Impact of Climate Change on Electricity-Free-Samples for Students

Question: Discuss about the Impact of Climate Change on Electricity Demand and Supply. Answer: Research Aim This research thus aims to determine the electricity forecast and the demand of electricity in different weather condition. Research objectives To find out the change in electricity consumption depending on the changing weather To identify the impact of the changing weather on the electricity consumption To recommend some effective strategies for accurate electricity forecasting Research Questions What is the change in electricity consumption depending on the changing weather? What are the impacts of the changing weather on the electricity consumption? Short literature review Introduction The prime objective of the research is to evaluate the impact of climate change on electricity demand and supply. Gans et al. (2013) stated that in the demand of the electricity is measured through the units of consumption by the people of a nation. In this literature review, proper data and research previously made on the similar research topic is highlighted. The following session shows that in which season the electricity consumption or the demand of the electricity is high. The process used for forecasting the material is also discussed in the following literature. Literature review However, the process of measuring the demands changed from conventional time to the contemporary period. In conventional time, electricity feedback is taken for analyzing the demand of the electricity. Electricity feedback was carried out mostly by psychologists and questionnaire containing set of questions is given to the consumers based on which the evaluation of the electricity consumption is evaluated (Auffhammer Mansur, 2014). Son and Kim (2017) depicted that direct and indirect feedback method is used, where assigned electricity staffs did self-meter-reading, assessing unit numbers in direct displays, keypad meters, meter reading in presence of energy adviser. On the other hand, in the process of indirect feedback, raw data processed by the utility and sent out to customers is taken into consideration. These feedbacks comprises of the data of high among of electricity by the nations consumers, electricity readings along with historical feedback, detailed annual or quarterly en ergy reports and disaggregated feedbacks (Moshovel et al., 2015). It is said that electricity use varies with the weather as depending on the weather and humidity the demand for the less or more electricity consumption depends. There are majorly three sectors that consume electricity- residential, commercial and industrial (Eia.gov., 2017). Image 1: Electric Power Monthly Consumption (Source: Eia.gov., 2017) It is also found that residential people shows much variation in their electricity consumption as there is a peak in demand of electricity mainly in summer and winter. The prime reason is that people have air-conditioners in their homes and they need power for AC in summer and heaters in winter that on the other hand also consumes electricity (Auffhammer and Wolfram, 2014). There is a less variation of electricity use for commercial sectors through it is found that demands in summer increases compared to the winter season. Lastly, in industrial sector the variation of the electricity needs is more flat with slight increase during the summer. Taken for incident, the household people and it is found that in winter and fall, the consumption of electricity is higher compared to other seasons that are- spring and summer in United States of America (Sexton, 2015). Non-renewable source of energy are used in all these cases. Moreover, during the fall, the electricity consumption is high in case of summer and fall but the consumption of renewable energy sources like geo-thermal, solar and biomass is low throughout the year. Thus, it can be stated that the highest amount of energy consumption is during the winter season. Image 2: Electricity consumption in U.S. throughout the year (Source: Sexton, 2015) Cooling versus Heating Electricity is used for heating to a very limited extent and is fueled by oil and gas; however, other aspects like refrigerators, washing machines and lighting, electricity are used (De Felice et al., 2013The above figure shows is relevant example of the concept of cooling and heating. Temperature should be associated with a rising electricity demand if it is intended to find the association between electricity consumption and summer temperatures. This result can be better understood in nations with higher base temperatures. The primary reason for this aspect is that climate discomfort should be convex in temperature levels and the process if known as cooling effect. On the other hand, the reduction in fuel consumption for heating purposes is known as heating process. It is also said by Dahl et al. (2017) that higher temperatures in spring and fall having more impact compared with the winter season. Moreover, gas and oil products are used for heating and thus it is expected that dema nds for these energy needs does not possess a positive relationship with average temperature levels (Da Silva et al., 2014). Thus, it can be said that the impact of the temperature of climate on the gas and oil products demands can be predicted but there is an increase in energy demand when the temperature is very low and when the temperature is very high. Non-linearity of the energy demand - temperature relationship It is mentioned earlier that energy demand may have affect in a non-linear way. De Felice et al. (2015) stated in the research work that the non-linearity may arise due to the different usage of different fuels, geographical differences and seasonal variations. Important variables that can be considered that address the research aims and objectives are- seasonal temperatures and cold-and-hot country. Bossavy et al. (2013) portrays the evidence of the importance of geographic variability as cooling and heating effect are felt in hot and colder regions respectively. In addition to that, cooling should occur during the summer, whereas heating depends on the duration of the cold season. Another aspect that can be considered is that temperature increases at the beginning and at the end of the cold season