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• Spatio-temporal correlation analysis is performed for different power supply characteristics. • A combination model of MI-DFS-K2-Bayes is proposed to solve the spatio-temporal problem. • The improved k -means algorithm improves the accuracy of multiple power center curves. • Matching of running scenarios to point predictions is solved by setting upper and lower bounds. This paper proposes a novel method for scenario generation and scenario forecasting considering wind-photovoltaic-hydro-thermal power sources. With the rapid development and application prospects of energy internet and grid modernization, accurate source-side multiple output modeling and forecasting is essential to observe and estimate the multiple curves. It is becoming more and more important to optimize the operation efficiency of power grid. The traditional methods mainly focus on multi-scene generation of a single power source. Grid scheduling requirements that could not take into account multiple time correlations and multiple scenario forecasting. The paper proposes a scenario forecasting method considering scenario generation. Firstly, mutual Information (MI) and depth-first traversal (DFS) are combined to analyze the factors associated with power generation and build the MI-DFS-K2-Bayes network to generate multiple scenarios of wind- photovoltaic-hydro-thermal power. Secondly, the improved k -means algorithm is proposed to obtain the center curve of each power source. The algorithm improves the accuracy of the multivariate clustering center curve. It solves the problem that the initial clustering center and the number of clusters are difficult to select, and builds the multivariate running state. Finally, the upper and lower interval of point forecasting constrained by confidence parameters is proposed to make the running state scene match the point forecasting. The future state curves of each power source in short time scale are obtained. The future state scenario is implemented. Numerical examples demonstrate that the proposed method reduces the modeling error of wind- photovoltaic-hydro-thermal power curves by 0.13%–0.4%. The overall forecasting accuracy is significantly better than the state of the study. [ABSTRACT FROM AUTHOR]

[Display omitted] • Novel cathode flow field (CFF) with water management system (WMCFF) was developed. • Superhydrophilic capillary network was fabricated on Ti substrate by micromachining. • Carbon papers are used as evaporation wings to collect and evaporate water into air. • The WMCFF can effectively remove water produced in cathode and avoid water flooding. • Performance of DMFC with WMCFF was significantly enhanced. Oxygen starvation caused by water flooding in cathode is a key performance bottleneck for air-breathing direct methanol fuel cell (DMFC). To solve this problem, we present a new water management system consists of superhydrophilic capillary network and evaporation wings in this work. The cathode flow field (CFF) with water management system (WMCFF) is obtained by fabricating capillary network on titanium substrates with micromachining techniques. A pair of evaporating wings made of carbon papers are used to collect water from the capillary network and further evaporate them into the air quickly. Results show that, before a long-term discharge test, the initial peak power density (P max) of WMCFF -DMFC is 38.9% higher than that of the conventional cathode flow field DMFC (CCFF -DMFC). After 2 and 4 h discharge, the performance gaps between the two DMFCs become larger, and the P max of WMCFF -DMFC increases by 55.6% and 74.3% compared with that of the CCFF -DMFC, respectively. Moreover, the corresponding P max decline rate in the WMCFF -DMFC is only about 12% and 27% of that in the CCFF -DMFC. This suggests that the new water management system can effectively prevent water flooding in cathode and significantly improve the performance and stability of the air-breathing DMFCs. [ABSTRACT FROM AUTHOR]

Financial issues have been always one of major priorities in scheduling of energy systems. Although these systems are able to serve several types of energy demands but generated emission by these systems is a challenging problem. Since improvement of each one of mentioned issues has negative effects on the other issue therefore a trade-off solution is necessary to be obtained between these issues. In this paper, a multi-objective model has been presented to satisfy both economic and environmental objectives of a hub energy system in the presence of demand response program. In the proposed paper, ε-constraint and max–min fuzzy satisfying methods have been employed to solve and select the trade-off solution. The main reason of implementation of demand response program is to reduce operation cost and improve environmental performance of hub energy system. In fact, demand response program transfers some percentage of load from peak periods to off-peak periods to flatten load curve which leads to reduction of cost and emission. A mixed-integer linear programming has been used to simulate the proposed model and general algebraic modeling system software has been utilized to solve it. A sample hub energy system containing renewable and non-renewable energy resources has been studied and comparison results are presented to validate efficiency of proposed techniques. [ABSTRACT FROM AUTHOR]

In this paper, an integrated approach to optimize electrical, natural gas, and district heating networks simultaneously is studied. Several interdependencies between these infrastructures are considered in details including a nonlinear part-load performance for boilers and CHPs besides the valve-point effect for generators. A novel approach based on selecting an appropriate set of state-variables for the problem is proposed that eliminates the addition of any new variable to convert irregular equations into a regular set while the optimization problem is still solvable. As a large optimization problem, the optimal solution cannot be achieved by conventional mathematical techniques. Hence, it is better to use evolutionary algorithms instead. In this paper, the well-known modified teaching–learning based optimization algorithm is utilized to solve the multi-period optimal power flow problem of multi-carrier energy networks. The proposed scheme is implemented and applied to a typical multi-carrier energy network. Results are compared with some other conventional heuristic algorithms and the applicability and superiority of the proposed methodology is verified. [ABSTRACT FROM AUTHOR]

Due to the characteristics of clean and renewable, wind power is significant to economic and environmental operation of electric power system so that it attracts more and more attention from researchers. This paper integrates wind power with hydrothermal scheduling to establish multi-objective economic emission hydro-thermal-wind scheduling problem (MO-HTWS) model with considering wind uncertain cost. To solve MO-HTWS problem with various complicated constraints, this paper extends NSGA-III by introducing the dominance relationship criterion based on constraint violation to select new generation. Moreover, the constraint handling strategy which repairs the infeasible solutions by modifying the decision variables in feasible zone according to the violation amount is proposed. Finally, a daily scheduling example of hydro-thermal-wind system is used to test the ability of NSGA-III for solving MO-HTWS problem. It is concluded from the superior quality and good distribution of the Pareto optimal solutions that, NSGA-III can offer an efficient alternative for optimizing MO-HTWS problem. [ABSTRACT FROM AUTHOR]

