Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to p...Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.展开更多
This study focuses on a national regional coordinated development strategy and adopts China Multi-Regional Computable General Equilibrium model to analyze the economic and social development, energy demand, and carbon...This study focuses on a national regional coordinated development strategy and adopts China Multi-Regional Computable General Equilibrium model to analyze the economic and social development, energy demand, and carbon emissions of the provinces during the 14th Five-Year Plan (FYP, 2021 2025) period based on the economic development and energy demand since the New Normal. The main conclusions are the following: 1) Under the guidance of the regional coordinated development strategy, 13 provinces/municipalities are expected to have a per capita gross domestic product (GDP) of more than US$15,000, and 16 provinces/municipalities will have a per capita GDP of US$10,000 15,000. All provincial economies are expected to achieve steady and rapid development by the end of the 14th FYP. 2) The total energy consumption of the provinces is expected to reach 5.45 Gtce (excluding Tibet) in 2025, and the average annual growth rate is approximately 1.5%. The growth of energy demand will remain in low speed. The key point of energy demand will gradually shift from the eastern to the middle area, while the proportion of energy use in the western provinces will remain stable, which is consistent with the economic development stage and regional coordinated development strategy. 3) The annual average carbon intensity (mainly considering carbon emissions from energy use) of the provinces will approximately with most provinces dropping by over 4.0%. The trend of a considerable decline in carbon intensity, as observed in recent years, is expected to continue.展开更多
In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the mod...In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).展开更多
An improved energy demand forecasting model is built based on the autoregressive distributed lag(ARDL) bounds testing approach and an adaptive genetic algorithm(AGA) to obtain credible energy demand forecasting result...An improved energy demand forecasting model is built based on the autoregressive distributed lag(ARDL) bounds testing approach and an adaptive genetic algorithm(AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization,and technological progress as the input variables. On the basis of historical annual data from1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.展开更多
"Economic transformation"has become the main path to promote China's social and economic development,and many regions have increased the importance and attention to"economic transformation",and..."Economic transformation"has become the main path to promote China's social and economic development,and many regions have increased the importance and attention to"economic transformation",and the Southwest region is no exception.Many cities in Southwest China are developing new energy sources to promote economic development and economic transformation.Economic transformation and economic development in Southwest China are mutually influencing and interacting,while energy development in Southwest China and its local economic development are mutually promoting and influencing,so economic transformation also affects energy demand and development in Southwest China.The importance of economic transformation should be taken into consideration.展开更多
As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on...As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on the demand side.This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimi-zation of multiple demand users.This analysis classifies scenarios into various game participant categories,in-cluding distributed energy prosumers,small-and mid-dle-sized users,and large energy consumers.The in-depth analysis of each scenario,covering non-cooperative game,cooperative game,Stackelberg game,Bayesian game,and evolutionary game,is conducted by analyzing market operation mechanisms,model assumptions/formulations,and solution methods.Based on a comprehensive review of such studies,it is concluded that game-theoretic appli-cations on the demand side can benefit both the grid and the users,e.g.,reductions in the peak-to-average ratios and energy costs of the users.The prospects for the ap-plications of game theory on the demand side are dis-cussed,including application scenarios and methodologies.The overview presented in this paper is expected to sup-port researchers in comprehending typical game-theoretic concepts,keeping with the latest research developments,and identifying new and innovative appli-cations for the energy demand side.Index Terms—Energy demand side,game theory,Game-theoretic application,demand response,deci-sion-making behavior.展开更多
With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Eva...With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).展开更多
This work aims to investigate the factors accelerating electric vehicle(EV)acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition.The study further in-...This work aims to investigate the factors accelerating electric vehicle(EV)acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition.The study further in-vestigates the high EV penetration scenario resulting from the technology acceptance model(TAM's 80%EV)and its impact on energy demand and CO_(2)emissions.The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance.The model under investigation was adapted from the famous Technology-Acceptance Models(TAMs)and modified with other significant predictors evidenced in the literature.Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing.Out of six predictors,only four factors were significant in accelerating the EV transition.Financial policies were found to be highly significant,followed by environmental concern,facilitating conditions and perceived ease of use.The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results.The results highlight the significant in-crease in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 MT instead of 61.6 MT by 2040.展开更多
Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages.As for now,we are in a stage of blossoming sciences and technologies related to carbon materia...Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages.As for now,we are in a stage of blossoming sciences and technologies related to carbon materials,as a result of which our evergrowing energy demand has been largely satisfied.Yet,the expected rise of carbon energy consumption and the emerging environmental concerns have prevented us from being optimistic.To build a sufficiently powered future,we have been revolutionizing our ways of carbon energy utilization by discovering and designing new carbon structures,exploring and enhancing their unique physicochemical properties,and pursuing environmentally friendly strategies.Emerging structures such as graphene and sp-bonded C18 have allowed us to discover carbon’s promising properties such as energy storage and superconductivity,while green energy solutions such as fuel cells and CO2 reduction are working synergistically to purify the ecospheric carbon cycle.Therefore,this essay timely discusses related carbon sciences and technologies that have been the milestones shaping our energy consumption,based on which our energy future can be envisioned to be green and prosperous.展开更多
Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by year.However,until now,the use of RE has only reached 2%of the existin...Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by year.However,until now,the use of RE has only reached 2%of the existing potential of 441.7 GW.The main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in Indonesia.National energy demand and targets in the long run during the 2017-2050 period are also discussed.Besides,government policies in supporting RE development are considered in this work.The results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for decades.The use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE development.The selling price of electricity generated from RE is cheaper than electricity generated from fossils;this makes economy is more affordable for people.Finally,the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE development.Thus,greenhouse gas emissions and the use of petroleum fuels can be reduced.展开更多
A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilis...A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.展开更多
The scientific evaluation of trends in China's future energy demands is highly important.Using provincial-level panel data from 1995 to 2015,we studied the relationships between the economic aggregate,the developm...The scientific evaluation of trends in China's future energy demands is highly important.Using provincial-level panel data from 1995 to 2015,we studied the relationships between the economic aggregate,the development of energy-intensive industries and energy demand from the perspective of changes in the proportion of energy-intensive industries in the national economy.We find that economic aggregate affects energy demand through energy-intensive industries and that changes in the economic structure are the key factor for change in energy demand.This means that China's future energy demand will be much lower than that contained in forecasts that did not consider this factor.Comprehensively promoting green-tech development and strengthening the regulation of energy-extensive industries will be one of the key options for realizing China's objective of controlling total energy consumption.展开更多
China National Petroleum Corporation(CNPC),Asia’s biggest oil and gas producer,will increase its investment in natural gas exploration and production,pipeline construction and gas sales in the coming decade,betting b...China National Petroleum Corporation(CNPC),Asia’s biggest oil and gas producer,will increase its investment in natural gas exploration and production,pipeline construction and gas sales in the coming decade,betting big on the country’s thirst for the clean fuel.The stateowned company,which currently allocates 70展开更多
<span style="font-family:Verdana;">The electricity potential in Ghana has become a huge challenge to the nation, </span><span style="font-family:Verdana;">which</span><sp...<span style="font-family:Verdana;">The electricity potential in Ghana has become a huge challenge to the nation, </span><span style="font-family:Verdana;">which</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> increases the country’s economic growth and reduces Nation’s development. The study highlights the trends on the power grid of the energy potential for the past ten years’ impact regarding the directions on the power grid, and to determine the economic potential viabilities couples with the sustainability of renewable energy sources in Ghana. The study relied on substantial reviewed literature and revealed that Ghana’s energy generation has passed through multiple stages, started from diesel generator supply systems owned by industries and factories to hydroelectricity, thermal power electric</span><span style="font-family:Verdana;">ity powered by natural gas or crude oil, and solar electricity. The study showed</span><span style="font-family:Verdana;"> that as of December 2017, Ghana had installed a total capacity of 4398.6 MW comprising Hydro, Thermal, and Solar Plants. Out of the full power, Hydropower generates 1580 MW representing 35.9%, Thermal gene</span><span style="font-family:Verdana;">rates 2796 MW, which also represents 63.6%, while 22.6 MW capacity represent</span></span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> 0.5%. The Long-range Energy Alternatives Planning system (LEAP) tools were employed to consider three different scenarios: energy demand, cost-benefit, and carbon limitation. 2018 was considered as the base year and 2048 as the end year. The results show that 17</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">800</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GWh was estimated as energy demand at base year while 44</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">000</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GWh at end year of 7% annual growth rate. The share of renewable power plants was almost zero at the current account</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> The share of solar thermal plants may reach 90% due to direct cost and externalities. The study adopted one-hundred-year direct GWP at the point of emissions to compare the Mitigation (MITG) and Reference (REF) scenarios</span><span style="font-family:Verdana;">.</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> The model indicated 1.3 Million tons of CO</span><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;"> saving and 4.0 billion U.S. dollars saving with a 5% discount rate in power generation until 2048, if only the country could afford to develop its generation system with the high deployment of RETs, additional benefits in the form of a sustainable safety environment and less emission carbon would be achieved in the next 30 years.</span></span>展开更多
Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population ...Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.展开更多
The building sector is one of the largest energy user and carbon emitters globally.To increase the utilization rate of renewable energy and reduce carbon dioxide emissions,the optimal technical scheme of active public...The building sector is one of the largest energy user and carbon emitters globally.To increase the utilization rate of renewable energy and reduce carbon dioxide emissions,the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied.In this study,the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software,combined with energy conversion efficiency and data released by the National Bureau of Statistics,and the total energy demand and total energy supply of public institutions were predicted using the load density method.Based on the coupling mechanism of the MARKAL model,the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined.Through the study of the number of public institutions in various regions of China,energy consumption characteristics,construction area,and other related data,the reverse energy flow method is creatively proposed,and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established.The results show that the central region has the highest utilization rate of renewable energy in the public sector,reaching 36.18%.The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%,and it was 12.82% in cold zones.By 2025,the proportion of renewable energy resources in China is expected to reach 29.2%.The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.展开更多
Universal two-child policy has been implemented since the end of 2015 in China.This policy is anticipated to bring a significant increase in the total population,with profound influences on the resources and environme...Universal two-child policy has been implemented since the end of 2015 in China.This policy is anticipated to bring a significant increase in the total population,with profound influences on the resources and environment in the future.This paper analyzes the changing dynamics of urban and rural population,and forecasts urban and rural population from 2016 to 2030 at national and provincial scale using a double log linear regression model.Drawing upon the results of these two predictions,the impact of the population policy change on Chinese resources consumption and environmental pollution are predicted quantitatively.Given the future total population maintains current levels on resources consumption and environmental emission,the additional demand of resources and environment demand for the new population is forecasted and compared against the capacity on supply side.The findings are as follows:after implementing the universal two-child policy,China's grain,energy consumption,domestic water demand,and pollutant emissions are projected to increase at different rates across provinces.To meet the needs arising from future population growth,food and energy self-sufficiency rate will be significantly reduced in the future,while relying more on imports.Stability of the water supply needs to be improved,especially in Beijing,Henan,Jiangsu,Qinghai,and Sichuan where the gap in future domestic water demand is comparatively larger.Environmental protection and associated governing capability are in urgent need of upgrade not least due to the increasing pressure of pollution.展开更多
In the analyses of the uncertainty propagation of buildings’energy-demand,the Monte Carlo method is commonly used.In this study we present two alternative approaches:the stochastic perturbation method and the transfo...In the analyses of the uncertainty propagation of buildings’energy-demand,the Monte Carlo method is commonly used.In this study we present two alternative approaches:the stochastic perturbation method and the transformed random variable method.The energy-demand analysis is performed for the representative single-family house in Poland.The investigation is focused on two independent variables,considered as uncertain,the expanded polystyrene thermal conductivity and external temperature;however the generalization on any countable number of parameters is possible.Afterwards,the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches.The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption,while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption.The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method.The most important conclusions are related to the computational cost reduction,simplicity of the application and the appropriateness of the proposed approaches for the buildings’energy-demand calculations.展开更多
