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In the last few decades, the energy demand is increasing globally which, given the present dependency of the energy generation on fossil fuels, results in a continuous increase in CO2 emissions. In an isolated electric grid system like in the SIDS, the majority of the electricity is produced by fossil fuels, therefore most of the SIDS nations are now focusing on Variable Renewable Energy sources (VREs). VREs such as wind and solar are hardly predictable and bring instabilities in the electric power system if not buffered by a storage system. Here we investigate the possibility of using Seawater Pump Storage Hydropower Systems (S-PSHS) as a renewable energy storage solution in an isolated electric grid. For this, the island of Curaçao (one of the SIDS nations) is used as proof of the concept. For detecting potential locations for the S-PSHS sites on the island, GIS application was developed. The application of this conceptual proposed solution in similar systems is straightforward and it can be easily upscaled in other geographies. The concept of using seawater for the pumped hydro project is not common in practice and it is anticipated to have technical, environmental and financial challenges which are discussed in this paper. • Implementation of SPSHS as a storage possibility for SIDS. • SIDS requires energy storage when share of VRES is high. • Island has energy storage demand of 133.88 MWh/day. • Detection of feasible SPSHS sites near the coast of the island. • Discussion on technical and environmental challenges regarding SPSHS. [ABSTRACT FROM AUTHOR]

The energy sector with 70% public budget influence in Turkey is unsurprisingly one of the sectors over which most policies have been developed. Although Turkey is rich in terms of renewable energy sources such as solar, water, wind, geothermal; as the speed of the consumption of the energy generated through these resources is higher than the production, the use of other non-renewable energy sources is at stake, and increases the emission amount released into the environment depending on this type of energy used. In this study, Energy Extended Input-Output Analysis has been conducted using the data of emission occurred depending on energy use within the framework of energy economy as an interdisciplinary field of study. And research has been carried out on the mutual interdependency between energy and economy. This paper employs an analysis recently published as an input-output table of the year 2014 of Turkey and energy use emission data of the same year in the World Input-Output Database. The emission occurred as a result of one single unit of energy use in the sector of electricity, gas, steam, and air conditioning supply, which are one of the two sectors including the whole components of the energy sector in Turkey, comes first with 51.4327 TJ/million-dollar coefficient, manufacture of coke and refined petroleum products, which is the other sector, ranks at the ninth row with the coefficient of 4.4556 TJ/million-dollar. As a result of the conducted analysis, while normalized total backward energy use emission linkages coefficients (BL j) equal 10.1074 TJ for Electricity, gas, steam, and air conditioning supply sector, they are 1.1676 TJ for the manufacture of coke and refined petroleum products. Normalized total forward energy use emission linkages coefficients (FL i) are 21.448 TJ for electricity, gas, steam and air conditioning supply, yet they are 1.3941 TJ for manufacture of coke and refined petroleum products. Policies on the sustainable use of energy in Turkey have been developed through the achieved results in the content of the study. Policies by focusing on the economy and prioritizing the role of energy in providing sustainable development, preferring environmentally friendly renewable energy sources, and encouraging less production of emission, have especially been mentioned. [ABSTRACT FROM AUTHOR]

Tobacco is traditionally used for cigarettes and cigars manufacturing and after the drying process a great quantity of dry tobacco stalks is treated as waste. In this framework, the scope of the paper is to examine the use of tobacco stems to feed a biomass boiler after a chipping process. The study is focused on southern Italy area thus, firstly, the assessment of tobacco availability in Italy and Campania region has been determined. Then, Burley tobacco stalks sampled out in an air-curing factory of south Italy crop have been characterized according to ASTM D5142 and ASTM D5373 standards. Thereby, biomass and natural gas fuelled boilers have been compared through an energy, environmental and economic analysis on simulative basis. Both systems satisfy the thermal energy demands of a multi-purpose condominium located in a cold Italian climatic zone near the factory. The results have demonstrated that boiler fed by tobacco chips ensures environmental and economic advantages compared to traditional system. In particular, it guarantees a reduction of CO 2 emissions up to 16.59 tCO 2 in one of investigated scenarios and a simple payback period and a net present value of about 5.6 years and 17,168 € (without economic support), respectively. • Tobacco stalks availability in Italy and Campania region is assessed. • Tobacco stems proximate and ultimate analyses show results similar to woody biomasses. • Boiler fed by tobacco chips and natural gas fuelled one are modelled and simulated. • A techno-economic and environmental performance comparison is implemented. • The energy, environmental and economic results reward plant fed tobacco by chips. [ABSTRACT FROM AUTHOR]

Depletion of fossil fuel reserves has lead to identification of renewable sources of energy as alternative sources of energy for power generation. The photovoltaic plant is more prominent in present day scenario. The power electronic converters interfacing the photovoltaic generator with the grid gives rise to various power quality problems such as harmonics in current injected by the inverter into the grid and low output power factor. This paper focuses on the design, analysis and control of single phase grid integrated photovoltaic systems. In this paper a dual stage photovoltaic inverter system is simulated using MATLAB/SIMULINK software. A digital predictive current controller based on model predictive control theory with delay compensation technique is introduced for output current control of the single phase voltage source inverter integrating the photovoltaic generator with the grid. The proposed control strategy exhibits excellent steady state performance and attains a high output power factor. It also achieves very fast transient response during change in reactive power demands and grid voltage distortions. The efficacy of the proposed controller is validated by experimental implementation using low cost digital signal processor. [ABSTRACT FROM AUTHOR]

For electric grids that rely primarily on liquid fuel based power generation for energy provision, e.g. one or more diesel gensets, measures to allow a larger fraction of intermittent sources can pay-off since the displaced is high cost diesel powered generation. This paper presents a case study of Sao Vicente, located in Cape Verde where a particularly high fraction of wind capacity of 5.950 MW (75% of the average demand) is installed, with diesel gensets forming the dispatchable source of power. This high penetration of intermittent power is managed through conservative forecasting and curtailments. Two potential approaches to reduce curtailments are examined in this paper: 1) an improved wind speed forecasting using a rolling horizon ARIMA model; and 2) energy storage. This case study shows that combining renewable energy forecasting and energy storage is a promising solution which enhances diesel fuel savings as well as enables the isolated grid to further increase the annual renewable energy penetration from the current 30.4% up to 38% while reducing grid unreliability. In general, since renewable energy forecasting ensures more accurate scheduling and energy storage absorbs scheduling error, this solution is applicable to any small size isolated power grid with large renewable energy penetration. [ABSTRACT FROM AUTHOR]

