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Wireless network resource allocation is an important issue for designing Internet of Things systems. In this paper, we consider the problem of wireless network capacity optimization that involves issues such as flow allocation, link scheduling, and power control. We show that it can be decomposed into a linear program and a nonlinear weighted sum-rate maximization problem for power allocation. Unlike most traditional methods that iteratively search the optimal solutions of the nonlinear subproblem, we propose to directly compute approximated solutions based on machine learning techniques. Specifically, the learning systems consist of both support vector machines (SVMs) and deep belief networks (DBNs) that are trained based on offline computed optimal solutions. In the running phase, the SVMs perform classification for each link to decide whether to use maximal transmit power or be turned off. At the same time, the DBNs compute an approximation of the optimal power allocation. The two results are combined to obtain an approximated solution of the nonlinear program. Simulation results demonstrate the effectiveness of the proposed machine learning-based algorithm.

This paper researches the capacity optimization problem of Iron-steel enterprises under the supply chain environment, builds up a mathematical model with a joint objective of maximize the capacity utilization rate of core enterprises, the demand satisfaction degree of downstream enterprises and the profit margin of downstream enterprises based on the minimum planned production amount required for each process constraints and demand expectation range constraints of downstream enterprises and so on. The PSO algorithm is applied and improved by heuristic rules in order to repair the infeasible solutions.

In this paper, the upper optimization, based on the dynamic model of heating network, solves the problem of pipe network failure caused by too low temperature, the lower layer optimizes the capacities of the devices at the heat sources with the objective of economy according to the results of upper layer. Through the case analysis, the result shows that after optimization, the difference between the pipeline temperature and standard temperature is reduced by 33%, the minimum temperature of the pipe is increased from 52.5 °C to 76.7 °C, and the problem of pipeline failure is effectively avoided. Compared to the traditional capacity optimization model, the capacity planning cost of the two heat sources is reduced by 3.37% and 4.14% respectively. The bi- level programming model proposed in this paper can effectively avoid the operation failure of pipe network, and has better economic benefits after considering the variable efficiency characteristics of energy device.

In this paper, we consider the physical layer security problem of the wireless communication system. For the multiple-input, multiple-output (MIMO) wireless communication system, secrecy capacity optimization artificial noise (SCO−AN) is introduced and studied. Unlike its traditional counterpart, SCO−AN is an artificial noise located in the range space of the channel state information space and thus results in a significant increase in the secrecy capacity. Due to the limitation of transmission power, making rational use of this power is crucial to effectively increase the secrecy capacity. Hence, in this paper, the objective function of transmission power allocation is constructed. We also consider the imperfect channel estimation in the power allocation problems. In traditional AN research conducted in the past, the expression of the imperfect channel estimation effect was left unknown. Still, the extent to which the channel estimation error impacts the accuracy of secrecy capacity computation is not negligible. We derive the expression of channel estimation error for least square (LS) and minimum mean squared error (MMSE) channel estimation. The objective function for transmission power allocation is non-convex. That is, the traditional gradient method cannot be used to solve this non-convex optimization problem of power allocation. An improved sequence quadratic program (ISQP) is therefore applied to solve this optimization problem. The numerical result shows that the ISQP is better than other algorithms, and the power allocation as derived from ISQP significantly increases secrecy capacity.

The development of hydrogen energy is one of the key paths to realize the clean and low-carbon transformation of the global energy system. Producing green hydrogen from renewable energy has broad prospects. This paper proposes a capacity optimization configuration model for island-operated microgrids coupled with wind/solar/green hydrogen systems, with the goal of minimizing the equivalent annual cost of the system's operating life. The model takes into account investment costs, operating costs, fuel costs, and value of lost loads, considering the operation constraint of microgrid, green hydrogen system and diesel engines. Taking annual wind speed, temperature, and light intensity as inputs, the optimal scheme for the type and capacity of the microgrid power source is obtained. Taking an island microgrid as an example for verification, the results show the effectiveness of the proposed optimal configuration method, which provides theoretical basis and technical support for the planning and design of island microgrid considering green hydrogen systems.

