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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]

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]

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]

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]

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]

Although a state-of-the-art performance has been achieved in pixel-specific tasks, such as saliency prediction and depth estimation, convolutional neural networks (CNNs) still perform unsatisfactorily in human parsing where semantic information of detailed regions needs to be perceived under the influences of variations in viewpoints, poses, and occlusions. In this paper, we propose to improve the robustness of human parsing modules by introducing a depth-estimation module. A novel scheme is proposed for the integration of a depth-estimation module and a human-parsing module. The robustness of the overall model is improved with the automatically obtained depth labels. As another major concern, the computational efficiency is also discussed. Our proposed human parsing module with 24 layers can achieve a similar performance as the baseline CNN model with over 100 layers. The number of parameters in the overall model is less than that in the baseline model. Furthermore, we propose to reduce the computational burden by replacing a conventional CNN layer with a stack of simplified sub-layers to further reduce the overall number of trainable parameters. Experimental results show that the integration of two modules contributes to the improvement of human parsing without additional human labeling. The proposed model outperforms the benchmark solutions and the capacity of our model is better matched to the complexity of the task. [ABSTRACT FROM AUTHOR]

In this paper, economic feasibility of installing small-scale solar photovoltaic (PV) system is studied at the residential and commercial buildings from an end-user perspective. Based on given scenarios, the best sizing methodology of solar PV system installation has been proposed focusing primarily on the minimum payback period under given (rooftop) area for solar PV installation by the customer. The strategy is demonstrated with the help of a case study using real-time monthly load profile data of residential as well as commercial load/customers and current market price for solar PVs and inverters. In addition, sensitivity analysis has also been carried out to examine the effectiveness of net metering scheme for fairly high participation from end users. Since Saudi Arabia's Electricity and Co-generation Regulatory Authority (ECRA) has recently approved and published the net metering scheme for small-scale solar PV systems allowing end users to generate and export energy surplus to the utility grid, the proposed scheme has become vital and its practical significance is justified with figures and graphs obtained through computer simulations. [ABSTRACT FROM AUTHOR]

This article deals with the extremely difficult problem faced by a large number of smaller ports: how to enable small container terminals to simultaneously receive two ships of post-Panamax size, and at the same time provide effective transshipment operations on an individual terminal subsystem, which would enable the fast turnaround of the ship in port and at the same time provide the port the possibility to increase annual traffic. For this purpose, a simulation approach was used in a hypothetical small size container terminal. The performed simulations covered all the berth-yard-berth operations focusing on the correct allocation of transfer mechanization to ensure optimal results on the sea side of the terminal. The results obtained were used to define the key parameters on the basis of which a Decision Support Tool was created. The aim of the Decision Support Tool is to help port operators to identify their current problems quickly and effectively and to give them an insight into the measures that need to be implemented to accommodate two post-Panamax ships at the terminal at the same time. [ABSTRACT FROM AUTHOR]

Now that China has outlined its goals of "carbon peak and carbon neutrality", the development of clean energy will accelerate, the connection between different energy systems will be closer, and the development prospects of the integrated energy service industry will be broader. Integrated energy services are promoting energy transformation and services. "Carbon peaking, carbon neutrality" and other aspects will also have multiple values and far-reaching significance. Before implementing integrated energy services, the top-level design of integrated energy system planning must be carried out, and how to achieve the optimal allocation of capacity in the field of integrated energy systems is an urgent problem to be solved in integrated energy system planning. This paper combines practical engineering experience and the latest theoretical research results to creatively introduce, for the first time, a comprehensive evaluation into the initial planning stage, and proposes, also for the first time, a three-level multi-element comprehensive energy system planning optimization method which combines multi-element requirements to carry out comprehensive energy system planning and optimization. The three-tier planning and optimization solution results in the optimal planning scheme of the integrated energy system, thereby making the scheme more specific and reliable. According to the demand data of an industrial park, this method was applied to complete a case analysis of integrated energy system planning, which verified the feasibility and effectiveness of the method. This method is easy to popularize, and it will guide the planning of integrated energy systems, promote integrated energy services, promote energy transition, and make positive contributions to achieve carbon neutrality as soon as possible. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) communication is regarded as a promising technology for lightweight Internet of Things (IoT) communications in narrowband-IoT (NB-IoT) systems deployed in rugged terrain. In such UAV-assisted NB-IoT systems, the optimal UAV placement and resource allocation play a critical role. Consequently, the joint optimization of the UAV placement and resource allocation is considered in this study to improve the system capacity. Because the considered optimization problem is an NP-hard problem and owing to its non-convex property, it is difficult to optimize both the UAV placement and resource allocation simultaneously. Therefore, a competitive clustering algorithm has been developed by exchanging strategies between the UAV and the adjacent IoT devices to optimize the UAV placement. With multiple iterations, the UAV and the IoT devices within the coverage area of the UAV, converge their clustering strategies, which are suboptimal, to satisfy both sides. The bordering IoT devices of the adjacent clusters are then migrated heuristically toward each other to obtain the optimal system capacity maximization. Finally, the transmission throughput is optimized using the Nash equilibrium. The simulation results demonstrate that the algorithms proposed in this study exhibit rapid convergence, within 10 iterations, even in a large environment. The performance evaluation demonstrates that the proposed scheme improves the system capacity of the existing schemes by approximately 28%. [ABSTRACT FROM AUTHOR]

Battery storage (BS) sizing problems for grid-connected microgrids (GCμGs) commonly use stochastic scenarios to represent uncertain natures of renewable energy and load demand in the GCμG. Though taking a large number of stochastic scenarios into consideration can deliver a relatively accurate optimal result, it can also highly deteriorate the computational efficiency of the sizing problem. To make an accuracy-efficiency trade-off, a computationally efficient optimization method to optimize the BS capacities based on the power exchanging process of the GCμG is proposed in this paper. According to the imbalanced power of the GCμG, this paper investigates the power exchanging process between the GCμG, BS and external grid. Motivated by the BS dynamics, a forward/backward sweep-based energy management scheme is proposed based on the power exchanging process. A heuristic two-level optimization model is developed with sizing BS as the upper-level problem and optimizing the operational cost of the GCμG as the lower-level problem. The lower-level problem is solved by the proposed energy management scheme and the objective function of the upper-level is minimized by the pattern search (PS) algorithm. To validate the accuracy and computational efficiency of the proposed method, the numerical results are compared with the mixed integer linear programming (MILP) method. The comparison shows that the proposed method shares similar accuracy but is much more time-efficient than the MILP method. [ABSTRACT FROM AUTHOR]

In the context of large-scale wind power integration and rapid development of electric vehicles (EVs), a joint operation pattern was proposed to use a centralized charging station (CCS) to address high uncertainties incurred by wind power integration. This would directly remove the significant indeterminacy of wind power. Because the CCS is adjacent to a wind power gathering station, it could work jointly with wind farms to operate in power system as an independent enterprise. By combining the actual operational characteristics of the wind farm and the CCS, a multidimensional operating index evaluation system was created for the joint system. Based on the wind farm's known capacity, a multi-target capacity planning model was established for CCS to maximize the probability of realizing diverse indices of the system based on dependent-chance goal programming. In this model, planning and dispatching are combined to improve the feasibility of results in the operational stage. This approach takes the effects of randomness into account for wind power and battery swapping. The model was solved through combining Monte Carlo simulation and a genetic algorithm (GA) based on segmented encoding. As shown in the simulation results, the proposed model could comprehensively include factors such as initial investment, wind power price, battery life, etc., to optimize CCS capacity and to ultimately improve the operating indices of the joint system. [ABSTRACT FROM AUTHOR]