Based on the current EU environmental policy, wind power industry has been growing fast in recent years. Knowledge of wind characteristics helps to define site requirements, choose a proper turbine design and estimate profits from the wind energy production. The paper describes and compares the techniques of available wind energy evaluation using Weibull distributions: two-parameter Weibull probability distribution, and three-parameter Weibull distribution. The two-parameter Weibull distribution is recognized as an appropriate model and the most widely used in the wind industry sector. In some cases, in which the probability of null wind is significant, the Weibull distribution cannot reveal good conformity for the low wind speed. In theory, it seems that the three-parameter Weibull distribution, which takes into account the frequency of null winds, may better represent wind ranges for the low wind speed. In the paper, the available wind energy is calculated for three different turbine locations. The results show that for the higher probability of the null wind the three-parameter Weibull distribution gives better results comparing to the two-parameter Weibull distribution and can be proposed as an alternative to wind energy estimation technique. Additional analyses confirm the observation. [ABSTRACT FROM AUTHOR]

This paper assesses the cost effectiveness of wind energy policy options by using a cost–benefit analysis approach. In comparison with other studies, the main contribution of this paper is the use of real financial and operational information of 318 wind projects in Spain between 2006 and 2013. The projects represent 83% of total new wind capacity additions in Spain during the 2006–2013 period. Under real market conditions, it is found that the most cost effective option for promoting wind energy development from the government perspective is to use an investment credit policy given the capital intensive nature of wind technology. The analysis shows that an investment credit yields similar results as other policy options like feed-in tariffs and feed-in premiums, but at a cheaper cost and with less risk to the taxpayers or electricity consumers because costs do not fluctuate with electricity market prices. [ABSTRACT FROM AUTHOR]

Accurate estimation of extreme wave conditions is critical for offshore renewable energy activities and applications. Wave power is the transport of energy by wind waves, and the capture of that energy to do useful work. Wave energy converter (WEC) devices are designed to sustain the wave-induced loads that they experience during both operational and survival sea states. The extreme values of these forces are often a key cost driver for WEC designs. These extreme loads often occur during severe storms; therefore careful examination of harsh wave conditions during the device design process is needed. Consequently the development of a specific extreme condition modeling method is essential. This paper presents a novel method for estimating extreme wave statistics, based on the hourly measured wave height maxima at the location of interest. Wave measurements, analyzed in this paper, were collected at SEM-REV offshore sea station located near the coast of France, during years 2001–2010. Note that applied statistical methodology is general and can be well applied to a measured WEC response, and its technology risk assessment. Accurate estimation of extreme wave conditions is critical for offshore renewable energy activities and applications. SEM-REV is known French wave energy test site. The method, referred to as ACER method, is presented in brief detail. ACER method provides an accurate extreme value prediction, utilizing available data efficiently. In this study the estimated return level values, obtained by ACER method, are compared to the corresponding return level values obtained by Gumbel method. Based on the overall performance of the proposed method, it is concluded that the ACER method can provide a robust and accurate prediction of extreme wave height at a given location. [ABSTRACT FROM AUTHOR]

This paper proposes a dc–dc converter with quadratic voltage gain able to drain ripple–free current from the source at a selectable duty cycle. Because of these features, the topology proposed herein is of special interest in renewable energy systems which have low voltage generation sources and are sensitive to current ripple from converters. The paper discusses the converter's principle of operation, focusing on modeling and design guidelines. The topology is validated through computer simulations and hardware prototyping. [ABSTRACT FROM AUTHOR]

Over the past few years, there has been a growing global consensus related to the importance of renewable energy to minimize the emission of greenhouse gases. The solution is an increase in the number of renewable power plants but unfortunately, this leads to a high dependence on climate variables which are already affected by climate change. Brazil is one of the largest producers of electricity by renewables through its hydro-dominant power generation system. However, hydro-generation depends on water inflows that are directly affected by climate change that consequently affect the electricity production. Therefore, these changes need to be considered in the operation and planning of a hydro-dominant power system. In this paper, we present the effects of different climate scenarios in the water inflows produced by the regional Eta climate model. Normally, studies use an optimization model to make decisions in case of a hydro-thermal scheduling problem and use the assured energy to evaluate the hydro-production. In this analysis, water inflows used in the optimization process consider different trends according to its associated climate scenario. Our paper shows that climate change may drastically impact the system assured energy and consequently, the system's capability to supply load. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of decision making in Unit Commitment in systems with a significant penetration of wind power. Traditional approaches to Unit Commitment are inadequate to fully deal with the uncertainties associated to wind, represented by scenarios of forecasted wind power qualified by probabilities. Departing from a critique of planning paradigms, the paper argues that a stochastic programming approach, while a step in the good direction, is insufficient to model all aspects of the decision process and therefore proposes the adoption of models based on a Risk Analysis paradigm. A case study is worked out reinforcing this perspective. In a multi-objective context, the properties of the cost vs. risk Pareto-optimal fronts are analyzed, where risk may be represented by aversion to a worst scenario or a worst event. It is shown that the Pareto-optimal front may not be convex, which precludes a simplistic use of tradeoff concepts. It is also shown that decisions based on stochastic programming may in fact put the system at risk. An evaluation of risk levels and cost of hedging against undesired events is proposed as the paradigm to be followed in Unit Commitment decision making. [ABSTRACT FROM AUTHOR]

Solar and wind power based hybrid energy system with energy storage unit provides a reliable and cost effective energy alternative above the conventional diesel generator based system commonly used by remote consumers. In this context, this paper explores the application of a new meta-heuristic algorithm called Cuckoo Search (CS) in the area of a hybrid energy system design problem. Cuckoo Search (CS) is applied for optimal sizing of three different system schemes viz. Photovoltaic-Battery, Wind-Battery and Photovoltaic-Wind-Battery system applicable to a remote area located in Almora district of Uttarakhand, India while minimizing total system cost and considering seasonal variation of load. The effectiveness of Cuckoo Search algorithm in solving hybrid energy system design problem is investigated and its performance is compared with other well known optimization algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. Optimization results further, shows that hybrid integration of photovoltaic, wind and battery storage gives most reliable and economical system scheme for the study area. In addition, this paper assesses the effect of wind turbine generator (WTG) force outage rate (FOR) on the optimal system reliability and economics. [ABSTRACT FROM AUTHOR]

