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In this work, 138 datapoints, including elemental composition and ultimate analysis of various types of sewage sludge, and the hydrothermal carbonization reaction conditions, are used to develop a prediction model for the nitrogen content of the hydrochar. The results suggested that a two-layer feedforward neural network with five (05) neurons in the hidden layer can accurately predict the nitrogen content of the hydrochar based on the reaction temperature and the contents of nitrogen, carbon, volatiles and fixed carbon in the feedstock. Over 100 runs, the R2 and RMSE are in [87.547–99.097%] and [0.243–1.431] wt.% (db), respectively. Moreover, a statistical and regression analysis revealed that the sewage sludge-N is the main contributor to the hydrochar-N. Mostly, 40–70% of sewage sludge-N goes to hydrochar-N. The results are consistent with previous experimental reports, and this model can be used to predict the sewage sludge-derived hydrochar-N.

Microbial fuel cell (MFC) technology allows renewable energy production from the decomposition of organic matter present in wastewater. This technology, however, poses challenges that hinder scaling up of its usage in practical implementation. Also, resource recovery from the available waste sources is attracting attention due to the immense amount of valuable nutrients present in the wastewater. Herein, the present study focuses on investigating high-performance anodes, that is, using a stainless-steel brush (SSB) and carbon brush (CB) to accommodate the MFC in terms of energy production, leading to transformation into a microbial nutrient recovery system (MNRS) for nutrients’ recovery from wastewater. The present novel SSB anode system generated a stable voltage of ∼0.4V and a maximum power density of ∼400 mW/m2 whereas CB anode system produced the voltage and maximum power density of ∼0.5 V and ∼800 mW/m2, respectively. Besides, using the MNRS process, recovery of nutrients in terms of PO43− (80–83.4% for CB-MNRS and 60–69.5% for SSB-MNRS when using feed PO43− concentration of 10–20 ppm) and NH4+ (63–80.64% for the CB-MNRS and 52–70% for the SSB-MNRS when using feed NH4+ with a concentration of 10–20 ppm) was achieved.

During the operation of industrial gasifier, the concentrations of CO2 and H2O in gasification media vary with the change of stages, causing the gasification process being affected accordingly. Therefore, dividing the gasification process into different stages to study the effect of multi-stage switching atmosphere is significant. The conversion rate, reaction characteristics and reaction process for the gasification of Xiaolongtan (XLT) coal char in different atmospheres (CO2/H2O/N2) switching in the early, middle and late reaction stages were investigated using thermo-gravimetric analysis (TGA). The results indicated that the average reaction rate of coal char with H2O was approximately 3 times that of CO2. Increasing H2O concentration improved the reaction and conversion rate of coal char, which was the most obvious in the early stage. With the H2O concentration increasing from 0 to 95%, the increment of conversion rate in the early, middle, and late stages were 7.13%, 4.40%, and 3.92%, respectively. Thus, considering the problem of H2O corrosion in practical production, the H2O concentration could be appropriately increased in the early stage to improve efficiency. Compared with the random pore model, the shrinking core model fitted the gasification process of coal char better in a highly reactive 95% H2O/5% N2 atmosphere.

Automotive engines are encouraged to utilize biofuels to lower the net CO2 and to enhance the usage of renewable energy. With well-proven biodiesel engine characteristics, a comprehensive study about the impact of biodiesel on the fuel filter is lacking in the literature. Hence the current study investigates the comparative impact of higher blends of biodiesel and diesel on the fuel filter. Here, the authors have conducted both experimental and simulation studies on the fuel filter with diesel, two biodiesel fuels namely karanja, and waste cooking oil. Filter blocking tendency (FBT) was measured for the test fuels following ASTM D2068 . Also, a comparative tensile strength analysis of an automotive filter media was examined. The test results reveal that FBT increases exponentially with an increase in biodiesel blend concentration with a maximum FBT value of 30 for karanja biodiesel. Further, the tensile strength of the filter media reduces significantly with soaking in biodiesel compared to fossil diesel. Filter pressure drop measurement results with biodiesel were observed to be 35% higher than diesel. Consequently, effective countermeasures such as fuel preheating, varying the filtration area and ethanol blending are proposed to reduce the filter pressure drop with biodiesel. Filter pressure drop with karanja can be reduced similar to diesel fuel by increasing the fuel temperature by 16 °C or enhancing the filtration area by 35%. Karanja blended with 20% ethanol was also observed to have lowered the pressure drop. The present study concludes that neat biodiesel operation affects the filtration process and the filter media of the conventional fuel filter significantly.

Bio-oil is produced as a promising fuel for improving the economics of biomass. However, bio-oil utilizations suffer from challenges due to its complex compositions. In this research three bio-oil samples (BSR, BPA and BFT) were blended with coal/char to prepare slurries as biofuels. The thermogravimetric analysis of combustion and ash compositions of biofuels were investigated. Results showed that bio-oils have more weight loss peaks than coal/char, and the Tm is concentrated in the range from 476.4 °C to 495.2 °C, which are much lower than char. The contribution of coal/char and bio-oils to biofuel blendings can be clearly seen in the combustion profiles. There is a synergistic effect between bio-oils and coal/char improving the combustion performance. Tb of lignite BSR slurry reduced up to around 40 °C in comparison with lignite. The temperature range for combustion became narrow. By blending BSR, the Ea of lignite, long flame coal and char reduced 5.65 kJ mol−1, 13.56 kJ mol−1 and 6.31 kJ mol−1, respectively. Lignite BSR slurry with the lowest activation energy presented best combustion characteristics among all biofuels. The improved performance may attribute to AAEMs contained in bio-oil and microexplosions occurred in combustion, and AAEMs also help form lower fusion temperature of ashes.

This study attempted to improve the properties and energy efficiency of torrefied biochar produced from the waste of two common street trees, namely, Washingtonia filifera and Sterculia foetida. The effects of various torrefaction temperatures (210–300 °C) and reaction times (30 and 60 min) were evaluated. W. filifera petiole (WFP) biochar exhibited a higher weight loss and energy yield than S. foetida follicle (SFF) biochar. As the torrefaction temperature and time increased, the higher heating values (HHVs) and fuel ratios of both types of biochar increased, and the H/C and O/C atomic ratios became similar to the atomic ratios of lignite and peat due to the decomposition of hemicellulose and cellulose in the biomass structures. Furans and acetic acid, respectively, were the major gaseous products of WFP and SFF torrefaction. The maximum energy return on investment (EROI) of 23.2 was achieved for WFP biochar torrefied at 210 °C for 30 min. Greenhouse gas (GHG) emissions can be reduced markedly by 74.7–91.6% if biochar is used as a substitute for bituminous coal. Moreover, cofiring WFP or SFF biochar with bituminous coal at power plants is sustainable and economically beneficial because of the high HHV, stability, thermal performance, sufficient EROI, and low GHG emissions.

