Assessment of energy savings from the revised building energy code of Thailand

The government of Thailand legislated an Energy Conservation Promotion Act (ECP Act) in 1992 and set bye-laws that identify designated buildings (DBs) and detail mandatory requirements for energy conservation for DBs in 1995. An Energy Conservation Promotion Fund (ENCON Fund) was also created to fund energy audits on 1900 DBs. Recently the requirements and procedures for energy conservation in buildings have been revised where system performance requirements for building envelope, lighting, air-conditioning, and hot water generation are adopted. Moreover, the new building energy code (BEC) distinguishes different categories of DBs, provides credit for use of solar energy, and introduces a new option of whole building energy compliance. The authors develop building models from data obtained from energy audit reports and use them to estimate savings on energy and peak demand from future new buildings using forecasted energy and peak demand data from the Load Forecast Subcommittee, a panel tasked to forecast future electric load of Thailand. From a modest level of energy saving in the first year that the code is expected to be enforced, the level of saving rise to over 10% and 20% annually of requirement of target buildings in 6 and 12 years respectively.     

A framework for targeting household energy savings through habitual behavioural change

This paper reviews existing up-to-date literature related to individual household energy consumption. The how and why individual behaviour affects energy use are discussed, together with the principles and perspectives which have so far been considered in order to explain the habitual consuming behaviour. The research gaps, which are revealed from previous studies in terms of the limitations or assumptions on the methodology to alter individuals’ energy usage, give insights for a conceptual framework to define a comprehensive approach. The proposed framework suggests that the individual energy perception gaps are affected by psychological, habitual, structural and cultural variables in a wider-contextual, meso-societal and micro-individual spectrum. All these factors need to be considered in order for a variety of combined intervention methods, which are discussed and recommended, to introduce a more effective shift in the conventional energy-consuming behaviour, advancing insights for successful energy policies.     

Short- and long-run time-of-use price elasticities in Swiss residential electricity demand

This paper presents an empirical analysis on the residential demand for electricity by time-of-day. This analysis has been performed using aggregate data at the city level for 22 Swiss cities for the period 2000−2006. For this purpose, we estimated two log–log demand equations for peak and off-peak electricity consumption using static and dynamic partial adjustment approaches. These demand functions were estimated using several econometric approaches for panel data, for example LSDV and RE for static models, and LSDV and corrected LSDV estimators for dynamic models. The attempt of this empirical analysis has been to highlight some of the characteristics of the Swiss residential electricity demand. The estimated short-run own price elasticities are lower than 1, whereas in the long-run these values are higher than 1. The estimated short-run and long-run cross-price elasticities are positive. This result shows that peak and off-peak electricity are substitutes. In this context, time differentiated prices should provide an economic incentive to customers so that they can modify consumption patterns by reducing peak demand and shifting electricity consumption from peak to off-peak periods.     

Modelling and forecasting Turkish residential electricity demand

This research investigates the relationship between Turkish residential electricity consumption, household total final consumption expenditure and residential electricity prices by applying the structural time series model to annual data over the period from 1960 to 2008. Household total final consumption expenditure, real energy prices and an underlying energy demand trend are found to be important drivers of Turkish residential electricity demand with the estimated short run and the long run total final consumption expenditure elasticities being 0.38 and 1.57, respectively, and the estimated short run and long run price elasticities being −0.09 and −0.38, respectively. Moreover, the estimated underlying energy demand trend, (which, as far as is known, has not been investigated before for the Turkish residential sector) should be of some benefit to Turkish decision makers in terms of energy planning. It provides information about the impact of past policies, the influence of technical progress, the impacts of changes in consumer behaviour and the effects of changes in economic structure. Furthermore, based on the estimated equation, and different forecast assumptions, it is predicted that Turkish residential electricity demand will be somewhere between 48 and 80 TWh by 2020 compared to 40 TWh in 2008.     

