where the SPH plants are located. The physicochemical and biological characteristics of the water streams were analyzed, as well as the riparian ecological quality. Results showed that the presence of the hydropower plants did not significantly influence the physical and chemical characteristics of the water. There were no alterations of the benthic macroinvertebrate community at any of the plants except for one, and the riparian habitat was in general classified as good quality or close to natural conditions for all plants.
... It is assumed that all power plants are new, i.e. this is the direction the power plant fleet tends to develop in the long run. This is screening curve method, presented for example by Tzimas et al. [34]. ...
... (I) Electricity demand. The Reference demand specified in Table 3 (Odenberger and Johnsson, 2010;EWI, 2011;Tzimas et al., 2009). b Estimated based on previous data (European Commission, 2010c;ECN, 2011;EEA, 2011;Eurostat, 2011a). ...
... c The low-end estimates represent the average net efficiencies of existing capacities for natural gas-, coal-, lignite-, and oil-based plants (Graus and Worrell, 2009), as well as biomass-, peat-, and wastebased plants (European Commission, 2009). The high-end estimates represent the average net efficiencies of new capacities (Tzimas et al., 2009;Graus and Worrell, 2009;Eurelectric, 2011). The efficiency levels of nuclear and renewable generation technologies are not given, as they are not required for calculations of CO 2 emissions. ...
This study assesses the prospects for presently available abatement technologies to achieve significant reductions in CO2 emissions from large stationary sources of CO2 in the EU up to year 2050. The study covers power generation, petroleum refining, iron and steel, and cement production. By simulating capital stock turnover, scenarios that assume future developments in the technology stock, energy intensities, fuel and production mixes, and the resulting CO2 emissions were generated for each sector. The results confirm that the EU goal for reductions in Greenhouse Gas Emission in the sectors covered by the EU Emission Trading System, i.e., 21% reduction by 2020 as compared to the levels in 2005, is attainable with the abatement measures that are already available. However, despite the optimism regarding the potential for, and implementation of, available abatement strategies within current production processes, our results indicate that the power and industrial sectors will fail to comply with more stringent reduction targets in both the medium term (2030) and long term (2050). Deliberate exclusion from the analysis of mitigation technologies that are still in the early phases of development (e.g., CO2 capture and storage) provides an indirect measure of the requirements for novel low-carbon technologies and production processes.
... Currently, one of the issues confronting the world is the challenge of achieving a truly sustainable energy system [8]. The present path of economic development in most of the industrialised and emerging countries overrelies on energy from fossil fuel. ...
Hydropower is an important renewable energy resource worldwide. However, its development is accompanied with environmental and social drawbacks. Issues of degradation of the environment and climate change can negatively impact hydropower generation. A sustainable hydropower project is possible, but needs proper planning and careful system design to manage the challenges. Well-planned hydropower projects can contribute to supply sustainable energy. An up-to-date knowledge is necessary for energy planners, investors, and other stakeholders to make informed decisions concerning hydropower projects. This is basically a review paper. Apart from using expert knowledge, the authors have also consulted extensively from journals, conference papers, reports, and some documents to get secondary information on the subject. The paper has reviewed the world energy scenario and how hydropower fits in as the solution to the global sustainable energy challenge. Issues of hydropower resource availability, technology, environment and climate change have been also discussed. Hydropower is sensitive to the state of environment, and climate change. With global climate change, though globally the potential is stated to slightly increase, some countries will experience a decrease in potential with increased risks. Adaptation measures are required to sustainably generate hydropower. These are also discussed in the paper.
... Human society needs energy for industry, transportation, household heat and electricity; in developed countries our lifestyle is totally dependent on electricity. Electricity accounted for 21% of the energy consumption of European Union (EU) in 2007: 27.4% for the industrial sector and 39.5% for service and households [1]; its demand has increased at an average rate of 2% annually since 1990 and the prediction is that this growth will continue in the future [1]. ...
... Human society needs energy for industry, transportation, household heat and electricity; in developed countries our lifestyle is totally dependent on electricity. Electricity accounted for 21% of the energy consumption of European Union (EU) in 2007: 27.4% for the industrial sector and 39.5% for service and households [1]; its demand has increased at an average rate of 2% annually since 1990 and the prediction is that this growth will continue in the future [1]. ...
The renewable energy emerged as a solution to the environmental problems caused by the conventional sources of energy. Small hydropower (SHP) is claimed to cause negligible effects on the ecosystem, although some environmental values are threatened and maintenance of an adequate water quality should be ensured. This work provides a characterization of the water quality status in a river stretch around a SHP plant on river Lérez, northwest Spain, for four years after its construction. The ecological and chemical status of the water as well as the ecological quality of the riparian habitat, were used as measures of quality. Data were compared with the water quality requirements. The variations in the quality parameters were analyzed over time and over the river sections with respect to the SHP plant elements. Two years after construction, the temperature and dissolved oxygen values achieved conditions for salmonid water and close to the reference condition, while pH values were low. The Iberian Biological Monitoring Working Party (IBMWP) index showed a positive trend from two years after the construction and stabilized at “unpolluted or not considerably altered water”. Quality parameters did not present significant differences between sampling points. The SHP plant construction momentarily altered the quality characteristics of the water.