This paper presents simultaneous allocation of distributed generations (DGs) and fast directional protection switches (FDPSs) to decrease energy losses and enhance reliability of the network. The main contribution of the paper is restricting the allowable size of DGs to ensure the correct operation of upstream installed FDPS in all conditions. The paper introduces a method based on genetic algorithm to solve the optimization problem. Finally, to confirm the effectiveness of the proposed method some simulations considering a 33 bus test network are performed. The optimization problem with and without applying protection constraint is solved. Customer’s load is modeled by a three level yearly load. Time value of money and load growth rate are also considered. To assess the importance of the proposed protection constraint, fault studies after adding DGs and FDPSs to a 33 bus test network are performed. Results confirm the importance of the proposed protection constraint. [ABSTRACT FROM AUTHOR]

Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic DG planning problem considering uncertainties related to the intermittent nature of the DG technologies such as wind turbines and solar units in addition to the stochastic economic conditions. The stochastic economic situation includes the uncertainties related to the fuel and electricity price of each year. The Monte Carlo simulation is used to generate the possible scenarios of uncertain situations and the produced scenarios are reduced through backward reduction program. The aim of this paper is to maximize the revenue of the distribution system through the benefit cost analysis alongside the encouraging and punishment functions. In order to close to reality, the different growth rates for the planning period are selected. In this paper the Covariance Matrix Adaptation Evolutionary Strategy is introduced and is used to find the best planning scheme of the DG units. The different DG types are considered in the planning problem. The main assumption of this paper is that the DISCO is the owner of the distribution system and the DG units. The proposed method is tested on a 9 bus test distribution system and the results are compared with the known genetic algorithm and PSO methods to show the applicability of the CMAES method in this problem. [ABSTRACT FROM AUTHOR]

This paper presents a novel method to solve security based optimal placement and parameter setting of unified power flow controller (UPFC) problem based on hybrid group search optimization (HGSO) technique. Firstly, HGSO is introduced in order to solve mix-integer type problems. Afterwards, the proposed method is applied to the security based optimal placement and parameter setting of UPFC problem. The focus of the paper is to enhance the power system security through eliminating or minimizing the over loaded lines and the bus voltage limit violations under single line contingencies. Simulation studies are carried out on the IEEE 6-bus, IEEE 14-bus and IEEE 30-bus systems in order to verify the accuracy and robustness of the proposed method. The results indicate that by using the proposed method, the power system remains secure under single line contingencies. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an approach for solving the generation scheduling problem of a complex system consisted of conventional and renewable energy sources (RES). Wind power plants are integrated into the system in order to minimize the total thermal unit fuel costs. The gained results for wind farm power production are used as input in the system to determine the optimal amounts of generated power for the thermal generating units and hydro generating units over the study period. The optimization problem consists of minimizing the total production costs, respecting power balance equations for each time interval and all operational system constraints. The proposed approach is applied on a specific system consisted of thermal power plants (TPPs), storage hydro power plants (HPPs), pumped-storage hydro power plant (PSHPP) and wind power plant (WPP). The benefits of energy production from WPP, in terms of reducing the production costs of conventional thermal power plants are also investigated. In the proposed paper two cases are analyzed. In the two analyzed cases power unit’s generation, thermal unit’s fuel costs and stream flows of hydro units are calculated over the study period. [Copyright &y& Elsevier]

Abstract: Contract capacity setting for industrial consumer with self-owned generating units (SOGUs) is a highly discrete and nonlinear optimization problem considering expenditure on the electricity from the utility and operation costs of the SOGUs. This paper proposes an improved Taguchi method that combines existing Taguchi method and particle swarm optimization (PSO) algorithm to solve this problem. Taguchi method provides fast converging characteristics in searching the optimal solution through quality analysis in orthogonal matrices. The integrated PSO algorithm generates new solutions in the orthogonal matrices based on the searching experiences during the evolution process to further improve the quality of solution. To verify feasibility of the proposed method, the paper uses the real data obtained from a large optoelectronics factory in Taiwan. In comparison with the existing optimization methods, the proposed improved Taguchi method has superior performance as revealed in the numerical results in terms of the convergence process and the quality of solution obtained. [Copyright &y& Elsevier]

• A synergy planning methodology of energy stations and networks is proposed. • The cost-based synergy planning model consists of power losses, siting and sizing, multiple constraints. • The model uses a hybrid approach including Genetic Algorithm, Dijkstra Algorithm and Simulated Annealing. • Four scenarios analyze the potential benefits of the methodology. • The proposed hybrid approach is an efficient tool for siting and sizing energy stations and networks. Siting and sizing of energy stations (ESs) and pipeline networks (PNs) significantly influence views on the economy of regional integrated energy system (RIES). In this paper, we present a synergy planning method for siting and sizing ESs and PNs. This study describes the basic structure of ESs and PNs in RIES, clearly points out influencing factors that should be considered in the planning model. Then, with flow velocity, pipe diameter, equipment capacity, and the location of ESs and PNs as variables, the cost-based function model is built based on many constraints. The Genetic Algorithm (GA) based heuristic algorithm and the PNs planning algorithm based on "Dijkstra Algorithm (DA) + Simulated Annealing (SA)" are used to solve the problem proposed in this paper. In the results and discussion, the proposed model and method are verified by a hypothetical region from OpenStreetMap. The economic changes are studied in a basic scenario and three comparison scenarios. The results show that synergy planning can save 293,320 $ and reduce the load peak-valley difference rate by 3.24%. Moreover, optimizing the flow velocity can reduce 0–32.04% lowest annual cost (LAC). [ABSTRACT FROM AUTHOR]

This paper presented a novel method of dissipating solar photovoltaic heat based on the technology of micro-heat-pipe array and the utilization of photovoltaic-cell waste heat. This novel technology solved the problems of low PV electrical efficiency and thermal failure caused by high cell temperature, greatly increased the comprehensive utilization efficiency of solar energy, and extended the service life of photovoltaic modules. One-year experiments were conducted to investigate the electrical and thermal performance of a forced-circulation, household-type photovoltaic/thermal system based on micro-heat-pipe array in Beijing, China. Test results showed that on the four typical days in different seasons, the average electrical efficiencies were 13.76%, 11.92%, 13.71%, and 14.65%; the average thermal efficiencies were 31.62%, 33.07%, 24.99%, and 17.24%; and the average total efficiencies were 45.38%, 44.99%, 38.70%, 31.89%, respectively. The system met the demand of power supply on sunny days and the demand of hot water between March and November, except in cloudy days. These experimental results can provide basis and reference for practical applications of the system. [ABSTRACT FROM AUTHOR]