A multi-pump, multi-piston power take-off wave energy converter (MP 2 PTO WEC) has been proposed for use with a novel renewable energy harvester termed the Ocean Grazer. The MP 2 PTO WEC utilizes wave motion to pump–via buoys connected to pistons–working fluid within a closed circuit and store it as potential energy that can be converted to electricity via turbines. This paper introduces the mechanical design and model-based performance prediction of a single-piston pump that constitutes the basic building block for the MP 2 PTO WEC. Results provide preliminary validation of aqueous lubrication as a viable means of reducing friction and wear, suggesting that water-based hydraulic fluids can prohibit solid contact at the piston-cylinder interface while reducing volumetric leakage, and allowing for an estimation of the energy extraction efficiency for the mechanical pumping system. Pending more thorough and extended tribological investigations using the methodology introduced in this paper, findings suggest that the overall system efficiency will be dictated by the hydrodynamics of the buoys actuating the pumping system. [ABSTRACT FROM AUTHOR]

Hybrid solar and geothermal utilisation is a promising option for effective exploitation of renewable energy sources. Concentrated solar power (CSP) systems with geothermal preheating are acknowledged as an attractive solution, with supercritical CO 2 (SCO 2) cycle systems adopted for power generation thanks to the favourable properties offered by CO 2 as a working fluid. In order to further improve the overall performance of such systems, organic Rankine cycle (ORC) systems can be added as bottoming cycles to recover the heat rejected from the topping SCO 2 cycle system and also to utilise surplus geothermal heat available after the brine is used for preheating in the SCO 2 system. This paper proposes four configurations of combined SCO 2 -ORC system for hybrid solar and geothermal power generation and performs detailed thermodynamic and economic assessments based on actual conditions in Seville, Spain. The results reveal that combined systems in which the geothermal-brine stream is split into two parallel flows and utilised separately by the topping SCO 2 cycle and bottoming ORC systems are preferable. A split geothermal-stream combined system with the ORC working fluid first utilising geothermal heat followed in series by heat from the topping SCO 2 cycle system delivers a net power output of 2940 kW, which is the maximum among all the proposed configurations and is 45% higher than that of a standalone SCO 2 plant. A similar combined system with a reversed ORC flow direction such that the organic fluid is preheated first by utilising heat from the SCO 2 cycle system and then by geothermal heat has a specific cost corresponding to the maximum net power output of 2880 $/kW, which is the lowest among all the configurations and is 22% lower than that of the standalone SCO 2 plant. Annual performance evaluation shows that the combined systems can achieve significant improvements, ranging from 22% to 45%, over the total electricity generation of the standalone SCO 2 plant, which demonstrates the significant potential of deploying such novel and promising combined systems for hybrid solar and geothermal power generation. [ABSTRACT FROM AUTHOR]

The Canary Islands are still largely dependent on expensive imported fossil fuels, are stressed by the increasing touristic impact and are extremely vulnerable to climate change due to water scarcity. Water desalinisation is an energy-demanding process and is essential to the sustainable development of these islands. The aim of this study is to explore the potential advantages of a hybrid installation, exploiting two different renewable energy sources, specifically waves and solar, to supply a large desalination plant in Tenerife. The paper ultimately provides a generally applicable procedure for the design of hybrid installations, including three steps: the assessment of available renewable energy sources, the optimal combination of these sources and finally the economic assessment. The wave and solar resources are assessed first, then the hybrid installation is conceptually designed, proposing a criterion for the optimal mixing of the renewable energy sources that can be applied to other resources and other sites. The basic idea is to maximise the exploitation of the renewable power, minimizing the need of the fossil-based back-up system. The costs of the hybrid installation are finally assessed considering the sensitivity to government incentives, showing that the project parity point is reached within the lifetime of typical desalination plants (i.e. 40 years) and can be significantly more attractive in case of Feed-In-Tariffs available in other European countries. [ABSTRACT FROM AUTHOR]

In most Sub Saharan African countries renewable energy has been part of the energy mix balance-sheet for quite a long time. However, the share of total primary energy from renewable sources have declined over time despite significant investment into the sector in the last decade. This paper pierce the veil of the energy sector to unearth the underlying factors driving renewable energy output in the region using a panel data of 32 SSA countries from 1990 to 2015. The study rely on the Driscoll-Kraay estimator to correct the problem of cross sectional dependence and serial correlation in the model. Findings from the study point to the underlying factors of renewable energy output in Co2 emissions per capita, income per capita, oil prices, trade openness, natural resource rents, urbanization, population growth rate, and climatic stress. The study proposes policies that incorporates environmental awareness in the national development plans of countries; increasing renewable energy consumption among the middle class; encourage regional renewable energy grid sharing; implementing and expanding the feed-in-tariff system; and granting tax incentives to companies that seek to invest and develop the renewable energy sector in the region. [ABSTRACT FROM AUTHOR]

Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection. • A novel fuzzy Bayesian network-based MADM model for OTW selection in busy waterways is proposed. • A three-layer decision-making framework is established for the OWT selection. • Comprehensively consider each influencing factor's qualitative and quantitative characteristics. • Model can not only conduct the uncertainty caused by ambiguity but also intuitively achieve optimal reasoning and evaluation. [ABSTRACT FROM AUTHOR]

Million miles of gravity-fed drinking water and sewage pipelines around the world, especially in rural and urban areas in mountain ranges, have introduced a new renewable energy sources (RES), i.e., in-pipe hydropower systems (IHS). Output power of this technology, similar to other types of RES, suffers from intermittency, while it is still more predictable in comparison to other technologies of RESs. Besides, energy storage systems (ESS) are introduced as a pivotal technology for dealing with the intermittent and non-dispatchable characteristics of IHS through spatio-temporal arbitrage. This paper aims to develop a stochastic mixed-integer linear programming (MILP) formulation that simultaneously determines the optimal location and size of ESS and IHS in a microgrid (MG) considering the correlation between prevailing uncertainties. In this regard, the proposed optimization problem minimizes the expected social cost of MG considering the AC power flow equations and operation constraints. Finally, the efficiency and applicability of the proposed model are illustrated using numerical simulations on the IEEE 33-bus distribution test system. • Investigating on a new renewable energy sources in mountainous areas, i.e., in-pipe hydropower system. • Developing a sizing and siting optimization problem for energy storage and in-pipe hydropower systems. • Considering the linearized AC power flow equations in the developed optimization problem. • Modeling the correlation between uncertainties using historical data. [ABSTRACT FROM AUTHOR]

