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This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization (ACO) algorithm. Image steganography, a technique of embedding hidden information in digital photographs, should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect. The contemporary methods of steganography are at best a compromise between these two. In this paper, we present our approach, entitled Ant Colony Optimization (ACO)-Least Significant Bit (LSB), which attempts to optimize the capacity in steganographic embedding. The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte, both for integrity verification and the file checksum of the secret data. This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images. The ACO algorithm uses adaptive exploration to select some pixels, maximizing the capacity of data embedding while minimizing the degradation of visual quality. Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement. The levels of pheromone are modified to reinforce successful pixel choices. Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30% in the embedding capacity compared with traditional approaches; the average Peak Signal to Noise Ratio (PSNR) is 40.5 dB with a Structural Index Similarity (SSIM) of 0.98. The approach also demonstrates very high resistance to detection, cutting down the rate by 20%. Implemented in MATLAB R2023a, the model was tested against one thousand publicly available grayscale images, thus providing robust evidence of its effectiveness. [ABSTRACT FROM AUTHOR]

Pumped hydro storage (PHS) is the most mature storage technology with the largest installed capacity. However, its power regulation rate is slow, and its adjustment flexibility is relatively lacking. Electrochemical storage, which is currently the fastest developing storage equipment, provides capability of flexible power regulation. A photovoltaic-battery-pumped hybrid energy storage system is constructed in this paper. The goal is to improve the flexibility of PHS by utilizling the complementary characteristics of different storage facilities. The power regulation capability of the unit based on the power oscillation interval description of pumped storage is described, and further defines the flexibility of PHS according to the regulation of single unit and the unit-commitmen of multiple units. Considering the flexibility enhancing constraints and the demand to improve the operational lifespan of PHS, a capacity optimization strategy for the battery in hybrid energy storage system is established. A case study is conducted using a PHS in Shandong province as an example. The findings reveal that configuring an electrochemical energy storage system with a rated power equal to 10% of the PHS capacity can improve the flexibility of the storage system by about 40%, and reduce generation start-up times by 14%. However, under the current electricity pricing structure, the peak-valley price gap needs to surplus 0.5 yuan per kW·h for the electrochemical storage facility to become profitable.

Abstract How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh.

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Car parks are essential for every new development, but they often result in inefficient use of space. To save space, basement car parks are adopted. However, their capacity is limited by the building footprint and immovable vertical structural elements. Current research on basement car parks mainly focuses on ventilation under extreme situations such as fires. The impact of specific design factors unique to basement car parks on capacity has not been fully studied yet. Therefore, this paper investigates the impact of basement car park design factors on capacity by studying three design aspects: parking aisle network, building footprint, and column positions. A parking lot placement algorithm is proposed to help model and automate the car park design process. Parking lots are optimized using a genetic algorithm (GA). A series of impact analyses have been conducted for three selected design factors. Four design principles from prior research were used to analyze and compare the impact of design factors on parking lots. It was found that the proposed design optimization method can automate basement car park design and facilitate the impact analysis of design factors. Based on the impact analysis, the increase in parking lots for each layout is not strictly positively correlated with the perimeter of the parking aisle. The long rectangle building footprint can have a larger capacity compared with other shapes. Adjusting column positions can provide more space for parking lots and further optimize car park capacity. This research has practical implications for the field of architectural engineering, benefiting architects, engineers, and designers. Architects and designers working on basement car park projects can utilize the research findings to optimize the layout of parking spaces. By considering nonadjustable column positions and vertical obstructions, they can make more efficient use of available space, thereby increasing car park capacity. The systematic analysis of design factors, including parking aisle networks, building footprints, and column positions, empowers professionals to make informed decisions during the design phase. They can tailor car park designs to meet specific needs, while adhering to structural constraints. The introduction of a novel parking lot placement algorithm with optimization capabilities offers a practical tool for architects and designers. It streamlines the process of positioning parking lots to maximize capacity, particularly in basement car parks with fixed columns and obstacles. The research provides valuable guidance for basement car park design, enabling architects to apply design principles derived from surface and single-use multistory car park design to basement configurations. In practice, these applications result in more efficient and flexible car park designs, addressing the unique challenges of basement car parks, while optimizing their capacity and functionality. [ABSTRACT FROM AUTHOR]