Active and reactive power flow in distribution networks can be effectively controlled by optimally placing distributed resources like shunt capacitors and distributed generators. This paper presents improved variant of Teaching Learning Based Optimization (TLBO) to efficiently and effectively deal with the problem of simultaneous allocation of these distributed resources in radial distribution networks while considering multi-level load scenario. Several algorithm specific modifications are suggested in the standard form of TLBO to cope against the intrinsic flaws of this technique. In addition, an intelligent search approach is proposed to restrict the problem search space without loss of diversity. This enhances the overall performance of the proposed method. The proposed method is investigated on IEEE 33-bus, 69-bus and 83-bus test distribution systems showing promising results. [ABSTRACT FROM AUTHOR]

To attain the highest performance of energy supply systems, it is necessary to rationally determine types, capacities, and numbers of equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. In the combinatorial optimization method based on the mixed-integer linear programming (MILP), integer variables are used to express the selection, numbers, and on/off status of operation of equipment, and the number of these variables increases with those of equipment and periods for variations in energy demands, and affects the computation efficiency significantly. In this paper, a MILP method utilizing the hierarchical relationship between design and operation variables is proposed to solve the optimal design problem of energy supply systems efficiently: At the upper level, the optimal values of design variables are searched by the branch and bound method; At the lower level, the values of operation variables are optimized independently at each period by the branch and bound method under the values of design variables given tentatively during the search at the upper level; Lower bounds for the optimal value of the objective function to be minimized are evaluated, and are utilized for the bounding operations at both the levels. This method is implemented into open and commercial MILP solvers. Illustrative and practical case studies on the optimal design of cogeneration systems are conducted, and the validity and effectiveness of the proposed method are clarified. [ABSTRACT FROM AUTHOR]

The method for estimation of I – V curves of photovoltaic (PV) panel by analytic expression is presented in the paper. The problem is defined in the form of an optimization problem. The optimization problem objective is based on data from I – V curves obtained by manufacturers’ or measured I – V curves. In order to estimate PV panel parameters, the optimization problem is solved by using an evolutionary strategy. The proposed method is tested for different PV panel technologies using data sheets. In this method the I – V curve approximation with two linear and a sigmoid function is proposed. The method for estimating the knee of the I – V curve and maximum power point at any irradiance and temperature is proposed. [ABSTRACT FROM AUTHOR]

Application of the Ångström–Prescott (A–P) model, one of the highly rated global solar radiation ( Rs ) models based on sunshine, is often limited by scarcity of model parameters. Increasing availability of its parameters in the absence of Rs measurements is an effective way to overcome this problem. Although the A–P parameters have been extensively studied and calibrated worldwide, models relating them to easily obtainable variables are less investigated. Furthermore, it is uncertain whether the existing models are applicable under Chinese climate. Using a two-step approach and data from 80 sites covering three agro-climatic zones in China, this paper focuses on spatial modeling of the A–P parameters in order to increase their availability and consequently to extend the applicability of the model. It was found that the countrywide average was a = 0.18, b = 0.56 and ( a + b ) = 0.75, and that a had the largest spatial variability followed by b and ( a + b ). Values of a and ( a + b ) increased from east towards west while b showed no regular trend. The influence of various variables on the parameters was investigated, finding that a was mostly affected by P (annual average precipitation) followed by n / N ‾ (annual average daily sunshine fraction), Z (altitude), T (annual average daily temperature), ϕ (longitude) and φ (latitude), while b was only influenced by Z , and the sum ( a + b ) mostly by Z followed by T , ϕ , P and n / N ‾ . Parameter models of varying complexity were proposed and validated. The three-variable model with Z , n / N ‾ and ϕ for a and Z , φ and ϕ for ( a + b ) performed the best. However, the best- and the poorest-performing model obtained Rs prediction differed insignificantly from the calibrated parameters. Particularly, both Z and P alone could be used with confidence to predict these parameters, implying a large potential for simplifying the current parameter models. We therefore recommend choosing the simplest one-variable models in applications. Meanwhile, we question the necessity of the earlier developed complex models. Variation in model for b affected little on Rs prediction, while accurate modeling for a was more important . This questions the current approach of modeling in b : are efforts for model of b aiming at improving its prediction worthwhile? Could b be considered as a constant across whole China? These questions deserve further investigation. The findings in this study using large datasets have both theoretical implication in guiding the research direction of the A–P model and practical implications in increasing parameter availability and thus in facilitating a wider application of the A–P model. [ABSTRACT FROM AUTHOR]

Project selection plays an important role in the entire life cycle of wind farm project and the multi-criteria decision making (MCDM) methods are very important in the whole wind farm project plan selection process. There are problems in the present MCDM methods decrease evaluation quality of the wind farm project plans: first, the information loss exists in the wind farm project plan evaluation process. Second, it is difficult to satisfy the independent assumption of the multi-criteria decision making methods used in the wind farm project plan evaluation in fact. Third, the compensatory problem of performance scores of the wind farm project plans is processed unreasonably. Hence the innovation points of this paper are as follows: first, the intuitionistic fuzzy numbers are used instead of fuzzy numbers or numerical values to reflect the experts’ intuitive preferences to decrease the probability of information loss; second, the fuzzy measure is used to rate the important degrees of criteria in order to avoid the independent assumption and to increase the reasonability; third, the partial compensatory problem of performance scores is well processed by using intuitionistic fuzzy Choquet (IFC) operator and generalized intuitionistic fuzzy ordered geometric averaging (GIFOGA) operator. These operators can deal with the compensatory performance scores and non-compensatory performance scores respectively. Finally, a case study demonstrates the effectiveness of decision framework. [ABSTRACT FROM AUTHOR]

The oil produced by the pyrolysis of biomass has potential for use as a substitute for fossil fuels. However, the oil needs to be upgraded since it contains high levels of oxygen, which causes low caloric value, corrosion problems, and instability. Generally, upgrading the pyrolysis oil involves the addition of a catalyst, solvent and large amount hydrogen, which can cost more than the oil itself. In this regard, the co-pyrolysis technique offers simplicity and effectiveness in order to produce a high-grade pyrolysis oil. Co-pyrolysis is a process which involves two or more materials as feedstock. Many studies have shown that the use of co-pyrolysis is able to improve the characteristics of pyrolysis oil, e.g. increase the oil yield, reduce the water content, and increase the caloric value of oil. Besides, the use of this technique also contributed to reduce the production cost and solve some issues on waste management. This article tried to review the co-pyrolysis process through several points of view, including the process mechanism, feedstock, the exploration on co-pyrolysis studies, co-pyrolysis phenomena, characteristics of byproducts, and economic assessment. Additionally, several outlooks based on studies in the literature are also presented in this paper. [ABSTRACT FROM AUTHOR]

