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This paper employs a four-dimensional coupled dynamic system and combines actual data from China to analyze the intrinsic connections and dynamic changes among new energy, economy, carbon emissions, and energy storage. The study reveals that new energy development faces an impossible triangle dilemma, wherein energy reliability, economy, and low carbon are difficult to achieve simultaneously. Although economic growth promotes new energy utilization, new energy development may inhibit economy at the initial stage. The development of new energy can effectively reduce carbon intensity. There exists a conflict between economic growth and carbon reduction, and a part of economic growth needs to be sacrificed to realize carbon emissions reduction. Further research indicates that market mechanism and government regulation are crucial for solving the energy trilemma, requiring coordinated efforts to achieve the optimal solution for energy, economy, and environmental goals. It is recommended to dynamically adjust the policy and market mechanism intensity based on the new energy industry's development stage. Increase government subsidies in the early stages and gradually transition to market dominance in the mid-term while improving market mechanism. In the field of energy storage, guide its participation in the electricity market to achieve scale development. [Display omitted] • 4D new energy coupled system are formed based on nonlinear system dynamics • New energy coupled system faces an impossible triangle dilemma • Market mechanism and government regulation are key to solve new energy trilemma • Energy storage can assist electricity market in achieving scale development [ABSTRACT FROM AUTHOR]

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age. • A decision-making optimization model of CCPs low-carbon technology is proposed. • A dynamic CET system with upper-lower layer interaction is introduced. • The proposed dynamic CET system results in 27% reduction in total costs. • The high-resolution data of coal power in Beijing-Tianjin-Hebei region are used. • The strategy to solve the conflict between cleanliness and flexibility is given. [ABSTRACT FROM AUTHOR]

This study assesses alternative energy technologies (i.e., PV and battery systems, electric heat pumps, hybrid gas heating with solar thermal energy) in terms of profitability and CO 2 emissions, for the case of two simulated typical households living in detached houses in Germany. Under the status-quo regulatory framework, the energy transition in the heating sector is fostered through grants for replacing old heating systems, whereas PV generation is fostered by feed-in tariffs and indirect subsidies for self-consumption. This study considers an alternative regulatory scenario with a more market-oriented approach, finding that a CO 2 -oriented reform of energy surcharges and taxes, as well as a reform of network charges, can support a more cost-efficient energy transition in the residential sector. The paper concludes with a discussion of the consistency, cost efficiency and effectiveness of past and current policies underpinning the energy transition in the residential sector. • Assessment of home energy systems across 2 households and 2 regulatory scenarios. • Consideration of PV, batteries, heat pumps and hybrid gas-solar heating systems. • Comparison of subsidies and flat energy tariffs vs. cost-reflective, dynamic tariffs. • Market-oriented approach incentivizes far more cost-efficient CO 2 emission reduction. • The German residential energy sector needs a consistent regulatory framework. [ABSTRACT FROM AUTHOR]

This paper aims at evaluating the best allocation of potential biomethane generation for the decarbonization of the transport system, presenting a case study in Italy. The country has some peculiar features, such as several operating biogas plants, additional potential feedstock for biogas/biomethane generation, a well-developed natural gas network and established relevant natural gas uses in different final sectors, including transport. Based on current estimates for sustainable biomethane potential by 2030, ranging from 2.3 to 7.6 billion cubic meters depending on the scenario, the analysis compares technologies for the generation, distribution and final use of biomethane. The results of the analysis confirm the potential interesting contribution of biomethane in decarbonizing the Italian transport system: a billion cubic meters of biomethane can lead to 2.33–4.37 Mt CO 2 e savings, depending on the feedstock mix and the application. On a national basis, annual climate emission savings in 2030 range from 10.0 to 26.7 Mt CO 2 e , depending on the scenario. Additional 3.1–8.1 Mt CO 2 of emissions can be avoided if the CO 2 captured during the biomethane upgrading can be stored or reused. The proposed methodology could be used to extend the analysis to other countries, and to the European context. • We analyze the climate emissions of biomethane for different supply chains. • We evaluate the effects of feedstock, distribution strategy and electricity mix. • The results confirm the important role of biomethane for transport decarbonization. • We discuss the best allocation in transport segments based on emissions savings. • We compare alternative biomethane scenarios for Italy in 2030. [ABSTRACT FROM AUTHOR]

