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Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [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]

With the increasing number of integrated energy system (IES) construction, a multi-agent system group composed of multiple IESs would be formed in a certain region. Moreover, complex uncertainties gradually existed in the IES which affect the system optimal situation and optimization results. In order to take full use of each agent's advantages and improve the performance of the whole region under uncertainty, this paper focuses on the multiple energy types' interaction of multi-agent IESs within the region to realize the stable operation, economic and environmental improvement of each agent and the whole region. To this aim, a multi-objective multiple energy types' multi-agent interactive optimization model considering energy demand and renewable energy output uncertainties was built. The model was based on Nash bargaining cooperative game theory and took economy, flexibility and carbon emission as objectives. Moreover, energy interaction scheduling and its corresponding price can be optimized through the model. The results of non-cooperative operation mode without interaction and cooperative operation mode with interaction were compared to analyze the superiority of interaction from the cost, carbon emission and flexibility aspects. The overall regional carbon emission and the economic cost has been reduced by 13.84% and 9.94%, for each IES, the carbon emission and economic cost could reduce by up to 41.5% and 44.5%. Moreover, the flexibility of the IES was correspondingly increased by nearly 47%. These results denoted that after multi-agent interaction, not only the overall region's energy, economic and environmental performance are improved, but also that of each agent are significantly improved. In addition, different energy types among multiple agents could be effectively used and regional joint energy storage is realized, improving the region flexibility level and resilience. • A multi-objective integrated energy systems interaction model is established. • The interaction model considers multiple energy types and uncertainties. • The model can improve economy and flexibility of each system agent and the region. [ABSTRACT FROM AUTHOR]

The flexibility of power systems is critical to cope with the variability and uncertainty caused by the integration of renewable energy resources. Incorporating district cooling systems (DCSs) into power systems is an emerging approach to provide flexibility for renewable energy utilization. However, the existing models of DCS components, such as ice-storage cooling sources, cooling pipelines, and cold warehouses, are simplified, failing to fully utilize that flexibility, which brings difficulties for renewable energy utilization. Therefore, this paper proposed a coordinated operational strategy for an integrated power and cooling system combining detailed operating characteristics of DCSs to exploit the potential flexibility of DCSs for better integration of renewable energy. The primary cooling source components, including the chiller, ice storage tank, pump, and cooling tower, are established to model a detailed process of cooling generation and storage. The thermal inertial of district cooling networks (DCNs) is formulated as a quasi-dynamic model by considering dynamical temperature characteristics and transmission delay to exploit the virtual energy storage capacity of the cooling water pipeline. Moreover, the thermal inertia of the demand side, such as buildings and cold warehouses, is considered to utilize thermal storage capacity. The objective function of the proposed optimization model is to minimize the overall operating cost of the integrated power and cooling system. Numerical simulations show that the proposed method could reduce the operating cost by 15.4% and the wind curtailment rate by 86.2%, indicating better performance of the proposed method in improving economic benefits and reducing renewable energy curtailment. • We proposed a coordinated operational strategy combining flexible resources of DCSs. • We established a detailed model of cooling source including chiller, ice storage tank, pump, and cooling tower. • The thermal inertial of district cooling networks is formulated as a quasi-dynamic model. • The combined cooling and power system can reduce the costs and wind curtailment. [ABSTRACT FROM AUTHOR]

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]

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]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

Ocean wave energy is a kind of renewable energy with huge reserves, which can play an important role in the future construction of smart oceans. The low capture efficiency and narrow bandwidth of traditional wave energy capture devices seriously restrict the large-scale application of wave energy. To address these issues, a rack-pinion-lever-spring (RPLS) adaptive bistable Power Take-Off (PTO) mechanism with a time-varying potential barrier is proposed and applied to a point absorber wave energy converter (WEC). In the proposed nonlinear PTO system, a rack-pinion-lever (RPL) mechanism, as a displacement amplifier, has an effect on enhancing the poor amplitude sensitivity issue of the negative stiffness mechanism. Based on linear wave theory, a special nonlinear one-and-half-degree of freedom dynamic model is developed to describe the adaptive bistable wave energy converter (AB WEC). In comparison with the traditional bistable WEC (TB WEC) and equivalent linear WEC, the numerical results show that the AB WEC can greatly enhance the capture efficiency of lower frequency energy, especially at small wave excitations. The energy capture efficiency of the AB WEC is close to that of the TB WEC even at large wave amplitudes and is higher than that of the equivalent linear WEC. Finally, the wave energy capture characteristics of the three kinds of systems are studied based on the wave distribution diagram of a certain sea area in the South China Sea. The results show that the AB WEC has a large wave height adaptability in the low frequency region. The AB PTO mechanism with a displacement amplifier proposed in this paper can provide a technical reserve for the highly efficient and wide bandwidth capture of low frequency ocean wave energy. [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]

