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For achieving the carbon peaking and carbon neutrality goals, the renewable energy and the battery energy storage will be developed more rapidly. The participation of the solar-wind-battery renewable energy system (SWBS) in black-start can improve the resilience of the power grid. Aiming at the balance between the capacity demand of SWBS participating in black-start and the benefit of SWBS, this paper proposes a positive-sum game-based SWBS capacity configuration planning method. First, a levelized cost of energy- (LCOE-) based full life cycle index is presented and constructs an economic benefit model of SWBS considering the participation of wind and solar power plants in carbon trading. Second, a black-start capacity demand model is constructed considering the continuous power demand on the auxiliary devices of the operated thermal power generator. Taking the economic benefit of SWBS and the black-start capacity requirement into account, this paper establishes a positive-sum game-based SWBS capacity optimal configuration model, in which maximizing the economic benefits of SWBS as an objective. Third, the simulation analysis is carried out by using a real power system, and results verify that the proposed method can achieve the optimal balance between the feasibility of black-start and the economic benefit of the SWBS. [ABSTRACT FROM AUTHOR]

Summary: In battery swapping station (BSS), the battery swapping of electric vehicle (EV) is not synchronous with the centralized charging of BSS. Based on new energy electricity generation and battery swapping demand information of EVs, how to formulate charging–discharging strategies of centralized charging stations is essential in BSS energy management system. Different from traditional BSS scheduling issues, considering that the supply–demand balance of power in isolated microgrid (IMG) could be regulated by the discharging of backup batteries, a service model of EV BSS is proposed with strong constraints, coupling relationships and randomness. Because the microgrid‐BSS capacity configuration optimization issue is nonlinear and multivariable, if traditional optimization algorithms are utilized, it would take a long time and the optimal solution may not be obtained. Moreover, there is no guarantee of complete convergence even with the standard particle swarm optimization (PSO) algorithm. In this paper, the quantum behavior particle swarm optimization (QPSO) algorithm is used to handle operation control issues of BSS. As shown in case results, with the QPSO algorithm proposed, both the reliability of battery swapping service and the maximum profit of BSSs could be implemented. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of new energy resources being generated in the power system, it is necessary to plan the capacity configuration of the power supply side through the coordination of power generation, grid, load, and energy storage, to create a relatively controllable power generation output and ensure the safe and stable operation of the power system is a key problem that needs to be solved at present. Therefore, combined with national and regional policies and resource constraints in China, this paper firstly determines the requirements and boundary conditions of various power supply planning in the regional power system and proposes a "generation-grid-load-energy storage" coordination mode. Secondly, establish a regional power system coordination planning model considering the proportion of new energy consumption and low-carbon cost. Finally, in three scenarios with a high, medium, and low proportion of new energy resources, the new installed capacity configuration of regional power supply is optimized to verify the effectiveness of the proposed model and provides reference for regional power supply planning. [ABSTRACT FROM AUTHOR]

To sustain the clean energy transition without interruption and to ensure the reliable operation of the transmission system, it is required to have enough additional transmission capacity in the future horizons. The transmission expansion planning (TEP) problem is a core issue in deciding additional transmission capacity in the planning activities. TEP aims to find the best expansion plan while satisfying technical and economic constraints. In this study, a new binary version of the original FBI algorithm called the BFBI (binary forensic-based investigation) algorithm is developed to solve the binary TEP problem. The effectiveness and performance of the developed BFBI are assessed by implementing it in two different test systems: the standard Garver 6-bus test system and the modified 400 kV Turkish grid. Seasonal scenarios are created for 5- and 10-year planning periods to cover all possible generation and load conditions and to assess the impact of the increased share of RES on the grid in the TEP studies conducted for the modified 400 kV Turkish grid created as a bulk realistic grid. The TEP problem is solved by including investment, reliability, and operational costs in two different objective functions for cases while considering the N-1 contingency criterion. The efficacy and robustness of the BFBI algorithm are justified by comparing it with well-known algorithms such as GA and PSO. [ABSTRACT FROM AUTHOR]

Decentralized regional multienergy system is one of the important development directions of energy and power systems, and researching on the optimization method of multienergy microgrid configuration could provide important support for the investment income guarantee and orderly development of regional multienergy systems. Based on a park-level multienergy microgrid, this paper proposed a multiobjective optimization model for a multienergy microgrid configuration based on the typical scenario set which was constructed by HMM. Besides, based on the actual historical data, the capacity configuration-oriented planning model and component configuration-oriented planning model were analysed and compared under different external environments. The results show that HMM has a good effect on the reduction and extraction of historical scenarios of the system. Compared with the traditional microgrid, the multienergy microgrid has better economic and emission reduction advantages. In addition, the capacity configuration-oriented planning model could reduce the investment cost by up to 62.4% compared with the component configuration-oriented planning model. [ABSTRACT FROM AUTHOR]

