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Concentrated solar power (CSP), a technology that provides electricity by concentrating solar energy into a single focal point, shows great potential for large-scale utilization. In this study, an integrated hybrid life cycle assessment (LCA) model for multiple regions of China is presented to evaluate the life cycle CO 2 emission, energy consumption, and energy payback time of a 10-MW power tower CSP plant located in China. Over its life cycle of 25 years, the CSP plant is estimated to have CO 2 emissions of 35 g/kWh, consume 514 kJ/kWh of energy, and have an energy payback time close to four years. Using the proposed hybrid model, a significant advantage is the ability to compare the life cycle impact sourced from each sector in input-output economic background. Based on the regionalization of economic background at the province level, the most noteworthy impact is from Hubei province; besides, the CO 2 emission from the electricity sector in each province is the largest contributor. The majority (54%) of the life cycle CO 2 emission of the CSP plant is contributed by the northern region of China due to the higher utilization of coal in the economy of that region, yet most of the energy consumption comes from the southern part of China because of the concentration of manufacturing there. For the life cycle impact of a CSP plant, thermal energy storage using molten salt, and solar collection demanding lots of steel are the two most significant contributors to CO 2 emission and energy consumption. Image 1 • Propose an integrated hybrid LCA model for Chinese situation. • Quantify the CO 2 emission and energy consumption for a CSP plant in China. • Discuss the regional contribution difference for the life cycle impacts. • Analyze a major contributor — manufacture phase — for the CSP's CO 2 emission. • Propose the solutions to decrease the impact sourced from fossil fuel consumption. [ABSTRACT FROM AUTHOR]

Despite of the high demand for liquefied natural gas (LNG) in demanding countries, an ideal means for establishing the LNG supply system has not yet been found in many regions. In this paper, a three-stage stochastic programming method has been proposed for LNG supply system infrastructure development and inventory routing in demanding countries. The minimum daily cost is set as the objective function; and the cost consists of the delivery cost, liquefaction cost, purchase cost, and construction cost. Under the constraints of delivery mode, volume, vehicle, time, and infrastructure construction, a multi-scenario MILP model was established and solved by a hybrid computational method (ACO-MILP), and the optimal infrastructure development and inventory routing were presented as the result. Finally, the method was successfully applied to the LNG supply system along the Yangtze River in China. Furthermore, compared with the other methods, the superiority of the method was verified. [ABSTRACT FROM AUTHOR]

Multi-product pipeline is the most effective mode for refined products transportation and is of vital importance in the energy supply chain. The essential task in actual pipeline operation is scheduling delivery and injection of numerous kinds of products. Despite much research was done on products pipeline scheduling issue, little of them focused on multiple pump stations which are significant for long-distance pipelines. The paper establishes a mixed-integer nonlinear programming model (MINLP) for products pipeline with single source and multiple pump stations. The model has taken factors such as batch migration, local electricity price, demand time window, avoidance of idle segment, change of minimum flow rate and nonlinear hydraulic related objective function into consideration. The model contains two parts and is solved by a hybrid computational approach, the ant colony optimization algorithm (ACO) and the simplex method (SM). Finally, the formulation is successfully applied to a virtual and a real-world pipeline to verify the stability, convergence and practicability. [ABSTRACT FROM AUTHOR]

Solar power generation (SPG) is essentially dependent on spatial and meteorological characteristics which makes the planning and operation of power systems difficult. To promote the integration of solar power into electric power grid, accurate prediction of geographically distributed SPG is needed. In this paper, we present a combined method for day-ahead SPG prediction of multi-region photovoltaic (PV) plants. First, automatic machine learning (AML) is applied to generate the most suitable ensemble prediction model with optimal parameters and then an improved genetic algorithm (GA) is implemented which processes the candidate features by assigning appropriate operators. To achieve more accurate forecast results as well as mine the interpretable relationship between SPG and related weather or PV system factors, the SPG physical model is taken into account. The method performance is evaluated by the real SPG data along with meteorological variables of multi-region PV plants in Hokkaido from 2016 to 2018. Results indicate that the combined method provides acceptable accuracy and outperforms several baselines and other methods used for comparison. • AML is introduced to establish the optimal combination of different forecast models. • The raw data of multi-region PV plants are reconstructed through suitable operators. • SPG mechanism model is applied to generate suitable candidate operators. • GA algorithm is developed to identify optimal operator set for different PV plant. [ABSTRACT FROM AUTHOR]

