scholarly journals Capacity Evaluation of AC/DC Hybrid Micro-grid-Distributed Generation Considering V2G

2021 ◽  
Author(s):  
Xuan Gong ◽  
Wenyi Li
Keyword(s):  
Distributed Generation ◽  
Electric Vehicles ◽  
Capacity Allocation ◽  
Capacity Optimization ◽  
Allocation Model ◽  
Capacity Evaluation ◽  
Proposed Model ◽  
Micro Grid ◽  
Optimization Allocation ◽  
The Cost

AbstractThe increasing number of DC loads, such as electric vehicles (EVs), has resulted in micro-grid undergoing difficulty in satisfying the various demands of such loads. The study develops a multi-objective capacity optimization allocation model for hybrid micro-grid on the bases of users’ satisfaction and the orderly charging/discharging of EVs. The proposed model aims to reduce the cost of the capacity allocation of AC/DC hybrid micro-grid and improve users’ satisfaction. Particle swarm optimization algorithm is used to address the capacity optimization of hybrid micro-grid in EVs across scenarios. This study verified the rationality and effectiveness of the proposed capacity optimization model by comparing and analyzing the influence of capacity optimization on the orderly/disorderly charging/discharging of EVs and users’ satisfaction.

scholarly journals Optimal Day-Ahead Scheduling of a Smart Micro-Grid via a Probabilistic Model for Considering the Uncertainty of Electric Vehicles’ Load

2019 ◽  
Vol 9 (22) ◽  
pp. 4872 ◽  
Author(s):  
Behnam Rasouli ◽  
Mohammad Javad Salehpour ◽  
Jin Wang ◽  
Gwang-jun Kim
Keyword(s):  
Electric Vehicles ◽  
Cost Minimization ◽  
Power Level ◽  
Market Price ◽  
Simulation Method ◽  
Mixed Integer ◽  
Proposed Model ◽  
Battery Capacity ◽  
Micro Grid ◽  
Cost Minimization Problem

This paper presents a new model based on the Monte Carlo simulation method for considering the uncertainty of electric vehicles’ charging station’s load in a day-ahead operation optimization of a smart micro-grid. In the proposed model, some uncertain effective factors on the electric vehicles’ charging station’s load including battery capacity, type of electric vehicles, state of charge, charging power level and response to energy price changes are considered. In addition, other uncertainties of operating parameters such as market price, photovoltaic generation and loads are also considered. Therefore, various stochastic scenarios are generated and involved in a cost minimization problem, which is formulated in the form of mixed-integer linear programming. Finally, the proposed model is simulated on a typical micro-grid with two 60 kW micro-turbines, a 60 kW photovoltaic unit and some loads. The results showed that by applying the proposed model for estimation of charging station load, the total operation cost decreased.

Research on Optimization Design for Independent Island Micro-Grid

2013 ◽  
Vol 827 ◽  
pp. 292-297
Author(s):  
Hong Da Liu ◽  
Li Zhang ◽  
Wen Hao Zhang ◽  
Sheng Yue Qu
Keyword(s):  
Distributed Generation ◽  
Power Supply ◽  
Optimization Design ◽  
Design Method ◽  
Capacity Optimization ◽  
Specific Design ◽  
Electricity Cost ◽  
Practical Project ◽  
Optimizing Design ◽  
Micro Grid

nland grid cannot reach and cover the islands, and it is also difficult to complete the construction of the cross-sea grid, for these islands are usually far from the mainland. The research on the island micro-grid (solar/wind/tide/battery) is carried out based on the corresponding practical project on a island. The multi-object optimizing design method mainly based on the Deficiency of Power Supply Probability (DPSP) and the Levelised Unit Electricity Cost (LUEC) which represent the reliability of the power supply and the economical efficiency of the micro-grid system, respectively. The traversal algorithm is employed to obtain the capacity optimization configuration for the distributed generation units and the battery bank in the independent micro-grid. Meanwhile, a software designed for the capacity optimization configuration of the island hybrid system is presented. At last, the specific design for a independent island micro-grid is proposed, which validate the effeteness of the method presented in this paper.

scholarly journals Optimal Allocation Model of Virtual Power Plant Capacity considering Electric Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Shiping Geng ◽  
Caixia Tan ◽  
Dongxiao Niu ◽  
Xiaopeng Guo
Keyword(s):  
Electric Vehicles ◽  
Optimal Allocation ◽  
Clean Energy ◽  
Capacity Allocation ◽  
Shanxi Province ◽  
Conditional Value At Risk ◽  
Virtual Power ◽  
Allocation Model ◽  
Virtual Power Plants ◽  
Optimal Allocation Model

To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.