This paper researches the different configuration modes of distributed generation, such as installation location, installation capacity and influence on distribution network, analyzes the running power factor, energy loss, network topology, voltage control mode with traditional voltage control equipment, and gives relevant conclusions, so that power system planning, dispatching and running engineer can process in time, effectively guide customers to connect distributed power to distribution power grid, minimize the distributed power impact to distribution power system. As for the access point and capacity optimization of distributed generation in distribution network, this paper proposes the intelligent optimization algorithm to solve the optimal access mode of distributed power supply. The objective is to maximize the capacity of distributed generation and minimize the system energy loss. At the same time, the upper and lower limits of branch current carrying capacity, main transformer capacity, section active power and node voltage are taken as constraints. By the calculation and analysis of the access capability of a real distribution network, the effectiveness of the proposed method is verified.

We consider a two-hop, hybrid collaborative radio communication system having regenerative and non-regenerative energy harvesting (EH) relays. The regenerative or non-regenerative relay harvests energy from its received radio frequency (RF) signals transmitted by the collaborative EH radio system’s source node. We employ simultaneous wireless information and power transfer (SWIPT) with the time-switching EH protocol. We propose an insightful hybrid cooperative relaying protocol and its performance analysis. Specifically, we formulate an ergodic capacity optimization problem with average power constraint on relay transmissions. The proposed hybrid relaying protocol analysis and numerical results provide valuable insights into the effect of various hybrid EH collaborative radio system parameters. These parameters include EH time and non-regenerative relay’s amplification factor on system performance in terms of normalized capacity. Furthermore, a comparison between multiple relaying protocols shows that hybrid relaying provides better fading-averaged capacity performance.

In a microgrid with multiple types of power sources, the distribution and capacity configuration of power sources will have a greater impact on the economics of the microgrid. Aiming at the grid-connected hybrid AC/DC microgrid of wind, solar, and storage, the power flow model with the goal of reducing the economic cost of the microgrid operation was established. Taking the self-balancing rate and converter loss of the microgrid as the evaluation indicators of the microgrid, a suitable operation control strategy is proposed. At the same time, using the actual data of a certain place, the genetic algorithm is used to optimize the solution, and the optimal scheme of its capacity configuration is given, which verifies the effectiveness of the control strategy proposed in this paper. The results show that increasing the battery capacity and coordinated configuration of wind turbines and photovoltaics can make the microgrid have better operating economy.

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%.

For semiconductor manufacturing, a qualification is a certification for a machine to process one operation of a specific product. A machine cannot process a product without the associated qualification. Qualifications are mandatory to ensure high yield of production lines and products of quality. Qualifications are used to improve the flexibility (ability to respond effectively to changing circumstances) and to configure production capacities of work centers in semiconductor factories. Because qualifications take time, up to several months, and can be expensive, only relevant qualifications must be planned and determined to optimize the utilization balance of the machines, throughput, fabrication time and demand satisfaction at the lowest cost. More precisely, the following question is answered: Given a horizon, a demand forecast by product and operation, processing times, qualification delays and costs and production capacities of machines, what are the most relevant qualifications?Answering these questions is actually complex because it is difficult to evaluate the utilization rate of a machine after multiple qualification decisions. Evaluating the utilization rates of the machines is yet primordial as the utilization rates of the machines is related to the throughput, the fabrication time and the demand satisfaction. Because qualification management is complex, we answer these questions from two standpoints by proposing relevant new optimization models and solution approaches. The first standpoint is an operational standpoint where most the follow up of qualifications is optimized. The second standpoint is a tactical standpoint where new qualifications must be planned and anticipated to satisfy the demand. We show throughout the thesis that a small number of relevant qualifications is often sufficient to optimize a given criterion., Pour la fabrication de composants semiconducteurs, une qualification est une certification pour une machine permettant de traiter une opération d'un produit spécifique. Une machine ne peut pas traiter un produit sans la qualification associée. Les qualifications sont impératives pour garantir un rendement élevé des lignes de production et des produits de qualité. Les qualifications sont utilisées pour améliorer la flexibilité (capacité à répondre efficacement à des circonstances changeantes) et servent à configurer la capacité de production des ateliers de fabrication dans les usines de semiconducteurs. Comme les qualifications peuvent prendre du temps et sont coûteuses, seules les décisions pertinentes de qualifications et de requalifications doivent être déterminées et planifiées pour optimiser l'équilibre de la charge de travail des machines, la capacité de production, le temps de fabrication et la satisfaction de la demande. Plus précisément, nous voulons répondre à la question suivante : Compte tenu d'un horizon, d'une prévision de la demande par produit et par opération, du temps opératoire par opération, des délais et des coûts de qualification et des capacités de production des machines, quelles les qualifications les plus pertinentes ? Répondre à cette question est en fait complexe parce qu'il est en général d'évaluer les taux d'utilisation des machines, qui sont liés à la capacité de production, le temps de fabrication et la satisfaction de la demande, après plusieurs décisions de qualifications. Parce que la gestion des qualifications est complexe, nous répondons à cette question sous deux angles en proposant des nouveaux modèles d'optimisation et des nouvelles approches de résolutions pertinentes. Le premier angle est un angle opérationnel où le suivi des qualifications est optimisé, le second angle est un angle tactique où les nouvelles qualifications doivent être planifiées et anticipées pour répondre à la demande. Nous montrons tout au long de la thèse qu'en général, un petit nombre de qualifications, du moment qu'elles sont pertinentes, est souvent suffisant pour optimiser un critère donné.