... Power plants can be classified, based on their mode of operation [3], as base load, load following, and peak load plants. This generic classification changes over time as a function of techno-economic criteria, but provides a basic taxonomy of generation units. ...
... The variable costs involve variable operating (fuel, start up, shut down, etc.) and maintenance costs. Indicative values of the annual electricity generation costs from fossil fuelled power plants can be found in [3]. Furthermore, the deployment of fossil fuelled generation also impacts the energy policy goals of the E.U., namely a drastic reduction of carbon dioxide emissions. ...
The underlying philosophy while planning the operation of the power system used to be based on maintaining security margins. However, this approach with increasing fluctuating energy sources involves higher costs and leads to assets under-utilisation. The system was mainly built by creating the supply side and minor attention was paid in controlling the demand side. With the effort to accommodate larger shares of renewable energy sources, while continuously maintaining the power balance and ensuring the reliability of the power system, the implementation of demand response mechanisms may provide considerable options to reshape the demand profile for electrical energy. This study aims to create further knowledge on the potentials of residential demand response concepts, while focusing on the Netherlands.
... Sources of Combustion in Northeastern Europe and Asia [32] The combustion factor derived from measurements in the north Atlantic and adjacent regions of the Arctic Ocean during ICEALOT reflects aerosol influenced by emissions from northeastern Europe and Russia. Coal burning is one of the main sources of heating, and there are also numerous coal burning smelters in that area [Hole et al., 2006a; Tzimas et al., 2009; Weiler et al., 2005]. The Kola Peninsula, northeast of Finland, has Cu and Ni producing industry which emits high levels of SO 2 and NO 2 [Weiler et al., 2005] . ...
... In addition to the correlations with the combustion and pollution tracers (Zn, Ca, Fe, S, and V), the concentrations of these metals were 5–65% higher in the European source region than the ICEALOT average. [33] About 50% of Europe's electricity is produced from fossil fuel combustion [Tzimas et al., 2009], and high alkane concentrations are found in petroleum combustion emissions from Europe [Blake et al., 1994 [Blake et al., , 2003. In eastern Europe, combustion of fuels such as coal and lignite lead to high levels of SO x , NO x , PM, and CO 2 emissions [Ross et al., 2002]. ...
1] The composition of Arctic aerosol, especially during the springtime Arctic haze, may play an important role in the radiative balance of the Arctic. The contribution of organic components to Arctic haze has only recently been investigated. Because measurements in this region are sparse, little is known about organic particle composition, sources, and concentrations. This study compares springtime measurements in the Arctic regions north of the Atlantic (ICEALOT, 2008) and Pacific (Barrow, Alaska, 2008 and 2009) oceans. The aerosol organic functional group composition from Fourier transform infrared (FTIR) spectroscopy combined with positive matrix factorization (PMF) and elemental tracer analysis indicate that mixed combustion sources account for more than 60% (>0.3 mg m −3) of the submicron organic mass (OM 1) for springtime haze conditions in both regions. Correlations with typical combustion tracers (S, Zn, K, Br, V) provide evidence for the contribution of combustion sources to the Arctic OM 1 . However, the two regions are influenced by different urban and industrial centers with different fuel usage. Highsulfur coal burning in northeastern Europe impacts the northern Atlantic Arctic region, while oil burning and forest fires in northeastern Asia and Alaska impact the northern Pacific Arctic region. Quadrupole and High Resolution Aerosol Mass Spectrometry measurements confirm the highly oxygenated nature of the OM 1 , with an oxygenated organic aerosol (OOA) spectrum from PMF. High coemissions of sulfate and organics from coalburning in northeastern Europe produce significant concentrations of organosulfate functional groups that account for 10% of OM 1 measured by FTIR spectroscopy during ICEALOT. These observations provide preliminary support for a heterogeneous mechanism of organosulfate formation on acidic sulfate particles.
... These calculations are performed using a model programmed in a commercial spreadsheet computer application. More details about the adaptation of the screening curve method to a computer model can be found in (Tzimas et al., 2009). ...
The paper presents a view into the long term future of fossil-fuelled power generation in the European Union, based on a number of alternative scenarios for the development of the coal, natural gas and CO2 markets, and the penetration of renewable and nuclear technologies. The new fossil fuelled capacity needed and the likely technology mix are estimated using a cost optimisation model based on the screening curve method, taking into consideration the rate of retirement of the current power plant fleet, the capacity already planned or under construction and the role of carbon capture and storage technologies. This analysis shows that measures to increase both non-fossil-fuel-based power generation and the price of CO2 are necessary to drive the composition of the European power generation capacity so that the European policy goal of reducing greenhouse gas emissions is achieved. Meeting this goal will however require a high capital investment for the creation of an optimal fossil fuel power plant technology mix.