[Display omitted] • A multi-energy operation simulation model with biomass energy flow balance is established. • An optimal planning strategy is developed to determine the portfolio of solar, biomass, etc. • A thermodynamics-based model is formulated to characterize the heat transfer in biogas production. • An ordinary least squares approach is applied to linearize the biogas production model. Recent years have witnessed an increasing proliferation of biomass technologies since biomass is recognized as a natural carbon-neutral fuel. Existing studies have paid extensive attention to the concept and modeling of zero-carbon energy systems or buildings. However, few studies have explored the multi-energy coupling method and techno-economic evaluation of biomass to achieve near-zero carbon systems. Therefore, we propose an optimal sizing framework for integrating waste-to-biogas in regional energy systems to achieve near-zero carbon emissions. To establish energy balance between supply and demand and regional self-sufficiency, biomass is added to the existing natural gas energy framework. Biomass produces biogas through anaerobic digestion reactions, and the biogas output can be constructed as an analytical function related to the anaerobic reaction environment temperature and reactant volume. Furthermore, this paper constructs a planning model with the goal of minimizing the investment and operating costs of biomass, solar and storage, respecting carbon emission constraints induced by local electricity and gas energy flows. The impact of carbon emission regulations on the investment of each component of the energy system is determined using a sensitivity analysis of carbon emissions. To solve the nonlinearity issues of the biogas production function, we propose a convexification method based on least squares fitting. Case studies based on realistic datasets demonstrate that, under the constraints of carbon emissions, the integration of biogas can significantly improve the supply adequacy of the energy system, reduce investment costs by up to 77.07%, and carbon emission reduction intensity is 5.11 kg CO 2 kg-1 Biogas, all while lowering the Levelized Cost of Energy (LCOE). In comparison to prior research, the model provided in this paper more accurately portrays the biogas production process, lowers the system investment cost and LCOE, and emphasizes the benefits of biomass. [ABSTRACT FROM AUTHOR]

The growing penetration of 5G base stations (5G BSs) is posing a severe challenge to efficient and sustainable operation of power distribution systems (PDS) due to their huge energy demand and massive quantity. To tackle this issue, this paper proposes a synergetic planning framework for renewable energy generation (REG) and 5G BS allocation to support decarbonizing development of future PDS. The problem is formulated as a multi-objective interval optimization (MOIO) model, wherein the 5G BS siting and sizing are co-optimized with the REG capacity allocation and their operating strategies to minimize the total economic cost and carbon emissions of the PDS. The potential flexibility benefits achievable from 5G BS operation (as responsive load demands to PDS) are explicitly considered in the proposed planning formulation by accounting for the effects of both transmit power control and on-site energy storage battery dispatch. Moreover, we adopt an interval approach to capture the uncertainties involved in the system (including REG output, market prices, carbon emissions factor, and power/communication load demands). In order to solve the proposed MOIO problem efficiently, a constrained interval multi-objective optimization evolutionary algorithm based on decomposition (CIMOEA/D) is developed by combining constraint handling, interval-based Tchebycheff aggregation function and interval ensemble comparison, which could achieve Pareto optimal solutions and an interval-formed Pareto front to the problem with all the uncertain information fully preserved in the decision-making process. The proposed model is demonstrated on a modified IEEE 33-bus test case, and the simulation results verified the effectiveness of the suggested approach. • Modeling of 5G BS operational flexibility considering both energy and communication characteristics. • Proposing a MOIO formulation to allow synergetic renewable and 5G BS integration. • Uncertainties are considered via the interval optimization approach. • A constrained interval multi-objective optimization evolutionary algorithm based on decomposition is developed. • Effectiveness of the proposed approach is verified via comparative analysis. [ABSTRACT FROM AUTHOR]