Integrated Energy System can realize the cascade utilization of energy, which improves the utilization of energy efficiently and reduce the carbon emission. Taking into account the uncertainty of multi-energy load and renewable energy forecasting, this paper presents a bi-level robust optimization model with demand response and thermal comfort, for the capacity planning and operation problem of Integrated Energy System. The outer level optimization is planning to find the optimal integrated energy system configuration to minimize economic investment, while the inner level is to robustly optimize the system scheduling to simultaneously reduce carbon emissions and dissatisfaction of residents’ participation in demand response. In the simulation, the Pareto front of the multi-objective problem is obtained via NSGA-II algorithm and Gurobi solver, and the best design plans on the Pareto front selected by Topsis method are analyzed and discussed. The results from three modes are compared in simulation, which illustrates the economic and environmental benefits from demand response and thermal comfort. In addition, the impact of different thermal time scales and forecast uncertainties on integrated energy system planning are also discussed. Finally the sensitivity of the influence of the low carbon grid constrains on the optimization is analyzed.

In view of the poor utilization of renewable energy, and the high cost of traditional battery energy storage, frequent deep charging and discharging can easily lead to reduced battery life, this paper proposes a consideration of hydrogen storage and battery mixing capacity optimization allocation strategy for multi-energy systems of energy storage. First, establish an island multi-energy system model with electricity/hydrogen coordinated supply centered on electricity/hydrogen conversion and storage. Secondly, with the goal of minimizing the sum of annual investment and operating costs of the island multi-energy system, establish a capacity optimization configuration model for the island multi-energy system. Finally, taking an island in my country as an example, the analysis of the calculation example shows that the hybrid energy storage system structure proposed by the model extends the service life of the battery, reduces the operating cost of the battery energy storage.

This article considers an incumbent’s product innovation decision within an uncertain framework, where the firm decides whether to continue selling the established product. The model being dynamic allows to analyze the trade-off between an early innovation where the new product only slightly improves the existing one, or innovating late with a much better new product. We find that the effect of uncertainty is that it raises the value of the strategy where the firm keeps on producing the old product after innovating. This results in earlier investment if the firm stays active on the established product market after adopting the new product, and that it keeps on producing the established product for a longer time after the product innovation. Limited uncertainty could lead to a non-monotonicity: with a better new product it is not optimal to innovate, whereas innovating is optimal with a worse one.