This article presents a novel active-mode indirect solar dryer consisting of highly efficiency through-flow evacuated tube collector for air heating. A DC fan powered with solar photovoltaic (PV) module is provided for forced air flow through this dryer. The dehydration chamber is of tray type with loading capacity of 6 kg–45 kg depending on the drying product. The dryer was evaluated to dry fenugreek leaves (Trigonella foenum-graecum) and turmeric (Curcuma longa) in semi-continuous mode; and respective overall thermal efficiency was found to be 34.1% and 23.6%. The respective thermal efficiency in open sun drying (OSD) was 5.7% and 5.4%. The quality of dried fenugreek leaves and turmeric was found better than OSD. Economics of the solar dryer indicate the capital cost is negligible in comparison to cumulative present worth of life cycle savings. The values are 211,262 INR (1 US$ = 74.57 INR) and 229,152 INR respectively for fenugreek leaves and turmeric; and capital cost of the dryer is only 50,000 INR. The low value of payback period also confirms economic viability of this dryer. The generalized economic curves have also been plotted.

Efficient, economic and environmentally friendly aircrafts and their operations have become the motivation of research nowadays after the COVID-19 pandemic has spread around the world. This paper presents a combined solid oxide fuel cell with a turbofan engine system in order to potentially improve the aviation performance. Two different analyses are conducted through exergy and exergoeconomic approaches. Also, the paper investigates five alternative fuels with five fuel blends on the system performance. It is founded that the overall exergetic performance is almost the same among the five fuel blends of 82% exergetic efficiency and 18% exergy destruction ratio. In addition, using the methanol and hydrogen fuel blend appears to be the most economical way of achieving a relative cost difference of 38% and minimum price of electricity generation in the range of 108–621 $/GJ for turbines and SOFC. However, using the dimethyl ether and hydrogen fuel blend is the least favorable.

Funding Information: This work was supported by the Academy of Finland , STORE-project [grant number 298317 ]. Publisher Copyright: © 2021 The Author(s) This paper introduces a novel application area for multivariate copulas in electric vehicle charging event simulation and dependency analysis. We propose a multivariate copula procedure that can be used to generate new synthetic charging events, which retain the complex dependency and correlation structures present in real-world charging events. The paper compares the most popular multivariate copula functions to discover the most reliable one to be used with electric vehicle charging event data. Accurate EV charging event simulation and analysis is crucial in multiple theoretical and practical applications such as charging load and demand response aggregation modelling. Based on multiple goodness-of-fit tests and charging load profiles of simulated charging events, the Student-t copula was found to be the most reliable multivariate copula to be used with EV charging data. Overall, the multivariate copula procedure is effective in analysis and simulation of EV charging events as it retains the inherent variability and complex dependencies of real charging events.

Value-added trade has increased rapidly in recent decades in the context of globalisation. This paper aims to measure the energy embodied in the value-added trade and the ecological relationships in the networks of the international construction industries. A hybrid method which combines ecological network analysis and the input–output method has been adopted. The direct energy intensities in most countries ranged from 0 to 3.0 trillion joules per million US$ and the dominant relationships were control and exploitation in the selected 15 years. For the construction sector, the negative relationships were dominant at 63.9% in Australia and 79.19% in China. The ratio of positive relationships increased from 24.3% to 31.5%, while the ratio of negative relationships declined from 75.7% to 68.5% over the 15 years. The mutualism index ranged from 0.72 to 0.98 over the 15 years, maintaining a value less than 1. In contrast, the synergism index had an increase of 12.2 over the 15 years. The results indicate that both direct energy intensity and the value-added ratio are important factors in relation to the energy embodied in value-added trade. Sectoral influence on the national industrial system has been interpreted in this paper to stimulate further research.

To alleviate the issues of climate warming and fossil resources depletion, this paper proposes a wind-photovoltaic-thermal energy storage-electric heater cogeneration system, which adopts the structure of concentrated solar power plant but replaces the expensive collector field and tower with wind and photovoltaic farms and electric heater that converts the excess electricity as thermal energy source. The cogeneration system can effectively regulate the fluctuating wind and photovoltaic output to satisfy electricity load by electric heater based on resistive heating of molten salt, two-tank molten salt thermal energy storage and power block based on steam Rankine cycle, and supply heat load directly from the molten salt heat transfer fluid and heat exchanger. This paper considers minimizing levelized cost of energy, loss of power and heat supply probability, energy reduction rate and lifecycle equivalent CO2 emissions as optimized objectives, and investigates the many-objective (having four or more objectives) capacity optimization problem by Nondominated Sorting Genetic Algorithm-Ⅲ coupled with Principal Component Analysis and Technique for order preference by similarity to ideal solution. Moreover, residual analysis is applied to consider the uncertainty of wind power prediction. The detailed optimization process and performance comparison of many-objective algorithms, the all-year energy flow and seasonal output characteristics analysis of the optimal-compromise solution are presented. Finally, the proposed cogeneration system is compared to the power-only system with the same installed capacity and equivalent load demand, and the comparison results show that the proposed cogeneration system performs better in all evaluation metrics. Therefore, the proposed renewable energy cogeneration system is feasible and cost-effective for practical applications.

As China's transportation system expands it is producing a large amount of end-use emissions. To derive cost-effective measures for CO2 mitigation, the authors of this paper develop a marginal abatement cost (MAC) curve for China's transport sector. Using the TIMES model, MAC curves are derived which identify the linkages among all transport modes, considering, among other uncertainties, the impacts of a carbon peaking policy and driver preferences for plug-in hybrid electric vehicles (PHEVs). The results underline that the intercity passenger transport sector has a cost advantage, with a huge potential for emissions reduction (accounting for almost 38.8% of all CO2 emissions mitigation). The freight sector is another potentially significant contributor to CO2 reduction, underlining its vital role for emission mitigation in the longer run. The carbon peak policy, while making alternative fuel vehicles (AFV) more efficient, is found to increase the overall cost of emissions reduction. A higher ratio of electricity to fossil fuel use by PHEVs drivers would help the transport sector to reduce CO2 much faster. The sensitivity analysis of these and other factors is also estimated in this paper. The final MAC curves provide valuable insights for future low-carbon energy development strategies of China's transport sector.