11% of Irish electricity was consumed by data centres in 2020. The Irish data centre industry and the cooling methods utilised require reformative actions in the coming years to meet EU Energy policies. The resell of ...11% of Irish electricity was consumed by data centres in 2020. The Irish data centre industry and the cooling methods utilised require reformative actions in the coming years to meet EU Energy policies. The resell of heat, alternative cooling methods or carbon reduction methods are all possibilities to conform to these policies. This study aims to determine the viability of the resell of waste heat from data centres both technically and economically. This was determined using a novel application of thermodynamics to determine waste heat recovery potential in Irish data centres, and the current methods of heat generation for economical comparison. This paper also explores policy surrounding waste heat recovery within the industry. The Recoverable Carnot Equivalent Power (RCEP) is theoretically calculated for the three potential cooling methods for Irish data centres. These are air, hybrid, and immersion cooling techniques. This is the maximum useable heat that can be recovered from a data centre rack. This study is established under current operating conditions which are optimised for cooling performance, that air cooling has the highest potential RCEP of 0.39 kW/rack. This is approximately 8% of the input electrical power that can be captured as useable heat. Indicating that Irish data centres have the energy potential to be heat providers in the Irish economy. This study highlighted the technical and economic aspects of prevalent cooling techniques and determined air cooling heat recovery cost can be reduced to 0.01 €/kWhth using offsetting. This is financially competitive with current heating solutions in Ireland.展开更多
China's energy service demand statistics for the passenger transport sector only cover travel activities for business purpose.The incomplete data have made it very difficult to conduct long-term scenario analysis ...China's energy service demand statistics for the passenger transport sector only cover travel activities for business purpose.The incomplete data have made it very difficult to conduct long-term scenario analysis for this sector.Thus,this study develops a methodology by refining and combining vehicle activity and travel behavior methods.By taking advantage of the two methods,the extent of uncertainty in estimates can be reduced.A detailed description of China's energy service demand in the passenger transport sector for the years 2013,2015,and 2017 are produced.Results show that there is a significant underestimation of total passenger transport turnover in governmental statistics.After reevaluation,the total passenger turnover doubled and increased by 146%,167%,and 187%,resulting in national total passenger turnover of 6783,8031,and 9406 billion passenger kilometers in 2013,2015,and 2017,respectively.The majority of missing statistics are caused by not accounting for non-operational road transport,which is dominated by private vehicles.After adjustment,the road sector share in total passenger turnover grew substantially from 30%to 40%to approximately 75%,with the proportion of urban travel continuing to increase.We found that without complete data on China's passenger turnover,it may result in more than three-fold overestimation for future travel demand,leading to inaccurate projections with much higher energy consumption and CO_(2) emissions.It is therefore very important to have a detailed and precise calculation of energy service demand in the passenger transport sector.The estimation framework and step-by-step process can also be applied to other developing countries which are confronting similar statistical issues.展开更多
文摘Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.
基金s This work was supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology (2016YFA0602601), Science and Technology Project of the State Grid Corporation of China Headquarters ''Research and Development of China Multi-regional Compre hensive Analysis and Forecast Mcxlel System for Energy Sup ply and Demand Fourth National Climate Assessment Report: Mitigation of Climate Change’’, and National Natural Science Foundation of China Program (71573145, 71573062).
文摘This study focuses on a national regional coordinated development strategy and adopts China Multi-Regional Computable General Equilibrium model to analyze the economic and social development, energy demand, and carbon emissions of the provinces during the 14th Five-Year Plan (FYP, 2021 2025) period based on the economic development and energy demand since the New Normal. The main conclusions are the following: 1) Under the guidance of the regional coordinated development strategy, 13 provinces/municipalities are expected to have a per capita gross domestic product (GDP) of more than US$15,000, and 16 provinces/municipalities will have a per capita GDP of US$10,000 15,000. All provincial economies are expected to achieve steady and rapid development by the end of the 14th FYP. 2) The total energy consumption of the provinces is expected to reach 5.45 Gtce (excluding Tibet) in 2025, and the average annual growth rate is approximately 1.5%. The growth of energy demand will remain in low speed. The key point of energy demand will gradually shift from the eastern to the middle area, while the proportion of energy use in the western provinces will remain stable, which is consistent with the economic development stage and regional coordinated development strategy. 3) The annual average carbon intensity (mainly considering carbon emissions from energy use) of the provinces will approximately with most provinces dropping by over 4.0%. The trend of a considerable decline in carbon intensity, as observed in recent years, is expected to continue.
基金supported by the Fundamental Research Funds for the Central Universities[Grant No.JBK1507159]
文摘In this article,we present an application of Adaptive Genetic Algorithm Energy Demand Estimation(AGAEDE) optimal model to improve the efficiency of energy demand prediction.The coefficients of the two forms of the model(both linear and quadratic) are optimized by AGA using factors,such as GDP,population,urbanization rate,and R&D inputs together with energy consumption structure,that affect demand.Since the spurious regression phenomenon occurs for a wide range of time series analysis in econometrics,we also discuss this problem for the current artificial intelligence model.The simulation results show that the proposed model is more accurate and reliable compared with other existing methods and the China's energy demand will be 5.23 billion TCE in 2020 according to the average results of the AGAEDE optimal model.Further discussion illustrates that there will be great pressure for China to fulfill the planned goal of controlling energy demand set in the National Energy Demand Project(2014—2020).