Many uncertain factors in wind power forecasting lead to large prediction errors. Various prediction technologies have been developed to reduce errors and improve the dispatch-ability of grid-connected wind power. To install energy storage systems is an effective approach to reduce the scheduling deviation in dispatching the grid-connected wind power. This paper considers the optimal capacity allocation, a key issue in smoothing the grid wind power generation and integration. Based on the analysis of wind power prediction technologies and the resultant prediction deviations, the relationship between the distribution characteristics of wind power prediction errors and energy storage capacity demand is first investigated. Then, an optimization method is proposed, considering the stability of grid operation and the relationship between compensation necessity and load changes. Further, load fitness factor is introduced in processing the deviation data samples, together with an economic dispatch model for the deviation compensation, considering the operation costs. Finally, based on the analysis of various factors, the technical route to achieve energy storage capacity allocation for scheduling deviation compensation is proposed. Case studies are also presented to demonstrate the effectiveness of the proposed approach.

The work analyzes the performance of a solar tunnel dryer for drying four different products (mango slice, citrus, beef, fish) under forced convection by using a DC blower run by a photovoltaic panel (200 w). An experimental setup of solar tunnel dryer has been designed and fabricated. Experiments have been performed during summer season. The system is operated between 30 and 69 °C. The dryer is simple in construction, at a low cost, with locally available materials. The performance of the designed drier is evaluated by carrying drying experiments at Nagercoil, Tamil Nadu, India (8.1700 °N, 77.4300 °E). RSM software was used to investigate estimation of capacity optimization and acceptability using desirability for four different products. The independent variables or responses were time, air temperature, and solar radiation. These researches are applicable to various process industries to analyze the probable utilization in various forms.

Buffer capacity allocation occupies an essential position in the designing of production systems. By means of polymorphism analysis on production capacity and capability, the buffer allocation for multi-stage production lines with unreliable machines, which simultaneously involves the maximization of theoretical production rate in system and minimization of system state entropy for a certain amount of buffers, was investigated in this paper. For repairable modular machines, the Markov models are established using stochastic process analysis and the corresponding theoretical steady-state probability in various states are obtained. Furthermore, the original system in combination with multi-state reliability measures of buffer stations is equivalent to a system with independent machines which can be expressed by vector u-functions. Based on the probability distributions of the states of subsystems, the composition operators for series connections and parallel connections are defined. Consequently, the entire system is simplified to one component represented by the polynomial UGF and the systematic multi-state reliability and structural complexity were assessed. Based on the theoretical production rate in system and system state entropy, a mathematical model for buffer capacity optimization was established and optimized using a specific genetic algorithm. The feasibility and effectiveness of the proposed method was verified by applying which to a production line of engine heads.

Capacity Optimization in Cloud computing is a common term for innovations used to progress storage use by dwindling stored data. Crucial methodologies used for capacity optimization in cloud computing are data compression and data deduplication. Our paper focuses Capacity Optimization in connection with Deduplication procedures, which stare for redundancy in series of bytes across comparison windows by using cryptographic hash functions as identifiers of unique sequences, then the sequences are matched to the history of other such sequences, and when possible, the first uniquely stored version of a sequence is referenced rather than stored again. Our work shows better results for capacity optimization, which are most frequently found in backup/recovery storage, where storage of iterating versions of backups day to day creates a chance for lessening of space in cloud computing. Hence this study has recognized exploration hole as vast number of capacity of copy records on cloud frameworks from various clients. Consequently, to diminish the amount of work brought about by copied documents, SHA3 calculation has been proposed in this study, which makes the hash esteem for each record and discover the information intelligent compression for capacity streamlining on cloud framework. Our Work also includes Authentication, which is based on Cryptographic hash function and Secure Data Deduplication allows cloud storage and optimization of storage to employ data with concealment.

Planning public services needs to promote equal access across geographic areas and between demographic groups. However, most location-allocation models emphasize efficiency such as minimal travel burden or maximal demand coverage while omitting the equality issue. This case study optimizes the emergency medical service (EMS) in Shanghai from a trade-off perspective by comparing two models. One is the 2-step optimization (2SO) model that uses the maximum covering location problem (MCLP) to site new facilities and then a quadratic programming (QP) method to optimize capacities, the other performs location selection and capacity optimization simultaneously through greedy optimization (GO). There are several findings from various simulation scenarios. First, the GO model is more effective in optimizing equality, but the 2SO model offers a more balanced approach by covering more people within the mandatory response time while improving access equality. Secondly, solutions of both models change as demands and travel costs vary over time and call for dynamic adjustment of resource allocation. Thirdly, it is important to coordinate EMS with other agencies to ensure reasonable road connectivity and make contingency plans in events such as floods, earthquakes and other natural disasters.