and thus, due to this season change, people avoid using heating systems. Image 3: Relationships between Energy Demand and Temperature Levels (Source: Bossavy et al., 2013) Furthermore, if the research can be made in-depth to the hours in a day for every season, different graphs have been obtained. However, in the cases, it has been found that at midnight the consumption of the electricity is low compared to other hours of the day. It is found that in winter season, the energy consumption is lower people do not use any electricity gadgets but during the day time, the consumption of electricity due to the use of other appliances like refrigerators, television, music systems are high (Hong et al., 2014). The electricity demand in the winter is high in Qubec and thus government in such nations helps them to limit the spike in demand. Image 4: Electricity consumption curve during a day in winter season (Source: Hong et al., 2014) However, in summer, the electricity consumption is high in all the hours compared to winter but in comparison with the summer season the electricity consumption at midnight is less for not using the home appliances. Image 5: Electricity consumption curve during a day in summer season (Source: Hong et al., 2014) The scenario, for the spring and fall curve illustrates that the energy consumption is greater in the day time compared to that of the midnight. However, in this case, consistency in energy consumption can be seen and thus, forecasting the unit of electricity consumed can be forecasted easily. Image 6: Electricity consumption curve during a day in spring and fall season (Source: Hong et al., 2014) Techniques for forecasting the electricity demands There are several techniques that help in determining the electricity forecasting- modeling techniques, projecting peak demand from energy, incorporating load management and conversation measures and determining capacity needs from demand forecasts (Alessandrini et al., 2015). However, Trotter et al. (2016) stated that the electricity forecasting affects due to several factors- economic activity, interruptible customers called upon, price of compelling fuel and weather. Usually, three different methods are used in forecasting- time series, econometric and end use (Hong et al., 2014). In the time series forecasting, conception trends is analyzed and can be furthermore categorized in three divisions- liner trend, polynomial trend and logarithmic trend. In all these trends, forecasting the demand of electricity is easy and it can be expected that the consumer demands will also follow the same unit of electricity as per as the developed line or unit of electricity consumed by consumer of that nation. In some cases weather ensembles are used so that demand in electricity can be utilized. In some cases, nonhomogeneous exponential model is used even through there is a gap between the forecasted electricity consumption results and actual consumption of the electricity. Xu et al. (2014) stated that electricity consumption forecasting is usually divided into two categories: short-term and mid/long-term. Experts also evaluate the estimation of GDP, price and GDP per capita elasticity in order to assess the non-domestic and domestic electricity consumption (Xu et al., 2014). In this case, example of Asia country can be taken into consideration. India being the large South Asian developing country and witnessed high increase in GDP and the co untry also suffers from energy shortage due to current lag. The mode developed in the research paper developed by Xu et al. (2014) shows the difference between the actual and the estimated forecasted electricity consumption graph. Image 7: Indias electricity consumption curve and respective comparison among different forecasting results (Source: Xu et al., 2014) Research plan The research project is estimated to be completed within 3 month. The topic selected for the research is already done in the first month. The topic that is selected for the research is impact of climate change on electricity demand and supply. The data that is selected for the research is only secondary data and it needs 4 weeks of time that is from week-1 to week-4. Moreover, framing of the research layout and formulation of the literature review is intended to complete in the 3rd and 4th week together. The data sources selected to develop literature review is different electricity distribution companys websites and government sites. In the next two months that is week 5 and 6, formation of the research plan and selection of appropriate research techniques is completed. The 7th and 8th week is taken for extracting relevant data from the selected data sources. The content of the sources are associated with the selected research topic. The analysis and conclusion drawn from the data c ollection and the research made on the selected topic is intended for the week 9 and 10. Lastly, the formulation of the rough draft and submission of the final project is made on 11th and 12th month. Proposed method of analysis Mackey and Gass (2015) stated that methods of analysis illustrate the research methodology for accomplishing the research project. The aim of the research is to investigate the impact of climate change on electricity demand and supply. Thus, in order to conduct a successful research, interpretivism research philosophy, deductive research approach and descriptive research purpose is utilized in the research. The data that are collected is secondary and analyzed through qualitative data analysis method. The data that are collected illustrate the change of demand in electricity based on the deferent weather and climate. This will help to forecast the electricity consumption of a particular nation. The sources that are selected for data collection are from websites of electricity distribution companies and government websites. The data are collected based on the outcome from the secondary research is then aligned with the research aims and objectives in order to obtain the research outco me. Reference List Alessandrini, S., Delle Monache, L., Sperati, S., Cervone, G. (2015). An analog ensemble for short-term probabilistic solar power forecast. Applied energy, 157, 95-110. Ang, B. W., Wang, H., Ma, X. (2017). 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