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This article proposed the architecture of a stand-alone photovoltaic connected system (SPVS) with energy storage. An SPVS with energy storage requires power management for various operating modes. A coordinate controller is often necessary to manage the change in control architecture depending on the operating mode. This proposed system contains a boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). The BC is used for two purposes. The first is extracting the maximum power from the PV. The second is to control the DC link voltage when the SPVS is not in MPPT mode; dual-loop PI controllers are used for this. The BSQZSDC is used for energy management and regulates DC link voltage when SPVS operates in MPPT mode. This paper addresses about the effective control approach that BSQZSDC used, i.e., the double-integral sliding mode control, to regulate the DC link voltage and battery current in various operating modes. Experimental results were carried out to depict that the controller reasonably achieves the control objectives and proper energy management. [ABSTRACT FROM AUTHOR]

In this paper, undoped BaBi2−x−yNb2ErxYbyO9 (BBN), Er3+ doped BBN, and a series of Er3+/Yb3+ co-doped BBN ferroelectric ceramic is synthesized by the solid-state method to study the structural, dielectric, ferroelectric, and energy storage behavior of the prepared ceramic. XRD spectra revealed orthorhombic geometry and Fmmm phase group of each prepared ceramic. SEM micrograph shows dense microstructures similar to square-shaped structures. Two FTIR bands at 619 and 822 cm−1 are observed, displaying the characteristic peaks of the Aurivillius phase. Four Raman bands are detected for undoped BBN, while twelve are visible in Er3+/Yb3+ doped BBN compositions. Further, this work utilizes a comparable method to correlate Nb–O stretching frequencies with their corresponding bond lengths using Herschbach's exponential function. Temperature-dependent dielectric studies show considerable dispersion below and above maximum temperature (Tm), and the dielectric constant (εʹ) decreases with an increase in frequency. The dielectric loss (ɛʺ) curves are quite diffused, and shifts in the maxima with frequency have been observed, thus validating the relaxor behavior of all the prepared BBN compositions. The slimmer PE curves were obtained under moderate electric fields ranging from 75 to 100 kV/cm. The energy storage parameters (W, Wrec, η) are improved with increasing applied electric field. The energy storage efficiency (η) obtained for undoped, Er3+ doped, and Er3+/Yb3+ co-doped BBN ceramics are 78.25%, 83.39%, and 90.87%, respectively. The energy storage parameters revealed good temperature stability from 303 to 415 K, indicating that the synthesized material might aid in developing modern electronic equipment for energy storage applications. [ABSTRACT FROM AUTHOR]

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031. [ABSTRACT FROM AUTHOR]

In pursuit of the "Dual Carbon Goals" and to mitigate the adverse effects of "power supply restrictions," a microgrid scheme integrating wind and solar power with hydrogen energy storage is proposed. This paper introduces the principles of system capacity configuration and establishes a mathematical model. This research offers a novel method for configuring wind and solar hydrogen storage systems called quantum-enhanced multi-objective collaboration. This work intends to address the complicated issues of achieving effective energy storage, reducing prices, and maximising renewable energy utilisation by leveraging the capabilities of quantum computing. A multi-objective capacity optimization configuration model for wind–solar–hydrogen energy storage is developed using Homer Pro software and an enhanced BAS-GA algorithm. Under off-grid operating conditions, the wind–solar–hydrogen energy storage system's capacity optimization configuration model is validated through practical examples. The results indicate that, in comparison to wind storage, solar storage, wind–solar storage, and wind–solar–diesel storage systems, the net present value and levelized cost of electricity of the wind–solar–hydrogen energy storage system decrease to 14.25 million RMB and 1.529 RMB/(kW h), respectively. The renewable energy utilization rate increases to 98.7%, and the load shedding rate decreases to 5.50%. [ABSTRACT FROM AUTHOR]

In this paper, stochastic synchronization of the wind and solar energy using energy storage system based on real-time pricing in the day-ahead market along with taking advantage of the potential of demand response programming has been analyzed. Since renewable energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the demand response program and real-time pricing system. [ABSTRACT FROM AUTHOR]

