Energy Efficiency Scenarios Research Articles

Renewable Energy Potential for Sustainable Long-Term Electricity Energy Planning: A Bottom-up Model Application

This paper presents a preliminary assessment of renewable energy potential for long-term electricity energy planning. In order to implement a sustainable supply-demand framework, renewable energy resources consist of geothermal and solar energy based power plants are included in a bottom-up accounting based model along with energy efficient and energy conservation approach in the demand side. The developed model is applied to a region comprises 5 cities or regencies in the eastern part of East Java province, Indonesia. The main purpose of the study is to compare several supply-demand scenarios with respect to the resources, costs, and environmental impacts over the study period. According to the analyses, the 2025 total electricity consumption of the observed area would be 6,873.8 GWh for the case of Business as Usual scenario, or increase by 203% compared to year 2014 as the baseline. Meanwhile, applying energy efficiency and conservation scenario, the growth would increase in a lower pace with 175% compared to the baseline or 6,222 GWh. Given the shortage of supply, more electricity should be imported from the interconnected system if the existing coal fired power plant is preserved through out the simulation period compared to planning involving geothermal and solar energy based power plants. In this study, geothermal and photovoltaic power plant would have their contribution toward the supply around 15.49% and 4.06%, respectively, from early year of their appearence until the last simulation year.

U.S. Biomass Opportunities for Value-added Biomass Exports based on the European Union Renewable Energy Share Targets

World energy demand is expected to continue increasing in the coming years. This situation has created a worldwide pressure for the development of alternative fuel and energy sources, pursuing a more environmentally friendly usage of biofuels. The EU has the target of generating 20% of its energy consumption from renewable sources by 2020. Member States have different individual targets to meet this overall target. Meanwhile in the United States, there are about 750 million acres [300 million hectares] of forestland, with slightly more than two-thirds classified as timberland or land capable of producing 20 cubic feet per acre [1.4 m3 per hectare] annually of roundwood. Given these circumstances, this research aimed to understand the U.S. opportunities to export woody biomass based on the targets that the European Union has imposed to its Member States. The data collected allowed several scenario developments by identifying the possible EU’s biomass deficits and U.S.’s capacity to supply the gaps. Considering the physical availability, the U.S. would be able to satisfy between 42 and 48% depending on the energy efficiency scenario. Nevertheless, when considering reasonable biomass prices, only a small portion of the EU demand could be covered by the U.S.

A semantic service-oriented platform for energy efficient buildings

The construction industry is under pressure to increase the sustainability of its practices to meet UK commitments for reducing energy consumption and alleviating climate change. The research uses a mixed-method approach drawn from recent studies to explore the readiness, maturity and level of engagement of construction stakeholders in adopting the UK government sustainability agenda. Limited positive energy practices and sustainability regulatory awareness, combined with information provision deficiencies, form some of the key barriers to sustainable construction faced by industry. A service-oriented platform that provides integrated access to sustainability resources in the form of interactive, dynamic and user-oriented services that fully exploit latest advances in computing technologies is proposed to address these barriers. In this paper, we specifically elaborate on how a service-oriented system can be efficiently used for performing (near) real-time energy optimisation in buildings, greatly contributing to engaging construction stakeholders with sustainability practices. The solution disseminates energy efficient practices and provides support for building managers in implementing energy efficient optimisation plans. The solution is tested and validated through a number of energy efficiency scenarios developed as part of the EU FP7 SportE2 project.

Artificial neural networks: a tool for understanding green consumer behavior

Purpose – The purpose of this paper is to study the usefulness of neural network to explain the gap between behavior intention and actual behavior in the consumption of green products. The paper draws the base from theory of planned behavior (TPB) and social dilemma theory. Design/methodology/approach – Artificial neural networks were used to analyze the data. A survey instrument was developed to understand the behavior pattern of customers while purchasing energy-efficient products. The outputs and input variables were identified and the input variables were divided into binary and discreet inputs. Findings – The research attempts to identify the factors that drive as well as avoid green consumerism. It also details the measures that can be adapted to address the social dilemma of green consumerism. In general the paper identifies with the literature in eliciting that environmental consciousness does not drive green consumerism. Research limitations/implications – The results of the study have important implications for practitioners as well as researchers. It is observed that neural network also provides inconclusive evidence for the intention behavior gap. This can be further explored by identifying different elements of environment consciousness and further testing. Practical implications – Marketers need to have strategies interwoven with traditional influencers to promote their green offerings. The consumers expect a clear and measurable benefit to the green offerings that the marketers are marketing. Originality/value – The research has its conceptual base in the TPB and social dilemma theory to understand the drivers of purchase behavior while evaluating an electronic product available in both energy efficient non-energy efficient rating scenario.

