Decarbonization Of Energy System Research Articles

A comprehensive multi-node multi-vector multi-sector modelling framework to investigate integrated energy systems and assess decarbonisation needs

The pathway towards decarbonised energy systems involves massive changes in adopted energy vectors, installed technologies, networks roles, and interaction capabilities. To investigate the combination of these effects, this work presents the OMNI-ES modelling framework (Optimisation Model for Network-Integrated Energy Systems), which offers a comprehensive approach to analyse multi-node, multi-vector, multi-sector energy systems. It adopts a detailed temporal and spatial resolution and implements multiple conversion options between energy vectors (electricity, hydrogen, natural gas, biomethane, biofuels, e-fuels, …). The formulation solves the energy vector balances at each time step, taking into account sources, sinks, conversion processes, and storage systems. CO2 flows are also tracked, allowing the introduction of CO2 emission constraints that account for all contributions (fossil and biogenic, direct and indirect) and mitigation measures (capture, re-use, sequestration). In the article, OMNI-ES is applied to investigate an Italian scenario for 2050, adopting a regional (NUTS-2) resolution. The model output yields the cost-optimal energy system configuration that is capable to support the demand with net-zero CO2 emissions. Results show that the need for CO2 balance closure calls in several technologies, including massive renewable power generation (up to 20 times today’s capacities), storage systems (batteries, hydrogen, pumped hydro), biogenic sources (residual biomass and biomethane), and CO2 capture (both on fossil and biogenic sources). Networks emerge as critical elements, as the need to transport energy vectors saturates the expected capacities of grid infrastructures, especially in the case of hydrogen.

System-driven design of flexible nuclear power plant configurations with thermal energy storage

Nuclear power plants are expected to make an important contribution to the decarbonisation of electricity supply alongside variable renewable generation, especially if their operational flexibility is enhanced by coupling them with thermal energy storage. This paper presents a system modelling approach to identifying configurations of flexible nuclear plants that minimise the investment and operation costs in a decarbonised energy system, effectively proposing a system-driven design of flexible nuclear technology. Case studies presented in the paper explore the impact of system features on plant configuration choices. The results suggest that cost-efficient flexible nuclear configurations should adapt to the system they are located in. In the main low-carbon scenarios and assuming standard-size nuclear power plants (1,610MWel), the lowest-cost system configuration included around 500MWel of additional secondary generation capacity coupled to the nuclear power plants, with 4.5GWhth of thermal storage capacity and a discharging duration of 2.2 h. Net system benefits per unit of flexible nuclear generation for the main scenarios were quantified at £29-33 m/yr for a wind-dominated system and £19-20 m/yr for a solar-dominated system.

Transit cost effectively to a secure decarbonized energy system

Transit cost effectively to a secure decarbonized energy system

Building back better: Granular energy technologies in green recovery funding programs

Building back better: Granular energy technologies in green recovery funding programs

러시아와 EU 간의 에너지 협력과 갈등에 대한 소고

Energy policies are planned in consideration of a number of factors that meet various social needs. Typical constituent factors are energy security, environmental sustainability, and energy equity. Energy cooperation between Russia and the EU is also a key issue. The Corona pandemic and the Russia-Ukraine war are having a huge impact on both Russia and the EU. War, not only Russia, but also the EU thinks that the balance of peace can be upset rather than maintaining the geopolitical balance. However, energy is required for industrial development and maintenance, and a significant part of the required energy is dependent on Russia. To avoid these risks, the EU is actively pursuing the establishment of a decarbonized energy system and the energy transition. Russia is also responding by diversifying its exports. Importantly, both Russia and the EU are at an inflection point for energy policy changes. Therefore, it can be said that a clear analysis of the changes in energy policies in Russia and the EU is necessary because the policy changes of both camps have a great impact on the global economy.

Multiobjective optimization and analysis of low-temperature district heating systems coupled with distributed heat pumps

