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The claim of a green data centre is generally made based on a net-zero CO2 emission through a ‘balance-sheet’ approach, which considers renewable electricity through on-site installation or purchase agreement as abatement measures against the use of fossil electricity from the electric grid on an annual basis. However, when the hourly dynamic fuel mix is accounted for in the assessment, the annual net-zero energy approach does not lead to a true carbon neutral data centre. In response, two approaches based on net-zero energy and net-zero CO2 emission respectively are proposed and investigated regarding the goal of net-zero CO2 emission. A data centre in Singapore with typical load profiles is used as a case study, different scenarios considering climate change and projected future energy are defined to examine the impacts of dynamic energy mix on the net CO2 emission of the data centre. The net-zero energy approach is found to result in significant amount of annual CO2 emissions. In comparison, the net-zero CO2 emission approach can assure a true net-zero CO2 emission, but this approach will require an increase of PV capacity by 20% and 60% as compared to the net-zero energy approach based on assessment for the year 2030 and 2050, respectively.

Researchers have often overlooked the emissions problem during the suitable selection of renewable energy (RE) systems. Therefore, there is a need for an integrated framework that simultaneously considers the economic, technical and environmental criteria for the selection of the appropriate configuration. Firstly, a multi-objective optimization is performed using the epsilon- constraint method with a simulated annealing algorithm. Then a scenario-based method with a hybrid multi-criterion decision-making approach is used to rank all the available configurations. Five operating strategies are developed to make different configurations, i.e. battery only, pumped hydro storage (PHS), battery-diesel generator (DG), PHS-DG, and hybrid pumped-battery storage. A total of seven scenarios are made based on the weightage given to each main criterion. The study reveals that solar-wind-PHS-DG was the top-ranking alternative under four scenarios, solar-wind-PHS ranked first in two scenarios, and solar-wind-DG-battery got preference under the no-preference scenario; this shows that preferential selection (assigning a weighting to each criterion) significantly affects results. Emissions of all considered RE-based alternatives range from 0.072 to 0.148 kg (CO2 equivalent) per kWh of the served load. Furthermore, sensitivity analysis reveals that technical criteria conflict more with economic criteria than with environmental criteria. The impact of land requirements (an environmental sub-criterion) is visible in this study, indicating the high requirement of land for RE systems. The most appropriate configuration type is selected depending solely on the priorities defined by investors and policy-makers.

Electricity Systems in europe are experiencing major changes due to targets for renewable energy integration, reducing greenhouse gas emissions, and energy efficiency. Different studies show that there is a growing need for more flexibility and active stakeholder involvement at all levels (from small consumers to pan-European networks) to ensure the efficient and reliable operation of the electricity system, particularly to deal with growing volumes of renewable energy sources, from transmission-level wind and solar farms to household-level photovoltaic generation. Other key evolutions that aim to decarbonize the energy sector beyond electricity, such as those based on the electrification of energy end uses (e.g., the development of electric vehicles and the electrification of heating), are also expected to have a substantial impact.

Although geothermal resources are practically independent of climate factors, those factors significantly condition the potential use of the Earth's natural heat resources. Unlike all the other factors limiting or facilitating the use of geothermal heat (like receivers' temperature expectation, financial issues or local regulations), climate factors remain immovable. Thus, climate remains the main factor influencing the effective use of geothermal resources. Volumes of sold energy, typical capacity factors and rapid changes in heat demand may all influence the financial and technological performance of an investment. In the current paper, climate factors are translated into heat demand based on historical data (meteorological and district heating logs) by means of a dedicated artificial neural network, and analysed in terms of possible constraints and facilitators that might affect the effective use of geothermal energy. The results of ANN simulation indicate that average and typical operation is expected without any turbulences, yet about 10% of operating hours may require additional technical measures, like peak source support, smart management and buffers in order to limit pumping ramp rate. With appropriate dimensioning and exploitation, capacity factors as high as 60% are available, proving the potential for financially and environmentally effective use of geothermal resources.

