Deep Decarbonization Research Articles

How to minimise the cost of green hydrogen with hybrid supply: A regional case study in China

Hydrogen produced from renewables-fuelled electrolysis has the potential to provide deep decarbonisation for sectors that are the most difficult to decarbonise, but the intermittency of renewable sources may result in low electrolyser utilisation and, consequently, high costs. This study determines the greenhouse gas (GHG) emissions and levelised cost of hydrogen (LCOH) associated with blending wind electricity with the grid to increase electrolyser utilisation. A novel cost model was developed to determine the production performance, costs and embodied CO2 emissions of an electrolyser with hybrid fuel supply (wind and grid) considering dynamic load constraints of alkaline (ALK) and Proton exchange membrane (PEM) electrolysers and using high-resolution historical weather data to determine wind production profiles in two case study regions: northern (Bayannur) and southern (Pingtan) China. The results show that using 52% of grid electricity in an ALK electrolyser in Bayannur reduces LCOH by 11%, increasing hydrogen production by 74% but with 17.4 kg CO2/ kg H2 generated in CO2 emissions. When a carbon price is accounted for, the optimal electrolyser utilisation is reduced to 40%, with the LCE 44% reduced. Emissions of 9.8 kg CO2/kg H2 are lower than for stream methane reforming with carbon capture. In Southern China (Pingtan), LCOH is higher than Bayanur because of the high electricity cost. The economic benefits of increasing grid power depend primarily on the total CAPEX cost, grid power cost, and carbon tax. By altering the carbon tax, the optimal blend of wind/ grid differs. The grid contribution to the optimal blend diminishes as a carbon tax approaches 35 USD/ t CO2.

Decarbonization potential collaborated with source industries for China's iron and steel industry towards carbon neutrality

Towards the target of carbon neutrality, the need for deep decarbonization in iron and steel industry is increasingly recognized. Existing studies rarely mention the effects of collaboration with its source industries for carbon emission reduction in ISI. Furthermore, the fundamental laws of CO2 emission reduction capacity with cost change of source substitutions for various steel production routes under market economics are unclear. To avoid the blind substitution of clean sources for meeting the urgent requirements for decarbonization in China's steel industry, which may result in increased carbon emissions and uneconomical carbon reduction from a lifecycle perspective. This paper integrates the production and utilization processes of source resources into the steel production process, establishes coupling models of carbon and value flows for four main types of steel production routes, and aims to quantitatively analyze the specific CO2 emission reduction potential and costs. Three economic market development scenarios from 2021 to 2060 are set, and the marginal carbon prices of various source substitutions are determined. Subsequently, decarbonization pathways with the goal of minimizing production costs are developed. Finally. The future resource demand and the carbon emission reduction contributions of source industries collaborated are predicted. The results show that (1) about 81.7–89.9 % of CO2 emissions could be reduced in the scenarios; (2) the demands of electric power and hydrogen energy will reach 54.48–511.90 BkWh and 7.47–10.31 Gt in 2060, respectively; and (3) collaboration with source industries could make 18.3–18.3% of CO2 emission reduction contributions. All in all, this study could provide scientific support for the low-carbon transformation of China's steel industry.

Electric-gas infrastructure planning for deep decarbonization of energy systems

The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors. Electricity and natural gas (NG), which are vital vectors of today’s energy system, are likely to be coupled in different ways in the future, resulting from increasing electrification, adoption of variable renewable energy (VRE) generation in the power sector and policy factors such as cross-sectoral emissions trading. This paper develops a least-cost investment and operations model for joint planning of electricity and NG infrastructures that considers a wide range of available and emerging technology options across the two vectors, including carbon capture and storage (CCS) equipped power generation, low-carbon drop-in fuels (LCDF) as well as long-duration energy storage (LDES). The model incorporates the main operational constraints of both systems and allows each system to operate under different temporal resolutions consistent with their typical scheduling timescales. We apply our modeling framework to evaluate power-NG system outcomes for the U.S. New England region under different technology, decarbonization goals, and demand scenarios. Under a global emissions constraint, ranging between 80%–95% emissions reduction compared to 1990 levels, the least-cost solution relies significantly on using the available emissions budget to serve non-power NG demand, with power sector using only 14%–23% of the emissions budget. Increasing electrification of heating in the buildings sector results in greater reliance on wind and NG-fired plants with CCS and results in similar or slightly lower total system costs as compared to the business-as-usual demand scenario with lower electrification of end-uses. Interestingly, although electrification reduces non-power NG demand, it leads to up to 24% increase in overall NG consumption (both power and non-power) compared to the business-as-usual scenarios, resulting from the increased role for CCS in the power sector. The availability of low-cost LDES systems reduces the extent of coupling of electricity and NG systems by significantly reducing fuel (both NG and LCDF) consumption in the power system compared to scenarios without LDES, while also reducing total systems costs by up to 4.6% for the evaluated set of scenarios.

