Impacts of a warmer world on space cooling demand in Brazilian households

Air Conditioning (AC) appliances are a highly effective adaptation strategy to rising temperatures, thus making future climate conditions an important driver of space cooling energy demand. The main goal of this study is to assess the impacts of climate change on Cooling Degree Days computed with wet-bulb temperature (CDDwb) and household space cooling demand in Brazil. We compare the needs under three specific warming levels (SWLs) scenarios (1.5 °C, 2 °C and 4 °C) to a baseline with historically observed meteorological parameters by combining CDDwb projections with an end-use model to evaluate the energy requirements of air conditioning. The effects of the climate change were isolated, and no future expansion in AC ownership considered. Carbon dioxide (CO2) emissions associated with AC energy demand are also calculated. Results show an increase in both average CDDwb and AC electricity consumption for the global warming scenarios in all Brazilian regions. The Northern region shows the highest increase in CDDwb (187% in CDDwb for SWL 4 °C), while the Southeast presents the highest AC energy consumption response (326% in the AC energy consumption for SWL 4 °C) compared to the baseline. At the national level, CDDwb and the AC energy consumption in all SWLs scenarios grow by 70%, 99% and 190%, respectively

Avaliação dos efeitos de padrões mais restritivos na regulamentação de eficiência energética no uso residencial de ar condicionado em Guayaquil, Equador

Orientadores: Arnaldo Cesar da Silva Walter, Guillermo Enrique Soriano IdrovoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: No mundo, o uso de energia para resfriamento de espaço tem o mais rápido crescimento nas edificações; em 2016, esse consumo foi de 2020 TWh, o que representou 18.5% do consumo total de eletricidade nesse setor e 12% de suas emissões totais de CO2. No Equador, o mais recente estudo nacional sobre usos finais de energia foi publicado em 1993 pelo ex-INECEL. Este estudo mostrou que a parcela de ar condicionado foi de 14.3% da demanda total de eletricidade e contribuiu com 12% da demanda pico na região litoral. Este trabalho estima a demanda de energia de equipamentos de ar condicionado (AC) no setor residencial de Guayaquil - Equador e quantifica os benefícios potenciais de aumentar o atual padrão mínimo de eficiência energética (PMEE) considerando dois cenários de crescimento econômico, com 2020 como ano base e um horizonte até 2030. O impacto de aumentar o padrão na difusão dos aparelhos também é quantificado, principalmente em residências de renda média, que apresentaram o maior potencial de crescimento no uso do ar-condicionado. A estimativa mostra que a demanda por AC representou 15.4% da demanda total de eletricidade no setor residencial (108 GWh) em 2000 e em termos absolutos, quase triplicou em 2019 (285 GWh), representando aproximadamente 19.5% da demanda total residencial. Com o atual padrão de eficiência energética (EER = 3.2 W/W), a demanda de eletricidade associada pode chegar a 489 GWh em 2030, representando 21.5% da demanda e 0,07 Mt CO2eq em emissões relacionadas ao uso de energia elétrica. Dados os preços da eletricidade, nível de renda das famílias e o aumento nos custos dos equipamentos associados aos diferentes PMEE analisados (oito no total), o PMEE melhor avaliado foi 4.3 W/W. A avaliação leva em consideração a perspectiva do consumidor em termos de economia nos custos de ciclo de vida e a perspectiva da sociedade em termos de valor presente líquido. Com o novo padrão, seria possível reduzir o consumo acumulado de eletricidade nos aparelhos de AC e as emissões de CO2 relacionadas à energia em até 11% entre 2020 e 2030. Porém desde o ponto de vista do consumidor, os benefícios serão pequenos em comparação ao aumento no custo do equipamento e, portanto, as famílias não estarão incentivadas a trocar os equipamentos por unidades mais eficientes, a menos que haja políticas adicionais para cobrir o custo adicionalAbstract: Worldwide, the energy use for space cooling has the fastest growth in buildings; in 2016, this consumption was 2020 TWh, which represented 18.5% of the total electricity consumption in this sector, and 12% of their total CO2 emissions. In Ecuador, the latest national end-use energy study was published in 1993 by the ex-INECEL. This study showed that the share of air conditioning was 14.3% of total electricity demand and contributed with 12% of the peak demand in the coast region. This work estimates the energy demand of air conditioning equipment (AC) in the residential sector of Guayaquil - Ecuador, and quantifies the potential benefits of increasing the current minimum energy performance standard (MEPS) considering two economic growth scenarios, with 2020 as the base year and a horizon until 2030. It also quantifies the impact of this increase in the diffusion of the appliance, especially in middle-income households, which presented the greatest growth potential in the use of air conditioning. The estimation shows that AC demand represented 15.4% of total residential electricity demand in 2000 (108 GWh) and in absolute terms, it almost tripled in 2019 (285 GWh) representing approximately 19.5% of total residential electricity demand. With the current energy efficiency standard (EER = 3.2 W/W), the associated electricity demand could reach up to 489 GWh in 2030, representing 21.5% of the total residential electricity demand and 0.07 Mt CO2eq in energy-related emissions. Given the electricity prices, the level of household income, and the increase in equipment costs associated with the different MEPS analyzed (eight in total), the best-rated MEPS was 4.3 W/W. The assessment take into account the perspective from a single household in terms of the life cycle cost savings and the society perspective in terms of the net present value. With the new standard, it would possible to reduce the aggregate AC electricity consumption and energy-related CO2 emissions up to 11% between 2020 and 2030. However, from the consumers¿ point of view the benefits will be small compared to the increase in the equipment cost and therefore would not be encouraged to change their equipment for more efficient units, unless there are additional policies to cover the additional upfront costMestradoPlanejamento de Sistemas EnergeticosMestre em Planejamento de Sistemas Energético

