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Across recent years, there has been a growing prevalence of extreme weather events throughout the United States, posing significant challenges to the reliable and resilient operation of power systems. Specifically, summer droughts threaten to severely reduce available generation capacity to meet regional electricity demand, potentially leading to power outages. This underscores the importance of accurate resource adequacy (RA) assessment to ensure the reliable operation of the nation's energy infrastructure. Accurately evaluating the usable capacity of regional generation fleets is a challenging undertaking due to the intricate interactions between power systems and hydro-climatic systems. This paper proposes a systematic and analytical framework to evaluate the impacts of extreme summer drought events on the available capacity of various generating technologies, incorporating both meteorological and hydrologic factors. The framework provides detailed plant-level capacity derating models for hydroelectric, thermoelectric, and renewable power plants, facilitating evaluations with high temporal and spatial resolution. The application of the proposed impact assessment framework to the 2025 generation fleet of the real-world power system within the PJM and SERC regions of the United States yields insightful results. By analyzing the daily usable capacity of 6,055 at-risk generators across the study region, it shows that the summer capacity deration is most significant for hydroelectric and once-through thermal power plants, followed by recirculating thermal power plants and combustion turbines. In the event of the recurrence of the 2007 southeastern summer drought event in the near future, the generation fleet could experience a substantial reduction in available capacity, estimated at approximately 8.5 GW, compared to typical summer conditions. The sensitivity analysis reveals that the usable capacity of the generation fleet would suffer an even more significant decrease under conditions of increasingly severe summer droughts. The proposed approach and the findings of this study provide valuable methodologies and insights, empowering stakeholders to bolster the resilience of power systems against the potentially devastating effects of future extreme drought events. [Display omitted] • A systematic summer droughts generating capacity assessment framework is developed. • Capacity derating models for various generation technologies are presented. • The impacts of summer droughts on generating fleet in PJM and SERC are studied. • Impact of extreme summer drought on usable capacity of generators is significant. [ABSTRACT FROM AUTHOR]

Reducing the dependence on traditional energy sources and shifting towards the utilization of renewable energy sources (RES) of energy in the maritime sector is imperative for reducing greenhouse gas emissions. Inherently, RES sources like solar and wind are intermittent and variable, resulting in inconsistent power availability and hence leading to energy supply fluctuations and potential shortages. In this respect, an efficient control strategy to maintain system stability and address intermittency effectively is essential. This work considers a hybrid marine microgrid with various energy sources like photovoltaics (PV), wind energy conversion system (WECS), marine biodiesel generator, Archimedes wave power generation, solid oxide fuel cell, and batteries. A 2-degree of freedom (2DOF) structure is designed and implemented with the tilt-integral-derivative filter (TIDN) to address frequency variations. Furthermore, an Archimedes optimization algorithm (AOA) is used to optimize the 2DOF-TIDN controller. The stability of the proposed microgrid system is assessed under various combinations of RES availabilities, including real-time data from WECS and PV. The AOA-based 2DOF-TIDN performance is compared to the following algorithms: genetic, Jaya, bat, grasshopper optimization, particle swarm optimization, and moth flame optimization. Simulation results obtained show that the AOA-based 2DOF-TIDN control strategy achieves shorter settling times in mitigating the changes of marine microgrid systems under different dynamic conditions as compared to the other algorithms. Finally, the controller being proposed in this paper was tested for robustness with parameter deviations of +25%, −20%, and − 40% from the nominal values, and proved to be the proposed 2DOF-TIDN controller parameters demonstrate significant robustness in effectively managing the uncertainties and parametric variations. • Maritime Microgrid system is designed for investigations with RE & storage units. • PV, Wind, MBG, AWPG, SOFC, and battery storage units are considered. • 2DOF-TIDN controller is developed to mitigate the changes in the system. • The AOA method is proposed for optimal gains of the 2DOF-TIDN under uncertainties. • Sensitivity analysis, Time delay analysis, and stability analysis are performed [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