This paper presents a mathematical model for estimating the optimal sizing and assessing a standalone hybrid power system's performance entirely based on variable renewable energy sources and coupled with a hybrid energy storage system. This study evaluates how different levels of the main components' capital cost and the loss of power supply probability would affect the cost of energy and the power system's optimal sizing. The case study selected for this study was Ometepe Island in Nicaragua, where the crater lake of an extinct volcano was considered a feasible upper reservoir of a pumped storage hydropower plant, reducing the investments associated with this component. The mathematical formulation considers energy storage losses and gains, and the Pareto efficient solutions of the multi-objective optimization model simultaneously increase reliability, reduce the cost of energy, and minimize curtailment energy. By employing time-series with an hourly resolution, the model allows assessing the impact of the interannual variability of renewable energy sources on the system's performance. As for the case study, the cost of energy obtained from the model results ranges between (sic)0.047/kWh and (sic)0.095/kWh, based on international reference values, and these values match the information available in the literature and other databases.

The Air Pollution Prevention and Control Action Plan proposed in 2013 has contributed to the fossil fuel reduction in central heating, but the consumption of scattered coal in rural areas should not be overlooked as it accounts for nearly 50% pollutant emissions during the heating season. Air Pollution Prevention and Control Action Plan for Beijing-Tianjin-Hebei and Surrounding Areas was issued to solve the problem of scattered coal consumption and improve the heating energy structure in 2017. In this paper, an air quality-heating model is established to evaluate the air quality improvement potential and far-reaching influence of scattered coal consumption reduction in rural areas. Distinct from other air quality evaluation models, the proposed model is to evaluate the regional air quality related to the heating factors. The results indicate that the coal substitution policy can reduce PM2.5 concentrations in the heating season by about 20 mu g/m(3) in Beijing-Tianjin-Hebei and surrounding areas. Furthermore, predictions about the variation of PM2.5 concentration in the heating season are conducted, which indicate that the PM2.5 concentration of Beijing, Tianjin, Hebei and surrounding areas in 2030 will drop by 48%, 35%, 29% and 23% respectively compared to that in 2016. The reduction could be further enhanced to be 55%, 40%, 32% and 27% with the consideration of clean power development. This study is conducive to evaluate the clean heating technologies role in improving the environment and air quality.

This study investigates the benefits of introducing Li-ion batteries as energy storage unit in the commercial sector by considering a representative building with a photovoltaic system. Only the costs and revenues related to the installation and operation of the battery are considered in this study. The operational strategy of the battery consists in balancing the following processes through day-ahead forecasts for both electricity consumption and photovoltaic production: shaving a targeted peak, performing price arbitrage, and increasing photovoltaic self-consumption. By reviewing the electricity price cost for commercial buildings from several companies around the world, a general electricity price structure is defined. Afterwards, a Monte Carlo Analysis is applied for three locations with different solar irradiation levels to study the impact of climate, electricity price components, and other seven sensitive parameters on the economic viability of Li-ion batteries. The Monte Carlo Analysis shows that the most sensitive parameters for the net present value are the battery capacity, the battery price, and the component of the electricity price that relates to the peak power consumption. For Stockholm, one of the investigated locations, the corresponding Pearson correlation coefficients are −0.67, −0.66, and 0.19 for the case were no photovoltaic system is installed. For the considered battery operational strategies, the current investment and annual operation costs for the Li-ion battery always lead to negative net present values independently of the location. Battery prices lower than 250 US$/kWh start to manifest positive net present values when combining peak shaving, price arbitrage, and photovoltaic self-consumption. However, the integration of a photovoltaic system leads to a reduced economic viability of the battery by reducing the revenues generated by the battery while performing peak shaving.

The power usage effectiveness (PUE) is commonly used as the key performance indicator to evaluate the energy performance of data centers. However, using only PUE cannot enable a fair comparison when data centers are operating in different regions, due to the unneglectable impacts of climatic conditions on the power consumption of cooling systems. To solve this problem, a new indicator, coefficient of PUE (COPUE), is proposed, which is defined as the ratio of the measured PUE of real data centers to the local benchmark PUE. The benchmark PUE is reckoned based on the current most commonly used cooling technology, which consists of water-cooled chillers and water cooling towers. A simplified method for calculating benchmark PUE is also developed. The degree hour of water cooling is introduced to consider the impacts of local climatic conditions. It presents the annual accumulated hours, in which chilled water is needed to satisfy the cooling demand of data centers. Through several case studies, COPUE has been proved to be an effective indicator for comparing the energy performance of data centers. When the same cooling technology is adopted, it can reflect how good the design and operation are; while, when different cooling technologies are adopted, it can be used to demonstrate which one is superior.