How to effectively utilize renewable energy and improve the economic efficiency of microgrid system and its ability to consume renewable energy has become one of the main problems facing China at present. In response to this challenge, this paper establishes a multiobjective capacity optimization model with the minimum levelized cost of energy, the maximum proportion of renewable energy consumption, and the minimum comprehensive system cost. Based on this model, a new improved beluga whale optimization algorithm is proposed to solve the multiobjective optimization problem in the capacity allocation process of wind–solar–storage microgrid system with the goal of ensuring that the microgrid can meet the maximum load demand at different moments throughout the year. In this paper, opposition‐based learning, artificial bee colony, dynamic opposite, and beluga whale optimization are combined to improve the population diversity and convergence accuracy, thereby enhancing the optimization performance of the algorithm. Finally, after finding the optimal Pareto front solution, the Technique for Order Preference by Similarity to an Ideal Solution is used to help decision‐makers select the optimal solution. Using real load data and meteorological data, the results of this paper show that the multiobjective capacity allocation optimization method of grid‐connected scenic storage microgrid system based on the improved beluga whale optimization algorithm can improve the economics of the wind–solar–storage microgrid system and promote the photovoltaic consumption simultaneously, providing a solution for the realization of low‐carbon power and regional economic development. The best‐found levelized cost of energy for the wind–solar–storage microgrid system is 0.192 yuan/kWh. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low production efficiency and inability to fully unleash production capacity in current semiconductor enterprises, a production efficiency optimization model for semiconductor enterprises has been studied and constructed. This model transforms the problem of low production efficiency into a problem of locating and solving the decoupling point of enterprise customer orders, and comprehensively considers the situation of sudden changes in enterprise production orders when locating and solving the decoupling point of customer orders. Propose to use a three-layer coding (TLC) mechanism to improve and optimize the biogeographical optimization algorithm, and use the improved biogeographical optimization (IOBO) algorithm to solve the production efficiency optimization problem of semiconductor enterprises. The results show that the proposed IBBO-TCL algorithm has a fast convergence speed and the minimum root mean square error after convergence. And this method can accurately solve the decoupling point of customer orders for semiconductor enterprises. The method proposed in the study has effectively improved the production efficiency of semiconductor enterprises and has guiding significance for optimizing enterprise structure.

After the Tohoku earthquake and tsunami (Japan, 2011), regulatory efforts to mitigate external hazards have increased both the safety requirements and the total capital cost of nuclear power plants (NPPs). In these circumstances, identifying not only disaster robustness but also cost-effective capacity setting of NPPs has become one of the most important tasks for the nuclear power industry. A few studies have been performed to relocate the seismic capacity of NPPs, yet the effects of multiple hazards have not been accounted for in NPP capacity optimization. The major challenges in extending this problem to the multihazard dimension are (1) the high computational costs for both multihazard risk quantification and system-level optimization and (2) the lack of capital cost databases of NPPs. To resolve these issues, this paper proposes an effective method that identifies the optimal multihazard capacity of NPPs using a multi-objective genetic algorithm and the two-stage direct quantification of fault trees using Monte Carlo simulation method, called the two-stage DQFM. Also, a capacity-based indirect capital cost measure is proposed. Such a proposed method enables NPP to achieve safety and cost-effectiveness against multi-hazard simultaneously within the computationally efficient platform. The proposed multihazard capacity optimization framework is demonstrated and tested with an earthquake–tsunami example.

Utility-scale (>10 MW) Wind-Photovoltaic-Electrolysis-Battery (WPEB) system is an emerging technology that adopts open loop "Power-to-H 2 " architecture for large-scale green hydrogen production. It applies to curtailment reduction in the area with abundant wind and solar energy resources. The traditional residential-scale (0–1 MW) or commercial/facility-scale (1–10 MW) WPEB systems usually adopt the close loop "Power-to-H 2 -to-Power" structure for power supply in the microgrid. The capacity optimization of the commercial-scale, residential-scale WPEB system have been studied extensively, but the researches for utility-scale WPEB system are rare. This work proposes a multi-constraints single objective capacity optimization method for the utility-scale WPEB system. The optimization objective is to minimize the levelized cost of electricity (LCOE) and the constraints of power balance, annual grid sales rate (AGSR), wind/PV/electrolysis station capacity are considered. The feasible domain is identified by the 8760-h production simulation and the gradient descent method is applied to search the optimal solution. The main conclusions are summarized below: (1) Increasing the wind-to-generation capacity ratio can reduce the LCOE, required grid sales capacity, and net generation curve fluctuation. (2) The lower and upper limits of the electrolysis station capacity are determined by the AGSR constraint and the LCOE target. (3) The battery bank time-shifts electricity to reduce the downtime of the electrolysis station. Thus, its power capacity should be no less than the minimum load ratio of the electrolysis station and the discharge duration greater than 1 h is preferred. Overall, this work provides a capacity optimization roadmap for the utility-scale WPEB system which plays a significant role in renewable electricity-based hydrogen production and curtailment reduction. Schematic of wind-photovoltaic-electrolysis-battery (WPEB) hybrid energy system for power and hydrogen generation. [Display omitted] • WPEB system capacity optimization method is proposed. • The wind-solar complementary metric is used to form a typical meteorological year. • Increased wind power capacity reduces LCOE, grid sales capacity and net generation volatility. • The optimal case is W190–P10-E95-B30 with LCOE and LCOH of 0.2692¥/kWh, 14.1574¥/kg. • The IRR and ROI of W190–P10-E95-B30 are 12.60% and 78.97%. [ABSTRACT FROM AUTHOR]

