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Abstract This work aims to predict the general performances of a pilot parabolic trough collector during transient periods. To do so, a one-dimensional thermal model has been developed. It has been validated with experimental results from two different experimental setups, in steady-state conditions, with a transmitted power maximum error of 3.4%. Since the model only predicts the collector's thermal behavior, the parabolic trough collector has been first optically qualified. Then, optical efficiencies were used as input for the model. Experimental results were obtained in steady-state conditions and compared to the model. Then, experimental and numerical results were compared during two period of time with varying inlet conditions (i.e. dynamic condition tests): the first one with stable conditions, and the other one with harsh conditions. The developed model showed a good capability of predicting the thermal behavior of the parabolic trough collector with unstable environment (DNI, mass flow, inlet temperature), with a 9.6% relative standard error in the worst case. As a conclusion, while previous studies only focused on steady-state conditions, it has been showed that this kind of model can be used to precisely predict the dynamic behavior of large power plants.

This paper deals the study of the lead-acid behavior and its modelling based on the Kinetic Battery Model. Results of experimental tests are used to realize the analysis and to estimate the model parameters. These last can serve for prediction and estimation of the battery lifetime according to the actual operating conditions.

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.

Abstract In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C# language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

Abstract The power system needs to satisfy its customers demand with minimum cost and emission. Fuel cost has straight relationship with energy cost. This paper presents an advanced parallelized particle swarm optimization algorithm (PSPSO) for finding optimal combination of power generation units that minimizes the total fuel cost and emission. In this algorithm, time requirements for solving complex large-scale CEED problem can be substantially reduced using parallel computation and it performs the update of positions and velocities in the end of each iteration. The proposed approach is applied on four test systems and compared with other techniques. The results represent that PSPSO has better convergence than other techniques.

Abstract Building-based active envelopes play an important role to reduce active heating supplies. Several techniques are developed to enhance the energy performances of active building envelopes; meanwhile, numerous numerical and mathematical models are also developed to conduct the performance analysis of these techniques. In this paper, we propose a state-space model for solar active wall-based Phase Change Materials (PCM). The advantage of this method remains in its simplicity to provide details of internal nodes and input/output parameters. The low-cost calculation is a supplementary advantage versus a heavy numerical method. The proposed numerical model is applied for a multi-layer wall with PCM Wallboards (PCMW) embedded between indoor and outdoor environments. The results show the ability of the state-space model to estimate the thermal behavior of the system, as well as the thermal characteristics of embedding PCM in the internal face of the wall. It significantly contributes to stabilize the indoor temperature and to ensure the thermal comfort.

Demand-Side Management systems aim to modulate energy consumption at the customer side of the meter using price incentives. Current incentive schemes allow consumers to reduce their costs, and from the point of view of the supplier play a role in load balancing, but do not lead to optimal demand patterns. In the context of charging fleets of electric vehicles, we propose a centralised method for setting overnight charging schedules. This method uses evolutionary algorithms to automatically search for optimal plans, representing both the charging schedule and the energy drawn from the grid at each time-step. In successive experiments, we optimise for increased state of charge, reduced peak demand, and reduced consumer costs. In simulations, the centralised method achieves improvements in performance relative to simple models of non-centralised consumer behaviour.

Nowadays, a large part of energy is provided by steam turbines; thus, increasing the efficiency and improving the steam turbines performance are of special importance. The presence of the liquid phase in the low-pressure stage of the steam turbine can cause energy loss, efficiency drop, and erosion/corrosion problems; therefore, one of the essential issues is to identify wet steam flow and try to reduce condensation loss. In order to decrease the liquid fraction, the drainage groove technique can be applied. The drainage groove sucks the water droplets from the turbine blade surface and drains them into the condenser. In this study, the effect of the drainage groove location on the surface of steam turbine blades has been investigated on the condensation, droplet radius, inlet mass flow, erosion rate, liquid drainage ratio, condensation losses, and total drainage ratio. For modeling the condensing flow, the Eulerian–Eulerian approach has been applied. The results show that the location of the drainage groove affects the groove performance and flow pattern in the turbine blade. In the selected drainage, the liquid drainage ratio, condensation losses, and erosion rate are reduced by 7.6%, 12%, and 88%, respectively, compared with the no-drainage groove case. Also, the total drainage ratio is 7.2% in the selected drainage. The outcomes of the present work have been a major step forward in the techniques having a great influence on the lifetime, repair and maintenance, and the output power of steam power generation facilities.

Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.