Climate targets by major steel companies: An assessment of collective ambition and planned emission reduction measures

This article systematically assesses the status, robustness, and potential impact of greenhouse gas emission reduction targets set by the largest steel producer companies as of mid-2022. The assessment covers the 60 largest steel companies by volume, accounting for more than 60 % of global steel production. Data on company-level greenhouse gas emission reduction targets and emission reduction measures were collected from publicly available documents.We found that only 30 companies have their own greenhouse gas emission reduction targets of varying timeframes between 2025 and 2060. Even when excluding the 15 Chinese state-owned companies that are under the national 2060 net zero target, 15 companies had no emission reduction targets. Twenty-one companies had long-term targets (2040 or after), of which 18 were net zero emission targets; all but one also had interim targets. If all climate targets identified among the 60 companies are achieved, annual CO2 emissions for the 60 companies could be reduced by up to 12 % by 2030 and up to 39 % by 2050 in comparison to a baseline scenario. Assuming a gradual increase in global crude steel demand from 1.9 Gt in 2019 to 2.5 Gt in 2050 and assuming similar trends for the rest of the global iron and steel sector as observed for the 60 companies, we estimate that the current ambition of the global iron and steel sector on emission reductions would lead to a reduction of 38 % to 53 % by 2050 from 2019 levels (3.4 GtCO2 to 1.6–2.1 GtCO2), or compared to a 32 % to 43 % reduction in a baseline scenario in 2050.Steel companies are also lagging in setting clear emission reduction plans to achieve their targets. We found that 14 out of the 30 steel producers with targets did not provide an emission reduction plan. The most popular measures amongst the 16 companies that identified at least one measure to achieve their target in their emission reduction plans were hydrogen-based DRI (n = 14), enhanced use of renewable electricity (n = 13) and Carbon Capture Utilisation and Storage (CCU/S) for blast furnaces (n = 9). While it is encouraging that the steel companies have started acting toward long-term deep decarbonisation, our findings suggest that there is a long way ahead and the action needs to be accelerated considerably.

Techno-economic impact of electricity price mechanism and demand response on residential rooftop photovoltaic integration

Rooftop distributed photovoltaics show great potential for deep decarbonisation in the residential sector. However, the intermittency and undispatchability of photovoltaic output are urgent problems in the large-scale deployment of rooftop distributed photovoltaics. This study develops a techno-economic evaluation framework for rooftop distributed photovoltaics by comprehensively considering and exploring the uncertain effects of electricity price mechanisms, battery energy storage, demand response for residential flexible loads, and residential electricity demand difference to increase self-consumption and the economic benefits of rooftop distributed photovoltaics. The results show that electricity price mechanisms are the main factors affecting the economic benefits of rooftop distributed photovoltaics, which are more economical under time-of-use prices than under flat prices. Installing battery energy storage and participating in demand response for residential flexible loads can significantly improve self-consumption and self-sufficiency rates, and bring more electricity bill savings and less feed-in revenue. Specifically, the relative economic benefits of installing battery energy storage and demand response were greater at lower feed-in tariffs. Moreover, the greater the ratio of annual residential electricity demand to photovoltaic power generation, the greater the self-consumption rate. Further, only when battery energy storage is installed, the smaller the ratio, the greater the self-sufficiency rate. Consequently, the energy sector can design more efficient time-of-use pricing mechanisms and adopt lower feed-in tariffs to encourage residential consumers to deploy both rooftop distributed photovoltaics and battery energy storage and to participate in demand response.