A hybrid renewable energy system (HRES) utilizes the coaction of diverse energy to enhance energy efficiency while improving economic benefit. Under the paradigm of the electricity market, the HRES decomposes bilateral contract (BC) signed in the medium to long-term market and bids in the spot market are a promising way to get more revenue while ensuring basic profits by exploiting the synergy of different markets. However, the scheme of BC decomposition and the uncertainties of renewable energy affect the revenue of HRES scheduling. This paper proposes an optimal scheduling model for HRES to perform the BC flexible decomposition while bidding in the day-ahead energy market to maximize total revenue. The HRES is a real system located in Southwest China, and its constituent units are a pumped storage, two hydropower, and two solar stations. The Wasserstein metric ambiguity set is adopted to portray the uncertainty of available solar power and the forecasted market price error. Then, the proposed model is formulated as a distributionally robust chance-constrained scheduling model. Finally, the conditional value-at-risk approximation and strong duality are employed to reformulate this model into a tractable problem. The numerical results show that the proposed model can increase the total profit of HRES by 2.26 % (i.e., 2.56 × 104 DKK) compared to the case of fixed BC decomposition and bidding. Moreover, the impact analysis related to the ambiguity set and risk tolerance of uncertainty on total revenue of the proposed model is also presented, which provides the measure for the HRES to trade-off the robustness and economy of the solution. • The dispatch of the hybrid renewable energy system in the power market is studied. • A distributionally robust chance-constrained model is proposed accordingly. • The bilateral contract flexible decomposition is more profitable for the system. • The model can balance the economy and conservativeness of the solution. [ABSTRACT FROM AUTHOR]

The large-scale integration of non-dispatchable renewable energy must be carefully planned, especially in poorly robust electrical energy systems. Tenerife is the most populated of the islands in the Canary Archipelago (Spain) and has an isolated electrical system. It is envisaged for the horizon of 2040 that the island will have a high contribution of non-dispatchable renewable energies (fundamentally wind and solar photovoltaic). Tenerife also has exploitable high enthalpy geothermal resources. An energy planning model is implemented in this paper based on which different scenarios with high non-dispatchable renewable energy penetration in the island's electrical system are simulated. Geothermal energy is also incorporated in the simulations as a dispatchable renewable energy source, with the power required sized according to the hypotheses of each scenario. The results are compared according to different technical and economic parameters. An evaluation is also made of the influence that the capacity of the electrical system to take on higher non-dispatchable renewable power and the presence of energy accumulation systems might have on the minimization of losses in the non-dispatchable renewable energy capacity factor. The results of the simulated scenarios show that geothermal energy could meet up to 28.8% of the island's electrical energy demand, increasing the stability, flexibility and supply guarantee of system. A 100% renewable system could be possible through the incorporation of this energy source and the planning of energy accumulation systems. In the case study, the economic saving for Spain's electrical system could exceed 400 €million per year. • Sustainable electricity generation in poorly robust insular electrical systems. • Large-scale integration management of non-dispatchable renewable energies (NDREs). • Exploitation of geothermal energy in 100% renewable electrical systems. • Importance of energy accumulation systems in NDRE integration. • LCOE decrease through exploitation of renewable energies. [ABSTRACT FROM AUTHOR]

Policies targeting energy efficiency and renewable energy are sometimes viewed as more politically feasible than carbon pricing in pursuit of emission reduction goals. This paper assesses underlying drivers of energy policy development. These factors include economic, social, environmental, and institutional variables. The between estimator for panel data is an appropriate method for our focus on some exogenous variables that vary more across countries than over time. We find that larger oil reserves per capita have a negative relationship with renewable energy policy development. Education and political globalisation have strong positive relationships with both energy-efficiency and renewable-energy policy development. These results suggest that greater participation in global political groups can be an indirect approach toward energy policy development, in cases where direct and immediate policies are hard to implement. OECD countries have higher policy scores by 33 and 25 points in energy efficiency and renewable energy respectively. Blinder-Oaxaca decomposition analysis shows that these higher scores are primarily due to social and political institution predictors rather than economics and physical endowments. • Large-n cross country international sample on continuous policy score measurement. • Political globalisation is positively linked with renewables and energy efficiency policies. • Education is positively linked to energy policy development, especially for efficiency. • Higher OECD countries' policy scores are primarily due to social and political predictors. • Higher incomes are unlikely to lead to automatic improvements in energy policies. [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]

Buildings are one of the major sectors responsible for high energy demand and GHG emissions worldwide, which is driving expectations for implementing net-zero emission buildings (NZEBs) in many countries. Specifically, most developed countries have a carbon-neutral target set up for 2050, mandating all new buildings from then to be constructed as net-zero emission. Therefore, this paper investigated the economic feasibility of implementing NZEBs in the United States residential sector by combining a heat pump system with two renewable energy sources that are heavily supported by the federal government: solar and geothermal technologies. First, this study clearly demonstrates that "net-zero emission" is the most challenging task among neutralizing the energetic, economic, and environmental by-products from building operations. Moreover, this study analyzed the payback periods of multiple NZEB scenarios by considering the potential future changes in technology and policy necessarily required to meet the net-zero emission target by 2050. For reference, the technological and institutional factors were represented by "PV energy conversion rate" and "CO 2 equivalent price of emission trading scheme (ETS)", respectively. The results show that improving the PV energy conversion rate is much more effective in reducing the payback period of NZEBs compared to raising the CO 2 equivalent price of ETS. However, the growing public awareness for this CO 2 regulation policy will soon bring synergistic effect with the advancement of PV technology. In conclusion, this research framework clarifies the rather vague technological and institutional challenges that should be addressed to allow NZEBs to become economically feasible in the United States residential sector. Therefore, many building engineers, PV technicians, and policymakers should actively contribute to improving the techno-economic drivers for implementing NZEBs by 2050. • Most developed countries are mandating all new buildings from 2050 to be constructed as net-zero emission. • Net-zero emission target is hardly achievable without the simultaneous energy supply from solar and geothermal. • PV energy conversion rate should reach 43.50% to meet the net-zero critical point. • Improving the PV energy conversion rate is highly essential for reducing the payback period of NZEBs. • The growing public awareness of ETS will bring synergistic effect with the advancement of PV technology. [ABSTRACT FROM AUTHOR]