To reduce large peak torque and improve the vehicle's dynamic performance during frequent stops and starts, a novel mechatronics-electro-hydraulic power coupling system (MEH-PCS) is proposed. It combines a conventional electric motor and a piston pump/motor into a single unit that converts electrical, mechanical, and hydraulic energy. After a theoretical analysis of the system power flow, the paper establishes a rule-based dynamic optimal energy management strategy to control energy distribution and the dynamic switching of working modes in real-time. Moreover, a prototype vehicle model (MEH-PCEV) based on the MEH-PCS is developed by analyzing the working modes of the system. Thereafter, the feasibility and superiority of MEH-PCEV are verified by comparing it with AMESim certified pure electric vehicles. The verification results indicate that the MEH-PCEV's battery consumption rate is reduced by 14.418% and 21.174%, respectively, in the new European driving cycle and the American city cycle. Furthermore, the time for the vehicle to reach the start-up speed threshold is enhanced, and the electric peak torque is substantially reduced. Ultimately, the paper proposes a fuzzy controller with vehicle speed and pressure of the accumulator as variables to optimize the logic threshold control strategy. The electric torque is substantially reduced while the overall efficiency of the motor operating point is enhanced. The consumption rate of electric energy has been improved somewhat. Under the drive strategy's control, the MEH-PCS is well suited for frequent stops and starts conditions. As a development, this research is expected to provide a reference for improving electric torque.

One technique for improving energy efficiency is combining two or more energy systems to form a hybrid one that utilizes a single energy source. The use of renewable energy, such as solar heat to power hybrid systems, is another advantage. This paper introduces a novel hybrid ejector cooling-desalination system driven by an evacuated tube solar collector. The ejector cooling cycle (ECC) is combined with a closed-air, open-water, water-heated humidification dehumidification (HDH) desalination system. A thermodynamic model of the hybrid system has been developed, validated, evaluated, and presented in this paper, considering different refrigerants for the ejector cycle. The results indicate that the hybrid system achieved an energy utilization factor (EUF) of 1.4 with R600 as the ECC refrigerant. This represents the highest value (about 4–27%) than the other selected refrigerants. Hence, R600 is selected as the best refrigerant for the hybrid system. A case study of the hybrid system has been presented, considering several cities in Saudi Arabia using solar energy as the only source of energy. Results indicate about 15% energy input saving by the hybrid system for the specified outputs for Qassim city.

In order to improve the total efficiency of power generation in a wind farm, this paper investigated the effect of different operating conditions of the upstream wind turbine on the power of the downstream wind turbine by Gaussian wake model. A three-bladed Horizontal Axis Wind Turbine with the generator capacity of 30 kW was used. Firstly, Gaussian model for evaluating the wake flow was developed, which fit well with experiment data. Then, the influence of the operating condition of the upstream turbine on the downstream turbine performance was estimated by calculating the energy amount resulted from the velocity deficit of the wake. Finally, the effect of the installation position of the downstream wind turbine was analyzed. In consequence, the total power coefficient Ptotal of the wind turbines was relatively higher when the upstream turbine was at tip speed ratio λ = 6.4 and pitch angle β = 2° among different test conditions. Meanwhile, the Ptotal showed the maximum value when the downstream turbine was installed at the streamwise location x/D = 2.0 and lateral offset location y/R = 1.5 when the inflow turbulence intensity is 0.22≤TIref ≤ 0.35. This paper provided a better guidance for optimizing the wind turbine layout and improving the total power output of the entire wind farm.

Existing traditional street lighting systems are characterized by a high level of energy consumption compared to automated intelligent systems that offer different operating modes depending on traffic and power system load. The most promising energy sources systems are hybrid installations that switch the load to the grid in adverse weather conditions. Such systems may increase the energy efficiency of the street lighting system, but they are not completely autonomous. In this case, the most important problem is to provide the street lighting system with energy in adverse weather conditions. In this paper, an autonomous street lighting system with adaptive energy consumption based on weather forecast was shown. The proposed street lighting system is completely independent of traditional power sources and is completely powered by solar panels. The main energy consumers of a street lighting system are lamps. The consumption of lamps can be changed to the minimum brightness level required by outdoor lighting standards. Forecasts of energy generation by solar panels can be obtained using LSTM. It is based on weather and solar radiation forecasts data for the coming days. The brightness levels of lamps are calculated and changed using the methods proposed in this paper. The probability of reaching the critical level of batteries does not exceed 0.10% and fluctuates around 0.05% most of the time when simulating for 1000 days under random weather conditions. Simulation of energy consumption by the street lighting system using the proposed method shows stable and sustainable performance in Almaty, Kazakhstan. The obtained results in this work can be used for designing autonomous street lighting and outdoor lighting systems.

This paper studies the energy conversion performance and strength of a wearable thermoelectric generator consisting of a thin thermoelectric layer bonded to a flexible substrate. The thermal conductivity, Seebeck coefficient and electrical resistivity used in this paper are calculated from the fundamental formulas of carrier motion as functions of temperature. This gives more accurate calculating results and in line with actual situation. Convective heat transfer between the thermoelectric layer and the environment and the contact thermal resistance between the thermoelectric layer and the human skin are considered. Analytical solutions of power output density and axial stress in thermoelectric layer are derived. It is found that the bending of thermoelectric layer can increase the open-circuit voltage. The power output density increases to a peak value and then decreases with the length of the thermoelectric layer. On the premise of guaranteeing the strength of thermoelectric layer, the optimized length of the thermoelectric layer for obtaining maximum power output density is given.

This paper proposes a collection of novel deliverability prediction models for underground natural gas storage (UNGS) in salt caverns based on machine learning algorithms. Considering that the natural gas supply chain is characterized by imbalances between demand and supply on a timely basis, effective and fast models for predicting the deliverability of UNGS would not only be a valuable tool to various stakeholders but also, of great benefit in competitive natural gas marketplace. In this paper, a first step in applying machine learning algorithms to predict the deliverability of UNGS in salt caverns is proposed. To achieve this, the capability of three machine learning algorithms namely, artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF) are examined. The predictive capabilities of these methods were investigated using different monthly field storage data samples for different years with varied data samples of 36 active UNGS in salt caverns in the United States. Experimental results reveal that the machine learning models developed in this study can serve as suitable tools for predicting the deliverability of UNGS in salt caverns with different performances. Overall result shows that RF model exhibits better prediction performance with varied data partitions over ANN and SVM models.