This paper deals with the numerical study of a conductive, convective and radiative cylindrical porous fin. At first, Runge–Kutta method-based numerical solution is obtained for calculating the temperature distribution, and then an inverse problem is solved for estimation of unknown parameters. Five critical parameters such as the porosity, emissivity, solid thermal conductivity, thickness and the permeability have been simultaneously predicted for satisfying a prescribed temperature distribution on the surface of the porous fin. This is achieved by solving an inverse problem using the hybrid evolutionary–nonlinear programming optimization algorithm. The effect of random measurement errors between ±10% has been considered. The estimated values of non-dimensional entities such as porosity and surface emissivity are found to be approximately within the range, 0.28–0.92 and 0.27–0.75, respectively. Additionally, the thermal conductivity, thickness and the permeability are found to be almost between 17 and 140 W/m K, 8.7 × 10 −4 to 0.007 m and 2 × 10 −11 to 5 × 10 −8 m 2 , respectively. The present study reveals that many feasible combinations of available materials satisfy the same temperature field, thus providing an opportunity for selecting any combination from the available alternatives. Moreover, the hybrid method is found to perform better and yield relatively faster convergence than individual methods. The sensitivity analysis reveals that the effect of fin permeability on the temperature field is considerably high than other parameters. [ABSTRACT FROM AUTHOR]

The oxygen-containing gases released during coal pyrolysis comprise more than a half of the devolatilization products, especially the noncondensible species. The mechanisms of CO formation reactions remain problematic, particularly the identity of the functional group precursors for low-temperature CO. In this paper, pyrolysis experiments of superfine pulverized coal were carried out in the N 2 and CO 2 atmosphere under non-isothermal conditions, applying a fixed-bed reactor. The CO formation mechanisms were investigated from a functional-group standpoint. The deconvolution method via numerical analysis was adopted to resolve the multi-component envelop profiles of CO evolution. Five constituent reaction complexes induced by different oxygenated groups and reactions are recognized, combining the X-ray photoelectron spectroscopy (XPS) analysis. In addition, the effects of coal type, particle size, pyrolysis atmosphere and heating rate on the CO evolution were analyzed. Finally, different CO formation mechanisms initiated from the primary decomposition of distinctive oxygenated functionalities, secondary pyrolysis reactions of tars, and gasification reactions of chars are summarized. [ABSTRACT FROM AUTHOR]

This paper introduces an efficient planning strategy using new hybrid interactive differential evolution (DE), adaptive particle swarm optimization (APSO), and pattern search (PS) for solving the security optimal power flow (SOPF) considering multi distributed static VAR compensator (SVC). Three objective functions such as fuel cost, power loss and voltage deviation are considered and optimized considering sever loading conditions. The main idea of the proposed strategy is that variable controls are optimized based on superposition mechanism, the best solutions evaluated by DE and APSO at specified stages are communicated to PS to exploit new regions around this solution, alternatively the new solution achieved by PS is also communicated to DE and APSO, this interactive mechanism search between global and local search is to balance the exploitation and exploration capability which allows individuals from different methods to react more by learning and changing experiences. The robustness of the proposed strategy is tested and validated on large practical power system test (IEEE 118-Bus, IEEE 300-Bus, and 40 units). Comparison results with the standard global optimization methods such as DE, APSO PS and to other recent techniques showed the superiority and perspective of the proposed hybrid technique for solving practical power system problems. [ABSTRACT FROM AUTHOR]

A new efficient optimization method, called the League Championship Algorithm (LCA) is proposed in this paper for solving the optimal power flow problem. This method is inspired by the competition of sport teams in an artificial sport league for several weeks and over a number of seasons. The proposed method has been applied to the Algerian power system network for different objectives. Furthermore, in order to assess the effectiveness of the proposed LCA method the obtained results using this method have been compared to those obtained using other methods reported in the literature. The obtained results and the comparison with other techniques indicate that the league championship algorithm provides effective and high-quality solution when solving the optimal power flow problem. [ABSTRACT FROM AUTHOR]

Chemical looping with oxygen uncoupling (CLOU) is a promising concept for efficient combustion of solid fuels with an inherent capture of the greenhouse gas CO 2 . This paper presents a CLOU process scheme with stoichiometric mass, energy, and exergy balances. A CLOU reactor system using medium volatile bituminous coal as fuel and silica-supported CuO as an oxygen carrier is analyzed. The analysis includes the estimation of various design and operational parameters, thermal considerations, and evaluation of the overall performance. The operation of a reactor system of two interacting circulating fluidized beds (CFBs) is greatly influenced by the hydrodynamics. For the CuO oxygen carrier, the hydrodynamic operating range appeared feasible considering the maximum solid circulation rates in current CFB boilers. Depending upon the reactor temperatures, oxygen carrier inventories of 400–680 kg/MW in the system were found necessary for stoichiometric combustion of the fuel. The temperature difference between the reactors should not exceed 50 °C, as otherwise, problems may arise with the heat balance. Exergetic efficiencies in the range of 63–70% were obtained for different combinations of relevant design parameters. It is evident that the possible operating conditions in the system are closely related to the properties of the chosen oxygen carrier. However, the calculation procedure and design criteria presented here are applicable to any oxygen carrier to be used in the process. [ABSTRACT FROM AUTHOR]

One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents a developed multi objective CIABC based on CLS theory for solving EED problem. [•] The EED problem is formulated as a non-convex multi objective optimization problem. [•] Considered three test systems to demonstrate its efficiency including practical constrains. [•] The significant improvement in the results comparing the reported literature. [Copyright &y& Elsevier]

Highlights: [•] This paper presents a new profit centric operating strategy of a hybrid power system under market environment. [•] The profit is ensured by optimal coordination of RES and load management approach. [•] The problem is formulated as dynamic optimization problem and solved using ABC algorithm. [•] Comparison shows that the proposed approach results more profit for the hybrid system. [Copyright &y& Elsevier]

