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• The model provides the best configuration to maximize crop yield and energy output. • The model gives the optimal height, spacing between tables, table size, and tilt. • The amount of solar irradiation available for crops under the panels are calculated. • The energy output and crop yield for each configuration of the system is evaluated. • The increase in crop yield is more sensitive to the expansion of panel row spacing. The global population is experiencing rapid growth, leading to increased demand for energy and food resources, necessitating the expansion of cultivated land. The construction of photovoltaic power plants to meet energy needs may result in competition for land between the agriculture and energy sectors. To address this issue, agrivoltaics systems are perceived as a solution, allowing for the coexistence of agricultural and energy production in the same area. However, the shading caused by solar panels can potentially. Therefore, a model has been developed to determine the best configuration for maximising both crop yields and energy production from the photovoltaic field. The purpose of this paper is to develop a model that optimizes energy production and crop yield within an agrivoltaics system. The model integrates factors such as elevation, spacing, tilt, panel technology and size to enhance the radiation under the photovoltaic panels, as well as to increase crop yield and the efficiency of photovoltaic array. It is constructed based on the climatic condition and the relationship between the shaded area and the sunlight distribution below the photovoltaic panels. Furthermore, the model relies on the correlation between the configuration used and the energy power delivered by the photovoltaic array. A set of equations that link configuration, sunlight, crop yield, and photovoltaic panel power was developed, and the model was implemented in MATLAB, using genetic algorithm optimisation techniques. The initial step involves the determination of radiation values under the panels, followed by the identification of the best scenarios for subsequent simulations aimed at evaluating crop yield and power generation from the photovoltaic array. A case study was conducted in Kamboinsin village (12°27′ N, 1°33′ W), in Burkina Faso, focusing onusing corn cultivation to validate the model. The results show that the model effectively identifies the optimal configuration for maximizing both crop yield and photovoltaic field output. The simulation results reveals that the distribution of radiation under the panels is significantly influenced by factors such as panel elevation height, spacing between table, and spacing between rows of table. Notably, the yield is more sensitive to the spacing between rows of panels. When comparing the effects of the different panel sizes, it is evident that utilizing smaller tables leads to higher crop yields. However, this approach results in a decrease in energy production from the photovoltaic field. For instance, on 1 ha of land, a table consisting of a single 100 Wp panel generated 92.8 % of the crop yield achieved in full sun with a nominal power of 96.9 kWp, whereas a table comprising 2 panels of 260 Wp produced 80.1 % of the yield with a nominal power of 378.56 kWp. [ABSTRACT FROM AUTHOR]

• A comprehensive review of intelligent predictors and two auxiliary methods in hybrid forecasting models. • Discussion on merits and limitations of intelligent predictors for wind energy forecasting applications. • Sources of diversity and ensemble strategies in ensemble learning are summarized and clarified. • Overview of underlying theories of metaheuristic optimization algorithms and their implementation details. Recent developments in renewable energy have highlighted the need for rational use of wind energy. Accurate prediction of wind speed and wind power is recognized as an essential part in realizing energy balance and scheduling decisions of power generation. In recent years, various wind energy forecasting models have been successfully proposed. Among them, intelligent models occupy an irreplaceable dominance and have tremendous potential due to their accuracy and robustness. This paper gives a broad literature survey of the intelligent predictors in the field of wind energy forecasting, including four types of shallow predictors (artificial neural network, extreme learning machine, support vector machine, and fuzzy logic model) and four types of deep learning-based predictors (autoencoder, restricted Boltzmann machine, convolutional neural network, and recurrent neural network). Their theoretical backgrounds, applications, merits, and limitations are thoroughly discussed. Then, two commonly used auxiliary methods for hybrid intelligent models are reviewed, i.e., ensemble learning and metaheuristic optimization. The ensemble learning models are categorized by the sources of diversity and ensemble strategies. According to the specific optimized objects, the metaheuristic optimization algorithms are classified into two groups. Moreover, the general process of metaheuristic optimization and differences between single-objective and multi-objective algorithms are also clarified. A group of representative models is summarized to show the frameworks of mainstream predictive models in artificial intelligence. Finally, this paper gives three possible development directions of wind energy forecasting for subsequent research. [ABSTRACT FROM AUTHOR]

• LNG vaporizer uses the multi-stream type with cold energy recovery. • Cryogenic liquid self-evaporating gas serves as the medium for heat exchange. • Three multi-stream heat exchangers integrate into a heat exchange network. • Multi-objective algorithm based thermal-hydraulic model evaluates performance. • The feasibility of this design verifies by liquid nitrogen gasification experiment. Vaporizers are the key heat transfer device in the regasification process of liquefied natural gas (LNG), especially for the conventional onshore LNG receiving stations. This paper has proposed a novel intermediate fluid vaporizer by employing multi-stream plate-fin heat exchanger (MPFHE-IFV). Compared to traditional shell-and-tube IFVs, it can not only achieve a higher heat transfer efficiency with more compact structure using MPFHE but also realize the recovery and reuse of LNG cold energy with intermediate fluid. In this ingenious design, the self-evaporating gas of cryogenic liquid is adopted as the intermediate medium of IFV so that the equipment freezing can be effectively avoided under large flow conditions. A thermal-hydraulic design model has been established for MPFHE-IFV to determine the detailed structural dimensions as well as the heat transfer performance, and a corresponding multi-objective optimization algorithm has been adopted to obtain the minimum equipment volume, the optimal channel arrangement and fin structure, the maximum cold energy recovery efficiency and the optimal number of internal cycles. Meanwhile, the transmission process of the cryogenic exergy in MPFHE-IFV has been revealed according to the analysis of the system temperature-entropy diagram. Finally, two experimental cases based on the liquid nitrogen regasification process are conducted to evaluate the practical performance of the novel MPFHE-IFVs using the design and optimization method proposed in this paper. The results indicate that this new type of IFV can reach the highest cold energy recovery efficiency up to 95% within the 8% error range when the heat transfer capacity is 11.5 kW and the heat medium flow rate is near 330 L/h. [ABSTRACT FROM AUTHOR]

Competitive and changing frame conditions, such as the need for decarbonization of energy supply or the growing share of renewable electricity generation, are driving the need for flexibility in different systems, e.g., in power supply and industrial production. Flexibility is often included in mathematical optimization applications. However, integrating it in optimization may create conflicting understandings of flexibility due to the properties of relativity and goal orientation. Establishing a common understanding of flexibility in industrial systems among all stakeholders, and a general but adaptable formulation for inclusion in mathematical optimization could better exploit the potential for flexibility in the future and make a greater contribution to energy transition. In this paper, an overview of flexibility, the incentives for it, and its integration into the decision variables, constraints, and objectives of mathematical optimization is provided. This enables the authors to conceptualize industrial flexibility in optimization and provide the basis for the presented approach: a three-step mixed-integer linear programming optimization model for integrating flexibility in the evaluation of cost-optimal energy supply systems. The presented approach is generally suitable for various flexibility types but is applied in this study to assess energy-source flexibility and elaborate its cost implications in a use case from the industry. For the identified energy supply systems with full energy carrier flexibility, the total annual costs increased by 62 to 112% compared to the cost-optimal solution with all available energy carriers, of which a relevant share was due to the investment cost increase by a factor of up to four. The two main conclusions are that (i) the results of the presented use case reveal the order of magnitude in cost increase for energy-source flexible supply systems, and (ii) the presented approach should be further used and developed to evaluate different types of industrial flexibility e.g., analysis of cost increase for robust energy systems in a changing environment, a potential contribution of flexibility for energy markets or power grids, or the flexibility to allow production changes. [Display omitted] • Interfaces of flexibility incentives and optimization for industry presented. • Set up of adaptable optimization framework for different flexibility aspects. • Cost increase quantified for energy-source flexibility in industrial use case. [ABSTRACT FROM AUTHOR]