As a fundamental parameter of the electric grid, obtaining spatial electric load distribution is the premise and basis for numerous studies. As a public, world-wide, and spatialized dataset, NPP/VIIRS night-light satellite image has been long used for socio-economic information estimation, including electric consumption, while little attention has been given to the electric load estimation. Additionally, most of the previous studies were performed at a large spatial scale, which could not reflect the electric information inner a city. Therefore, this paper proposes a method to estimate electric load density at a township-level spatial scale based on NPP/VIIRS night-light satellite data. Firstly, we reveal the different fitting relationships between EC (Electric Consumption)-NLS (Night-Light Sum) and EL (Electric Load)-NLI (Night-Light Intensity). Then, we validated the spatial-scale's influence on the estimation accuracy by experiment via generating a series of simulated datasets. After working out the super-resolution night-light image with the SRCNN (Super-Resolution Convolutional Neural Network) algorithm, we established a finer spatial estimation model. By taking a monthly data of Shanghai as a case study, we validate the model we established. The result shows that estimating electric load at township-level based on night-light satellite data is feasible, and the SRCNN algorithm can improve the performance. • Electric load estimation is conducted based on night-light satellite data. • Fitting relationships between electric Load and night-light intensity is revealed. • The spatial-scale's influence on the estimation model fitting is validated. • A finer electric load estimation method based on the SRCNN algorithm is conducted. [ABSTRACT FROM AUTHOR]

Climate change and population growth pose fundamental challenges to urban food and energy resilience and intensify the land-use competition. By co-locating photovoltaic with vegetation on urban rooftops offers an Agrivoltaics solution to achieve sustainable cities with clean energy supply and 'zero food mile' vegetable production. However, the first and foremost question is on how much food and energy can the Agrivoltaics nexus produce. This study proposes a multi-disciplinary assessment framework to unlock the potential of urban rooftop Agrivoltaics by integrating Geographic Information System (GIS), biogeochemical simulation, and solar power simulation. A case study in Shenzhen, China, reveals that bringing the Agrivoltaics (e.g., planting lettuce under photovoltaic panels) on the 854,000 number of rooftops (i.e., 105 km2 identified) can yield 9.84 × 105 tonnes/year lettuce to fulfil the whole city's demand. Meanwhile, the solar PV installed capacity is 2106 MW on average with 1899 GWh/year generated electricity, which is equivalent to 0.2% of the whole city's electricity demand. Besides, 4.11 × 106 tonnes/year extra freshwater is needed for irrigation. Overall, the Agrivoltaics indicates greater contribution to the Sustainable Development Goals (SDGs) and the proposed integrated framework provides an overall picture and assessment tool to explore the urban Agrivoltaics as a key multifunctional land-use mechanism for urban sustainability. [Display omitted] • Integrate GIS, biogeochemical simulator, PV simulator to assess Agrivoltaics potential. • Barriers, uncertainties, SDG performance, and the future directions are discussed. • Quantify Shenzhen's city-level Agrivoltaics' potential over 854,000 number of rooftops. • 9.84 × 105 tonnes/year lettuce yield is projected on 105 km2 identified rooftops. • 2.1 GW solar PV installed capacity is projected with 1899 GWh/year generation. [ABSTRACT FROM AUTHOR]

