Search

Renewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens. Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users' electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally. [ABSTRACT FROM AUTHOR]

Deforestation is a central topic in the ongoing environmental degradation stemming from global economic expansion and population growth. This study delved into the effects of electricity production from renewable sources, GDP per capita, and urbanization on forest area growth in Finland during the over-three-decade research period, 1990–2022, using an Autoregressive Distributed Lag (ARDL) model. Both the ARDL bounds test and the Bayer–Hanck cointegration tests proved the existence of a long-term cointegrating relationship between the variables, and the constructed error correction model (ECM) evaluated short-term relationships. The results showed that: (i) forest area growth is positively connected with electricity production from renewable sources and urbanization; (ii) forest area growth is negatively connected with economic growth; (iii) in the short run, forest area growth is positively connected with all regressors. The utilized ARDL-ECM model, characterized by its robustness and appropriateness, validated the time-series dynamics. The obtained results were scrutinized, and their policy implications were thoroughly examined. Additionally, recommendations are provided to ensure the sustainability and success of forest conservation efforts. [ABSTRACT FROM AUTHOR]

With the energy structure transition and the development of the green power market, the role of electricity retailers in multi-market green power trading has become more and more important. Particularly in China, where aggressive green energy policies and rapid market transformations provide a distinct context for such studies, the challenges are pronounced. Under demand-side response, electricity retailers face the uncertainty of users’ electricity consumption and incentives, which complicates decision-making processes. The purpose of this paper is to explore the optimization decision-making problem of multi-market green power trading for electricity retailers under demand-side response, with a special focus on the Chinese market due to its leadership in implementing substantial green energy initiatives and its potential to set precedents for global practices. We first construct a two-party benefit optimization model, which comprehensively considers the profit objectives for electricity retailers and utility maximization for users. Then, the model is solved by the Lagrange multiplier method and distributed subgradient algorithm to obtain the optimal solution. Finally, the effectiveness of the incentive optimization strategy under the multi-market to promote green power consumption and improve the profit of electricity retailers is verified by arithmetic simulation. The results of this study show that the incentive optimization strategy under multi-market, particularly within the Chinese context, is expected to provide a reference for electricity retailers to develop more flexible and effective trading strategies in the green power market and to contribute to the process of promoting green power consumption globally.

This study investigated the psychological factors affecting individuals' intention to use green energy within the Theory of Planned Behavior (TPB) framework. The study's results are expected to significantly contribute to increasing the effectiveness of social marketing strategies aimed at encouraging the intention to use green energy. The survey data from 703 participants in five major cities in Turkiye was analyzed using descriptive statistics, confirmatory factor analysis, and structural equation modeling. Green energy usage intentions were significantly influenced by attitude (β:0.67), subjective norms (β:0.45), perceived behavioral control (β:0.14), and egoistic value (β:0.36). The findings suggest that these factors can positively influence green energy intention, enabling effective social marketing strategies and comprehensive political initiatives, including training and awareness campaigns.

The splitting of the German–Austrian electricity bidding zone in October 2018 established limits for cross-border exchanges of power between Germany and Austria, which led to the decoupling of German and Austrian wholesale power prices. We analyze the impact of the split on the investment in lithium-ion grid-scale battery energy storage systems (BESS) used for intertemporal arbitrage in German and Austrian day-ahead power markets with hourly contracts operated by the Energy Exchange of Austria. The paper was written in response to the increasing share of green power in the national energy mix of the two countries, followed by a similar development in the volume of green power traded. Arbitrage with green power was analyzed and compared to arbitrage with power from conventional sources using a robust mixed-integer linear programming (MILP) model extended with a battery degradation process. The model was solved for different levels of depth of discharge (DOD) to identify the optimum between cycle life and calendar aging with respect to prevailing market conditions. Using predicted costs of battery, a break-even point was reached with 60% DOD identified as the optimal pattern for the dispatch of the BESS. A separate scenario was considered for the analysis of the impact of moving from the perfect knowledge of future prices to a situation where day-ahead prices are forecasted using the seasonal autoregressive integrated moving average (SARIMA) model.

