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The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated. • Short-term solar power generation prediction model established. • An influential factors analysis is designed to obtain the optimal combination of the influential factors. • A novel decomposition-ensemble framework is proposed to mine the intrinsic modes of the solar power generation sequence. • An attention-based Bayesian Seq2Seq method is presented. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]

The increase in energy demand makes it urgent to explore green energy sources. And wind energy is one of the most promising renewable energy sources. In this paper, a rotating hybrid nanogenerator with a combination of a direct-current triboelectric nanogenerator (DC-TENG) and an electromagnetic generator (EMG) is designed; the design concept is to harvest wind energy. First, an optimized ternary DC-TENG is designed based on the ternary dielectric triboelectrification effect. A soft contact structure is designed, which can effectively increase the frictional area as well as reduce the frictional resistance. Then, by introducing the EMG unit, the output power of the device and the speed of charging the capacitor are increased. The high output voltage of the DC-TENG and the high output current of the EMG complement each other to produce excellent power output. At 180 rpm/min, the output power of DC-TENG and EMG can reach 1.78 mW and 215 mW, respectively. With a simple power management circuit design, the device is successfully implemented to power small electronic devices. By charging a suitable capacitor, the device can be used as a power supply to power the sensor. Together with the sensor and power management circuit, it forms a self-powered sensing system that can drive the sensor for continuous operation. This work provides a simple, convenient, and reliable design that offers new insights into energy harvesting in nature. • A ternary dielectric triboelectrification effect triboelectric nanogenerator. • A soft contact structure is designed to minimize frictional resistance. • Introduction of an electromagnetic generator to reduce the capacitor charging time. • Energy supply for the continuous operation of the self-powered sensing systems. [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance. [ABSTRACT FROM AUTHOR]

The increasing energy demand in harbour areas, coupled with the need to reduce pollutant emissions, has led to the development of renewable energy-based polygeneration systems to face the carbon footprint of ports and ships at berth. In this way, in the coming years, ports can be converted into modern energy hubs. From this point of view, this paper presents a new dynamic simulation model for assessing and optimizing the energy and economic impact of ports. Here, energy systems and renewable sources can be designed to be connected to national electricity and natural gas grids and can include also alternative fuels (hydrogen, biomethane, etc.) and thermal energy networks, as well as different biomass fluxes (to be exploited for energy aims). Energy availability/demands of near towns and port buildings/infrastructures, as well as on-shore power supply are also included in the dynamic assessments. Hourly weather data and different prices for all the considered energy carriers are taken into account hour by hour. A multi-objective optimization approach is also implemented in the model considering energy and economic indexes to be optimized. The whole model is implemented in a computer tool written in MATLAB. For showing the capability of the developed model, a novel case study referred to the port of Naples (South-Italy) is presented. Here, several renewable energy sources are considered, including an anaerobic biodigester for producing biogas from the organic waste of docked cruise ships. A combined heat and power system (fed by biogas) is implemented in the port energy hub also for supplying absorption chillers. PV panels, and marine power generators are also included. In the conducted analysis, optimization targets are the maximization of system self-consumption and self-sufficiency as well as the minimum simple payback period. The proposed system can effectively contribute to the decarbonization of the port energy demand and reduce harmful pollutant emissions. Results showed that very high rate of renewable energy produced on-site can be exploited (up to 84%) by the considered port facilities, ensuring increasing independency from utility power grid (self-sufficiency index up to 40%). By the obtained results and through the developed simulation/optimization tool, novel design and operating criteria can be achieved for future port energy hubs featured by renewables and bi-directional energy exchange between ships and port. [ABSTRACT FROM AUTHOR]

Unsustainable and uncontrolled development of urbanization, as well as the expansion and increase of environmental challenges, has caused extensive activities in recent years to reduce environmental pollution and efficient use of energy resources, worldwide. One of the appropriate solutions to reduce carbon dioxide (CO 2) emission and the optimal use of energy sources is the construction of an industrial energy park (IEP). Given the pivotal role of energy parks in different energy networks, accurate techno-economic-environmental assessment of energy parks, along with the promoted energy conversion facilities and flexibility options has become a challenging task. To address this open issue, this paper focuses on the coordinated operation of IEPs with a variety of thermal, electrical, and cooling loads to satisfy economic and environmental benefits. The proposed combined cooling, heat, and power (CCHP)-based energy park is equipped with multiple energy conversion facilities, such as absorption and electric chiller, power-to-heat facility, multi-energy storage, as well as demand response (DR) program as the flexibility option to supply energy demands, while has energy exchanging (power and heat exchanged) with the corresponding markets. The main objectives of this paper are to minimize the total operation cost of the energy park and decrease CO 2 emission rates. The coordinated model is exposed to the fluctuation of wind power, electrical and heat loads, and power prices, so the hybrid stochastic/information gap decision theory (IGDT)-based robust approach is used to handle them. The technical implications of using the proposed strategy are: (1) Creating a robust platform for industrial park operators to actively participate in different energy markets and take advantage of existing economic opportunities to meet the required energy demands at the lowest cost; and (2) Encouraging industrial park operators to participate in DR programs to reduce operating costs, increase energy efficiency, and contribute to the development of energy network plans. The proposed stochastic-IGDT multi-objective model is examined on the sample park and numerical results are discussed for different cases. Results reveal that under the proposed coordinated approach, total operation cost and emission pollution are decreased up to 3.1%, and 2.2%, respectively. • Evaluating the operation of the CCHP-based IEP under coordinated strategy • Providing a hybrid IGDT/stochastic approach for operation of the IEP • Developing economic-environmental scheduling for IEP under hybrid approach • Integrating multi-carrier storage, DR, boiler, CHP, chiller, P2H, and RES in IEP [ABSTRACT FROM AUTHOR]