• Introducing a static-based PV array reconfiguration method called 8-Queen's that has no dependence on PV dimensions and shadow size. • Ability to implement the proposed method without the need for sensors and switches in the PV system, which in this regard is cost-effective compared to other conventional techniques. • Overcoming the limitations of classical and electrical array reconfiguration methods such as the use of high connections and complex wiring are obvious advantages of the 8-Queen technique. • Implementing the proposed method in a short time such as a few microseconds on the PV system. • Providing a comparative approach to evaluate the results of the 8-Queen's method with other common methods used to PV array modules reconfiguration. Non-uniform irradiance due to partial shading conditions (PSCs) reduces the power delivered by the photovoltaic (PV) cell. The output power reduction in the PV arrays directly depends on the shading pattern and type of array configuration which is selected. So far, many dynamic and static reconfiguration methods have been used for maximum power point tracking under PSCs in the PV arrays. However, most conventional methods suffer from some major problems such as the need for additional equipment and sensors, complex wiring, the use of expensive sensors, production of complex switching matrices, high costs, and inability to reconfigure PV arrays with very small, large, and non-square sizes. Accordingly, this paper, after reviewing the dynamic and static PV array reconfiguration methods, presents a novel static-based technique called 8-Queen's for reconfiguring the PV modules corresponding to the Total-Cross-Tied (TCT) inter-connection PV array. The 8-Queen's technique has a great ability to apply on high dimensions and rectangular shapes PV arrays and is based on the movement of 8 queens on the chessboard so that none of the queens can attack the others. The effectiveness of the suggested method is expressed by implementing it on 7 cases of the TCT PV array in different sizes and various PSCs. In a comparative scenario, the performance and effectiveness of the proposed 8-Queen's technique are evaluated compared to other conventional methods. Indicators of global maximum power point (GMPP), fill factor, power efficiency, and mismatch losses evaluate the results of the employed methods. The evaluation of results represents the effectiveness of the 8-Queen's technique compared to other used methods. In addition, the performance evaluation of the proposed technique in real-world PV arrays is performed by modeling a sample PV array taking into account measurement errors. The results in this step also show that the proposed technique can also provide acceptable performance for solving problems related to maximum power point tracking under PSCs in PV systems. [ABSTRACT FROM AUTHOR]

Abstract: The improvement of the hydraulic valves depends on the careful analysis of the coherent structures driving the motion of the working fluid. In the past those devices have been studied by experimental tests; during the last 15years also several numerical works have been presented, solving the flow on body-fitted computational grids by RANS methods. In this study a different approach is proposed for the axisymmetric analysis of a directional valve (4/3, closed center): whereas the RANS techniques are based on the time-averaged equations of the flow, in the present work the unsteady Navier–Stokes equations have been solved using the Direct Numerical Simulation (DNS), which provides important details on the instantaneous structures of the flow, affecting the valve performance. Furthermore, while in the previous numerical studies the computational domain has been discretized by conformal grids, in this case the fluid-body interaction has been represented by an immersed-boundary (IB) method on a Cartesian grid. The analysis of the discharge coefficient and the flow forces for different openings s and pressure drops Δp is presented in this paper. The behavior of those global parameters is justified also considering the time-averaged and the instantaneous fields. For small openings and pressure drops the flow is steady and attached to the wall of the discharge chamber on the side of the restricted section. When s and Δp are increased the jet separates at the restricted section and it re-attaches downstream (Coanda effect), keeping the steady state. Finally, for large openings and pressure drops the flow becomes strongly unsteady: it is organized like a free jet and is dominated by large vortices. [Copyright &y& Elsevier]

Abstract: As Gencos are responsible for providing active and reactive power generation, they should devise good bidding strategies for energy and reactive power market. The paper describes a method for analyzing the competition among transmission-constrained Gencos with incomplete information. The proposed methodology employs the Supply Function Equilibrium (SFE) for modeling a Genco’s bidding strategy in energy market and uses Expected Payment Function (EPF) to construct a bidding framework in the reactive power market. The problem of finding the optimum strategy of Gencos is modeled as a bi-level optimization problem, where the upper sub-problem represents individual Genco’s payoff and the lower sub-problem solves the ISO’s market clearing problem. The ISO’s market clearing model is modified with applying generator Active Participation Factors to improve the voltage stability margin. The IEEE 39 bus test system is used to verify the effectiveness of the proposed method. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach based on Benders decomposition (BD) to solve hydrothermal unit commitment problem with AC power flow and security constraints. The proposed method decomposes the problem into a master problem and two sets of sub-problems. The master problem applies integer programming method to solve unit commitment (UC) while the sub-problems apply nonlinear programming solution method to determine economic dispatch for each time period. If one sub-problem of the first set becomes infeasible, the corresponding sub-problem of the second set is called. Moreover, strong Benders cuts are proposed that reduce the number of iterations and CPU time of the Benders decomposition method. All constraints of the hydrothermal unit commitment problem can be completely satisfied with zero penalty terms by the proposed solution method. The methodology is tested on the 9-bus and IEEE 118-bus test systems. The obtained results confirm the validity of the developed approach. [Copyright &y& Elsevier]

This paper has the intention of discussing about a recently published paper by M.T. Hagh et al. (2014). In the discussed paper the combined heat and power economic dispatch (CHPED) problem solved by applying an improved group search algorithm. To show the performance of the algorithm, three large scale test systems were simulated. In modelling, consideration of both valve point effect and prohibited zones in the operation of power units distinguishes discussed paper from earlier works. However, there are some errors in the reported results. This discussion clarifies calculations and presents corrigendum to the results. [ABSTRACT FROM AUTHOR]

Abstract: This paper developed a new method for solving the economic dispatch (ED) problems considering the valve-point effects in power systems. The method is based on a hybrid algorithm consisting of chaotic particle swarm optimization (CPSO) algorithm and sequential quadratic programming (SQP) techniques. The CPSO is the main optimizer of the algorithm and the SQP is used to fine tune its results to improve the solution. The proposed method was applied to three different cases of power systems. The economic effect, solution quality, convergence property and computation efficiency of the proposed method have been explored through the comparison with the existing techniques for the ED problems considering the valve-point effects. The simulation results demonstrate the applicability and effectiveness of the proposed algorithm to the practical ED problems. [Copyright &y& Elsevier]