Long term infrastructure investment planning for the electricity sector in Small Island Developing States typically optimizes generation and transmission investments sequentially. Compared to a simultaneous planning method, the current practice may result in a misallocation of scarce resources. To address this, this paper makes two contributions. First, it presents a framework for assessing two features of long-term planning models while accounting for economic and geographic idiosyncrasies of small island states. These are: i) the simultaneous vs sequential treatment of generation and transmission investments and ii) the impact of loop flow (a phenomenon intrinsic to electricity transmission networks) on long-term investment planning. Second, it quantifies the magnitude of omitting these model features using Jamaica as a test case. Depending on the initial conditions of the network, a simultaneous planning approach yields cost-efficiency gains in the order of 3.3%–3.6%. This is substantial when converted to financial costs and excess infrastructure investments. Importantly, energy modelers may want to think carefully about whether or not their results are liable to suffer from omitting these features and by making data and program codes publicly available, this paper broadens the scope for energy economic research in small island states.

Currently, it is of great significance to develop energy-saving technologies. Rectisol wash technology is becoming more and more popular in the industrial field due to its excellent performance. In order to reduce the energy consumption, a semi-lean solution process based on the Rectisol wash technology is proposed in this paper. The Cubic-Plus-Association (CPA) and Predictive Soave–Redlich–Kwong (PSRK) property methods are selected for the simulations in Aspen Plus. Two scenarios are proposed for the energy and exergy analysis. Results show that 50% SP3-reflux ratio is the optimal point in both scenarios from the energy view, and the optimal SP3-reflux ratios for scenario 1 and scenario 2 are 40% and 43% from the exergy view, respectively. In addition, the analysis results of related parameters in both scenarios show that increasing the SP1-spitting ratio or decreasing the SP2-spitting ratio has a positive effect on CO2 production; the H2S concentration in CO2 product first increases and then decreases while either ratio increases. The stripping N2 flowrate has a significant impact on the H2S concentration in Claus gas. This paper may guide for further study of the semi-lean solution process and provide a better understanding for Rectisol technology.

Decarbonizing the energy intensive iron and steel industry is in urgent need to meet the ambitious environmental goal. Efficient and clean use of the blast furnace gas (BFG) through combined-cycle gas turbine (CCGT) power plant provides feasible pathway to realize a near-term CO2 reduction when integrated with carbon capture. This paper presents effective control strategies to coordinate the operation of BFG-fired CCGT plant and solvent-based post-combustion CO2 capture (PCC) process based on the in-depth understanding of the interactions among process dynamics in different time-scales for carbon, heat and electricity. The energy storage capability of the PCC process is explored in addition to the CO2 capture and the reboiler steam flowrate used for solvent regeneration is incorporated into the BFG-fired CCGT control loop. Considering this, two coordinated control strategies are developed for the BFG-fired CCGT-PCC, first based on the conventional PI control and then with advanced model predictive control (MPC) approaches. The coordinated strategies are demonstrated to improve the power ramping performance of the CCGT with little degradation on the PCC operation, thus providing better support for the reliability of the power system in the context of increasing penetration of renewable energy resources. Moreover, by considering the impact of disturbances into the predictive models, the MPC-based coordinated control can well alleviate the influence of BFG fluctuations, guaranteeing a stable operation of the integrated plant. This paper points to the new direction of using PCC for more flexible power regulation of adjustable sources in low-carbon energy systems with penetration of intermittent renewable powers.

The key core contribution of the paper is to solve the problem of optimal sizing of hybrids systems with multiple storage facilities and minimizing the Levelised cost of electricity under the premise of ensuring the load supply. To achieve this, we have developed a new extended algorithm based on the Power Pinch Analysis (PPA) and the Modified Electric System Cascade Analysis (MESCA) which we previously used to size hybrid systems in previous papers [1,2]. Meanwhile, The ESCEA is used in the research to obtain the optimal combination of the contributions (%) of each type of generator adopted in an autonomous hybrid system (photovoltaic panels PV, wind turbines generators WT, and a concentrated solar power system CSP), with a thermal energy storage system (TES) to store heated fluid simultaneous with batteries for storing electrical energy (BESS). The ESCEA algorithm applied in a case study with data from the city of Oujda with an electrical load of 50 MWe indicated that it is possible to achieve 0.18 $/kWh of the Levelized Cost of Electricity (LCOE) for 0% Loss of Power Supply Probability (LPSP) with specific contributions of each generator type.

In this paper a concept of an integrated energy system for residential applications has been presented. The prosumer system consists of a renewable source of electricity and an electrical energy storage. It is complemented with a reliable, gas-fueled source of heat and electricity which is a micro-cogeneration unit. The proposed solution presents a significant potential of covering the energy demand of the individual consumers while being considered as a natural replacement for traditional energy solutions which may increase low-stack emissions. A model of the analyzed micro-cogeneration system was previously developed by the authors and validated using experimental data. This paper proposes an original idea to use a reliable method of calculation for analyzing an innovative green-promoting energy system. By using the proposed integrated prosumer system, consumer electrical self-sufficiency may reach up to 99% (using 13 kWh battery), while covering total thermal energy demand. An economic assessment was made using dynamic methods and the areas of potential market competitiveness have been presented. The micro scale-generation in the context of electricity is characterized by significant investment costs, however, it may bring an important ecological effect in the future.

Understanding the dynamic relationship between oil prices and renewable energy is beneficial to promoting the development of the renewable energy market and the transition from fossil fuels to renewable energy, which will finally protect the environment. Based on the data of G7 countries from 1980 to 2018, this paper analyzes the short-term and long-term dynamic relationship between oil prices and renewable energy consumption (REC) by using linear and nonlinear autoregressive distributed lag models under the condition of structural fractures and then discusses its asymmetric characteristics. The results show that, except for France and Germany, the oil prices of other countries have a significant asymmetric effect on REC, but there is great heterogeneity between the countries. Specifically, in Canada, the U.S., and Italy, a positive change in oil prices has a greater impact on REC than a negative change, whereas in England and Japan, the results are just the opposite. Therefore, based on the heterogeneity between countries and the long- and short-term dynamic relationships reflected in the research results, this paper further discusses the policy implications.

District heating networks transport thermal energy from one or more sources to a plurality of consumers. Lowering the operating temperatures of district heating networks is a key research topic to reduce energy losses and unlock the potential of low-temperature heat sources, such as waste heat. With an increasing share of uncontrolled heat sources in district heating networks, control strategies to coordinate energy supply and network operation become more important. This paper focuses on the modeling, control, and optimization of a low-temperature district heating network, presenting a case study with a high share of waste heat from high-performance computers. The network consists of heat pumps with temperature-dependent characteristics. In this paper, quadratic correlations are used to model temperature characteristics. Thus, a mixed-integer quadratically-constrained program is presented that optimizes the operation of heat pumps in combination with thermal energy storages and the operating temperatures of a pipe network. The network operation is optimized for three sample days. The presented optimization model uses the flexibility of the thermal energy storages and thermal inertia of the network by controlling its flow and return temperatures. The results show savings of electrical energy consumption of 1.55%–5.49%, depending on heat and cool demand.

