The article deals with the optimization of the azimuthal tracking mechanism for a photovoltaic (PV) platform, which uses linear actuators as actuation elements for both movements (diurnal and elevation). In the case of diurnal movement, where the platform’s angular field of orientation is large, a mechanism with a relatively simple structure is used for amplifying the actuator’s stroke and avoiding the risk of the system locking itself (by limiting the values of the transmission angle). The optimization study targets the mechanical device, the control device, and the bi-axial tracking program (embodied by the laws of motion in time for the platform’s diurnal and elevation angles) with the purpose of obtaining a high input of solar radiation, with a minimal energy consumption to achieve tracking. The study is carried out by using a virtual prototyping platform, which includes Computer Aided Design (CAD), Multi-Body Systems (MBS), and Design for Control (DFC) computer applications. The m... [more]

Due to the high complexity of detailed sector-coupling models, a perfect foresight optimization approach reaches complexity levels that either requires a reduction of covered time-steps or very long run-times. To mitigate these issues, a myopic approach with limited foresight can be used. This paper examines the influence of the foresight horizon on local energy systems using the model DISTRICT. DISTRICT is characterized by its intersectoral approach to a regionally bound energy system with a connection to the superior electricity grid level. It is shown that with the advantage of a significantly reduced run-time, a limited foresight yields fairly similar results when the input parameters show a stable development. With unexpected, shock-like events, limited foresight shows more realistic results since it cannot foresee the sudden parameter changes. In general, the limited foresight approach tends to invest into generation technologies with low variable cost and avoids investing into d... [more]

In this study, a standalone hybrid wind turbine (WT)/photovoltaic (PV)/biomass/pump-hydro-storage energy system was designed and optimized based on technical, economic, and environmental parameters to provide the load demand with an objective function of minimum cost of energy (COE). The constraints of the proposed approach are the loss of power supply probability, and the excess energy fraction. The proposed approach allows the combination of different sources of energy to provide the best configuration of the hybrid system. Therefore, the proposed system was optimized and compared with a WT/PV/biomass/battery storage-based hybrid energy system. This study proposes three different optimization algorithms for sizing and minimizing the COE, including the whale optimization algorithm (WOA), firefly algorithm (FF) and particle swarm optimization (PSO) and the optimization procedure was executed using MATLAB software. The outcomes of these algorithms are contrasted to select the most effec... [more]

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both... [more]

An efficient approach to the geometry optimization problem of a non-axisymmetric flow channel is discussed. The method combines geometrical transformation with a computational fluid dynamics solver, a multi-objective genetic algorithm, and a response surface. This approach, through geometrical modifications and simplifications allows transforming a non-axisymmetric problem into the axisymmetric one in some specific devices i.e., a scroll distributor or a volute. It results in a significant decrease in the problem size, as only the flow in a quasi-2D section of the channel is solved. A significantly broader design space is covered in a much shorter time than in the standard method, and the optimization of large flow problems is feasible with desktop-class computers. One computational point is obtained approximately eight times faster than in full geometry computations. The method was applied to a scroll distributor. For the case under analysis, it was possible to increase flow uniformit... [more]

In this paper, two universal reinforcement learning methods are considered to solve the problem of maximum power point tracking for photovoltaics. Both methods exhibit fast achievement of the MPP under varying environmental conditions and are applicable in different PV systems. The only required knowledge of the PV system are the open-circuit voltage, the short-circuit current and the maximum power, all under STC, which are always provided by the manufacturer. Both methods are compared to a Fuzzy Logic Controller and the universality of the proposed methods is highlighted. After the implementation and the validation of proper performance of both methods, two evolutionary optimization algorithms (Big Bang—Big Crunch and Genetic Algorithm) are applied. The results demonstrate that both methods achieve higher energy production and in both methods the time for tracking the MPP is reduced, after the application of both evolutionary algorithms.

In this study, coconut oils have been transesterified with ethanol using microwave technology. The product obtained (biodiesel and FAEE) was then fractional distillated under vacuum to collect bio-kerosene or bio-jet fuel, which is a renewable fuel to operate a gas turbine engine. This process was modeled using RSM and ANN for optimization purposes. The developed models were proved to be reliable and accurate through different statistical tests and the results showed that ANN modeling was better than RSM. Based on the study, the optimum bio-jet fuel production yield of 74.45 wt% could be achieved with an ethanol−oil molar ratio of 9.25:1 under microwave irradiation with a power of 163.69 W for 12.66 min. This predicted value was obtained from the ANN model that has been optimized with ACO. Besides that, the sensitivity analysis indicated that microwave power offers a dominant impact on the results, followed by the reaction time and lastly ethanol−oil molar ratio. The properties of the... [more]