Developing district heating can help decarbonizing energy systems. Modern concepts of district heating revolve around low operational temperature to reduce heat losses and enhance the efficiency of the supply plants with technologies such as cogeneration, condensing boilers or geothermal heat pumps. However, temperature reduction in district heating networks may be limited by technical requirements on the building side. Booster heat pumps on the end-users’ side can be a solution to bridge the network-building temperature gap. However, the current state-of-the-art on such systems does not provide ways for optimizing in real-time the operating temperature, which is needed to fully exploit its benefits, and proposed control strategies focus solely on reducing costs without considering environmental aspects. This article proposes a new multi-objective optimization framework to determine the real-time supply temperature for low-temperature district heating with booster heat pumps. The optimization approach uses rolling horizon and both operating costs and GHG emissions are minimized simultaneously. The model on which the optimization relies includes efficiency curves for equipment and water transport delays in the network. Many scenarios are simulated to understand how key factors affect the optimal supply temperature, such as the fuel type (i.e., wood chips or natural gas), price of electricity, emission factor of electricity, and relative weight of costs and emissions in the objective function. Results show that the proposed framework can indeed reduce costs and emissions, but the viability of the system depends strongly on the above-mentioned factors. In the case-study with typical electricity costs and emissions for Quebec (Canada), the average operating temperature could be reduced to 42 and 66 °C depending on the type of boilers in the system. While emissions were always smaller than with a conventional network, operating costs could be higher (with wood chip boiler) or lower (with natural gas boiler). This work can contribute to the deployment of efficient low temperature networks.

Physics-Constrained Vulnerability Assessment of Deep Reinforcement Learning-Based SCOPF

The decarbonization of energy systems has posed unprecedented challenges in system complexity and operational uncertainty that render it imperative to exploit cutting-edge artificial intelligence (AI) technologies to realize real-time, autonomous power system operation and control. In particular, deep reinforcement learning (DRL)-based approaches in power systems are extensively studied and implemented in several trials worldwide. Nevertheless, the vulnerability of DRL brings new security threats to power systems that have not been well identified and investigated in the literature. To this end, this paper proposes a physics-constrained vulnerability assessment methodological framework for the DRL-based power system operation and control, with a special focus on the problem of security-constrained optimal power flow (SCOPF). In particular, we develop a novel adversarial example generation method, defined as a false data injection attack against the DRL-based SCOPF (FDIAI), to realize a targeted adversarial attack considering the nonlinear physical constraints in power systems via two main stages of constructor function design and unconstrained optimization problem transformation. In this way, the proposed FDIAI can significantly influence the decision-making procedure of DRL while successfully evading the bad data detection mechanism in power systems. Case studies are conducted to explore the stealthiness and effectiveness of FDIAI and then show its severe impacts on system operation and control on the winners' models of the Learning to Run a Power Network (L2RPN) competitions, including L2RPN IJCNN 2019 (IJCNN), L2RPN WCCI 2020 (WCCI), and L2RPN NeurIPS 2020 (NeurIPS).

Energy communities in Europe: An overview of issues and regulatory and economic solutions

As part of the ongoing transition towards decarbonisation and decentralisation of energy systems, collective self-consumption and Energy Communities (ECs) have become increasingly relevant in Europe as several concepts have emerged, framed by two EU directives. However, their potential to become a standard organisational model is still uncertain, as the structure of ECs may jeopardise existing market principles and increase system costs. This article provides an overview of the state of the art of national transpositions of ECs by reviewing recent studies on European implementation and energy-sharing strategies, particularly regarding peer-topeer trading. These approaches raise fundamental questions about the financial viability and development of the electricity system and are compared to the latest experimental findings. Hence significant barriers are pointed out and key implications for self-consumption and EC policies are given to elaborate adapted national conditions for ECs and to ensure adequate tariff reforms. This paper identifies the need for further investigation on national approaches to enable the efficient and sustainable development of ECs. Moreover, in the interest of the consumer's well-being and the well-functioning of the market, a multidisciplinary approach should be integrated that incorporates adapted regulations and appropriate framework conditions for ECs according to the prevailing economic and social context.

Improving Ecological Efficiency of Gas Turbine Power System by Combusting Hydrogen and Hydrogen-Natural Gas Mixtures

Currently, the issue of creating decarbonized energy systems in various spheres of life is acute. Therefore, for gas turbine power systems including hybrid power plants with fuel cells, it is relevant to transfer the existing engines to pure hydrogen or mixtures of hydrogen with natural gas. However, significant problems arise associated with the possibility of the appearance of flashback zones and acoustic instability of combustion, an increase in the temperature of the walls of the flame tubes, and an increase in the emission of nitrogen oxides, in some cases. This work is devoted to improving the efficiency of gas turbine power systems by combusting pure hydrogen and mixtures of natural gas with hydrogen. The organization of working processes in the premixed combustion chamber and the combustion chamber with a sequential injection of ecological and energy steam for the “Aquarius” type power plant is considered. The conducted studies of the basic aerodynamic and energy parameters of a gas turbine combustor working on hydrogen-containing gases are based on solving the equations of conservation and transfer in a multicomponent reacting system. A four-stage chemical scheme for the burning of a mixture of natural gas and hydrogen was used, which allows for the rational parameters of environmentally friendly fuel burning devices to be calculated. The premixed combustion chamber can only be recommended for operations on mixtures of natural gas with hydrogen, with a hydrogen content not exceeding 20% (by volume). An increase in the content of hydrogen leads to the appearance of flashback zones and fuel combustion inside the channels of the swirlers. For the combustion chamber of the combined-cycle power plant “Vodoley”, when operating on pure hydrogen, the formation of flame flashback zones does not occur.