The study presents a comprehensive investigation of solar thermal systems with varying capacities and Thermal Energy Storage (TES) durations in the existing fossil fuel-run Thermal Power Plant at Ar'Ar, Saudi Arabia. The main objective is to assess the feasibility, economic viability, and environmental impact of these systems for sustainable power generation. In pursuit of sustainable energy solutions, parabolic trough systems with capacities ranging from 10 MW to 50 MW and TES durations from 0 to 8 hours were analyzed. The evaluation includes thermal and electrical assessments, field performance evaluations, and detailed cost analysis for each configuration. Multi-Criteria Decision Making (MCDM) was utilized to identify the best TES for every Concentrated Solar Power (CSP) systen with the 4 hr TES ranking first among all capacities. The research uncovers significant positive correlations between system capacity and thermal and electrical output. The 50 MW system exhibits the highest thermal output of 280.899 MW and electrical output of 180580 MW. Incorporating 4 hr TES emerges as a critical factor in enhancing system performance, optimizing the cost of electricity, and achieving a payback period within 12 years. Furthermore, the integration of solar thermal energy demonstrates substantial reductions in fossil fuel consumption. Across all capacities, the 4-hour TES system yields considerable fuel savings, ranging from 18.84 tons/hour for the 10 MW system to 96 tons/hour for the 50 MW system. These reductions correspondingly translate to considerable cost savings, with the 50 MW system reducing fuel costs by $5760. Moreover, the study highlights the crucial environmental benefits of solar thermal systems, leading to substantial CO2 emission reduction, with the 50 MW system achieving a reduction of 93452.8 kg/hour. [ABSTRACT FROM AUTHOR]

In response to fluctuations in the power levels within the link connecting the direct current transmission system to the upper-level power grid, we propose an optimization approach for determining the ability of a Hybrid Energy Storage System (HESS). Our first step involves calculating the power agreement for the link and the total power requirements for the HESS, taking into account the system’s net load power and the dynamic pricing of electricity based on time-of-use. Subsequently, we utilize the Ensemble Empirical Mode Decomposition (EEMD) technique to break down the total energy generated by the HESS into its constituent components. Incorporating instructions for both powering up and losing power of the HESS across varying filter orders, we solve the capacity allocation model for the HESS using a particle swarm algorithm, with rated power and rated capacity as decision variables. Finally, we identify the filter order that yields the best objective function value and the corresponding energy storage configuration scheme. An illustrative analysis, conducted on an enhanced IEEE-14 node DC distribution network, demonstrates that the proposed HESS configuration effectively mitigates tie-line fluctuations and enhances system stability.

In the microgrid, the battery energy storage system (BESS) is used in combination with renewable energy to solve the problem of renewable energy accommodation. Meanwhile, power sources with the BESS can participate in valley filling and reduce electric generation costs. However, the BESS cannot contribute sufficient capability to the microgrid and reduce costs due to its limited capabilities. To tackle this challenge, the output power model of the combined cooling, heating and power microgrid (CCHPM) with the BESS is proposed in this paper. The model takes the micro-gas turbine, the photovoltaic unit, the wind turbine, the electric refrigerator and the BESS as a whole CCHPM. Meanwhile, a capacity optimization method of the BESS is developed to select the appropriate capacity and promote renewable energy accommodation. The minimum cost of the objective function is established. The electric power balance of the CCHPM, charge state constraints of the BESS, capacity constraints and ramp-up/down constraints of the units are derived to optimize the output power of units and the capacity of the BESS. The effectiveness of the proposed method is verified by the system simulation of YALMIP and GUROBI. Compared with the traditional method, the total cost of the microgrid is decreased by 1.4%, and the microgrid’s renewable energy accommodation is enhanced by 12.2%. This capacity optimization method can enhance the applicability of BESS in CCHPM.

Energy storage devices are frequently included to stabilize the fluctuation of offshore wind power’s output power in order to lessen the effect of intermittency and fluctuation on the electrical grid but doing so will raise operators’ investment costs. To obtain the best economic benefits, this paper presents a hybrid energy storage system based on batteries and super-capacitors and its capacity configuration optimization method. First, the wind power output is divided using the wavelet packet decomposition method, and then power is distributed between the batteries and the super-capacitors. Then, the mathematical model of energy storage system optimization is established to optimize the capacity configuration of hybrid energy storage with the objective of minimizing the daily input cost of energy storage, and the configuration schemes of single energy storage and hybrid energy storage are compared. Finally, using the measured data of a domestic offshore wind farm for simulation, several energy storage schemes are compared to verify the feasibility and effectiveness of the proposed method, at the same time, using the hybrid energy storage devices to participate in the grid-connected stabilization task of offshore wind power is more advantageous than the single energy storage, the results show that the daily input cost of the hybrid energy storage scheme is 2.79% and 3.84% lower than that of the single energy storage scheme using only battery or super-capacitor.