The islanded microgrid (IMG) is universally accepted as an important method to solve the island power supply problem. The optimal capacity of the hybrid energy storage system (HESS) is necessary to improve safety, reliability, and economic efficiency in an IMG. To improve the IMG ability to deal with uncertainty, this paper proposed a flexible islanded microgrid (FIMG) model with real-time price (RTP)-based demand response (DR). Through RTP to guide users’ electricity consumption behaviors more in line with renewable energy sources (RES) output law, the capacity allocation of the HESS was optimized and the system had sufficient flexibility in each scheduling period. In this paper, two decision variables of the HESS, the number of lead-acid batteries and supercapacitors, were determined based on the objective of comprehensive operating cost (COC) and flexibility insufficiency rate (FIR) minimization. Firstly, the non-dominated sorting genetic algorithm II (NSGA-II) was devoted to obtaining the Pareto set that satisfies the FIMG day-ahead scheduling. Then, both the loss of produced power probability (LPPP) and the loss of power supply probability (LPSP) corresponding to each Pareto solution were gotten based on the real-time dispatch strategy. Finally, taking COC, FIR, LPPP, and LPSP as evaluation indicators, the improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied to select the optimal solution from the Pareto set. In the case study, a FIMG on an island in Guangdong province was analyzed and discussed. By studying typical days in winter and summer of the FIMG, the superiority of the proposed model and methods was verified.

Nowadays, new energy is an important tool to deal with global energy shortage crisis and environment pollution. The volatility and uncertainty of intermittent new energy injection will dramatically impact the economy of regional planning and system operation. Thus, it is vital to accurately describe the volatility of new energy and construct the reasonable new energy capacity optimization model. A novel regional new energy planning is proposed to accurately calculate the new energy capacity, first, analyzing the correlation between regional new energies, and then, the typical minute-level new energy output scenarios are derived by fuzzy C-means (FCM), finally, an double-layer new energy optimization model is built with taking net value of whole life cycle of new energies including WT and PV as the upper objective function and system volatility rate as the lower objective function, respectively. The regional new energy planning can achieve a tradeoff between system volatility and planning economy. The simulation results validate the reasonability of the proposed double-layer model.

Microgrid is an important form of utilizing distributed renewable energy as well as a key link to realize low carbonization of the power grid. In this paper, a double-layer optimization method considering carbon emission cost is proposed to determine the optimal capacity of a microgrid. Specifically, the outer-layer optimization focuses on solving the optimal capacity of microgrid and the inner-layer optimization focuses on optimizing the allocation of different power generation units considering carbon emission cost. Besides, the formulated double-layer optimization model also considers uncertainties in multiple-time scales. In the outer layer, a robust coordination indicator is proposed to deal with energy balance in the long-time scale, while in the inner layer, a forecasting error indicator is proposed to deal with power balance in the short-time scale. Finally, an island microgrid is taken as an example to testify the impact of carbon emission cost on microgrid planning. Simulation results verify the validity of the proposed model and method.

This paper proposes a multi-objective capacity optimization method to determine the size of the integrated energy system (IES). A novel IES configuration composed of wind turbine, solar photovoltaic, combined cooling, heating, and power system, and compressed air energy storage is designed, which could improve energy efficiency and reduce emissions. Based on a developed modeling of all sub-systems composing the IES, the capacity is optimized to minimize the net present cost and environmental cost simultaneously. Non-dominated sorting genetic algorithm-II (NSGA-II) is applied to find the Pareto frontier of the constructed multi-objective formulas. Finally, taking an industrial park located in Jinan, China, as an example. The optimization results using the proposed approach provided a set of design solutions for the investor to select the optimal configuration.