Operation optimization is an effective but quite challenging way to reduce the energy consumption of large-scale pipeline networks. The thermal-hydraulic performance of natural gas, combined with the detailed constraints of compressors, creates many nonlinear constraints that make optimization models difficult to solve. Previous studies aimed at optimizing large-scale natural gas pipeline networks have often simplified multiple compressors into a single compressor station and ignored the operating envelopes of centrifugal compressors. This oversimplification can lead to solutions that deviate from reality. To address this issue, this paper presents an optimization model that is more practical to real-world scenarios. The model incorporates thermal-hydraulic constraints, reverse processes, and detailed constraints of compressors, including the operating envelopes of centrifugal compressors. To solve the Mixed Integer Nonlinear Programming (MINLP) model, an iterative four-stage decomposition algorithm is proposed. The proposed model and algorithm are verified by three large-scale natural gas pipeline networks, demonstrating advantages in solving time and scalability. Specifically, the solving time only requires 9.9 s for a pipeline network containing 406 nodes, even when considering the operating envelopes of centrifugal compressors. These characteristics of the model and algorithm enable them to be portable for similar problems and future research. • A MINLP model optimizing the operation of gas networks has been developed. • Detailed constraints of compressors containing operating envelopes are considered. • A novel decomposition algorithm is proposed to solve the complex MINLP model. • Gas networks containing 406 nodes and 165 compressors are optimized within 9.9 s. [ABSTRACT FROM AUTHOR]

Wind power is one of the most promising renewable energy for its abundant resources, economically competitive, and environmentally friendly. Nevertheless, the wind power is challenging used in the power generation system due to its intermittency. Therefore, to improve the utilization ratio of wind power, the common method is adopting a prediction model for scheduling the generation industries. However, the information offered by single-step models hardly assists managers control their generators, and existing multi-step prediction models ignore the temporal dependence among predicted steps. Thus, a hybrid method based on a deep-chain echo state network (DCESN) and variational mode decomposition (VMD) is proposed to enhance the mapping capability for wind power multi-step prediction. The multiple reservoirs of deep-chain echo state network are concatenated as a chain structure, which could congregate the temporal relations among future steps shown in visualized graphs. Three comparative experiments demonstrate that the proposed hybrid method has promising performance on wind power multi-step prediction. • A novel hybrid model explains the temporal dependence in multi-step prediction. • Deep-chain echo state network maps the correlation in prediction. • Variational mode decomposition further improves the hybrid model's performance. • A temporal dependence coefficient visualizes the correlation among the steps. [ABSTRACT FROM AUTHOR]

Compressed air energy storage (CAES), a technology that stores energy in the form of compressed air at times of excess supply and releases it to meet the higher demand in peak load periods, has been considered for numerous applications, most notably to support the electric grid for load leveling applications. Nonetheless, one of the downsides of CAES is the large energy losses incurred in the form of waste compression heat. Considering this issue, a novel CAES system integrating two separate compression process in sequence was proposed in this study. The near-isothermal compression pressurized the air from adiabatic pre-compression could enhance the thermodynamic performance of liquid piston and eliminate the generation of compression heat fundamentally. Through setting up a systematic model, the thermodynamic performance of the system was investigated in detail and the most outstanding advantage characterized with the efficient near-isothermal compressed air process within the liquid piston due to (after) the pre-compression was explored. For the comprehensive utilization of system energy, the intercooler, compressor, and expander were found to be the main contributors to exergy destruction and accounted for 25.8%, 17.3%, and 15.7% of the total exergy destruction, respectively. The significant effects of the liquid piston on thermal management led to the improvement in thermal performance. Moreover, with an optimum inlet pressure of liquid piston, the round-trip efficiency and exergy efficiency were achieved to be 62.6% and 55.4%, respectively, in actual operating conditions. • A novel isothermal compressed air energy storage system was proposed. • Combination of adiabatic pre-compression and near-isothermal compression was employed. • Dynamic mathematical model of the proposed system was built for performance analyzing. • Comprehensive performance of the proposed system performance was evaluated. [ABSTRACT FROM AUTHOR]