In this study, we propose an efficient algorithm for the optimization of pilot assignment to reduce pilot contamination in multi-cell multi-user massive multiple-input multiple-output (MIMO) systems. The object function of the proposed optimization approach is based on the cumulative average uplink Signal to Interference plus Noise Ratio (SINR) values. This method effectively optimizes pilot-user matchings using an efficient search which wisely reduces both search spaces of users and pilots. It dynamically calculates search space reductions after updating each cell iteratively. This reduction is performed by selecting only the users whose SINR value is below the average SINR of the each cell. On the other hand, the pilot's search space is reduced by choosing only the least-used pilots within close neighboring cells' pilot usages. This calculation is performed for each cell to increase the cumulative average SINR (global SINR) value. Pilot contamination is reduced to minimum levels with less complexity and memory requirements with the proposed method in this manner. The simulation results indicate that the proposed method outperforms the recent popular analytical and deep learning-based pilot assignment approaches. The achieved improvement is even more prominent when all the pilots are fully used. [ABSTRACT FROM AUTHOR]

The installed capacity of energy storage in China has increased dramatically due to the national power system reform and the integration of large scale renewable energy with other sources. To support the construction of large-scale energy bases and optimizes the performance of thermal power plants, the research on the corporation mode between energy storage and thermal energy, including the optimization of energy-storage capacity and its operation in large-scale clean energy bases. In this paper, a large-scale clean energy base system is modeled with EBSILON and a capacity calculation method is established by minimizing the investment cost and energy storage capacity of the power system and constraints such as power balance, SOC, and power fluctuations. The research proposed a method of using coupled system of thermal energy storage systems primarily based on molten salt thermal storage and thermal power generation for rough modulation and using battery energy storage system for fine modulation tasks. Example of fine modulation includes frequency modulation and heating demand of the district, which significantly reduces the energy storage investment by more than 95%. A case study of a 10 MW clean energy base is conducted. The result shows that the overall pre-tax internal rate of return of the base project is 8%, which has good economic benefits. [ABSTRACT FROM AUTHOR]

Wind power generations are widely used in power system as a type of clean renewable energy. However, the random fluctuation of wind power output has a negative effect on the power quality of power system. The energy storage system has been applied to suppress the voltage and power fluctuation by wind power access to power system because of its flexible bidirectional regulation ability. In this paper, a multi-objective energy storage optimal allocation model is established based on system voltage deviation, daily active power loss and total energy storage access capacity as the objective function. A beetle antennae search-improved multi-objective particle swarm optimization (BAS-IMOPSO) algorithm which is formed by combining BAS and IMOPSO is used to solve the model, and the optimal energy storage allocation scheme is selected in Pareto solution set by using the technique of order preference by similarity to ideal solution based on information entropy. The proposed model has been applied to IEEE 33-node distribution system. The simulation results show that the energy storage system optimized by BAS-IMOPSO algorithm has significantly higher ability to reduce voltage deviation and active power loss of distribution network than MOPSO algorithm does, and it can effectively improve the power quality with the ability of peak load shifting. The model shows the effectiveness of the proposed BAS-IMOPSO algorithm.

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario. [ABSTRACT FROM AUTHOR]

This paper proposes a microgrid (MG) system integrating distributed renewable energy (RE) and hybrid energy storage system (HESS), which is an effective solution of power supply for remote areas and islands. The distributed generation (DG) unit of the system includes photovoltaic (PV) power and wind power, and the HESS includes lithium-ion (Li-ion) battery, super capacitor (SC) and compressed air energy storage (CAES). In this study, the Confidence Interval Method (CIM) is used to establish the wind power output and PV power output model, and the DG power curves under different confidence levels are calculated. Taking the minimum annual total cost of the system as the optimization objective, the capacity allocation of the wind-PV-Li-ion battery system is optimized by using the genetic algorithm (GA) tool. Based on the quadratic moving average filtering method, the energy storage power is divided into different frequencies, and the rated power, rated capacity and initial state of charge (SOC) of three kinds of ESSs are calculated. The results show that the wind-PV-HESS can better meet the power demand of users with a good economic performance.

In order to achieve the carbon peak and neutrality targets and to build a new low-carbon energy system, a two-stage optimization strategy was adopted to optimize the capacity configuration of the power supply system of a large industrial park in Northern China. Two optimization objectives were set the maximum consumption rate of new energy generation and the minimum levelized cost of energy (LCOE) for the system. Firstly, the structure model of the integrated source-grid-load-storage system in the park was established, and the first-level optimization was to cap wind and solar abandonment rate at 5% . Secondly, the output results of the first-level optimization were used as the initial values of HOMER software to carry out the second-level optimization, using the lowest LCOE as the objective function. Lastly, the second-level optimization results were analyzed to obtain the best power system configuration in terms of best economic aspect, lowest carbon emission, and highest power supply reliability. The optimization results demonstrate that using HOMER’s enumeration method, the second-level optimization also takes real engineering problems such as land use and transformer capacity into consideration, hence a more practical optimization than the direct HOMER optimization method. The results of the study provide an optimized source-grid-load-storage system for the industrial park, serving as a case study for future planning of such systems for other industrial parks. [ABSTRACT FROM AUTHOR]

