Nowadays, the world is encountering multiple challenges of energy security, economic recovery, and the effect of global warming. Investing in new fossil fuels only locks in uneconomic practices, sustains existing risks and increases the threats of climate change. In contrast, renewable energies, such as photovoltaic energy, constitute one of the most promising technologies in combating global increase in temperatures. Given its simplicity and low maintenance costs, photovoltaic energy is the most effective alternative to address the issues above. However, the standard test conditions (STCs) of PV modules are, in most cases, different from the real working conditions of a solar module. For instance, high levels of incident irradiation in an arid climate may cause the temperature of a module to rise by many degrees above the STC temperature of 25 °C, lowering the module’s performance. To effectively simulate and control PV systems for a given location, it has become paramount to develop... [more]

This paper presents a fully-decentralized peer-to-peer (P2P) electricity and gas market for retailers and prosumers with coupled energy units, considering the uncertainties of wholesale electricity market price and prosumers’ demand. The goal is to improve the overall economy of the proposed market while increasing its flexibility. In this market, the retailers are equipped with self-generation and energy storage units and can bilaterally negotiate for electricity and gas transactions with prosumers to maximize their profit. Furthermore, they can sell power to the upstream market in addition to prosumers. The prosumers have access to several retailers to supply their required electricity and gas and can freely provide their energy needs from every retailer, contributing to dynamicity in the proposed market. Given that they have an energy hub consisting of boiler units, combined heat and electricity (CHP) units, and electric pumps, they can switch their energy supply source from electri... [more]

The wide application of power electronic devices brings an increasing amount of undesired harmonic and interharmonic tones, and accurate harmonic phasor estimation under a complex signal input is an important task for smart grid applications. In this paper, an optimization of least-square dynamic harmonic phasor estimators, considering multi-interference and harmonic frequency variance, is proposed. A comprehensive error index (CEI) composed of the fundamental-leakage-led harmonic amplitude estimation error, harmonic mutual interference, out-of-band interference, and harmonic frequency deviation is employed. The largest CEI part of least-square algorithms using three different signal decomposition models is analyzed for the first time, and variables to reduce this error component are then introduced using singular value decomposition. With the CEI and defined variables, a minimum-error estimation of harmonic phasors under various interference and harmonic frequency change is discussed.... [more]

In the rural regions of southern Brazil, electricity is largely directed to irrigation activities on rice crops at restricted periods of the year. Typically, customers in these regions are called “irrigators”, and have some characteristics different from loads in urban centers, such as high demand levels and sharp load variations. These characteristics can result in problems of excessive loading on distribution grids at certain times of the day, generating concerns for the power utilities in relation to the security of the electrical system, energy supply to customers, and the integrity of electrical equipment. An alternative to avoid or mitigate these possible problems may be the application of a demand management model to irrigator customers. In this context, a load shifting strategy can be inserted to reduce demand in more critical periods and move it to intervals with lower load on the power grid. In this context, this article presents a demand-side management methodology in distri... [more]

Comprising a total of seven articles divided into five research articles, one review article, and one editorial article, this Special Issue is dedicated to new techniques for piezoelectric energy harvesting and its design, optimization, applications, and analysis [...]

Optimization studies are an important task in reservoir engineering practices such as production optimization and EOR (Enhanced Oil Recovery) assessments. However, they are extensive studies with many simulations that require huge computational effort and resources. In terms of EOR, CO2 injection is one of the most common methods employed due to a high recovery potential and environmental benefits. To assess the feasibility of CO2-EOR projects, a reservoir design study must be conducted before optimization is performed. Some studies have demonstrated the advantages of employing proxy models to perform this task in terms of saving huge amounts of computer memory space and time. In this study, proxy models were developed to solve a multi-objective optimization problem using NSGA-II (Non-dominated Sorting Genetic Algorithm II) in two selected reservoir models. The study was performed for a CO2-WAG (Water Alternating Gas) application, where gas and water injection rates and half-cycle leng... [more]

Investments in the development of the district heating system require a thorough analysis of the technical, economic, and legal aspects. Regarding the technical and economic context, a mathematical model of the district heating system has been proposed. It optimizes both the technical and economic aspects of the function and development of a district heating system. To deal with non-linearities, the developed linear programming model is divided into three phases: flow, thermal, and pressure. Therein, non-linear dependencies are calculated between the linear optimization phases. This paper presents the main assumptions and equations that were used to calculate the parameters of the heating system, along with the optimal level of heat production, the flow rate of the heating medium in the heat nodes and edges of the network graph, the heat, power, and temperature losses at each edge, and the purchase costs of heat and its transmission. The operation of the model was tested on a real-worl... [more]

To address the problems of “difficult to consume” renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and “electric-heat coupling” characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management.