Auction is a dominant way to allocate transport capacities for the aim of profit, fairness, and transparency. Previous related work focuses on the natural gas pipeline and cannot be applied to the petroleum pipeline for its special technique of transporting multiple batches. This paper designs a bi-level model for capacity auction of the petroleum pipeline, which facilitates fair competition and free flow of petroleum products in market. The upper-level model simulates the decision-making process of pipeline company, and the lower-level one determines if shippers bid up prices for capacities somewhere. A real-world pipeline is taken as a case to explore the impact of different auction designs and proration on capacity allocation. New shippers obtain all the nominations of 18,700 m3 by auction but lost the capacities of 3,523 m3 by proration. Compared to proration, auction leads to a profit growth of 181,545 CNY and 150,019 CNY to pipeline company and new shippers respectively but a decrease of 4,586 CNY to regular shippers. Different orders of offering price lead to different outcomes regarding fairness and revenue. Letting new shippers offer price first is suggested as a base to design auction for capacity allocation since it generates a reduction of 0.2% to regular shippers but an increase of over 12% to others. • Bi-level model – company decision maker and shippers bidding level • The law behind the capacity allocation results obtained from auction and proration • New shippers obtain all the nominations by auction but 18.8% lost by proration • Let new shipper bid first- 14.8% and 12% profit growth for new shippers and carrier [ABSTRACT FROM AUTHOR]

The smart building-integrated photovoltaic (SBIPV) systems have become the important source of electricity in recent years. However, many sociological and engineering challenges caused by temporal and spatial changes on demand-side and supply-side remain. In this paper, the barriers and traditional data utilization of SBIPV system causing the above challenges are summarized. Data-driven SBIPV was firstly proposed, including four aspects: Data Sensing, Data Analysis, Data-driven Prediction, and Data-driven Optimization. Data sensing goes beyond the technical limitations of a single measurement and can build the bridge between demand- and supply-side. Then, the demand-side response and electricity changes in supply-side under various environmental changes will also become clear by Data Analysis. Data-driven Prediction of load and electricity supply for the SBIPV is the basis of energy management. Data-driven Optimization is the combination of demand-side trading and disturbed system optimization in the field of engineering and sociology. Furthermore, the perspective of data-driven SBIPV, technologies and models, including all four data-driven features to make automated operational decisions on demand- and supply-side are also explored. The data -driven SBIPV system requiring much greater policy ambition and more effort from both supply and demand side, especially in the areas of data integration and the mitigation of SBIPV system. • The innovations of SBIPV were reviewed from the perspective of data. • The barriers and challenges of traditional data utilization were summarized. • Data-driven Sensing, Analysis, Prediction, and Optimization were illustrated. • The perspectives of data-driven SBIPV systems were proposed. [ABSTRACT FROM AUTHOR]

With the promotion of renewable energy, natural gas's advantage is being cut as its prices fluctuate largely. This study proposes a robust mathematical model to simulate the changes in electricity structure and hydrogen supply with the fluctuation of natural gas prices. The simulation model is applied to explore the impact of soaring natural gas prices on the electricity market in China. The results demonstrate that high natural gas prices are more likely to promote the development of low-cost nonrenewable power generation rather than renewable power generation under a policy of low-carbon tax. Coal power still dominates the electricity market with a share of 42% and accounts for 66% in hydrogen production due to its low price and mature craftsmanship. Natural gas is the indispensable transitional energy for the transformation of the power structure despite its high prices, occupying 17% in the electricity market and 15% in hydrogen production. In conclusion, fossil energy will gradually change from direct combustion and utilization to the production of renewable energy such as hydrogen energy. Finally, policy recommendations for the structure of the electricity market and the supply of hydrogen energy are proposed. • A R-MILP model has been developed to simulate the changes in electricity market. • Energy prices uncertainty is considered for reflecting energy competitiveness. • A universal approach for analyzing the evolution of energy structure is proposed. • Explore energy structure changes brought by high gas prices in China. [ABSTRACT FROM AUTHOR]