The rapidly growing state of energy consumption and the urgency of mitigating the harmful effects of climate change have accelerated our transition to renewable energy. This paper proposes a multi-level framework for exploring the global competition to develop renewable energy technologies. Through a combination of text mining techniques and by analyzing a corpus of patents covering six renewable energy technologies from 1970 to 2019, we reveal the lifecycle of these technologies, the original sources of these technologies, their potential markets, and the different supply and demand patterns of the major countries/regions involved. The results show that solar and wind energy are currently the most widely used renewable energy sources, yet biomass is the only energy source still in its growing stage. China's patents have seen a rapid upward trend, spurred on by strong policy promotion, which runs counter to the decrease in patent applications seen in most other major countries in this area. However, China's technology imports still far outweigh its exports, indicating that there is still a sizeable gap between China and some of the more developed countries like the United States, Germany, France, and the United Kingdom in terms of high-quality, high-value patents. Accordingly, China, the United States, and Japan are currently the leading in the world's renewable energy markets. That said, the market potential of Europe, Canada, and Australia should not be underestimated. These and more themes are discussed from a political, economic, environmental, and global governance perspective. This paper provides useful insights into the global renewable energy technology landscape, particularly the competitive dynamics at play. The aim is to guide policy makers or managers on the next steps in our progress toward applying and using renewable energy at scale. [ABSTRACT FROM AUTHOR]

With the goals of carbon peaking and carbon neutrality, China's renewable energy has entered a stage of rapid development. The government gives financial and power-rate subsidies to clean power while putting great support on its construction. However, there is currently a big gap in the government's subsidy on new energy power generation, which is challenging to meet the demand for renewable energy consumption and sustainable development. In this regard, participating in the market is inevitable for renewable energy development, which is significant for promoting renewable energy power consumption and alleviating the subsidy dilemma. Considering the market-based mechanism, this paper regards the electricity market, the tradable green certificate market, and the carbon emissions allowance trading market as an integrated energy platform system that couples carbon emissions with electricity, linking generators and retailers. We consider two scenarios, including green electricity–green certificate separation and unity, and introduce the intermittency of new energy generation to construct the optimal pricing decision for this platform supply chain. We explore the optimal price of tradable green certificate for renewable power and the equilibrium demand for green power and thermal power in two circumstances. The results show that: (1) there is an equilibrium solution in various conditions. Moreover, the optimal price of green certificate in condition two, namely green electricity–green certificate unity, is higher when the on-grid price of green electricity and thermal one and the substitution effect of these two powers satisfy a certain constraint, which can stimulate generator and retailer to generate and consume green electricity as much as possible. (2) The price of carbon emissions and the responsibility weight of renewable electricity consumption both have a positive influence on the optimal price of green certificate and the share of equilibrium demands for green power, whereas intermittency of new energy generation has a negative impact on them. Adjusting the carbon price and incentive responsibility weights and taking relevant measures to reduce intermittency in different situations help to reduce thermal power demand and promote green power consumption. (3) When the prices of green certificates in diverse circumstances are in a certain interval, the impact intensity of changes in green certificate price on electricity demand will arise. It is conducive to performing the facilitative role of green certificate price on the power market better and stimulates the generation and consumption of new energy power in the carbon-electricity coupling market. • Through the carbon-electricity coupling perspective, the optimal TGC price was explored. • Introducing substitution effects of green power and thermal power to analyze optimal TGC prices. • A higher incentive responsibility weight of renewable consumption in scenario two can promote the retailer to consume. • Impact intensity of changes in green certificate price on power demand will arise when the price is in a certain interval. [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]

Global warming, climate change, and resource depletion have forced us to reconsider energy usage and efficiencies over the last few decades. Residential and commercial buildings are both large energy consumers, so improving energy and material usage efficiency in this sector is a common research topic. According to a recent study, the Building Sector (BS) accounts for 40% of greenhouse gas emissions. The primary objective of this paper is to examine and assess the potential of Renewable Energy Systems (RES) and their combinations for enhancing energy efficiency in the BS. Specifically, the focus will be on converting low energy-efficient buildings into highly efficient ones. The potential of the RES and their combinations for the BS is evaluated based on payback durations, energy generation, and reduction of CO 2 emissions. The optimization flow charts for the RES, feasibility studies, commercialization road maps of energy storage systems and the necessity of control mechanisms for enhancing RES efficiency were discussed. Additionally, the technology drawbacks are discussed, along with various innovative techniques recommended to direct future study in this area. Finally, this article assists the audience clear idea in the selection of the right combination of potential RES based on different conditions to achieve deep decarbonization in BSs. • Buildings sector (BS) responsible for 40% of GHG emissions. • Study assesses potential of Renewable Energy System (RES) in BS. • RES evaluated on payback, energy generation, and CO 2 reduction. • Optimization, feasibility and commercialization studies were discussed. • Article recommends right RES combinations for deep decarbonization in BS. [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]