A Resource Optimization Allocation Strategy for China’s Shipbuilding Industry Green Innovation System

2020 ◽  
Vol 17 (04) ◽  
pp. 2050029
Author(s):  
Kexin Bi ◽  
Kwangil An ◽  
Xiang Li
Keyword(s):  
Innovation System ◽  
Pso Algorithm ◽  
Resource Optimization ◽  
Allocation Strategy ◽  
Green Innovation ◽  
Allocation Model ◽  
Shipbuilding Industry ◽  
Proposed Model ◽  
Resource Data ◽  
Optimization Allocation

In order to realize the resource optimization allocation in the green innovation system of China’s shipbuilding industry under the internet environment, to improve the level of green innovation and to reduce the resource consumption, a resource optimization allocation model and the corresponding allocation strategy are proposed. The model integrates and shares the innovation resource data through Internet of Things (IOT) technology, and optimizes the allocation decision by using the cooperative differential game and Particle Swarm Optimization (PSO) algorithm. At the same time, it ensures the robustness of green innovation system and realizes the optimization allocation of resources. A case study is given to illustrate the feasibility of the model. The results show that the green innovation subject can carry out strategic interaction by adjusting the allocation proportion of innovation resources through the proposed model, so as to optimize the overall green innovation benefits of the system.

scholarly journals Calculation Model of Railway Capacity Price in the Czech Republic

2021 ◽  
Vol 33 (1) ◽  
pp. 91-102
Author(s):  
Jaromír Široký ◽  
Petr Nachtigall ◽  
Jozef Gašparík ◽  
Jiří Čáp
Keyword(s):  
Ad Hoc ◽  
Capacity Allocation ◽  
Calculation Model ◽  
Operational Conditions ◽  
Railway Infrastructure ◽  
Proposed Model ◽  
Railway Capacity ◽  
The Individual ◽  
Annual Working ◽  
The Cost

This paper presents a pricing model of railway infrastructure capacity allocation functioning as a regulatory measure while fulfilling the regulatory requirements on railway infrastructure capacity allocation. The prices of railway infrastructure capacity allocation will be modelled with regard to all economically justifiable costs of railway infrastructure capacity allocation. The structure of model has been developed as a set of calculation sheets in Microsoft Excel. The recommended prices for railway capacity have been found by simulation of a set of variants and the recommendation is done for different operational conditions in an individual way. It analyses different products offered by the railway infrastructure capacity allocator both in the annual working timetable mode, and in the individual ad hoc mode. The aim of the proposed model is to motivate not only railway undertakings, but also the railway infrastructure capacity allocator to submit requests for railway infrastructure capacity in the annual working timetable mode rather than in the individual ad hoc mode. The total price is then verified to the cost of railway infrastructure capacity allocation. This process then ensures the regulation of the demand of railway undertakings on the given route and can influence the decision about the use of the product offered.

Energy After COVID

2020 ◽  
Vol 119 (820) ◽  
pp. 317-322
Author(s):  
Michael T. Klare
Keyword(s):  
Renewable Energy ◽  
Electric Vehicles ◽  
Fossil Fuels ◽  
Fossil Fuel ◽  
White Collar ◽  
Peak Oil ◽  
Business Travel ◽  
Reduce Energy Consumption ◽  
The Cost ◽  
Energy Companies

By transforming patterns of travel and work around the world, the COVID-19 pandemic is accelerating the transition to renewable energy and the decline of fossil fuels. Lockdowns brought car commuting and plane travel to a near halt, and the mass experiment in which white-collar employees have been working from home may permanently reduce energy consumption for business travel. Renewable energy and electric vehicles were already gaining market share before the pandemic. Under pressure from investors, major energy companies have started writing off fossil fuel reserves as stranded assets that are no longer worth the cost of extracting. These shifts may indicate that “peak oil demand” has arrived earlier than expected.

scholarly journals Combining regional to local restoration goals in the Brazilian Atlantic forest

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Cassia M.G. Lemos ◽  
Pedro R. Andrade ◽  
Ricardo R. Rodrigues ◽  
Leticia Hissa ◽  
Ana P. D. Aguiar
Keyword(s):  
Cost Effectiveness ◽  
Atlantic Forest ◽  
Natural Regeneration ◽  
Regional Level ◽  
Target Area ◽  
Brazilian Atlantic Forest ◽  
Regeneration Potential ◽  
Allocation Model ◽  
Alternative Scenarios ◽  
The Cost

AbstractTo achieve regional and international large-scale restoration goals with minimum costs, several restoration commitments rely on natural regeneration, a passive and inexpensive strategy. However, natural regeneration potential may vary within the landscape, mainly due to its historical context. In this work, we use spatially explicit restoration scenarios to explore how and where, within a given region, multiple restoration commitments could be combined to achieve cost-effectiveness outcomes. Our goal is to facilitate the elaboration of forest restoration plans at the regional level, taking into consideration the costs for active and passive restoration methods. The approach includes (1) a statistical analysis to estimate the natural regeneration potential for a given area based on alternative sets of biophysical, land cover, and/or socioeconomic factors and (2) the use of a land change allocation model to explore the cost-effectiveness of combining multiple restoration commitments in a given area through alternative scenarios. We test our approach in a strategic region in the Brazilian Atlantic Forest Biome, the Paraiba Valley in São Paulo State. Using the available data for 2011, calibrated for 2015, we build alternative scenarios for allocating natural regeneration until 2025. Our models indicate that the natural regeneration potential of the region is actually very low, and the cost-effectiveness outcomes are similar for all scenarios. We believe our approach can be used to support the regional-level decision-making about the implementation of multiple commitments aiming at the same target area. It can also be combined with other approaches for more refined analysis (e.g., optimization models).

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