In light of the global energy and climate crises, integration of low-carbon technologies into energy systems is being considered to mitigate the high energy costs and carbon footprint. The wide range of available capacities, efficiencies, and investment costs of these technologies and their different possible operating schedules can unlock several pathways towards decarbonisation. This paper presents an optimisation framework for a public healthcare facility to determine the optimal operation schedule of the site's energy system. A detailed techno-economic analysis of low-carbon power generation, conversion, and energy storage technologies that can be incorporated into the system based on real historical data was carried out for different scenarios. The results reveal that a heat pump with a capacity of 1800 kW can replace gas boilers on-site to meet the heat demand while recovering the investment in 5 years and providing an operating and carbon cost saving of 22.47% compared to the base case. The analysis shows that a more electrified mode of operation is favoured during high gas prices, thus making electrical energy storage more attractive than thermal energy storage. While handling real data, the optimisation algorithm was sensitised to discriminate conventional energy supplies from clean energy sources by considering their carbon impact so that it minimises energy bills in a smart and eco-friendly way. The optimisation algorithm and the subsequent techno-economic analysis provide a comprehensive framework to decision-makers for facilitating energy investment decisions. The framework can be used based on the short and long term goals of the energy system, visualising the evolution of financial benefits over equipment lifetime, and understanding the environmental impacts of integrating renewable energy. • Heat pumps are the most favourable decarbonisation technology for the hospital. • The moderate variations in energy demand limit the scope of energy storage. • Fuel price surcharge favours EES over TES and reduces payback period of retrofits. • PVT's savings scale linearly with roof space until 3 times the available capacity. • Monetising the carbon footprint shortens the DPP of clean retrofit technologies. [ABSTRACT FROM AUTHOR]

Negative emissions systems differ from net-zero systems by deploying significantly more negative emission technologies. The emerging literature on negative emission power systems assumes straight transition pathways from the present systems to final negative emissions systems. Such straight transition paths are unlikely, as deployment of negative emission technologies such as direct air capture might occur via crash course to respond to climate crises and utilities are investing towards net-zero systems instead of negative emissions systems. In this paper, to inform policymakers of the different outcomes of planning for negative emissions systems at different timelines, we quantify the value of planning for a negative emissions power system beginning now versus after achieving a net-zero emissions system in 2050. We apply a macro-scale capacity expansion planning model to the Eastern Interconnection power system to quantify the technological deployments and cost consequences of these two decarbonization pathways to reach a negative emission power system. We find advanced planning for a negative emissions system favors more deployment of storage technologies, which increases system flexibility and allows for better utilization of renewable generation to reduce investments in other technologies, including transmission lines. This change in distribution of investments across technologies leads to small annual cost saving of $6 billion, or 1%. We also find that further delay in planning for a negative emission system after reaching net-zero by 5, 10, and 15 years could significantly increase total system cost by 29%, 35%, and 41% respectively due to higher deployment of negative emission technologies. Our results indicate that, given small annual cost saving from advanced planning for a negative emission power system, economically utilities are on the right path in planning for a net-zero power system by 2050. However, planning should not be delayed further after reaching net-zero to avoid missing cumulative emission targets or significant cost consequences. • We compare negative emissions Eastern power system planned now versus after net-zero. • Advanced planning increases system flexibility by increasing storage deployments. • Advanced planning reduces investments in non-storage technologies. • Advanced planning reduces total annual system cost by $6 billion (1%) across scenarios. • Delayed planning for a negative emission system after net-zero incurs significant costs. [ABSTRACT FROM AUTHOR]