Highlights • A mathematical model developed by jointly solving analytical and numerical equations. • All related effects, nonlinear material properties and irreversible factors involved. • Impacts of occupied air zone, thermal and electrical contact resistances evaluated. • No fixed ratio of thermoelectric leg's length to area exists for a maximum output. • A geometric optimization method first applied based on the Hill-climbing algorithm. Abstract This paper develops a one-dimensional simulation model of a thermoelectric module aiming at its characteristic exploration and output performance prediction. Almost all related effects are considered including Seebeck, Peltier, Thomson effects, the spatial- and temperature-dependent thermoelectric material properties, as well as the other irreversible factors. In order to ensure its high accuracy and computational efficiency, the differential equations built for components of non-linear physical properties, and analytical equations for the others of constant properties are jointly solved by numerical methods and implemented in MATLAB integrated development environment. A small deviation of 1.44% exists between its calculated output power and that by a previously reported three-dimensional model under a specific wide load range (0–250 Ω), validating its accuracy of this model. Besides, it indicates that both the proportions of heat leakage through the occupied air zone and its maximum output power to the input total heat flow have the same order of magnitude. A rising proportion for the former has to be paid attention in case of a higher temperature difference applied. The assumption of regarding its electrical contact resistance as an identical external resistance connected with the load in series is computationally proved to generate a negligible discrepancy in terms of TEM characteristics and its output performance. Furthermore, with respect to the impacts of TEM geometry, the pre-set fixed substrate area weakens the equivalence of regarding a thermoelectric module with larger cross-section area of thermoelectric legs as multiple identical modules with smaller leg areas connected in parallel. It accounts for the better linearity of rising output power by a long leg than that by a short leg as the leg area increases. A long thermoelectric leg means an increased induced electrical potential and inner resistance as well, thus leading to a single peak of the output power as the leg length increases. Finally, a thermoelectric geometric optimization approach abased on the Hill-climbing algorithm is introduced and first applied for the maximum output. Its accuracy and efficiency are validated and analyzed detailedly by supplying different variables as initial search point. All the methods and conclusions in this paper can assist thermoelectric generators' performance estimation and guide the geometric optimizations regarding their large-scale applications in the future. [ABSTRACT FROM AUTHOR]

• Vehicle speed computation designed to give driver indications based on target speed. • Target speed calculation based on Predictive Cruise Control for energy reduction. • Speed computation algorithm valid to any vehicle type and powertrain architecture. • Simulated energy reduction achieved on real scenarios between 3% and 11%. • Algorithm suitable for on board implementation and real time use on vehicles. Energy reduction in automotive applications is addressed since many years by both academia and vehicle manufactures. Recently, the introduction of innovative technical solutions, based, for instance, on internet connectivity and Advanced Driver-Assistance Systems (ADAS), allowed the on-board implementation of complex Energy Management Strategies (EMS) based on advanced optimization algorithms. However, these solutions require, in most cases, high computational capabilities and are generally customized on vehicle type and addressed to hybrid powertrain configurations. In this regard, this paper presents a vehicle speed management algorithm that suggests in real time to the driver the speed that shall be followed to achieve fuel consumption reduction. The algorithm requires road information, such as speed limits and slope variation, but it can be applied to any vehicle (e.g., light-weight cars, busses, trucks, etc.) and powertrain configuration (conventional, hybrid, pure electric, etc.). The basic structure relies on Predictive Cruise Control (PCC) approach, without introducing any complex mathematical optimization algorithm. The algorithm performance is here investigated in simulated environment by analyzing different vehicle types and routes. Under all the addressed scenarios, the algorithm can allow fulfilling energy consumption reduction (within the range of 3%-11% for real scenarios) with respect to a given reference speed profile under the same travel time. [ABSTRACT FROM AUTHOR]

• This paper proposes a distributed thermoelectric model based on discretization. • The thermoelectric model is compared with experiments at different module geometry. • Parametric studies were conducted to study the effect of thermoelectric module geometry. • Optimal geometry parameters are identified. The majority of existing commercial thermoelectric modules have fixed geometry, with customers purchasing those modules without adjusting its geometry for a specific application. However, previous investigations show that thermoelectric module geometry can have a significant influence on its output power – careful design considerations are therefore required. In this study, both simulation and experimental investigations are conducted to optimize the geometry of thermoelectric modules, in order to achieve higher power while maintaining the cost low. The experimental setup is built, and three thermoelectric modules with different geometries but same material are tested. The documented experimental results agree well with the simulation results. Based on parametric studies, optimal thermoelectric module height to achieve maximum output power is found to be 1.1 mm at the given thermal condition, slightly lower compared with the value used for most commercial products, which are around 1.5 mm. The effect of geometry design parameters on efficiency and power per material cost are also discussed, and the optimal design parameters are identified. Further improvements are proposed based on the simulation and experimental results. [ABSTRACT FROM AUTHOR]

• The work and efficiency of advanced gas turbine cycles are analytically optimized. • Polytropic efficiencies are used for the compression and expansion processes. • The overall and intermediate pressure ratios which maximise the net-work are given. • The ranges of the pressure ratios yielding a benefit in the efficiency are found. • The maximum-efficiency point is analytically given for the intercooled & reheat cycle. The paper presents a theoretical analysis of advanced gas-turbine cycles. Specifically, three cycles are investigated, that is the intercooled, the reheat, and the intercooled and reheat cycles. The internal irreversibilities, which characterise the compression and expansion processes, are taken into account through the polytropic efficiencies of the compressors and turbines. New analytical formulations for the overall and intermediate pressure-ratios which maximise the net work of the three aforementioned cycles are proposed along with an order relation between these optimum pressure-ratios. Moreover, the thermal efficiency of these cycles is also analysed providing, among other findings, the ranges of the intermediate pressure-ratios returning a benefit in the thermal efficiency in comparison with the simple cycle. Finally, for the sole intercooled and reheat cycle, a novel analytical expression for the maximum point of the thermal efficiency is given. It is also shown that, for the intercooled and reheat cycle, there is a unique value of the overall pressure-ratio which simultaneously maximises the net work and the thermal efficiency. To give some quantitative information, consider a maximum to minimum cycle-temperature ratio equal to 1573/300 and a compressor (resp. turbine) polytropic-efficiency equal to 0.8 (resp. 0.88). The net work and the thermal efficiency are maximised by a set of overall pressure-ratios obeying an order relation. The simple, the reheat, the intercooled, and the intercooled and reheat cycles reach the maximum net-work (resp. thermal efficiency) for increasing values of the overall pressure-ratio, that is 8.550 (resp. 18.260), 14.950 (resp. 25.039), 20.846 (resp. 39.984), and 73.109 (resp. 73.109). The reheat cycle achieves a 34.899% (resp. 6.077%) gain in the net work (resp. thermal efficiency), while the intercooled cycle returns a 31.477% (resp. 15.970%) increment. The maximum net-work of the intercooled and reheat cycle exactly doubles that of the simple cycle. Finally, the maximum thermal efficiency of the intercooled and reheat cycle yields a 24.966% improvement in comparison with the simple one. [ABSTRACT FROM AUTHOR]

• Lossless coupling of PV and TEG systems to enhance power generation efficiency. • An efficient SSA algorithm is used for MPPT of PV-TEG system. • Twelve methods are used as comparative algorithms. • Four case studies were used to validate the effectiveness of SSA. • Two constructive recommendations are proposed for future development. This paper proposes an innovative strategy to integrate thermoelectric generator (TEG) and photovoltaic (PV) systems, aiming to enhance energy production efficiency by addressing the significant waste heat generated during traditional PV system operation. Additionally, photovoltaic-thermoelectric generator (PV-TEG) hybrid system encounters the dual challenge of partial shading conditions (PSC) and non-uniform temperature distribution (NTD). Thus, salp swarm optimization (SSA) is introduced to simultaneously tackle the negative impacts of PSC and NTD. In contrast to alternative meta -heuristic algorithms (MhAs) and conventional mathematical approaches, the streamlined and effective optimization mechanism inherent to SSA affords a shorter optimization time, while mitigating the risk of the PV-TEG hybrid system's optimization outcomes being confined to local maximum power points (LMPP). Furthermore, the optimization performance of SSA for PV-TEG hybrid systems is assessed via four case studies, including start-up test, stepwise variations in solar irradiation at constant temperature, stochastic change in solar irradiation, and field measured data for typical days in Hong Kong, in which simulation results show that SSA evinces unparalleled global exploration and local search capabilities, yielding heightened energy output (up to 43.75%) and effectively suppressing power fluctuations in the PV-TEG hybrid system (as evidenced by Δ V avg and Δ V max). [ABSTRACT FROM AUTHOR]

Reducing the energy consumption of a plant often conflicts with the investment required for heat recovery. This paper presents a design study of shell and tube heat exchanger and direct-contact heat exchanger in three retrofit configurations. Multiobjective optimizations are employed to find optimal solutions that increase exergy efficiency at justifiable costs. A numerical modelization of heat transfer equipements is developed using heat transfer, pressure drop and cost correlations from the open literature. In order to verify the capability of the proposed approach, a case study for heat recovery in a pulp and paper plant is presented. In which multiple structural modifications of existing heat recovery systems are proposed based on an analysis of the Grand Composite Curve pinch targeting method. Each proposed modification is subject to multiobjective optimization based on the fast non-dominant sorting genetic algorithm (NSGA-II). The case study’s results shows significant steam operation cost reduction of up to 89% reducing exergy destruction by 82%. It has also been shown that for some heat recovery modifications the most cost effective solution is close to the minimum exergy destruction solution subject to equipment design constraints. [ABSTRACT FROM AUTHOR]