Offshore wind energy is one of the most important sources of renewable energy to make sure supplying enough electricity for the world in the future. The floating offshore wind turbine (FOWT) operates under many influences such as both the aerodynamic force, the hydrodynamic force and coupled motion between the two. Therefore, in this paper about the effect of collective pitch control and cyclic pitch control on power and loads of FOWT are discussed under the uniform wind velocity of 10 m/s with wind tunnel and numerical investigation. FAST tool (Fatigue, Aerodynamics, Structures, and Turbulence) is used to simulate FOWT under the same wind conditions as wind tunnel experiments. A two-bladed downwind turbine model is used for this experiment to estimate the fluctuations of the power coefficient, thrust coefficient and moments. The pitch moment and the yaw moment are measured at the top of the tower and the fluctuation of loads is compared. The phase angle of the pitch angle is controlled by three actuators through a swash plate equipment. From the simulated results, the effect of the pitch angle change on the power coefficient is higher than the yaw angle change. For the yawed wind condition, the load of the wind turbine at the phase angle of ξ = 60° was equal to the optimum operating point. Thence, the load of the wind turbine was decreased when the cyclic pitch control method was applied. • The phase angle of the pitch angle is adjusted by a swash plate. • Aerodynamic force is simulated by FAST under the uniform wind condition. • Power and thrust coefficients fluctuated when the pitch angle and the yaw angle change. • Loads of wind turbine were decreased by the cyclic pitch control method. • The simulated results were in good agreement with the experimental data. [ABSTRACT FROM AUTHOR]

Hydropower plants are one of the most convenient option for power generation, as they generate energy exploiting a renewable source, they have relatively low operating and maintenance costs, and they may be used to provide ancillary services, exploiting the large reservoirs of available water. The recent advances in Information and Communication Technologies (ICT) and in machine learning methodologies are seen as fundamental enablers to upgrade and modernize the current operation of most hydropower plants, in terms of condition monitoring, early diagnostics and eventually predictive maintenance. While very few works, or running technologies, have been documented so far for the hydro case, in this paper we propose a novel Key Performance Indicator (KPI) that we have recently developed and tested on operating hydropower plants. In particular, we show that after more than one year of operation it has been able to identify several faults, and to support the operation and maintenance tasks of plant operators. Also, we show that the proposed KPI outperforms conventional multivariable process control charts, like the Hotelling t 2 index. • A novel SOM-based indicator is proposed for condition monitoring in hydropower plants. • The indicator detects anomalous operating conditions and specific faulty components. • The proposed methodology is tested in a real case study for more than one year. • The proposed KPI outperforms classic process control tools such as Hotelling charts. • It is a first step for digitalizated and unsupervised operation of hydropower plants. [ABSTRACT FROM AUTHOR]

In this paper, conventional and renewable energy sources for sustainable development of energy sector in India are evaluated from multiple perspectives including economic, technical, social, environmental, political, and flexible criteria. An integrated Shannon's entropy multi-criteria decision making (MCDM) method has been used for the evaluation and assessment of these sources. Thermal, gas, nuclear, solar, wind, biomass, and hydro energy options are used as the alternatives in the decision model. Shannon's entropy method is applied to determine the weights of decision criteria, and fuzzy analytical hierarchy process (AHP) method is applied to prioritize sustainable energy alternatives. The output of the proposed model was compared with six different fuzzy MCDM techniques for the establishment of correlation index. Solar energy was shown to be particularly well suited for India followed by wind and hydro energy sources. Later, the study has developed fourteen scenarios, considering the first five sustainable energy sources (solar, wind, hydro, biomass, and gas power), to evaluate the optimal energy mix scenario for the sustainable development of energy sector in India. An optimal energy mix scenario carries the heroic development of solar, wind, and hydro energy with cross border import-export facility for the time frame of the year 2030. • Reviewed the overview, challenges and development of Indian energy sector. • Developed integrated Shannon entropy fuzzy MCDM approach. • Evaluated the most sustainable energy alternatives in India. • Results validated with six fuzzy MCDM approaches and correlation coefficients. • Evaluated the optimal energy mix scenario in India for the time frame of 2030. [ABSTRACT FROM AUTHOR]

In recent years, as a result of the rapid increase in population and industrial development, the need for energy has increased. Many investments are made especially for electrical energy which is an inevitable requirement for the industry. Most of these investments are made by using fossil resources in countries' lands. Fossil sources are known to be consumable as well as too poisonous gases they give to the atmosphere during energy production. This situation causes global climate change. To prevent climate change, the carbon emitted to the atmosphere has to be brought under a certain control mechanism. In this context, the first Kyoto Protocol and then Paris Climate Agreement was signed. Under these agreements, countries will be responsible for the level of their greenhouse gases in the atmosphere. This situation led to the carbon trade among countries. This situation accelerated the transition of countries from fossil sources to renewable energy sources. In this study, the effect of hydroelectric power plants, one of the renewable energy sources, on carbon emissions was investigated. According to the results, based on fossil sources of 408,533.57 tCO 2 per year from Turkey Gökçekaya dam has been shown to reduce the carbon footprint. • The paper provides a comparison of carbon emissions between hydroelectric and fossil fuel energy sources. • The article examines carbon emission reduction from a perspective of carbon markets and the legal framework. • The study also provides an analysis based on the example of Gökçekaya HEPP in Turkey. • The study provides an economic analysis of the Gökçekaya HEPP with the feasibility analysis. [ABSTRACT FROM AUTHOR]

This paper discusses how flexibility metrics can be used in analysis of the balancing challenges arising from power system transition towards high shares of variable renewable energy sources. Using some metrics suggested in the literature as a starting point, new metrics are developed that combine the advantages of existing metrics and improve the connection to flexibility properties and balancing capability metrics. The metrics are applied to scenarios with 50% variable renewable energy in Japan and Sweden as a case study. The results show differences and similarities in the balancing challenges of these two systems. While diurnal variations increase more in Japan, Sweden is challenged by inter-week and seasonal variations. The results also show how the feasibility of inflexible base power production depends on large volumes of energy storage in the system. • Comparison of metrics for balancing need and balancing capability. • Power regulation, energy regulation and storage volume requirement. • Case study of 50% variable renewable energy in Japan and Sweden. [ABSTRACT FROM AUTHOR]