As the demand of fossil fuel declines, improving the infrastructure development of fossil fuel supply chain is the efficient way to reduce cost and carbon emissions. This paper analyzes the impact of declining oil demand on the infrastructure development of the oil products delivery system. A mathematical model is proposed to simulate the transportation and storage schemes, and then is generalized to different forms based on four disposal strategies of idle depots and five sub-scenarios of declining demand. The discussed disposal strategies include storage of other fuels, relocation, destruction and to be sold. A comprehensive evaluation system is also formulated to evaluate the utilization efficiency of the transit depots. Finally, the oil products delivery system in Northwestern China is used to validate the proposed method. Compared with the current system, improving the utilization rate of the depot under declining demand sub-scenarios can improve the economic efficiency by 10.6%, 8.9%, 17.3%,21.3% and 21.6%. With the decline in demand, the economy of destruction of transit depot is better than others, while the environmental benefit of storing other fuels is better than other strategies. • A mathematical model simulated the schemes of the oil products delivery system is developed. • A comprehensive evaluation system of depot's utilization efficiency is proposed. • The simulation model is generalized to different models based on four depot disposal strategies. • Different disposal strategies bring different economy and environment effects. [ABSTRACT FROM AUTHOR]

Electrolysis hydrogen generation technology is one of the feasible ways to alleviate the problem of wind electricity curtailment. One promising hydrogen value-added application is to blend hydrogen into the natural gas grid and sell it as the heat energy carrier. This paper aims to discuss the feasibility of a roadmap to urban energy internet with wind electricity-natural gas nexus. Firstly, a framework is raised to integrate wind electricity generation, electrolysis hydrogen generation, and hydrogen-natural gas blending systems. Secondly, a series of reasonable hydrogen supply profiles are provided based on annual electricity curtailment and realistic natural gas scheduling. Then, an energy optimisation model and a techno-economic model are applied to simulate the generation of electricity and hydrogen, as well as determine the most economical hydrogen supply scheme. Finally, a case study in the Beijing-Tianjin-Hebei region of China is taken to validate the benefits of the proposed roadmap. The preferred scheme is worked out with the net present value of 88.8 M$, including the economy configurations of the electricity-hydrogen hybrid generation system, as well as the hydrogen-natural gas blending plan. The results also indicate that annual electricity curtailment and annual carbon emission are decreased by 204 GWh (48.8%) and 40.2 kt (49.9%). • Raise a roadmap to urban energy internet with wind electricity-natural gas nexus. • Set a framework to integrate electrolysis hydrogen generation and blending systems. • Propose an overall optimisation model for economic and environmental analysis. • Hydrogen supply depends on wind electricity curtailment and natural gas scheduling. • The improved economic and environmental benefits in a real case are identified. [ABSTRACT FROM AUTHOR]

This article is devoted to discussing the feasibility and the optimal scheme to implement an electric-thermal carbon emissions neutral industrial park and perform a 3E analysis on various scenarios. A carbon emissions neutral framework of electric-thermal hydrogen-based containing MILP energy optimisation model is constructed. Photovoltaic power generation, electrolysis hydrogen production are considered in the model. There is an optimal scheme for realising carbon emissions neutrality in industrial parks, which will cost a relatively high price and the compromise scheme which can implement low carbon emissions is also worth studying. The results of the real-world case show that the present value of the cost of achieving carbon emissions neutrality is $ 8.61 billion (109). The scheme presents a compromise between environment and economy will cost $3.95 billion while bringing a 61 % reduction in emission compared with the baseline, which relies upon on-grid and natural gas. And the scheme is most affordable when hydrogen energy accounts for 95 %. Finally, in order to verify the generalisation of the roadmap, the analysis method has been applied to other industrial park cases of typical cities around the world, and the feasibility of achieving the carbon emissions near-neutral goal in these areas is evaluated. • A roadmap covered schemes of compromised low carbon and carbon emissions neutral was proposed. • A carbon emissions neutral framework of an electric-thermal hydrogen-based system is constructed. • An overall 3E analysis method coupled with an optimisation model is proposed. • Scheme to achieve carbon emissions neutral of an industrial park and various scenarios were studied. • Feasibility analysis of carbon emissions neutral cases along with typical cities worldwide. [ABSTRACT FROM AUTHOR]