Partial shading on photovoltaic (PV) modules significantly affects their productivity. Therefore, different PV reconfiguration approaches were employed as solutions for distributing the shade across the array. This paper proposes an advanced four-square (FS) sudoku technique for configuring the 9 × 9 total-cross-tied (TCT) PV array via moving the modules physically for one time to enhance the harvested maximum power under all possible shading patterns. The proposed FS is validated under different shading classes, including deterministic and random shading patterns. The performance of the proposed strategy is assessed versus six different approaches representing the literature, including basic sudoku (SDU), optimal sudoku (OSDU), chaos map (CMP), skyscraper (Sky-S), odd-even prime (OEP), and ancient chinese magic square (ACMS). The assessment process is conducted using the global maximum power point, execution ratio (ER), fill factor (FF), percentage power losses (% P L), power enhancement (% P H), and array output power-voltage (P-V) characteristic. Moreover, an extensive analysis of the cost and the energy saving during a day is performed to investigate the reliability of the proposed FS strategy. Furthermore, a critical evaluation is performed among the FS and recent dynamic reconfiguration approaches, including particle swarm optimizer (PSO), harris hawk optimization algorithm (HHO), genetic algorithm (GA), butterfly optimization algorithm (BOA), coyote optimization algorithm (COA) and artificial ecosystem-based optimization (AEO). The results of this paper reveal that the rearranged scheme-based FS approach can disperse different categories of shading efficiently with a high generated energy from the entire system and high revenue compared to TCT and other state-of-the-art arrangements with achieving smooth uni-peak P-V characteristics. • A novel static reconfiguration approach using four square sudoku (FS) is proposed. • FS is proposed as a universal scheme for dispersing several shade patterns. • FS is evaluated versus several static and dynamic reconfiguration approaches. • FS reliability is assessed by determining the energy savings over a day. • FS enhanced the power by 38.016% in the existence of random failed modules. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel optimization approach is developed for the Food-Energy-Water Nexus. • The energy and food industries exhibit synergy and competition for water use. • Synergy and competition scenarios quantify the state of industrial water use. • The inter-industry competitive and unsustainable water use are prominent. • Response paths are proposed based on administrative means and market allocation. Studies on the Food-Energy-Water Nexus can help researchers, policy makers, practitioners, and stakeholders identify opportunities to maintain the nexus' synergies and trade-offs. Water, the most sensitive element in the Food-Energy-Water Nexus, readily influences the stability, cooperativity, and safety of the nexus. The key initiative to ensure water security in the Food-Energy-Water Nexus is properly handling water for food and energy production, but the existed conceptual framework and evaluation system are incomplete. This paper uses the Driver-Pressure-State-Impact-Response model and the water footprint theory to construct an optimization approach to evaluate the synergy and competition for water between food and energy at five levels. This optimization approach was tested and implemented based on a case study of 31 provinces in the Chinese Mainland from 1997 to 2016. The results showed that the blue water footprint of 31 provinces was 263.48 Gm3 in 2016, and the gray water footprint was 1518.57 Gm3, which led to inter-industry competitive water use and water unsustainability. In 2016, the 31 provinces had developed into Industry Synergy Sustainability scenario (1 province), Industry Synergy Unsustainability scenario (9 provinces), Industry Competition Unsustainability scenario (16 provinces), and Industry Competition Sustainability scenario (5 provinces), presenting a spatially clustered distribution pattern. Except for Xinjiang and Jilin, the remaining 29 provinces gradually developed into sustainable or synergistic scenarios. The total production water footprint in the Industry Competition Unsustainability scenario reached 4.08 m3/kg in 2016, while the Industry Synergy Sustainability scenario was only 3.67 m3/kg. This paper proposes two response paths, based on market allocation and administrative means, to facilitate the gradual evolution of the Industry Competition Unsustainability scenario into the Industry Synergy Sustainability scenario. These paths contribute to the efficient and sustainable integrated management of food, energy, and water globally. [ABSTRACT FROM AUTHOR]

• Diagnostic approach to evaluate the health state of District heating Networks (DHNs). • Detection and identification of leakages, anomalous heat and pressure losses. • Detection and identification of single and multiple faults. • Validation of the diagnostic approach that correctly detects and identifies all implanted faults. District Heating Networks (DHNs) are composed of numerous pipes that can be threatened by faults that affect DHN operation and management. Thus, reliable diagnostic methodologies are essential to identify DHN health state and hinder DHN malfunctioning and performance deterioration. To this purpose, a novel diagnostic approach that couples a DHN simulation model with an optimization algorithm for detecting and identifying both thermal and hydraulic faults, i.e., water leakages, anomalous heat and pressure losses, is presented in this paper. In the current paper, the novel diagnostic approach is challenged at evaluating the health state of the DHN of the campus of the University of Parma, where different faults are artificially implanted, by using a digital twin of the DHN. The faulty datasets account for both single and multiple faults, as well as different fault types and causes. The novel diagnostic approach proves to correctly detect and identify all simulated faults, by also correctly estimating their magnitude even in the most challenging scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new improved optimization method is proposed. • The standard deviation of the optimal solutions decision variables increased by 70%. • The implementation of the optimal solutions has been improved. • The requirements of overall planners and implementers are comprehensively considered. The multiobjective optimization of the system usually focuses on the optimization of the objective functions while ignoring the influence of decision variables on the implementation of the solution. This paper proposes a new improved optimization method by embedding the decision variable diversification mechanism in the optimization process, adopting the discretization mechanism in the multisource complementary heating model, and improving the search space. The new improved optimization method and the original method have similar performance in obtaining the Pareto front, and the hypervolume of the two algorithms differs by only 1.37% on average. The standard deviations of the decision variables in the optimal solutions obtained by the improved algorithm are increased by 70%, and it has a higher diversity of solutions in the decision space. The equipment capacity obtained by the improved algorithm is discretized, and avoids equipment with lower capacity which is beneficial to construction. In this paper, the optimal implementation solution is obtained through the selection of the objective functions by the overall planners and the construction preference of the solution implementers. In this way, the overall planners' requirements for energy conservation, emission reduction and economy, as well as the solution implementers' choice of implementation solutions can be comprehensively considered. In addition, this paper also obtains another optimal solution for adopting the carbon pricing method. [ABSTRACT FROM AUTHOR]

Highlights • A novel model of composite insulation system (SOFI, MLI, VDMLI and VCS) for LH 2 tank. • Heat reduction of VDMLI + VCS is 83.12% than MLI and 66.32% than VDMLI. • VDMLI and VCS are of mutual promotion with improvement greater than their simple sum. • With vacuum changing, influence of VCS on composite insulation system is analyzed. Abstract Hydrogen has more energy per unit mass (141.8 MJ/kg) than any other fuel but also has the lowest gaseous density (0.084 kg/m3), and liquid hydrogen (LH 2) storage is a solution with high energy density. However, LH 2 storage has the characteristics of low temperature (20 K) and easy evaporation, putting forward higher requirements for insulation system. At present, the improvement and optimization of insulation system consisting of spray on foam insulation (SOFI), multilayer insulation (MLI) and variable density MLI (VDMLI) is a hot topic. Considering the considerable sensible heat of hydrogen, this paper introduces self-evaporation vapor cooled shield (VCS) into the above-mentioned insulation systems to fully recover its sensible heat to improve insulation performance. A thermodynamic model has been established to study the insulation properties of composite insulation system for LH 2 tank, and the results have good agreement with test data. Coupling effects among them are analyzed and the optimization strategies of MLI, VDMLI and VCS are quantitatively explained to obtain better insulation performance. The heat flux and temperature profile of four insulation structures (MLI, VDMLI, MLI + VCS and VDMLI + VCS) have also been quantitatively analyzed, which can provide guidance for insulation system optimization. The insulation performance of composite insulation system under different vacuum conditions have also been compared. [ABSTRACT FROM AUTHOR]