The world is experiencing an unprecedented transition towards a low-carbon and renewable-based energy system, driven by the imperatives of mitigating climate changes, building climate resilience and ensuring sustainable development. Recently, polygeneration systems based on renewable energies have emerged as a viable solution to support the energy transition since they can improve resource utilization and energy efficiency, thus reducing the dependency on fossil fuels and the environmental impacts on ecosystems. Renewable polygeneration systems can play a significant role in the decarbonization of the food processing industry due to its high thermal energy demand at low and medium temperature levels. This paper presents a detailed analysis of a renewable polygeneration system integrating solar, geothermal and biomass energies and able to produce electricity, heat and cold water. The novelty of this study is the analysis of polygeneration system configurations relying solely on renewable energies and the optimization of the strategy to control the energy exchange between power systems, storage facilities and utilities. Two system configurations based on different power technologies and control strategies are investigated and compared, considering as end-user a milk production process with hot and cold water requirements at different temperature levels and load profiles. To this end, a dynamic model in Matlab/Simulink has been developed to simulate the behaviour of the polygeneration system over time and evaluate its overall energy performance parameters. For each system configuration, the optimal operating strategy has been identified by maximizing the energy conversion efficiency and minimizing the unmet thermal and cooling loads. Simulation results highlighted that, compared to polygeneration systems relying only on fluctuating renewable energies, those including a backup heat source (i.e., a biomass boiler) were able to meet the thermal energy needs of larger and more complex milk production processes. [ABSTRACT FROM AUTHOR]

In the last decade, the significant level of carbon emission in the atmosphere has increased the temperature of the Earth by 50% compared to the pre-industrial period. In recent years, considering renewable energy sources integrated with different energy systems such as cooling, heating, and power has become widespread around the globe as the ambition is to produce sustainable and clean energy. However, as the conventional methods for energy generation are highly reliable, their usage for supplying energy demands is inevitable. Therefore, in such renewable-based generation systems, the issues arising from uncertainties are the main challenges to the high penetration of these technologies. In this paper, the aim is to appraise such challenges in a multi-energy combined heat and power (CHP)-based microgrid integrated with power-to-gas (P-2-G) technology. In the proposed system, the downside risk constraint (DRC) approach as an effective risk management method is considered for risk assessment. The proposed model is a mixed integer linear program that is modeled as a multi-objective risk-based problem, for which the epsilon constraints method is used to achieve the optimal solutions for the objectives. According to the results, using P-2-G as an environmentally friendly tool can reduce approximately 55% of the emission cost. Further, the operating cost is increased nearly 13% to obtain a zero-risk condition for optimal management. [Display omitted] • Risk-constrained emission reduction and flexibility improvement in the fossil-fueled CHP-based multi-vector system. • A stochastic-based risk-aware multi-objective optimization framework with iterative solution mechanism. • Risk analysis with downside risk constraint (DRC) approach for the multi-vector uncertainties. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization strategy for hybrid-renewable energy systems in microgrids. The multi-objective optimization approach is formulated for a PV-wind-diesel-battery hybrid system. Its main objectives are to minimize the levelized cost of energy (LCOE) and loss of power supply probability (LPSP) whilst maximizing the use of renewable energy sources (RES). The proposed optimization strategy combines the Taguchi method with a novel fuzzy decision-maker-based multi-objective optimization algorithm. It implements, a) an energy management strategy to optimize the use of the energy sources, b) a Taguchi method to determine the upper bounds of the model's decision variables, c) a multi-objective moth flame optimization algorithm to optimize the size of the renewable energy sources, and d) a fuzzy decision-making approach to obtain the best Pareto front. The proposed strategy was implemented to optimize the design of a hybrid renewable energy system based on three different scenarios consisting of 10, 15, and 20 residential houses located in Sønderborg, a town in the Region of Southern Denmark. The results of the proposed model in terms of loss of power supply probability and levelized cost of energy for the scenarios I, II, and III are [0.224, 0.754], [0.313, 0.612], and [0.368, 0.547] respectively. In addition, the optimal design of the hybrid renewable-based microgrid system for scenarios I, II, and III are [PV: 32 kW, AD: 4.86, WT: 6], [PV: 36 kW, AD: 4.73, WT: 7], and [PV: 47 kW, AD: 5, WT: 7], respectively. The effectiveness of the proposed multi-objective optimization algorithm (MOMFO) in solving the optimization problem was examined and the results were further compared with those of the Non-Dominated Sorting Algorithm II(NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Social Engineering Optimizer (MOSEO). [ABSTRACT FROM AUTHOR]