Owing to the increasing utilization of renewable resources such as wind turbines (WT), photovoltaic (PV) into a microgrid (MG), optimal planning has become important to satisfy the energy demand due to inherent uncertainties. This paper proposes a new model of planning based on renewable energy uncertainty and demand response and electric vehicles (EVs) in order to minimize the electricity market’s total cost. Considering uncertainty challenges, energy storage system (ESS) and demand response programs based on time-of-use (TOU) are employed as a solution for managing the power flow in MG to warranty the essential load supporting and voltage stability and satisfy electrical and heat demands. Moreover, in this paper, the influence of price-based demand response (DR) for industrial, commercial, and residential loads is taken into account. Finally, the proposed problem is modeled as an optimization problem while the related decision variables are adjusted by a modified version of virus colony search (VCS) algorithm based on chaos theory in order to increase the exploitation and exploration terms. The proposed approach is tested on an MG system with several scenarios through analyzing the effect of DR programs based on the total cost reduction. As shown in the simulation results, DR highly reduced total cost (20–26% related to the case without DR), in which voltage dip (maximum 1.4%) and power deviation (maximum 1.2%) were enhanced.

The promotion of hydrogen fuel cell vehicles (HFCVs) is an effective way to decarbonize the transportation industry. However, the shortage and difficulty in the layout of hydrogen refueling stations (HRSs) are the key obstacles to the HFCVs development. This paper constructs a two-stage location optimization model based on geographic information big data to provide technical support for the HRSs layout. The first stage is suitability analysis led by the geographic information system; the second is decision analysis based on the multi-criteria decision-making (MCDM) approach. First, the analysis elements and indicators used in the two-stage model are respectively given: including ten types of geographic information elements and six evaluation indicators. Then, the key steps and core algorithms of the two-stage model are introduced in detail. Thirdly, this paper takes the Weifang City of Shandong Province as the study area to carry out an empirical case to verify the model. The weight results show that traffic convenience is the key determinant of suitability (more than 25%); the economic development level and public acceptance of the alternative site area are the critical indicators in the decision-making stage (more than 25%). The model gives the suitability score for all available land in the study area. The suitability score is between 3.521 and 8.047 (10 points scale). Taking the median value of 5.784 as the threshold value, eight alternative sites are selected from the green areas suitable for layout based on the distribution of interest points, which are included in the planning for the next two years. The layout priority rankings show that alternative sites A1, A6, and A5 are ranked high. The reliability and uniqueness of the model are proved by comparative analysis. The results of sensitivity analysis and scenario analysis show that the sensitivity preference of decision-makers could significantly affect the priority ranking. The empirical study will provide effective reference and enlightenment for Shandong to develop hydrogen energy strategy.

Nowadays, photovoltaic technology has been recognized as a popular energy source in most parts of the world. The temperature rise in photovoltaic cells is one of the major weak points of this technology which impacts on its electricity generation. Using nanofluid circulation as the coolant of photovoltaic modules, is one of the methods concerned by scholars. In this paper, using carbon black nano particles in water was investigated experimentally, and the concentration range of 0–0.4 wt% were tested. Results showed an unexpected cooling behavior of carbon black/water nanofluid. So that, the concentration of 0.21 wt% was found to be the best case. This concentration led to produce 7% more output power in comparison with pure water as coolant and 54% more output power in compare with the conventional photovoltaic module. In order to precisely calculate the electrical energy efficiency, the consumed pumping power was considered, too. The exergy assessment is another investigated matter in this paper and a modified method for exergy efficiency calculation is presented. Accordingly, the overall exergy efficiency rises 15.2% and the entropy generation depresses 1.4% compared with the conventional module, for the best case.

The ceramics industry is the second largest energy consuming sector in Europe. The main energy used in the ceramics industry is heat generated through burners using natural gas. The main area can be identified in three stages, the drying stage and the firing stage, and the cooling stage. The firing stage represents about 75% of the total energy cost. The roller hearth kiln technology is considered to be the most cost-effective solution for ceramic tile manufacturing. The kiln is separated into two sections, the firing stage and the cooling stage. The cooling stage generates large amounts of waste heat as the exhaust of the kiln is composed of a challenging flue gas for heat recovery. The recovery of this heat in an efficient way with no cross contamination has been achieved with a heat pipe heat exchanger (HPHE) system, which was designed, manufactured and installed on a roller hearth kiln and is presented in this paper. The heat pipe heat exchanger located next to the cooling section exhaust stack managed to recover up to 100 kW at steady state without cross contamination or excess fouling. The return on investment of the system has been evaluated at 16 months with a saving of £30,000 per year. This paper will present a deep row by row theoretical analysis of the heat pipe heat exchanger. The Computational Fluids Dynamics will also be presented to investigate the fluid dynamics within the evaporator and condenser section. Both investigations have then been validated by the experimental investigation carried out on a full-scale industrial system. The design approach used in this paper will highlight the benefits of this type of technology and provide a guideline for the design of novel heat pipe heat exchangers.

The main objective of this paper is to investigate the usage of organic fluids as the working fluid in the two-phase natural circulation system. Also, this paper discusses the effects of fluid material properties on the performance of the system. For this purpose, five working fluids are selected, include water, R113, R11, Cyclohexane, and R245fa. The mathematical simulation of the system is done by applying a two-phase flow model. The mass flow rate of the two-phase natural circulation for five working fluids are shown in different heating power and sub-cooling conditions. To analyze the transient state behavior of the two-phase natural circulation loop, the stability maps of the system for five working fluids are sketched. According to obtained results, using organic fluids instead of ordinary water can increase both the stability and capability of removal heat. Furthermore, the second law of thermodynamic is applied to calculate the amount of entropy generation, and consequently to study the thermal-hydraulic performance of the different working fluid used in the loop.

This paper presents a new expansion planning model of integrated electricity and gas networks with the high penetration of renewable energy resources, using a multi-objective mixed integer linear programming approach. The proposed method concludes the generation and transmission expansion planning of electricity network and natural gas network expansion planning. Since the outage of natural gas-fired units due to gas network faults is possible, in this paper, integrated electricity and gas network expansion planning is studied by applying N − 1 contingency to these systems. Photovoltaic power plants are used for generation expansion planning to reduce emission and greenhouse gases. Reduced disjunctive model is applied to decrease the calculation time of applying N − 1 criterion to power systems. A scenario-based stochastic method is exploited to study the correlated uncertainty of the photovoltaic generation and electrical loads. The proposed model is applied to two case studies, including modified Garver’s system & 5-nodegas network and IEEE-RTS system & Belgian gas network. The numerical results show that using photovoltaic generation in the expansion planning problem while considering contingency in both electrical and gas networks decreases costs by 9.08% and 7.08%, and reduces emission by 38.71% and 34.62% in the first and second systems, respectively.