What political institutions support Bus Rapid Transit? Evidence from a survival analysis of 92 cities in low- and middle-income countries

Decarbonization of urban energy systems will require significant adoption of mass transit infrastructure over the next decades, especially in cities in low- and middle-income countries that are not yet locked into car-centric transportation systems. The political contexts which could enable or even accelerate widespread transit development, however, remain ill understood. To advance our understanding of the relationship between political institutions and low-carbon infrastructure outcomes, this paper leverages a novel dataset of 138 low- and middle-income countries, of which 21 countries adopted 92 Bus Rapid Transit (BRT) systems in cities between 1970 and 2017. It examines whether political decentralization and democratic governance, alongside international non-profit organizations, are associated with greater chances of BRT adoption. The results from three sets of survival regressions suggest they are: politically decentralized countries are associated with significantly higher adoption prospects when they are governed democratically. Moreover, countries that host international non-profit organizations advocating for BRT are associated with domestic demonstration projects. These results suggest that political institutions critically condition the adoption of low-carbon infrastructure systems. They caution that demands to empower subnational governments with enhanced regulatory authority ought to be qualified by political regime type. In authoritarian political contexts, decentralization appears to reduce the likelihood of mass transit development and, by extension, the chances of avoiding carbon lock-in.

Local integrated energy system operational optimization considering multi‐type uncertainties: A reinforcement learning approach based on improved TD3 algorithm

AbstractLocal integrated energy system (IES), usually a multi‐building heating and cooling system incorporating cogeneration systems and distributed energy resources (DERs), is becoming an efficient energy infrastructure for energy system decarbonization. However, for small and medium‐scale local IESs, the fluctuation of user loads seriously influences energy system operation, and the increasing DER penetration also enlarges the influence. Given the necessity of exploring an efficient way to handle the uncertainties in local IES, this paper proposes a reinforcement learning (RL) approach based on the improved TD3 algorithm. The mathematical model of local IES is first established considering supply‐ and load‐side flexible resources. The local IES dispatch problem is formulated as a Markov decision process (MDP), in which multi‐type uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved twin delayed deep deterministic policy gradient (TD3) algorithm is proposed with a dynamic balance mechanism of exploration noise. Based on a local IES testbed in the Nantong Central Innovation District, China, a comparison analysis is conducted to verify the promoting effect of flexible resources on the operation economy and renewable energy consumption. The system operating cost reduces by 18.46%, and surplus renewable energy can all be accommodated considering flexible resources. The dispatch policies obtained by the deep deterministic policy gradient (DDPG), the improved TD3, the original TD3 and traditional optimization algorithms are also compared. The results reveal that the convergence stability and solving accuracy of the improved TD3 outperform the other two RL algorithms. Specifically, the system operating cost of the improved TD3 reduces by 2.76% compared with the DDPG, and the energy supply imbalance decreases by around 88%. Meanwhile, the improved TD3 exhibits better operation economy and adaptability to the uncertain environment than the deterministic optimization and intraday rolling algorithms.

The role of hydrogen for deep decarbonization of energy systems: A Chilean case study

In this paper we implement a long-term multi-sectoral energy planning model to evaluate the role of green hydrogen in the energy mix of Chile, a country with a high renewable potential, under stringent emission reduction objectives in 2050. Our results show that green hydrogen is a cost-effective and environmentally friendly route especially for hard-to-abate sectors, such as interprovincial and freight transport. They also suggest a strong synergy of hydrogen with electricity generation from renewable sources. Our numerical simulations show that Chile should (i) start immediately to develop hydrogen production through electrolyzers all along the country, (ii) keep investing in wind and solar generation capacities ensuring a low cost hydrogen production and reinforce the power transmission grid to allow nodal hydrogen production, (iii) foster the use of electric mobility for cars and local buses and of hydrogen for long-haul trucks and interprovincial buses and, (iv) develop seasonal hydrogen storage and hydrogen cells to be exploited for electricity supply, especially for the most stringent emission reduction objectives.

