Energy For Bulgaria For The 5th Grade Free Download ((INSTALL))

29 December 2023 - The more the energy transition progresses, the more questions and challenges arise. Here are the most important topics, according to the readers of Balkan Green Energy News.

Local Law 33 of 2018 amended the Administrative Code of the City of New York in relation to energy efficiency scores and grades for buildings required to benchmark their energy and water consumption. These energy efficiency scores and grades for these buildings are assigned and disclosed in accordance with the new section 28-309.12 annually, based on benchmarking reporting consistent with Federal energy efficiency standards.

Energy For Bulgaria For The 5th Grade Free Download

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An energy efficiency score is the Energy Star Rating that a building earns using the United States Environmental Protection Agency online benchmarking tool, Energy Star Portfolio Manager, to compare building energy performance to similar buildings in similar climates. As per Local Law 95 of 2019 grades based on Energy Star energy efficiency scores will be assigned as follows:

The energy label includes both a letter grade and the energy efficiency score of the building. Please reference the following document for more information Local Law 33 as amended by LL95 of 2019 Steps to Compliance.

Global efforts for mitigating climate change through the reduction of greenhouse gasses target the accelerated deployment of renewable energy sources (RES) and the adoption of energy efficiency (EE) measures (Irena, 2017, 2018; Hesselink & Chappin, 2019). In this frame, the EU aims to achieve by 2030 a 32% RES share, compared to less than 18% in 2017, and a 32.5% EE improvement (European Commission, 2020).

These efforts are linked strongly with the residential sector which is characterized as an important sector for contributing to the internationally set climate targets of the Paris Agreement due to a globally growing population and its increasing energy demand (Hesselink & Chappin, 2019). Together, buildings and construction sectors have a 36% share in global final energy use and 39% of the energy-related carbon dioxide (CO2) emissions including upstream power generation (International Energy Agency and the United Nations Environment Programme, 2019; Irena, 2018; UN Environment, 2017).

As for RES, their contribution to the total final energy demand of EU buildings was 22% in 2015. Almost half was attributed to biomass, and the other half to electricity and district heat derived from RES. The contribution of solar thermal was relatively small (2% of renewable consumption) (IRENA & EC, 2018). The most common RES technologies to deliver heating/cooling services in households and become part of the energy renovationFootnote 1 are solar thermal, biomass boilers, and high coefficient of performance heat pumps (European Commission, 2016). The penetration of RES technologies depends on several factors, including building stock turnover. Estimations refer to a possible double final consumption of RES in EU buildings by 2030 compared to 2010 levels (IRENA & EC, 2018).

Under this context, the paper (i) presents the already evaluated impact of behavioral barriers for the examined case, (ii) incorporates these barriers in energy modeling using the HERON Decision Support Tool (HERON-DST), (iii) develops scenarios with LEAP for the case study of the Bulgarian residential sector focusing on the combination of available EE/RES technologies and policy instruments for reducing the impact of selected barriers, and (iv) evaluates the policy mixtures of the developed scenarios with the AMS evaluation method.

It is the main energy consumer within the national buildings sector (Sustainable Energy Development Agency, 2018b). Also, it is the third largest sector in terms of final energy consumption (24%), after transport (35%) and industry (28%) (Fahy et al., 2019).

The country has high population shares in energy poverty (linked with the inadequate implementation of EE measures). It is indicative that in 2017, around 63.5% of the most socially deprived households were still unable to keep their homes warm (European Commission, 2017 and Eurostat, 2017a). This percentage is lower compared to that of 2005 which was 79% but remains significantly above the EU average of 23% and makes Bulgaria the worst performer in the EU on that metric (European Commission 2017).

In 2017, there were 3.95 million dwellings (NSI 2018a). Residential buildings were 2.07 million, out of which 45% were constructed until 1960, another 45% between 1961 and 1990, and only 10% after 1990 (NSI, 2018b). According to the 2011 census results (NSI, 2011), 49% of the dwellings were in single-family buildings and the remaining in multi-family buildingsFootnote 2; 68.6% of these dwellings were inhabited (NSI, 2011). It is reasonable to assume that energy is consumed only in the inhabited dwellings.

Among the inhabited buildings, 97.5% are privately owned and almost all of them are owned by individuals (NSI, 2011). In multi-family buildings, individual owners usually undertake partial energy refurbishment measures (replacement of windows, wall insulation, etc.) limited to their own dwelling unless they apply for a grant (e.g., under the Energy Efficiency of Multi-Family Residential Buildings National Programme) in which case they need to comply with the grant requirements to refurbish the whole building.