Artificial lighting systems are used in commercial greenhouses to ensure year-round yields. Current Light Emitting Diode (LED) technologies improved the system efficiency. Nevertheless, having artificial lighting systems extended for hectares with power densities over 50W/m2 causes energy and power demand of greenhouses to be really significant. The present paper introduces an innovative supervisory and predictive control strategy to optimize the energy performance of the artificial lights of greenhouses. The controller has been implemented in a multi-span plastic greenhouse located in North Italy. The proposed control strategy has been tested on a greenhouse of 1 hectare with a lighting system with a nominal power density of 50 Wm−2 requiring an overall power supply of 1 MW for a period of 80 days. The results have been compared with the data coming from another greenhouse of 1 hectare in the same conditions implementing a state-of-the-art strategy for artificial lighting control. Res... [more]

This review critically evaluates the latest trends in fuel cell development for portable and stationary fuel cell applications and their integration into the automotive industry. Fast start-up, high efficiency, no toxic emissions into the atmosphere and good modularity are the key advantages of fuel cell applications. Despite the merits associated with fuel cells, the high cost of the technology remains a key factor impeding its widespread commercialization. Therefore, this review presents detailed information into the best operating conditions that yield maximum fuel cell performance. The paper recommends future research geared towards robust fuel cell geometry designs, as this determines the cell losses, and material characterization of the various cell components. When this is done properly, it will support a total reduction in the cost of the cell which in effect will reduce the total cost of the system. Despite the strides made by the fuel cell research community, there is a need... [more]

The paper is related to the problem of modeling and optimizing power systems supplying, among others, nonlinear loads. A software solution that allows the modeling and simulation of power systems in the frequency domain as well as the sizing and allocation of active power filters has been developed and presented. The basic assumptions for the software development followed by the models of power system components and the optimization assumptions have been described in the paper. On the basis of an example of a low-voltage network, an analysis of the selection of the number and allocation of active power filters was carried out in terms of minimizing losses and investment costs under the assumed conditions for voltage total harmonic distortion (THD) coefficients in the network nodes. The presented examples show that the appropriate software allows for an in-depth analysis of possible solutions and, furthermore, the selection of the optimal one for a specific case, depending on the adopte... [more]

In contemporary energy markets, the Retailer acts as the intermediate between the generation and demand sectors. The scope of the Retailer is to maximize its profits by selecting the appropriate procurement mechanism and selling price to the consumers. The wholesale market operation influences the profits since the mix of generation plants determines the system marginal price (SMP). In the related literature, the SMP is treated as a stochastic variable, and the wholesale market conditions are not taken into account. The present paper presents a novel methodology that aims at connecting the wholesale and retail market operations from a Retailer’s perspective. A wholesale market clearing problem is formulated and solved. The scope is to examine how different photovoltaics (PV) penetration levels in the generation side influences the profits of the Retailer and the selling prices to the consumers. The resulting SMPs are used as inputs in a retailer profit maximization problem. This approa... [more]

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the co... [more]

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditiona... [more]

This work presents a numerical study that investigates the optimum post-injection strategy and internal exhaust gas recirculation (iEGR) application with intake valve re-opening (2IVO) aiming to optimize the brake specific nitric oxide (bsNO) and brake specific soot (bsSoot) trade-off with reasonable brake specific fuel consumption (BSFC) via 1D engine cycle simulation. For model validation, single and post-injection test results obtained from a heavy-duty single cylinder diesel research engine were used. Then, the model was modified for 2IVO application. Following the simulations performed based on Latin hypercube DoE; BSFC, bsNO and bsSoot response surfaces trained by feedforward neural network were generated as a function of the injection (start of main injection, post-injection quantity, post-injection dwell time) and iEGR (2IVO dwell) parameters. After examining the effect of each parameter on pollutant emission and engine performance, multi-objective pareto optimization was perfo... [more]

Solar thermal systems have been widely used to increase energy efficiency in the building sector, since the use of renewable energy sources became one of the top priorities to meet environmental targets. The main objective of this study is the thermo-economic optimization of solar thermal systems for residential building applications, considering a multi-objective approach. The simulations were performed through a MatLab code by implementing an elitist variant of Non-dominated Sorting Genetic Algorithm-II (NASGA-II). The solar collection area and the linear loss coefficient as well as the tank storage volume were defined as decision variables. A two-dimensional Pareto front was obtained, considering as objective functions the minimization of the annualized investment cost and the maximization of the solar collection efficiency. Based on the best trade-off between both objectives and considering that the solar thermal systems can operate for a period of at least 15 years, the Pareto ana... [more]