文摘An improved energy demand forecasting model is built based on the autoregressive distributed lag(ARDL) bounds testing approach and an adaptive genetic algorithm(AGA) to obtain credible energy demand forecasting results. The ARDL bounds analysis is first employed to select the appropriate input variables of the energy demand model. After the existence of a cointegration relationship in the model is confirmed, the AGA is then employed to optimize the coefficients of both linear and quadratic forms with gross domestic product, economic structure, urbanization,and technological progress as the input variables. On the basis of historical annual data from1985 to 2015, the simulation results indicate that the proposed model has greater accuracy and reliability than conventional optimization methods. The predicted results of the proposed model also demonstrate that China will demand approximately 4.9, 5.6, and 6.1 billion standard tons of coal equivalent in 2020, 2025, and 2030, respectively.
文摘"Economic transformation"has become the main path to promote China's social and economic development,and many regions have increased the importance and attention to"economic transformation",and the Southwest region is no exception.Many cities in Southwest China are developing new energy sources to promote economic development and economic transformation.Economic transformation and economic development in Southwest China are mutually influencing and interacting,while energy development in Southwest China and its local economic development are mutually promoting and influencing,so economic transformation also affects energy demand and development in Southwest China.The importance of economic transformation should be taken into consideration.
基金supported by the National Natural Science Foundation of China(No.52107100)the Basic Science(Natural Science)Research Pro-ject of Jiangsu Higher Education Institutions(No.23KJB470020).
文摘As an essential characteristic of the smart grid,energy demand users are being transformed from passive roles to active decision-makers.To analyze their decision-making behaviors,game theory has been widely applied on the demand side.This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimi-zation of multiple demand users.This analysis classifies scenarios into various game participant categories,in-cluding distributed energy prosumers,small-and mid-dle-sized users,and large energy consumers.The in-depth analysis of each scenario,covering non-cooperative game,cooperative game,Stackelberg game,Bayesian game,and evolutionary game,is conducted by analyzing market operation mechanisms,model assumptions/formulations,and solution methods.Based on a comprehensive review of such studies,it is concluded that game-theoretic appli-cations on the demand side can benefit both the grid and the users,e.g.,reductions in the peak-to-average ratios and energy costs of the users.The prospects for the ap-plications of game theory on the demand side are dis-cussed,including application scenarios and methodologies.The overview presented in this paper is expected to sup-port researchers in comprehending typical game-theoretic concepts,keeping with the latest research developments,and identifying new and innovative appli-cations for the energy demand side.Index Terms—Energy demand side,game theory,Game-theoretic application,demand response,deci-sion-making behavior.
基金supported by The Indian Institute of Technology-Bombay(Institute Postdoctoral Fellowship-AO/Admin-1/Rect/33/2019).
文摘With the existence of several conventional and advanced building thermal energy demand forecast models to improve the energy efficiency of buildings,it is hard to find an appropriate,convenient,and efficient model.Evaluations based on statistical indexes(MAE,RMSE,MAPE,etc.)that characterize the accuracy of the forecasts do not help in the identification of the efficient building thermal energy demand forecast tool since they do not reflect the efforts entailed in implementation of the forecast model,i.e.,data collection to production/use phase.Hence,this work presents a Gini Index based Measurement of Alternatives and Ranking according to COmpromise Solution(GI-MARCOS),a hybrid Multi Attribute Decision Making(MADM)approach for the identification of the most efficient building energy demand forecast tool.GI-MARCOS employs(i)GI based objective weight method:assigns meaningful objective weights to the attributes in four phases(1:pre-processing,2:implementation,3:post-processing,and 4:use phase)thereby avoiding unnecessary biases in the expert’s opinion on weights and applicable to domains where there is a lack of domain expertise,and(ii)MARCOS:provides a robust and reliable ranking of alternatives in a dynamic environment.A case study with three alternatives evaluated over three to six attributes in four phases of implementation(pre-processing,implementation,post-processing and use)reveals that the use of GI-MARCOS improved the accuracy of alternatives MLR and BM by 6%and 13%,respectively.Moreover,additional validations state that(i)MLR performs best in Phase 1 and 2,while ANN performs best in Phase 3 and 4 with BM providing a mediocre performance in all four phases,(ii)sensitivity analysis:provides robust ranking with interchange of weights across phases and attributes,and(iii)rank correlation:ranks produce by GI-MARCOS has a high correlation with GRA(0.999),COPRAS(0.9786),and ARAS(0.9775).