In this paper, the optimum operation of a distribution network in the presence of renewable resources, and a new combined system of liquid carbon dioxide energy storage is investigated. A comprehensive structure is proposed for modeling and optimizing the combination of wind turbine and solar sources with the new energy storage system by considering their converters. Then, a multipurpose structure in the presence of these resources and the new storage resource is presented to overcome the uncertainty of the output power of renewable resources and improve the reliability of the grid. To this end, first, by considering the probabilistic nature of wind and solar resources, a relatively comprehensive modeling of the system is presented. Economic costs, including the cost of setting up units, cost of generation, and emission of these units, are presented as optimization problem. The effects of the storage system on the load curve are investigated and discussed. The proposed objective function is determined based on optimal load distribution and technical constraints. The multi-objective group teaching optimization algorithm is used to solve the microgrid optimal operation problem. The proposed model considering three cases, is successfully implemented on a 33-bus RBTS distribution network. The results indicated that the total cost of the proposed system in the first, second, and third case is decreased by about 2.88%, 21.78% and 21.2% compared to the base case study. The cost reduction in the first case is due to the use of renewable energy sources, and this reduction in the second case is more due to the use of new storage. Finally, in the third case, due to the consideration of uncertainty and reliability index, compared to the second case, the amount of cost has increased slightly, but compared to the main case, there is a significant decrease, which indicates the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

A comprehensive understanding of the impact of renewable/non-renewable energy consumption on ecological quality and economic growth can serve to the sustainability of energy, ecology, and economy. This paper provides a new perspective of the comprehensive system of the 3E model, and introduces indicator of social factor to examine the non-linear effects of urbanization on energy, economy, and the environment. A threshold panel regression model is developed using the data of 120 countries in the last 20 years. In the proposed model, renewable energy and non-renewable energy are explanatory variables, economic growth and ecological footprint are explained variables, and urbanization are threshold variable. Non-linear relationships are concretized and quantified in the research of global and different income groups. The results show that global renewable energy can promote economic growth while improving the environment. As the urbanization rate increases, the negative effect of renewable energy on the ecological footprint first weakens and then increases, and the positive coefficient on the economy maintains a growth trend. Non-renewable energy has a more obvious positive effect on economic growth, but it increases the ecological footprint. The development of urbanization strengthens its promotion of the economy and reduces its pressure on the environment. From the perspective of different income groups, renewable energy does not always suppress the ecological footprint. The energy transition reduces economic growth in the initial stages of urbanization in certain regions. After crossing the threshold, the coefficient changes of different income groups have similarities and differences. Based on the conclusions, some recommendations are put forward, including promoting the transformation of energy consumption structure, improving energy efficiency and accelerating the process of urbanization. The model in this paper is suitable for investigating the nonlinear impact of urbanization on the 3E system through panel data, and can provide theoretical support for studying the threshold effect of a country or economy. This study also shows the importance of renewable energy and the indirect effects of urbanization, which is helpful for formulating global sustainable and coordinated development policies. [Display omitted] • A new perspective of the energy-economy-environment (3E) model is proposed. • Threshold models enable the non-linear relationships to be concretized and quantified. • Global renewable energy improves the environment while promoting economic growth. • Urbanization reduces the pressure on the environment from energy consumption. • Urbanization strengthens the positive effect of energy on economic growth. [ABSTRACT FROM AUTHOR]

Military expenditures in the Asia-Pacific region have grown rapidly in tandem with economic growth over the past few decades. The military sector's impact on environmental change has not been given enough attention. Renewable energy utilization in the Asia-Pacific region also has increased significantly. This paper examines the relationship between militarization, economic growth, renewable energy utilization, and environmental change in the 15 countries participating in the Regional Comprehensive Economic Cooperation (RCEP) agreement using data that covers 1990–2018. The panel cointegration and cross section autoregressive distributed lag (CS-ARDL) models are used to examine the long- and short-term relationships among variables. The empirical results show that economic growth and rising levels of militarization have a positive impact on a country's ecological footprint. The increase in military capital intensity has aggravated the environmental harm caused by the military. Improving the utilization rate of renewable energy can alleviate detrimental environmental effects in the long term. This study also puts forward policy recommendations from the policy perspective of sustainable development to reduce the level of regional militarization and improve the utilization of renewable energy. • Militarization is the cause of environmental degradation. • The increase of military capital intensity will aggravate its damage to the environment. • Renewable energy use can ease ecological footprint expansion. [ABSTRACT FROM AUTHOR]

With the rise of renewable energy sources (RES) due to detrimental impacts of climate change, the potential for clean energy sources such as hydrogen has gained attention. The development of various technologies, such power-to-gas (P2G) and hydrogen-based energy systems (HES), enables the integration of these systems with conventional ones in this task. This approach will help improve efficiency and resolve the substantial carbon emission problem on the demand and supply sides. This paper focuses on using hydrogen as a primary source of energy and uses wind turbine (WT) and Photovoltaic (PV) as RES. Hot- and chilled water storages besides a HTS are implemented to improve energy efficiency. Upstream grid and hydrogen markets are connected to this system to compensate for the energy deficiency in some periods. P2G functions as a carbon recycling system by reducing carbon emissions and generating extra heat from its chemical reaction for end-users. The proposed hydrogen-based energy system combined with Power-to-gas (HES-P2G) is based on stochastic programming, and takes into account various uncertainties. The nonlinearity in some restrictions is converted using the piecewise linearization technique. The epsilon-constraint method is used as a multi-objective optimization to achieve both economic and environmental goals. The fuzzy satisfying technique is used to obtain a trade-off solution between two objectives. The numerical results show the impacts of using P2G in HES in reducing carbon emission and operating cost. The current HES can significantly reduce carbon emissions; however, using P2G influences environmentally and economically. P2G will thereby cut carbon emissions by 14.2% and operating expenses by 11.7%. [Display omitted] • Multi-objective optimization of a hydrogen-based renewable penetrated multi energy system. • Investigating the impacts of carbon capture system on recycling the carbon emission of system. • Using the scenario-based approach for uncertainty modeling of stochastic resources and loads. • Using the fuzzy-based method for satisfying the objectives and obtaining an optimum point. [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]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