Performance enhancement of hybrid absorption-compression heat pump via internal heat recovery

Heat pumps, especially the air-source systems, have been regarded as promising technology for deep decarbonization of thermal energy, but its industrial application is strongly constrained by its small temperature lift. Three-stage hybrid absorption-compression heat pump cycles have been proven as an effective way to achieve large temperature lift, but suffer from performance degradation under high temperature lift. In this study, different internal heat recovery configurations for the three-stage hybrid cycle are investigated for performance enhancement under high temperature lift. Results show that the three-stage hybrid cycles with internal heat recovery can reach output temperature of 100–190 °C with COP of 1.26–2.23, under the ambient temperature of 30 °C. By effectively recovering the condensation heat of absorption sub-cycle, the maximum output temperature and maximum temperature lift of three-stage hybrid cycles are increased by 10 °C and 20 °C, respectively, and the COP under 100 °C temperature lift is improved by 13 %. More importantly, sub-zero operation of the three-stage hybrid cycle is enabled by the internal heat recovery. The comprehensive enhancement of three-stage hybrid cycle with internal heat recovery shows the great potential in promoting the industrial application of air-source heat pump.

Differential effects of climate change on average and peak demand for heating and cooling across the contiguous USA

While most electricity systems are designed to handle peak demand during summer months, long-term energy pathways consistent with deep decarbonization generally electrify building heating, thus increasing electricity demand during winter. A key question is how climate variability and change will affect peak heating and cooling demand in an electrified future. We conduct a spatially explicit analysis of trends in temperature-based proxies of electricity demand over the past 70 years. Average annual demand for heating (cooling) decreases (increases) over most of the contiguous US. However, while climate change drives robust increases in peak cooling demand, trends in peak heating demand are generally smaller and less robust. Because the distribution of temperature exhibits a long left tail, severe cold snaps dominate the extremes of thermal demand. As building heating electrifies, system operators must account for these events to ensure reliability.

Exploring decarbonization pathways for USA passenger and freight mobility

Passenger and freight travel account for 28% of U.S. greenhouse gas (GHG) emissions today. We explore pathways to reduce transportation emissions using NREL’s TEMPO model under bounding assumptions on future travel behavior, technology advancement, and policies. Results show diverse routes to 80% or more well-to-wheel GHG reductions by 2050. Rapid adoption of zero-emission vehicles coupled with a clean electric grid is essential for deep decarbonization; in the median scenario, zero-emission vehicle sales reach 89% for passenger light-duty and 69% for freight trucks by 2030 and 100% sales for both by 2040. Up to 3,000 terawatt-hours of electricity could be needed in 2050 to power plug-in electric vehicles. Increased sustainable biofuel usage is also essential for decarbonizing aviation (10–42 billion gallons needed in 2050) and to support legacy vehicles during the transition. Managing travel demand growth can ease this transition by reducing the need for clean electricity and sustainable fuels.

Author Correction: China’s bulk material loops can be closed but deep decarbonization requires demand reduction

Author Correction: China’s bulk material loops can be closed but deep decarbonization requires demand reduction

Impact of Glasgow Climate Pact and Updated Nationally Determined Contribution on Mercury Mitigation Abiding by the Minamata Convention in India.

India is one of the largest emitters of atmospheric anthropogenic mercury (Hg) and the third-largest emitter of greenhouse gases in the world. In the past decade, India has been committed to the Minamata Convention (2017) in addition to the Paris Climate Change Agreement (2015) and the Glasgow Pact (2021). More than 70% to 80% of India's mercury and carbon dioxide emissions occur because of anthropogenic activities from coal usage. This study explores nine policy scenarios, the nationally determined contribution (NDC) scenario, and two deep decarbonization pathways (DDP) with and without mercury control technologies in the energy and carbon-intensive sectors using a bottom-up, techno-economic model, AIM/Enduse India. It is estimated that NDC scenarios reduce mercury emissions by 4%-10% by 2070; while coal intensive (DDP-CCS) pathways and focus on renewables (DDP-R) reduce emissions by 10%-54% and 15%-59%, respectively. Increase in the renewables share (power sector) can result in a significant reduction in the costs of additional pollution-abating technologies in the DDP-R scenario when compared with the coal intensive DDP-CCS scenario. However, the industry sector, especially iron and steel and metal production, will require stringent policies to encourage installation of pollution-abating technologies to mitigate mercury emissions under all the scenarios.

Progress in Photochemical and Electrochemical C-N Bond Formation for Urea Synthesis.

