Grid Emission Factor Research Articles

Dynamic grid emission factors and export limits reduce emission abatement and cost benefits of building PV systems

Photovoltaic (PV) installations in the building sector are expected to play a crucial role in Switzerland's efforts to achieve decarbonization targets. Nevertheless, most analyses on PV system design prioritize economic factors, and overlook the impact of angle-dependent PV generation, dynamic grid greenhouse gas (GHG) emissions, and export limitations on their emission abatement potential during operation. Our study sheds light on the cost- and emission-optimal orientation and sizing of PV systems in buildings, factoring in hourly grid GHG emission intensity and curtailment measures. We employ mixed-integer linear programming (MILP) and illustrate the methodology for a case study building under different scenarios, aiming to minimize costs and emissions by considering a combination of rooftop and façade PV systems, and a battery. We show that assuming a constant annual grid GHG emission intensity may overestimate annual emission abatement potential of PV generation by a factor of two, emphasizing the need for dynamic grid GHG emission intensity. When considered, cost-optimal solutions favour larger systems that maximize total production. In contrast, emission-optimal solutions increase self-consumption via inclusion of battery systems. Curtailment due to export limitations reduces the cost and emission benefits of excess generation (from feed-in tariff payments and carbon credits), prompting a trend towards smaller PV installations with steeper panel tilts. Thus, the emission abatement potential of PV generation heavily depends on the option to export excess electricity production.

Optimal design of building envelope towards life cycle performance: Impact of considering dynamic grid emission factors

Building-related carbon emissions in the construction and operation stages account for more than one-third of global energy-related emissions. Optimizing during the early design stage is an effective way to reduce building carbon emissions. However, current studies adopt a constant grid emission factor (GEF) to assess the environmental impacts of buildings while ignoring the dynamic changes of the future electricity mix. This will overestimate the operational carbon emissions of the building in the context of the increasing share of renewable energy in power generation. In this study, a dynamic life cycle assessment model based on dynamic GEF is constructed to evaluate the life cycle environmental impacts of buildings. On this basis, a building optimization design framework considering dynamic GEF is proposed, and the impact of considering future variations of GEF on the optimal design of the building envelope is explored. The study is conducted in three steps. First, dynamically changing GEFs are predicted under the future electricity mix based on scenario analysis. Second, a multi-objective optimization framework is established to minimize the life cycle carbon emissions (LCCE) of the building while optimizing its life cycle costs (LCC) and indoor discomfort hours (IDH). Finally, the proposed optimization framework is applied to a prototype office building in Shanghai, and a comparative analysis is conducted for the optimal schemes between a static and two dynamic scenarios. The result reveals that ignoring future variations will lead to overestimating the operational carbon emissions by 49.0%-64.8%, which in turn will result in an over-insulated design of the envelope (i.e., additional material consumption). In addition, compared to the static scenario, consideration of dynamic GEF results in a 38.7%-51.6% reduction in LCCE, a 5.2%-6.1% reduction in LCC, and a 5.3%-9.5% increase in IDH for the optimal solutions. This research illustrates the importance of considering dynamic GEF in identifying the optimal design parameters and can help decision-makers reasonably choose the optimal schemes during early-stage design.

Research on optimization strategy of grid carbon emission factor for promoting electricity-carbon market coupling

Abstract The grid emission factor is an essential parameter for accounting for carbon emissions caused by electricity consumption, and its update frequency and spatial accuracy affect the quality of the emission factor. Based on the existing regional grid emission factors, this study proposes an improved calculation method to get the revised emission factors. The revised emission factors can address barriers such as delayed updating, inequitable accounting between regions, and double measurement of green power emission reduction benefits. Therefore, the study can support the construction of the carbon market and the realization of carbon neutrality goals.