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Green hydrogen generation driven by solar-wind hybrid power is a key strategy for obtaining the low-carbon energy, while by considering the fluctuation natures of solar-wind energy resource, the system capacity configuration of power generation, hydrogen production and essential storage devices need to be comprehensively optimized. In this work, a solar-wind hybrid green hydrogen production system is developed by combining the hydrogen storage equipment with the power grid, the coordinated operation strategy of solar-wind hybrid hydrogen production is proposed, furthermore, the NSGA-III algorithm is used to optimize the system capacity configuration with the comprehensive performance criteria of economy, environment and energy efficiency. Through the implemented case study with the hydrogen production capacity of 20,000 tons/year, the abandoned energy power rate will be reduced to 3.32% with the electrolytic cell average load factor of 64.77%, and the system achieves the remarkable carbon emission reduction. In addition, with the advantage of connect to the power grid, the generated surplus solar/wind power can be readily transmitted with addition income, when the sale price of produced hydrogen is suggested to 27.80 CNY/kgH2, the internal rate of return of the system reaches to 8% which present the reasonable economic potential. The research provides technical and methodological suggestions and guidance for the development of solar-wind hybrid hydrogen production schemes with favorable comprehensive performance.

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because the BESS has a limited lifespan and is the most expensive component in a microgrid, frequent replacement significantly increases a project's operating costs. This paper proposes a capacity optimization method as well as a cost analysis that takes the BESS lifetime into account. The weighted Wh throughput method is used in this paper to estimate the BESS lifetime. Furthermore, the well-known Particle Swarm Optimization (PSO) algorithm is employed to maximize battery capacity while minimizing the total net present value. According to simulation results, the optimal adjusting factor of 1.761 yields the lowest total net present value of US$200,653. The optimal capacity of the BESS can significantly reduce the net present value of total operation costs throughout the project by extending its lifetime. When applied to larger power systems, the proposed strategy can further reduce total costs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) and wind power generation are very promising renewable energy sources, reasonable capacity allocation of PV–wind complementary energy storage (ES) power generation system can improve the economy and reliability of system operation. In this paper, the goal is to ensure the power supply of the system and reduce the operation cost. The PV, wind and ES system models are analyzed. The differential evolutionary (DE) algorithm is adopted to optimize the particle swarm optimization (PSO) algorithm, and the parameters of the PSO algorithm are changed through the DE algorithm to obtain better performance. We use MATLAB to verify that when the system is composed of 100 kW PV and 100 kW wind power, the battery capacity obtained by PSO algorithm is 400 kWh, while the algorithm proposed in this paper only requires 330 kWh. Although the loss of load probability of the system is improved by about 0.12%, the cost is saved by 17.5%. To improve the system operation reliability, we recommend increasing PV, wind and ES capacity at the same time rather than increasing ES capacity separately. [ABSTRACT FROM AUTHOR]

Under the background of carbon neutrality, the traditional coal chemical industry needs to upgrade the industrial park to reduce the carbon emission in the production process. Therefore, this paper studies a method to transform the coal chemical industry park by using renewable energy to produce hydrogen, and proposes an energy management scheme coupled with renewable energy to produce hydrogen in the coal chemical industry park. Firstly, based on time of use electricity price, renewable energy generation and load, an energy management strategy aiming at maximizing daily income is proposed to obtain the optimal energy management scheme with the highest daily income. Then, based on this strategy, the annual revenue maximization optimization model considering the investment and operating costs of photovoltaic, hydrogen production, hydrogen storage and electricity storage is established, and the optimal capacity configuration of different equipment in the system is obtained. The simulation model is established on the MATLAB-YALMIP platform, and GUROBI is used to solve the optimization model. The proposed scheme can save about 76,800 tons of standard coal and reduce carbon emissions by 181,247.95 tons per year. In addition, a system that only considers photovoltaic and electricity storage coupling is also established as a comparison, and the net profit of the system under this scheme is reduced by 36.47 % compared with the proposed PV-storage-hydrogen system, which highlights the economic advantages of the proposed photovoltaic, electricity storage and green hydrogen coupling system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Strategy-based optimization for a renewable hydrogen system operating 8,760 h. • Economic, energy and environmental performance analysis on hydrogen production. • A comprehensive life cycle assessment of PV power to hydrogen systems. • LCOH of ultra-light grid-connect scenario is 31% lower than the off-grid scenario. • GWP of ultra-light grid-connect scenario is 12% lower than the off-grid scenario. With the growing global emphasis on reducing greenhouse gas emissions and transitioning to a low-carbon economy, renewable hydrogen is expected to play a vital role in the chemical industry. However, there is a contradiction between the continuous and stable hydrogen demand of chemical process systems and the intermittent and fluctuating nature of renewable energy systems when coupling them together in the industrial chemical production. There are currently few studies addressing year-round stable hydrogen load systems while considering renewable energy intermittency and capacity optimization. Moreover, the comprehensive life cycle environmental impact study of such stable renewable-hydrogen production system is extremely limited. Therefore, this study focuses on a photovoltaic power-to-hydrogen system that can operate stably for 8,760 h per year, specifically targeting its application in the chemical industry. Two operating strategies of the hydrogen system were considered: fully off-grid and ultra-light grid-connected scenarios. The capacity configuration under each scenario was optimized using gird search algorithm, with the objective of minimizing the levelized cost of hydrogen. Based on the optimization results, the energy flow network, cost-effectiveness, and comprehensive life cycle environmental impacts of such system were analyzed. The results show that the ultra-light grid-connected hydrogen production system, due to the support of the power grid, has improved the reliability of the hydrogen production system and significantly reduced the capacity configuration of system components, thereby demonstrating better performance in terms of economics and overall lifecycle environmental impact compared to a completely off-grid hydrogen production system. Its global warming potential (GWP) and levelized cost of hydrogen under electricity sales scenario are 12% and 31% lower, respectively, compared to fully off-grid hydrogen production. Considering China's future demand for large-scale renewable hydrogen, ultra-light grid-connected hydrogen production may become the primary transitional pathway in the near to medium term. [ABSTRACT FROM AUTHOR]