Impacts of a warmer world on space cooling demand in Brazilian households

Air Conditioning (AC) appliances are a highly effective adaptation strategy to rising temperatures, thus making future climate conditions an important driver of space cooling energy demand. The main goal of this study is to assess the impacts of climate change on Cooling Degree Days computed with wet-bulb temperature (CDDwb) and household space cooling demand in Brazil. We compare the needs under three specific warming levels (SWLs) scenarios (1.5 °C, 2 °C and 4 °C) to a baseline with historically observed meteorological parameters by combining CDDwb projections with an end-use model to evaluate the energy requirements of air conditioning. The effects of the climate change were isolated, and no future expansion in AC ownership considered. Carbon dioxide (CO2) emissions associated with AC energy demand are also calculated. Results show an increase in both average CDDwb and AC electricity consumption for the global warming scenarios in all Brazilian regions. The Northern region shows the highest increase in CDDwb (187% in CDDwb for SWL 4 °C), while the Southeast presents the highest AC energy consumption response (326% in the AC energy consumption for SWL 4 °C) compared to the baseline. At the national level, CDDwb and the AC energy consumption in all SWLs scenarios grow by 70%, 99% and 190%, respectively

PERFORMANCE AND APPLICATIONS OF RESIDENTIAL BUILDING ENERGY GREY-BOX MODELS

The electricity market is in need of a method to accurately predict how much peak load is removable by directly controlling residential thermostats. Utilities have been experimenting with residential demand response programs for the last decade, but inconsistent forecasting is preventing them from becoming a dependent electricity grid management tool. This dissertation documents the use of building energy models to forecast both general residential energy consumption and removable air conditioning loads. In the models, complex buildings are represented as simple grey-box systems where the sensible energy of the entire indoor environment is balanced with the flow of energy through the envelope. When internet-connected thermostat and local weather data are inputs, twelve coefficients representing building parameters are used to non-dimensionalize the heat transfer equations governing this system. The model's performance was tested using 559 thermostats from 83 zip codes nationwide during both heating and cooling seasons. For this set, the average RMS error between the modeled and measured indoor air temperature was 0.44°C and the average daily ON time prediction was 1.9% higher than the data. When combined with smart power meter data from 250 homes in Houston, TX in the summer of 2012 these models outperformed the best traditional methods by 3.4 and 28.2% predicting daily and hourly energy consumption with RMS errors of 86 and 163 MWh. The second model that was developed used only smart meter and local weather data to predict loads. It operated by correlating an effective heat transfer metric to past energy data, and even further improvement forecasting loads were observed. During a demand response trial with Earth Networks and CenterPoint Energy in the summer of 2012, 206 internet-connected thermostats were controlled to reduce peak loads by an average of 1.13 kW. The thermostat building energy models averaged forecasting the load in the 2 hours before, during, and after these demand response tests to within 5.9%. These building energy models were also applied to generate thermostat setpoint schedules that improved the energy efficiency of homes, disaggregate loads for home efficiency scorecards and remote energy audits, and as simulation tools to test schedule changes and hardware upgrades

Model Predictive Control for Demand Response of Thermostatically Controlled Loads