Energy efficiency and management is certainly one of the key drivers of human progress. Thus, the trends in the energy research are a topic of interest for the scientific community. The aim of this study is to highlight global research trends in this field through the analysis of a scientific journal indexed exclusively in the energy and fuels category. For this purpose, a journal has been selected that is in the center of the category considering its impact factor, which is only indexed in this category and of open access, Energies of the publisher MDPI. Therefore, a bibliometric analysis of all the contents of the journal between 2008 and 2020, 13,740 documents published, has been carried out. Analyzing the articles that are linked to each other by their citations, 14 clusters or research topics have been detected: smart grids; climate change−electric energy community; energy storage; bioenergy sources; prediction algorithms applied to power; optimization of the grid link for renewab... [more]

District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In th... [more]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is ben... [more]

In this work we study long-term maximization of hydroelectric energy generation from complex multi-purpose reservoir systems, using the reservoir system of the Aliakmon River, Greece, as an application example. This system serves various purposes, like urban water supply, irrigation, hydroelectric energy production, cooling thermoelectric power plants and flood control, while preserving environmental flow. The system operator uses institutional rules for the annual scheduling of the outflows of the 2 largest reservoirs (Ilarion and Polyfyto) for additional safety and smooth distribution of energy production through the year. In this work, we focus on maximization of energy production. We have considered three different hydrological scenarios (dry, average and wet), both for the current and for anticipated future water demand. The multi-reservoir system’s operation was simulated and then optimized using a rather simple form of genetic algorithms, in order to maximize hydro energy produc... [more]

Agrivoltaic systems, which consist of the combination of energy production by means of photovoltaic systems and agricultural production in the same area, have emerged as a promising solution to the constraints related to the reduction in cultivated areas due to solar panels used in agricultural production systems. They also enable optimization of land use and reduction in conflicts over land access, in order to meet the increasing demand for agricultural products and energy resulting from rapid population growth. However, the selected installation configurations, such as elevation, spacing, tilt, and choice of panel technology used, can have a negative impact on agricultural and/or energy production. Thus, this paper addresses the need for a review that provides a clear explanation of agrivoltaics, including the factors that impact agricultural and energy production in agrivoltaic systems, types of panel configurations and technologies to optimize these systems, and a synthesis of mode... [more]

Anthocyanins are one of the bioactive compounds in roselle that has many medicinal proposes. Anthocyanins are placed in the inner part of the roselle; therefore, combinations of two methods were applied to extract the anthocyanins. The first stage is employing supercritical carbon dioxide (ScCO2) to break the particle surface or outer layer of the roselle based on the total phenolic compounds (TPC) recovery, and the second step was to apply subcritical water extraction (SWE) for the extraction of anthocyanins. The objective is to determine the best conditions to obtain high yields of total anthocyanins compounds (TAC) from the roselle (Hibiscus sabdariffa) by employing a combination of ScCO2 and SWE. The optimal conditions of ScCO2 (first stage) were 19.13 MPa, 60 °C, and 4.31 mL/min, yielding 18.20%, and 80.34 mg/100 g TPC, respectively. The optimum conditions of SWE (second stage) were 9.48 MPa, 137 °C, and 6.14 mL/min, yielding 86.11% and 1224.61 mg/100 g TAC, respectively. The appl... [more]

This paper proposes a methodology based on a genetic algorithms (GA) to calibrate the parameters of a chlorine decay model in a water distribution system (WDS). The proposed methodology first contemplates that a GA is implemented using historical measurements of chlorine concentration at some sensed nodes to calibrate the unknown values corresponding to both the bulk and wall reaction coefficients. Once both parameters are estimated, the optimal-fit chlorine decay model is used to predict the decay of chlorine concentration in the water at each node for any concentration input at the pumping station. Then, a second GA-based algorithm is implemented to obtain the minimal chlorine concentration needed at the input to ensure that every node in the system meets the official normativity requirements for free chlorine in a WDS. The proposed methodology performed satisfactorily for a WDS simulated in EPANET with a GA implemented in MATLAB, both for the estimation of the reaction coefficients... [more]

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover,... [more]

Modern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 × 104 times f... [more]

The paper proposes an optimal siting and sizing methodology to design an energy storage system (ESS) for railway lines. The scope is to maximize the economic benefits. The problem of the optimal siting and sizing of an ESS is addressed and solved by a software developed by the authors using the particle swarm algorithm, whose objective function is based on the net present value (NPV). The railway line, using a standard working day timetable, has been simulated in order to estimate the power flow between the trains finding the siting and sizing of electrical substations and storage systems suitable for the railway network. Numerical simulations have been performed to test the methodology by assuming a new-generation of high-performance trains on a 3 kV direct current (d.c.) railway line. The solution found represents the best choice from an economic point of view and which allows less energy to be taken from the primary network.