Summary: Integrated energy system (IES) can improve energy efficiency, reasonable configuration of low‐carbon equipment in IES can effectively reduce carbon emissions and achieve carbon peaking and carbon neutrality. Under the background of carbon peaking and carbon neutrality, this paper proposes a configuration method of low‐carbon equipment in IES considering life cycle carbon emissions and ladder‐type carbon trading cost. Firstly, the IES architecture of animation and cartoon industry parks in Sino‐Singapore Tianjin eco‐city is analyzed, and the models of "electrical/heating/cooling" energy supply equipment and integrated energy storage equipment are built. Secondly, the two‐stage operation of power to gas (P2G) is refined, and P2G is subdivided into equipment combination of electrolyzer, methanation reactor, hydrogen fuel cell to study the various benefits of hydrogen energy. Then, the life cycle carbon emission calculation method of renewable energy power generation and P2G equipment is proposed, the life cycle carbon emission model of IES is established, and the ladder‐type carbon trading mechanism is introduced to restrict IES carbon emission. On this basis, aiming at minimizing carbon emission cost, energy purchased cost, equipment investment, operation and maintenance cost, and renewable energy power generation curtailment cost, a joint optimal configuration model of renewable energy power generation and P2G is established in order to realize the low‐carbon economic operation of IES. Finally, through multi‐scenario comparative analysis, the correctness and effectiveness of the optimal configuration method of low‐carbon equipment in IES are verified, and the influence of carbon trading mechanism and refining two‐stage operation of P2G on the optimal configuration results are discussed. [ABSTRACT FROM AUTHOR]

The Integrated Energy System fulfilled coordinated planning, optimized operation, coordinated management, which can improve energy efficiency and reduce energy costs. A multi-level planning optimization model with integrated energy system scenario reasoning, equipment selection, and capacity optimization is proposed. The minimum life cycle cost of integrated energy system is taken as the objective function, and the constraints of scenario reasoning, equipment selection and capacity optimization are also taken into account, including the similarity between planning scenarios and historical scenarios, equipment model matching and mutual exclusion, and operational constraints. In addition, an improved particle colony-ant colony optimization algorithm is used to solve the model. The K-proximity reasoning method is constructed based on multiple data types for reasoning of the equipment scenario in the park. The optimized results are transferred to the case library to further enrich the planning scenario case library. Finally, a park is taken as an example to analyze the impact of equipment-type changes on the planning results and provides important suggestions for the park construction. [ABSTRACT FROM AUTHOR]

Full-duplex (FD) communication has been attractive as the breakthrough technology for improving attainable spectral efficiency since the 5G mobile communication system. Previous research focused on self-interference cancellation and medium access control (MAC) protocol to realize the FD system in wireless networks. This paper proposes an optimal achievable transmission capacity (OATC) scheme for capacity optimization in the FD multihop wireless networks. In this paper, the proposed OATC scheme considers the temporal reuse for spectral efficiency and the spatial reuse with transmit power control scheme for interference mitigation and capacity optimization. OATC scheme controls the transmit power to mitigate interference and optimizes the transmission capacity, which leads to the optimal achievable network capacity. We conduct the performance evaluation through numerical simulations and compare it with the existing FD MAC protocols. The numerical simulations reveal that considering only the concurrent transmissions in the FD system does not guarantee optimal transmission capacity. Moreover, the hybrid mechanism, including the sequential transmissions, is also crucial because of the interference problem. Besides, numerical simulation validates that the proposed OATC scheme accomplishes the optimal achievable network capacity with lower interference power and higher achievable throughput than the existing MAC protocols. [ABSTRACT FROM AUTHOR]

In order to improve the power grid stroke permeability, a hybrid energy storage system consisting of batteries, supercapacitors and pumped storage has proposed to build a two-layer capacity optimization model to alleviate the instability of wind power grid connection. Firstly, the extraction schemes of typical wind power scenarios and extreme wind power scenarios are proposed to obtain the set of scenarios that fully approximate the original wind power output scenarios. Secondly, the wavelet packet analysis method is used to configure the capacity of batteries and supercapacitors to suppress the wind power fluctuation, and the pumped storage is used to cut peak and fill valley in the power system, and the two-layer hybrid energy storage capacity optimization model is established to optimize the energy storage capacity. Finally, the improved RTS-96 system is simulated and analyzed using the output power data of a wind farm for one year. The calculation results show that the capacity optimization model of two-layer hybrid energy storage increases the wind power permeability under the condition of ensuring the economy. [ABSTRACT FROM AUTHOR]

