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Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. [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]

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]

Bi0.5Na0.5TiO3 (BNT) is a lead-free ferroelectric ceramic that has received much attention in recent years. However, the pure BNT presents a tetragonal structure with considerable remanent polarization at room temperature, which lead to its low energy storage efficiency thus limiting its application in energy storage. In this paper, on the basis of the introduction of Ba0.3Sr0.7TiO3 (BST), NaNbO3 (NN) into BNT to form a relaxor ferroelectric, Gd2O3 is further doped to refine grain and reduce oxygen vacancy concentration thereby improving the breakdown strength, which is beneficial to improve its energy storage performance. A series of ceramic samples of 0.9 (0.5BNT-0.5BST)-0.1NN + xwt%Gd (x = 0, 0.5, 1, 2.5, 3) were prepared by the solid-state reaction method. The effect of Gd2O3 on the microstructure and electrical properties of the systems was investigated. The results show that a suitable doping level of Gd2O3 can significantly refine the grains and improve the breakdown strength of the system. For x = 2.5 ceramic sample obtained a high energy storage density of Wrec = 4.86 J/cm3 and an energy storage efficiency of η = 86% at 250 kV/cm. Furthermore, this sample exhibited a stable discharge density (Wd ≥ 1.44 J/cm3) and an ultra-fast discharge rate (t0.9 ≤ 0.056 µs) in the temperature range of 140 kV/cm, 40∼140 °C. [ABSTRACT FROM AUTHOR]

As a new type of energy storage, shared energy storage (SES) can help promote the consumption of renewable energy and reduce the energy cost of users. To this end, an optimization clearing strategy for a multi-region electricity-heat joint market is proposed with consideration of SES and integrated demand response (DR). Firstly, the concept of shared energy storage station (SESS) is proposed, its business operation model is analyzed and its advantages over traditional energy storage are compared. Secondly, to exploit the potential for user flexibility, an integrated DR model that includes shiftable, transferable, interruptible electricity and heat load is constructed. Then, a multi-region electricity-heat joint market clearing model is constructed considering SESS and integrated DR. Finally, a three-region electricity-heat joint market connecting the external power grid, gas grid, and the SESS is used as an example. A comparison is made between the configuration of independent energy storage in each region and the configuration of SESS, which concludes that the introduction of the SESS and integrated DR can reduce the energy cost of users and improve the utilization rate of the energy storage facilities. [ABSTRACT FROM AUTHOR]

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In order to improve energy efficiency, reduce dependence on fossil fuels, and enhance the sustainable development capability of the power system, this paper proposes a virtual grid and thermal power joint peak shaving optimization model based on the power Internet of Things. Establish an objective function to optimize the charging and discharging loss cost of energy storage equipment, the interruption compensation cost of interruptible loads, and the operating cost of thermal power units, in order to improve the optimization scheduling effect of the power system. Set power balance constraints, interruptible load constraints, energy storage constraints, and power constraints for thermal power units to maximize their operational efficiency, and construct a virtual grid and thermal power joint peak shaving optimization model. Using ant colony algorithm to solve the model and obtain the optimal peak shaving value that meets the development needs of the power system. The simulation results show that the mean r2 of our method is 0.97, and the average RMSE is 0.17. It has been proven that the model has good joint shaving optimization ability and high optimization accuracy. It can ensure the output of thermal power units and lower the peak shaving pressure of the power system, thereby promoting the sustainable development and stable operation of the power system. [ABSTRACT FROM AUTHOR]

Thermal-integrated pumped thermal electricity storage (TI-PTES) could realize efficient energy storage for fluctuating and intermittent renewable energy. However, the boundary conditions of TI-PTES may frequently change with the variation of times and seasons, which causes a tremendous deterioration to the operating performance. To realize efficient and flexible energy storage in operating conditions, a novel composition-adjustable TI-PTES is proposed, and the operating performance is investigated and compared with composition-fixed TI-PTES. Simulation results show that, compared to composition-fixed TI-PTES, the energy storage efficiency of TI-PTES could be enhanced by the absolute value of 4.4–18.3% by introducing composition adjustment method under various boundary conditions. Besides, tuning sub-system composition could simultaneously adjust the capacities of power input, heat storage and power output, realizing a more flexible operating range for TI-PTES. A case study for an isolated energy community shows that composition-adjustable TI-PTES could realize 100% conversion of off-peak electric energy and reduce daily investment by 35.6% compared with composition-fixed TI-PTES. [ABSTRACT FROM AUTHOR]

The surge in energy storage systems and the increasing involvement of demand-side participation can be attributed to their favorable characteristics, including their seamless integration into electrical networks and their capacity to offer operational flexibility during critical periods. This scholarly article focuses on enhancing energy utilization in an autonomous electrical grid by incorporating hydrogen storage and demand-side participation. The optimization of the stand-alone electrical grid is based on maximizing efficiency and minimizing energy consumption costs as the main objective functions are modeled. The modeling efficiency is formulated considering the ratio of the energy not supplied (ENS) to energy generation by resources. And costs of energy consumption are modeled as consumption of fuel costs by resources. The consumers' participation is proposed based on an incentive approach to consumers for demand shaving in peak times. Also, the hydrogen storage system is installed in the stand-alone electrical grid to improve the main objectives. The particle swarm optimization (PSO) algorithm for energy optimization and solving objective functions is applied. In the end, numerical simulation is carried out in some case studies to confirm and supremacy of energy optimization with the participation of the hydrogen storage system and consumers. The case studies based on non-participation and participation of the storage system and consumers in energy management are implemented. The implementation of case studies examines the impact of both non-participation and participation of the storage system and consumers in energy management. The findings reveal that when the storage system and consumers actively participate, there is a significant improvement in efficiency and a substantial reduction in energy costs. Specifically, the efficiency is enhanced by 3% and the energy costs are reduced by 29.5% compared to the scenario where they do not participate. [ABSTRACT FROM AUTHOR]

Volatile electrical energy prices are a challenge and an opportunity for small and medium-sized companies in energy-intensive industries. By using electrical energy storage and/or an adaptation of production processes, companies can significantly profit from time-depending energy prices and reduce their energy costs. We consider a time-discrete optimal control problem to reach a desired final state of the energy storage at a certain time step. Thereby, the energy input is discrete since only multiples of 100 kWh can be purchased at the EPEX SPOT market. We use available price estimates to minimize the total energy cost by a rounding based dynamic programming approach. With our model non-linear energy loss functions of the storage can be considered and we obtain a significant speed-up compared to the integer (linear) programming formulation. [ABSTRACT FROM AUTHOR]