The future of energy flexibility in microgrids (MGs) is steering towards a highly granular control of the end-user customers. This calls for more highly accurate uncertainty forecasting and optimal management of risk and flexibility options. This paper presents a novel data-driven model to optimize the operation of MGs based on a risk-averse flexi-intelligent energy management system (RFEMS), considering the rising challenge of global climate change. It considers the presence of renewables, a diesel generator, and flexibility resources (FRs) containing a demand response program (DRP), distributed electric vehicles (EVs), and electric springs (ESs). In the first phase, the proposed model, by means of a novel hybrid deep-learning (DL) model, forecasts uncertain parameters associated with wind and solar generations, load demand, and day-ahead energy market price. The architecture of the proposed hybrid forecasting model is composed of several stacked restricted Boltzmann machines and a cascade neural network. In the second phase, the MG operator (MGO), based on the obtained uncertainty forecasting results, in the context of a hybrid risk-controlling model, optimizes the MG operation using the provided demand-side flexibility. The proposed optimization problem is linearized stochastic programming with robust concepts, subject to AC optimal power flow constraints, MG flexibility restrictions, and operating limits of local resources. Finally, the efficiency of the proposed RFEMS by using real German datasets on a 33-bus test MG is analyzed. Numerical results demonstrate the superior performance of the proposed forecasting model over several hybrid DL models. In particular, the root mean square error (RMSE) index for wind, solar, load, and price forecasting is improved by 53.35%, 73.24%, 80.24%, and 58.1%, respectively. Further analysis of the proposed RFEMS reveals that operating indices in two 33-bus and 69-bus test networks are significantly improved. It paves pathways to risk-averse, flexible, and economic operation of smart active distribution networks. [Display omitted] • A data-driven model is proposed to optimize microgrids' operation. • A hybrid deep-learning model is proposed to forecast uncertain parameters. • Uncertain parameters include wind and solar generations, load, and energy price. • The forecasting is composed of Boltzmann machines and cascade neural network • Hybrid stochastic/robust optimization is proposed to handle uncertainty. [ABSTRACT FROM AUTHOR]

• This paper systematically reviews the research progress of RS technology applied to various stages of PV system development. • We conclude that RS plays a significant role in PV potential assessment, large-scale data analysis and PV health monitoring. • We discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. Developing solar photovoltaic (PV) systems is an effective way to address the problems of limited fossil fuel reserves, soaring world energy demand and global climate change. The earth observation information provides a promising perspective for estimating the PV energy potential and understanding the status of the PV system development, which is critical for making scientifically sound and cost-optimal sustainable planning strategies. Remote sensing (RS), a versatile technology that captures surface information at various temporal and spatial scales, is now widely applied in different fields of the PV development. However, despite the rapid growth of related research, there is still a lack of comprehensive review on the application of RS to different stages (i.e., planning, site selection, installation, maintenance, etc.) of the PV system development. This paper systematically reviews the research progress of RS technology applied throughout various stages of the PV system development. The reviewed literatures are organized as four major parts: i) PV potential estimation, ii) PV array detection, iii) PV fault monitoring and diagnosis, and iv) other cross-cutting areas where RS can facilitate PV development. We conclude that RS technology can bridge the gap caused by the traditional methods in effective assessment of resource potential, large-scale data analysis and PV health monitoring, which can provide strong support in assisting the planning, management, and decision-making of PV systems. Finally, we discuss future challenges and opportunities for RS technology in PV applications for advancing the research in this area. [ABSTRACT FROM AUTHOR]

Schematic illustration of the double-layer PDRC coating. [Display omitted] • The idea of introducing masking layer for double-layer PDRC coating is proposed. • PDRC coating achieves high solar reflectivity (94.0%) at a thin thickness. • The PDRC coating reduces the indoor temperature of container room by 5.0 °C. • The prepared PDRC coating can realize large-scale long-time outdoor applications. The realization of spontaneous sub-ambient cooling in daytime typically requires the solar reflectance and thermal emittance of passive daytime radiative cooling (PDRC) material to be > 90%. Toward real-world applications, however, to achieve high optical performance and excellent durability, PDRC coating is often cost-prohibitive due to its high thickness (500 μm or higher). To solve the contradiction between high optical performance and thin thickness of PDRC coating, the idea of introducing masking layer for PDRC coating is proposed in this paper to prepare the water-based double-layer PDRC coating and explore its long-term outdoor application potential. The PDRC coating can achieve high solar reflectance (94.0%) and "atmospheric transparent spectrum window" emittance (93.0%), while the thickness and cost are only 40% and 60% of existing water-based PDRC coating. The PDRC coating shows efficient cooling performance when applied as the external coating of large temporary buildings, with maximum temperature reduction of 20.8 °C and 5.0 °C on the roof/indoor, which are equal to or greater than recorded values. The 150-day large-scale outdoor application shows that the coating has excellent durability, which offers a reference for the long-term outdoor application of PDRC coating. The application of the PDRC coating on rooftops of containers can achieve a cooling energy savings of up to 15.3% compared to conventional steel roofs, translating to an annual energy savings of 413.2 MJ and a reduction of approximately 114 kg in carbon dioxide emissions, which provide compelling evidence for the ability of PDRC coating to mitigate carbon emissions and address global climate change. [ABSTRACT FROM AUTHOR]