Distributed energy systems (DES) with hybrid sources has become a more effective way to increase the flexibility of energy supply. Current relevant research on DES design optimization strives to improve system efficiency and sustainability through independent application of subsystem interconnection and demand response. This paper aims to analyze the coupling impact of the above two strategies on the performance of DES. A general multi-objective mathematical programming model is proposed to work out the optimal design and operation scheme of DES, as well as flexible management of electricity, cooling energy, heating energy and steam. The minimum annual cost, carbon emission and energy demand deviation are set as objective functions. Additionally, the augmented ε-constraint method is introduced to search for the trade-off among three indicators. A composite community in China is taken as a case and three scenarios are carried out for comparison. Results reveal that the annual cost and carbon emission are further decreased by 7.3% and 32.1% under the coupling impact of subsystem interconnection and demand response. Eventually, the preferred strategy is analyzed in detailed to illustrate how this coupling strategy further improves the performance of DES. • The coupling impact of subsystem interconnection and demand response is analyzed. • DES is evaluated by economic, environmental and demand deviation indicators. • A tri-objective MILP model is built to determine the optimal coupling strategy. • The AUGMECON is introduced to search for the trade-off between three indicators. • The annual cost and carbon emission are further decreased by 7.3% and 32.1%. [ABSTRACT FROM AUTHOR]

Nowadays, environmental problems caused by petroleum industries need to be alleviated urgently for the transit to low-carbon oilfields. Carbon Capture & Storage-Enhanced Oil Recovery (CCS-EOR) is effective to increase oil production while reducing carbon emissions, but few studies evaluated the cyclic projects that connect capture and injection inside the oilfield. Aiming at Recycle-CCS-EOR (RCE) project, this study proposes a comprehensive technical-economic model to analysis the economic and environmental benefits, considering the uncertainties of geological conditions, prices, and CO 2 injection volume. Taking six blocks in real-world oilfields as the example, this model simulates four scenarios under different CO 2 acquisition methods and carbon taxes. The results show that the breakeven oil price of RCE is 29.4 $/barrel for carbon tax and 25.6 $/barrel for no tax. The recommendable CO 2 injection volume of RCE is higher than 33.5% HCPV for tax and 22.8% HCPV for no tax. Under the current range of CO 2 market price, RCE is more profitable when the capture investment is less than 80.9 M$ for carbon tax and 40.7 M$ for no tax. Compared with traditional EOR production, RCE project reduces carbon emissions by about 54.7%, indicating its technical feasibility, economic and environmental advantages in practical engineering. • Develop a Recycle–CCS–EOR model with economic and environmental advantages. • Show the applicability of the model through comparative analysis in four scenarios. • Identify the impact of critical parameters to the model's performance. • Evaluate the economic and environmental benefits of different blocks. [ABSTRACT FROM AUTHOR]

In coal-dominated regions, hydrogen by-product from refineries can be used to replace coal as heating fuel. This can be a viable method for reducing CO 2 emissions. Given that hydrogen injection into existing natural gas pipeline networks is an economical way to utilise this clean gas, this paper explores its benefits by studying its supply chain optimisation and analysing its reliability. First, a mixed-integer linear programming model is developed to minimise transport and dissatisfaction costs. Material balance, supply and transport of natural gas, operation of pipeline networks, and hydrogen injection are considered as constraints. The operating plans of a regional natural gas supply chain (NGSC), with and without hydrogen injection, are optimised. Then, three types of indicators – economic indices, coal and hydrogen as percentages of total energy consumption, and CO 2 emissions – are applied to compare the performance of the NGSC in different situations. A case verifies the proposed model and shows the influence of utilising hydrogen in an NGSC. The optimal operating plans under one standard and two non-standard conditions are calculated, and the reliability of the supply chain is analysed. Results illustrate that injecting hydrogen in the NGSC is beneficial in reducing CO 2 emissions and operating costs. • Implementing hydrogen by-product to replace the coal reduces CO 2 emissions. • A Framework is proposed to evaluate the economic and environmental benefits. • An MILP model is developed to minimise transport and dissatisfaction costs. • A case is studied to show the influence of utilising hydrogen on an NGSC. • The performance of the NGSC in several conditions are analysed. [ABSTRACT FROM AUTHOR]