The reduction of CO 2 by moving from fossil to renewable energy sources is currently high on the agenda of many governments. Simultaneously these governments are also forcing the reduction of energy consumption. The primary focus of these agendas is on mobility, building, and industrial sectors. For the latter, energy-efficient shop floors and machining processes assist the reduction of energy consumption. Previous research has focused on energy-efficient machining strategies during machining processes. However, an energy-efficient start-up of these machines or their spindle axis start-up has been neglected until now. This paper focuses on this neglected issue by comparing the energy-efficiency, production time, and cost-efficiency of the CNC (computer numeric control) machine by varying the power input at the spindle axis. This is done by analysing the high-frequency data (500 H z) of the machine from machining operations that is retrieved via the edge device. Concepts of data analytics and especially EDA (exploratory data analytics) were used to interactively visualize the inter-dependencies and develop results. It is shown that optimized reduction of spindle power input value leads to both: peak power smoothing from 20kW to 10kW and lowering of overall energy consumption by approximately 1.4%. Moreover, the costs and production time are marginally affected (0.518% and 0.523% respectively) by this optimized reduction of spindle power input value. Thus, this paper highlights a novel method from data acquisition to process improvement towards energy-efficient and sustainable machining. [Display omitted] • Energy-efficient machining via innovative technique of data acquisition and analysis. • Sustainable peak smoothing for spindle operations. • Utilization of high-frequency machine data for enhancement of process. [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]

Energy hubs as a multi-carrier energy system with various energy carriers and conversions have been integrated with smart grids to increase the efficiency, flexibility, and reliability of the network. Today, scheduling multi-energy hubs (MEH) is one of the challenging issues in smart grids. In this paper, the optimal schedule of interconnected MEHs based on the robust optimization in the presence of electric vehicles (EVs) is proposed. A robust optimization approach is considered to deal with uncertainties of electrical price, renewable energy resources (RESs) generations, and electrical loads. In this paper, MEHs network include commercial, industrial, and residential energy hubs. The energy consumption of MEHs consist of electrical, water, gas, and heat. RESs, demand response (DR) programs, heat and electrical energy storage devices are used to supply the energy demand. The aim of the proposed model is reducing the total cost of operation and carbon emission. The price-based demand response program is applied to shift the power consumption from on-peak to off-peak times in response to the price signal. Finally, the problem is solved for the three cases with CPLEX under YALMIP MATLAB. The results illustrate the effectiveness of the proposed model to achieve optimal scheduling of MEHs. The proposed problem is organized in three scenarios, among which the scenario I determines optimal scheduling of MEHs, using robust optimization method. The scenario II investigates the problem with deterministic model. The scenario III evaluates the problem applying robust optimization method without DR program and energy storage device consideration in autonomous EHs. The numerical results show that the total operation cost is increased 15.46% in scenario I compared with the scenario II due to applying the worst-case of uncertainties in robust optimization approach. • An optimal scheduling of Multi-Energy Hubs is proposed with energy costs and emission. • Electrical, heat, gas and water demands, RESs, storages, and EVs is considered with DR. • A robust optimization is used to cover uncertainties of price, generation of RESs and loads. • Three interconnected EHs as MEHs including commercial, industrial and residential sectors. • The proposed problem is optimized in three cases and the results are compared with each other. [ABSTRACT FROM AUTHOR]

Renewable energy consumption has a strong driving force for promoting energy conservation and emission reduction, and is a new way to achieve clean and low-carbon development. Based on this, this paper uses the data of renewable energy consumption, carbon productivity, GDP, population, energy efficiency and technological innovation of 30 provinces in China from 2011 to 2020, and empirically analyzes the impact of renewable energy consumption on carbon productivity in 30 provinces in China, using STIRPAT model, comprehensively considers spatial horizontally and temporal vertically dimension. The empirical results show that: (1) China's carbon productivity shows a significant spatial spillover effect, its impact can spill over to adjacent areas within 1000 km; (2) China's renewable energy power consumption plays a significant role in promoting the improvement of carbon productivity. (3) Renewable energy consumption in the western region plays the most important role in driving carbon productivity. Finally, according to the temporal and spatial relationship between renewable energy consumption and carbon productivity in different regions, we provide specific regional policy suggestions. • The STIRPAT model is employed to explore temporal and spatial impact, while it considers sub regions. • Spatialy, the impact can spill over to adjacent areas within 1000km. Temporally, the impact fluctuates and rises. • The positive role of renewable energy consumption is found in China's western region. [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]