With the rising threat of climate change, green hydrogen is increasingly seen as the high-capacity energy storage and transport medium of the future. This creates an opportunity for low- and middle-income countries to leverage their high renewable energy potential to produce, use, and export low-cost green hydrogen, creating environmental and economic development benefits. While identifying ideal locations for green hydrogen production is critical for countries when defining their green hydrogen strategies, there has been a paucity of adequate geospatial planning approaches suitable to low- and middle-income countries. It is essential for these countries to identify green hydrogen production sites which match demand to expected use cases such that their strategies are economically sustainable. This paper therefore develops a novel geospatial cost modelling method to optimize the location of green hydrogen production across different use cases, with a focus on suitability to low- and middle-income countries. This method is applied in Kenya to investigate the potential hydrogen supply chain for three use cases: ammonia-based fertilizer, freight transport, and export. We find hydrogen production costs of €3.7–9.9/kg H 2 are currently achievable across Kenya, depending on the production location chosen. The cheapest production locations are identified to the south and south-east of Lake Turkana. We show that ammonia produced in Kenya can be cost-competitive given the current energy crisis and that Kenya could export hydrogen to Rotterdam with costs of €7/kg H 2 , undercutting current market prices regardless of the carrier medium. With expected techno-economic improvements, hydrogen production costs across Kenya could drop to €1.8–3.0/kg H 2 by 2030. [Display omitted] • We develop a novel GIS method to identify least-cost green hydrogen production locations. • This is applied in Kenya for ammonia, freight transport, and export use cases. • Hydrogen production costs of €3.7–9.9/kg H 2 are currently achievable in Kenya. • The cheapest production locations are to the south/south-east of Lake Turkana. • Kenya could export hydrogen and undercut current market prices with any carrier. [ABSTRACT FROM AUTHOR]

• Developing countries face the dual challenges of industrialising and 'catching up' while mitigating CO 2 emissions. • Medium- and high-technology manufacturing is associated with lower emissions than low-technology manufacturing, with differences varying by the income levels of countries. • A shift to more technology-intensive manufacturing could be one aspect of environmentally sustainable industrialisation in developing countries. Industrialisation is an important engine of growth and 'catch up', but also associated with harmful carbon dioxide emissions and hence with climate change. This poses a challenge for sustainable industrial development, particularly for late industrialisers on how to industrialise while also mitigating carbon dioxide emissions. This paper investigates the effect of technology intensity across manufacturing industries on carbon dioxide emissions: are medium- and high-technology manufacturing industries less emissions-intensive than low-technology manufacturing industries in developing countries? The paper analyses this relationship for a panel of 68 developing and emerging economies over the period 1990–2016, by adapting the environmental Kuznets curve and the stochastic effect by regression on population, affluence and technology approaches. Using two alternative measures of emissions and estimating generalised method of moments model, the results show that medium- and high-technology manufacturing industries are associated with lower carbon dioxide emissions than low-technology manufacturing industries. The results also show that these differences vary by the income levels of countries. These findings have important policy implications, suggesting that a shift towards more technology-intensive manufacturing production processes may be a more environmentally sustainable industrialisation path for developing countries. [ABSTRACT FROM AUTHOR]