Abstract: In this paper, a new practical method is presented for solving the non-convex security constraint unit commitment (SCUC) problem in power systems. The accuracy of the proposed method is desirable while the shorter computation time makes it useful for SCUC solution of large-scale power systems, real-time market operation and long-term SCUC problems. The proposed framework allows inclusion of the valve point effects, warmth-dependent start-up costs, ramp rates, minimum up/down time constraints, multiple fuels costs, emission costs, prohibited operating zones and AC power flow limits in normal and contingency conditions. To solve the non-convex problem, combination of a modified Branch-and-Bound method with the Quadratic Programming is used as an optimization tool and a developed AC power flow algorithm is applied for considering the security and contingency concerns using the nonlinear/linear AC model. These modifications improve the convergence speed and solution precision of SCUC problem. In the proposed method, in contrast with traditional SCUC algorithms, unit commitment solution, checking and satisfying the security constraints are managed simultaneously. The obtained results are compared with other reported methods for investigating the effectiveness of the proposed method. Also, the proposed method is applied to an Iranian power system including 493 thermal units. [Copyright &y& Elsevier]

Abstract: This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. [Copyright &y& Elsevier]

Abstract: Economic dispatch (ED) is one of the key optimization problems in electric power system operation. The problem grows complex if one or more units produce both power and heat. Combined heat and power economic dispatch (CHPED) problem is a complicated problem that needs powerful methods to solve. This paper presents a harmony search (EDHS) algorithm to solve CHPED. Some standard examples are presented to demonstrate the effectiveness of this algorithm in obtaining the optimal solution. In all cases, the solutions obtained using EDHS algorithm are better than those obtained by other methods. [ABSTRACT FROM AUTHOR]

Abstract: Power system stability is an important problem for power system operation. Determination of different stability margins can result in the optimum utilization of power system with minimum risk. Voltage stability is an important subset of power system stability. To correctly analyze the voltage stability of a power system, suitable dynamic models are usually required. However, static analysis tools can give us useful information about long term voltage stability. Especially, maximum loadability point (MLP) of a power system can be effectively estimated by modal analysis of load flow Jacobians. MLP is one of the important boundaries of voltage stability feasible region that loading beyond which is of little practical meaning. In this paper, MLP boundary of power system is analyzed by means of static analysis tools and its differences with the other boundaries of voltage stability, like saddle node bifurcation, are discussed. Effect of reactive power limits of generators and different static load models on the MLP border is also evaluated. [Copyright &y& Elsevier]

Abstract: This paper proposes an improved merit order (IMO) combined with an augmented Lagrangian Hopfield network (ALHN) for solving short term hydrothermal scheduling (HTS) with pumped-storage hydro plants. The proposed IMO-ALHN consists of a merit order based on the average production cost of generating units enhanced by heuristic search algorithm for finding unit scheduling and a continuous Hopfield neural network with its energy function based on augmented Lagrangian relaxation for solving constrained economic dispatch (CED). The proposed method is applied to solve the HTS problem in five stages including thermal, hydro and pumped-storage unit commitment by IMO and heuristic search, constraint violations repairing by heuristic search and CED by ALHN. The proposed method is tested on the 24-bus IEEE RTS with 32 units including 4 fuel-constrained, 4-hydro, and 2 pumped-storage units scheduled over a 24-h period. Test results indicate that the proposed IMO-ALHN is efficient for hydrothermal systems with various constraints. [Copyright &y& Elsevier]

Abstract: In this paper, a modified quantum-behaved particle swarm optimization (QPSO) method is proposed to solve the economic dispatch (ED) problem in power systems, whose objective is to simultaneously minimize the generation cost rate while satisfying various equality and inequality constraints. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability of the algorithm. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zones, and nonsmooth cost functions are considered when the proposed method is used in practical generator operation. The feasibility of the QPSO–DM method is demonstrated by three different power systems. It is compared with the QPSO, the differential evolution (DE), the particle swarm optimization (PSO), and the genetic algorithm (GA) in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed QPSO–DM method is able to obtain higher quality solutions stably and efficiently in the ED problem than any other tested optimization algorithm. [Copyright &y& Elsevier]

Abstract: The need to obtain automatic filters to treat wind data is clear due to the huge amount of data available in any wind farm analysis. Automatic detection of performance deviations of wind turbines is required for maintenance purposes. In spite of some alarms being registered by the wind farm’s SCADA system, many times automatic use of real data use is not valid due to its low quality. In this paper, the problems to carry out the characterization of the turbines in a wind farm are stated and a new technique to filter the data is presented. The robust statistical technique of the Least Median of Squares (LMedS) combined with a random search is adapted to deal with this problem with promising results. A mathematical model representing the dependency of the wind turbine behaviour in function not only of the wind speed but also of other variables such as the direction of the wind is also proposed. The results achieved are tested with long term real data from wind farms in Spain. [Copyright &y& Elsevier]

Abstract: In this paper, a particle swarm optimization (PSO) technique is proposed for solution of the generation expansion planning (GEP) problem in a competitive electricity market. GEP is one of the important decision-making activities in electric utilities and acquires a principle role in the deregulated environment for electricity trading. It is highly constrained non-linear dynamic optimization problem that can only be fully solved by complete enumeration and is computationally impossible for a real world GEP problem. The utility has to take both independent power producers participation and environment impact (CO2 emission) with satisfying all electrical and energy market constraints simultaneously. The proposed PSO based method has a strong ability to find most optimistic results and is robust for solution problem featuring non-linearity, non-differentiability and high-dimensionality. The proposed method is compared with a GA based technique to illustrate its robust performance considering different purchase prices for IPPs and CO2 emission limits. Analysis reveals that the proposed approach is an inherent, effective and economical tool for the solution of the competitive GEP problem that is easy to implement and is also superior to the GA based technique. [Copyright &y& Elsevier]

Abstract: Transmission network expansion planning (TNEP) is an important component of power system planning. It determines the characteristic and performance of the future electric power network and influences the power system operation directly. Up till now, various methods have been presented for the solution of static TNEP (STNEP) problem. However, in all of them, the role of bundle lines in TNEP problem considering the expansion of substations from the voltage level point of view has not been investigated. Thus, in this paper, the role of bundle lines in STNEP problem is being studied considering expansion cost of substations from the voltage level point of view using decimal codification genetic algorithm (DCGA). The effectiveness of the proposed idea is tested on an actual transmission network of the Azerbaijan regional electric company, Iran. The results reveal that bundle lines have effective role in transmission expansion planning and subsequent determining the network arrangement. In addition, considering the bundle lines in a power system is caused that the expansion cost of lines and substations decreases and therefore the total expansion cost of network is minimized. Also, it can be said, although cost of bundle lines are more than those which have not bundle conductor, constructing this type of lines in a transmission network with different voltage levels prevents useless expansion of unbundled lines in separate corridors and therefore the network expansion planning is optimized. [Copyright &y& Elsevier]