The integration of distributed generation (DG) into the power grid has increased in recent years due to its techno-economic benefits for utilities and consumers. However, due to the fact that distribution systems were not originally designed to accommodate such DG units, many challenges are being faced by utilities to seamlessly integrate them into their systems. One of the critical challenges is their effect on protection system settings and coordination. The DG units will affect the pickup current settings of the protection relays, coordination between the primary and secondary relays, and even the direction of the fault current. Failing to consider DG’s effect on the protection system may lead to serious equipment damage or system failure, causing huge financial setbacks for utilities. To that end, this work proposes a new dynamic approach to optimally allocate different types of DG units over the planning horizon. The objective is to minimize the overall costs of the system while t... [more]

This study focuses on the determination of optimum layout configurations for a linear array of identical mutually interacting truncated cylinders. Optimum configurations correspond to those that maximize either the total heave exciting force acting on all cylinders of the array or the heave exciting force applied on pairs of cylinders within the array. For achieving this goal, we developed and applied an efficient optimization numerical process (ONP), where a robust hydrodynamic numerical model, capable of solving the diffraction problem of the examined multi-body arrangement in the frequency domain, was appropriately coupled with a genetic algorithm solver in an integrated computational environment. Initially, the efficiency of the ONP is demonstrated by comparing results with those of other investigations that resulted from the deployment of classical optimization methods. Then, ONP is applied for a linear array of nine cylinders for determining the optimum layout configurations unde... [more]

This study discusses a numerical study that was developed to optimize the ventilation system in a power substation prior to its installation. We established a multiobjective particle swarm optimizer to identify the best approach for simultaneously improving, first, the ventilation performance considering the most appropriate inlet size and outlet openings and second, the reduction of the synthetic noise of the ventilation and power consumption from the exhaust fan equipment and its operation. The study used building information modeling to construct indoor and outdoor models of the substation building and verified the overall performance using ANSYS FLUENT 18.0 software to simulate the air velocity and air temperature distribution within the building. Results show that the exhaust fan of the B1F cable finishing room and the 23 kV gas insulated switchgear (GIS) room optimize the reduction of horsepower by approximately 1 Hp and 0.5 Hp. The combined noise is reduced by 4 dBA and 2 dBA; t... [more]

This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, env... [more]

Solar radiation and human activity generate ubiquitous temperature gradients that could be harvested by thermoelectric generators (TEGs). However, most of these temperature gradients are in the range of very few degrees and, while TEGs are able to harvest them, the resulting output voltages are extremely small (a few hundreds of mV), and DC−DC converters are necessary to boost them to usable levels. Impedance matching between TEGs and DC−DC converter plays a fundamental role in the energy harvesting efficiency. Therefore, it is essential to determine the output power of the system in different configurations, in order to decide on the optimum TEG connection. Here, we present an electronic circuit to measure the maximum power that can be harvested with low-voltage TEGs connected to a DC−DC converter. The developed circuit is an electronic controlled load that drains the maximum current from the output of the DC−DC converter while maintaining its output voltage at the maximum allowed val... [more]

This paper proposes a method to improve the performance of a distribution system by optimizing volt−var function of a smart inverter to alleviate the voltage deviation problem due to distributed generation connection. In order to minimize voltage deviation and line losses which represent the performance of a distribution system, this paper proposes an algorithm that optimally sets the parameters of the volt−var function. In the process of optimizing the parameters of the volt−var function, the algorithm proposed in this paper considers minimizing the contribution of the reactive power in order not to affect the output of the distributed generation. In order to apply to the field, the distribution system in South Korea considering the configuration and operation regulation was selected as a test model for algorithm verification. As a result, the system performance was successfully improved by optimally setting the volt−var function of the smart inverter which is an effective way to solv... [more]

We designed and synthesized two zinc phthalocyanine sensitizers (PcS27 and PcS28), substituted with branched or cyclic alkoxy chains, to investigate the structural effect of peripheral alkyl chains on the performance of dye-sensitized TiO2 solar cells. The bulky cyclic alkyl chains of PcS28 decreased the adsorption density of PcS28 on the TiO2 electrode, while the terminal branches of alkoxy chains of PcS27 did not influence the adsorption density in comparison to the previously published PcS20 with linear alkoxy chains. Under one sun conditions, PcS27 cells exhibited higher open-circuit voltage but a slightly lower energy conversion efficiency, 6.0% less than PcS20. These results suggest that the small alternation of alkoxy chains resulted in decreasing electron pushing ability of peripheral phenoxy units, giving lower short-circuit current.

This paper deals with the problem of optimal location and reallocation of battery energy storage systems (BESS) in direct current (dc) microgrids with constant power loads. The optimization model that represents this problem is formulated with two objective functions. The first model corresponds to the minimization of the total daily cost of buying energy in the spot market by conventional generators and the second to the minimization of the costs of the daily energy losses in all branches of the network. Both the models are constrained by classical nonlinear power flow equations, distributed generation capabilities, and voltage regulation, among others. These formulations generate a nonlinear mixed-integer programming (MINLP) model that requires special methods to be solved. A dc microgrid composed of 21-nodes with existing BESS is used for validating the proposed mathematical formula. This system allows to identify the optimal location or reallocation points for these batteries by im... [more]