Pathways Toward Improving the Energy Efficiency of Residential Air-Conditioning Systems in Saudi Arabia

Abstract In Saudi Arabia, the residential electricity consumption approaches 50%, primarily driven by air conditioners (AC). This study explores the potential energy savings and carbon dioxide (CO2) emission reductions up to 2030 through three scenarios: business as usual (BAU), continuous improvement scenario (CIS), and accelerated improvement scenario (AIS). BAU scenario assumes that the current energy efficiency ratio (EER) of 11.8 BTU/Wh is maintained until 2030. CIS considers a 5% EER improvement in new AC stock every two or five years, while AIS assumes a 10% improvement in the EER at the same intervals. Additionally, energy savings and emission reductions possible from varying adoption levels of a new refrigerant (R32) are estimated for three scenarios. Finally, the CO2 emission reduction under each scenario is computed for two extreme cases of grid emission factor. BAU scenario predicts energy savings of up to 17.7 TWh in 2030 compared to 2020 energy consumption figures. AIS with two-year intervals results in additional energy savings of 10.1 TWh in 2030 and cumulative energy savings of 37.1 TWh over a decade compared to the BAU scenario. Even CIS with five-year intervals yields additional energy savings of 1.69 TWh in 2030 and 5.1 TWh cumulatively compared to the BAU scenario. In comparison, the introduction of the new refrigerant results in cumulative energy savings of 10.2 TWh in the best-case scenario. These findings emphasize the importance of enhancing the EER of residential AC systems as a priority in energy efficiency policy.

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

This work studies synergies arising from combining industrial demand response and local renewable electricity supply. To this end, we optimize the design of a local electricity generation and storage system with an integrated demand response scheduling of a continuous power-intensive production process in a multi-stage problem. We optimize both total annualized cost and global warming impact and consider local photovoltaic and wind electricity generation, an electric battery, and electricity trading on day-ahead and intraday market. We find that installing a battery can reduce emissions and enable large trading volumes on the electricity markets, but significantly increases cost. Economically and ecologically-optimal operation of the process and battery are driven primarily by the electricity price and grid emission factor, respectively, rather than locally generated electricity. A parameter study reveals that cost savings from the local system and flexibilizing the process behave almost additively.

Navigating Sustainability through Greenhouse Gas Emission Inventory: ESG Practices and Energy Shift in Bangladesh’s Textile and Readymade Garment Industries

As the world's demand for textiles and clothing rapidly increases, this industry's greenhouse gas (GHG) emissions are becoming a major environmental concern. Bangladesh, a key player in the global textile supply chain and one of the top producers, contributes significantly to these emissions. However, accessible data on activity and GHG emissions, crucial for researchers, the private sector, and policymakers in decision-making, is scarce. To address this gap, this study combines a detailed field survey with expert interviews to establish a comprehensive emission inventory. This inventory aims to identify hotspots and facilitate the adoption of effective mitigation strategies. Focusing on a prominent industrial zone's textile and readymade garments (RMG) industries, the research employs a mix of top-down and bottom-up approaches and follows the IPCC guidelines to develop a GHG emission inventory for 2022. The study evaluates various emission sources, including scope 1 (onsite fuel combustions), scope 2 (grid electricity usage), and scope 3 (waste and wastewater treatment). In the total emissions (6043.5 Gg CO2eq.), textile and RMG industries contribute 67.8% and 32.2%, respectively, with scope 1 emissions dominating at 85%. Notably, scope 2 emissions exhibit significant uncertainty (−10.4% to +11.9%), largely due to variations in national grid emission factors. This study forecasts GHG emissions until 2030, considering current trends (26 thousand Gg CO2 eq.). It also explores various energy mix scenarios, factoring in the depletion of existing natural gas reserves (ranging from 8 thousand to 33 thousand Gg CO2 eq.). This study delves into the impact of the Environmental, Social, and Governance (ESG) system on industries' GHG emissions. Besides improving worldwide emission databases and identifying hotspots, this research aims to promote a sustainable transition in both Bangladesh and other developing textile manufacturing nations across the globe.