Highlights • Energy consumption in higher education institutions (HEIs) modelled. • For the first time, panel data used, with cross-sectional and time-series variations. • Over time and across HEIs, energy use increases as income and size increase. • Economies of scale as HEIs grow, but energy use still rising with sector growth. • Research universities more energy intensive. Abstract Among the various sustainability goals of higher education institutions (HEIs), reducing energy use and carbon emissions are particularly important. However, not much is known about energy demand from the higher education sector – especially since there is a lack of robust models of energy demand in this sector. This paper, the first to utilize a panel dataset and advanced panel econometric techniques in order to model energy use in higher education, investigates variations in energy use between HEIs (cross-sectional analysis), and also changes in energy use over time (temporal analysis), using the UK as a case study. We argue that panel dataset and methods are more useful for understanding growth (and reduction) in energy use within the HE sector than the methods used within previous cross-sectional studies. Results show that, over time and also across the sector, energy consumption in the HEIs increases with increases in income and floor space, but at a slower rate. As HEIs grow overall (in terms of income, floor space, student and staff number) over time, they become more 'energy efficient' (using less energy per unit of area, population or income), indicating economies of scale in the temporal dimension. Results also show that after controlling for income and size, research intensive HEIs consume more energy. We also find a small but statistically significant effect of energy prices on energy consumption, as might be expected. Simulation using the model parameters for an example scenario suggests that energy consumption will continue to increase unless there is a significant change in the policies driving income growth and spatial expansion in the HE sector in the UK. [ABSTRACT FROM AUTHOR]

Highlights • Review of decomposition studies on CO 2 emissions from electricity generation. • Lack of consensus on preferred decomposition identity. • Systematic analyses of differences and linkages across decomposition identities. • Implications of findings on decomposition identity selection. • Future extension of studies on drivers of CO 2 emissions from electricity generation. Abstract The quantification of drivers of CO 2 emissions from the electricity generation sector is an area that has attracted a great deal of attention among researchers and policy makers. Index decomposition analysis is a technique that has been widely used for this purpose. The increasing interest, however, has led to the usage of a variety of formulae in the decomposition. In this paper, a literature survey on the related publications is first compiled. Based on the survey, key features of studies and development trends are discussed. The decomposition formulae used are systematically analysed to elucidate the differences and relationships among them. An approach which quantifies the share of fossil fuels in the electricity mix and considers the generation efficiency by fuel type is recommended as it leads to the most comprehensive and meaningful decomposition results. Further extensions to the current practice to include factors such as transmission and distribution losses as well as carbon capture and storage are also discussed. The findings and recommendations provide insights into the analysis of CO 2 emissions from the electricity generation sector where major structural changes are expected to take place in future. [ABSTRACT FROM AUTHOR]

Consumption-based carbon emission accounting is able to clarify consumers’ responsibility for the carbon emissions from a power system. The responsible amount of carbon emissions for each consumer can be calculated based on the power consumption and the accordant carbon emission flow (CEF). Distribution of the CEF in the network may vary significantly under different transmission network configurations, resulting in different attributed carbon emission responsibilities of consumers. This paper describes how transmission expansion planning (TEP) and consumption-based carbon emission accounting affect each other. A novel TEP model considering the consumption-based carbon emission accounting is presented. A new index named CO 2 allocation equity coefficient (CAEC) is introduced to quantify the equity performance of the consumption-based carbon emission accounting system. As such, the requirement for different equity performances can be explicitly incorporated into the TEP model as a constraint to determine its effect on TEP. The proposed TEP model is tested on Garver’s 6-bus system and a modified IEEE 39-bus system. The results show that the methodology is able to obtain the transmission expansion planning, in general, more lines must be planned to achieve better equity performance, with more even consumption-based carbon emission, but leading to an overall increasing tendency in the annualized transmission investment cost. [ABSTRACT FROM AUTHOR]

