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This paper aims to develop a selective energy optimisation of the photovoltaic–thermal (PV/T) system performance. The PV cell inside the PV/T system could be periodically manipulated to produce domestic hot water without applying an external power supply.

A numerical simulation model of the proposed PV/T model was developed in MATLAB/Simulink to analyse the selective energy optimisation of the model. The extrinsic cell resistance (R_se) is adjusted to control the ratio of thermal to the electrical energy, generated from the PV cell inside the PV/T system. Therefore, the internal heat of the PV cell inside the PV/T system is periodically used as a thermal element to produce electrical power and hot water.

The optimisation of PV/T energy shows that the electrical power efficiency can increase by 11.6% when R_se was 0 Ω, and the 200 L water tank temperature increased by 22ºC when R_se was 50 Ω.

This study showed that the use of the PV cell could be extended to domestic hot water and space heating, and not only for electricity.

The main purpose of this study is to present a non-similar analysis of two-dimensional boundary layer flow of non-Newtonian nanofluid over a vertical stretching sheet with variable thermal conductivity. The Sisko fluid model is used for non-Newtonian fluid with an exponent (n* > 1), that is, shear thickening fluid. Buongiorno model for nanofluid accounting Brownian diffusion and thermophoresis effects is used to model the governing differential equations.

The governing boundary layer equations are converted into nondimensional coupled nonlinear partial differential equations using appropriate transformations. The resultant differential equations are solved numerically using implicit finite difference scheme in association with the quasilinearization technique.

This analysis shows that the temperature raises for thermal conductivity parameter and velocity ratio parameter while decreases for the thermal buoyancy parameter. The thermophoresis and Brownian diffusion parameter that characterizes the nanofluid flow enhances the temperature and reduces the heat transfer rate. Skin friction drag can be effectively reduced by proper control of the values of thermal buoyancy and velocity ratio parameter.

The wall heating and cooling investigation result in the analysis of the control parameters that are related to the designing and manufacturing of thermal systems for cooling applications and energy harvesting. These control parameters have practical significance in the designing of heat exchangers and solar thermal collectors, in glass and polymer industries, in the extrusion of plastic sheets, the process of cooling of the metallic plate, etc.

To the best of authors’ knowledge, it is found from the literature survey that no similar work has been published which investigates the non-similar solution of Sisko nanofluid with variable thermal conductivity using finite difference method and quasilinearization technique.

The problem of diverse cash flows associated with a construction “project” appear in four progressive cycles. These are the initiating construction cycle and closing deconstruction cycle (devaluation cycle). The effectiveness of any project is given by capitalisation cycle. The optimisation of payback (credit return) cycle is critical for any project.

For calculate of activity durations, cash flows and even we may use the spreadsheet table as a tool for expression of calculation formulas. This approach may offer a mechanism for answers regarding the sensitivity of manageable parameters (say changes in costs, construction speeds, duration of activity). The problem of optimal capacity expansion of construction work as a time dependent problem is studied in many different applied contexts. Traditional capacity planning usually begins with a forecast of demand on the basis of organisational or technological needs.

The implementation of a technical project carried out in conditions of high production speeds and low time reserves requires changes in technologies, organisation and preparation of construction. In each specific case, a civil engineer needs to know the economic impacts (the capability of applicable calculations).

It is obvious from the given example, which has the same features as the execution of a series of construction projects in recent years, that the myth of the importance of executing works in large volumes ahead of the deadlines has significant financial consequences.

Reverse osmosis (RO) has become an important method of desalination to meet the ever-growing water needs around the world. Its integration with renewable energy source (RES) reduces the environmental impact of gas emissions and cost of conventional fossil energy sources. The optimal sizing of energy sources to power RO desalination system is intended mainly to minimize the annualized cost of the system and by extension minimize freshwater cost while maximizing production.

In this study, a mathematical optimization approach is used to determine the optimal energy mix, which includes grid power, diesel generator and a photovoltaic (PV) module to supply an RO desalination unit. Three cases of optimal sizing approach were compared. Case 1 is a system with only grid power and diesel generator as energy sources; Case 2 has PV incorporated in the energy supply mix while Case 3 has the three energy sources and a Time of Use (TOU) demand response program on the demand side.