Limiting the impact of human use of energy on the environment is necessary to maintain the Earth's ecosystems such that they can continue to provide the ecosystem services on which humans and all other living organisms depend. Because the methods available to limit the impact on the environment of energy production and use require energy, energy used for this purpose—termed ecosystem maintenance energy (ESME)—reduces the net energy available. A capacity to estimate the energy that can be produced while sustaining ecosystem services, termed green energy, is critical to future energy use. This paper sets out a method to determine ESME by assigning energy consumption to the processes needed to remove the source of the impact on the ecosystem that results from the energy system lifecycle. We illustrate the method by the use of a case study based on a concentrated solar power plant (CSP) with natural gas back up. Green energy production is assessed in terms of the green energy return on energy invested, EROIg. CSP ecosystem impact is determined via a lifecycle assessment of the plant's operations for varying degrees of natural gas back-up. ESME is then determined by calculating the energy consumption of the impact source removal (ISR) process needed to remove the ecosystem impact. We show that, although the CSP plant produces increasing amounts of net energy with increasing natural gas back-up, the capacity to produce net green energy reduces with increasing natural gas back-up once ESME is included. For the CSP plant examined, EROI increases, while EROIg decreases for increasing use of natural gas. This indicates the large impact of ESME on the plant's energy return and the necessity of its inclusion in energy assessments. • Ecosystem maintenance energy (ESME) should be counted as energy cost. • For green energy production, EROI is greatly affected by ESME. • Even renewable energies have large ESME costs affecting their EROIg. • ESME is computed using the ISR energy associated with the lifecycle impact. [ABSTRACT FROM AUTHOR]

This paper examines the nexus between renewable energy consumption (REC) and Gross Domestic Product (GDP) in G7 countries. To this end, we analyse data from 1980 to 2020 employing Toda and Yamamoto (1995) time causality test, Lemmens et al. (2008) frequency causality test and Hatemi-J's (2012) asymmetric causality approach. The findings differ across countries and are as follows. The conservative hypothesis is validated for the US and Canada. Specifically, we find significant and positive causal effect from GDP to REC in the long-term horizons (periods greater than 15 years for the US and 7 years for Canada). The growth hypothesis is confirmed for Germany. Precisely, significant and positive causal impact is shown from REC to GDP in the long-run horizons (periods longer than 7 years). The neutrality hypothesis is supported for France, the UK, Italy and Japan as there is no significant causal link at any time horizon. This is likely because the clean energy consumption does not reach a threshold beyond which it will start to have considerable interaction with economic growth. This study provides policymakers a better understanding of the REC-economic growth nexus to formulate appropriate policies. • We examine the nexus between renewable energy consumption and economic growth in G7 countries. • We use Toda and Yamamoto (1995) and Lemmens et al. (2008) tests and Hatemi-J's (2012) asymmetric causality approach. • For the US and Canada, significant and positive causal effect is shown from GDP to REC in the long-term horizons. • For Germany, significant and positive causal impact is displayed from REC to GDP in the long-run horizons. • For France, the UK, Italy and Japan, no significant causal link is found between REC and economic growth. [ABSTRACT FROM AUTHOR]

All economies need energy for growth, particularly emerging ones with continued demand for fossil fuels that move them to unconventional energy sources such as heavy oil. However, the presence of large quantities of heteroatoms -sulfur, nitrogen, oxygen, and metals – in heavy oil is a major contributor to environmental pollution during the combustion phase. This requires the crude and residue to be upgraded and fractionated within tight specifications. Hot compressed water (HCW), at or beyond the critical point (374 °C, 22.1 MPa), has emerged as a highly promising environmentally benign and efficient method for heavy oil upgrading to replace conventional toxic solvents. The synergistic effect of HCW with energy generated from renewable sources is a new avenue into a cleaner carbon energy system for fuel production with the benefits of reduced energy consumption and lower carbon dioxide emission. This paper provides a comprehensive review in multi-scale which underlines the recent evolution and the challenges in this field. In addition, the critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. Eventually, a set of suggestions for integrating renewable energy technologies with heavy oil upgrading in HCW and future perspective has been reported. [Display omitted] • The synergistic effect between HCW and heavy oil as a cleaner carbon conversion system is reviewed. • A comprehensive view in multi-scale which underlines the recent evolution and the challenges in this field. • Critical factors and emerging gaps influencing the heavy oil upgrading in HCW are appended. • A logical integration of renewable technologies with carbon-based energy conversion system in HCW is suggested. • Possible research suggestions and directions on this field have been proposed. [ABSTRACT FROM AUTHOR]

The increasing global consumption of energy and its negative impacts on the environment has been an issue discussed both by academics and practitioners, including in the health sector. Hospitals are major consumers of energy, which leads to increased operating costs, decreased competitiveness, and negative environmental impacts. Energy management in hospitals must be understood as a way to contribute to environmental sustainability and as a way to improve competitiveness. Therefore, the objective of this paper is to propose guidelines for efficient and sustainable energy management in hospitals, developed based on the literature review, the challenges faced and the good practices of sustainable energy management in the hospitals studied, the cross analysis of the case studies, and the authors' expertise. As a result, the eight proposed guidelines are related to culture change, energy consumption management, energy management standard, purchase of renewable energy, green technologies, sustainable hospital buildings, social responsibility programs focused on energy use, and sustainable energy public policies. These guidelines contribute in an applied/managerial way to help managers of public and private hospitals overcome the challenges of energy management and contribute to UN's SDG7 due to its potential to improve energy efficiency and environmental performance, therefore, being a movement towards clean energy. This study also theoretically/scientifically contributes to the academic debate since the empirical findings found in the hospitals studied and the proposition of guidelines deepen and expand the key elements identified in the literature on efficient energy management in hospitals, providing the basis for developing new research on the subject. Energy management in hospitals is discussed from the perspective of sustainability. Challenges need to be overcome for sustainability benefits to be achieved. This work can assist public and private hospitals in developing sustainability. Guidelines for sustainable energy management in hospitals are proposed. [ABSTRACT FROM AUTHOR]