• Organic photovoltaic was applied to photovoltaic thermal system. • The photocatalysis was combined with photovoltaic thermal system. • Organic cells might operate longer account for the separation from absorber plate. • The performance of the novel system was analyzed under multi-work conditions. Organic photovoltaic cells compensate for the decline in the electrical efficiency of photovoltaic cells as temperatures rise. Photocatalytic technology solves the problem of low utilization of ultraviolet photons by photovoltaic thermal systems. Therefore, this paper proposes a photovoltaic thermal system that combines photocatalytic technology and organic photovoltaic cells, which combines power generation, air purification and domestic hot water. In this study, a heat and mass transfer model is established for the novel system and the thermal, electrical and air purification performance of the system under different working conditions are studied. The results show that with the increase of solar radiation and ambient temperature, the electrical and thermal efficiencies of the system are increasing. The maximum instantaneous thermal efficiency of the system can reach 65% and the electrical efficiency can reach 17.7%. The higher the temperature of the inlet water stream, the lower the thermal efficiency of the system, but the electrical efficiency of the system is elevated. The increase of solar radiation is beneficial to photocatalytic degradation of formaldehyde, but the increase of relative humidity in the air hinders the photocatalytic reaction, and the photocatalytic efficiency decreases significantly when the relative humidity exceeds 0.5. The maximum formaldehyde single-pass efficiency of the system is 60%, which can produce 675 m3 of clean air per day. The novel PC-OPV/T system provides theoretical guidance for the application of PV/T in high-temperature fields and air purification. [ABSTRACT FROM AUTHOR]

• A broadband rotary piezoelectric-electromagnetic hybrid generator (BRHG) is proposed. • The BRHG effectively collects extremely low-speed rotation energy as low as 0.01 Hz. • The energy conversion efficiency and speed are 8.2 and 60 times that of a cantilever. • Wide-flow-rate fluid energy is harvested, including wind, water, and two-phase flow. • This work contributes significantly to in situ power supply of underwater devices. The ocean contains copious available natural resources and energy. Ocean in situ power generation technology, directly harvesting energy from ocean fluids, is the crux of solving the problem of power supply for eupelagic or deep-sea exploration equipment. However, the ultralow frequency and wide-flow-rate characteristics of ocean fluids have brought severe challenges to in situ power generation in the ocean. In this paper, we develop a broadband rotary piezoelectric-electromagnetic hybrid generator to effectively harvest the energy of wide-flow-rate and ultralow frequency fluids. By using rotational torque to directly drive deformation of piezoelectric films, the hybrid generator effectively collects extremely low-speed rotational energy (as low as 0.01 Hz), with the efficiency and speed of energy conversion of the piezoelectric component of the generator being respectively 8.2 times and 60 times higher than that of a traditional piezoelectric cantilever beam harvester. The integration of the electromagnetic component further improves the energy harvesting performance of the generator over a wide flow velocity range. Compared with existing energy harvesters, the water-driven power generation per unit flow rate is enhanced by a factor of 75.5. The output power density of the proposed underwater bubble power generation system using the hybrid generator is 287 mW/m3, which is 275 times higher than previous devices. This work provides a viable approach to blue energy exploitation, especially ocean in situ power generation, and opens up many prospects for underwater cable-free power supply to eupelagic or deep-sea equipment. [ABSTRACT FROM AUTHOR]

• Multi-criteria optimisation is performed on an industrial thermocline tank. • Objective function includes exergy efficiency and a normalised environmental indicator. • Pareto set is obtained, bounded by the exergy and LCA-optimised solutions. • Exergy optimisation leads to a tapered tank shape. • Environmental optimisation leads to a square tank shape. Thermocline thermal energy storage is one of the most promising solutions for recovering waste heat in industrial plants. This paper aims to optimise the shape of a thermal energy storage to minimise its environmental impacts and maximise its exergy efficiency. The reference storage is an existing industrial high-temperature air/ceramic packed-bed heat storage called EcoStock®. The physical model used to determine the performances of the tank is a one dimensional model with two equations: one for the heat transfer fluid and one for the filler material. The environmental impacts are analysed using a life cycle assessment through four selected indicators: cumulative energy demand, global warming potential, abiotic depletion potential and particulate matter. To solve this multi-criteria problem, a particle swarm optimisation algorithm was applied with several exergy and environmental weighting factors. A Pareto set is obtained, bounded by the single exergy or environmental optimisations. Favouring exergy efficiency reduces the volume of the tank. However, environmental footprint of the tank is increased: the indicators of cumulative energy demand and abiotic depletion potential are considerably higher. The shape of the tank evolves with the exergy weight, from a square shape (environmental optimisation) to a tapered shape (exergy optimisation). [ABSTRACT FROM AUTHOR]

• Total sixty-four PV array reconfiguration techniques are elaborated. • Various computational evaluation criteria are discussed. • A comprehensive table summarizes and compares all the reconfiguration techniques. • The authors' own research on PV array reconfiguration are provided. • Several constructive recommendations are given for future development. An intractable but common problem in photovoltaic systems is that the power generated by photovoltaic will reduce seriously due to partial shading. In order to solve this problem, the photovoltaic array reconfiguration methods are developed to mitigate the impact of partial shading and increase output power. This work aims to undertake a comprehensive review on state-of-the-art photovoltaic array reconfiguration methods through a thoroughly investigation of 125 recently published papers. Compared with prior reviews, this work makes a more exhaustive classification, in which sixty-four methods are thoroughly categorized into nine groups. In addition, nine evaluation criteria are summarized for researchers to choose according to their specific requirements. Furthermore, a comprehensive comparison is provided based on ten specific indicators, such as monitor variables, complexity, response speed, rate of shadow dispersion, merits-demerits and application range, etc. Among these methods, the dynamic methods represented by meta-heuristic algorithms show more desirable performance than the static methods due to their faster response speed and prominent adaptability (e.g. the water cycle algorithm has the best performance with a power enhancement of 28%–37%, the TomTom algorithm has inferior performance with power enhancement of only 5%–25%). Finally, this review proposes six constructive suggestions and perspectives to offer technical inspirations for future research in the related fields. [ABSTRACT FROM AUTHOR]