Bottom-up modeling of small-scale energy consumers for effective Demand Response Applications

In contemporary power systems, small-scale consumers account for up to 50% of a country׳s total electrical energy consumption. Nevertheless, not much has been achieved towards eliminating the problems caused by their inelastic consumption habits, namely the peaks in their daily power demand and the inability of energy suppliers to perform short-term forecasting and/or long-term portfolio management. Typical approaches applied in large-scale consumers, like providing targeted incentives for behavioral change, cannot be employed in this case due to the lack of models for everyday habits, activities and consumption patterns, as well as the inability to model consumer response based on personal comfort. Current work aspires to tackle these issues; it introduces a set of small-scale consumer models that provide statistical descriptions of electrical consumption patterns, parameterized from the analysis of real-life consumption measurements. These models allow (i) bottom-up aggregation of appliance use up to the overall installation load, (ii) simulation of various energy efficiency scenarios that involve changes at appliance and/or activity level and (iii) the assessment of change in consumer habits, and therefore the power consumption, as a result of applying different pricing policies. Furthermore, an autonomous agent architecture is introduced that adopts the proposed consumer models to perform simulation and result analysis. The conducted experiments indicate that (i) the proposed approach leads to accurate prediction of small-scale consumption (in terms of energy consumption and consumption activities) and (ii) small shifts in appliance usage times are sufficient to achieve significant peak power reduction.

Increasing the efficiency of high temperature furnaces through a topping cycle cogeneration—a case study

Based on the energy requirements of a set of continuous metallurgic heat treatment furnaces, this paper describes and compares several energy efficiency and cogeneration scenarios in a Portuguese metallurgic plant in terms of operating costs vs. savings. Such scenarios are analyzed in terms of hourly and annual savings. As part of improved heat integration, an energy cascade was designed and several hypotheses were studied to better match the decreasing thermal requirements of various heat treatment furnaces, each with different set point temperature and operating times. The scenario which presents the highest primary energy and economic saving adopts a special kind of gas turbine, one that due to its own characteristics requires a larger amount of excess air, hence being capable of providing enough O2 for a post-combustion in the existing burners. This setting is essential so that the furnaces are able to reach the required set point temperatures. This solution, along with additional simple energy saving actions, accounted for primary energy savings of 7.9 %, a decrease in 5 % in carbon dioxide emissions and a reduction of the energy costs over 40 %.

A New Intelligent Neuro–Fuzzy Paradigm for Energy-Efficient Homes

Demand response (DR), which is the action voluntarily taken by a consumer to adjust amount or timing of its energy consumption, has an important role in improving energy efficiency. With DR, we can shift electrical load from peak demand time to other periods based on changes in price signal. At residential level, automated energy management systems (EMS) have been developed to assist users in responding to price changes in dynamic pricing systems. In this paper, a new intelligent EMS (iEMS) in a smart house is presented. It consists of two parts: a fuzzy subsystem and an intelligent lookup table. The fuzzy subsystem is based on its fuzzy rules and inputs that produce the proper output for the intelligent lookup table. The second part, whose core is a new model of an associative neural network, is able to map inputs to desired outputs. The structure of the associative neural network is presented and discussed. The intelligent lookup table takes three types of inputs that come from the fuzzy subsystem, outside sensors, and feedback outputs. Whatever is trained in this lookup table are different scenarios in different conditions. This system is able to find the best energy-efficiency scenario in different situations.

Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning

Improving the representation of end-use energy efficiency, and of the effects of policies and programs to promote it, is an emergent priority for electricity load forecasting models and methods. This paper describes a “hybrid” load forecasting approach combining econometric and technological elements that is designed to meet this need, in a novel application to long-run electric power transmission planning in the western United States. A twenty-year load forecast incorporating significant increases in energy-efficiency programs and policies across multiple locations was developed in order to assess the potential of efficiency to reduce load growth and requirements for expanded transmission capacity. Load forecasting and transmission planning background is summarized, the theoretical and empirical aspects of the hybrid methodology described, and the assumptions, structure, data development, and results of the aggressive efficiency scenario are presented. The analysis shows that substantial electricity savings are possible in this scenario in most residential and commercial end-uses, and in the industrial sector, with magnitudes depending upon the specific end-use as well as upon the geographic location of the utility or other entity providing the electricity.