Highlights • A new TLBO (ITLBO) is proposed for parameters estimation of solar cells/modules. • The ITLBO is based on improved teaching and learning strategies. • The accuracy and reliability of ITLBO is verified through different PV models. • The ITLBO performs better than most reported algorithms. Abstract Accurate and reliable parameter extraction of photovoltaic (PV) models is urgently desired for the simulation, evaluation, control, and optimization of PV systems. Although many meta-heuristic algorithms have been used to extract the PV parameters, the extracted parameters are usually not very accurate and reliable. To accurately and reliably extract the parameters of different PV models, an improved teaching-learning-based optimization (ITLBO) algorithm is proposed in this paper. The novelty of ITLBO lies primarily in the improved teaching and learning strategies with two improvements: (i) the teacher adopts different teaching strategies according to learner levels in the teacher phase; and (ii) in the learner phase, a new learning strategy is proposed to balance exploration and exploitation. The performance of ITLBO is verified by extracting the parameters of the single diode model, the double diode model, and three PV modules. The experimental results indicate that ITLBO obtains better performance with respect to accuracy and reliability compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Fin-like receivers are proposed to improve the optical efficiency of solar tower. • Geometries of the new receivers are optimized, and optimal results are obtained. • An increase of 3.2 percent points in optical efficiency is gained by new receiver. • Much higher flux is achieved by the new receiver compared with the original one. • Multi-point aiming strategy is found to be able to reduce the peak flux reasonably. Abstract The optical efficiency of the solar receiver is important to the global efficiency of the solar power tower plant. However, the present commercial cylindrical receiver causes a considerable optical loss because most of the sunrays reflected by the receiver surface are lost to the environment. To improve optical efficiency, three novel receivers with vertical finned structures and two novel receivers with horizontal finned structures are proposed in this paper. The optimal configurations of these receivers are obtained after parametric optimization. The optical efficiency and solar fluxes distributions of the optimal novel receivers are compared with those of the traditional cylindrical receiver at the noon of spring equinox. The results show that there is an optimal fin number of 6 for all the receivers with vertical finned structures. The receiver with vertical finned structures and the flat bottom shows the maximal optical efficiency of 63.9%. It indicates an improvement of 3.2 percentage points compared with that of the traditional cylindrical receiver. The receiver with horizontal fins with flat bottom shows the minimal optical efficiency of 60.3% at the noon of the spring equinox. It indicates a decrease of 0.4 percentage points compared with that of the traditional cylindrical receiver. Additionally, the peak solar fluxes on the novel receivers are quite close to each other and are nearly double that of the traditional cylindrical receiver. In the end, a multi-point aiming strategy is applied to the optimal novel receivers, and the results show that the peak fluxes can be obviously reduced. It is concluded that optical efficiency can be effectively improved by using the receivers with vertical finned structures. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A novel AP-DEACM method is proposed. • Performance evaluation and energy optimization model is obtained. • This proposed model is efficient in energy saving and carbon emission reduction. • The energy saving potential of ethylene and PTA production plants are 2.78% and 1.26%. • Carbon emission reduction potential is 3.62% in ethylene production plants. Abstract Data Envelopment Analysis (DEA) has been widely used in performance and energy efficiency evaluation. However, in the traditional DEA, the effective of each decision making unit (DMU) is evaluated through its own optimized perspective and regardless of other DMUs influence, which may result in too many effective DMUs. And the DEA cross-model (DEACM) can distinguish the effective DMUs better by constructing a cross-efficiency matrix, but the optimal weight of the DMU may not be unique, so the cross efficiency of the DEACM may be different. Therefore, this paper proposes a novel DEACM based on the affinity propagation (AP) clustering algorithm (AP-DEACM). Through the AP clustering algorithm, the high impact data affecting the performance capacity and energy saving are obtained. Then the better effective DMU is identified through the high discrimination of the AP–DEACM. Finally, the proposed AP-DEACM is used for performance evaluation and energy optimization modeling of the Pure Terephthalic Acid (PTA) production process and the ethylene industrial process in complex petrochemical industries. The experimental results show that the energy saving potential of PTA production plants and ethylene production plants are 2.78% and 1.26%, respectively, and the average value of carbon emission savings potential is 3.62% in ethylene production plants. [ABSTRACT FROM AUTHOR]

Highlights • A no-frost heat pump combined with liquid desiccant is proposed and investigated. • The most exergy destruction rates take place in the regenerator and dehumidifier. • Solution flow rate and temperature are the key factors on exergoeconomic performance. • The total product unit cost can decrease by 12.64% with exergoeconomic optimization. Abstract Frost build-up on the evaporator of air source heat pump severely deteriorates the heat transfer and subsequently reduces the performance of the system. Combining liquid desiccant dehumidification with heat pump system is an efficient strategy to achieve frost-free operation by dehumidifying the inlet air during winter in humid areas. In this paper, detailed energy, exergy and exergoeconomic analyses are performed for a frost-free air-conditioning system with integrated liquid desiccant dehumidification and compressor-assisted regeneration, in which the diluted liquid desiccant can be efficiently regenerated under low temperature condition. Parametric studies are conducted to appraise the influence of the important operation parameters on the performance of the proposed system. Then the hybrid system is optimized and compared from the exergoeconomic perspective. It is found that, the first and second largest exergy destructions take place in the regenerator and dehumidifier, which can be improved by increasing the investment costs. When the proposed system is optimized based on the exergoeconomics, the coefficient of performance and exergy efficiency increase by 13.02% and 12.73%, respectively. The total product unit costs can decrease by 12.64% compared to that under the basic operation condition. [ABSTRACT FROM AUTHOR]

Highlights • A novel model based on online sequential outlier robust extreme learning (OSORELM) is proposed. • The hybrid mode decomposition (HMD) method is used to deeply decompose the original wind speed. • The OSORELM with HMD decomposition is used for short-term multi-step wind speed prediction. • The crisscross optimization (CSO) is applied to optimize the parameters of the model. Abstract As the wind energy developing, wind speed prediction is important for the reliability of wind power system and the integration of wind energy into the power network. This paper proposed a novel model based on hybrid mode decomposition (HMD) method and online sequential outlier robust extreme learning machine (OSORELM) for short-term wind speed prediction. In data pre-processing period, wind speed is deeply decomposed by HMD, which is comprised of variational mode decomposition (VMD), sample entropy (SE) and wavelet packet decomposition (WPD). The crisscross algorithm (CSO) is applied to optimize the input-weights and hidden layer biases for OSORELM, which have impact on the forecasting performance. The experiment results show that: (a) HMD is an effective way of wind speed decomposition, which can capture the characteristics of wind speed time series accurately and thus promote the prediction performance; (b) the OSORELM performs better than offline models in practical forecasting; (c) the proposed forecasting model has greatly improved the accuracy in multi-step wind speed forecasting. [ABSTRACT FROM AUTHOR]

Highlights • We present two models for the optimal design of distributed energy systems. • We perform a comparison of the two methods in a case study with six buildings. • The total annual cost and the carbon emissions are the two objective functions. • Each method provides different results for each solution. • We propose a combined use of the two methodologies. Abstract Energy supply in an economic and environmentally friendly configuration is a major challenge for urban energy systems. This paper presents two multi-objective models for the design of Distributed Energy Systems (DES) for satisfying local needs in heating, cooling and electricity. DES can be used to promote energy efficiency and reduce carbon emissions which is crucial to tackle climate change. A multi-objective MILP framework is presented, comparing two main methodologies of designing DES using Total Annual Cost (TAC) and carbon emissions as objective functions. The first one, namely "Method A", which performs a simultaneous sizing and the second one, namely "Method B", in which technologies and their respective capacity are predefined. Furthermore, three scenarios regarding the available technologies are developed to assess DES for both methods and a case study is carried out. Results show that in both methods Scenario 3 (a DES coupled with district heating network, microgrid and storage technologies) has the best performance. In terms of comparison between the two methods, "Method A" provides better solutions in terms of cost and emissions as it has more degrees of freedom. Structure, design and operational results are presented and discussed analytically, as well as the possibility of comparing the two methods for a more detailed design. [ABSTRACT FROM AUTHOR]