After 2011, the residential economic consumption steadily increased, while the sectoral energy intensity steadily declined (SEDA, 2018a). The specific reasons for the consumption increase after 2014 are as follows (SEDA 2018a):

There are several studies that assess the theoretical and technical potential of renewables that can be used in households, such as biomass, solar, and geothermal energy Koleva & Mladenov, 2014; BSREC, 2012; MEET, 2009). The studies demonstrate that the technical potential of all of these resources is much higher than their current utilization.

RES share of 25% (to be increased to 27%, following EC feedback) in the gross final energy consumption, i.e., expected shares of RES in the electricity, heating, and transport, respectively, 17%, 44%, and 14%.

The 2030 energy saving target is substantially below the overall EU ambition of 32.5% (non-binding at the Member State level), as set out in the revised EE Directive (2018/2002/EU). Similarly, the national RES target (25% in 2030 compared to 16% in 2020) involves less progress than the overall EU target (32% in 2030 compared to 20% in 2020) (European Commission, 2018).

individual targets for the energy suppliers with annual sales of electricity or heat exceeding 20 GWh, gas exceeding 1 million m3, or non-transport liquid fuels exceeding 6500 tons to achieve energy savings amounting to 1,5% of their annual sales in 2018, 2019, and 2020 (EEA 2018).

National Programme for Renovation of Residential Buildings, providing up to 100% grant for the energy renovation of multi-family buildings (MRDPW, 2018) and with a total capitalization (as of the end of 2017) of 2 billion BGN (1.02 billion euros)Footnote 3;

property tax exemption for up to 10 years for high-energy performance buildings constructed before 2005, as stipulated in the Bulgarian Excise Duties and Tax Warehouses Act. The concrete period depends on both the energy class of the building and the availability of RES utilization.

development of scenarios and their assumed policy mixture based on the incorporated behavioral barriers and selecting the most promising combination of EE and RES technologies for the energy modeling;

The calculated IFs allow the incorporation of barriers in energy modeling. The total impact of the assumed barriers on a certain technology/practice or measure is expressed by the total impact factor (TIF) also calculated with HERON-DST. Consequently, the penetration of each one of the EE/RES technologies/practices is linked with the relevant barriers through their TIFs also provided by HERON-DST (Mavrakis & Konidari, 2017). The tool provides the user with promising combinations of EE/RES technologies/practices in a hierarchical order. The user has the option to select which of the related barriers are to be tackled in view of achieving maximum or efficient progress toward targets. These promising combinations of EE/RES technologies/practices are identified based on (i) their higher number of common barriers and (ii) the lower TIF of barriers that they have as a combination of selected technologies compared to all other combinations (Mavrakis & Konidari, 2017). Mathematical details are explained in the published work (Mavrakis & Konidari, 2017).

A. Business as usual (BAU) scenario: It concerns possible current trends until 2030 with policy measures/instruments already implemented. 2030 is selected due to EU decisions on energy and climate change policy issues.

B. B0 scenario: It reflects a forward-looking path leading to the desired situation, i.e., to achieve the maximum possible amount of EE improvements and RES penetration based on the national potential. It is the synthesis of sub-scenarios, each one concerning one of five (5) household energy needs leading to EE improvements and/or increased RES use. The Bulgarian EE/RES market was investigated through bibliographic research, and only the available in-country technological options were used. More specifically:

2. Building shell improvement (building fabric upgrade): improvement of insulation (in walls, windows, roof, and floor), resulting in decreased energy intensity of the space heating for all housing types. By 2030, the renovated buildings need to comply with the Class A energy certificate. Building shell improvement assumed targets are differentiated for single and multi-family buildings in LEAP.

The third step is for grading the performance of the evaluated objects (policy instruments/mixtures, scenarios) under a criterion/sub-criterion. MAUT and SMART are used for assigning the grades. MAUT is used when there are available and credible numerical data for the evaluated objects under the examined sub-criterion. If such data are not available, then SMART is used for assigning grades for the evaluated objects based on the experience of the expert and the available qualitative information. The grades of SMART are calculated into normalized grades on the scale [0, 100], the same as the MAUT scale of grades (Konidari & Mavrakis, 2007), i.e., e24fc04721