The shipping industry has reached a higher level of maturity in terms of its knowledge and awareness of decarbonization challenges. Carbon-free or carbon-neutralized green fuel, such as green hydrogen, green ammonia, and green methanol, are being widely discussed. However, little attention has paid to the green fuel pathway from renewable energy to shipping. This paper, therefore, provides a review of the production methods for green power (green hydrogen, green ammonia, and green methanol) and analyzes the potential of green fuel for application to shipping. The review shows that the potential production methods for green hydrogen, green ammonia, and green methanol for the shipping industry are (1) hydrogen production from seawater electrolysis using green power; (2) ammonia production from green hydrogen + Haber–Bosch process; and (3) methanol production from CO2 using green power. While the future of green fuel is bright, in the short term, the costs are expected to be higher than conventional fuel. Our recommendations are therefore as follows: improve green power production technology to reduce the production cost; develop electrochemical fuel production technology to increase the efficiency of green fuel production; and explore new technology. Strengthening the research and development of renewable energy and green fuel production technology and expanding fuel production capacity to ensure an adequate supply of low- and zero-emission marine fuel are important factors to achieve carbon reduction in shipping. [ABSTRACT FROM AUTHOR]

Transparent luminescent solar concentrator (LSC) windows with edge-attached photovoltaic (PV) cells have the potential for improving building efficiency without compromising aesthetics and comfort. Optimization of such windows requires an inexpensive simulator for experimenting with various designs. We report, for the first time to the best of our knowledge, the simulator of a transparent LSC window in the form of a plastic container filled with a colloid of photoluminescent nanoparticles (NPs) in an organic solvent (1-propanol). The exemplary NPs were produced by ball milling of the powder of rare earth (RE)-doped phosphor NaYF4:Yb3+,Er3+ synthesized by the wet method. The NPs converted the ultraviolet (UV) solar spectrum into visible/near infrared (NIR) via spectral down-shifting and down-conversion (quantum cutting). With a photoluminescence quantum yield (PLQY) of the phosphor

On-grid integration of renewable energy, also called "green power", is attracting more and more attention nowadays. Green power futures can be effective in increasing returns to suppliers and increasing market liquidity. However, compared to traditional futures, green power feed-in tariffs may be subject to integrity problems due to lower power factors; therefore, existing margin calculation methods for the futures market are no longer applicable. A SPAN-based margin calculation method that considers the power factor risk is proposed in this paper. The method provides the classification policies of the green power futures, based on the historical power factors of green power suppliers, and allows the margin amount to be adjusted as per the classification. To verify the effectiveness of the proposed method, empirical validation is presented by applying actual transaction data. Results prove that the proposed method can reduce the margin amount while covering the risk effectively. [ABSTRACT FROM AUTHOR]

Enzymatic Biofuel Cell (EBC) represents a promising green source since it is capable of harvesting electricity from renewable and abundantly available biofuels using enzymes as catalysts. Nevertheless, nowadays long-term stability and low power output are currently the main concerns. To this end, several research studies focus on using complex tridimensional and highly expensive nanostructures as electrode support for enzymes. This increases cell performance whilst drastically reducing the economic feasibility needed for industrial viability. Thus, this paper analyzes a novel flow-based EBC consisting of covalent immobilized GOx (bioanode) and Laccase (biocathode) on a commercial flat conductive polymer. A suitable immobilization technique based on covalent ligands is carried out to enhance EBC durability. The experimental characterization demonstrates that the cell generates power over three weeks, reaching 590 mV and 2.41 µW cm−2 as maximum open circuit voltage and power density, respectively. The most significant contributions of this configuration are definitely ease of implementation, low cost, high scalability, and reproducibility. Therefore, such a design can be considered a step forward in the viable EBC industrialization process for a wide range of applications. [ABSTRACT FROM AUTHOR]