ConspectusHere, we discuss recent advances and pressing challenges in achieving sustainable urea synthesis. Urea stands out as the most prevalent nitrogen-based fertilizer used across the globe, making up over 50% of all manufactured fertilizers. Historically, the Bosch-Meiser process has been the go-to chemical manufacturing method for urea production. This procedure, characterized by its high-temperature and high-pressure conditions, reacts ammonia with carbon dioxide to form ammonium carbamate. Subsequently, this ammonium carbamate undergoes dehydration, facilitated by heat, producing solid urea. A concerning aspect of this method is its dependency on fossil fuels, as nearly all the process heat comes from nonrenewable sources. Consequently, the Bosch-Meiser process leaves behind a considerable carbon footprint. Current estimates predict that unchecked, carbon emissions from urea production alone might skyrocket, reaching a staggering 286 MtCO2,eq/yr by 2050. Such projections paint a clear picture regarding the necessity for more eco-friendly, sustainable urea production methods. Recently, the scientific community has shown growing interest in forming C-N bonds using alternative methods. Shifting toward photochemical or electrochemical processes, as opposed to traditional thermal-based processes, promises the potential for complete electrification of urea synthesis. This shift toward process electrification is not just an incremental change; it represents a groundbreaking advancement, the first of many steps, toward achieving deep decarbonization in the chemical manufacturing sector. Since the turn of 2020, there has been a surge in research focusing on photochemical and electrochemical urea synthesis. These methods capitalize on co-reduction of carbon dioxide with nitrogenous reactants like NOx and N2. Despite the progress, there are significant challenges that hinder these processes from reaching their full potential. In this comprehensive review, we shed light on the advances made in electrified C-N bond formation. More importantly, we focus on the invaluable insights gathered over the years, especially concerning catalytic reaction mechanisms. We have dedicated a section to underline key focal areas for up-and-coming research, emphasizing catalyst, electrolyte, and reactor design. It is undeniable that catalyst design remains at the heart of the matter, as managing the co-reduction of two distinct reactants (CO2 and nitrogenous species) is complex. This process results in a myriad of intermediates, which must be adeptly managed to both maintain catalyst activity and avoid catalyst deactivation. Moreover, the electrolytes play a pivotal role, essentially dictating the creation of optimal microenvironments that drive reaction selectivity. Finally, reactor engineering stands out as crucial to ensure optimal mass transport for all involved reactants and subsequent products. We touch upon the broader environmental ramifications of urea production and bring to light potential obstacles for alternative synthesis routes. A notable mention is the urgency of accelerating the uptake and large-scale implementation of renewable energy sources.

Transformative disruptiveness or transition? Revealing digitalization and deep decarbonization pathways in the Italian smart electricity meter roll-out

Energy infrastructure digitalization is proposed as key for a just deep decarbonization. However, an integrative literature review reveals the lack of a more profound exploration on normative considerations tied to world perspectives and values, as levers of a lasting transformation in the context of socio-technical transitions. Thus, the paper employs qualitative research methods to investigate how energy infrastructure digitalization contributes to transformative disruption, along which deliberated deep decarbonization pathways, using Italy's smart electricity meter roll-out as a case study. While the roll-out represents a substantial effort in introducing digital devices into the residential sector, concerns persist about its effectiveness, also due to potential societal disruptions. Through in-depth interviews with experts (N = 17) and citizens (N = 23) and content-oriented analysis, the research unveils evolving pathways between ever more disengaged two grand narratives and value systems: energy as a commodity and energy as a common good. The first opts for technological progress, changing consumer behavior, to address energy poverty and social justice, building on market-based instruments and new business models. Conversely, the second harbors skepticism towards technological fixes, emphasizes energy sufficiency and questions energy opulence, linked to environmental justice considerations, and calling for corrective principles by regulatory means. Finally, the paper presents a conceptual framework to re-shape deep deliberative transformation, highlighting the importance of re-connecting to defined values, re-generating actionable knowledge, restructuring institutions in societal experimentation, and recognizing underlying processes such as justice claims. It argues for further research into discourse coalitions capitalizing on defined narratives, to better understand their impact on policy-making processes.