• A novel decarbonization concept applied to offshore natural gas wells benefiting CCUS technology. • A comprehensive CFD model considers real gas EOS and the behaviours of gas, droplets, liquid film. • Compatible with pure CO 2 and CH 4 -CO 2 models to study condensation and separation characteristics. • The maximum CO 2 condensation amount under high pressure and low temperature is 10.33 ton/h. • The maximum CO 2 capture capacity is 4.43 ton/h with high heterogeneous droplet mass concentration. Carbon capture, utilisation and storage (CCUS) is of unique significance for building a green and resilient energy system, and it is also a key solution to tackle the climate challenge. The concept of supersonic decarburization, a joint product of non-equilibrium condensation and swirling separation, can contribute to CCUS technology in a clean way. In this paper, a numerical model is established and validated to investigate the complex physical phenomena of supersonic decarbonization in a high-pressure environment based on the real gas equation of state. The model is compatible with the pure CO 2 model and CH 4 -CO 2 model. Through the simulation of the supersonic nozzle and supersonic separator, the condensation and separation performance of supersonic decarbonization technology was evaluated. For the condensation performance of carbon dioxide, the results show that higher pressure makes it much easier to achieve the condensation process. When the pressure is supercritical, the decrease of inlet temperature or the increase of inlet mole fraction of CO 2 leads to a higher liquid fraction. For separation performance, when the mass concentration of inlet heterogeneous droplets increases from 0.1 kg/m3 to 7.5 kg/m3, the carbon separation amount increases from 3.33 ton/h to 4.43 ton/h, while the exergy loss of condensed CO 2 drops from 436.57 kJ/kg to 329.56 kJ/kg. It demonstrates that the decarburization process is easier, and exergy required for condensation decreases when the concentration of the foreign core is larger. This new concept is beneficial to CCUS technology and can be applied to carbon capture in offshore natural gas processing. [ABSTRACT FROM AUTHOR]

Due to anthropological climate change, climate scientists project a significant change in climate in the coming years. Despite the advances in climate modeling, future climate is still subject to large uncertainties. To mitigate greenhouse gas emissions and slow down climate change, the energy system must undergo a fundamental transition. Thereby, energy system modeling tools are used to optimize the design of future energy systems. As many components of an energy system depend on climate variables, changing climate and its uncertainties influence the outcome of energy system models. Energy systems with high shares of renewable energies are thereby especially vulnerable to changing climate conditions. To find how the changing climate and its uncertainties are assessed in energy system modeling, a systematic literature review was conducted. Thereby we address three major issues: First, we depict the most common techniques to convert climate data into energy system input data. As large amounts of climate data are necessary to depict future climate developments, we secondly review data reduction techniques which can be used to restrain computational time. Around 46% of the studies in this review use those techniques with scenario building being the most common one. Finally, we review methods to explicitly assess climate uncertainty in energy system models. The majority of energy system optimization models searches for individual solutions for the scenarios, while only 22% use methods that include the uncertainty explicitly in the optimization process. We see the need for further research especially in the use of more advanced optimization methods and the combination of those with well known data reduction techniques. The review provides a wide synopsis of different methods for the three major issues and can therefore enable energy system modelers to choose the right methods for the specific challenges. • Systematic review including 160 research papers. • Review of methods for assessing climate uncertainty in energy system optimization. • Presentation of synergies between methods and identification of research needs. [ABSTRACT FROM AUTHOR]

• A multi-model and multi-method ensemble approach for wind resource projection is proposed. • Future wind potential in Hong Kong is assessed for the first time. • Robustness of the climate change signal is evaluated. • An increase in wind power density during summer (+23.7 %) and a decrease during autumn (–15.1 %) in 2081–2100 are projected for SSP5-8.5. Harvesting renewable wind energy is among the most cost-efficient means of reducing carbon emissions and achieving carbon neutrality. However, wind resource is susceptible to climate change impacts since the global temperature increase will reshape atmospheric circulation patterns. To facilitate the evaluation of fine-scale wind energy potential under climate variability, this paper proposes and validates a multi-model and multi-method ensemble wind resource projection approach. Then this approach is utilized to investigate the future variation of wind resources in Hong Kong based on the combined use of global climate models from Coupled Model Intercomparison Project Phase 6 and long-term observations from meteorological stations. It is found that there is a significant increase in future wind resources during summer, while a remarkable decline is projected during autumn. Nevertheless, the variations of wind resources in winter and spring are relatively insignificant. The outcomes of this study are expected to offer a framework for fine-scale wind resource assessment under climate change, and facilitate the economic and risk assessments of future wind farm projects. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [ABSTRACT FROM AUTHOR]