Today, with the increasing penetration of microgrids, the degree of complexity and non-linearity of power systems has increased, causing conventional and inflexible controllers not to perform well in a wide range of operating points. In this paper, a self-tuning proportional-integral (PI)-controller based on a soft computation of a combination of genetic algorithm (GA) and artificial neural network (ANN). The GA-ANN is used to control the frequency of a microgrid in an island mode to automatically adjust and optimize the coefficients of a PI-controller. The proposed PI-controller is located in the frequency control secondary loop of an island microgrid. Since the ANN is a local search algorithm and can be located in local minimum points and on the other hand improving its performance requires a lot of training data. The ANN parameters are optimized using the GA algorithm's proposed controller. Train ANN online to adapt to the system and change the PI-control coefficients without a lot of training data, in addition to avoiding being in the local minimum points.The microgrid tested included various distributed generation units including battery energy storage that tried to create a more realistic frequency response for the microgrid by considering nonlinear factors on the model of these resources. Finally, the simulation results with different perturbations indicate the proper performance of the proposed controller. [ABSTRACT FROM AUTHOR]

With the development of aircraft electrification, the problem of thermal management has become increasingly prominent. It is necessary to propose a new aircraft energy management method to satisfy the needs of aircraft thermal management while maintaining high efficiency. This study addresses a compressed carbon dioxide energy storage system applied in aircraft energy management. Especially, this is the first time carbon dioxide has been used for aircraft energy storage. In this system, carbon dioxide can be used as a cold source to dissipate heat for the equipment. The system can also provide electrical energy for avionics, achieving combined cooling and power generation with a compact structure. Moreover, the energy storage unit increases the flexibility of the system. In this paper, the novel system is modeled and simulated. Results indicate that the system can operate stably to satisfy energy demand. The energy utilization efficiency of the system is 69% under design conditions. When the energy is stored first and then released, the exergy efficiency of the system can reach 81.66%, which has good economic benefits. The system is efficient, compact and flexible. It realizes the cascade utilization of energy while working, and simultaneously meets the energy requirements of cold, heat and electricity. [ABSTRACT FROM AUTHOR]

In dual-motor drive systems, a supercapacitor is connected to a common direct current (DC) bus through a DC/DC converter for the storage and utilization of regenerative energy, which is an effective energy saving method. However, the uncoordinated control of this type of system results in undesirable power circulation and reduced energy utilization efficiency. In this paper, an optimal power tracking control strategy based on a power flow predictive model is proposed. The power flow of the system is analyzed and a power flow predictive model is established. In addition, an objective function is deduced from the perspective of optimal performance tracking and minimum grid side energy consumption. The reference power of a supercapacitor is obtained in real time under constraints. The power flows among the grid side, the motors, and the energy storage unit are fully coordinated to realize a reasonable energy distribution. Experimental results indicate that the energy utilization efficiency of the system is improved by 25.4% in comparison with double closed-loop control in one working period. [ABSTRACT FROM AUTHOR]

In this paper, promising nanocomposite materials based on carbon and titanium are considered. It is shown that the use of a highly porous matrix is of particular interest. Materials based on such matrices have minimal weight and high strength characteristics. The paper also describes composites based on porous carbon fibers with metal oxides. The directions for producing composites can be divided into three types: matrix method, coating of finished nanoparticles with an inert shell, and the formation of nanoparticles and matrices in one process. The coating of nanoparticles with an inert shell prevents their oxidation and preserves the necessary magnetic properties. When using methods such as IR pyrolysis, arc evaporation third-party metal-carbon phases are formed that contaminate the resulting material. To avoid this, reducing agents are used, for example, hydrogen when coking nanoparticles in methane plasma current restores metal particles from its sol-gel and prevents them from reacting with carbon. However, with this method, it is difficult to control the particle size. Using a ready-made matrix allows us to control the size of the nanoparticles. However, this method uses high temperatures, and sometimes hydrogen, which complicates the production process. The main problem in the field of nanocomposites is the search for more technological, simple, cheap, and environmentally friendly methods for obtaining nanocomposites with high performance characteristics. The developed technology for forming the pore space of the initial carbon matrix does not have the above disadvantages. This technology has a simple, cheap, and environmentally friendly design; moreover high temperatures are not used in the process of producing nanocomposites and third-party metal-carbon phases are not formed. The resulting nanocomposite materials are used as electrodes for ultra-high-volume capacitor structures. When studying the capacitance and electrical characteristics of samples, it is found that the formation of metal on a porous carbon matrix can significantly reduce the internal resistance of the cell and increase the specific energy consumption. [ABSTRACT FROM AUTHOR]