With the rapid development of renewable energy power in Qinghai province, the utilization rate of renewable energy has become to a new challenge. Large-scale battery energy storage system (BESS) is an effective way to promote the utilization of renewable energy. In this paper, a method on capacity optimization and economic evaluation of BESS is proposed based on sequential production simulation technology. An optimization model is established to maximize the utilization of renewable energy, considering constraints of system power balance, operation mode of BESS and conventional units. The annual power outputs of renewable energy are incorporated as inputs for optimizing the hourly charging and discharging power of BESS. The discount cash flow method is adopted to evaluate the economic benefits of BESS. In case studies, a set of scenarios for Qinghai province with different power rating and energy capacity of BESS are tested, and the best plans of BESS in Qinghai province is obtained. The testing results verify the effectiveness of the proposed method.

As traditional optimal planning strategies in multi-energy gathering center are only dominated by the economic index, while the flexibility index has not been taken into consideration. This paper creatively proposes a comprehensive evaluation metric of flexibility and economic index based on the multi-scenario operation process and the optimal planning method. A two-level capacity optimization model of different generations in multi-energy gathering center is established in this paper. This model consists of two objectives, the economy index and the flexibility index. The outer model applies the capacity of two flexible resources, thermal power units and battery energy storage units, as decision variables. And the inner model aims at the operating cost and flexibility index as objectives. The combined method of NSGA-II, linear programming and quadratic optimization is adopted to solve the above two-level optimization problem on the basis of fully considering multiple scenarios. Finally, based on the actual data and energy characteristics in a multi-energy gathering center in Yunnan, the proposed planning strategy is verified. Simulation results show that, compared with the traditional planning strategy only considering the economic cost as the main index, this proposed strategy can significantly reduce the energy abandonment and loads’ cutting rate. As a result, the proposed strategy reduces the possibility of grid risks and improves the system reliability and flexibility.

Aiming at the problem of flexibility regulation demand caused by the random fluctuation of photovoltaic (PV) output and load, the concentrating solar power (CSP) plant is used as a flexibility regulation source in the CSP-PV power generation system, and an optimal configuration method of the thermal storage capacity of the CSP plant considering the regulation demand constraints is proposed. First, the constraint conditions for flexibility regulation demand in respond to the fluctuation of load and photovoltaic output are defined. Then, with the goal of the loss of power supply probability, investment operation cost and PV penetration rate, a multi-objective optimal configuration model of CSP power storage capacity are established. Finally, the non-dominant sorting genetic algorithm (NSGA-II) with elite strategy is adopted to optimize the storage capacity of CSP plant. Taking northwest China as an example, the effectiveness and rationality of the optimal configuration scheme proposed in this thesis considering the regulation demand is verified. The results show that the reasonable configuration of the storage capacity of CSP plant in the system can effectively ensure the reliability of power supply while improving the economy.

The marine energy reserves available for development in China are huge, and the coastal areas are highly concentrated in China’s power load area, so the research and development of marine energy is of great significance. Aiming at the problem of large fluctuation and poor predictability of offshore wind power generation, a combined power generation system of offshore wind power and tidal current is proposed. This paper analyzes the time and space complementary characteristics of offshore wind power and tidal current energy, designs the integrated power generation platform, establishes the system model based on DC bus network, and determines the evaluation index of the system. Based on the Homer software, the optimal allocation of system capacity is analyzed, which verifies that the economy of wind tide combined power generation system is better.

High penetration of renewable energy sources (RES) requires flexibility options as storage to keep system stability. The main barrier to implement the storage systems is the high costs combined with short lifetimes. To define a cost-optimal storage solution for a microgrid, an optimal design strategy for a hybrid storage system is proposed that consists of thermal energy storage (TES) and battery energy storage (BES). The hybrid energy storage system is expected to bring the high potential for cost-saving and capacity optimization to guarantee the minimal possible implementation cost. Furthermore, a new flexible tariff system called white tariff has been implemented to assess the potential savings achieved by a different configuration of storage implementation. BES has been compared with TES in a grid-connected settlement area equipped with PV generation and a centralized heat pump (HP). The storage systems have been compared under white and flat tariffs. The results show that the hybrid storage system has more potential in terms of cost-saving when it is used with the white tariff system.