For making earlier realization on peak carbon dioxide emissions and carbon neutrality, hydropower development in countries all over the world can effectively reduce the Greenhouse Gas (GHG) emissions and solve the problem of global climate change. This paper proposes a novel Optimized initial condition and Seasonal division based Grey Seasonal Variation Index (OSGSVI) model to accurately predict the hydropower generation in some countries. Firstly, for enhancing the fitting accuracy, the initial condition is optimized based on the weighted average methods and the data grouping with OSVI is utilized by seasonal divisions. Secondly, an OSGSVI model is established coupled optimization on both optimized initial conditions and seasonal divisions. Thirdly, the Whale Optimized Algorithm (WOA) is employed to determine estimated parameters to further enhance the fitting accuracy for the hydropower generation. Finally, the experimental results of the prediction study show that three error measure values are all the smallest in all the fitting results and the MAPE values are converged before 30 iterations by utilizing our proposed model when compared with a set of baseline prediction models. It demonstrates the superiority of our proposed model over the others on the fitting accuracy with fast-convergence for the hydropower generation in these selected countries. • An OSGSVI model is proposed to accurately predict the hydropower generation. • The model uses the seasonal data grouping and OGSVI to enhance the predictive accuracy. • The WOA is proposed to further improve the predictive accuracy. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

• Developing a novel Stochastic-IGDT approach for optimal scheduling of isolated microgrids. • Considering failure of components in the developed model via IGDT. • Considering other uncertainties using scenarios. • Developing a solution methodology for the considered optimal scheduling model. • Performing simulations to validate the new proposal and analyse the role of demand response programs. In power systems, contingencies and outages are more frequent nowadays due to climate changing effects. This circumstance along equipment aging may lead to unexpected failures and outages in power and energy systems. This issue is especially critical in isolated microgrids, which must be supplied by means of own resources and onsite assets. In these systems, unexpected failures may notably provoke a detriment of the economy and users' satisfaction. In order to minimize the impact of these incidents, this paper proposes a novel energy management tool for isolated microgrids that are robust against failures. To this end, a novel stochastic-IGDT formulation is developed, by which typical uncertainties are comfortably modelled via scenarios while components' failures are treated in a robust fashion using IGDT. A solution procedure is proposed in which the operation cost is also considered in order to reproduce useful results and limit the cost of reliability. A variety of simulations are conducted in order to validate the developed model and discuss the particularities derived from considering failures in the energy management task. Moreover, the role of demand response programs is also foregrounded. In particular, the demand response programs allow reducing the operation costs by 3 % while the scheduling result admits up to 13 h more of accumulated failures, thus confirming a positive effect of such initiatives in both economy and robustness against failures. [ABSTRACT FROM AUTHOR]

As the first legally binding global agreement on climate change, the Paris Agreement aims to limit long-term global warming to less than 2 °C, preferably below 1.5 °C compared to pre-industrial levels. Road transport, as one of the primary sources of energy consumption and carbon emissions, has great potential for energy conservation and emission reduction in achieving the climate goals. However, current approaches cannot efficiently reduce road travel, making it challenging to decarbonize in the transport sector. Therefore, in this paper, we propose STRICTs, a blockchain-enabled motor vehicle restrictive and trading system based on the carbon emissions cap. STRICTs allows automated carbon emission auditing and carbon emission violation punishment without relying on third-party escrow. Through a decentralized enforcer, STRICTs also enables carbon permit trading reliably and transparently. Finally, we implement a proof-of-concept prototype of the system based on Hyperledger Fabric and conduct experiments for comprehensive performance evaluation through CO2 Emission by Vehicles dataset. The experiment results show the practically affordable performance of STRICTs. [Display omitted] • Carbon emission calculation and carbon emission permit allocation method is developed. • Vehicle data chain is built to guarantee the reliability and correctness of collected vehicle data. • Restrictions on carbon emission allowance is presented to reduce carbon emissions. • A smart-contract-based decentralized enforcer is proposed to audit user's carbon emission behavior. [ABSTRACT FROM AUTHOR]