Climate change will impact renewable resources and electricity demand, usually not jointly considered when designing future decarbonized power systems. This paper assesses how sensitive the Portuguese carbon-neutral power sector is to climate change by 2050 and what are the implications for the formally approved Portuguese Carbon Neutrality Roadmap. The future capacity factors for wind, solar and hydropower and electricity demand response to temperature are estimated for 22 climate projections along the Representative Concentration Pathway 4.5 and 8.5. The eTIMES_PT optimization model is used to assess its combined impact on the cost-optimal configuration of the power sector by 2050. Results show that climate change lowers hydropower generation by 20% (in median terms). Improving spatial and temporal resolution and including future climate patterns, results also in lower cost-effectiveness of solar photovoltaic vis-a-vis the Carbon Neutrality Roadmap. While future climate does not impact onshore wind production, offshore wind power generation is positively affected, being a climate-resilient carbon-neutral option for Portugal. Annual electricity unitary costs at final users (excluding taxes and levies) only increase up to 4% with climate change, but seasonal costs have higher variability. This analysis highlights that climate change affects the cost-optimal annual carbon-neutral power sector and needs to be included in energy planning. info:eu-repo/semantics/publishedVersion

Large-scale heat pumps (HPs), biomass CHPs and excess heat (EH) from industry and data centres are promising district heating (DH) sources. Electricity and thus power transformers (PTs) will be an important part of the future energy system, which opens the possibility to use the thermal losses occurring in PTs for DH. The present paper analyses high voltage PTs in Denmark as DH sources. First, we employ a thermodynamic model of PTs to determine the EH they produce. Subsequently, we analyse thermodynamic properties of heat exchangers and HPs necessary to utilise EH for DH. Finally, we perform a GIS analysis to link the PTs with specific DH networks. From the theoretical amount of excess heat from power transformers (EHPT) available for DH of 0.28 TWh per year, 0.12 TWh or 0.5% of Danish DH demand can reach the consumers. 0.07-0.21 GWh can reach the consumers below the average DH price. The entire EHPT potential can be utilised through HPs, working with an average COP of 4. The sensitivity analysis showed that the EHPT can supply up to 2.26% of the Danish DH demand. Therefore, EHPT is a relatively small DH source on the national scale but could be an important local option.

The European Green Deal has been heralded as the“Europe's man on the moon moment” as it aims to achieve 100% GHG reductions by 2050. Achieving the decarbonization of the energy system will be driven by a combination of factors and synergies between technological development, policy exertion and societal attitudes. In this paper, we present an original set of future storylines until the year 2050 to inspire modelers, policy makers, industry actors, and the public to understand: Without technological developments in the next decades, to what extent can we rely on societal commitment or stronger cooperation within the EU to achieve climate targets? What technological innovations could become a cornerstone of an effective European energy transition? Through analyzing four pathways (shaped by the crossroads of policy-technology-society developments), results indicate that high electrification rates are imminent to achieve a rapid decarbonization. This implies that technology development and deployment must go hand-in-hand with strong policy enforcement in the short-term to speed-up the energy transition. Then, based on a review of European energy transition scenarios, these and other insights are compared vis-a-vis with other scenarios studies to identify similarities of pathway results and to elaborate on consolidated findings relevant to the EU Green Deal.

The paper presents a practical mathematical model for determining the optimal wind turbine (WT) design based on the known wind statistics at WT location and wind shear exponent. The basic WT optimization parameters are hub height (HH), WT rotor diameter (RD) and WT rated power (RP). The proposed model varies WT parameters and calculates the total actualized WT costs as well as the annual energy production (AEP). The optimal values of WT parameters are those at which the minimum levelized cost of energy (LCOE) is achieved. Firstly, the LCOE model is built up and WT optimization problem is defined. Then, genetic algorithm is presented and improved. A quantitative assessment of the drop in LCOE, had the unoptimized WT been used is done. Lastly, the practical applicability of the proposed model is demonstrated on real examples of WTs installed at locations with different wind conditions. It is indicated that by optimization of WT parameters, LCOE can be reduced effectively. The developed algorithm and mathematical model have a general character and can be used to optimize utilization of wind potential at a particular location and also provide maximization of the profit generated by the wind power plant within its life-span.

Scrubbers are more and more used to recover waste heat from coal-fired exhaust flue gas (EFG). But the heat and mass transfer mechanism in the counter flow direct contact scrubber is not clear enough, and the heat recovery rate (E) needs to be improved cost effectively. This paper proposes to use a simulation model to study the heat and mass transfer mechanism and thus to improve the scrubber design without intensive experiments. A mathematical model based on the two-film theory was established, and the finite difference method was used to solve it. The model was validated using experimental data and heat recovery equation is obtained with relation to the main influencing factors. The results show that the most sensitive factors on E are the liquid-to-gas ratio rwf and the equivalent diameter of water droplets rd. In addition, height to diameter ratio (H/D) is more important than flue gas velocity. E can be increased by 25% and 36% when rwf increases from 4 to 6 and rd decreases from 0.002 m to 0.0015 m, respectively. Intensive simulations help determine the scope of the influencing factors to improve scrubber design.

This paper proposes a real-time indirect reinforcement learning based strategy to reduce the fuel consumption. In order to improve the real-time performance and achieve learning online, the simulated experience from environment model is adopted for the learning process, which is called indirect reinforcement learning. To establish an accurate environment model, a high-order Markov Chain is introduced and detailed, which is more precise than a widely used first-order Markov Chain. Corresponding with the model, how the reinforcement learning algorithm learns from the simulated experience is illustrated. Furthermore, an online recursive form of the transition probability matrix is derived, through which the statistical characteristics from the practical driving conditions can be collected. The induced matrix norm is chosen as a criterion to quantify the differences between the transition probability matrices and to determine the time for updating the environment model and triggering the recalculation of the reinforcement learning algorithm. Simulation results demonstrate that, compared with the direct RL, the proposed strategy can effectively reduce the learning time while maintains satisfied fuel economy. Furthermore, a hardware-in-the-loop experiment verifies its real-time capability and actual applicability.