Heterogeneity of Grid Emission Factor Accounting in China: Temporal Resolution, Spatial Resolution, and Energy Distribution

Grid emission factor has become the most critical variable in carbon market quota allocation and carbon footprint accounting of international trade products. Different grid emission factors will lead to great differences in carbon emissions of enterprises or products. It is found that the temporal resolution, spatial resolution and energy distribution of cogeneration have great influence on the emission factors of power grid. This paper uses the data of China’s power industry to carry out empirical analysis. For each dimension, we set different choices and explore their specific impacts. It is found that the different choices of the three dimensions will cause 5.01%, 20.59%, 4.38% differences in the calculation results of grid emission factors. Finally, based on the research results, this paper puts forward suggestions from three aspects: authoritative guidance, data accuracy and international communication, in order to provide reference for Scope 2 greenhouse gas emissions accounting and reporting.

Towards standardized grid emission factors: methodological insights and best practices

Grid emission factors from official sources vary. Nine relevant aspects were identified and their influence quantified. The recommended set of aspects best represents emissions from grid electricity consumption.

Low-carbon technology selection and carbon reduction potential assessment in the shipbuilding industry with dynamically changing grid emission factors

The shipbuilding industry is a vital sector involved in manufacturing ships and marine structures. It serves critical roles in shipping, maritime development, and national defense, which contributes significantly to employment, international trade, and national security. Nevertheless, the shipbuilding industry is under enormous pressure to curtail carbon emissions. The International Maritime Organization (IMO) requires a 40% reduction in greenhouse gas emissions (GHG) from both existing and new ships by 2030 compared to the 2008 baseline. This stems from the industry's high-carbon emission nature, making it imperative to adopt sustainable solutions that minimize its environmental impact. Given the capital- and energy-intensive nature of the shipbuilding industry, the implementation of suitable technologies can also improve the overall operational efficiency while meeting shipowners' sustainability standards and enhancing competitiveness. We selected a typical shipbuilding company in South China and categorized the 11 key low-carbon technologies into four categories based on their principles and application scenarios, i.e., equipment improvement, process upgrading, intelligent upgrading, and fuel alternative. The Long-range Energy Alternatives Planning system (LEAP) model was employed in this study to simulate the energy consumption structure of this shipbuilding enterprise and explore pathways to reach carbon emissions peak under six low-carbon technology scenarios. In addition, the impact of dynamically changing grid emission factors on long-term carbon emission reduction is taken into consideration. The results show that a sustained reduction in grid emission factors will have a positive impact on carbon emission reduction. The shipyard's planned requirements to achieve carbon peak before 2030 and reduce its carbon intensity by 18% from 2020 are only achievable under the application of all low-carbon technologies. The shipyard will achieve carbon peak in 2026, with a maximum emission reduction potential of 13.2 thousand tons. The primary contributors are equipment improvement and process upgrading technologies, with reductions of 6.73 and 3.11 thousand tons, respectively. Therefore, from a technical point of view, these can be prioritized for development, although some of the equipment has a high upfront investment. Notably, the technology diffusion is rapid, with most technologies projected to reach penetration saturation by 2025, leading to a payback period of 3–4 years thereafter.

Determination of circular economy targets based on absolute sustainability: A case study on plastics

Plastic production significantly contributes to greenhouse gas emissions, making up around 4.5% of total emissions. As plastic consumption is expected to rise, it’s crucial to address the environmental impact of this industry urgently. Circular economy strategies, like recycling, are proposed solutions to reduce plastic’s carbon footprint. However, these strategies lack specific carbon footprint targets for plastics used in various applications. This study focuses on plastics in the automotive industry, establishing specific carbon footprint goals for plastics used in passenger cars. It also examines mechanical recycling’s role and the share of secondary plastics in meeting these absolute targets. The analysis of mechanical recycling technology reveals that even with 100% recycled plastics, the carbon footprint targets aren’t achieved due to high process energy consumption. Investigating grid emission factors, results show that meeting targets requires emission factors lower than 0.13 kgCO2-eq./kWh. Despite successful scenarios, it’s presently infeasible to use 100% recycled plastics in passenger car production. This necessitates the need to further improve plastic recycling technologies and increase the share of secondary plastics used in passenger car production.