文摘This work aims to investigate the factors accelerating electric vehicle(EV)acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition.The study further in-vestigates the high EV penetration scenario resulting from the technology acceptance model(TAM's 80%EV)and its impact on energy demand and CO_(2)emissions.The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance.The model under investigation was adapted from the famous Technology-Acceptance Models(TAMs)and modified with other significant predictors evidenced in the literature.Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing.Out of six predictors,only four factors were significant in accelerating the EV transition.Financial policies were found to be highly significant,followed by environmental concern,facilitating conditions and perceived ease of use.The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results.The results highlight the significant in-crease in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 MT instead of 61.6 MT by 2040.
基金DOE Office of Energy Efficiency and Renewable Energy,Vehicle Technologies OfficeDOE Office of Science by UChicago Argonne,Grant/Award Number:DE‐AC02‐06CH11357。
文摘Carbon is the central element driving the evolution of our human society towards prosperity over several historical stages.As for now,we are in a stage of blossoming sciences and technologies related to carbon materials,as a result of which our evergrowing energy demand has been largely satisfied.Yet,the expected rise of carbon energy consumption and the emerging environmental concerns have prevented us from being optimistic.To build a sufficiently powered future,we have been revolutionizing our ways of carbon energy utilization by discovering and designing new carbon structures,exploring and enhancing their unique physicochemical properties,and pursuing environmentally friendly strategies.Emerging structures such as graphene and sp-bonded C18 have allowed us to discover carbon’s promising properties such as energy storage and superconductivity,while green energy solutions such as fuel cells and CO2 reduction are working synergistically to purify the ecospheric carbon cycle.Therefore,this essay timely discusses related carbon sciences and technologies that have been the milestones shaping our energy consumption,based on which our energy future can be envisioned to be green and prosperous.
文摘Abundant potential of renewable energy(RE)in Indonesia is predicted to replace conventional energy which continues to experience depletion year by year.However,until now,the use of RE has only reached 2%of the existing potential of 441.7 GW.The main overview of this work is to investigate the availability of RE that can be utilized for electricity generation in Indonesia.National energy demand and targets in the long run during the 2017-2050 period are also discussed.Besides,government policies in supporting RE development are considered in this work.The results show that the potential of RE in Indonesia can be utilized and might replace conventional energy for decades.The use of RE for electricity generation can be achieved by employing a government policy that supports the investor as the executor of RE development.The selling price of electricity generated from RE is cheaper than electricity generated from fossils;this makes economy is more affordable for people.Finally,the target set by the government for utilizing RE as the main energy in Indonesia can be done by implementing several policies for the RE development.Thus,greenhouse gas emissions and the use of petroleum fuels can be reduced.
基金The authors would like to acknowledge the funding received from the Department of Science and Technology,Government of India(DST/TMD/UKBEE/2017/17)Projects:Zero Peak Energy Demand for India(ZED-I)and Engineering and Physics Research Council EPSRC(EP/R008612/1).
文摘A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.
基金the project of the National Natural Science Fund of China,“Research on Household Energy Consumption in China”(71774165)the Research Fund of Renmin University of China,i.e.,the Basic Research Funds for the Central Universities(17XNS001 and 11XNL004).
文摘The scientific evaluation of trends in China's future energy demands is highly important.Using provincial-level panel data from 1995 to 2015,we studied the relationships between the economic aggregate,the development of energy-intensive industries and energy demand from the perspective of changes in the proportion of energy-intensive industries in the national economy.We find that economic aggregate affects energy demand through energy-intensive industries and that changes in the economic structure are the key factor for change in energy demand.This means that China's future energy demand will be much lower than that contained in forecasts that did not consider this factor.Comprehensively promoting green-tech development and strengthening the regulation of energy-extensive industries will be one of the key options for realizing China's objective of controlling total energy consumption.