Microgrid systems are becoming a very promising solution to meet the power demand growth especially in remote areas where diesel generators (DG) are commonly used as a main energy source. Photovoltaic (PV) systems are commonly used as a sustainable energy source to economize DG fuel. Due to the intermittent and fluctuating behavior of PV generators, energy storage systems (ESS) such as electrochemical battery are suggested. PV and ESS are usually connected using one inverter/charger called hybrid inverter. The power management is crucial to optimize the fuel consumption and operate efficiently ESS. Additionally, in an off-grid operation, the microgrid frequency becomes sensible due to the slow dynamic of DG which requires an additional control tool to improve the frequency regulation. This paper proposes a new power management based on Mamdani fuzzy logic. The proposed controller considers the targets mentioned above by only controlling the hybrid inverter. Simulation results prove that fuzzy-based controller reduces the DG fuel consumption by more than 12% compared to classical hysteresis management control. Moreover, the proposed controller performs efficiently regarding the conventional frequency regulation, which is widely used in microgrid control. [ABSTRACT FROM AUTHOR]

This paper analyzes the mode provisioning and scheduling, in light of the aggregation over distributed energy storage system for improving the interactions and energy trading decisions under the smart grid networks. Further a new smart power system equipped with energy storage devices yields efficiency and robustness in a novel structure, which can identify and react on the energy market equilibrium in a timely manner. An energy consumption and stochastic linear programming game in the distributed structure is proposed for the energy payments, so that scheduling for appliances and storage devices can be used here as well. Furthermore, it is easy to implement a proposed two-phase DSLPM (distributed stochastic linear programming management) algorithm to bring about optimality with both energy provider and users to approach payoff sharing under uncertainty. With the incomplete information, a price equilibrium scheme is proposed. Experimental results are shown to verify the consumed energy, payment, and convergence properties of the proposed models. [ABSTRACT FROM AUTHOR]

This paper develops a real-time and reliable data collection system for big scale emotional recognition systems. Based on the data sample set collected in the initialization stage and by considering the dynamic migration of emotional recognition data, we design an adaptive Kth average device clustering algorithm for migration perception. We define a sub-modulus weight function, which minimizes the sum of the weights of the subsets covered by a cover to achieve high-precision device positioning. Combining the energy of the data collection devices and the energy of the wireless emotional device, we balance the data collection efficiency and energy consumption, and define a minimum access number problem based on energy and storage space constraints. By designing an approximate algorithm to solve the approximate minimum Steiner point problem, the continuous collection of emotional recognition data and the connectivity of data acquisition devices are guaranteed under the energy constraint of wireless devices. We validate the proposed algorithms through simulation experiments using different emotional recognition systems and different data scale. Furthermore, we analyze the proposed algorithms in terms of topology for devices classification, location accuracy, and data collection efficiency by comparing with the Bayesian classifier-based expectation maximization algorithm, the background difference-based moving target detection arithmetic averaging algorithm, and the Hungarian algorithm for solving the assignment problem. [ABSTRACT FROM AUTHOR]

This work presents a new adaptive scheme for energy management in an independent microgrid. The proposed energy management system has been developed to manage the utilization of power among the hybrid resources and energy storage system in order to supply the load requirement based on multi-agent system (MAS) concept and predicted renewable powers and load powers. Auto regressive moving average models have been developed for predicting the wind speed, atmospheric temperature, irradiation, and connected loads. The structure proposed in this paper includes renewable sources as primary source and storage system as secondary source. A wind generator and solar PV array system together acts as primary source, which supplies power to the local load most of the time in this energy management strategy. When they fail to meet the load demand, the secondary source present in the system will assist the primary source and help to attain the goal of satisfying load demand without interruption. If the primary source and secondary source together are not able to meet the load demand then load shedding will be executed according to the priority set. Thus the developed MAS algorithm co-ordinates the hybrid system components and achieves energy management among renewable energy sources, storage units, and load under varying environmental conditions and varying loads. STATCOM based compensation has been implemented to balance the reactive power demand and to mitigate the voltage fluctuations and harmonics on the AC bus. The proposed microgrid has been simulated with MAS concept in Matlab/Simulink environment. The results presented in this paper show cases the effectiveness of the proposed energy management controller. [ABSTRACT FROM AUTHOR]

