Energy Management of Battery Switch Station of Electric Vehicles in Two Settlement Electricity Market

Battery switch station (BSS) can resolve the conflict between life and charging time of batteries of electric vehicles by offering quick replacement services though pre-charging with small power. This paper proposed an energy management model for BSS in two settlement electricity markets. The present program focused on how to satisfy demands of electric vehicles with lowest cost in electricity markets, which are the key issue of lowering operating costs of BSS. The program adopted the mean-variance theory to control financial risks of uncertainties of prices, and simulation results showed the effectiveness of the proposed program

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Optimal Scheduling and Economic Analysis of Hybrid Electric Vehicles in a Microgrid

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2018-0104 ◽

2018 ◽

Vol 19 (6) ◽

Cited By ~ 1

Author(s):

George Fernandez Savari ◽

Vijayakumar Krishnasamy ◽

Josep M. Guerrero

Keyword(s):

Economic Analysis ◽

Electric Vehicles ◽

Electricity Market ◽

Hybrid Electric Vehicles ◽

Shuffled Frog Leaping Algorithm ◽

Charging Time ◽

High Penetration ◽

Shuffled Frog Leaping ◽

Charging Stations ◽

Priority Algorithm

Abstract A projected high penetration of electric vehicles (EVs) in the electricity market will introduce an additional load in the grid. The foremost concern of EV owners is to reduce charging expenditure during real-time pricing. This paper presents an optimal charging schedule of the electric vehicle with the objective to minimize the charging cost and charging time. The allocation of EVs should satisfy constraints related to charging stations (CSs) status. The results obtained are compared with the two conventional algorithms and other charging algorithms: Arrival time-based priority algorithm (ATP) and SOC based priority algorithm (SPB), Particle Swarm Optimization (PSO) and Shuffled Frog Leaping Algorithm (SFLA). Also, the CS is powered by the main grid and the microgrid available in the CSs. The EVs charging schedule and the economic analysis is done for two cases: (i) With Grid only (ii) With Combined Grid & microgrid. The load shifting of EVs is done based on the grid pricing and the results obtained are compared with the other cases mentioned.

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A Novel LCOE Pricing Model for Renewable Energy with Power Purchase Agreement: A Case Study in China

Processes ◽

10.3390/pr9101780 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1780

Author(s):

Jun Dong ◽

Dongran Liu ◽

Yaoyu Zhang ◽

Yuanyuan Wang ◽

Xihao Dou

Keyword(s):

Renewable Energy ◽

Electricity Markets ◽

Electricity Market ◽

Power Plants ◽

Operating Cost ◽

Operating Costs ◽

Pricing Model ◽

Correlation Degree ◽

Energy Companies

To reach Carbon Peak in 2030 and Carbon Neutrality in 2060, China is developing renewable energy at a fast pace. Renewable energy enterprises will participate in the power market in an all-round way as China gradually improves its electricity market. Signing the Power Purchase Agreement (PPA) helps renewable energy companies to avoid market risk and achieve sustainable development. Therefore, a novel PPA pricing model is proposed in our research. Based on the theory of the Levelized Cost of Energy (LCOE), our model considers system operating costs in China’s dual-track electric power sector, which is both government-guided and market-oriented. First of all, key influencing factors of the PPA agreement are analyzed in view of the developments of the renewable energy and electricity markets in China. Next, the design of pricing strategies for renewable energy power plants to cope with market challenges is presented through a photovoltaic project case study. The results show that when the operating costs of the system are considered and other conditions remain unchanged, the investment payback period of the new energy power station will change from 10.8 years to 13.6 years. Furthermore, correlation degree and sensitivity coefficient (SAF) were introduced to conduct correlation analysis and sensitivity analysis of key elements that affect the pricing of the PPA. Finally, it is concluded that the utilization hours of power generation have the most significant effect on the PPA price, while the system’s operating cost is the least sensitive factor. The study expands the application of LCOE, and provides a decision-making solution for the PPA pricing of renewable energy power enterprises. It is expected to help promote power transactions by renewable energy companies.

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