Spatio-temporal electrical grid emission factors effects on calculated GHG emissions of buildings in mixed-grid environments

This study compares the calculated greenhouse gas (GHG) emissions of buildings using two different methodologies in mixed-grid environments. Simulations were conducted using virtual models of 25 buildings and actual meteorological data over 2016–2018. The “Annual Method” using yearly average emission factors and the “Hourly Method” using consumption-based hourly emission factors were used to calculate GHG emissions. The study found that the hourly method provided a more accurate representation of GHG emissions, especially during peak grid demand. Furthermore, the study recommends using a zonal approach to building codes in terms of electrical grids similar to climate zones in current codes and standards while also prioritizing building types with the largest potential for emissions reductions. A case study in Ontario, Canada found that electrification via heat pump always results in GHG savings independent of year, building model, and city if keeping the calculation method the same between fuel-switching models. Future research is needed to improve the accuracy of GHG emissions calculations and understand the relationship between electrical load and GHG emissions.

Multi-objective optimization of gas-steam-power system for an integrated iron and steel mill considering carbon emission reduction and cost

The gas-steam-power system (GSPS) optimization is a complicated optimization problem considering economic and environmental benefits. This paper presents a multiperiod mixed integer linear programming (MILP) model for the GSPS in iron and steel enterprises simultaneously optimizing total cost and carbon emission reduction. It was used to determine the optimal operating strategy for the GSPS under multiobjective conditions. The model considers the influence of a blast furnace with top-gas recycling (TGR-BF) technology on the by-product gas supply and steam and power cogeneration system (SPCS) operation. The results showed that after the optimization aiming at the minimum total cost (Scenario A) and the maximum carbon emission reduction (Scenario B), the system total cost was the lowest (7.78 billion CNY) when the top gas recovery rate was 4%, and the carbon emission reduction peaked at 465,296.76 tCO2 when the top gas recovery rate was 12%. After multiobjective optimization (Scenario C), it was found that the system achieved carbon reduction only when the total cost attained a certain value. A sensitivity analysis revealed that a reduction in the grid emission factor and an increase in the green power increased the carbon emission reduction. When the coal price increased to 1800 CNY/t or the power price decreased to 0.2 CNY/kWh, the optimal system operation strategy was consistent under different optimization objectives. These research results provide guidance for steel enterprises to reduce carbon emissions and total cost.

Techno-economic assessment of energy and environmental impact of waste-to-energy electricity generation

This study explored cumulative 127.5MW waste to energy (WtE) potential in five populous cities of Pakistan based on local waste characterization profiles and global standards. The 50MW WtE plant in Lahore using National electricity regulator codes and practices resulted in an attractive Levelized cost of electricity (LCOE) of US¢ 7.86/kWh over 25 years with a $151.5 million investment cost. The net savings to Lahore Waste Management Company can be $103.4 and $137.7 million respectively with and without tipping fees on account of waste disposal cost, bricks revenue using bottom ash, and waste fee. The project developers can get net savings of $16.9 and $51.5 million respectively with and without tipping fees other than LCOE. Furthermore, the greenhouse gas emissions of 216.6 million tons of CO2eq can be saved throughout plant life against 279 GWh/year energy generation, in terms of grid emission factor and current methane release into the atmosphere from the dumping site.

GHG emissions reduction patterns from waste sectors after forced source separation