This article determines the optimal capacities of small wind turbine (SWT) and battery energy storage (BES) for a grid-connected household (GCH) with or without an electric vehicle (EV) to minimize the overall cost of electricity (COE). Rule-based home energy management systems (HEMSs) are developed for two different configurations of the GCH: 1) with only SWT and 2) with SWT and BES. For each configuration, the HEMSs are developed for two cases: with and without an EV in the premises of the GCH. Uncertainties are also included in the arrival time, departure time, and initial state of charge (at arrival) of the EV. The abovementioned technique is then applied to a typical household in South Australia using the yearly load profile of the household and actual yearly wind speed data at an interval of one hour. To investigate the effects of stochastic nature of household load, EV, and wind power generation on various results, the optimization process is repeated using 10-year of actual wind speed data and probabilistic load and EV uncertainties. The results of several sensitivity analyses of various system parameters are presented. It has been found that the SWT can effectively decrease the COE of the household for both cases (with and without an EV). However, the current price of battery may not be in favor of further reducing the COE of the household. [ABSTRACT FROM AUTHOR]

This article proposes a novel capacity optimization configuration method of battery energy storage system (BESS) considering the rate characteristics in primary frequency regulation to improve the power system frequency regulation capability and performance. By analyzing the charge or discharge rate characteristics of BESS, combined with the equivalent conversion method of the action time at different rates, the capacity optimization configuration method of BESS for frequency regulation considering the rate characteristic is proposed. Taking typical peak load working condition of a certain power grid as an example, the capacity configuration of three batteries (BESS 1, BESS 2 and BESS 3) considering the rate characteristics under different control methods is compared and analyzed. The results show that rate characteristics take most advantages of the corresponding control method and the high rate battery, and BESS requires a much lower capacity configuration for the same effect in grid frequency regulation. This indicates that the proposed optimal configuration method for BESS considering rate characteristics in frequency regulation can effectively reduce the configuration capacity, and this helps to realize the economic potential of BESS.

Hybrid energy system is one main mean to deal with the energy crisis and its capacity optimization is a key to obtain an economical and reliable supply of energy. In this paper, the capacity optimization of a novel hybrid system composed of wind turbine, concentrated solar plant and electric heater is modeled as a multi-objective two-stage stochastic problem, where capacity optimization minimizing the life cycle cost (LCC) and energy management strategy optimization minimizing the loss of power supply probability (LPSP) are integrated. The scenario-based approach is applied to reflect the random characteristics of wind and solar resources. The Pareto set of the problem is obtained by non-dominated sorting genetic algorithm (NSGA-II), whose effectiveness is validated by algorithm comparisons with multi-objective particle swarm optimization (MOPSO) and multi-objective evolutionary algorithm based on decomposition (MOEA/D). Furthermore, techno-economic comparisons with a reference hybrid energy system without electric heater are performed to investigate economic benefits of the electric heater. The comparison results show that the electric heater is beneficial for a lower LCC which is reduced by 1.21%, 1.34%, 1.68%, 3.14%, 4.94% and 4.55% respectively when the LPSP is 0%, 1%, 2%, 3%, 4% and 5%.

In view of the remote areas with abundant wind/photovoltaic（PV） resources, the capacity allocation model and coordinated operation strategy of wind-solar storage independent microgrid were proposed. Firstly, a mathematical model of distributed power was established, considering the loss of power supply probability（LPSP） and energy exceed rate（EXR）, the total cost of microgrid allocation was taken as the objective function which was calculated by improved particle swarm algorithm, and then the battery allocation result obtained by the first optimize phase; Secondly, the result from first phase was used as the input of the second optimization phase, and virtual energy storage based on electric springs（ES） was added. With the lowest total cost of the energy storage system as the objective function to calculate the number of batteries equivalent to the virtual energy storage, the number of battery configurations was reduced, and the final allocation result was gotten. Finally, the feasibility and economy of the method were demonstrated through a calculation example, which can provide a reference for the capacity allocation of the microgrid system. [ABSTRACT FROM AUTHOR]

For the fabrication of high‐energy and high‐power all‐solid‐state batteries (ASSBs), easily synthesizable solid electrolytes are needed, which enable fast ion transport inside the composite cathode as well as good contacts between cathode active material and solid electrolyte particles. Regarding the latter, the size ratio of the particles inside the composite cathode has to be optimized. Here, we use a wet ball milling process for the synthesis of agyrodite‐type Li5.5PS4.5Cl1.5 solid electrolyte particles and study the influence of milling time on particle size and ionic conductivity. With longer milling time, both the solid electrolyte particle size and the ionic conductivity decrease, which exert an opposing influence on the cathode performance. We show that a milling time of approximately 2 h leads to an optimum cathode performance, as this time is sufficient for a favorable particle size ratio, while a strong drop of the ionic conductivity of Li5.5PS4.5Cl1.5 is avoided. [ABSTRACT FROM AUTHOR]