Simulating flexibility, variability and decentralisation with an integrated energy system model for Great Britain

Energy system models allow the development and assessment of ambitious transition pathways towards a sustainable energy system. However, current models lack adequate spatial and temporal resolution to capture the implications of a shift to decentralised energy supply and storage across multiple local energy vectors to meet spatially variable energy demand. There is also a lack of representation of interactions with the transport sector as well as national and local energy system operation. Here, we bridge these gaps with a high-resolution system-of-systems modelling framework which is applied to Great Britain to simulate differences between the performance of decarbonised energy systems in 2050 through two distinct strategies, an electric strategy and a multi-vector strategy prioritising a mix of fuels, including hydrogen. Within these strategies, we simulated the impacts of decentralised operation of the energy system given the variability of wind and across flexibility options including demand side management, battery storage and vehicle to grid services. Decentralised operation was shown to improve operational flexibility and maximise utilisation of renewables, whose electricity supplies can be cost-effectively converted to hydrogen or stored in batteries to meet peak electricity demands, therefore reducing carbon-intensive generation and the requirement for investment in expanding the electricity transmission network capacity.

The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum

Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite – Agent-based Simulation (EMIS-AS) model. We apply clean energy targets of 45%–100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the “right” market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non-incremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as capacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve-based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in capacity and operating reserve markets can impact deployment and operations, even for nonrestricted technologies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities.

Process Simulations of High-Purity and Renewable Clean H2 Production by Sorption Enhanced Steam Reforming of Biogas

Renewable clean H2 has a very promising potential for the decarbonization of energy systems. Sorption enhanced steam reforming (SESR) is a novel process that combines the steam reforming reaction and the simultaneous CO2 removal by a solid sorbent, such as CaO, which significantly enhances hydrogen generation, enabling high-purity H2 production. The CO2 sorption reaction (carbonation) is exothermic, but the sorbent regeneration by calcination is highly endothermic, which requires extra energy. Biogas is one of the available carbon-neutral renewable H2 production sources. It can be especially relevant for the energy integration of the SESR process since, due to the exothermic sorption reaction, the CO2 contained in the biogas provides extra heat to the system, which can help to balance the energy requirements of the process. This work studies different process configurations for the energy integration of the SESR process of biogas for high-purity renewable H2 production: (1) SESR with sorbent regeneration using a portion of the produced H2 (SESR+REG_H2), (2) SESR with sorbent regeneration using biogas (SESR+REG_BG), and (3) SESR with sorbent regeneration using biogas and adding a pressure swing adsorption (PSA) unit for hydrogen purification (SESR+REG_BG+PSA). When using biogas as fuel (Cases 2 and 3), these configurations were studied using air and oxy-fuel combustion atmospheres in the sorbent regeneration step, resulting in five case studies. A thermodynamic approach for process modeling can provide the optimal process operating conditions and configurations that maximize the energy efficiency of the process, which are the basis for subsequent optimization of the process at the practical level needed to scale up this technology. For this purpose, process simulations were performed using a steady-state plant model developed in Aspen Plus, incorporating a complex heat exchanger network (HEN) to optimize heat integration. A comprehensive parametric study assessed the effects of biogas composition, temperature, pressure, and steam to methane (S/CH4) ratio on the process performance represented by the selected key performance indicators, i.e., H2 purity, H2 yield, CH4 conversion, cold gas efficiency (CGE), net efficiency (NE), fuel consumption for the sorbent regeneration step, and CO2 capture efficiency. H2 with a purity of 98.5 vol % and a CGE of 75.7% with zero carbon emissions can be achieved. When adding a PSA unit, nearly 100% H2 purity and CO2 capture efficiency were achieved with a CGE of 77.3%. The use of oxy-fuel combustion during regeneration lowered the net efficiency of the process by 2.3% points (since it requires an air separation unit) but allowed the process to achieve negative carbon emissions.

