Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

Achieving efficiency, or maximizing the outputs achieved per unit of resource invested, is of great interest to governments, donors and other stakeholders in the health sector. Many studies consider efficiency in public health using Cost Effectiveness Analyses which estimate the health outcomes achieved per unit of cost. Others employ Technical Efficiency Analysis to understand which health system units, usually hospitals, provide the most health services per unit of resource. However, very little is known about demand-side efficiency or how efficiently individuals convert available health resources into health outcomes. To address this gap, I developed and piloted a two-stage methodology using Amartya Sen's Capability Approach as a theoretical framework mapping the process by which individuals convert resources into outcomes. The first stage estimates conversion efficiency using Order-m Efficiency Analysis then identifies the social groups most likely to be efficient using regression analysis. The second stage undertakes focus group discussions and semi-structured interviews to investigate how and why the social groups identified in the quantitative stage were more likely to be efficient. I conducted my analysis in Ba Vi district, northern Vietnam looking specifically at how efficiently pregnant women converted maternal health resources—including health facilities and human resources for health—into both appropriate care and healthy pregnancy and delivery. I found that ethnic minorities and women in non-mountainous areas were more likely to be efficient at achieving appropriate care while ethnic minorities and less educated women are more likely to be efficient at achieving healthy pregnancy and delivery outcomes. Through qualitative feedback, women who were ethnic majorities, better educated and generally more affluent expressed stronger technology preference, greater use of the private sector, less continuity of care, tendencies towards overnutrition, less focus on mental and emotional health and more varied sources of health information including advertising and the internet. Evidence links each of these themes to adverse care and/or health outcomes. Consequently, the more affluent populations, who also have a greater endowment of public health resources, may be less likely to achieve good outcomes—explaining at least in part why they are found to be less efficient. My findings highlight that the development process and attendant epidemiological and nutrition transitions give rise to a new set of challenges not solely for public health, but also for the efficiency with which it is achieved using existing health system resources.

It is more than a fact that electrical energy is a main production factor of every economic activity. Since electrical power is not easy to store, it needs to be consumed as it is generated in order to keep a constant balance between supply and demand. As a result, for developing an efficient energy market it is significant to create a method for accurately forecasting the electricity consumption. This thesis describes a method for analyzing data provided by the ENTSO-E transparency platform. The ENTSO-E (European Network of Transmission System Operators) is a network of electricity operators from 36 countries across Europe. Its main objective is to provide transparency concerning data of electricity generation and consumption in Europe in order to promote the development of efficient and competitive electricity markets. By using the method described in this thesis, one may use historical data provided by ENTSO-E to forecast the electricity consumption of an EU country for the years to come. As an example, data of electricity consumption in Greece during the years 2015-2018 have been used in order to calculate the average load demand of a weekday during the year 2030. On the other hand, in order to correctly predict the electricity demand of a specific region over the next decade, one should take into account some crucial parameters that may influence not only the evolution of the load demand, but also the fuel mix that will be used in order to cover our future electricity needs. Advances in power generation technologies, evolution of fuel prices, expansion of electricity grid and economic growth are a subset of parameters that should be taken into account for an accurate forecast of the electricity consumption in the long run. Particularly for Greece, a set of parameters that may affect the electricity consumption are being computationally analyzed in order to evaluate their contribution to the load demand curve by the year 2030. These include the interconnection of Greek islands to the mainland, the development of Hellinikon Project and the increase of the share of electric vehicles. The author of this thesis has developed code in Python programming language that can be found in the Appendix. These scripts and functions that implement most of the calculations described in the following chapters can also be used for forecasting the load demand of other EU countries that are included in the ENTSO-E catalogue. The datasets used as input to these algorithms may also be used from the readers to identify more patterns for predicting the load demand for a specific region and time. A sustainable energy system is based on consumers with environmental awareness. As a result, citizens living inside the European Union should become a member of a community that promotes energy saving measures, investments in renewable energy sources and smart metering applications.

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.