Highlights • A decomposition technique is proposed for different types of FC models. • A generic framework by using the nature-inspired optimization method is developed. • Extensive tests are carried out on 32 instances of the PEMFC and SOFC models. Abstract Fast and accurate parameter extraction of fuel cells is crucial to the control and performance analysis of fuel cell power systems. Unfortunately, due to the multi-variable and nonlinear features of fuel cell models, it is a difficult task to identify the parameters of the models. In this paper, we propose a decomposition technique, where the unknown parameters are divided into two groups: nonlinear and linear. The optimization techniques only need to optimize the nonlinear parameters, and then the linear parameters are determined based on the nonlinear ones. With the help of the decomposition technique, a generalized framework by using the nature-inspired optimization method is proposed to try to fast and accurately extract the parameters for different types of fuel cells. To test the performances of our approach, two widely used types of fuel cells are studied, i.e., proton exchange membrane fuel cell and solid oxide fuel cell. Extensive simulation tests with thirty-two instances are carried out for comparing our approach with existing approaches. The comparison demonstrates the efficiency of the decomposition technique. Moreover, the results show that our approach can not only significantly reduce the computational resources, but also yields high quality solutions. [ABSTRACT FROM AUTHOR]

Highlights • An improved whale optimization algorithm named IWOA is proposed. • Two prey searching strategies are proposed to enhance the performance of IWOA. • IWAO can conquer prematurity and accelerate the searching process simultaneously. • IWOA is firstly applied to three benchmark test PV models. • IWOA is then applied to two practical PV power station models in the Guizhou Power Grid of China. Abstract Parameter extraction of solar photovoltaic (PV) models is a typical complex nonlinear multivariable strongly coupled optimization problem. In this paper, an improved whale optimization algorithm (WOA), referred to as IWOA, is proposed to accurately extract the parameters of different PV models. The original WOA has good local exploitation ability, but it is likely to stagnate and suffer from premature convergence when dealing with complex multimodal problems. To conquer this concerning shortcoming, IWOA develops two prey searching strategies to effectively balance the local exploitation and global exploration, and thereby enhance the performance of WOA. Three benchmark test PV models including single diode, double diode and PV module models, and two practical PV power station models with more modules in the Guizhou Power Grid of China are employed to verify the performance of IWOA. The experimental and comparison results comprehensively demonstrate that IWOA is significantly better than the original WOA and three advanced variants of WOA, and is also highly competitive with the reported results of some recently-developed parameter extraction methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology for the optimisation of off-grid hybrid systems (photovoltaic–diesel–battery systems). A stochastic approach is developed by means of Monte Carlo simulation to consider the uncertainties of irradiation and load. The optimisation is economic; that is, we look for a system with a lower net present cost including installation, replacement of the components, operation and maintenance, etc. The most important variable that must be estimated is the batteries lifespan, which depends on the operating conditions (charge/discharge cycles, corrosion, state of charge, etc.). Previous works used classical methods for the estimation of batteries lifespan, which can be too optimistic in many cases, obtaining a net present cost of the system much lower than in reality. In this work, we include an advanced weighted Ah-throughput model for the lead-acid batteries, which is much more realistic. The optimisation methodology presented in this paper is applied in the optimisation of the electrical supply for an off-grid hospital located in Kalonge (Democratic Republic of the Congo). At the moment, the power supply relies on a diesel generator; batteries are used in order to ensure the basic supply of energy when the generator is unavailable (night hours). The optimisation includes the possibility of adding solar photovoltaic (PV) panels to improve the supply of electrical energy. The results show that optimal design could achieve a 28% reduction in the levelised cost of energy and a 54% reduction in the diesel fuel used in the generator, thereby reducing pollution. Furthermore, we discuss possible improvements to the telecommunications of the hospital. [ABSTRACT FROM AUTHOR]

In this paper, strategic energy management in a micro grid is proposed incorporating two types of storage elements viz. unitised regenerative fuel cell (URFC) and electric vehicle (EV). Rather than a simple approach of optimizing micro grid operation to minimize line loss in the micro grid, this paper deals with multi objective optimization to minimize line loss, operational cost and maximize the value of stored energy of URFC and EV simultaneously. Apart from URFC, two operation strategies are proposed for EV enabling V2G operation to reduce overall system cost of operation. To address the complexity, non-linearity and multi dimensionality of the objective function, particle swarm optimization-a heuristic approach based solution methodology along with forward and back sweep algorithm based load flow solution technique is developed. Combined with particle swarm optimization (PSO), forward and backward sweep algorithm resolves the complexity and multi dimensionality of the load flow analysis and optimizes the operational cost of micro grid. The simulation results are presented and discussed which are promising with regard to reduction in line loss as well as cost of operation. Scheduling strategy of the micro grid with both URFC and EV enabling V2G operation presents a promising approach to minimize line loss and cost of operation. [ABSTRACT FROM AUTHOR]

The large potential for energy saving by cogeneration and trigeneration in the building sector is scarcely exploited due to a number of obstacles in making the investments attractive. The analyst often encounters difficulties in identifying optimal design and operation strategies, since a number of factors, either endogenous (i.e. related with the energy load profiles) and exogenous (i.e. related with external conditions like energy prices and support mechanisms), influence the economic viability. In this paper a decision tool is adopted, which represents an upgrade of a software analyzed in previous papers; the tool simultaneously optimizes the plant lay-out, the sizes of the main components and their operation strategy. For a specific building in the hotel sector, a preliminary analysis is performed to identify the most promising plant configuration, in terms of type of cogeneration unit (either microturbine or diesel oil/natural gas-fueled reciprocate engine) and absorption chiller. Then, sensitivity analyses are carried out to investigate the effects induced by: (a) tax exemption for the fuel consumed in “efficient cogeneration” mode, (b) dynamic behavior of the prime mover and consequent capability to rapidly adjust its load level to follow the energy loads. [ABSTRACT FROM AUTHOR]

This paper goes through a sophisticated ecological function for irreversible regenerative closed Brayton cycle. Moreover, aforementioned irreversible regenerative closed Brayton cycle is optimized by implementing ecological function. With the aim of the first and second laws of thermodynamics, an equivalent system is initially specified. Developed multi objective evolutionary approaches (MOEAs) on the basis of NSGA-II method is implemented throughout this work. To accomplish the above mentioned target of this paper, three objective functions which includes the power output ( P ), the ecological function ( E ) and thermoeconomic criterion ( F ) are perceived in optimization process simultaneously. Three aforementioned objective functions are maximized at the same time. [ABSTRACT FROM AUTHOR]

This paper presents a thermo-economic optimization of a combined cycle power plant obtained via the Proper Orthogonal Decomposition-Radial Basis Functions (POD-RBF) procedure. POD, also known as "Karhunen-Loewe decomposition" or as "Method of Snapshots" is a powerful mathematical method for the low-order approximation of highly dimensional processes for which a set of initial data is known in the form of a discrete and finite set of experimental (or simulated) data: the procedure consists in constructing an approximated representation of a matricial operator that optimally "represents" the original data set on the basis of the eigenvalues and eigenvectors of the properly re-assembled data set. By combining POD and RBF it is possible to construct, by interpolation, a functional (parametric) approximation of such a representation. In this paper the set of starting data for the POD-RBF procedure has been obtained by the CAMEL-Pro™ process simulator. The proposed procedure does not require the generation of a complete simulated set of results at each iteration step of the optimization, because POD constructs a very accurate approximation to the function described by a relatively small number of initial simulations, and thus "new" points in design space can be extrapolated without recurring to additional and expensive process simulations. Thus, the often taxing computational effort needed to iteratively generate numerical process simulations of incrementally different configurations is substantially reduced by replacing much of it by easy-to-perform matrix operations. The object of the study was a fossil-fuelled, combined cycle powerplant of nameplate power Pel=160MW, for which a set of operational and costing data was available: different combinations of the relevant process parameters were considered and the corresponding process variables calculated for each simulation were collected. In our case the aim of the application of the procedure was to find the combination of process parameters which corresponds to the minimum the thermo-economic cost of the products. The results obtained by the application of this model have been validated by comparison with literature data obtained by the application of genetic algorithm optimization. [ABSTRACT FROM AUTHOR]

The organic Rankine cycle (ORC) has been demonstrated to be a viable approach to recover low-grade waste heat and has been widely investigated in recent years. In the current research focused on the multi-objective optimization problem of ORC systems, few scholars consider the variation in turbine efficiency with the cycle parameters. This paper focused on the comparison of multi-objective optimization with variable turbine efficiency and that with constant turbine efficiency. The results obtained for the two types of turbine efficiency were compared, and the differences were analyzed. Flue gas at 523.15 K was used as the heat source, and pentane, hexane, heptane, cyclohexane, benzene and toluene were selected as working fluid candidates. The one-dimensional radial-inflow turbine efficiency prediction model was applied to replace constant turbine efficiency. The multi-objective model in conjunction with the turbine efficiency model was constructed by defining the net power output and system total cost per unit net power output as the objective functions. The non-dominated sorting genetic algorithm-II (NSGA-II) was used to optimize the evaporation temperature and condensation temperature as the decision variables. With the aid of the ideal point, the optimal solution of each working fluid was selected from the Pareto frontier. The results showed that the turbine efficiency varies with changes in evaporation temperature and condensation temperature. In the multi-optimization with constant turbine efficiency, toluene and cyclohexane are the optimal working fluids, whereas with variable turbine efficiency, benzene is the optimal working fluid. In the sensitivity analysis, the optimal exergy efficiency shows opposite trends for the multi-objective optimization with constant and variable turbine efficiency. [ABSTRACT FROM AUTHOR]