SUMMARY This paper addresses some important issues related to airport apron capacity planning and management. An overview of existing apron models for supporting planning studies and for optimizing available resources utilization is given, with an emphasis on analytical models for apron capacity estimation. Constraints on apron usage, physical and operational with respect to different users, are discussed in detail, together with their impact on apron capacity. Simple extension of existing apron capacity estimation models is suggested accounting for constraints both on aircraft types and dominant users. Further on, instead of expressing apron capacity through a single number, an apron capacity envelope is used to illustrate capacity changes, that is, an apron's ability to accept various mixes of dominant users in demand. The apron capacity envelope provides information on capacity for one apron configuration (with respect to stand size and policy of usage) and a given fleet mix, for different shares of dominant users in demand. Finally, apron capacity flexibility is discussed with respect to its role in apron capacity planning and management. It is suggested how to express and interpret apron capacity flexibility. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Capacity reliability is defined as the probability that the network capacity can accommodate a certain volume of traffic demand at a required service level. It is a supply-side reliability measure for assessing the adequacy of a degradable transportation network. The network capacity model used to calculate the capacity reliability measure is based on the concept of reserve capacity, which requires preserving a pre-determined origin-destination (O-D) demand pattern. In this paper, we relax this assumption by allowing a non-uniform growth in the spatial distribution of the O-D demand pattern. By using this non-uniform O-D growth approach, two network capacity models related to the concepts of ultimate capacity and practical capacity are developed to estimate alternate capacity reliability measures. Numerical results are provided to analyze the features of three capacity reliability measures for transportation networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Capacity allocation and optimal scheduling of virtual power plants (VPP) are important aspects to ensure the effectiveness of system investment and operational economy. In this study, a two-stage optimization model for capacity planning and regulation operation of VPPs considering source-load-storage resources is constructed. In the first stage, a capacity optimization model is constructed for the VPP with the lowest annual economic cost under the refinement constraints of source-storage resources. In the second stage, based on the capacity allocation results and load characteristics, a source-load interactive operation optimization model with the lowest typical daily operating cost and incentive-based demand response is constructed under the resource capacity constraint, so as to realize the capacity allocation and energy control of the VPPs in all stages of source-load-storage resources. Finally, a planning solver is applied to solve the algorithm. The proposed model is validated. The results show that the presence or absence of demand response, the form of demand response, and the charge state of energy storage all have an impact on the allocation and operation results. Adequate consideration of the source-side, load-side, and storage-side interactions can provide a reference for more accurate planning and optimization. The research results are intended to be able to provide VPPs investors and operators with a full process of construction and operation solutions. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear Goal Programming Model (GPM) for solving the problem of admission capacity planning in academic universities. Many factors of university admission capacity planning have been taken into consideration among which are number of admitted students in the past years, total population in the country, number of graduates from secondary schools, desired ratios of specific specialties, faculty-to-students ratio, and the past number of graduates. The proposed model is general and has been tested at King Abdulaziz University (KAU) in the Kingdom of Saudi Arabia, where the work aims to achieve the key objectives of a five-year development plan in addition to a 25-year future plan (AAFAQ) for universities education in the Kingdom. Based on the results of this test, the proposed GPM with a modified differential evolution algorithm has approved an ability to solve general admission capacity planning problem in terms of high quality, rapid convergence speed, efficiency, and robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a model for estimating capacity of on-ramp merging section of urban expressway based on dynamics and gap acceptance theory, considering lane-changing processes and time headway loss. Survey data were collected from on-ramp merging sections of shanghai urban expressway system and showed that capacity drop of on-ramp merging section is caused by drivers' lane-changing which may lead to unsteady speed of vehicles and so prolonged time headway compared to the minimum time headway corresponding to the maximum capacity. Three parameters (optimal time headway, time headway loss, and interference quantity of lane-changing) are given and a methodology by accumulating time headway loss due to lane-changing is developed to estimate the capacity drop. Results' comparisons between real data and microsimulation of on-ramp merging sections and sensitivity analysis show that the proposed model can produce reliable and accurate results. This study also reveals that ramp flow and the difference between the optimal speed and the lane-changing speed of fleet have a great impact on capacity drop. This study is beneficial to evaluate congestion levels, to understand complex traffic phenomena, and so to find efficient solutions. [ABSTRACT FROM AUTHOR]