In accordance with the main aim of the study, a widely accessible, modifiable tool was created for parties interested in maintaining the national electricity supply network and parties interested in informing policy on plug-in vehicle adoption schemes and recharging behaviour control. The Parry Tool enables the user to incorporate present limits to plug-in vehicle recharging demand scheduling as imposed by the state of present technology (no third party mechanism for monitoring and control of recharging), present human travel behaviour needs and existing patterns in electricity usage; into the investigation of the impacts of recharging demand impacts and the design of mitigation measures for deflecting (parrying) worst case scenarios. The second aim of the project was to demonstrate the application of the Parry Tool. The multidisciplinary/interdisciplinary information gathered by the Parry Tool was used to produce national demand profiles for plug-in vehicle recharging demand, calculated using socioeconomic and travel behaviour-estimated population sizes for plug-in eligible vehicles and vehicle usage patterns, which were added to existing national electricity demand for a chosen test week – this was the first scenario subsequently tested. The information gathered by the Parry Tool was then used to inform the design of two demand management methods for plug-in vehicle recharging: Recharging Regimes and weekly recharging load-shifting – these were the second and third scenarios subsequently tested. Unmitigated simultaneous recharging demand in scenario 1 (all vehicles assumed to recharge at home upon arrival home every day) severely exacerbated peak demand, raising it by 20% above the highest peak in existing demand for the year 2009 over half an hour from 58,554 MW to 70,012 MW – a challenge to the generation sector. This increased the difference between daily demand minima and maxima and made the new total demand have sharper peaks – a challenge for grid regulators. Recharging Regimes in scenario 2 split the estimated national plug-in vehicle populations into groups of different sizes that started recharging at different times of the day, with the word ‘regime’ being applied because the spread of start times changed over the course of the test week from workdays to weekend. This avoided exacerbation of the peak and reduced the difference between daily demand minima and maxima by raising minima, providing a load-levelling service. Scenario 3 embellished the Recharging Regimes with workday-to-weekend recharging load-shifting that therefore took better advantage of the often overlooked weekly pattern in existing demand (demand being higher on workdays than weekends), by allowing partial recharging of a segment of the plug-in vehicle population. Limited consideration of the impact of changing vehicle energy usage (for which distance travelled was assumed to proxy in this study) showed that the more vehicles used their batteries during the day, the better the levelling effect offered by Recharging Regimes. Greater utilisation of battery capacity each day, however, can also be assumed to lessen the potential for workday-to-weekend load levelling, because load-shifting depends upon vehicles being able to partially recharge or defer recharging to later days and still meet their travel needs plus keep a reserve State Of Charge (SOC) for emergency and other unplanned travel. Whilst altering vehicle energy usage did not change the finding that unmitigated simultaneous recharging exacerbated existing peak demand, it was noted that when limited mileage variation was considered this sharpened the profile of total demand – the rise and fall of the new peak far steeper than that of the original peak in existing demand. The Parry Tool combines a series of integrated methods, several of which are new contributions to the field that use UK data archives but may potentially be adapted by researchers looking at energy issues in other nations. It presents a novel fossil-fuel based justification for targeting road transport – acknowledging energy use of fossil fuel as the originator of many global and local problems, the importance of non-energy use of petroleum products and subsequent conflicts of interest for use, and a fossil fuel dependency based well-to-wheel assessment for UK road transport for the two energy pathways: electricity and petroleum products. It presents a method for the recalculation and ranking of top energy use/users using national energy use statistics that better highlights the importance of the electricity industry. It also presents the first publicly documented method for the direct consultation and extraction of vehicle-focused statistics from the people-focused National Travel Survey database, including a travel behaviour and household income-based assessment of plug-in vehicle eligibility, used to scale up to national estimates for battery electric and plug-in electric hybrid vehicle (BEV and PHEV) national population sizes. The work presented here is meant to allow the reader to perceive the potential benefits of using several resources in combination. It details the Parry Tool, a framework for doing so, and where necessary provides methods for data analysis to suit. It should however be noted that methods were kept as simple as possible so as to be easily followed by non-specialists and researchers entering the field from other disciplines. Methods are also predominantly data-exploratory in nature: strong conclusions therefore should not be drawn. Rather, the work here should be seen as a guideline for future work that may more rigorously study these combined topics and the impacts they may have upon plug-in vehicle ownership, usage behaviour, impacts of recharging upon the national network and the design of mitigation measures to cope with this new demand.