In the present research, multi-objective optimization (MOO) was applied to a natural gas-to-liquids (GTL) process with a multi-stage Fischer-Tropsch (FT) reactor. A CO 2 stream was fed into the GTL plant’s endothermic steam reformer (SMR) to adjust the H 2 to CO ratio of the SMR effluent. Three objective functions were considered in this paper; minimizing the amount of CO 2 released to the atmosphere, maximizing carbon efficiency, and maximizing wax production. The degrees of freedom for the GTL process with single-stage FT reactor were recycle ratio to process and recycle ratio to the FT reactor. In addition, for the GTL process with two-stage FT reactor, the synthesis gas split ratio between the stages was considered as another degree of freedom. Maximizing wax production rate results in lower carbon efficiency and higher CO 2 emission to the atmosphere. The amount of CO 2 intake to the SMR in all cases was 256 tons/hr. With the same amount of utilized CO 2 in the SMR, sectioning of the FT reactor to two similar stages notably increases the carbon efficiency and mass flow of wax compared to the single stage case. [ABSTRACT FROM AUTHOR]

The thermodynamic performance of a novel combined heating and power system integrated with biomass partial gasification and ground source heat pump has been investigated previously, while system performances are greatly dependent on system component capacity and operation strategy. In this paper, a multi-objective optimization model of proposed integration system is presented, which considers the energetic, economic and environmental performances. The optimal parameters are mainly consists of three parts: the rated power of power generation unit, carbon conversion ratio and the temperature of warm water. Genetic algorithm is adopted to optimize the component capacity and operation strategy of proposed system. In order to demonstrate the optimal model, a case study is adopted. The optimal results show that the primary energy saving ratio, annual total cost saving ratio, carbon dioxide emission reduction ratio and performance indicator are 7.61%, 23.62%, 66.52%, 32.58%, respectively. In addition, the influences of related economic factors on proposed system performances are also analyzed. Finally, the research shows that the multi-objective optimization model provides a new way for the optimization design of the integration system. [ABSTRACT FROM AUTHOR]

In this paper, a solar double absorption heat transformer (SDAHT) operating with the LiBr-H 2 O solution is proposed to provide high temperature energy. The flat plate collector (FPC) is used to supply the demanded heat input in the present configuration, which will greatly broaden the application scope of the FPCs. The thermoeconomic concept is applied to the evaluation and optimization of the SDAHT, aimed at minimizing its annual capital cost per kilowatt heat capacity ( CPK ) and payback period ( PP ). A dedicated computer model in the software Engineering Equation Solver was developed to conduct the study by means of a parametric analysis. The results show that there exists an optimum absorber/evaporator temperature at which CPK and PP can obtain the minimum values. With solar radiation intensity at 600 W/m 2 , generation (evaporation) temperature at 75 °C, condensation temperature at 38 °C, absorption temperature at 135 °C and economizer efficiency set to 0.8, the optimum absorber/evaporator temperature and corresponding CPK and PP are 104.0 °C, 928.1 euros/kW and 4.0 years, respectively. The cost of the FPCs takes the major part of CPK . Moreover, the effects of the operating parameters such as the generation (evaporation), condensation, absorption temperatures and design parameters including the first and second economizer efficiencies on the optimum absorber/evaporator temperature and corresponding CPK and PP have been analyzed in detail. Besides, some suggestions derived from the results are also given to assist the engineers in estimation of the economic performance. [ABSTRACT FROM AUTHOR]

Hydraulic performance of a district heating system is a significant aspect in designing. In the context of multiple heat sources and looped topology layout, severe non-linear hydraulic characteristics have markedly impacts on the hydraulic performance design of a district heating network. Design or select variable-speed circulating pumps with suitable capacities and hydraulic characteristics can be a rather difficult issue especially when there are fluctuating renewable heat sources. In this paper, an optimization model is proposed to achieve the lowest power consumption of all pumps and still fulfill the hydraulic head demands of all substations simultaneously. Based on the proposed model, a holistic design method is presented for optimal hydraulic design of the variable-speed pumps in each heat source. Then a case study is illustrated to validate the proposed method. With consideration of fluctuating heat load of a renewable heat source, the optimal rated capacities of all pumps and variable speed hydraulic characteristic curves are obtained in the designed heating schemes from 100% to 40% of full heat load. Moreover, when all pumps operating on their optimal regime, there always exists one substation branch whose excess head would reach the preset minimum value. The sensitivity analysis results show that the variation of total work of pumps can be from 0.16% to 7.56% when the heat load of the renewable heat source fluctuated 1.0% in different designed heating schemes. [ABSTRACT FROM AUTHOR]

Parameters identification of photovoltaic (PV) models based on measured current-voltage characteristic curves is significant for the simulation, evaluation, and control of PV systems. To accurately and reliably identify the parameters of different PV models, an improved JAYA (IJAYA) optimization algorithm is proposed in the paper. In IJAYA, a self-adaptive weight is introduced to adjust the tendency of approaching the best solution and avoiding the worst solution at different search stages, which enables the algorithm to approach the promising area at the early stage and implement the local search at the later stage. Furthermore, an experience-based learning strategy is developed and employed randomly to maintain the population diversity and enhance the exploration ability. A chaotic elite learning method is proposed to refine the quality of the best solution in each generation. The proposed IJAYA is used to solve the parameters identification problems of different PV models, i.e., single diode, double diode, and PV module. Comprehensive experiment results and analyses indicate that IJAYA can obtain a highly competitive performance compared with other state-of-the-state algorithms, especially in terms of accuracy and reliability. [ABSTRACT FROM AUTHOR]

One of the possible solution for conventional coal-fired heat sources of district heating systems is repowering with a cogeneration of heat and power) unit. Thermal and economic optimization of the such modernization is the subject of the paper. The total capacity of the existing heat generating plant reaches 210 MW and the generated heat is delivered to the city consuming central heating and hot domestic water. The optimization of the cogeneration system (regarding unit size) was aimed at finding a solution which is an optimal in terms of the economic efficiency. The optimal size of gas turbine was found and it has been proven that the retrofitting coal-fired district heating source with a gas turbine cogeneration unit has technical and economic justification and always leads to the energetic and ecological effects. However, the optimal size of the unit is highly sensitive to electricity and natural gas prices. [ABSTRACT FROM AUTHOR]

A new global particle swarm optimization (NGPSO) algorithm is proposed to solve the economic emission dispatch (EED) problems in this paper. NGPSO is different from the traditional particle swarm optimization (PSO) algorithm in two aspects. First, NGPSO uses a new position updating equation which relies on the global best particle to guide the searching activities of all particles. Second, it uses the randomization based on the uniform distribution to slightly disturb the flight trajectories of particles during the late evolutionary process. The two steps enable NGPSO to effectively execute a number of global searches, and thus they increase the chance of exploring promising solution space, and reduce the probabilities of getting trapped into local optima for all particles. On the other hand, the two objective functions of EED are normalized separately according to all candidate solutions, and then they are incorporated into one single objective function. The transformation steps are very helpful in eliminating the difference caused by the different dimensions of the two functions, and thus they strike a balance between the fuel cost and emission. In addition, a simple and common penalty function method is employed to facilitate the satisfactions of EED’s constraints. Based on these improvements in PSO, objective functions and constraints handling, high-quality solutions can be obtained for EED problems. Five examples are chosen to testify the performance of three improved PSOs on solving EED problems with or without transmission losses. Experimental results show that NGPSO is the most efficient approach on solving the single objective optimization (fuel cost or emission minimization) and multi-objective optimization (fuel cost and emission minimizations) problems. [ABSTRACT FROM AUTHOR]