Charakteristickým rysem moderní energetiky je narůstající podíl výroby elektřiny z obnovitelných zdrojů. To přináší řadu výhod z pohledu kvality životního prostředí. Výroba elektřiny z obnovitelných zdrojů má však výrazně stochastický charakter a integrace většího množství takto vyrobené elektřiny do elektrizační sítě není možná, pokud nebudou vytvořeny nové metody řízení spotřeby elektřiny, nové technologie pro skladování elektrické energie a vyspělá řídicí a komunikační infrastruktura. Na straně spotřeby elektrické energie připadá významný podíl termostaticky řízeným spotřebičům. Ty jsou navíc obvykle těsně propojeny s velkými tepelně akumulačními kapacitami. Jsou proto zvláště vhodné pro řízení spotřeby elektřiny a nákladově efektivní akumulaci energie. Z této motivace vychází zaměření této disertační práce na pokročilé algoritmy pro řízení termostatických spotřebičů.Jakékoliv řízení nutně předpokládá, že existuje vhodný řídicí signál, kterým můžeme chování řízené soustavy ovlivňovat. V této práci pracujeme s nepřímým řídicím signálem: cenou elektřiny proměnnou v reálném čase. Tento koncept je používán v řadě pilotních projektů v USA i v EU. Z řady hledisek je tento koncept výhodný: zákazníci si mohou sami rozhodnout, jak na proměnnou cenu budou reagovat bez toho, že by jejich komfort byl ohrožen. Rovněž tak není nutné instalovat složitá rozhraní pro přímé ovládání spotřebičů a monitorování jejich stavu. Návrh vhodných algoritmů pro to, jak reagovat na proměnné ceny však zůstává stále do značné míry otevřeným problémem. Tato práce je zaměřena na dva aspekty tohoto problému.První část práce se zabývá problematikou řízení jednotlivých velkých termostatických spotřebičů, které reaguje na proměnnou cenu elektřiny. Tyto spotřebiče jsou zde popsány obecně jako lineární časově proměnné systémy a jejich řízení je navrženo jako lokální ekonomické prediktivní řízení. Tento ekonomický prediktivní regulátor musí vzít v úvahu časově proměnný charakter řízené soustavy. Tím, že provádí lokální ekonomickou optimalizaci, napomáhá tento regulátor udržet rovnováhu výroby a spotřeby v elektrizační soustavě. Tato část práce vznikla v rámci projektu H2020 SmartNet a jako případovou studii používá jedno z pilotních experimentálních zařízení tohoto projektu: vyhřívaný plavecký bazén. Časová proměnnost matematického modelu tohoto bazénu pramení ze změn součinitele přestupu tepla mezi vodou a vzduchem v závislosti na rychlosti větru.Druhá část práce je zaměřena na menší termostatické spotřebiče, které sice mají jednotlivě zanedbatelný příkon, mohou však hrát významnou roli, pokud je jejich větší počet sdružen dohromady. Struktura navrhovaného řídicího systému je hierarchická. Ekonomický prediktivní regulátor na vyšší rovině řízení reaguje na proměnnou cenu elektřiny a mění žádané hodnoty termostatů na nižší rovině. Cíl řízení je stejný jako v první části práce: cena provozu celé skupiny spotřebičů je minimalizována a to napomáhá udržení rovnováhy v síti. Vzhledem k velkému počtu spotřebičů však není možné, aby prediktivní regulátor pracoval s modely všech jednotlivých spotřebičů, ale bylo nutné vyvinout a ověřit sdružený model dynamiky celé skupiny. Tento model je nelineární a ekonomický prediktivní regulátor musí řešit úlohu nelineárního smíšeného celočíselného programování. Efektivita navržené strategie řízení byla prokázána pomocí simulačních experimentů.Increasing the share of renewable electricity generation is a characteristic feature of modern energy systems. Renewable electricity generation has important environmental benefits, however, it is also marked by significant stochasticity and its large scale integration into power grid is not possible without new methods for control of electricity consumption, new energy storage technologies and communication infrastructure. Thermostatically controlled loads represent a significant share of total electricity consumption and