Decarbonization of the electric grid has become an important world-wide priority and is actively happening in many ways by introducing innovations and new technologies from the generation sectors to the demand sectors. In particular, one promising pathway toward such net-zero carbon emissions is to utilize the demand-side flexibility with the increasing number of flexible loads in distribution networks. In this paper, we explore a load shifting strategy with the emerging concept of location marginal emissions (LMEs) to reduce carbon emissions. LMEs measure the impact of carbon emissions including the locational aspect in more granular way and thus provide a novel mechanism for the system operator and load aggregators to design the LME-based load shifting strategy, which can efficiently guide consumers and thus adjust their consumption behaviors. Simulation case studies on the IEEE test networks are performed to validate the capability of the proposed load shifting method to reduce carbon emissions. We also compare this with other relevant strategies to discuss multiple scenarios and corresponding results. While each provides a different level of flexibility, all the explored strategies tested have led to solutions that have lower carbon emissions, indicating the great potential of demand-side flexibility in reducing carbon emissions for future distribution networks. • Novel demand response with locational marginal emissions to reduce carbon emissions. • The user-centered optimization approach for flexible loads to shift load temporally. • Comprehensive numerical case studies to discuss great potential of the proposed work. • The proposed demand response decreases overall carbon emissions and operating cost. [ABSTRACT FROM AUTHOR]

China has proposed its ambitious cap targets of carbon emissions in both carbon intensity (CO 2 emissions per unit of GDP) and carbon scale (gross carbon emissions). Since mining sector is the foundation of the whole industrial production as well as a carbon intensive sector, it is critical to uncover the key driving factors on inducing corresponding carbon emissions so that appropriate mitigation policies can be raised. Under such a circumstance, this paper aims to fill such a research gap by employing a novel index decomposition method, namely, Generalized Divisia Index Method (GDIM), so that the driving factors of energy-related carbon emissions changes in China’s mining sector and its five sub-sectors over the period of 1999–2013 can be identified. In addition, a scenario analysis approach is applied in order to seek the feasible mitigation pathways on China’s mining sector and its five sub-sectors. The results indicate that output scale effect is the primary contributor of the increase in carbon emissions of both mining sector and its five sub-sectors and energy use effect also plays a positive role, while carbon intensity effect contributes most to the decrease in carbon emissions. All sub-sectors have achieved the target of 45% carbon intensity reduction except the extraction industry of petroleum and natural gas. Nevertheless, more efforts should be made for the whole mining sector in order to achieve the 2030 peak target of carbon scale. [ABSTRACT FROM AUTHOR]

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%. • Experimentally demonstrated an MPC for a campus-scale cooling plant system. • Present MPC's real performance for renewable integration and decarbonization. • First MPC demonstration in response to a real time grid carbon signal. • Achieved reductions of the excess PV power by 25%, and of the CO2 emission by 10% • Share lessons-learned during the demonstration to facilitate MPC deployment. [ABSTRACT FROM AUTHOR]

In the United States, buildings account for 35% of total energy-related carbon dioxide emissions, making them important contributors to decarbonization. Carbon intensities in the power grid are time-varying and can fluctuate significantly within hours, so shifting building loads in response to the carbon intensities can reduce a building's operational carbon emissions. This paper presents a rule-based carbon responsive control framework that controls the setpoints of thermostatically controlled loads responding to the grid's carbon emission signals in real time. Based on this framework, four controllers are proposed with different combinations of carbon accounting methods and control rules. To evaluate their performance, we performed simulation studies using models of a 27-home, all-electric, net zero energy residential community located in Basalt, Colorado, United States. The carbon intensity data of four future years from the Cambium data set are adopted to account for the evolving resource mix in the power grid. Various performance metrics, including energy consumption, carbon emission, energy cost, and thermal discomfort, were used to evaluate the performance of the controllers. Sensitivity analysis was also conducted to determine how the control thresholds and intervals affect the controllers' performance. Simulation results indicate that the carbon responsive controllers can reduce the homes' annual carbon emissions by 6.0% to 20.5%. However, the energy consumption increased by 0.9% to 6.7%, except in one scenario where it decreased by 2.2%. Compared to the baseline, the change in energy cost was between −2.9% and 3.4%, and thermal discomfort was also maintained within an acceptable range. The little impact on energy cost and thermal discomfort indicates there are no potential roadblocks for customer acceptance when rolling out the controllers in utility programs. [ABSTRACT FROM AUTHOR]