Due to the widespread deployment of distributed energy resources, renewable energies and battery energy storage, the peer to peer (P2P) energy trading schematic has gained the staple attention for improving the energy efficiency and energy flexibility of power grids. This is while, smart demand response programming (DRP) is considered as the bridge between these two indicators of smart grid. Moreover, the subtle point of proliferating P2P schematics is the regulation towards the maximization of social welfare leading to economic profitability of customers and owner's of microgrid and, eventually, reduction of pollutant emission of fossil fuels. Also, uncertainty, aroused by electrical consumption and renewable energy resources, is the core of every considerations, which has to be dealt with intelligent algorithms for strengthening the stability of transactions. On the other hand, compatibility with upper grid's regulations, i.e. power loss and voltage deviation, along with determining fair price of energy trading are the subjects of P2P-based tactics. Therefore, this paper proposes a P2P-based transactive energy sharing architecture, as two stage mixed integer non-linear programming, using smart DRP integrated with machine learning approach, i.e. radial basis neural network. Firstly, the uncertainty of electrical demand and renewable energies are relaxed through short term forecasting. Doing so, the day-ahead transactions of peers are obtained based on their energy management objective, targeting the energy reliability of customers, which energy not supplied criterion has to be equal to zero. Then, participation of customers in DRP, cost of customers, revenue of microgrid's owner and transactions of real time programming are optimally acquired based on Pareto front technique. Also, the simulations are conducted on IEEE 85 bus test system to realize the considerations. The results convey that the profitability of customers and owners is tied with the implementation of smart DRP and accurate forecasting of uncertain variables. In addition, the maximum improvements towards maximizing the revenue of owners and minimizing the cost of customers take place at hours which the electrical consumption is shifted from peaks to off-peaks and mid-peaks, certifying the performance of proposed methodology. [ABSTRACT FROM AUTHOR]

The spatial distribution of biomass resources is not uniform. At present, the zoning of biomass energy development suitability is primarily based on resource potential, whereas energy demand is ignored as an important factor affecting the degree of local energy self-sufficiency. Under the constraints of high transportation costs, solar energy with low geographical constraints can be a supplementary energy source for biomass despite its instability. Based on the energy utilization potential of solar energy and biomass resources, this paper evaluates the energy surplus factor (ratio of renewable resource potential to energy demand) of each city in the study area, takes meeting the specific energy surplus factor as the construction prerequisites of biomass combined heat and power (CHP) plants, calculates the biomass resource utilization rate and energy surplus factor in the study area under different conditions, and provides zoning suggestions and data reference for the unified planning of biomass CHP plants. The minimum energy surplus factor that is suitable for the development of biomass energy is set as the construction threshold of biomass CHP plants (The construction threshold is the minimum condition for the construction of biomass CHP plants. Only areas where the energy surplus factor reaches the preset construction threshold are allowed to build biomass CHP plants). The results show that the construction threshold is negatively correlated with the utilization rate of biomass resources. When the construction threshold is less than 1.4, the utilization rate of biomass resources decreases rapidly with the increase of the construction threshold; when the construction threshold is greater than 1.4, the utilization rate of biomass resources decreases slowly and tends to be flat. It is suggested that biomass resources should be developed in different regions and in stages, and priority should be given to developing areas with an energy surplus factor greater than 1.4. Areas with an energy surplus factor of 0.3–1.4 are considered secondary development areas, and areas with an energy surplus factor of less than 0.3 are not recommended for development; these can meet 31% and 59% of the total energy demand and reduce 50319380 tons and 180524400 tons carbon emissions so that the limited construction cost can meet higher energy demand and biomass resource utilization. • Priority development areas are identified based on solar and biomass potential. • Energy surplus factor is calculated to determine the development suitability. • Strategies for zoning and phased planning of biomass resources are proposed. [ABSTRACT FROM AUTHOR]