Ultra-low district heating (ULTDH) networks are operated at temperatures below 30 °C. However, the temperatures supplied are still too low for direct heating of the consumers. Heat pumps use the heating network as a source and can supply the consumers with space heating and domestic hot water as needed. Although individual manufacturers provide performance figures for the source temperature of 20 °C, these are usually derived from extrapolation from standard conditions with source temperatures of 0 °C and 10 °C. Up to now practical measurements are missing how efficiently heat pumps work in an ULTDH network environment and how the network temperature spread affects the performance. In this paper test rigs are used to investigate heat pumps under the conditions in ULTDH networks at a network supply temperature of 20 °C. It is shown that heat pumps can operate up to twice as efficient as with a geothermal probe. Furthermore, the network temperature spread has a significant influence on the performance of the heat pump. Heat pumps operate significantly better with a low source temperature spread.

Excessive preheating energy consumption is a persistent issue in natural gas expansion power plants. In this paper, a novel system is proposed to solve this problem by using multi-stage expansion and gas engine-driven heat pumps (GHP). A dynamic model based on phase equilibrium theory is established to predict hydrate formation. A control strategy with continuously variable transmission (CVT) for optimizing engine efficiency is investigated through simulation and experiment. Finally, the efficiency and economic performance are compared among GHP, electric heat pump (EHP), and cogeneration (CHP) in a case study. The results show that the engine efficiency of CVT is 4.89% higher than that for multi-level transmission. Compared to single-stage expansion, multi-stage design can reduce preheating temperature by 46%, preheating load by 4.41%, and improve the performance coefficient (COP) of GHP 1.4-fold. The COP of GHP is 33.38% higher than that of EHP, and the primary energy ratio (PER) is 1.81 times that of CHP accordingly. The annual average profit margin for GHP is 1.89 times that of CHP and 7.15 times that of EHP. Thus, the proposed system has significant energy savings and economic advantages.

In this paper, we present a comprehensive evaluation of low-rank fuel compositions obtained using multi-criteria decision-making methods. Here we used a unique combination of parameters, especially important for gaining an insight into the prospects of industrial combustion of water slurries based on waste. The research involved six sets of criteria (cost, energy efficiency, ease of preparation and storage, atomization, fragmentation, and emissions) including a total of seventeen characteristics determined experimentally. An integral efficiency indicator was calculated in each set using the Weighted Sum Model. After that, an overall indicator was calculated accounting for all the key factors. The integral indicator ranged from 0.4 to 0.9 in all the six categories of criteria. The lowest overall efficiency indicators (0.239–0.487) were typical of the fuel containing 50% of flame coal filter cake, 40% of water, and 10% of turbine oil. According to the analysis, the blends based on coal slime and water can be considered the best fuels. The main application of the research findings is the development of compositions to provide positive effects at the stages of fuel preparation, storage, transportation, atomization, and combustion.

Sustainable energy development in the farming sector is an essential strategy to respond the combined challenge of achieving a reliable and affordable solution but including mitigation and adaptation to climate change. Intensive breeding farms require maintaining an adequate indoor thermal environment that results in high energy demands, usually covered by fossil fuels and electricity. This paper addresses the application of the combined slurry technology for a particular pig farm that currently uses a diesel boiler to supply the piglet heating energy needs. The study also considers different options based on closed ground source heat pump systems. After the design of the slurry alternative and the geothermal ones, notable advantages are detected compared to the existing diesel system. Results show that the implementation of the slurry technology implies an important reduction of the operational costs, which, in turn, involves short amortization periods for this system in relation to the diesel one. Greenhouse gases emissions are also highly reduced in the slurry alternative based on the low electricity use of the heat pump. The environmental side is reinforced by the reduction of polluting substances such as methane of ammonia derived from the descent of temperature of the slurry.

Ground source heat pump systems (GSHP) for residential building heating, cooling, and hot water are highly energy efficient but capital intensive when sized for peak demands. The use of supplemental sources of energy with GSHP systems enables improved life-cycle economics through the reduction in the size and cost of the GSHP components. This paper investigates the life-cycle economics of hybrid solar-assisted ground source heat pump systems (SAGSHP) using simulations validated from field data. The economics and optimal sizing of SAGSHP systems for heating dominant climates in four locations in Australia and ten locations elsewhere are evaluated in order to explore the suitability and relative merits of SAGSHP systems in a range of heating dominant climates. In locations having high or moderate levels of solar irradiation, high electricity prices, and high or moderate gas prices, SAGSHP systems are shown to have the lowest life cycle cost amongst alternatives, with predicted savings of up to 30%.

In order to achieve the targets of the EU climate protection strategy, the household sector with its overall high energy consumption offers great potential for efficiency improvements. Better insulated buildings and efficient heating and ventilation technologies can reduce the demand for energy regarding space heating and cooling. Efficiency improvements in domestic appliances can only be achieved with great effort. This paper presents an innovative system concept in which domestic appliances are thermally connected to the heating and ventilation system via the energiBUS, using a heat pump as the central heating and cooling device. The system benefits from the replacement of internal heating and cooling devices of the respective domestic appliances and the simultaneous utilization of both energy flows – warm and cold – of the heat pump. The system is analyzed by means of a detailed dynamic simulation model using MATLAB/Simulink®. The component models included are experimentally validated and have deviations of less than 5%. The results show that a reduction of 35 kWh/a in total energy consumption is achieved and the COP of the heat pump can be improved from 3.3 to 3.5. In this context, the integrated design of the system control still has potential for optimization.

The world has witnessed a surge in renewable power installed capacity in recent years, and there is an emerging trend of renewable penetration in electricity production. However, there is a lack of quantitative comparative study on disaggregated renewable power sources concerning their generation efficiency performance, regional heterogeneity, development potential, and influencing mechanism in the existing literature. In the case of China's 30 provinces, this paper evaluates the hydropower, solar power, and wind power generation efficiency by stochastic frontier analysis method, and then reveals the distribution characteristics and deployment potential of different renewable sources. Furthermore, from the perspective of spatial heterogeneity, geographical detector model is utilized to study the influence mechanism of the generation efficiency of different renewable sources. The main results are as follows. Firstly, production inefficiency prevails in hydropower, solar power, and wind power generation industries. The installed capacity, utilized hours, and auxiliary power consumption have positive impacts on the three renewable energy sources. Every 1% increase in auxiliary power consumption leads to 0.16% increase in solar power generation, which is quite larger than the increase in hydropower and wind power. Secondly, on average, hydropower has the highest level of generation efficiency, followed by wind power and solar power. Kernel density curves indicate the generation efficiency of hydropower, solar power, and wind power displays distinct aggregation characteristics. Different energy types show significant regional differences in deployment potential. Thirdly, annual precipitation accounts for 76% of the spatial heterogeneity in hydropower generation efficiency, followed by hydropower technology innovation and power structure. As for solar power generation efficiency, the most important influencing factors are electricity investment and economic development. By contrast, wind power generation efficiency is primarily affected by power structure, electricity investment, and urbanization. Additionally, there exist distinct synergistic effects among different variables. These results provide insightful policy support for the improvement of renewable power generation efficiency. The study can be extended to the global scale using country-level data.