Pro-environmental behavior in a common-resource dilemma: The role of beliefs

Despite the overwhelming scientific evidence about the anthropogenic nature of climate change, a vocal minority continues to spread skepticism. This inhibits pro-environmental action and fosters a false perception of social reality, leading people to underestimate the pro-environmental intentions and actions of others required to facilitate rapid and deep decarbonization. Previous efforts to address these beliefs through environmental interventions have yielded inconsistent outcomes. In two studies, we jointly examine the role of first-order climate change beliefs and beliefs about others' behavior (Study 1) as well as second-order climate change beliefs (Study 2) in pro-environmental decision-making in controlled laboratory settings. The studies involve a common-resource dilemma – in which a negative environmental externality is triggered depending on the group's collective decision-making. Our findings show that while first-order climate change beliefs weakly predict pro-environmental behavior, second-order climate change beliefs do not correlate with participants' choices when accounting for first-order climate change beliefs. However, beliefs about others' behavior strongly predict people's choices. We discuss the results in terms of the role different types of beliefs play in environmental decision-making.

Perspectives for the green hydrogen energy-based economy

Hydrogen as an energy vector offers enormous potential compared to other renewable energy routes in the deep decarbonization of industrial sectors. However, globally, developments across the entire hydrogen value chain are still emerging. The specific facets of hydrogen energy, like production, storage, transportation, applications, and policies, are individually evaluated. Nevertheless, a comprehensive viewpoint on the prospects and challenges across the entire hydrogen value chain is essential, and such holistic information is rare. This study addresses the knowledge gap by mapping the potential of the hydrogen economy from an all-rounded perspective. In addition to the advancements in materials, wastewater recycling and the use of microbes or enzymes in electrolysis, offer potential alternative routes to support a hydrogen-based economy. Significant technological advancements are warranted for safe hydrogen transportation and storage. A vast network of refueling stations is essential for hydrogen fuel penetration in the transportation sector, and conducive governing policies are needed to decarbonize heavy industries. Lastly, government incentives are crucial to promote the socioeconomic acceptance of the transition from fossil fuels to green hydrogen.

Role of climate finance beyond renewables: hard-to-abate sectors

This paper investigates the role of climate finance for hard-to-abate sectors in developing countries. It focuses on international transport, heavy industries and greenhouse gas removal technologies. The research is conducted in two parts: firstly, qualitative documentary-based secondary analysis through evidence reviews of the academic literature, landscape reviews of existing Funds in multilateral organisations, and legal reviews of OECD DAC Codes were undertaken to determine the degree of academic literature and practitioner activities on climate finance for hard-to-abate sectors, and the aid-eligibility of these groups of sectors; secondly, qualitative primary research is undertaken using the Delphi technique to identify the applicability of pull mechanisms previously undertaken for vaccine development to industrial decarbonisation in developing countries, analysing a case study of the applicability of an Advance Market Commitment (AMC) for cement decarbonisation in India. The outputs from the first part of the analysis produced a theoretical and conceptual framework for mapping the impacts of decarbonising hard-to-abate sectors against the Sustainable Development Goals and three common goals of climate finance-badged Official Development Assistance (ODA): economic development, poverty reduction and climate action. The paper finds that industrial decarbonisation is ODA-eligible, decarbonising international transport is only ODA-eligible in very specific applications in relation to poorer local communities surrounding airports and ports, and the theory of change for the ODA-eligibility of greenhouse gas removals is weak. The outputs from the second part of the analysis found that an AMC’s design needs to act as a large enough incentive (marked by the size of the funding committed to the AMC) and remain credible (through credible funding sources and transparent and trustworthy relationships between the actors involved), which are influenced by the type of actors involved, as well as their relationships, and that the process design needs to be tailored to the specific context of the industrial sub-sector and country. The paper argues for a greater role of pull mechanisms, such as AMCs, to address deep decarbonisation challenges in heavy industries in developing countries, and that (ODA-badged) climate finance can help to facilitate this.

Equity implications of net-zero emissions: A multi-model analysis of energy expenditures across income classes under economy-wide deep decarbonization policies