This paper presents a case study on power control and energy management for a 60 apartments' residential building with solar generation and energy storage tied to the grid in Santiago, Chile. A new energy management algorithm based on energy homeostasis is designed for a small electro thermal generation system (nanogrid), with smart metering. The test bed employs supervisory control with energy management that regulates the temperature inside a large room by the action of an HVAC (Heating/Ventilating/Air Conditioning) unit. The main objective of supervisory control is to allow temperature comfort for residents while evaluating the decrease in energy cost. The study considers a room with rooftop grid-tie nanogrid with a photovoltaic and wind turbine generation plant, working in parallel. It also has an external weather station that allows predictive analysis and control of the temperature inside the abode. The electrical system can be disconnected from the local network, working independently (islanding) and with voltage regulation executed by the photovoltaic generation system. Additionally, the system has a battery bank that allows the energy management by means of the supervisory control system. Under this scenario, a set of coordination and supervisory control strategies, adapted for the needs defined in the energy management program and considering the infrastructure conditions of the network and the abode, are applied with the aim of efficiently managing the supply and consumption of energy, considering Electricity Distribution Net Billing Laws 20.571 and 21.118 in Chile (https://www.bcn.cl/historiadelaley/historia-de-la-ley/vista-expandida/7596/), the electricity tariffs established by the distribution company and the option of incorporating an energy storage system and temperature control inside the room. The results show the advantage of the proposed tariffs and the overall energy homeostasis management strategy for the integration of distributed power generation and distribution within the smart grid transformation agenda in Chile. [ABSTRACT FROM AUTHOR]

A recent trend has been a move toward greater reliance on renewable or "green" energy sources, especially in the residential sector. Using a choice experiment, this paper examines how providing information regarding the efficiency, cost, and environmental impacts of different power-generating sources impact consumers' stated preferences for selecting voluntary green-power plans. Based on 21,000 plan choices from two different samples totaling over 1,800 respondents, our results indicate that information nudges significantly impact respondents' choice of plan. Promoting the advantages of the green plan or the disadvantages of the "gray" plan increase green plan selection. The magnitudes of these estimated effects are economically significant being roughly equivalent to a change in the monthly green price premium of $4/month. We also find that promoting the advantages of the green plan is more effective when the green plan premium is relatively small, while highlighting the drawbacks of the gray plan is more effective when the green plan premium is relatively large. Our results suggest that information nudges have the potential to be a plausible, economical, and effective mechanism to increase adoption of voluntary green-power plans. [ABSTRACT FROM AUTHOR]

The influences of renewable and conventional energy consumption on ecological sustainability remain unclear because of the dynamic economic and innovative framework. This investigation gives a new perception by exploring the association between the production of various sources of renewable energies (e.g., hydroelectric, wind, solar PV, geothermal, and biomass power) and economic growth encapsulating capital, government spending, and trade openness. This research used a heterogeneous approach for panel data and second generational tools for econometrics, which allow for cross-sectional reliance and slope heterogeneity. This study has revealed the substantial reason to back up the feedback assumptions between renewable energy sources and economic growth, using the Dumitrescu and Hurlin analysis. In terms of policy, this empirical analysis suggests enacting impactful policies that encourage green power and economic reform in an attempt to lessen CO2 concentrations in the biosphere.