Neste trabalho foi desenvolvida uma metodologia para realizar a gestão ativa da demanda de energia elétrica de consumidores, providos de armazenamento de energia elétrica e geração solar fotovoltaica, inseridos em redes inteligentes. Tal metodologia pode ser utilizada em instalações residenciais e comerciais. Utilizando estratégias de otimização e inteligência artificial, a metodologia configura um sistema de tomada de decisão para o gerenciador do conversor da bateria, que realiza a gestão da energia armazenada, visando reduzir o custo com energia elétrica para o consumidor final. Esta gestão propicia contribuição com a distribuidora em forma de aumento da reserva de capacidade da rede elétrica nos casos em que a tarifa de energia elétrica for mais cara no horário de pico. De qualquer forma, há potencial postergação da necessidade de expansão da rede elétrica e redução de impactos ambientais advindos da geração convencional de energia elétrica, uma vez que tal gestão de energia propicia redução de consumo de energia elétrica da rede. O mesmo sistema de tomada de decisão do gerenciador do conversor da bateria pode ser utilizado em vários consumidores com características semelhantes (mesmo tipo, localização e tarifação de energia elétrica, e perfil de consumo similar), uma vez que tal sistema é composto por uma rede neural treinada com dados locais. Estudo de caso foi conduzido considerando consumidor residencial na cidade de São Paulo. Foram construídos quinze perfis de consumo, que foram combinados com três perfis de geração solar. A metodologia apresentou desempenho satisfatório, tanto na avaliação da etapa de otimização quanto de treinamento da rede neural, uma vez que as curvas de armazenamento de energia apresentaram comportamentos próximos aos esperados. O sistema de tomada de decisão também respondeu de forma adequada, alterando a curva de carga do consumidor vista pela rede de forma a reduzir o custo diário com energia elétrica e o consumo de energia no horário de pico da residência em todos os casos estudados. A análise econômica apontou a necessidade de encontrar formas de tornar a iniciativa positiva do ponto de vista econômico no estudo de caso realizado. / This work presents a methodology developed to perform the active demand side management for consumers, provided with energy storage and solar photovoltaic power, inserted in smart grids. Such methodology can be used in residential and commercial installations. Using optimization and artificial intelligence strategies, the methodology sets up a decision-making system for the battery converter manager, which performs energy storage management, in order to reduce the cost with electricity for the final consumer. This management contributes with the utility increasing the grid reserve capacity when the electricity tariff is more expensive during peak hours. Anyway, there is potential postponement of the need to expand the grid, and environmental impacts reduction from conventional power generation, since such power management provides a reduction of the grid electricity consumption. The same decision-making system of the battery converter manager can be used in several consumers with similar characteristics (same type, location and electricity tariff, and similar consumption profile), since this system is composed by a neural network trained with local data. A case study was conducted considering household in the city of São Paulo. Fifteen consumption profiles were built, which were combined with three solar generation profiles. The methodology presented satisfactory performance both in the evaluation of the optimization stage and the neural network training stage, since the energy storage curves presented behaviors close to those expected. The decision-making system also responded adequately, changing the consumer load curve seen by the grid in order to reduce the daily electricity cost, and energy consumption at peak hours of the household in all cases studied. The economic analysis pointed to the need to find ways to make the initiative positive from an economic point of view in the case study carried out.