Coal-fired energy generation is the backbone of India's power sector and considered a driver of its economic development. However, it is associated with detrimental environmental and health impacts in India and its fleet is currently struggling with overcapacity and inefficiency problems. One solution to address these challenges is the early retirement of some of India's coal-fired power plants. In this paper, we introduce multidimensional indices that identify plants for retirement based on comprehensive criteria that include technical and economic characteristics of plants as well as their environmental impacts. We implement an ensemble approach, where we formulate 8008 indices based on all possible combination of seven relevant parameters and rank plants accordingly. This approach facilitates a comprehensive analysis of the plants' performance on different parameters and provides a new outlook on plant retirements that differs from the common approach of retiring plants based solely on technical characteristics such as age, capacity, and heat rate. Our results show that top plants recommended for early retirement are typically 7 years older, 13% more expensive and have around 40% higher population exposure to emissions compared to an average plant in India. We estimate the potential costs saved from the retirement of the worst-performing 50 GW of generating capacity to be $21 billion resulting from shifting ownership towards a cheaper cost of capital and replacing coal by more competitive sources such as solar power. • Identify plants for early retirement based on technical, economic and environmental indicators. • Worst performing 50 GW capacity are older, more expensive to operate and have higher population exposure. • Costs saved from retiring 50 GW capacity and replacing them with solar are $21 billion. • A multidimensional approach is essential for a politically feasible phaseout of coal. [ABSTRACT FROM AUTHOR]

In view of increasing concerns about climate change and global warming, various renewable energy sources are widely adopted in power systems to reduce greenhouse gas emissions. However, the widespread adoption of renewable energy sources exacerbates the problem of harmonics pollution. In order to improve power quality, it is urgent to develop a new approach for monitoring harmonics. By leveraging the structured sparsity and spatial sparsity of harmonic sources, this paper proposes a group sparse Bayesian learning method for solving harmonic state estimation problems. The proposed algorithm can employ a small number of measurements less than the number of state variables to pinpoint harmonic sources and to estimate the harmonic magnitudes and angles. Moreover, the proposed method achieves automatic hyperparameter adjustments and solves the system states in complex domain without splitting them into real and imaginary parts. Extensive results on a benchmark IEEE 14-bus system are presented to substantiate the superiority of the proposed method in terms of localization accuracy and harmonic magnitude and phase angle estimation. • A group sparse Bayesian learning method is proposed for harmonic state estimation. • The proposed algorithm achieves automatic hyperparameter adjustments. • The proposed method can directly handle the measurements in the complex domain. • Using a small number of measurements pinpoints the harmonic sources. • The proposed method has high localization precision and small estimation error. [ABSTRACT FROM AUTHOR]

• Weather files defined with different methodology and dataset are compared. • Projections of climate changes are considered in the construction of weather files. • The case study is a residential building in a cooling dominated climate. • The composition of weather files greatly influences the energy performance prevision. • Traditional efficiency measures are not resilient for the expected climate changes. The methodology, the site and the dataset as well as the emissions scenario considered in the weather file definition influence the numerical evaluation of efficiency measures resilience. With a complete statistical and critical approach, the paper analyzes the importance of these aspects by means of a residential case study simulated in Benevento, a city of south Italy. Using data monitored from 2015 to 2020, a current weather file is built with different methodologies. The comparison indicates that there is not repeatability of the year chosen as a reference for the various months and thus the resolution of the building energy balance could bring different results. Some future climate projections are also generated on medium (2050 s) and long (2080 s) term considering different emission scenarios. With long term projection, the heating degree days are reduced also of − 21% meanwhile the cooling degree days are more than double compared with the current condition. This suggests a remarked transition towards a dominant cooling climate for Benevento. Moreover, when the climate change is considered, the insulation intervention and the installation of double glazed low emissive window is not resilient because the heating energy need decreases also of −56%, but the cooling energy need increases of + 62% (2080 s). If the efficiency measures include also the cool roof and the external shadings, the cooling demand could be reduced until –33% in some scenarios (e.g. RCP 4.5-50th percentile) and increased (+31%) in some others (e.g. 2080 s). [ABSTRACT FROM AUTHOR]