Despite the fact that research on multigeneration and polygeneration systems are available, more efficient and effective systems that fulfill several energy demands at the same time are still needed. Furthermore, efficient use and harvesting of renewable energy sources are critical for global emission reductions. Accordingly, this paper proposes a novel geothermal-solar-based hybrid system for simultaneous power and freshwater production. The overall objective of the study is to model the proposed cogeneration system with energy and exergy exergoeconomic, and economic viewpoints, and obtain the optimum performances of the system using non-dominated sorting genetic algorithm II (NSGA-II) method and LINMAP decision-making approach. According to the obtained results, at the base operating conditions, the exergy destruction rate for the CPVT is the highest with 311.3 k W amongst other components, followed by the RO unit with a value of 159 k W. Also, the net output electricity of the total system and freshwater generation rate are computed to be 775.3 k W and 10.14 k g. s − 1 . Also, for the first optimization scenario (S U C P − η e x ), the exergy efficiency and sum unit cost of the products are obtained by 42.31 % and 16.32 $. G J − 1 , respectively. And, the produced electricity and freshwater are computed to be 755.3 k W and. 16.44 k g. s − 1 • Proposal and study of a newly designed electricity/freshwater co-production system. • Smart use of solar and geothermal energies for the performance enhancement. • Energy, exergy, exergy costing, and NPV methods together with NSGA-II optimization. • The optimum exergy efficiency and unit cost are separately 42.31% and 16.32 $/GJ. • The optimum payback period of the system is estimated to be 1.07 years. [ABSTRACT FROM AUTHOR]

As a result of the increased demand for energy combined with the limitations of traditional energy sources, it is necessary to turn to renewable energy sources such as solar energy, which is one of the most promising technologies to assist the transition towards sustainable development. With the increasing interest in renewable energies in general and solar energy in particular, attempts have been made to ensure that solar energy technologies provide an amount of energy equal to or close to the amount of energy currently consumed. Solar photovoltaic farm applications have become so widespread that they are now relied upon to produce massive amounts of energy for use in factories and homes. In this regard, identifying and prioritizing convenient areas for the construction of expensive solar photovoltaic farms is an important issue. Additionally, determining the suitable geographical locations for photovoltaic farms is not only related to the amount of solar radiation received, but many dimensions must be taken into accounts such as technical, economic, environmental, socio-political dimensions, risks, and their sub-indicators. To overcome the problem of prioritizing and selecting the best location for solar photovoltaic farms to achieve sustainability factors resulting from the presence of many different indicators, this paper applies a hybrid multi-criteria decision-making approach under a neutrosophic environment. Initially, the Delphi method is employed to capture preference and to determine the dimensions and their indicators in addition to provide preference among sub-indicators and available alternatives. The proportional importance of the designated dimensions and their sub-indicators was evaluated by the Decision-Making Trial and Evaluation Laboratory. Then, Vlse Kriterijumska Optimizacija Kompromisno Resenje is adopted to rank seven solar photovoltaic farm locations in Egypt. The Benban solar photovoltaic farm is shown to be the most sustainable alternative among the solar photovoltaic farm locations followed by Darb Al Arbaeen solar photovoltaic farm and so has priority for increased installation investments in solar energy production. To validate the proposed approach, results comparisons were implemented to indicate the advantages of the hybrid multi-criteria decision-making methods. Sensitivity analysis reveals that changing criteria weightings does not affect the ranking order. [ABSTRACT FROM AUTHOR]

As one of the environmentally friendly technologies, heat pumps have been widely utilized in residential and commercial buildings all over the world. The use of renewable energy sources and environmentally friendly refrigerants are two major challenges associated with heat pumps applications. Due to the advantages of cascade heat pumps over single-stage heat pumps and the higher energy demand met by cascade heat pumps, investigation of the effects of geographic conditions (weather and soil conditions) as well as the simultaneous use of two refrigerants for high and low temperature cycles on performance of cascade heat pumps would provide critical information for practical applications. This paper compares the performance of air source and ground source cascade heat pumps using refrigerants by low global warming potential and zero ozone depletion potential in top 10 coldest cities of Russia. The effects of variations in temperature of evaporator and cascade heat exchanger on optimal operational conditions are investigated based on energy, exergy, economic and environmental analysis using Pareto front curve. Optimization results show that there is an optimal point for evaporator temperature and cascade heat exchanger temperature where the maximum COP, maximum exergy efficiency and minimum total cost rate for ASHP and GSHP systems are obtained. It is found that at low air temperature, since the underground temperature is higher than ambient air, the efficiency of GSHP system is better than efficiency of ASHP system. The maximum COP of 2.357 and 2.44 are respectively obtained for ASHP and GSHP systems in Saint Petersburg. In addition, Yakutsk city has the maximum exergy efficiency of 40.7% and 43.2% and maximum total cost rate of 14714 $ year−1 and 13693 $ year−1 for ASHP and GSHP systems, respectively. Finally, it is concluded that Saint Petersburg is the best city to employ ASHP system while GSHP system is suggested for Khabarovsk city. • Performance of ASHP and GSHP is compared in 10 coldest cities of Russia, thermoeconomically. • Maximum COP of 2.357 and 2.44 are respectively obtained for ASHP and GSHP in Saint Petersburg. • Maximum exergy efficiency of 40.7% and 43.2% are achieved for ASHP and GSHP in Yakutsk, respectively. • Yakutsk also has the maximum total cost rate of 14714 $ year−1 and 13693 $ year−1 for ASHP and GSHP. • Saint Petersburg and Khabarovsk are the best cities to employ ASHP and GSHP. [ABSTRACT FROM AUTHOR]