In order to reduce the comprehensive power cost of the independent microgrid and to improve environmental protection and power supply reliability, a two-layer power capacity optimization model of a microgrid with electric vehicles (EVs) was established that considered uncertainty and demand response. Based on the load and energy storage characteristics of electric vehicles, the classification of electric vehicles was proposed, and their mathematical models were established. The idea of robust optimization was adopted to construct the uncertain scenario set. Considering the incentive demand response, a two-layer power capacity optimization model of a microgrid was constructed. The improved pelican optimization algorithm (IPOA) was proposed as the two-layer model. In view of the slow convergence rate of the pelican optimization algorithm (POA) and its tendency to fall into the local optimum, methods such as elite reverse learning were proposed to generate the initial population, set disturbance inhibitors, and introduce Lévy flight to improve the initial population of the algorithm and enhance its global search ability. Finally, an independent microgrid was used as an example to verify the effectiveness of the proposed model and the improved algorithm. Considering that the total power capacity optimization cost of the microgrid after addition of electric vehicles was reduced by CNY 139,600, the total power capacity optimization cost of the microgrid after IOPA optimization was reduced by CNY 49,600 compared with that after POA optimization.

We consider the problem of optimal fleet sizing in a vehicle sharing system. Vehicles are available for short-term rental and are accessible from multiple locations. A vehicle rented at one location can be returned to any other location. The size of the fleet must account not only for the nominal load and for the randomness in demand and rental duration but also for the randomness in the number of vehicles that are available at each location because of vehicle roaming (vehicles not returning to the same location from which they were picked up). We model the dynamics of the system using a closed queueing network and obtain explicit and closed form lower and upper bounds on the optimal number of vehicles (the minimum number of vehicles needed to meet a target service level). Specifically, we show that starting with any pair of lower and upper bounds, we can always obtain another pair of lower and upper bounds with gaps between the lower and upper bounds that are independent of demand and bounded by a function that depends only on the prescribed service level. We show that the generated bounds are asymptotically exact under several regimes. We use features of the bounds to construct a simple and closed form approximation that we show to be always within the generated lower and upper bounds and is exact under the asymptotic regimes considered. Extensive numerical experiments show that the approximate and exact values are nearly indistinguishable for a wide range of parameter values. The approximation is highly interpretable with buffer capacity expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times, (2) buffer capacity that is protection against vehicle roaming, and (3) a correction term. Our analysis reveals important differences between the optimal sizing of standard queueing systems (where servers always return to the same queue upon service completion) and that of systems where servers, upon service completion, randomly join any one of the queues in the system. We show that the additional capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. This paper was accepted by Baris Ata, stochastic models and simulation. [ABSTRACT FROM AUTHOR]

In standalone microgrids, the Battery Energy Storage System (BESS) is a popular energy storage technology. Because of renewable energy generation sources such as PV and Wind Turbine (WT), the output power of a microgrid varies greatly, which can reduce the BESS lifetime. Because the BESS has a limited lifespan and is the most expensive component in a microgrid, frequent replacement significantly increases a project’s operating costs. This paper proposes a capacity optimization method as well as a cost analysis that takes the BESS lifetime into account. The weighted Wh throughput method is used in this paper to estimate the BESS lifetime. Furthermore, the well-known Particle Swarm Optimization (PSO) algorithm is employed to maximize battery capacity while minimizing the total net present value. According to simulation results, the optimal adjusting factor of 1.761 yields the lowest total net present value of US$200,653. The optimal capacity of the BESS can significantly reduce the net present value of total operation costs throughout the project by extending its lifetime. When applied to larger power systems, the proposed strategy can further reduce total costs.

This paper develops a novel demand side management (DSM) approach to incorporate in optimal sizing of solar photovoltaic (PV), wind turbine (WT), and battery storage (BS) for a standalone household. The DSM strategy is based on the state-of-charge level of battery and day-ahead forecasts of solar insolation and wind speed. The core of the DSM is a fuzzy logic method which decides for efficient load shifting and/or load curtailment. The day-ahead forecasting errors, obtained by an artificial neural network technique, are considered not only in the DSM strategy but also in maintaining an operating reserve. The battery capacity degradation is calculated using the Rainflow counting algorithm to obtain a realistic battery model and estimate its lifetime. A typical household in South Australia (SA) is considered as a case study. Three different configurations (PV-BS, WT-BS, and PV-WT-BS) of the electricity supply system are optimized using the proposed method. It is found that the PV-WT-BS system is the best configuration that provides the lowest cost of electricity for both with and without applying the proposed DSM strategy. Comparison of the results of the best system configuration with an actual system in SA and two recently published articles indicates that the proposed method is very effective in lowering the electricity cost with zero-emission. [ABSTRACT FROM AUTHOR]