解决江西电煤供需不平衡的对策分析

从江西省资源概况出发,基于2004--2008年江西统计年鉴历史数据,对全省历年电煤产运和电力发展进行了因素分析,对因电煤发展不平衡引发的能源供应紧张以及其对经济发展带来的影响进行了具体分析,提出了建立省级煤炭储备基地的设想。围绕储备基地建设设想,对项目建设进行了简单的效益分析,详细阐述了建设的必要性、可行性以及建立储备基地的措施建议和现实而长远的意义。     

Household electricity and gas consumption for heating homes

Energy consumption has been drastically changed because of energy source depletion, price fluctuations, development and penetration of alternative energy sources, and government policies. Household energy sources are interrelated, and energy price and household characteristics, such as income level and dwelling size, affect the usage. To supply energy consistently and achieve a balance between production and consumption, stakeholders must understand consumer energy-consumption behavior. Therefore, this study identifies household heating energy usage patterns and the substitutive and/or complementary relationships between electricity and gas. Based on a multiple discrete-continuous extreme value model, household utility structure is identified from data on gas-heating usage. Results show greater utility and the smallest satiation values for gas boilers than for electric heaters and electric heating beds. The effects of consumer socioeconomic and environmental characteristics on the choice of heating energy sources were analyzed. Also, for further comparison, the respondents were split into high and low categories for income, heating degree days, dwelling size, and gas usage. Gas was found to be the most economical heating choice for households.     

Residential energy efficiency investment and behavioural response under different electricity pricing schemes: a physical-microeconomic approach

ABSTRACT

The purpose of this paper is to analyze the effects of electricity price changes and energy efficiency subsidy on household energy efficiency purchase and/or behavioural adjustment decisions. The analysis adds energy efficiency investment to a methodology that merges the physics of energy with microeconomic principles. The physical side informs the amount of electricity used to satisfy services that people desire, while the microeconomic side imposes a utility function that represents a household’s welfare. Several electricity pricing schemes and energy efficiency options are examined, with costs and benefits of each option explicitly modeled in the physical representation. Several insights are derived from performing an analysis for archetypical villas across Saudi Arabia. One, energy efficiency purchases lower the need for energy conservation. Households also lessen the extent to which they practice conservation as energy efficiency subsidies are raised. Additionally, as energy efficiency subsidies and electricity prices rise, the difference in household spending on other goods and services widens between the highest efficiency case and no added efficiency. This indirect rebound causes a situation where firms would increase their production, and thus energy use, to meet the additional demand by households for their goods.     

Weather, the estimation of residential energy demand relationships and the reporting of data

The need for adjusting energy related data, especially weather data, prior to estimating demand relationships is pointed out. A previous study by Rushdi in Energy Economics and other studies are used as examples. The lack of such adjustments could affect greatly the estimated results. The desirability of making data available along with an article is also pointed out.     

Optimized electrolyzer operation: Employing forecasts of wind energy availability,hydrogen demand,and electricity prices

One of the main advantages of fuel cell based mobility over other sustainable mobility concepts is the flexible production of hydrogen via electrolysis. To date, it is unclear how electrolysis at hydrogen refueling stations should be operated in order to achieve the lowest possible costs despite the constraints of hydrogen demand. This study proposes and evaluates an intelligent operating strategy for electrolysis capable of exploiting times of low electricity prices while participating in the spot market and maximizing wind energy utilization when combined with a wind farm. This strategy is based on a simulation model considering imperfect forecasts (e.g. of wind availability or energy prices) and non-linear electrolyzer behavior. Results show that this approach reduces hydrogen production costs by up to 9.2% and increases wind energy utilization by up to 19%, respectively.     

加强需求侧管理降低油田电能损耗

扼要说明了实施DSM的意义 ,并根据中原油田供配电系统的现状及存在的主要问题 ,提出了切实可行降低线损的DSM技术方案。通过实施达到了节能降耗之目的 ,取得了显著的经济效益。     

Forecasting electricity price and demand using a hybrid approach based on wavelet transform,ARIMA and neural networks

Demand and price forecasting are extremely important for participants in energy markets. Most research work in the area predicts demand and price signals separately. In this paper, a model is presented which predicts electricity demand and price simultaneously. The model combines wavelet transforms, ARIMA models and neural networks. Both time domain and wavelet domain variables are considered in the feature set for price and demand forecasting. The best input set is selected by two‐step correlation analysis. The proposed model is better adapted to real conditions of an energy market since the forecast features for price and demand are not assumed as known values but are predicted by the model, thus accounting for the interactions of the demand and price forecast processes. The forecast accuracy of the proposed method is evaluated using data from the Finnish energy market, which is part of the Nordic Power Pool. The results show that the proposed model provides significant improvement in both demand and price prediction accuracy compared with models using a separate frameworks approach. Copyright © 2013 John Wiley & Sons, Ltd.     