The shipping industry has reached a higher level of maturity in terms of its knowledge and awareness of decarbonization challenges. Carbon-free or carbon-neutralized green fuel, such as green hydrogen, green ammonia, and green methanol, are being widely discussed. However, little attention has paid to the green fuel pathway from renewable energy to shipping. This paper, therefore, provides a review of the production methods for green power (green hydrogen, green ammonia, and green methanol) and analyzes the potential of green fuel for application to shipping. The review shows that the potential production methods for green hydrogen, green ammonia, and green methanol for the shipping industry are (1) hydrogen production from seawater electrolysis using green power; (2) ammonia production from green hydrogen + Haber–Bosch process; and (3) methanol production from CO2 using green power. While the future of green fuel is bright, in the short term, the costs are expected to be higher than conventional fuel. Our recommendations are therefore as follows: improve green power production technology to reduce the production cost; develop electrochemical fuel production technology to increase the efficiency of green fuel production; and explore new technology. Strengthening the research and development of renewable energy and green fuel production technology and expanding fuel production capacity to ensure an adequate supply of low- and zero-emission marine fuel are important factors to achieve carbon reduction in shipping.

Utility voluntary green power programs that allow energy consumers to pay a premium for electricity generated from renewable energy sources have gained great momentum worldwide in recent years. While there is growing literature examining customer behaviors related to green power purchases, limited work has been done to systematically evaluate the efficacy of voluntary green power programs implemented in conjunction with government-led renewable energy policies. In this research, we construct an innovative panel dataset for 49 U.S. states and Washington, D.C. from 2001 to 2013, with the goal to understand factors influencing total sales and customers of voluntary green power programs. Hybrid random effects regression models are estimated to investigate the impact of state renewable portfolio standards (RPSs) on the performance of voluntary green power markets. We find that for a given state, an increase in RPS policy stringency by one unit would lead to a reduction of green power program customer enrollment by 12.37%, and a reduction in green power program sales by 12.98%. Our results suggest that the competing effect is dominant between the voluntary green power market and the renewable energy regulatory regime: state RPSs tend to crowd out cheap renewable-sourced electricity that is available for sale through utility green power programs. [Display omitted] [ABSTRACT FROM AUTHOR]

• Integration of blockchain characteristics into the collaborative carbon emission reduction game model of both the supply and demand sides of electricity is proposed to encompass source control and terminal suppression. • Blockchain is utilized to trace electricity types within the electricity market and to quantify the carbon emissions generated by the demand side of electricity. • By incorporating blockchain, users can actively engage in individual-level carbon trading to control and reduce carbon emissions. • Blockchain facilitates enhanced carbon emission reduction efforts by the power supply side. The task of carbon emission reduction is severe in the power industry in China under the national goal of "carbon peaking and carbon neutrality". The current carbon reduction is mainly based on supply side, but the effect is limited. To solve this problem, this paper proposed a coordinated supply–demand carbon emission reduction strategy based on blockchain technology. Based on the carbon emission reduction of complementary thermal power and renewable energy under the carbon trading mechanism of power supply side, users are made to participate in the individual level carbon trading mechanism with the help of blockchain technology, and the carbon emission reduction in power demand side is guided through the market mechanism, thus forming a supply–demand collaborative carbon emission reduction strategy of source side control and terminal inhibition. By analyzing the decision changes of both the supply and demand sides of electricity before and after the introduction of blockchain, quantifying the influence of blockchain on electricity quantity, electricity price and users' utility in turn, and establishing the personal carbon trading mechanism supported by blockchain, a game model of two-side interaction between supply and demand was constructed. The simulation results show that the collaborative carbon emission reduction strategy based on blockchain gives full play to the potential of carbon reduction in power demand side, and the personal carbon trading mechanism can better inhibit terminal carbon emissions, which is conducive to the deep carbon emission reduction of the power industry. [ABSTRACT FROM AUTHOR]

On-grid integration of renewable energy, also called “green power”, is attracting more and more attention nowadays. Green power futures can be effective in increasing returns to suppliers and increasing market liquidity. However, compared to traditional futures, green power feed-in tariffs may be subject to integrity problems due to lower power factors; therefore, existing margin calculation methods for the futures market are no longer applicable. A SPAN-based margin calculation method that considers the power factor risk is proposed in this paper. The method provides the classification policies of the green power futures, based on the historical power factors of green power suppliers, and allows the margin amount to be adjusted as per the classification. To verify the effectiveness of the proposed method, empirical validation is presented by applying actual transaction data. Results prove that the proposed method can reduce the margin amount while covering the risk effectively.