文摘China National Petroleum Corporation(CNPC),Asia’s biggest oil and gas producer,will increase its investment in natural gas exploration and production,pipeline construction and gas sales in the coming decade,betting big on the country’s thirst for the clean fuel.The stateowned company,which currently allocates 70
文摘<span style="font-family:Verdana;">The electricity potential in Ghana has become a huge challenge to the nation, </span><span style="font-family:Verdana;">which</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> increases the country’s economic growth and reduces Nation’s development. The study highlights the trends on the power grid of the energy potential for the past ten years’ impact regarding the directions on the power grid, and to determine the economic potential viabilities couples with the sustainability of renewable energy sources in Ghana. The study relied on substantial reviewed literature and revealed that Ghana’s energy generation has passed through multiple stages, started from diesel generator supply systems owned by industries and factories to hydroelectricity, thermal power electric</span><span style="font-family:Verdana;">ity powered by natural gas or crude oil, and solar electricity. The study showed</span><span style="font-family:Verdana;"> that as of December 2017, Ghana had installed a total capacity of 4398.6 MW comprising Hydro, Thermal, and Solar Plants. Out of the full power, Hydropower generates 1580 MW representing 35.9%, Thermal gene</span><span style="font-family:Verdana;">rates 2796 MW, which also represents 63.6%, while 22.6 MW capacity represent</span></span><span style="font-family:Verdana;">s</span><span style="font-family:Verdana;"> 0.5%. The Long-range Energy Alternatives Planning system (LEAP) tools were employed to consider three different scenarios: energy demand, cost-benefit, and carbon limitation. 2018 was considered as the base year and 2048 as the end year. The results show that 17</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">800</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GWh was estimated as energy demand at base year while 44</span><span style="font-family:Verdana;">,</span><span style="font-family:Verdana;">000</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">GWh at end year of 7% annual growth rate. The share of renewable power plants was almost zero at the current account</span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> The share of solar thermal plants may reach 90% due to direct cost and externalities. The study adopted one-hundred-year direct GWP at the point of emissions to compare the Mitigation (MITG) and Reference (REF) scenarios</span><span style="font-family:Verdana;">.</span><span style="font-family:;" "=""><span style="font-family:Verdana;"> The model indicated 1.3 Million tons of CO</span><sub><span style="font-family:Verdana;">2</span></sub><span style="font-family:Verdana;"> saving and 4.0 billion U.S. dollars saving with a 5% discount rate in power generation until 2048, if only the country could afford to develop its generation system with the high deployment of RETs, additional benefits in the form of a sustainable safety environment and less emission carbon would be achieved in the next 30 years.</span></span>
文摘Our dependency on energy is so vital that it makes it difficult to imagine how humans can live on our planet earth without it.The demand for electricity,for example,is directly related to the growth of the population worldwide,and presently,to meet this demand,we need both renewable and nonrenewable energy.While nonrenewable energy has its shortcomings(negative impact on climate change,for example),renewable energy is not enough to address the ever-changing demand for energy.One way to address this need is to become more innovative,use technology more effectively,and be aware of the costs associated with different sources of renewable energy.In the case of nuclear power plants,new innovative centered around small modular reactors(SMRs)of generation 4th of these plants make them safer and less costly to own them as well as to protect them via means of cyber-security against any attack by smart malware.Of course,understanding the risks and how to address them is an integral part of the study.Natural sources of energy,such as wind and solar,are suggesting other innovating technical approaches.In this article,we are studying these factors holistically,and details have been laid out in a book by the authors’second volume of series title as Knowledge Is Power in Four Dimensions under Energy subtitle.
基金supported by National Natural Science Funds(52078308)Liao Ning Revitalization Talents Program(XLYC2007003)the Educational Commission of Liaoning Province of China(lnzd202003).
文摘The building sector is one of the largest energy user and carbon emitters globally.To increase the utilization rate of renewable energy and reduce carbon dioxide emissions,the optimal technical scheme of active public institutions and coupled utilization of renewable energy is studied.In this study,the energy consumption of three types of public institutions in various regions of China was simulated by using DeST building energy consumption software,combined with energy conversion efficiency and data released by the National Bureau of Statistics,and the total energy demand and total energy supply of public institutions were predicted using the load density method.Based on the coupling mechanism of the MARKAL model,the optimal proportion of renewable energy in the energy supply of public buildings in different regions is determined.Through the study of the number of public institutions in various regions of China,energy consumption characteristics,construction area,and other related data,the reverse energy flow method is creatively proposed,and the active and renewable energy coupling algorithm from the energy demand side of public institutions to the energy supply side is established.The results show that the central region has the highest utilization rate of renewable energy in the public sector,reaching 36.18%.The use of renewable energy in public buildings in hot summer and warm winter zones decreased to 35.08%,and it was 12.82% in cold zones.By 2025,the proportion of renewable energy resources in China is expected to reach 29.2%.The energy coupling model and algorithm constructed in this paper can provide a basis for the coupling macro configuration of renewable energy in public institutions in China.