臺灣水資源多年來供不應求的問題，由於供給面管理已面臨瓶頸，不得不尋求以強調經濟誘因，效率使用為理念的需求面管理。水權交易制度是需求面管理政策中最符合價格機能運作的制度，本文由臺灣的水資源財產權演進的歷史，以及理論模型的推導，加上既有文獻的探討，和國外施行的經驗，認為此制度確實可行。它的優點是讓用水者面對真實的機會成本，可以增加用水效率，除此之外，還可分散缺水風險，使分配更具彈性，反應留川使用效益的公共價值，並且可以顧及公平性和增進整體社會福利。雖然此制度有其缺陷，但是藉制度的設計，政府適當的管制，應可克服這些問題。 　　國內現行的水利法不允許水權自由交易，其中充滿政府管制色彩。在制定之初幾乎不顧及經濟法則，但是隨著由供需主導的經濟力量，例如嘉南平原水權之爭，已迫使水利法的修正，允許需由政府核准的附帶補償的移轉，這新增條文的精神已離自由交易不遠。不過由各種跡象顯示，政府對水資源的管理態度將更強調對水資源調配的主控權，但是此主控權的行使，四十年以來一直拋不開各種包袱，在未來也很難相信主管機關能做到社會最適的調配。 　　本文所提出的理論模型，是為了做政策上的選擇。參考Baumol and Oates(1988)，徐世勳（民國80年），蕭代基（民國81年）等的模型架構，將留川及離川使用交互影響的關係，以及污染排放因素模型化。並討論在確定及不確定下的政策選擇，比較Pareto最適條件及市場均衡條件，在水量方面，主要是是探討水權費及水權交易制度，在水質方面，比較污染排放稅和可轉讓污染染排放許可證。論文結果發現，在確定情況下，上述政策無差異。在供給函數不確定下，應採水權交易制度和污染排放稅。本文最後提出政策建議，以集水區地方自治組織，來輔助市場交易，以及外部效果內部化工作。本文目的除了在財產權歷史和理論上做進一步探討，也希望提供臺灣成立水權交易制度政策上的參考。

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

The transport sector is seen as particularly problematic when concerns about climate change and dependency on fossil energy are discussed. Because of this, bioenergy is strongly promoted for use in the transport sector, both on a European level and nationally in Sweden. Even though bioenergy is considered one of the key solutions, it is generally agreed that both supply- and demand-side measures will be needed to achieve a change to a more sustainable transport system. One of the reasons for this is the limited availability of biomass, especially agricultural feedstocks competing with food or feed production. Woody biomass, however more abundant, is also exposed to tough competition from other sectors. In this thesis, the role of biogas as a vehicle fuel in a future sustainable transport system is discussed together with the prerequisites needed to realise such a transport system. Biogas is a biofuel that could be produced in several different ways: by anaerobic digestion, which is a first-generation production route, by gasification, which is a second-generation process, and by catalytic reduction of carbon dioxide, a third-generation technology. The main focus in this thesis is on biogas produced by anaerobic digestion and the results show that there is a significant potential for an increase compared to today’s production. Biogas from anaerobic digestion, however, will only be able to cover a minor part of the demand in the Swedish transport sector. Considering biogas of the second and third generations, the potential for production is more uncertain in a mid-term future, mainly due to competition for feedstock, the possibility to produce other fuels by these processes, and the present immaturity of the technology. The limited potential for replacing fossil vehicle fuels, either by biogas or other renewable fuels, clearly shows the need for demand-side measures in the transport system as well. This thesis shows the importance of technical and non-technical means to decrease the demand for transport and to make the transport as efficient as possible. The results show that both energy-efficient vehicles and behavioural and infrastructural changes will be required. Policies and economic incentives set by governments and decision-making bodies have a prominent role to play, in order to bring about a shift to a more sustainable transport system, however, measures taken on individual level will also have a great impact to contribute to a more sustainable transport system. / <p>QC 20121116</p>

The objective of the proposed research is to analyze automated residential energy management technology using primary energy source utilization. A residential energy management system (REMS) is an amalgamation of hardware and software that performs residential energy usage monitoring, planning, and control. Primary energy source utilization quantifies power system levels impacts on power generation cost, fuel utilization, and environmental air pollution; based on power system generating constraints and electric load. Automated residential energy management technology performance is quantified through a physically-based REMS simulation. This simulation includes individual appliance operation and accounts for consumer behavior by stochastically varying appliance usage and repeating multiple simulation iterations for each simulated scenario. The effect of the automated REMS under varying levels of control will be considered. Aggregate REMS power system impacts are quantified using primary energy source utilization. This analysis uses a probabilistic economic dispatch algorithm. The economic dispatch algorithm quantifies: fuel usage and subsequent environmental air pollution (EAP) generated; based on power system generating constraints and electric load (no transmission constraints are considered). The analysis will comprehensively explore multiple residential energy management options to achieve demand response. The physically-based REMS simulation will consider the following control options: programmable thermostat, direct load control, smart appliance scheduling, and smart appliance scheduling with a stationary battery. The ability to compare multiple automated residential energy management technology options on an equal basis will guide utility technology investment strategies.