Nowadays, due to the raising research interest in net and nearly zero-energy concepts at community level, great efforts are spent to investigate self-sustaining and energy-independent communities. Small and off-grid islands represent one of the best places to focus the attention on, and to suit pilot projects of self-sufficient energy communities. In this context, the dynamic simulation represents a useful tool for designing, analysing, and optimizing islands' energy system layouts, especially in case of renewable energy system adoption (due to the performance unpredictability of renewable energy sources). Therefore, powerful yet flexible dynamic simulation tools are increasingly in demand. In this framework, this paper presents a novel dynamic simulation model, developed in TRNSYS environment, purposely conceived to investigate innovative technologies, novel system layouts, and advanced control logics, in the case of small and off-grid islands, aiming at reaching the nearly/net-zero energy goal at a community level. To prove the potential of the novel dynamic simulation tool, a suitable case study referring to the existing island community of El Hierro (Canary Island) was considered and investigated. Specifically, to enhance the island energy independence, diverse scenarios and system layouts, including different renewable energy based technologies (e.g. wind turbines, hydroelectric power plants, solar thermal collectors, etc.) have been investigated by the novel developed simulation tool. Results demonstrate the potentiality of the adopted method as well as the feasibility of the proposed system layouts. Specifically, the investigated scenarios provide up to 85% of the annual electricity demand and about 79% of the annual thermal energy needs by renewable energy sources with substantial economic savings. [Display omitted] • Modelling and simulation of small islands hybrid multi-generation energy systems. • Using dynamic simulation to enhance off grid-islands energy independence. • Development of a design tool to boost renewable energy penetration of small islands. • Energy, economic and environmental optimization of islands' grid energy fluxes. • Exploitation of energy storage to mitigate fuel consumption in off-grid communities. [ABSTRACT FROM AUTHOR]

Generation of electricity comes with the emission of toxic gases into the atmosphere by the fossil fueled generators. Along with the promotion in the utilization of renewable energy sources (RES), it is also the duty of the power engineers to arrive at a compromised solution such that less emission of toxic gases occurs with economic generation of electricity. This paper proposes a balanced trade-off method for solving environment constrained economic dispatch (ECED) problems. A novel comparative analysis is performed among proposed ECED method with existing price-penalty-factor (PPF) and fractional programming (FP) methods for solving combined economic emission dispatch (CEED) problems on a 3-unit dynamic test system to sort out the method, which yields a better trade off solution between generation cost and pollutants emitted. An algorithm, following the hunting strategy of wolves, is improvised by incorporating strategies from population-based sine-cosine algorithm along with position updating methods of crows to form a robust hybrid algorithm, which was used as the optimization tool for the study. Involvement of RES diminished the generation cost to 5.5% for both economic dispatch and PPF based CEED, and 6.5% decrease in emission of pollutants was observed due to the same. The generation cost and amount of emitted pollutants, evaluated using proposed ECED approach, were much closer to the economic dispatch and emission dispatch values respectively compared to PPF based and FP based CEED solution. Furthermore, statistical analysis endorses the superiority of the proposed hybrid optimizer over other algorithms presented in the state-of-art literature. • Three different types of CEED methods are studied. • Subject test system is a 3-units RES integrated microgrid system. • Comparative analysis among the generation costs and pollutants emitted is performed. • A novel HMGWO algorithm was proposed as the optimization tool for the study. [ABSTRACT FROM AUTHOR]

Among the main components of a smart city, the energy system plays a vital and core role in the transition towards a sustainable urban life. Furthermore, the utilization of renewable energy sources has been demonstrated as a significant contribution to reducing pollutant emissions and enhancing the quality of the living environment. Therefore, designing the energy systems based on clean and renewable criteria is considered a sustainable solution for smart cities. Indeed, the deep and rapid penetration of renewable energy-based technologies have been believed to very well fit into a smart city under various scales, this could supply a secure basis for a modern society with a low-carbon economy. In this review paper, the main components and roles of renewable energy resources (such as solar, wind, geothermal, hydropower, ocean, and biofuels) for the smart city were fully introduced. Besides, integrating the renewable sources form into the energy systems of smart cities was thoroughly analyzed on the basis of technical and economic criteria. Finally, existing challenges and future scenarios were also discussed in detail to clarify the progress and perspective of smart renewable energy systems for the smart city. In general, the integration of renewables into energy systems of the smart city is a sagacious perspective and solution aiming to achieve cleaner process and more sustainable development. However, the optimization issues of the energy system for integrating of renewable components, ensuring good stability, maximizing the operating range, and minimizing the investment costs should be critically evaluated in the future works. • Designing tools and key criteria for smart renewable energy systems. • Efficiency and prospect of integrating renewables into energy system for smart city. • Existing problems and challenges for renewable-based energy system in smart city. • Future scenarios for the sustainable development of energy system in smart city. [ABSTRACT FROM AUTHOR]