Agriculture is one of the most energy intensive activities within the European Union. The common absence of electric infrastructure in these rural settings has led to the need for in situ generation in many cases. The existing excess pressures in large pressurised irrigation networks makes the generation of micro hydropower here a possible alternative renewable energy source. Pump-as-turbines have been proposed as a cost-effective technology for such purposes, taking advantage of the excess pressure in pipe networks to produce energy. This paper presents a methodology for the design of a plant for micro hydropower generation in an agricultural farm and outlines the predicted benefits of the installation. These predicted benefits were also compared with the measured performance of the plant for the 2019 irrigation season. The main aim of the plant was to replace a diesel generator that supplied energy to the farm. The excess pressure found in the pipe network varied between 0m and almost 60m. The nominal power of the pump-as-turbine was selected to supply the maximum energy requirements of the farm. The predicted operation time of the plant was estimated at up to 3199 h concentrated between April and September. An annual savings of approximately €2950 and 11 t eCO 2 were also estimated. The measured results showed an actual operation time of 2443 h between May and September, as in April the monitoring system was not operational. For this operation time, the savings were €2258 and 8.4 t eCO 2. Considering the April theoretical irrigation time obtained, the savings raised up to approximately €2434 and 9.1 teCO 2. The return on investment of the installation was computed to be recovered in less than ten years. • Real pilot PAT installation operating during an entire irrigation season. • Electricity was brought to remote places with no access to grid connection. • Diesel energy source was fully replaced by micro hydropower technology. • Solar and micro hydro hybrid solution was used for self-consumption. • More than 9 tCO 2 and €2400 were saved during the 2019 irrigation season. [ABSTRACT FROM AUTHOR]

Whilst present buildings are still often energivorous systems, in the near future they will have to be converted to (or replaced by) zero energy buildings, also capable to export green energy (produced on-site by renewables) towards other buildings and/or users. Specifically, the paper focuses on the performance of naturally ventilated BIPV façade elements taking into account the total heat flow inside the building and the temperature of the building walls. 3 Building integrated photovoltaic collectors In the next few years, a further diffusion of PV collectors will be achieved by Building Integrated Photovoltaic (BIPV) devices, that will also overcome the unesthetic and functional issues of stand-alone and building-adjacent PV systems. 7 Building envelope technologies In order to reduce the heating and cooling energy demands of buildings and/or to improve the related comfort performance, different innovative envelope materials and/or design solutions for opaque and transparent building elements can be adopted. [Extracted from the article]

This paper evaluates the anaerobic digestion of agricultural waste, manure and slaughterhouse residues of species typical of the Andes, such as llama, vicuña and guinea pig; quinoa, amaranth and wheat straw in order to be able to use them as a renewable energy source and boost rural development in the region. Has been used sludge from a WWTP as inoculum, evaluating the effect of two relationships between the substrate and inoculum (RSI) for both livestock and agricultural waste. The highest cumulative maximum methane production rate was obtained for the residues of flame manure and quinoa straw for an RSI of 1:2 with productions of 376.08 ml CH 4 /g SV and 377.02 ml CH 4 /g SV respectively. In these materials an RSI of 1:2 obtained increases of 22.56% and 37.54% compared to the RSI 1:1. Also, the kinetic analysis showed that the modified Gompertz model is the one that best fits performance, with constant differences of 7.06% between the experimental and predicted values. On the other hand, the modified Gompertz model was adjusted to the experimental results with an r2 of (0.998) and an RMSE of 4.09 ml/g SV at 17.12 ml/g SV. • The addition of an inoculum increased the rate of methane and biogas production. • The camelid manure generated methane yields between 235.49 and 377.02 ml CH 4 /g VS. • The three and four parameter sigmoid prediction models better estimated the BMP tests. • Flame manure residues provided 70.74% biodegradability results. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization framework to simultaneously optimize the saline and freshwater water sources as well as decentralized renewable and conventional energy sources, which consequently results in an economic and efficient water-energy system operation. The inextricable links between water and energy systems are taken into considerations, and a mixed integer nonlinear programming formulation is used to solve the proposed optimization model. A hybrid electrodialysis-reverse osmosis is integrated in the water distribution system and its electricity consumption is minimized. Also, the day-ahead economic dispatch problem of the energy system incorporates the random behavior of solar and wind generation units, along with charging and discharging modes of battery storage systems. The outcomes of the proposed models highlight: 1) integrating the hybrid desalination module in the water-energy system will reduce peak demands by more than 30% if the water and energy systems are solely supplied by saline water and renewable energy sources, respectively; 2) presence of an extra water storage tank also decreases peak demands by 13%. The impact of key elements of the combined system such as pumps' speeds, water tank constraints, and presence of renewable sources on the output cost, generated energy, and hydraulic parameters is investigated through case studies. Image 1 • An optimization model for a combined water-energy system is proposed. • Water system involves a hybrid electrodialysis-reverse osmosis desalination plant. • Effect of water storage tank on energy reduction is studied. • Effect of pump's speed on resource allocation of water-energy system is studied. • Operating water system with renewable energy and battery storage is optimized. [ABSTRACT FROM AUTHOR]

Energy transition is prevalent nowadays, and Colombia is no exception to this phenomenon. Energy coming from non-conventional renewable resources is one of the main feature of this phenomenon. Consequently, this paper evaluates the impact of this new kind of renewable energy resource in the Colombian wholesale electricity market. This evaluation uses a counterfactual scenario based on a structural model of an energy firm's behavior that offers 1000 MW in 2018. Our results suggest that the spot price decreases on average 12.75 COP$/kWh (4.31 US$/MWh) per day. We find that 6.75 COP$/kWh (2.28 US$/MWh) is due to the firms' strategies, and 6 COP$/kWh (2.03 US$/MWh) is a merit order effect. • We developed a structural model to evaluate the effect that the inclusion of non-conventional renewable energy would have on the spot price of electricity. • We estimate the aggregate economic benefits for one year of the inclusion of renewable energy in electricity markets. • We decompose the merit order effect into two parts: a pure merit order effect and an effect associated with the strategic behavior of firms in the market. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the world's biggest energy consumers and CO 2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO 2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO 2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO 2 emissions. This indicates that curbing CO 2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal. • Renewable energies, coal consumption, economic growth, and CO 2 we examine. • We investigate the causal predicted relationship on China, India, and the USA. • An advanced methodology in Machine Learning is used. • India has a prediction of an increase in emissions of CO 2. • China and the U.S. present a more promising path to sustainability than India. [ABSTRACT FROM AUTHOR]