In this paper, we investigate the access control problem in uplink integrated satellite-terrestrial networks (ISTN) while considering spectrum sharing between the satellite and terrestrial subnetworks. Specifically, a game-theoretic approach is employed by the hybrid network control center (HNCC) to analyze the integrated access control problem in ISTN for simplifying implementation. To this end, the total capacity optimization can be converted as maximizing the individual transmission rate of each user in a non-cooperative game. To obtain the CE solution of the game, an adaptive regret-matching algorithm based on no-regret learning is properly designed. Finally, representative numerical simulations are given to verify the correctness and efficiency of the proposed algorithm.

Multi-document summarization (MDS) aims at giving a brief summary for a cluster of related documents. In this paper, we consider the MDS task as an optimization problem with a novel measure named soaking capacity being the objective function. The origin of our method is the classic hypothesis: the summary components are the sinks of information diffusion. We point out that the hypothesis only gives the role of summary but does not cover how well a summary acts as this role. To fill in the gap, soaking capacity is formally defined to quantify the ability of summary to soak up information. We explicitly demonstrate its fitness as an indicator for both the saliency and the diversity goal of MDS. For solving the optimization problem, we propose a greedy algorithm named Soap by adopting a surrogate of soaking capacity to accelerate the computation. Experiments on MDS datasets across various domains show the great potential of Soap as compared with the state-of-the-art MDS systems.

In this paper, an improved topology for unified power quality conditioner (UPQC) is proposed. By adding a coupling capacitor to the LC filter branch in series side of UPQC, the improved topology can reduce the output voltage of the inverter, and provide a large part of the load-reactive power, thereby effectively reducing the overall capacity and dc-link voltage level of the UPQC system. Because of these prominent characteristics, the overall utilization and the cost performance of the device can be improved. Firstly, the circuit topology and working principle of the proposed UPQC are introduced. Secondly, the two working modes of UPQC under different working conditions are introduced in detail, including: power regulation mode under the normal steady-state condition and voltage compensation mode under the voltage sag condition. Then, the proposed topology is compared with other topologies to show its advantages in reducing overall capacity. Finally, the validity and feasibility of the proposed topology are verified by experimental results.

Based on the common characteristic that liquid hydrogen storage system (LHSS) and superconducting magnetic energy storage (SMES) has similar requirement of refrigeration, This paper analyzes advantages of joint operation of LHSS and SMES based on their performance characteristics. Based on the charge-discharge characteristics of each part (LHSS, SMES) of the joint system, an optimal capacity optimization method is proposed with the goal of best revenue rate with participating in ancillary services. Also, the effectiveness of the proposed strategy is verified by case study.

Second use of electric vehicle (EV) batteries used in the battery swapping station (BSS) is an effective way to improve economic benefits of the life span of the BSS and EV batteries. In this paper, we propose the physical structure of BSS for installing the energy storage system (ESS) of second use of EV batteries, and then establish the operation model of a BSS according to the operation mechanism between the internal components. In order to maximize the annual net present value of a BSS, the economic benefit model is established under the full consideration of the time of use (ToU) electricity price mechanism and the EV batteries charging demand. Finally, the proposed method is simulated, and the results show that according to the configuration results and operation strategies obtained by this paper, the economic benefits of BSS can be improved, and meanwhile, the volatility of the original load can be reduced.

The inhomogeneity among individual cells after several charge/discharge cycles leads to premature deterioration of series-connected Lithium-ion battery string. Therefore, cell balancing has been served as an indispensable role on the battery life preservation and the usable capacity optimization of the battery stack. This paper introduces the low-component-count and high-speed DC/DC converter based non-dissipative balancing scheme, which realizes energy interchange among the cells by charging and discharging of the inductors through switches. The justified comparison with the conventional topology demonstrates that the proposed battery equalizer performed a higher-level balancing speed because the energy transfer is not only cell by cell, but also from a cell to the multiple cells.