System wide impacts of fuel usage patterns in the Ethiopian highlands: Potentials for breaking the negative reinforcing feedback cycles

Due to scarcity of firewood, farmers in rural Ethiopia are forced to use cattle dung as a complementary energy source. By looking at patterns of firewood and cattle dung use for energy generation and their implications for food crop production, forest regeneration and community level emission reduction potentials, this study explores system interactions using a community living next to the Menagesha Suba state forest in Ethiopia as a case study. Mixed methods were used including household surveys, nutrient content analyses of firewood and cattle dung, and calculations of fuel and emission reductions for four cooking energy efficiency scenarios. It was found that the community and surrounding environment is stuck in a negative feedback cycle. Therefore shifts in current practices and systems are needed to break this cycle, for example by enhancing firewood supply, improving soil fertility, improving energy efficiency and enabling access to carbon financing for emissions reduced.

LEAPs and Bounds—an Energy Demand and Constraint Optimised Model of the Irish Energy System

This paper builds a model of energy demand and supply for Ireland with a focus on evaluating, and providing insights for, energy efficiency policies. The demand-side comprises sectoral sub-models, with a detailed bottom–up approach used for the transport and residential sectors and a top–down approach used for the industry and services sectors. The supply side uses the linear programming optimisation features of the Open Source Energy Modelling System applied to electricity generation to calculate the least-cost solution. This paper presents the first national level model developed within the Long Range Energy Alternatives Planning software to combine detailed end-use analysis on the demand side with a cost-minimising optimisation approach for modelling the electricity generation sector. Through three scenarios over the period 2009–2020, the model examines the aggregate impact on energy demand of a selection of current and proposed energy efficiency policies. In 2020, energy demand in the energy efficiency scenario is 8.6 % lower than the reference scenario and 11.1 % lower in the energy efficiency + scenario.

Assessment of the impact of renewable energy and energy efficiency policies on the Macedonian energy sector development

The main goal of this paper is to examine and quantify how the development of the energy system in Macedonia could be influenced by policies and programs that promote energy efficiency and renewable energy, in terms of energy security and diversification, economic competitiveness, and CO2 mitigation. For this purpose, the MARKAL-Macedonia national energy planning model was used. These policies are explored by comparing a reference (business-as-usual) development of energy system with three alternative policy scenarios: (1) Renewable Energy (RE) Scenario—introducing a renewable energy target by 2020 in line with that proposed by the Energy Community; (2) Energy Efficiency (EE) Scenario—exploring the range of energy efficiency measures that are the most cost-effective means to meet national targets aimed at reducing final energy consumption; and (3) Combined RE and EE Scenario—a combination of supply-side and demand-side approaches that examine the resulting synergies of these policy goals. For each scenario, the implications of least-cost investment options for new capacity builds, investment spending requirements, electricity prices, fuel expenditures, and carbon dioxide emissions were assessed. The analyses have shown that even under the Reference scenario, the reliance on electricity import will be reduced, as a result of increased gas import. The RE scenario further reduces the overall import by 3% while the EE scenario by 12%. Total discounted costs of the energy system in the Reference scenario is estimated to nearly €36 billion, with investment of €3.773 billion for 2.803 GW of new electricity generation capacity needed to meet the increased demand by 2030. The proposed RE target increases the cost of the energy system only by 0.13%, due to the availability of additional renewables, particularly hydro, biomass, and biofuels. The combination of the policies that promote increased energy efficiency will decrease the discounted energy system costs of 0.24% (€87 million net present value). The baseline evolution of the energy system results in 59% increase of CO2 emissions over the planning horizon. The analyzed RE and EE policies show strong synergies with a move to a lower carbon economy, resulting in cumulative CO2 emissions reduction of 3.7% triggered by an overall drop in consumption and less use of fossil fuels.

Pumps as turbines (PATs) in water distribution networks affected by intermittent service

A hydraulic model was developed in order to evaluate the potential energy recovery from the use of centrifugal pumps as turbines (PATs) in a water distribution network characterized by the presence of private tanks. The model integrates the Global Gradient Algorithm (GGA), with a pressure-driven model that permits a more realistic representation of the influence on the network behaviour of the private tanks filling and emptying. The model was applied to a real case study: a District Metered Area in Palermo (Italy). Three different scenarios were analysed and compared with a baseline scenario (Scenario 0 – no PAT installed) to identify the system configuration with added PATs that permits the maximal energy recovery without penalizing the hydraulic network performance. In scenarios involving PAT on service connections, the specification of PAT operational parameters was also evaluated by means of Monte Carlo Analysis. The centralized solution with a PAT installed downstream of the inlet node of the analysed district, combined with local PATs on the larger service connections, proves to be the most energy-efficient scenario.