Compressed air energy storage technologies can improve the supply capacity and stability of the electricity grid, particularly when fluctuating renewable energies are massively connected. While incorporating the combined cooling, heating and power systems into compressed air energy storage could achieve stable operation as well as efficient energy utilization. In this paper, a novel combined cooling, heating and power based compressed air energy storage system is proposed. The system combines a gas engine, supplemental heat exchangers and an ammonia-water absorption refrigeration system. The design trade-off between the thermodynamic and economic objectives, i.e., the overall exergy efficiency and the total specific cost of product, is investigated by an evolutionary multi-objective algorithm for the proposed combined system. It is found that, with an increase in the exergy efficiency, the total product unit cost is less affected in the beginning, while rises substantially afterwards. The best trade-off solution is selected with an overall exergy efficiency of 53.04% and a total product unit cost of 20.54 cent/kWh, respectively. The variation of decision variables with the exergy efficiency indicates that the compressor, turbine and heat exchanger preheating the inlet air of turbine are the key equipment to cost-effectively pursuit a higher exergy efficiency. It is also revealed by an exergoeconomic analysis that, for the best trade-off solution, the investment costs of the compressor and the two heat exchangers recovering compression heat and heating up compressed air for expansion should be reduced (particularly the latter), while the thermodynamic performance of the gas engine need to be improved significantly. [ABSTRACT FROM AUTHOR]

The uncertainty and nonstationary of wind speed have compelled the power system operators and researchers to search for more accurate and reliable techniques to implement wind speed forecasting (WSF). In allusion to this phenomenon, this paper presents an adaptive ensemble of model for the probabilistic WSF, which is based on combination of the adaptive ensemble of on-line sequential ORELM (OS-ORELM) and the time-varying mixture copula function (TVMCF) to perform multi-step WSF. An OS-ORELM with forgetting mechanism based on Cook’s distance ( λ CDFF OS-ORELM) serves as a basic WSF model and an on-line ensemble using ordered aggregation (OEOA) technique is employed to improve the prediction performance. In the data pre-processing period, the Bernaola Galvan algorithm (BGA) is employed to partition the raw wind speed series into segments and the adaptive variational mode decomposition (AVMD) is used to decompose each segment into sub-series with different sub-band. Each transformed sub-series is well-modeled with the application of λ CDFF OS-ORELM-OEOA, which is optimized by modified crisscross optimization algorithm (CSO). Eventual forecast results are obtained through aggregate calculation. Then the probabilistic prediction intervals (PIs) of wind speed are established in a TVMCF framework by modeling the conditional forecasting error. Case studies using the real wind speed data from the National Renewable Energy Laboratory (NREL) demonstrate that the proposed model can not only improves point forecasts compared with benchmark methods, but also constructs higher quality of probabilistic PIs. [ABSTRACT FROM AUTHOR]

Estimating circuit model parameters of PV cells/modules represents a challenging problem. PV cell/module parameter estimation problem is typically translated into an optimisation problem and is solved by metaheuristic optimisation problems. Particle swarm optimisation (PSO) is considered as a popular and well-established optimisation algorithm. Despite all its advantages, PSO suffers from premature convergence problem meaning that it may get trapped in local optima. Personal and social acceleration coefficients are two control parameters that, due to their effect on explorative and exploitative capabilities, play important roles in computational behavior of PSO. In this paper, in an attempt toward premature convergence mitigation in PSO, its personal acceleration coefficient is decreased during the course of run, while its social acceleration coefficient is increased. In this way, an appropriate tradeoff between explorative and exploitative capabilities of PSO is established during the course of run and premature convergence problem is significantly mitigated. The results vividly show that in parameter estimation of PV cells and modules, the proposed time varying acceleration coefficients PSO (TVACPSO) offers more accurate parameters than conventional PSO, teaching learning-based optimisation (TLBO) algorithm, imperialistic competitive algorithm (ICA), grey wolf optimisation (GWO), water cycle algorithm (WCA), pattern search (PS) and Newton algorithm. For validation of the proposed methodology, parameter estimation has been done both for RTC France PV cell and Photowatt-PWP 201 PV module. In all terms of mean, best and standard deviation of achieved results, the proposed TVACPSO outperforms other compared algorithms. [ABSTRACT FROM AUTHOR]

The powertrain system of a typical proton electrolyte membrane hybrid fuel cell vehicle contains a lithium battery package and a fuel cell stack. A multi-objective optimization for this powertrain system of a passenger car, taking account of fuel economy and system durability, is discussed in this paper. Based on an analysis of the optimum results obtained by dynamic programming, a soft-run strategy was proposed for real-time and multi-objective control algorithm design. The soft-run strategy was optimized by taking lithium battery size into consideration, and implemented using two real-time algorithms. When compared with the optimized dynamic programming results, the power demand-based control method proved more suitable for powertrain systems equipped with larger capacity batteries, while the state of charge based control method proved superior in other cases. On this basis, the life cycle cost was optimized by considering both lithium battery size and equivalent hydrogen consumption. The battery capacity selection proved more flexible, when powertrain systems are equipped with larger capacity batteries. Finally, the algorithm has been validated in a fuel cell city bus. It gets a good balance of fuel economy and system durability in a three months demonstration operation. [ABSTRACT FROM AUTHOR]

Accurate modeling and parameter extraction of solar cells play an important role in the simulation and optimization of PV systems. This paper presents a Lambert W -function based exact representation (LBER) for traditional double diode model (DDM) of solar cells, and then compares their fitness and parameter extraction performance. Unlike existing works, the proposed LBER is rigorously derived from DDM, and in LBER the coefficients of Lambert W -function are not extra parameters to be extracted or arbitrary scalars but the vectors of terminal voltage and current of solar cells. The fitness difference between LBER and DDM is objectively validated by the reported parameter values and experimental I – V data of a solar cell and four solar modules from different technologies. The comparison results indicate that under the same parameter values, the proposed LBER can better represent the I – V and P – V characteristics of solar cells and provide a closer representation to actual maximum power points of all module types. Two different algorithms are used to compare the parameter extraction performance of LBER and DDM. One is our restart-based bound constrained Nelder-Mead (rbcNM) algorithm implemented in Matlab, and the other is the reported R cr -IJADE algorithm executed in Visual Studio. The comparison results reveal that, the parameter values extracted from LBER using two algorithms are always more accurate and robust than those from DDM despite more time consuming. As an improved version of DDM, the proposed LBER is quite promising for PV simulation and thus deserves serious attention. [ABSTRACT FROM AUTHOR]

This paper proposes an accurate computational technique for the two-diode model of PV module. Unlike previous methods, it does not rely on assumptions that cause the accuracy to be compromised. The key to this improvement is the implementation of a hybrid solution, i.e. by incorporating the analytical method with the differential evolution (DE) optimization technique. Three parameters, i.e. I PV , I o 1 , and R p are computed analytically, while the remaining, a 1 , a 2 , I o 2 and R s are optimized using the DE. To validate its accuracy, the proposed method is tested on three PV modules of different technologies: mono-crystalline, poly-crystalline and thin film. Furthermore, its performance is evaluated against two popular computational methods for the two-diode model. The proposed method is found to exhibit superior accuracy for the variation in irradiance and temperature for all module types. In particular, the improvement in accuracy is evident at low irradiance conditions; the root-mean-square error is one order of magnitude lower than that of the other methods. In addition, the values of the model parameters are consistent with the physics of PV cell. It is envisaged that the method can be very useful for PV simulation, in which accuracy of the model is of prime concern. [ABSTRACT FROM AUTHOR]

In this paper, the performance of an integrated Rankine power cycle with parabolic trough solar system and a thermal storage system is simulated based on four different nano-fluids in the solar collector system, namely CuO, SiO 2 , TiO 2 and Al 2 O 3 . The effects of solar intensity, dead state temperature, and volume fraction of different nano-particles on the performance of the integrated cycle are studied using second law of thermodynamics. Also, the genetic algorithm is applied to optimize the net output power of the solar Rankine cycle. The solar thermal energy is stored in a two-tank system to improve the overall performance of the system when sunlight is not available. The concept of Finite Time Thermodynamics is applied for analyzing the performance of the solar collector and thermal energy storage system. This study reveals that by increasing the volume fraction of nano-particles, the exergy efficiency of the system increases. At higher dead state temperatures, the overall exergy efficiency is increased, and higher solar irradiation leads to considerable increase of the output power of the system. It is shown that among the selected nano-fluids, CuO/oil has the best performance from exergy perspective. [ABSTRACT FROM AUTHOR]