Mobile operators are deploying energy-harvesting heterogeneous networks due to their foreseen advantages such as self-sustainable capability and reduced operating expenditure, which cannot be offered by conventional grid powered communications. However, the used energy storage is subject to irreversible aging mechanisms, requiring intelligent management that considers both the energy cost and battery life cycle. In this paper, we propose a cognitive energy management strategy for small cell base stations powered by local renewable energy, a battery, and the smart grid to simultaneously minimize electricity expenditures of the mobile operators and enhance the life span of the storage device. Non-linear battery models and aging processes are considered to formulate the energy cost optimization problem. Simulation results in different configurations show that a degradation-aware policy significantly improves the battery lifetime, while achieving considerable cost savings. [ABSTRACT FROM AUTHOR]

Achieving high energy efficiency is becoming a very important challenge in designing future storage systems. This paper proposes to incorporate block correlations to perform energy-aware scheduling, thereby significantly improving energy efficiency of disk systems. Since the probability that an access to a file A will be followed by the same file (e.g. file B) that followed the last access to A is very high, we can infer the block correlations between files A and B. For example, block X of file A and block Y of file B may correlate with each other. We discover that energy-saving opportunities occur when the time interval between two accesses of block X and block Y is sufficiently large, and there are only a few sporadic requests distributed between these two accesses. Thus, when there is one access to block X, we can switch the disk drive to the low power state, properly arrange the sporadic requests, and transfer the disk back to the active state to serve the block access Y when saved energy is larger than energy penalty. Our experimental results demonstrate that the proposed energy-aware disk scheduler can strike a good balance between the energy saving, system performance, and reliability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel sensor scheduling scheme based on adaptive dynamic programming, which makes the sensor energy consumption and tracking error optimal over the system operational horizon for wireless sensor networks with solar energy harvesting. Neural network is used to model the solar energy harvesting. Kalman filter estimation technology is employed to predict the target location. A performance index function is established based on the energy consumption and tracking error. Critic network is developed to approximate the performance index function. The presented method is proven to be convergent. Numerical example shows the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Improving the thermal performance of building envelope is an important way to save building energy consumption. The phase change energy storage building envelope is helpful to effective use of renewable energy, reducing building operational energy consumption, increasing building thermal comfort, and reducing environment pollution and greenhouse gas emission. This paper presents the concept of ideal energy-saving building envelope, which is used to guide the building envelope material selection and thermal performance design. This paper reviews some available researches on phase change building material and phase change energy storage building envelope. At last, this paper presents some current problems needed further research. [ABSTRACT FROM AUTHOR]

In this paper, we present an experimental and theoretical investigation of the energy storage performance of a model solar pond with a surface area of 2.096 m and a depth of 2.0 m which is built in Cukurova University in Adana, Turkey. The system consist of three different densities zones (e.g., upper convective, non-convective and heat storage). During the months of a year, a data acquisition device was used to determine the temperature at various locations vertically within the pond. In the theoretical part, a mathematical model is used based on finite differences. For this system, the heat losses have been analyzed and modelled by using the Heat 2 software. We developed a performance model to determine the efficiencies of the solar pond. As a result, the total heat losses in side walls and inner insulation zones of the system are found. The experimental and the theoretical heat storage performance of the heat storage zone of the solar pond is determined the highest energy efficiency as 28.41 and 30.16 % for August, the lowest energy efficiency as 8.27 and 10.74 % for January, respectively. The results are compared and hence a good agreement is found. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s11051-021-05233-1 [ABSTRACT FROM AUTHOR]