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The ever-increasing electricity price and energy consumption in high-speed railway industry push railway companies to seek a promising way to realize their sustainable developments. Making full use of the solar resource along with high-speed railways can be a potential solution to cut the electricity bill, bring more profit to railway companies and realize the decarbonization of high-speed railway industry. This paper studies the optimal planning of distributed photovoltaic generation (DPVG) and energy storage system (ESS) for the traction power supply system (TPSS) of high-speed railway. A quantitative method is proposed to study the time and space characteristics of photovoltaic generation and electricity demand of high-speed trains. An integrated cost-benefit analysis framework is developed to evaluate the effect of DPVG and ESS on the economy of TPSS. To derive the optimal planning scheme and energy management strategy of DPVG and ESS, a mathematical programming model with the objective of minimizing the total cost is proposed to seek the most economical solution. A hybrid global optimal solution approach is developed to solve the model. A real-world case of Beijing-Baoding high-speed railway in China is used to illustrate the capability and characteristics of the proposed model. The computational results show that DPVG is able to supply 32.5 % electricity demand of high-speed trains. The integration of DPVG and ESS can help railway company save 4.2 million CNY each year in Beijing-Baoding high-speed railway. This paper demonstrates the potential and applicability of DPVG and ESS in high-speed railway industry. • A novel distributed photovoltaic generation model is proposed for the traction power supply system of high-speed railway. • A global optimal algorithm is provided to derive the most economical planning scheme and energy-management strategy. • Photovoltaic generation can help Beijing-Baoding high-speed railway save 4.2 million China Yuan each year. • Solar energy is able to supply 32.5% electricity demand of Beijing-Baoding high-speed railway. [ABSTRACT FROM AUTHOR]

The development of electricity retailers with energy storage systems expands the energy use ways of users, promotes the consumption of clean energy power generation, and facilitates the development of electricity market. However, due to the imperfect trading mechanism and uncertainties of power supply and demand, the business risk continues to be large, which leads to limited sustainable development of electricity retailers. Therefore, based on the design of a trading mechanism of electricity retailers with energy storage systems in the electricity market, this paper constructs a multi-objective stochastic optimization model of the retailers' flexible purchase of electricity, aiming at minimizing the cost of electricity retailers and maximizing the consumption of clean energy power generation. In the model, the uncertainty of supply and demand and the demand response are taken into consideration. The results show that: (1) Compared with the conventional optimization model, considering the uncertainty in the medium-and-long-term electricity trading, although the demand for clean energy is reduced, the retail cost of temporary electricity in the day-ahead trading is also reduced. (2) The retailers make full use of flexible power charging and discharging, so the extra cost of purchasing electricity in the day-ahead trading caused by the uncertainty of clean energy is reduced via the energy storage demand response. (3) A price-based demand response can change the electricity purchase demand of retailers, improve the transaction volume between retailers and clean power enterprises in the medium-and-long-term electricity trading, and reduce the energy purchase cost while improving the consumption of clean energy power generation. (4) Due to the impact of initial investment cost and other factors, there is no obvious linear relationship between the capacity of energy storage systems and the reduction of retail energy purchase cost; therefore, it is necessary to apply energy storage systems reasonably to achieve the optimal economic benefits. • The trading strategy for ER-ESS participating in electricity market is formulated. • The energy storage and price-based demand response models are constructed. • The respective solving algorithms for the regular and uncertainty model are built. [ABSTRACT FROM AUTHOR]