In the recent time, because of global warming caused by the growing environmental disaster, government and non-government agencies around the world are actively involved in the implementation of energy saving and carbon reducing products and plans. Our team has targeted and improved the problem of water getting colder as it is being used. It can, as well, effectively improve the efficiency of the consumption of heat and electricity. And this solves the problem of solar water-heaters. In order to solve the weakness of the first generation water-heating tank. The second generation water-heating tank, which put a partition disk in the tankhas been invented. The partition disk likes partition wall, separating two room inside the tank. There is a aqueduct between the left room and right room. When cold water flows in the tank, it flows in right room first, and flows in left room slowly by aqueduct. This research is willing yo find the best position to set partition disk, and make it bring into maximum affect.

Nowadays, due to the utilization of an abundant amount of fossil fuels environmental pollution increases rapidly. In order to rectify such a major problem, the power sector focused on distributed generation technology which increases the quality, sustainability, and reliability of electricity supply. There are different kinds of distributed generation resources accessible in the electricity market in the range of 5 KW to 5 MW. To mitigate transmission loss and to increase the stability of the power sector, distributed generation resources are located near be end-user load. This article presents a comparative analysis of traditional and modern distribution generation technologies including general information, size, fuel and rate of emission of gases, efficiency, applications, and also its pros and cons. This article has described the prime topology, namely, the microgrid of the distribution network. The article elaborates on the primary structure known as the microgrid within the distribution network. A thorough survey is presented, focusing on the current advancements in distributed generation and its associated technologies. The efforts in two different areas (traditional and modern) distributed generation technologies are reflected in this survey. Finally, this article ends with the concluding notes. • Comprehensive survey on distributed generation. • Comparative analysis of modern and traditional distribution generation technologies. • Comparative analysis of ac, dc, and hybrid ac-dc microgrid architectures and their associated aspects. • Outcomes of distributed generation technologies in modern power systems. • Assessment of integration of distributed energy resources and its impact on power network stability.. [ABSTRACT FROM AUTHOR]

• Utilizing surplus PV capacity of net-zero energy buildings is evaluated in this study; • Electric vehicle charging stations in nearby parking lots can consume the surplus electricity effectively; • The study identifies four distinct clusters representing different types of parking lot improvements; • The findings highlight the positive energy, environmental impact and economic viability of PV installations. Achieving net-zero energy (NZE) in buildings involves laying down photovoltaics (PV) over large building areas, and the issue of dissipating surplus PV capacity has been a challenge. With the popularity of electric vehicles (EVs), parking lots attached to buildings offer such a possible solution. In order to assess the possibility of applying PV surplus power to electric vehicle charging stations (EVCSs) in parking lots, a workflow based on the p-center model was developed in this study using scenario analysis. Considering New York City as the research focus, 100 buildings satisfying the prescribed criteria were screened. Subsequently, the potential of the parking facilities of the selected buildings was evaluated for dissipating surplus PV capacity, focusing on achieving both NZE and green power goals. The results show that the NZE goal is achieved between 3 % and 13 %, while the GP goal is determined as 20 %. The shape factor of the buildings and the goal completion rate from the four clusters reflect the improvements in the four types of parking lots. The average reduction in greenhouse gas (GHG) emissions for the five PV installation scenarios was 67.10, 112.94, 147.41, 171.00, and 187.24 MTCO 2 e, with an average payback period of 0.71, 1.87, 2.21, 2.47, and 2.22 years, respectively. In addition, 13 samples met the NZE goal, and 29 samples met the GP goal, representing 13 % and 29 % of the sample size sample, respectively. The study confirms the advantages of the building-to-vehicle-to-building (B2V2B) model, and the results can be used as a tool and reference for formulating PV development strategies in cities. [ABSTRACT FROM AUTHOR]