This research focused on real-time optimization control to improve the fuel consumption of power-split hybrid electric vehicles. Particle swarm optimization (PSO) was implemented to reduce fuel consumption for real-time optimization control. The engine torque was design-variable to manage the energy distribution of dual energy sources. The AHS II power-split hybrid electric system was used as the powertrain system. The hybrid electric vehicle model was built using Matlab/Simulink. The simulation was performed according to US FTP-75 regulations. The PSO design objective was to minimize the equivalent fuel rate with the driving system still meeting the dynamic performance requirements. Through dynamic vehicle simulation and PSO, the required torque value for the whole drivetrain system and corresponding high-efficiency engine operating point can be found. With that, the two motor/generators (M/Gs) supplemented the rest required torques. The composite fuel economy of the PSO algorithm was... [more]

This paper provides a literature review of the cascade refrigeration system (CRS). It is an important system that can achieve an evaporating temperature as low as −170 °C and broadens the refrigeration temperature range of conventional systems. In this paper, several research options such as various designs of CRS, studies on refrigerants, and optimization works on the systems are discussed. Moreover, the influence of parameters on system performance, the economic analysis, and applications are defined, followed by conclusions and suggestions for future studies.

Electricity markets are nowadays flooded with uncertainties that rise from renewable energy applications, technological development, and fossil fuel prices fluctuation, among others. These aspects result in a lumpy electricity prices for consumers, making it necessary to come up with risk management tools to help them hedge this associated risk. In this work a portfolio optimization applied to electricity sector, is proposed. A mixed integer programming model is presented to characterize the electricity portfolio of large consumers. The energy sources available for the portfolio characterization are the day-ahead spot market, forward contracts, and self-generation. The study novelty highlights the energy portfolio characterization for players denoted as large consumers, which has been overlooked by the scientific community and, focuses on the Iberian electricity market as a real case study. A multi-objective methodology is explored, using a weighted-sum approach. The expected cost and... [more]

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced wh... [more]

In recent years, numerous countries have introduced or considered capacity markets as remuneration mechanisms for long-term capacity adequacy. Since adequacy is frequently linked with thermal power generation, there is an ongoing debate as to whether this instrument could impact decarbonisation. In this context, the paper presents a quantitative assessment of the consequences of introducing a capacity market on decarbonisation pathways. The Polish power system is taken as an example due to its heavy dependence on fossil fuels. To this end, a computable model of the Polish power system is developed and applied to the study of two research scenarios. The first scenario presents the power system without introducing a capacity market, while the latter considers the system with a capacity market in place. The analysis shows that the introduction of a capacity market delays the decarbonisation of the power system and has a negative impact on carbon neutrality. Even though coal-fired units ar... [more]

The Clean Energy for all Europeans Package by the EU aims, among other things, to enable collective self-consumption for various forms of energy. This step towards more prosumer-based and decentralized energy systems comes at a time when energy planning at a neighborhood scale is on the rise in many countries. It is widely assumed that—from a prosumer’s cost-perspective—shared conversion and storage technologies supplying more than a single building can be advantageous. However, it is not clear whether this is the case generally or only under certain conditions. By analyzing idealized building clusters at different degrees of urbanization (DOU), a linear-optimization approach is used to study the cost difference between shared energy infrastructure (smart energy neighborhoods, SENs) and individually planned buildings. This procedure is carried out for various emission reduction targets. The results show, that with higher emission reduction targets the advantage of SENs increases within... [more]

CO2 capture and reinjection process (CCRP) can reduce the used CO2 amount and improve the CO2 storage efficiency in CO2 EOR projects. To select the best CCRP is an important aspect. Based on the involved equipment units of the CCRP, a novel techno-economic model of CCRP for produced gas in CO2 EOR and storage project was established. Five kinds of CO2 capture processes are covered, including the chemical absorption using amine solution (MDEA), pressure swing adsorption (PSA), low-temperature fractionation (LTF), membrane separation (MS), and direct reinjection mixed with purchased CO2 (DRM). The evaluation indicators of CCRP such as the cost, energy consumption, and CO2 capture efficiency and purity can be calculated. Taking the pilot project of CO2 EOR and storage in XinJiang oilfield China as an example, a sensitivity evaluation of CCRP was conducted based on the assumed gas production scale and the predicted yearly gas production. Finally, the DRM process was selected as the main CC... [more]

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box−Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of 93.90%, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a fi... [more]

DC microgrids have advantages over AC microgrids in terms of system efficiency, cost, and system size. However, a well-designed overcurrent protection approach for DC microgrids remains a challenge. Recognizing this, this paper presents a novel differential evolution (DE) based protection framework for DC microgrids. First, a simplified DC microgrid model is adopted to provide the analytical basis of the DE algorithm. The simplified model does not sacrifice performance criterion in steady-state simulation, which is verified through extensive simulation studies. A DE-based novel overcurrent protection scheme is then proposed to protect the DC microgrid. This DE method provides an innovative way to calculate the maximum line current, which can be used for the overcurrent protection threshold setting and the relay coordination time setting. The detailed load condition and solar irradiance for each bus can be obtained by proposed DE-based method. Finally, extensive case studies involving f... [more]