they are often tightly connected with large thermal storage capacities. For these reasons they can be used for controlling electricity consumption and cost effective energy storage. This motivates the focus of this thesis on advanced control algorithms for thermostatically controlled loads.Any control requires a suitable control signal. In this thesis, an indirect control signal is used - the role of the control signal is played by variable electricity price. This concept is considered in many pilot projects both in the USA and in the EU. It has certain advantages: the customers can choose the preferred strategy for responding to the needs of the grid, so their comfort is not compromised; also there is no need to install significantly more complex interfaces for direct control of the loads and monitoring of their states. However, the design of suitable control algorithms for responding to variable prices is still a largely open problem. The thesis focuses on two aspects of this problem.The first part of the thesis considers the control of a single large thermostatically controlled load that responds to the price signal. This load is described by a linear time varying system and a local economic model predictive controller is designed for it. This controller must account for the time varying dynamics of the controlled load. By performing local economic optimization this controller helps to balance supply and demand in the electricity grid. This part of the thesis was created within the framework of H2020 SmartNet project and it considers one of the project pilot demonstrations: heated swimming pool. The time varying character of the model of this pool is due to the changes of the heat transfer coefficient between water and air depending on the wind speed.The second part of the thesis focuses on smaller thermostatically controlled loads. They are negligible individually, but they can play an important role if a larger population is aggregated. The structure of the proposed control system is hierarchical. Economic model predictive controller in the upper level responds to varying electricity price and changes the temperature setpoints of the thermostats in the lower level. The objective of the control system is the same as in the first part of the thesis: the cost of the operation of this population is minimized and this helps to keep the balance in the grid. However, the high number of the loads does not allow individual modelling of each load in the model predictive controller and an aggregate model had to be developed and tested. This model is non-linear and economic model predictive controller has to solve mixed integer non-linear optimization problem. The effectiveness of the proposed control strategy was demonstrated by simulation

Impact of Air-to-Air Heat Pumps on Energy and Climate in a Mid-Latitude City

Exploring the potential effects of transitioning entirely to air-to-air heat pumps (AAHPs), we use an integrated weather and heat pump model to understand their performance across several building and weather conditions in Toulouse, France. In central Toulouse, where electric and gas heating are similarly adopted, a shift to AAHPs cuts annual electric consumption. Yet, during colder periods, a drop in their efficiency can cause a spike in electricity use. In regions predominantly relying on non-electric heaters, such as gas boilers, introducing AAHPs is expected to increase electricity demand as the heating system transitions to all-electric, though to a lesser extent and with much greater efficiency than traditional systems such as electric resistive heaters. In a separate analysis to evaluate the impact of AAHPs on local climate conditions, we find that AAHPs have a small influence of about 0.5 {\deg}C on the outdoor air temperature. This change is thus unlikely to meaningfully alter AAHPs' performance through feedback.Comment: Submitted manuscrip