Both global climate change and the decreasing cost of lithium-ion batteries are enablers of electric vehicles as an alternative form of transportation in the private sector. However, a high electric vehicle penetration in urban distribution grids leads to challenges, such as line over loading for the grid operator. In such a case installation of grid integrated storage systems represent an alternative to conventional grid reinforcement. This paper proposes a method of coordinated control for multiple battery energy storage systems located at electrical vehicle charging parks in a distribution grid using linear optimization in conjunction with time series modeling. The objective is to reduce the peak power at the point of common coupling in existing distribution grids with a high share of electric vehicles. An open source simulation tool has been developed that aims to couple a stand alone power flow model with a model of a stand alone battery energy storage system. This combination of previously disjointed tools enables more realistic simulation of the effects of storage systems in different operating modes on the distribution grid. Further information is derived from a detailed analysis of the storage system based on six key characteristics. The case study involves three charging parks with various sizes of coupled storage systems in a test grid in order to apply the developed method. By operating these storage systems using the coordinated control strategy, the maximum peak load can be reduced by 44.9%. The rise in peak load reduction increases linearly with small storage capacities, whereas saturation behavior can be observed above 800 kWh. [Display omitted] • Open Source model for detailed co-simulations of storage systems and MV-grids. • Impact assessment of different control strategies on peak load reduction. • Using the coordinated control strategy, the peak load can be reduced by 44.9%. • Storage systems are evaluated using KPIs along with its impacts on the grid. [ABSTRACT FROM AUTHOR]

Recently, renewable energy is rapidly integrated into the power grid to prevent climate change, and accurate forecasting of renewable generation becomes critical for reliable power system operation. However, existing forecasting algorithms only focused on reducing forecasting errors without considering error compensability by using a large-scale battery. In this paper, we propose a novel strategy called error compensable forecasting. We switch the objective of forecasting from reducing errors to making errors compensable by leveraging a battery, which in turn reduces the dispatched error , the difference between forecasted value and dispatched value. The challenging part of the proposed objective lies in that the stored energy at current time is affected by the previous forecasting result. In this regard, we propose a deep reinforcement learning based error compensable forecasting framework, called DeepComp, having forecasting in the loop of control. This makes an action as a continuous forecasted value, which requires a continuous action space. We leverage proximal policy optimization, which is simple to implement with outstanding performance for continuous control. Extensive experiments with solar and wind power generations show that DeepComp outperforms the conventional forecasting methods by up to 90% and achieves accurate forecasting, e.g., 0.58–1.22% of the mean absolute percentage error. • A novel forecasting strategy is proposed to make errors compensable using a battery. • A framework having forecasting in the loop of control is developed. • Deep reinforcement learning is applied for continuous control of forecasting. • Any forecasting algorithms can be combined with the proposed framework. • Accurate forecasting is achieved by outperforming the existing methods by up to 90%. [ABSTRACT FROM AUTHOR]

In this paper, we present a newly developed eXplainable artificial intelligence (XAI) model to analyze the impacts of climate change on the cooling energy consumption (E c) in buildings, predict long-term E c under the new shared socioeconomic pathway (SSP) climate change scenarios, and explain the underlying reasons behind the predictions. Such analyses and future predictions are imperative to allow decision-makers and stakeholders to accomplish climate-resilient and sustainable development goals by leveraging the power of meaningful and trustworthy projections and insights. We demonstrated that the XAI is capable of predicting the E c under future climate scenarios with high accuracy ( R 2 > 0. 9) and reveals the critical inflection points of the daily average outdoor air temperature (T a) beyond which the E c increase exponentially. We applied the XAI model for residential and commercial buildings in hot–humid and mixed–humid climate regions to quantify the incremental impacts of climate change on E c under the different SSPs. The XAI-based analysis concluded positive and persistent incremental changes in the E c from 2020 to 2100 under all future SSP scenarios, with the maximum incremental impact of 24.5%, 33.3%, 57.8%, and 87.2% in hot–humid and 37.1%, 47.5%, 85.3%, and 121% in mixed–humid climate regions under the sustainable green energy (SSP126), business-as-usual (SSP245), challenges to adaptation (SSP370), and increased reliance on fossil fuels (SSP585) scenarios, respectively. Potential increases in the E c in future climates could have significant adverse impacts on the local and regional economy if necessary adaptation and mitigation measures are not implemented a priori. • XAI predicts building cooling energy demand under climate change. • XAI reveals the critical reason for increase in cooling energy consumption. • Space cooling energy demand is predicted to increase exponentially after 2050. • Climate change impacts on cooling energy needs are worse in residential buildings. [ABSTRACT FROM AUTHOR]