Integrated dynamic modeling for energy optimization in the building: Part 1:The development of the model

Considering the magnitude of energy loss in Iran’s buildings, development of energy saving and energy optimization methods appears to be essential. To date, many energy saving methods, such as energy auditing, have been developed in this area, though none of them enjoyed high accuracy and efficiency. Optimization methods aided by energy modeling are the most useful tools for energy saving as well as energy loss reduction. In this study, a new model has been developed based on mathematical equations of mass and energy balance between different parts of a building and its surrounding area. Generally, this model integrates two methods of load and heating, ventilation and air conditioning modeling. Furthermore, the presented model allows for simulation of energy-efficient scenarios and their optimization based on life cycle cost analysis method. In this part, capabilities of the model have been closely analyzed based on historical records of existing models. The relations covering boundary conditions at external–internal surfaces; energy equations for building systems; interzone airflow and infiltration; heating, ventilation, and air conditioning models and economic models; and their optimization method and possible solutions have been explained in detail. Complete details about the application of this model for the XYZ case study building will be given in the second part of this article.

Assessment of energy efficiency and sustainability scenarios in the transport system

Energy Policy is one of the main drivers of Transport Policy. A number of strategies to reduce current energy consumption trends in the transport sector have been designed over the last decades. They include fuel taxes, more efficient technologies and changing travel behavior through demand regulation. But energy market has a high degree of uncertainty and the effectiveness of those policy options should be assessed. A scenario based assessment methodology has been developed in the frame of the EU project STEPS. It provides an integrated view of Energy efficiency, environment, social and competitiveness impacts of the different strategies. It has been applied at European level and to five specific Regions. The results are quite site specific dependent. However they show that regulation measures appear to be more effective than new technology investments. Higher energy prices could produce on their turn a deterioration of competitiveness and a threat for social goals.

Energy demand projections and relevance of income dynamics in Gauteng’s residential sector

Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others. In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households’ income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis. This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories – high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng’s residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.

Coherent neutrino scattering in dark matter detectors

Coherent elastic neutrino-nucleus and weakly interacting massive particle-nucleus interaction signatures are expected to be quite similar. This paper discusses how a next-generation ton-scale dark matter detector could discover neutrino-nucleus coherent scattering, a precisely-predicted standard model process. A high-intensity pion- and muon- decay-at-rest neutrino source recently proposed for oscillation physics at underground laboratories would provide the neutrinos for these measurements. In this paper, we calculate raw rates for various target materials commonly used in dark matter detectors and show that discovery of this interaction is possible with a $2\text{ }\text{ }\mathrm{ton}\ifmmode\cdot\else\textperiodcentered\fi{}\mathrm{year}$ GEODM exposure in an optimistic energy threshold and efficiency scenario. We also study the effects of the neutrino source on weakly interacting massive particle sensitivity and discuss the modulated neutrino signal as a sensitivity/consistency check between different dark matter experiments at the Deep Underground Science and Engineering Laboratory. Furthermore, we consider the possibility of coherent neutrino physics with a GEODM module placed within tens of meters of the neutrino source.

The impact of CHP (combined heat and power) planning restrictions on the efficiency of urban energy systems

Cities account for approximately two-thirds of global primary energy consumption and have large heat and power demands. CHP (combined heat and power) systems offer significant primary energy-efficiency gains and emissions reductions, but they can have high upfront investment costs and create nuisance pollution within the urban environment. Urban planners therefore need to understand the tradeoffs between limitations on CHP plant size and the performance of the overall energy system. This paper uses a mixed-integer linear programming model to evaluate urban energy system designs for a range of city sizes and technology scenarios. The results suggest that the most cost-effective and energy-efficient scenarios require a mix of technology scales including CHP plants of appropriate size for the total urban demand. For the cities studied here (less than 200,000 people), planning restrictions that prevent the use of CHP technologies could lead to total system cost penalties of 2% (but with significantly different cost structures) and energy-efficiency penalties of up to 24% when measured against a boiler-only business-as-usual case.