• A bottom-up estimation of CO 2 mitigation potential of combined options for a case-study refinery. • A dynamic CO 2 mitigation potential over time is based on measure interactions for each pathway. • Co-processing account for 60% of the portfolio followed by CCS with 23%, GE with 7%, and H 2 with 6% • The oil and gas industry could reach carbon neutral operation at factory gate. • The methodological approach brings insights to define optimal transition routes of the industry. The oil and gas industry is responsible for 6% of total global CO 2 emissions, from exploration to downstream petrochemical production and account for another 50% when including the use of its products. Thus, this industry has a significant role in realising the target of net "zero" CO 2 emissions by 2070, essential to limit global warming to 1.8 °C [2], as introduced under the Paris agreement. Currently, the interactions of an extensive set of individual and combined CO 2 mitigation measures along the value chain and over time are poorly assessed. This paper aims to assess a bottom-up CO 2 mitigation potential for a complex refinery, including portfolios of combined mitigation options, considering synergies, overlap, and interactions over time for more realistic insight into the costs and constraints of the mitigation portfolio. A total of 40 measures were identified, covering a wide range of technologies such as energy efficiency measures (EEM), carbon capture and storage (CCS), bio-oil co-processing, blue and green hydrogen (BH 2 , GH 2), green electricity import, and electrification of refining processes linked to the transition of the Colombian energy systems. Five deployment pathways were assessed to achieve different specific targets: 1-base case scenario, 2-less effort, 3-maximum CO 2 avoidance, 4-INDC, and 5-measures below 200 €/t CO 2. Two scenarios (3 and 5) gave the highest GHG emission reduction potentials of 106% and 98% of refining process emissions, respectively. Although significant, it represent only around 13% of the life-cycle emissions when including upstream and final-use emissions of the produced fuels. Bio-oil co-processing options account for around 60% of the mitigation options portfolio, followed by CCS (23%), green electricity (7%) and green H 2 (6%). The devised methodological approach in this study can also be applied to assess other energy-intensive industrial complexes and shed light on the bias for estimating CO 2 mitigation potentials, especially when combining different mitigation options. This is turn is vital to define optimal transition pathways of industrial complexes. [ABSTRACT FROM AUTHOR]

Abstract: As a representative of many emerging economies, BRICS’ economies have been greatly developed in recent years. Meanwhile, the proportion of energy consumption of BRICS to the whole world consumption has increased. Therefore, it is significant to analyze and compare the energy efficiency among them. This paper firstly utilizes a Super-SBM model to measure and calculate the energy efficiency of BRICS, then analyzes their present status and development trend. Further, Bootstrap is applied to modify the values based on DEA derived from small sample data, and finally the relationship between energy efficiency and carbon emissions is measured. Results show that energy efficiency of BRICS as a whole is low but has a quickly increasing trend. Also, the relationship between energy efficiency and carbon emissions vary from country to country because of their different energy structures. The governments of BRICS should make some relevant energy policies according to their own conditions. [Copyright &y& Elsevier]

Energy policy is too often not designed for energy consumers in a low-cost and consumer-friendly manner. This paper proposes a novel Stackelberg game and Blockchain-based framework that enables consumer-centric decarbonization by automating iterative negotiations between policy makers and consumers or generators to reduce carbon emissions. This iterative negotiation is modeled as a Stackelberg game-theoretic problem, and securely facilitated by Blockchain technologies. The policy maker formulates carbon prices and monetary compensation rates to dynamically incentivize the carbon reduction, whereas consumers and generators schedule their power profiles to minimize bills and maximize profits of generation, respectively. The negotiating agreement is yielded by reaching a Stackelberg equilibrium. The exchanged information and controlling functions are realized by using smart contracts of Blockchain technologies. Case studies of GB power systems show that the proposed framework can incentivize 9% more bill savings for consumers and 45.13% more energy generation from renewable energy sources. As a consumer-centric decarbonization framework, it can at least reduce carbon emissions by 40%. [Display omitted] • A novel consumer-centric and negotiation-based framework to decarbonize power systems. • A Blockchain based platform to integrate the exchanged information and controlling functions of the power system scheduling into data structures, nodes, and smart contracts of Blockchain networks. • How Blockchain based platform can prevent cyber attacks is discussed. • Case studies demonstrate the effectiveness of the proposed framework in the context of Great Britain power systems. [ABSTRACT FROM AUTHOR]