[Display omitted] • A BOG reliquefaction system using liquid air was developed. • Mathematical optimization was performed to minimize annual expenses. • Techno-economic analysis was conducted for three different voyage duration scenarios. • The proposed system can reduce both onboard energy consumption and capital investment. The boil-off gas (BOG) reliquefaction of cryogenic liquefied natural gas (LNG) for its intercontinental transport is challenging. This paper proposes a novel BOG reliquefaction system using liquid air and two different process designs: (i) low-pressure liquid air (LPLA) and (ii) high-pressure liquid air (HPLA). In the proposed system, an open refrigeration system using liquid air reliquefies BOG, thereby replacing the current commercial closed refrigeration cycle. As the temperature of liquid air is in the cryogenic range, the energy consumption to reliquefy BOG onboard approaches zero. However, the additional total weight of the ship is required to store liquid air, thus necessitating increased propulsion energy. Nevertheless, the reduction in energy needed for BOG reliquefaction exceeds the energy requirement for the added engine load. Thus, the LPLA shows a massive energy reduction from 4,913.07 to 642.67 kW compared to the commercial BOG reliquefaction process involving the nitrogen expander cycle. The HPLA process shows further improved results of 436.34 kW by expanding the vaporized high-pressure air. In addition, the proposed HPLA design exhibits an exergy efficiency of 77.4 %, which is 37.6 % higher than that of the commercial nitrogen expander process. A techno-economic analysis, including the purchase and transport cost of liquid air, is conducted to reflect an effort on on-shore liquid air production and transportation. Despite the additional liquid air tanks, the overall process configuration is much simpler than that of the current commercial BOG reliquefaction system. Thus, the proposed HPLA process is more economical than commercial processes for all representative voyage duration scenarios (8, 13, and 21 days). [ABSTRACT FROM AUTHOR]

• Propose a local hybrid energy system based on solar energy for a residential district. • Apply BTES to store solar energy in non-heating seasons, and release in heating ones. • Apply genetic algorithms to optimize the system performance and costs. • 38%-58% of total heating demand can be covered by the optimal solutions. • Annually reduce CO2 emissions by 102–217 tons with the rate of 31–66% Utilizing solar energy for heat supply can reduce CO 2 emissions and mitigate global climate change. In the Nordic region (e.g., Iceland and Finland), a tremendous seasonal mismatch exists between the availability of solar radiation and building heating demand. This paper proposes a local hybrid energy system based on solar energy for a residential district. It applies a borehole thermal energy storage to store solar energy in non-heating seasons, and uses stored energy for part of total heating demand in a residential neighbourhood in heating seasons. Photovoltaic panels are used to generate electricity for heat pump operation. To find out cost-optimal and eco-friendly solutions, the local energy system was first modelled and simulated in TRNSYS. Then, genetic algorithms were applied to optimize the system performance and costs. In optimal solutions, 38%-58% of total heating demand could be covered by on-site heat energy with the levelized cost of energy of 110–184 €/MWh. On this basis, importing additional electricity from grid to increase the utilization rate of air-to-water heat pumps can further increase the on-site heat energy fraction to 41%-88% with the levelized cost of energy of 108–201 €/MWh. Compared with the situation of fully district heating input, the proposed system can annually reduce CO 2 emissions by 102–217 tons with the rate of 31–66%. Although the initial cost of the studied system is higher than that of district heating, the local hybrid energy system is worth further developing considering decentralizing heat energy production and reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

• A model predictive control based optimization framework for heat pump system of a residential district is proposed. • Heating load and indoor state is predicted by data-driven model as the input of the optimization process. • Data-driven models and traditional fitting models for system components are combined in the optimization process. • Onsite validation is conducted. Heat pump system is widely used in district heating because of its high energy efficiency and economic benefits. Because of the complexity of large heat pump systems, achieving optimal operation in practical project remains challenging. Model predictive control is a promising method for operation optimization. This paper represents a data-driven optimization framework based on model predictive control for optimal district energy supply. Four sub-models of the framework including heating load prediction model, heat pump performance model, main pump performance model and indoor state prediction model were built via machine learning and fitting methods. The particle swarm optimization algorithm was adopted to determine the optimal operation strategy. The framework' effectiveness was verified by the operation data of a district energy system in Shanghai by means of both offline and onsite validation. The optimization results under low load conditions show that the energy-saving ratio can reach 9.43%, correspondingly, 12.37% for medium load conditions and 4.50% for high load conditions. In onsite validation, the Mean Absolute Percentage Error and Root Mean Square Error of heating load prediction are 6.72% and 104.21 kW, respectively. Compared with the day under similar weather conditions, the framework helps operators to obtain 12.9% energy-saving ratio in practice. [ABSTRACT FROM AUTHOR]

This paper studies the optimization of a small-scale central air-conditioning system, in which the cooling is provided by a ground source heat pump (GSHP) equipped with an on/off capacity control. The optimization strategy aims to optimize the overall system energy consumption and simultaneously guarantee the robustness of the space air temperature control without violating the allowed GSHP maximum start-ups number per hour specified by customers. The set-point of the chilled water return temperature and the width of the water temperature control band are used as the decision variables for the optimization. The performance of the proposed strategy was tested on a simulation platform. Results show that the optimization strategy can save the energy consumption by 9.59% in a typical spring day and 2.97% in a typical summer day. Meanwhile it is able to enhance the space air temperature control robustness when compared with a basic control strategy without optimization. [ABSTRACT FROM AUTHOR]

The effect of fin design parameters on the performance of plate-fin heat exchanges was investigated in the paper, in which an improved algorithm combing a Kriging response surface and multi-objective genetic algorithm was used. An ideal gas was adopted as the working fluid and ε -NTU method was utilized to determine the heat transfer and pressure drop. The fin height h , fin space s , fin thickness t and interrupted length l of serrated fin and channel inlet Reynolds number are firstly optimized, while the j factor, f factor and JF factor are optimization goals. The results show that when the inlet channel flow is in the laminar flow ( Re < 1000), it is beneficial to trade off the j factor and f factor. Furthermore, the total heat flow rate, total annual cost and number of entropy production units of plate-fin heat exchanges are optimized with the specified mass flow rate under given space by multi-objective optimization. Results obtained from the first two objectives and three objectives show that the fin design parameters are very similar except that the latter interrupted length is smaller slightly. A comparison between the results obtained by the previous approaches and the proposed algorithm shows that under the same effectiveness, the annual cost of the proposed algorithm is about 10% lower than the previous ones and it is faster to be converged. Therefore, this study demonstrated that the proposed algorithm is able to optimize the fin design parameter of serrated fin and the obtained results are beneficial to guide the design of plate-fin heat exchanges. [ABSTRACT FROM AUTHOR]

Concentrating solar power plants use a number of reflecting mirrors to focus and convert the incident solar energy to heat, and a power cycle to convert this heat into electricity. This paper evaluates the use of a high temperature Kalina cycle for a central receiver concentrating solar power plant with direct vapour generation and without storage. The use of the ammonia-water mixture as the power cycle working fluid with non-isothermal evaporation and condensation presents the potential to improve the overall performance of the plant. This however comes at a price of requiring larger heat exchangers because of lower thermal pinch and heat transfer degradation for mixtures as compared with using a pure fluid in a conventional steam Rankine cycle, and the necessity to use a complex cycle arrangement. Most of the previous studies on the Kalina cycle focused solely on the thermodynamic aspects of the cycle, thereby comparing cycles which require different investment costs. In this study, the economic aspect and the part-load performance are also considered for a thorough evaluation of the Kalina cycle. A thermoeconomic optimization was performed by minimizing the levelized cost of electricity. The different Kalina cycle simulations resulted in the levelized costs of electricity between 212.2 $ MWh −1 and 218.9 $ MWh −1 . For a plant of same rated capacity, the state-of-the-art steam Rankine cycle has a levelized cost of electricity of 181.0 $ MWh −1 . Therefore, when considering both the thermodynamic and the economic perspectives, the results suggest that it is not beneficial to use the Kalina cycle for high temperature concentrating solar power plants. [ABSTRACT FROM AUTHOR]

In the urban areas the demand of solar power is increasing due to better awareness about the emission of green house gases from conventional thermal power plants and significant decrease in the installation cost of residential solar power plants. But the land cost and the under utilization of available space is hindering its further growth. Under these circumstances, solar photovoltaic system installation needs to accommodate the maximum number of solar panels in either roof-top or land-mounted category. In this article a new approach is suggested to maximize the total number of solar panels in a given area with enhanced energy output without compromising the overall efficiency of the system. The number of solar panels can be maximized in a solar photovoltaic energy generation system by optimizing installation parameters such as tilt angle, pitch, gain factor, altitude angle and shading to improve the energy yield. In this paper mathematical analysis is done to show that the capacity and generated energy can be enhanced by more than 25% for a given land area by optimization various parameters. [ABSTRACT FROM AUTHOR]