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (WTs) are crucial in offshore wind energy development. However, the dynamic responses of WTs are subject to significant uncertainties which are usually not properly considered. To the end, this paper proposes an efficient method for quantifying the uncertainties in WTs' dynamic responses based on cumulative distribution function (CDF)-manifold learning. First, a probabilistic model is developed to represent the environmental parameters and sampling for aerodynamic-hydraulic-servo-elastic simulations. Then, the CDF is obtained by statistically analyzing the simulated data. To tackle the higher dimensionality resulting from discretizing the CDF, a manifold learning-based approach is subsequently proposed to reduce its dimensionality and obtain a manifold space. Furthermore, a mapping relation is established between the environmental parameters and the low-dimensional data to efficiently obtain the response CDF under different environmental parameters, leading to the construction of a probability box (P-box) model. To demonstrate the effectiveness of the proposed method, the National Renewable Energy Laboratory (NREL) 5 MW offshore WT on an Offshore Code Comparison Collaboration (OC3) monopile is selected as a case study and analyzed accordingly. The results show P-box models of seven WT responses and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Large-scale distributed renewable energy sources as well as emerging controllable loads such as electric vehicles connected to the distribution grids have posed challenges to the efficient management of these distributed energy resources. Promoting energy-sharing of distributed energy resources through economic incentives is a cost-effective and equitable solution to the challenge. This paper proposes a two-stage transactive energy control mechanism by the virtual power plant (VPP) for energy-sharing of multiple prosumers. Firstly, a VPP internal price mechanism based on the supply-demand ratio of prosumers in the energy-sharing alliance is proposed. Then, a two-stage economic optimization model is developed. Trading prices between VPP and prosumers are established in the first stage, and prosumers make operating plans based on those prices in the second stage. Then, considering that prosumers and VPP are independent stakeholders, a decentralized optimization algorithm based on the analytical target cascading method is developed. The upper-level VPP coordinates the energy-sharing alliance and sets the transaction price following the overall supply and demand ratio. Lower-level prosumers independently make decisions and feedback on the trading power of the VPP. Then, iterative calculations provide a distributed and independent solution. Finally, the case study verifies that the proposed energy-sharing model can reduce the operating cost of prosumers by 14.29 %. Moreover, prosumer privacy protection is achieved by the analytical target cascading-based distributed model at a cost of less than 1.5 % accuracy. [ABSTRACT FROM AUTHOR]

PHPS is a mature energy storage technology, leading to more efficient energy management and further integration of Renewable Energy Sources (RES). This paper investigates the implementation of such a system at Kastraki, Greece, combined with a second upstream reservoir with a total capacity of 10 hm3 and evaluates its efficiency. The position and the geometric characteristics of the second reservoir are determined with ArcGIS. When excess of energy exists, water is pumped from Kastraki reservoir and stored in the upper one. Otherwise, the stored water is used for energy production. The simulation is performed for 40 years with a monthly time step. Furthermore, another simulation for a typical day in July is conducted, when water and energy needs are increased. The system is evaluated in terms of covering the water supply and energy needs, as well as in terms of efficient energy management. Several results can be obtained, as follows: i) the reliability of the PHPS system for the coverage of monthly water supply and irrigation needs is 82.84 %. ii) The coverage of water supply needs for a typical day in July is 99.99 %. iii) The average monthly coverage of energy needs amounts to 83.86 %. [ABSTRACT FROM AUTHOR]

The China Certified Emission Reduction scheme (CCER) and the Cap-and-Trade (CaT) mechanisms jointly constitute the Emission Trading Scheme (ETS), which is expected to have a profound impact on China's social and economic decarbonization. However, previous studies have primarily focused on the impact of the CaT mechanism, neglecting the fact that CCERs serve as a carbon offset trading mechanism. This paper focuses on the impact of the CCER mechanism on China's energy economy and environment by constructing a recursive dynamic computable general equilibrium model that couples CaT and CCER mechanisms. Introducing the CCER mechanism based on the CAT mechanism leads to increased output and reduced prices in renewable energy sectors, cascading positive effects across various energy-utilizing sectors to counter the decline in Gross Domestic Product (GDP) and residential welfare. The finding suggests that China's unique CCER mechanism is poised to play a more important role in promoting renewable energy to mitigate global warming, given its potential to transform the structure of energy production and consumption. Finally, targeted policy recommendations are proposed for implementing the CCER mechanism. [ABSTRACT FROM AUTHOR]

Offshore wind turbines (WTs) are crucial in offshore wind energy development. However, the dynamic responses of WTs are subject to significant uncertainties which are usually not properly considered. To the end, this paper proposes an efficient method for quantifying the uncertainties in WTs' dynamic responses based on cumulative distribution function (CDF)-manifold learning. First, a probabilistic model is developed to represent the environmental parameters and sampling for aerodynamic-hydraulic-servo-elastic simulations. Then, the CDF is obtained by statistically analyzing the simulated data. To tackle the higher dimensionality resulting from discretizing the CDF, a manifold learning-based approach is subsequently proposed to reduce its dimensionality and obtain a manifold space. Furthermore, a mapping relation is established between the environmental parameters and the low-dimensional data to efficiently obtain the response CDF under different environmental parameters, leading to the construction of a probability box (P-box) model. To demonstrate the effectiveness of the proposed method, the National Renewable Energy Laboratory (NREL) 5 MW offshore WT on an Offshore Code Comparison Collaboration (OC3) monopile is selected as a case study and analyzed accordingly. The results show P-box models of seven WT responses and validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Ocean renewable energy (ORE) is one of the most important clean sources of energy and a major player towards the EU ambitions of being net zero emission by 2050. However, at present, there are few examples of commercially viable ORE technologies and no large-scale projects currently under implementation. Together with social and environmental analyses, the assessment of economic impacts is one of the key elements to help policy makers build a compelling case to gain local community acceptance and implement ORE projects. This paper presents a systematic methodology to assess local economic impacts of renewable energy projects, including jobs creation and impacts on gross value added and income. By combining the use of Location Quotients – which are indexes informing on local industrial specialisation – with the Input-Output multipliers the method can be used to map the supply-chain potential of a local economy and estimate local impacts compared to global ones. The method has been applied to a tidal project carried out in Orkney, Scotland. The research demonstrates the merit of early economic assessments for understanding the economic benefit of ORE projects, particularly for the local communities in which they are located, and it provides a methodological framework to be tested in other case studies. [Display omitted] [ABSTRACT FROM AUTHOR]