How do energy & environmental policy goals and instruments affect electricity demand? A framework for the analysis

Several measures in the environment and energy realms are currently being implemented in the EU and its Member States. Three of these instruments, with an impact on the electricity market, are demand side management activities, promotion of electricity from renewable energy sources and measures aimed at the mitigation of Greenhouse Gas (GHG) emissions. The objective of this paper is to analyse the impact of these energy efficiency and environmental goals and instruments on electricity demand and costs to electricity consumers when electricity markets are either national or international and when those policies are implemented by a national or an international institution. The paper shows that the effectiveness and impact of those measures largely depends on the demand response in the electricity market. An additional conclusion is that, when either the electricity markets or the support policies are national, distortions may occur, i.e. the reductions in electricity demand in one country may be subsidised by consumers or taxpayers in another country.     

A model to estimate energy demand and CO2 emissions for the Indian cement industry

A modelling framework based on linear dynamic programming techniques is presented which has been used to estimate energy demand as well as CO₂ emissions associated with the Indian cement industry for different scenarios during the period 1992–2021. Copyright © 1999 John Wiley & Sons, Ltd.     

Improved technical efficiency and exogenous factors in transportation demand for energy: An application of structural time series analysis to South Korean data

This paper stresses the importance of incorporating the effects of improved technical efficiency and exogenous factors when estimating energy demand functions. Using annual time series data for the period 1973–2007 in the STSM (structural time series model) developed by Harvey et al. [26] the paper estimates price and income elasticities of demand for energy as well as the annual growth of the stochastic trend at the end of the estimation period. The results of the study reveal a long-run income elasticity of 1.37 and a price elasticity of −0.19. In addition, the underlying trend is generally stochastic and negatively sloping during the greater part of the estimation period. Finally, the estimated result from the structural time series is compared with the results from the Johansen Cointegration. These results suggest that income is the dominant factor in energy consumption. In addition, the coefficient of linear trend is negative, supporting the results from the STSM.     

Analysis of energy demand and CO2 emissions for the Indian aluminium industry using a dynamic programming model

This paper presents a capacity planning model for the Indian aluminium industry based on a linear dynamic programming technique. The model has been used to analyse energy demand and CO₂ emission for the period 1992–2021. Copyright © 2000 John Wiley & Sons, Ltd.     

Development of a thermally enhanced frame wall with phase‐change materials for on‐peak air conditioning demand reduction and energy savings in residential buildings

This paper presents the development of a thermally enhanced frame wall that reduces peak air conditioning demand in residential buildings. A frame wall that integrates a highly crystalline paraffin phase‐change material (PCM), via macro‐encapsulation, was developed, constructed, and evaluated. This prototype wall is referred to as phase‐change frame wall (PCFW). Results from field testing show that the PCFW reduced wall peak heat fluxes by as much as 38%. For a period of several days that included walls facing different directions, the average wall peak heat flux reduction was approximately 15% when PCFWs with a 10% concentration of PCM (based on indoor sheathing weight) were used and approximately 9% when a 20% PCM concentration was used. The average space‐cooling load was reduced by approximately 8.6% when 10% PCM was applied and 10.8% when 20% PCM was used. The level of insulation in the PCFWs that were tested was 1.94m²K/W (R‐11). Copyright © 2005 John Wiley & Sons, Ltd.     

Comparison of neural network,conditional demand analysis,and engineering approaches for modeling end-use energy consumption in the residential sector

This paper investigates the use of conditional demand analysis (CDA) method to model the residential end-use energy consumption at the national level. There are several studies where CDA was used to model energy consumption at the regional level; however the CDA method had not been used to model residential energy consumption at the national level. The prediction performance and the ability to characterize the residential end-use energy consumption of the CDA model are compared with those of a neural network (NN) and an engineering based model developed earlier. The comparison of the predictions of the models indicates that CDA is capable of accurately predicting the energy consumption in the residential sector as well as the other two models. The effects of socio-economic factors are estimated using the NN and the CDA models, where possible. Due to the limited number of variables the CDA model can accommodate, its capability to evaluate these effects is found to be lower than the NN model.     