A 2050 CO2 EMISSIONS FORECAST MODEL OF URBAN RESIDENTIAL BUILDINGS IN SHANGHAI

These years residential energy consumption has been increasing sharply in China along with the high economic growth. Thus people pay much more attention to the issues of reducing the energy consumption and CO2 emission. This study chose Shanghai, a big city of China as a subject, and developed a Macro-model to forecast the CO2 emission of residential buildings up to 2050. By combining such Global warming count measures into the Macro-model it was able to forecast the CO2 emission of Shanghai’s residential buildings in several energy-efficient scenarios.

Alternative Road Vehicles, Electric Rail Systems, Short Flights: An Environmental Comparison

In a context of global warming, the energy-efficiency topic has never been as hot as it is now, especially as far as transportation is concerned. Road and rail transportation are both included in what currently is the main attempt to tackle climate change on a worldwide scale: the Kyoto protocol. The achievement of the objectives put forward in this international agreement might be reached thanks to the high energy efficiency of electric drives for both road and rail transportation. International aviation emissions however, have not been included in the targets of the Kyoto protocol. Nevertheless, as in some cases aviation competes with surface transport, the energy efficiencies and/or climate impacts of different alternative drives or transport modes for surface transport should be compared to (mainly short-haul and very short-haul) aviation.In this paper the environmental relevance of a planned prohibition of very short-haul flights in Belgium is assessed. This is done through the case-study of an abandoned project to operate a 737-400 flight between the Belgian cities of Liège and Charleroi (75 km distance), before taking off to Casablanca, Morocco (approximately 2100 km from Charleroi). The emissions due to the Liège-Charleroi leg of the flight are compared to alternatives for passenger transport, such as road transport and rail transport. Concerning road transport, different alternatives (conventional, CNG, hybrid, electric drive cars and buses) will be considered. After an assessment of the different transfer possibilities, an evaluation of the most climate-friendly and energy efficient scenarios is performed. Moreover an estimation of the yearly CO2-equivalent savings is carried out.

Analysis of the Syrian long-term energy and electricity demand projection using the end-use methodology

A comprehensive analysis of the possible future long-term development of Syrian energy and electricity demand covering the period 1999–2030 is presented. The analysis was conducted using the IAEA's model MAED, which relies upon the end-use approach. This model has been validated during the last two decades through the successful application in many developing countries, even those having partial market economy and energy subsidy. Starting from the base year, final energy consumption distributed by energy forms and consumption sectors, the future energy and electricity demand has been projected according to three different scenarios reflecting the possible future demographic, socio-economic and technological development of the country. These scenarios are constructed to cover a plausible range, in which future evolution factors affecting energy demand are expected to lie. The first is a high economy scenario (HS) representing the reference case, which is characterized by high gross domestic product (GDP) growth rate (average annual about 6%) and moderate improved technologies in the various consumption sectors. The second is an energy efficiency scenario (ES), which is identical to HS in all main parameters except these relating to the efficiency improvement and conservation measures. Here, high technology improvement and more effective conservation measures in all consumption sectors are proposed and the role of solar to substitute fossil energy for heating purposes is considered effectively. The third is a low economy scenario (LS) with low GDP growth rate (average annual about 3.5%) and less technology improvement in the consumption sectors. As a consequence, the improvement in the energy efficiency is low and the influence of conservation measures is less effective. Starting from about 10.5 mtoe final energy in the base year, the analysis shows that the projected energy demand will grow annually at average rates of 5%, 4.5% and 3% for the HS, ES and LS, respectively. Comparing to the HS, the conservation measures and technology improvement in the ES would save ca. 0.8 mtoe in 2010, 2.3 mtoe in 2020 and 5.7 mtoe in 2030. The final electricity demand of about 16.7 TWh in the base year will grow annually at average rates of 5.5%, 5% and 3.4% for the HS, ES and LS, respectively. During the study period 1999–2030 the annual electricity per capita will increase from about 1000 kWh to 2800, 2500 and 1500 kWh for the HS, ES and LS, respectively. The final energy intensity 1 1 The consumed final energy in kilograms oil equivalent to produce one currency amount of GDP. will decrease continuously from about 0.73 kgoe/US$ in the base year to 0.48 kgoe/US$, 0.42 kgoe/US$ and 0.67 kgoe/US$ 2 2 All monitory values of US$ are in the constant price of the year 1995. for the HS, ES and LS, respectively in the year 2030. These values indicate intensive final energy consumption in Syria compared to developing countries.