Waste management is the bridge to balance resource conservation and climate change protection, and source separation is the pre-requirement to improve the recycling rate and normal human behavior. In this work, greenhouse gas (GHG) emission patterns were investigated from waste sectors from 2016 to 2021 in Shanghai, where force MSW source separation was first implemented in China since 2019. The GHG emission in the waste sector increased from 3.06 (2016) to 9.28 (2021) Mt CO2-eq due to the increase of waste disposal amounts from 7.35 (2016) to 11.83 Mt (2021). With the implementation of forced source separation in Shanghai on July 1st, 2019, the proportion of kitchen waste and water content decreased by 37.31% and 20.74% in dry waste, resulting in the increase of low heating value (LHV) by 99.39% (13,160 kJ/kg). The waste disposal system was optimized by employing resource recycling and anaerobic digestion of wet waste, and the corresponding GHG emission intensity decreased from 0.29 to 0.24 t CO2-eq per ton MSW. Three scenario analyses, including the Business-As-Usual (BAU) scenario, New Policy (NP) scenario, and Low-carbon (LC) scenario, were conducted to study the influence of the grid emission factor, wet waste disposal capacity, incineration power generation efficiency, and lower waste plastic proportion on the GHG emissions. The results showed that the GHG emission intensity would reach 0.28, 0.21, and 0.12 t CO2-eq per ton MSW in the BAU, NP, and LC scenarios. Optimizing waste disposal mode and reducing waste plastic from sourced separation was critical to reduce GHG emissions more effectively from the waste sector.

Spatiotemporal analysis of CO2 emissions and emission reduction potential of Beijing buses using smart card data

Human activities, primarily carbon dioxide emissions, have undeniably caused global warming. The transportation sector contributes about a quarter of global CO2 emissions. While replacing traditional buses with electric ones has reduced emissions, it is crucial to consider the indirect emissions resulting from electricity consumption. This study proposes a framework for modeling bus emissions using smart card data, integrating spatiotemporal distribution characteristics and emission reduction potentials. Our analysis reveals that routes spanning 10–30 km contribute to 81% of total bus emissions, with an average emission rate of 56.2 gCO2/per-km for residents traveling by bus. Bus emissions also exhibit cyclical variations during holidays, weekdays, and weekends, indicating spatial clustering and trends. Although the area within Beijing's 4th Ring Road constitutes only 13% of the total area within the 6th Ring Road, it generates almost half of the CO2 emissions. With urban expansion, total bus emissions increase gradually, but emission intensity decreases. This study emphasizes the potential for reducing emissions through improved public transportation operations. It recommends fully electrifying the bus fleet and employing low grid emission factors, which could reduce emissions by up to 71% compared to diesel options. Electrification of buses and optimizing power generation on the grid are essential priorities for emission reduction.

Grid-supported electrolytic hydrogen production: Cost and climate impact using dynamic emission factors

Hydrogen production based on a combination of intermittent renewables and grid electricity is a promising approach for reducing emissions in hard-to-decarbonise sectors at lower costs. However, for such a configuration to provide climate benefits it is crucial to ensure that the grid electricity consumed in the process is derived from low-carbon sources. This paper examined the use of hourly grid emission factors (EFs) to more accurately determine the short-term climate impact of dynamically operated electrolysers. A model of the interconnected northern European electricity system was developed and used to calculate average grid-mix and marginal EFs for the four bidding zones in Sweden. Operating a 10 MW electrolyser using a combination of onshore wind and grid electricity was found to decrease the levelised cost of hydrogen (LCOH) to 2.40–3.63 €/kgH2 compared with 4.68 €/kgH2 for wind-only operation. A trade-off between LCOH and short-term climate impact was revealed as specific marginal emissions could exceed 20 kgCO2eq/kgH2 at minimum LCOH. Both an emission-minimising operating strategy and an increased wind-to-electrolyser ratio was found to manage this trade-off by enabling simultaneous cost and emission reductions, lowering the marginal carbon abatement cost (CAC) from 276.8 €/tCO2eq for wind-only operation to a minimum of 222.7 and 119.3 €/tCO2eq respectively. Both EF and LCOH variations were also identified between the bidding zones but with no notable impact on the marginal CAC. When using average grid-mix emission factors, the climate impact was low and the CAC could be reduced to 71.3–200.0 €/tCO2eq. In relation to proposed EU policy it was demonstrated that abiding by hourly renewable temporal matching principles could ensure low marginal emissions at current levels of fossil fuels in the electricity mix.