文摘Universal two-child policy has been implemented since the end of 2015 in China.This policy is anticipated to bring a significant increase in the total population,with profound influences on the resources and environment in the future.This paper analyzes the changing dynamics of urban and rural population,and forecasts urban and rural population from 2016 to 2030 at national and provincial scale using a double log linear regression model.Drawing upon the results of these two predictions,the impact of the population policy change on Chinese resources consumption and environmental pollution are predicted quantitatively.Given the future total population maintains current levels on resources consumption and environmental emission,the additional demand of resources and environment demand for the new population is forecasted and compared against the capacity on supply side.The findings are as follows:after implementing the universal two-child policy,China's grain,energy consumption,domestic water demand,and pollutant emissions are projected to increase at different rates across provinces.To meet the needs arising from future population growth,food and energy self-sufficiency rate will be significantly reduced in the future,while relying more on imports.Stability of the water supply needs to be improved,especially in Beijing,Henan,Jiangsu,Qinghai,and Sichuan where the gap in future domestic water demand is comparatively larger.Environmental protection and associated governing capability are in urgent need of upgrade not least due to the increasing pressure of pollution.
文摘In the analyses of the uncertainty propagation of buildings’energy-demand,the Monte Carlo method is commonly used.In this study we present two alternative approaches:the stochastic perturbation method and the transformed random variable method.The energy-demand analysis is performed for the representative single-family house in Poland.The investigation is focused on two independent variables,considered as uncertain,the expanded polystyrene thermal conductivity and external temperature;however the generalization on any countable number of parameters is possible.Afterwards,the propagation of the uncertainty in the calculations of the energy consumption has been investigated using two aforementioned approaches.The stochastic perturbation method is used to determine the expected value and central moments of the energy consumption,while the transformed random variable method allows to obtain the explicit form of energy consumption probability density function and further characteristic parameters like quantiles of energy consumption.The calculated data evinces a high accordance with the results obtained by means of the Monte Carlo method.The most important conclusions are related to the computational cost reduction,simplicity of the application and the appropriateness of the proposed approaches for the buildings’energy-demand calculations.
文摘11% of Irish electricity was consumed by data centres in 2020. The Irish data centre industry and the cooling methods utilised require reformative actions in the coming years to meet EU Energy policies. The resell of heat, alternative cooling methods or carbon reduction methods are all possibilities to conform to these policies. This study aims to determine the viability of the resell of waste heat from data centres both technically and economically. This was determined using a novel application of thermodynamics to determine waste heat recovery potential in Irish data centres, and the current methods of heat generation for economical comparison. This paper also explores policy surrounding waste heat recovery within the industry. The Recoverable Carnot Equivalent Power (RCEP) is theoretically calculated for the three potential cooling methods for Irish data centres. These are air, hybrid, and immersion cooling techniques. This is the maximum useable heat that can be recovered from a data centre rack. This study is established under current operating conditions which are optimised for cooling performance, that air cooling has the highest potential RCEP of 0.39 kW/rack. This is approximately 8% of the input electrical power that can be captured as useable heat. Indicating that Irish data centres have the energy potential to be heat providers in the Irish economy. This study highlighted the technical and economic aspects of prevalent cooling techniques and determined air cooling heat recovery cost can be reduced to 0.01 €/kWhth using offsetting. This is financially competitive with current heating solutions in Ireland.
基金supported by the National Natural Science Foundation of China(72004043)Shenzhen City Stable Support Plan(GXWD20201230155427003-20200823215025001).
文摘China's energy service demand statistics for the passenger transport sector only cover travel activities for business purpose.The incomplete data have made it very difficult to conduct long-term scenario analysis for this sector.Thus,this study develops a methodology by refining and combining vehicle activity and travel behavior methods.By taking advantage of the two methods,the extent of uncertainty in estimates can be reduced.A detailed description of China's energy service demand in the passenger transport sector for the years 2013,2015,and 2017 are produced.Results show that there is a significant underestimation of total passenger transport turnover in governmental statistics.After reevaluation,the total passenger turnover doubled and increased by 146%,167%,and 187%,resulting in national total passenger turnover of 6783,8031,and 9406 billion passenger kilometers in 2013,2015,and 2017,respectively.The majority of missing statistics are caused by not accounting for non-operational road transport,which is dominated by private vehicles.After adjustment,the road sector share in total passenger turnover grew substantially from 30%to 40%to approximately 75%,with the proportion of urban travel continuing to increase.We found that without complete data on China's passenger turnover,it may result in more than three-fold overestimation for future travel demand,leading to inaccurate projections with much higher energy consumption and CO_(2) emissions.It is therefore very important to have a detailed and precise calculation of energy service demand in the passenger transport sector.The estimation framework and step-by-step process can also be applied to other developing countries which are confronting similar statistical issues.