Research on the Participation of Household Battery Energy Storage in the Electricity Peak Regulation Ancillary Service Market

Household battery energy storage (HBES) is expected to play an important role in the transition to decarbonized energy systems by enabling the further penetration of renewable energy technologies while assuring power grid stability. However, the hitherto high installation cost is a key barrier for further deployment of HBES. Therefore, in order to improve its economic feasibility, we will study how HBES participates in the electricity peak regulation ancillary service market (PRASM) in China, which can add new sources of income for HBES. When participating in PRASM, the market mechanism first needs to be understood, and the framework for participating in PRASM needs to be established. In this framework, HBES needs to be aggregated into a cluster by the aggregator to participate in PRASM. In this participation process, the aggregator first needs to determine the controllable capacity of HBES and analyze its uncertainty. After the upper limit of the controllable capacity is determined, the aggregator will be able to more accurately formulate the bidding strategy considering the reserve capacity and charging power allocation strategy to maximize the net income. In this paper, particle swarm optimization and chaos optimization are combined to solve this problem, and finally different scenarios are analyzed through example analysis. The results of the case analysis show that the bidding strategy considering the reserve capacity proposed in this paper can effectively reduce the output deviation value and has a relatively higher economy.

Meeting the challenges of large-scale carbon storage and hydrogen production

There is a pressing need to rapidly, and massively, scale up negative carbon strategies such as carbon capture and storage (CCS). At the same time, large-scale CCS can enable ramp-up of large-scale hydrogen production, a key component of decarbonized energy systems. We argue here that the safest, and most practical strategy for dramatically increasing CO2 storage in the subsurface is to focus on regions where there are multiple partially depleted oil and gas reservoirs. Many of these reservoirs have adequate storage capacity, are geologically and hydrodynamically well understood and are less prone to injection-induced seismicity than saline aquifers. Once a CO2 storage facility is up and running, it can be used to store CO2 from multiple sources. Integration of CCS with hydrogen production appears to be an economically viable strategy for dramatically reducing greenhouse gas emissions over the next decade, particularly in oil- and gas-producing countries where there are numerous depleted reservoirs that are potentially suitable for large-scale carbon storage.

The potential role for new nuclear in the U.S. power system: A view from electricity system modelers

There are diverse views on the role nuclear power might play in a decarbonized energy system, with some vocal viewpoints suggesting nuclear should either have no role or that nuclear should be a key pillar. Here we present a perspective on the role of nuclear power based on our collective experience in modeling nuclear power within the decarbonized U.S. power systems. We summarize the costs of current reactor development projects, and compare their stated costs with the costs that our models indicate is necessary in order to see deployment of new nuclear. We also discuss aspects of new nuclear deployment that are not included in our models but can influence decisions about whether to invest in new nuclear capacity.

The heterogeneous impact of particulate matter on solar performance in a megacity: The case of Seoul

Megacities play an essential role in accelerating the energy transition. Megacities need to decarbonize energy systems by taking transformative actions on photovoltaic (PV) plants. However, particulate matter 10 (PM10) accumulated on PVs may decrease the amount of photovoltaic power generation (PPG), preventing the further expansion of PV installation. This study identifies the heterogeneous effects of PM10 by using quantile regression and by including small-scale PVs to understand subgroup differences across the distribution of PPG. This differs from previous studies, which only addressed its homogeneous effects. The results show that the direction of PM10′s impact on PPG is consistent with previous studies and that the effect of PM10 is different for each quantile. It turned out that accumulated PM10 had a heterogeneous effect on the PPG quantile, and the absolute value of PM10 was the largest when the PPG quantile was low. This enables a richer explanation of the impact of PM10 on PPG. By identifying possible obstacles related to PM10, this research contributes to facilitating renewable-led municipal energy policy and proposes effective management to improve the reliability of solar energy systems.

Regional disparity of residential solar panel diffusion in Australia: The roles of socio-economic factors

In following a pathway towards decarbonisation of energy systems, Australia has successfully accelerated the adoption of solar technologies, with approximately three million small-scale distributed solar panels installed on rooftops of Australian homes. This study, in seeking to explore the effects of socio-economic variables on solar energy uptake, firstly explored the historical diffusions of household solar panels across 2668 Australian postcode areas, and classified the postal areas into three groups through K-means clustering according to their solar penetration status, namely the regions with low, medium, and high solar-level. Subsequently, this study carried out a hierarchical regression analysis investigating the significant socio-economic factors impacting solar adoption in each of the three region types. The results indicate that the three solar groups have different significant impacting factors. In addition, factors, such as detached house and multi-person household, positively contribute to the solar panel uptake, while factors like population density and middle-aged people provide negative contributes. The findings suggest new directions that could be taken for future policies and viable business models, and that specific measures for the varied socio-economic factors to be considered at different stages of solar energy development.