ENERGY DECARBONIZATION STRATEGIES IN RETROFITTED SINGLE-FAMILY HOMES

Legislative work on minimizing greenhouse gas emissions becomes one of the issues of the sustainable approach in the architectural and construction sector. An example of such work is Chicago’s 2022 Climate Action Plan, where nearly 70% of Chicago’s greenhouse gas emissions come from urban environments. While energy consumption in new construction is declining due to tightening building codes, insufficient regulations do not limit emissions in existing buildings, especially single-family housing. The study evaluates building structures and focuses on three decarbonization strategies for retrofitted single-family homes. It interrogates and compares building electrification, envelope retrofitting and the use of renewable energy. Therefore, this study may be helpful in further prioritization of the energetic modernization of existing buildings. The case study is founded on the single-family houses built in Chicago before 1980. The paper presents possible interventions of available retrofit methods regarding grid emission factors and considers the CO2 cycle in the building’s lifetime.

Comparison of mono and bifacial modules for building integration and electric vehicle charging: A case study in Sweden

Future energy systems have a major difficulty in ensuring a reliable supply of electricity without affecting the environment, and numerous innovative renewables-based solutions are being introduced to meet this issue. This paper proposes a building photovoltaic (PV) system design for residential and electric vehicle (EV) charging demand and evaluates the techno-economic and environmental performance of the system in Orebro, Sweden, as it aims to become net zero carbon economy by 2045. Literature review shows that, although many studies exist, most of them did not fully consider the techno-economic and environmental aspects of PV systems for residential and EV charging loads in the chosen location. Two different PV technologies monofacial and bifacial monocrystalline panel in three different roof slopes 15°,30° and 45° has been analyzed to find the optimized system that can meet a typical house's annual energy demand. Economic indicators such as cumulative cash flow, levelized cost of electricity (LCOE), payback period and cost of EV charging have been evaluated for the PV system without discount and with discount which affects the system's profitability. PVSyst software was used to simulate the system for energy generation. Results have shown that the bifacial PV system performed better in energy generation, which is approximately 10% higher than the monofacial panel. However, in terms of economics, Case 6, a bifacial PV system with a roof angle of 45°, shows the lowest payback period of 7.3 years. In contrast, monofacial PV system with roof slope of 30° showed LCOE of 0.8988 Swedish Krona per kilowatt hour (SEK/kWh), EV charging cost of 0.1471 Swedish Krona per kilometer (SEK/km). Environmental parameters such as greenhouse gases (GHG) reduction have been analyzed. Results showed that GHG savings due to EV was higher than PV plant as Sweden's grid emission factor is very low due to less dependency on fossil fuels. The significance of this study will enable us to understand the performance of PV systems in Swedish aspect and methods can be extended to other countries for meeting location-specific energy demand.

Calculation of Emission Factors of the Northwest Regional Grid Based on Linear Support Vector Machines

Machine learning can perform correlation analysis on a large amount of data and explore their relationship. Thus, the status quo can be analyzed, and the values of specific power industry indicators can be predicted in the future. This paper deduced the industry indicators that affect the grid emission factor by analyzing the grid emission factor algorithm. The study used the support vector machine regression algorithm to model and analyze collected power grid data and discussed the regression effect from five main aspects, including RMSE, MSE, MAE, coefficient and running time. Finally the most fitted regression model parameters were obtained. And the optimal model predicted the emission factors of the Northwest power grid.

Emission factors measurement of regional power grid based on Gaussian process regression

Machine learning can analyze and study the huge amount of big data on electricity, explore and mine the correlation between electricity data, and then analyze the current situation and predict the future values of certain electric power industry indicators. In this paper, various social, economic and industrial indicators affecting the grid emission factors are derived through algorithmic analysis of the grid emission factors. The Gaussian process regression analysis algorithm was used to compare and analyze the collected electricity data, focusing on five aspects of regression effects, including root mean square error, mean square error, mean absolute error, fitting coefficient, and running time, to obtain the optimal regression model parameters. Finally, the trained model was completed to predict the emission factors of the East China Power Grid from 2020 to 2060.