• Socio-environmental and techno-economic factors are proposed for system sizing. • Grasshopper Optimization Algorithm outperforms Particle Swarm optimization. • 15.9% of the energy cost is incurred due to Socio-environmental factor. • Kurtosis value indicates that the proposed algorithm can escape local minima. • Grasshopper optimization algorithm is effective in reducing the system cost. Hybrid renewable energy systems (HRESs) are penentrating into the distributed energy market, which is being proved to be more economical than extending grid-electricity through long distance thereby incurring transmission losses. But, the major concern is the accountability issues resulted from socio-environmental factors that are still the hindrace to the sustainability of the HRES. In this paper, two major socio-environmental factors, viz. land cost and carbon emission penalty are integrated with the important techno-economic factors for designing and sizing of an HRES that comprises PV, biogas generator, and pumped hydro storage (PHS) system. The approach is applied for remote area electrification of an Indian town. The component level modeling of the HRES is performed considering the ambient conditions of the location. In this work, for the first time GOA based meta -heuristic optimizer is developed for solving the optimization problem. The simulation results obtained using GOA are compared with the famous meta -heuristic PSO algorithm to understand the robustness of the former optimizer in terms of computational efforts and statistical performance based on skewness and kurtosis of the total net present cost (TNPC). It is found that the contribution of the socio-environmental factors in the present case is 6% of the TNPC as compared to 94% in regard to techno-economic factors. Comparision of the two optimizers demonstrate shorter computational time and lower cost in respect of GOA. The higher kurtosis value of the TNPC obtained through GOA optimizer indicates the capability of this optimizer to escape from local minima with a greater capability to approximate the global optimum. From GOA, the optimal size of the HRES is obtained as 57.5 kW (341 m2) of PV system, 8 kW of BG plant, 378.4 kWh of energy storage and 33 kW of converter size. On comparing the optimized cost of the present HRES with a very recent work under similar load condition, the present design is found to exhibit lower value of cost of energy. [ABSTRACT FROM AUTHOR]

• A DRL-based multi-objective optimization model for IPHNGE system is developed. • Uncertainty of customer's demand and variability of wind power are achieved. • Advanced deep reinforcement learning approach is applied. As an essential development direction of energy internet, integrated energy system with interdisciplinary techniques is of great significance to promote multi-energy cooperation, realize low-carbon economic operation, and improve the flexible scheduling potential. This paper presents the study of an integrated power, heat and natural-gas system consisting of energy coupling units and wind power generation interconnected via a power grid. A deep reinforcement learning –based energy scheduling strategy is proposed to optimize multiple targets, including minimizing operational costs and ensuring power supply reliability. By scheduling the output of energy units, the economy and reliability of the considered system are improved. Taken diversified uncertainties into account, like intermittent of wind power and flexibility of load demand, the stochastic dynamic optimization problem is modeled as Markov decision process, and a soft actor-critic algorithm is introduced to solve the complex scheduling problem. The optimized decision-making action can be identified by the soft actor-critic algorithm through empirical learning without prediction information and prior knowledge. In the simulation, the proposed SAC-based agent has robust performance on solving optimization problems of different scenarios. Besides, the comparison study is carried out among benchmark reinforcement learning and heuristic algorithms, parameters of which are specifically given. The results demonstrate that when optimizing the comprehensive profits, the developed strategy reduced costs by up to 21.66%, compared to other algorithms. [ABSTRACT FROM AUTHOR]

• Chemical precooling technology is proposed to improve the engine performance. • A new chemical precooled engine cycle is put forward. • The chemical heat sink of selected fuel is measured by experiments. • The chemical precooled cycle can dramatically improve the specific impulse. • Future research directions and potential applications are summarized. The precooling technology is an effective way to expand the flight envelope of the turbine engine. To solve the problem of engine performance degradation caused by excessive fuel consumption, a new type of chemical precooled engine cycle is proposed, in which part of the heat energy from the incoming air is converted into the chemical energy by pyrolytic reaction. To evaluate the engine performance, a thermodynamic model is established. Based on this model, the experiments are performed to obtain the chemical heat sink of selected fuels. Through the simulation, it is found that the specific impulse is greatly improved because the fuel consumption is effectively reduced by introducing the chemical precooling process. Moreover, the highest operating Mach number (HOMN) of the precooled turbine channel is effected by different component parameters, among which the increase of the turbine inlet temperature has the most significant effect on rising the HOMN. Furthermore, the optimal fuel ratio can be obtained when the equivalence ratio is equal to 1.0, and the fuel consumption can be saved by 42% ~ 52% at this time. In general, the results in this paper are beneficial for developing high-performance turbine combined engines. [ABSTRACT FROM AUTHOR]

• BOA is employed to reconfigure the partially shaded PV array optimally. • Comparison with SP-TCT, NS puzzle pattern, Shade dispersion with NS and GWO is performed. • Five shadow patterns, regular and irregular, are investigated. • GMP obtained via BOA is enhanced by 27.43% compared to SP-TCT arrangement. • Statistical parameters Wilcoxon test are implemented for GWO and BOA for all studied shadow patterns. • The obtained results confirmed the superiority of the proposed BOA. The operation of the photovoltaic (PV) array under partial shadow conditions (PSCs) has negative effects on the extracted global maximum power (GMP) which is decreased due to the presence of power loss. Rearranging the shaded panels in the array is essential to enhance the GMP and diminish the effect of shadow, this process is known as PV array reconfiguration. Most of reported approaches did not guarantee the GMP and applicable to specific dimension of the PV array. Therefore, this paper proposes a novel methodology incorporated recent metaheuristic approach of butterfly optimization algorithm (BOA) to reconfigure the shaded PV array optimally and extract the GMP. BOA is selected due to its multiple advantages like ease of implementation, simple in construction, requirement of less controlling parameters, and effectiveness in solving real-time problems. Five shadow patterns are studied and the obtained results via BOA are compared to series–parallel total-cross-tied (SP-TCT), novel structure (NS) puzzle pattern, shade dispersion with NS and grey wolf optimizer (GWO) based arrangements. The proposed BOA succeeded in achieving maximum GMP enhancement of 27.43% compared to SP-TCT configuration. Moreover, Wilcoxon test is investigated for the results of the proposed BOA and GWO. Furthermore, the statistical parameters of both approaches are calculated. The obtained results confirmed the availability of the proposed BOA in reconfiguring the PV array operated under PSCs optimally. [ABSTRACT FROM AUTHOR]