Green financing is essential because it guarantees that initiatives that seek to reduce the world's reliance on fossil fuels receive the necessary funding to be successful. Green financial metrics can be simultaneously made possible by specialized technological innovation and natural resource rent, thereby encouraging the adoption of sustainable energy. In this paper, we investigate the mediating mechanisms underlying the relationship between green financial indicators and renewable energy consumption RNEC and we contribute to the ongoing discussion surrounding this relationship. Our observational example is derived from data collected for 66 countries between 2004 and 2019 using a panel threshold regression model defined with the difference generalized method of moments (GMM). We find that green funding initiatives have accelerated the adoption of renewable energy sources by encouraging increases in the proportion of renewable energy in the fuel supply. Multiple analyses of reliability corroborate the existence of this link. Green funding has been shown to have an encouraging effect on the transition to renewable energy, and our findings indicate that technological advancement and natural resource leasing serve to mitigate this effect. To assist policymakers in reducing the world's reliance on polluting energy, our study demonstrates how green financial markets can be established through improved technological innovation and natural resource management. [ABSTRACT FROM AUTHOR]

The increasing generation of renewable energy and its concurrent technological innovation are driving the transformations of the energy sector, but little effort has been made at the regional level to specify the heterogeneous impacts of different types of renewable energy development on multi-dimensional energy security. Therefore, this paper establishes a four-dimensional energy security assessment system based on the current energy market situation and economic context of China. Renewable energy generation and technological innovation were simultaneously used as the core proxies for renewable energy development. Then, the panel quantile regression method was used to analyze the heterogeneity of the impact of solar, hydro, and wind energy on energy security, covering 30 provinces in China from 2006 to 2019. The empirical results show that renewable energy power generation generally promotes energy security, but it cannot be effectively applicable to all levels of energy security. Renewable energy technology innovation has surprisingly hindered energy security. These results can serve as a policy and practical guide, suggesting that provincial governments in China should implement energy security and renewable energy policies in line with local characteristics. • The redefined provincial energy security presents obvious spatial heterogeneity. • Panel quantile regression was applied to balanced provincial panel data. • Hydropower contributes only to medium and low levels of energy security. • Wind and solar power contribute differently to all levels of energy security. • Renewable energy technological innovation impedes energy security. [ABSTRACT FROM AUTHOR]

Open Cycle - Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dynamic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly influences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 °C variation in temperature gradient from 18 °C to 19 °C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes. [ABSTRACT FROM AUTHOR]

The global energy sector is experiencing a transition towards renewable energy primarily driven by issues related to climate change and energy security. In this paper, we investigate the impact of uncertainties and potential drivers connected to energy security on the volatilities and returns of renewable energy stocks. Further, we examine how uncertainty and potential drivers connected to energy security affect the volatilities and returns of renewable energy stocks. By applying the MS-GARCH (1,1) and MS-GJR-GARCH (1,1) approach we calculate the Value at Risk (VaR) and Conditional Value-at Risk (CVaR). In addition, we estimate a fixed effects model to determine the impact of the uncertainty variables on the estimated conditional volatility and returns. Our findings indicate that economic policy uncertainty (EPU) positively impacts the returns of renewable stocks, attributing to an increased engagement towards a renewable transition. However, the prices of crucial green metals were found to have a negative impact on renewable stocks suggesting that the transition to renewable energy might impose implications regarding energy security if not managed correctly. The findings of this study have important implications for policymakers, market participants, and governmental agencies in devising a roadmap to promote the transition process to renewable energy sources. [ABSTRACT FROM AUTHOR]

Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5–15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analyzed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of colored pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons. [Display omitted] • Enhanced Convolutional Neural Network (ECNN) is proposed by exploiting auxiliary information (exogeneous parameters) which are encoded on images of the All-Sky Imager. • The performance of ECNN, CNN, and other baseline models are used for very short-term GHI forecast (nowcasting). • The ECNN outperforms naïve persistence models in case of partially cloudy days and highly variable weather conditions. [ABSTRACT FROM AUTHOR]

—As a result of the increasing demand for different energies with technological development, and with the rapid development of energy hubs (EHs), issues such as computation speed, big data and information privacy, robustness, etc. are becoming increasingly vital. Also, the problem of communication packet loss leads to an incompatibility between the generated and consumed energy. In response to the above problems, this paper proposes an Internet of things-Fog-Cloud distributed consensus-based energy management algorithm to optimally solve the energy mismatch problem in an EH environment considering communication information loss and uncertainties of some EH elements. A MATLAB/Simulink-based model of a hypothetical EH system is modified to support real-time communication. Two case studies are applied to explain the performance and effectiveness of the proposed algorithm under different packet loss scenarios. The system's sensitivity was studied depending on the number of iterations and the uncertainties of the renewable energy sources, energy prices, and load demands. Also, a comparison was made between the results obtained by the proposed method and the optimization results using the genetic algorithm and particle swarm optimization techniques. The results prove the applicability and feasibility of the proposed method for the demand management system in EHs. The results show that the proposed algorithm decreases the daily system operating cost from 372.421 to 369.941 $ and the total CO 2 emissions cost from 4848.5 to 4103.1 $. In addition, the daily energy mismatch between the generated and consumed values decreases from 579.85 to 0 kWh in the electrical system and from 435.14 to 0.073 kWh th in the heating system. Based on the previous results, the proposed algorithm of EHs contributes significantly to the development of sustainable cities. [ABSTRACT FROM AUTHOR]