The results of implementing the optimization models show that Case 3 turnout the highest freshwater production (1,521 m³/day) at a unit cost of 1.36$/m³ when compared to Case 1 with daily freshwater production of 1,250 m³/day at a unit cost of 1.68$/m³ and Case 2 having a daily freshwater production of 1,501 m³/day at a unit cost of 1.33$/m³.

The integration of RES to power desalination system with application of TOU demand response is the significance of this study.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

Nanotechnology has developed gradually in recent years and it is encountered in various applications. It has many usage area especially in energy systems. The purpose of this study, in a photovoltaic thermal system, thermal behaviours of a PV panel has been investigated by energy and exergy analysis method using a phase change material inserted 5 per cent weighted Al₂O₃ nanoparticle.

In this study, one of the three different PV panels was kept normally, the other one was filled with a phase changing material (paraffin-wax) and the last panel was filled with the mixture of a nanoparticle and paraffin-wax.

After the analyses, especially during the time intervals when the radiation is high, it is found that the panel with Np-paraffin mixture has a high electrical and thermal efficiency. In addition, as a result of the exergy analyses, average exergy efficiency of the panel with Np-paraffin mixture has been determined as 10 per cent, whereas that of the panel with paraffin as 9.2 per cent.

Nanoparticles had not been used with PCMs in photovoltaic–thermal systems in the studies made before.

The purpose of this paper is to focus on the efficiency of Dutch secondary schools. In particular, the size of the schools' management is benchmarked.

The methodology used is an advanced micro‐econometric technique called stochastic frontier analysis.

The method used is applicable for identifying the optimum allocation, in particular the size of management. The overall result is that there is no systematic over or under allocation of management in Dutch secondary schools.

Each school received an individual benchmark. Schools can position themselves in respect with other schools and have information on how to adjust allocation of resources.

The paper contributes to the discussion about the size of management costs of Dutch secondary schools. The analysis is based on state‐of‐the‐art methodologies and has not been applied to the educational process.

The purpose of this paper is to propose a model of assembly strategy generation and selection for human and robot coordinated (HRC) cell assembly. High‐Mix, Low‐Volume production in small production manufacturing industry, tends to employ more flexible assembly cells. The authors propose innovative human and robot coordinated assembly cells to solve the problem of persistent growing cost for human resources and occasional changes in programs and configurations for robots. The first issue is to find out an optimal way to allocate the assembly subtasks to both humans and robots.

A dual Generalized Stochastic Petri Net (GSPN) model is theoretically studied and then off line built based on a practical assembly task for human and robot coordination. Based on GSPN, Monte Carlo method is carried out to study the time cost and payment cost or possible strategies, and Multiple‐Objective Optimization (MOOP) method related Cost‐effectiveness analysis is adopted to select the optimal ones.

It is discovered that human and robot coordinated assembly can reduce the assembly time and meanwhile reduce the assembly cost. The authors demonstrate the effectiveness of this approach by comparing the simulation and experimental results.

The novelty with this work is that the human and robot coordinated flexible assembly cell, as the authors proved, is the main stream in small production in future due to the higher human source pressure from society and cost pressure upon the company. Based on this innovative work, the authors proposed a dual GSPN model to model the assembly task allocation process for human and robot, the model of which is also effective in modeling the possible robot and human behaviors.

This study aims to solve optimal pricing and power bidding strategy problem for integrated combined heat and power (CHP) system by using a modified heuristic optimization algorithm.

In electricity markets, generation companies compete according to their bidding parameters; therefore, optimal pricing and bidding strategy are solved. Recently, CHP units are significantly operated by generation companies to meet power and heat, simultaneously.

For validation, it is shown that profit is improved by 0.04%–48.02% for single and 0.02%–31.30% for double-sided auctions. As heat price curve is extracted, the simulation results show that when CHP system is integrated with other units results in profit increase and emission decrease by 3.04%–3.18% and 2.23%–4.13%, respectively. Also, CHP units significantly affect bidding parameters.

The novelties are pricing and bidding strategy of integrated CHP system is solved; local heat selling is considered in pricing and bidding strategy problem and heat price curve is extracted; the effects of CHP utilization on bidding parameters are investigated; a modified heuristic and deterministic optimization algorithm is presented.