Multiple effects of energy storage units on combined cooling,heating and power (CCHP) systems

Albeit numerous studies discussing manifold issues of combined cooling, heating and power (CCHP) systems, there is still lack of theoretical studies indicating to what extent the energy mismatch and the deviating working conditions affect the CCHP performance, absence of reports systematically summarizing the multiple effects of energy saving units (ESUs), and deficiency of research quantifying the benefits from ESUs to energy savings. The shortage of such studies will confuse some CCHP designers when a CCHP system is designed. Therefore, in this research, theoretical discussions have been undertaken about the energy mismatch issue between CCHP systems and their users as well as the multiple effects of ESUs on CCHP systems. An improved calculational method of energy storage rate (ESR) has been adopted to evaluate the energy savings performance of CCHP systems. Two general heat‐to‐electricity ratios (R_user for CCHP users and R_CCHP for CCHP systems) have been used to quantify the energy mismatch between CCHP systems and their users. In the regime of ‘priority of providing cooling’, the ESR reaches its maximum when R_user is equal to R_CCHP. Otherwise, the ESR tends to decrease rapidly, especially when the electrical demand must be supplemented from the grid. Furthermore, when the CCHP system produces more electricity than required, the payment mode of extra electricity from the CCHP system will significantly affect the ESR. Therefore, it is imperative to reach an international consensus regarding the dispose of extra CCHP products. The theoretical analyses also corroborate the advantages of incorporating an ESU into a CCHP system. The ESU enables the CCHP system components to operate at their optimal working conditions. Meanwhile, the power generation unit and the absorption refrigerator capacities can then be reduced. Moreover, the ESU also promotes the productivity of electricity and ensures an undiminished ESR regardless of what extra electricity payment mode is adopted. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi-objective optimization model for sustainable Indonesian electricity system: Analysis of economic,environment, and adequacy of energy sources

This paper presents a multi-objective optimization model for a long-term generation mix in Indonesia. The objective of this work is to assess the economic, environment, and adequacy of local energy sources. The model includes two competing objective functions to seek the lowest cost of generation and the lowest CO₂ emissions while considering technology diffusion. The scenarios include the use of fossil reserves with or without the constraints of the reserve to production ratio and exports. The results indicate that Indonesia should develop all renewable energy and requires imported coal and natural gas. If all fossil resources were upgraded to reserves, electricity demand in 2050 could be met by domestic energy sources. The maximum share of renewable energy that can be achieved in 2050 is 33% with and 80% without technology diffusion. The least cost optimization produces lower generation costs than the least CO₂ emissions, as well as the combined scenario. Total CO₂ emissions in 2050 are five to six times as large as current emissions. The least CO₂ emissions scenario can reduce almost half of the CO₂ emissions of the least cost scenario by 2050. The proposed multi-objective optimization model leads some optimal solutions for a more sustainable electricity system.     

Assessment and optimization of an integrated energy system with electrolysis and fuel cells for electricity,cooling and hydrogen production using various optimization techniques

In this research study, a novel integrated solar based combined, cooling, heating and, power (CCHP) is proposed consisting of Parabolic trough solar collectors (PTSC) field, a dual-tank molten salt heat storage, an Organic Rankine Cycle (ORC), a Proton exchange membrane fuel cell (PEMFC), a Proton exchange membrane electrolyzer (PEME), and a single effect Li/Br water absorption chiller. Thermodynamics and economic relations are used to analyze the proposed CCHP system. The mean of Tehran solar radiation as well as each portion of solar radiation during 24 h in winter is obtained from TRNSYS software to be used in PTSC calculations. A dynamic model of the thermal storage unit is assessed for proposed CCHP system under three different conditions (i.e., without thermal energy storage (TES), with TES and with TES + PEMFC). The results demonstrate that PEMFC has the ability to improve the power output by 10% during the night and 3% at sunny hours while by using TES alone, the overnight power generation is 86% of the power generation during the sunny hours. The optimum operating condition is determined via the NSGA-II algorithm with regards to exergy efficiency and total cost rate as objective functions where the optimum values are 0.058 ($/s) and 80%, respectively. The result of single objective optimization is 0.044 ($/s) for the economic objective in which the exergy efficiency is at its lowest value (57.7%). In addition, results indicate that the amount of single objective optimization based on exergetic objective is 88% in which the total cost rate is at its highest value (0.086 $/s). The scattered distribution of design parameters and the decision variables trend are investigated. In the next step, five different evolutionary algorithms namely NSGA-II, GDE3, IBEA, SMPSO, and SPEA2 are applied, and their Pareto frontiers are compared with each other.