With companies, states, and countries targeting net-zero emissions around midcentury, there are questions about how these targets alter household welfare and finances, including distributional effects across income groups. This paper examines the distributional dimensions of technology transitions and net-zero policies with a focus on welfare impacts across household incomes. The analysis uses a model intercomparison with a range of energy-economy models using harmonized policy scenarios reaching economy-wide, net-zero CO2 emissions across the United States in 2050. We employ a novel linking approach that connects output from detailed energy system models with survey microdata on energy expenditures across income classes to provide distributional analysis of net-zero policies. Although there are differences in model structure and input assumptions, we find broad agreement in qualitative trends in policy incidence and energy burdens across income groups. Models generally agree that direct energy expenditures for many households will likely decline over time with reference and net-zero policies. However, there is variation in the extent of changes relative to current levels, energy burdens relative to reference levels, and electricity expenditures. Policy design, primarily how climate policy revenues are used, has first-order impacts on distributional outcomes. Net-zero policy costs, in both absolute and relative terms, are unevenly distributed across households, and relative increases in energy expenditures are higher for lowest-income households. However, we also find that recycled revenues from climate policies have countervailing effects when rebated on a per-capita basis, offsetting higher energy burdens and potentially even leading to net progressive outcomes. Model results also show carbon Laffer curves, where revenues from net-zero policies increase but then decline with higher stringencies, which can diminish the progressive effects of climate policies. We also illustrate how using annual income deciles for distributional analysis instead of expenditure deciles can overstate the progressivity of emissions policies by overweighting revenue impacts on the lowest-income deciles.

Model-based economic analysis of off-grid wind/hydrogen systems

Hydrogen has emerged in the context of large-scale renewable uptake and deep decarbonization. However, the high cost of splitting water into hydrogen using renewable energy hinders the development of green hydrogen. Here, we provide a cost analysis of hydrogen from off-grid wind. It is found that the current cost evaluation can be improved by examining the operational details of electrolysis. Instead of using low-resolution wind-speed data and linear electrolysis models, we generate 5-min resolution wind data and utilize detailed electrolysis models that can describe the safe working range, startup time, and efficiency variation. Economic assessments are performed over 112 locations in seven countries to demonstrate the influence of operational models. It is shown that over-simplified models lead to less reliable results and the relative error can be 63.65% at most. Further studies have shown the global picture of producing green hydrogen. Based on the improved model, we find that the levelized cost of hydrogen ranges from 1.66$/kg to 13.61$/kg. The wind-based hydrogen is cost-competitive in areas with abundant resources and lower investment cost, such as China and Denmark. However, it is still costly in most of the studied cases. An optimal sizing strategy or involving a battery as electricity storage can further reduce the hydrogen cost, the effectiveness of which is location-specific. The sizing strategies of electrolyzers differ by country and rely on the specific wind resource. In contrast, the sizing of batteries presents similar trends. Smaller batteries are preferred in almost all the investigated cases.

Deep decarbonization and U.S. biofuels production: a coordinated analysis with a detailed structural model and an integrated multisectoral model

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

Modelling large-scale hydrogen uptake in the Mexican refinery and power sectors

Due to the emissions reduction commitments that Mexico compromised in the Paris Agreement, several clean fuel and renewable energy technologies need to penetrate the market to accomplish the environmental goals. Therefore, there is a need to develop achievable and realistic policies for such technologies to ease the decision-making on national energy strategies. Several countries are starting to develop large-scale green hydrogen production projects to reduce the carbon footprint of the multiple sectors within the country. The conversion sectors, namely power and refinery, are fundamental sectors to decarbonise due to their energy supply role. Nowadays, the highest energy consumables of the country are hydrocarbons (more than 90%) causing a particular challenge for deep decarbonisation. The purpose of this study is to use a multi-regional energy system model of Mexico to analyse a decarbonisation scenario in line with the latest National Energy System Development Program. Results show that if the country wants to succeed in reducing 22% of its GHG emissions and 51% of its short-lived climate pollutants emissions, green hydrogen could play a role in power generation in regions with higher energy demand growth rates. These results show, regarding the power sector, that H2 could represent 13.8 GW or 5.1% of the total installed capacity by 2050, while for the refinery sector H2 could reach a capacity of 157 PJ/y, which is around 31.8% of the total share, and it is mainly driven by the increasing demands of the transport, industry, and power sectors. Nevertheless, as oil would still represent the largest energy commodity, CCS technologies would have to be deployed for new and retrofitted refinery facilities.

Battery and Hydrogen Energy Storage Control in a Smart Energy Network with Flexible Energy Demand Using Deep Reinforcement Learning

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind, which are key for the deep decarbonisation of energy production. However, given the variability of the renewables as well as the energy demand, it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper, we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices, renewable energy production, and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this, we propose a multi-agent deep deterministic policy gradient approach, which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69%, improve cost savings by 23.5%, and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network.