The purpose of this paper is to make a research on solar power plants integration in an electric power system, taking into account all costs arising from circular economy criteria. Four scenarios of solar power plant installation are analyzed. The methodology relies on nature-inspired optimization. The evolutionary multiobjective genetic algorithm is applied. The optimization is based on the costs of electricity production, encompassing not only the costs of technology, but also the costs of environmental protection and sustainable development. These costs are included in the model and the objectives are formulated accordingly. The objectives are based on maximization of electricity production up to the level of demand, minimization of total electricity costs and minimization of greenhouse gas emissions. In addition, grid losses as well as other limitations and constraints of the electric power system are included into the model. The loss distribution is calculated in proportion to the distance between the supplier and the end user. The solar power plant losses equal zero, because it is assumed that the plant is located in a local community, close to consumers. The results show that the emissions and costs are higher without an introduced solar power plant. Introduction of solar capacities reduces the costs and emissions to a certain level. The developed model may be useful as a decision-support system generator.

The artificial ground freezing (AGF) method is widely used for soil reinforcement in underground engineering. The AGF method has been confirmed very effective, however, it consumes a great deal of energy during construction. Therefore, it is of great significance to study its energy conversion and utilization, which is very important for better and more energy-efficient employment of this method. In this paper, a novel method, which could effectively calculate the energy utilization rate (EUR), is proposed to evaluate the energy conversion efficiency of a typical AGF project. First of all, the brine temperature and ground temperature are monitored and analyzed. Secondly, according to the monitored data and actual layout of freezing pipes, a sophisticated three-dimensional solid model is established, which considers the thermal-mechanical coupling and ice-water phase change. Based on the numerical simulation, the variation of the temperature field as well as the expansion process of the frozen soil curtain are analyzed, and the effectiveness of the numerical model is further validated by comparison between the measured and simulated results. Thirdly, variations of three key parameters including the output energy, elastic strain energy and heat energy during the active freezing period are calculated and analyzed. Finally, the EUR curve during the AGF process is acquired based on the calculated indicators. The trend of EUR can be divided into four stages, while the EUR is zero in the initial stage, reaches a peak of 0.79 in the rise stage, and then decreases slowly in the decline stage and stable stage. According to the EUR curve, constructive measures to improve energy conversion and utilization are proposed. It is concluded that this novel method is very effective for the assessment of energy utilization in AGF projects.

Accurate forecast of mid-term power demand ensures the stable and efficient operation of power systems, and is essential for the construction of energy interconnections and renewable energy microgrids. However, the implementation of strategies aimed at reducing carbon emissions such as electric energy substitution increases the uncertainty of power demand. In order to effectively extract the changing characteristics of electricity demand, this paper firstly proposes a secondary decomposition model based on a seasonal-trend decomposition procedure based on Loess (STL) and variational mode decomposition (VMD) to reduce sequence complexity. Then, different models such as grey wolf optimized support vector regression (GWO-SVR) for different sequences were used to achieve the best prediction effect. In addition, this study used the Markov chain model to further improve the prediction accuracy based on interval optimization. To verify the effectiveness of the hybrid model, a case study was conducted on the monthly electricity consumption in Zhejiang Province, China. The results show that the proposed model effectively extracts the characteristics of changes in electricity demand and greatly improves the forecast accuracy.

The integration of renewable electricity generation poses challenges for today's energy system. Since renewable potentials are geographically unevenly distributed, solutions to overcome bottlenecks such as grid congestion are necessary to raise as much renewable potentials as possible. In specific, a part of Austria is characterised with high photovoltaic installation inquiries and low electricity demand. Electricity grids are designed to cope with today's loads, therefore the integration of inquired photovoltaic would overstrain the built infrastructure. In this paper we investigate how power-to-gas can ease this photovoltaic related grid strain. The feed-in of hydrogen into natural gas transmission pipelines and the usage of hydrogen and biogas from local fermentation plants for methanation feeding synthetic natural gas (SNG) into local distribution grid is assessed in various scenarios. Based on the technical assessment results, an economic evaluation of each scenario is performed to determine generation costs of hydrogen or SNG. Depending on the electrolysers location in the electricity grid, the installable photovoltaic power can be increased significantly without causing electricity grid congestion. Costs for hydrogen and SNG vary across each scenario, mainly influenced by CapEx for electrolyser and methanation as well as electricity purchasing costs.

Evacuated cylindrical tube receiver (ECT) is a promising type of solar collector system, due to its mass production, low cost, and low heat loss. In this paper, a solar dish collector system with evacuated cylindrical tube receivers (SDEC) is proposed, designed and trial-manufactured. The optical-thermal efficiency and economic analysis are carried out to validate the comprehensive performance of the SDEC. The results show that the thermal efficiency of the SDEC is about 47.3%. The nonuniformity of the solar flux and temperature distributions at the top of the absorber are verified through the coupled multiphysics simulation with the result of 0–50 mm. In addition, over 10 years of the lifetime of the SDEC with 20 ECTs, the cumulative present worth of annual saving for heat production turned out to be approximately 34,041 $ and 13,460 $ with respect to electricity coiler and gas-fired boiler, respectively, when the cost of heat production per kWh is 0.0264 $ and the payback period is 1.88 years.

Several countries are adopting plans to reduce the contaminant emissions from the energy sector through renewable energy integration and restrictions on fossil fuel generation. This process poses important computational and methodological challenges on expansion planning modeling due to the operational details needed for a proper analysis. In this context, this paper develops a planning model including an effective representation of the operational aspects of the system to understand the key role of flexible resources under strong decarbonization processes in highly renewable power systems. A case study is developed for the Chilean power system, which is currently undergoing an ambitious coal phase-out process, including the analysis of a scenario that leads to a completely renewable generation mix. The results show that highly renewable generation mixes are feasible, but rely on an effective balance of the key flexibility attributes of the system including ramping, storage, and transmission capacities. Further, such balance allows for faster decarbonization goals to remain in a similar cost range, through the deployment of flexible capacity in earlier stages of the planning horizon.