For realizing environment protection and carbon emission reduction strategy, the dominant position of renewable energy in the future power system has been clarified. Planning economic and efficient flexible resource system is the basis to balance multi fluctuations caused by large amount of renewable energy integration and ensure the safe operation of the whole power system. Considering that the flexibility adjustment demands of different links of the power system are not independent, but influence and restrict each other, this paper focuses on how to collaboratively allocate multi-flexible resources and achieve corresponding balancing interaction strategy. Firstly, a flexible resource system is composed through a fuzzy TODIM improved PROMETHEE-II model. Secondly, a multi-flexible resource collaborative allocation optimization model is proposed, which considers the Stackelberg game relationship of flexibility adjustment demand and ability in different links of the power system under varied fluctuation scenarios. The scientificity and effectiveness of the proposed model are verified by a practical case, and key incentive factors and specific development suggestions for improving flexible resources application efficiency are identified simultaneously. Since the collaborative planning of flexible resource system have not been deeply discussed by scholars yet, thus, this research contributes to the literature and expand the knowledge. • Design an effective flexible resources system through TODIM improved PROMETHEE-II model under multiple application scenarios. • Construct flexible resources optimal allocation scenarios according to the power generation and consumption characteristics of the new power system. • Construct a two-layer flexible resources equilibrium allocation model based on Stackelberg game theory. • Propose a multi-stage flexible resources equilibrium allocation model solution method. • Identify key factors influencing the allocation strategy of flexible resources in the new power system. [ABSTRACT FROM AUTHOR]

In order to accelerate the transition from carbon fuels to renewable energy sources, it is essential to extend our knowledge of the resources' availability to further improve or adjust the design of extraction devices. In the present paper, a first characterization of the tidal stream resource along the coast of The Netherlands is performed using a high-resolution unstructured grid implementation of the Thetis model. Extensive validation of the sea surface elevations was done by comparing with existing networks of tide gauges in the North Sea. The simulations from this study show that the highest tidal current intensities are generated mainly at Den Helder and Oost Vlieland, reaching values > 1.5 m s − 1 and power density estimates that are most frequently close to 300 W m − 2 and that can reach values ≥ 900 W m − 2 . Given the relatively reduced depths where these "hot spots" are found, most existing stream turbines will require further development to operate. Nevertheless, the existence of higher current intensities zones, along a commonly considered "low energy" coast, opens the door to include the tidal stream resource in near future plans to diversify the energy supply in The Netherlands. [Display omitted] • First tidal stream resource assessment for The Netherlands. • Identification of high energy sites for potential extraction. • Investigation on the persistence and magnitude of Dutch "hot-spots". [ABSTRACT FROM AUTHOR]

Identifying the research topics of China's energy transition policy and predicting future research trends are crucial for policymakers to make informed decisions and advance the energy transition. This study applied the Latent Dirichlet Allocation (LDA) to extract 26 major research topics from the 13,976 abstracts of Chinese policy research papers in web of science (WOS), conceptualized the five most popular topics and used the ARIMA model to predict trends over the next 36 months. The results show that except for Economic and Environmental Impact of Renewable Energy Consumption (Topic13), which will first experience unanticipated fluctuations and then show strong growth capacity, all other topics will continue to remain positive growth trend. Research and Development (Topic3) is driven by the urgency of China's energy transformation. Economic and Environmental Impact of Renewable Energy Consumption (Topic13) is driven by the potential for the development of renewable energy. Economic Impact of Climate Change (Topic26) is driven by the climate change debate. Economic Growth, Urbanization, and Energy Consumption (Topic7) is driven by a discussion of their relationship and the mechanisms influencing them. Regional Efficiency and Productivity Analysis (Topic19) is driven by regional differences in China's energy policies. [ABSTRACT FROM AUTHOR]

In this paper, a risk-based robust energy scheduling model for microgrids is proposed to address the uncertainty of renewable energy sources (RESs). The proposed model introduces a novel adjustable convex hull based uncertainty set (ACHUS), which is a subset of the maximum uncertainty set, to quantify the uncertainty of RESs. The operational risk of an ACHUS is evaluated by a historical data-based method. This method defines the operational risk as the expected penalty cost of all historical data when the operational safety of the microgrid is guaranteed in the ACHUS. Based on the evaluation of the operational risk, a day-ahead energy scheduling model with the ACHUS for microgrids is formulated as a mixed integer nonlinear programming. By introducing auxiliary variables and constraints, the mixed integer nonlinear programming is converted into a solvable mixed integer linear programming. The effectiveness of the proposed method is validated on the IEEE 6-bus and 118-bus systems. The simulation results show that the proposed method can effectively reduce the conservativeness of uncertainty quantification and the improve the economic efficiency of the microgrid. • An adjustable convex hull based uncertainty set is used to quantify the uncertainty. • A historical data-based method is proposed to evaluate the risk of microgrids. • An energy scheduling model considering the adjustable uncertainty set is formulated. [ABSTRACT FROM AUTHOR]

Tidal current energy is regarded as a promising renewable energy with strong predictability and high energy density advantages. Tidal current power generation farm (TCPGF) planning has obtained great concerns to improve the exploitation of tidal current energy. Substantial work has been conducted to derive a TCPGF planning scheme with high economy and engineering value. However, there is no overall review of existing TCPGF planning methods so far. To give a systematic overview for the methodologies, characteristics, and challenges on this topic, this paper presents a thorough review for TCPGF planning methods. First, the macro-sitting methods of TCPGFs, which are the premise and basis of TCPGF planning, are discussed in detail. Then, the progress in tidal current turbine (TCT) layout and electrical collector system (ECS) planning are thoroughly surveyed. Finally, the challenges in TCPGF planning and the potential applications of artificial intelligence in TCPGF planning are proposed to promote the development of tidal current energy. • A multi-indicator evaluation framework for macro-sitting is established. • The progress in TCT layout and ECS planning are surveyed. • The future research prospects and challenges are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a model for optimizing the life cycle economic and environmental impacts of a hybrid renewable energy and battery storage system – as energy supply technologies (EST) for off-grid farms. Micro-hydropower and photovoltaics represent the primary renewable energy (RE) sources, with batteries and a diesel generator considered as possible back-up systems. A modified Non-dominated Sorting Genetic Algorithm II (NSGA-II) was developed and applied to a case study farm in Southern Spain and provided different EST solutions to optimize a design based on economic (life cycle costing or LCC) and environmental (life cycle assessment or LCA) constraints. The results highlighted that cases with a strong dependence on batteries could achieve minimum costs and lower impacts for climate change and energy resources (non-renewable fuels). In contrast, the optimal system for minimizing the consumption of material resources (metals/minerals) resulted in higher fuel use, which was up to 25% more expensive. The optimal EST solutions used up to 37% of the total energy generated; however, the results were sensitive to energy production-demand distributions. This methodology can help identify the most cost-effective solutions with the lower environmental impacts for decarbonizing the agricultural sector. [Display omitted] [ABSTRACT FROM AUTHOR]