At the ends of the distribution network, the long power supply distance and weak grid structure often lead to the power supply problems, especially when seasonal and periodic load fluctuations occur. For these intermittent loads, the peak-to-valley power difference can achieve up to nearly ten times. This difference could lead to power quality problems such as custom-side voltage sag and three-phase voltage imbalance. In this paper, a hybrid energy storage system (HESS) including both power-density and energy-density storage components is used to suppress the power fluctuation of the intermittent load. The intermittent load was modelled by analyzing its frequency characteristics with an algorithm combining empirical mode decomposition (EMD) and Hilbert spectrum analysis. Thus, the high and low frequency components of the intermittent load are determined. Then, according to the proposed power allocation strategy, the rated capacity of the HESS was designed. Finally, simulation experiments based on actual examples verified the effectiveness of the proposed methods.

The battery energy storage system (BESS) is an effective means to compensate the photovoltaic (PV) power prediction errors, so as to improve the reliability of the PV power prediction results as the power grid dispatching reference. A bi-level capacity optimization model of BESS is established to solve the problem that the economy and technology are difficult to balance. Based on the prediction errors compensation strategy considering the energy balance of BESS charge-discharge, the operation layer optimizes the operating parameters by minimizing the index for the balance of BESS charge-discharge and the penalty cost of prediction errors. And the economic layer optimizes the energy storage capacity with objective of maximizing the net income of PV energy storage system. Then, through the harmony search and multi-objective particle swarm optimization (HS-MOPSO) algorithm, a comprehensive optimal capacity configuration scheme is obtained. Finally, the validations based on practical data show the advantages of the proposed method.

Power to hydrogen is a promising method to consume surplus renewable energy existing in the world. However, due to bottlenecks in hydrogen storage and transportation, converting hydrogen into ammonia which is a kind of hydrogen carrier with satisfied quality in storage and transportation is considered. Industrial ammonia synthesis reactors always work in constant conditions with constant flow rate of the feed gas, while hydrogen output of electrolyzers may vary sharply across renewable energy, thus hydrogen buffer tanks are required between electrolyzers and reactors to eliminate the fluctuation to some extent. The capacity of the buffer needs to be optimized due to relatively expensive cost of tanks, and the optimal capacity of the buffer actually depends on the operation flexibility of the reactor which is rarely discussed in existing literatures. This paper studies operation flexibility of the reactor first and establishes operation model of ammonia synthesis system under variable working conditions, on this basis the capacity optimization model is proposed and verified by a P2A system. At last the operation performance and the economy of P2A system are discussed, and case studies show that about 24% of total investment cost can be reduced with the consideration of operation flexibility of the reactor.

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach.

High penetration of renewable energy generation is an important trend in the development of power systems. However, the problem of wind and solar energy curtailment due to their inherent randomness and fluctuation remains to be solved. Multienergy complementary operation based on the complementarity between different renewable energy units is an important means to improve the consumption. In this paper, a complementarity evaluation index is proposed to describe the matching ability of the total output of renewable power generation to the load. The interaction between the proposed index and an optimal allocation model of wind and photovoltaic capacity is analyzed in detail. Based on the analysis of the interaction, we can get a satisfying allocation result simply but effectively by maximizing the index. Compared with the existing complementarity indices, the proposed index can describe the complementarity between renewable energy sources in different capacity allocation results.

This paper investigates the energy-efficient communications and user capacity in the cognitive multiple input multiple output (MIMO) symmetric system of underlay mode. The advantage of the improved energy efficiency provided by the orthogonal slot allocation for time division multiple access (TDMA) has a side effect of restricting the number of users accessing the system. To tackle the above problem, this paper proposes a nonorthogonal time resource allocation method with sparse code multiple access (SCMA), which allows one to convert the orthogonal slot units of TDMA into non-orthogonal shared slot units for multiple secondary users (SUs). The method of adding virtual users is adopted, wherein each SU is treated as multiple SUs who occupy a shared unit, to facilitate the packet access. Finally, the greedy algorithm is applied to optimize the time slot allocation of unoccupied shared slot units. The simulation results show that SCMA-based nonorthogonal slot allocation can reduce not only the energy consumption by nearly 40%, but also the average power interference from SUs to primary users by nearly 2 dB if their number is relatively high. Moreover, in the case of satisfying the rate requirement of the users, the system user capacity increased by more than 50%.