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• The Monte Carlo method is used to construct the charging load of EV in the planning area divided into four different vehicles type. • The Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines. • Proposed collaborative planning modeling promotes the time-of-use electricity price scheme to change the temporal and spatial characteristics of EV charging load. • The growth of EV charging load in the planning area has not increased the investment and operation-maintenance costs of DES equipment, and reduced carbon transaction costs as well as improved economic of system operation. • Simulations are conducted to verify the effeteness of proposed method. Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment. [ABSTRACT FROM AUTHOR]

Abstract Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock. Highlights • There are significant time-varying correlations and long-run persistence of shocks to the DCCs between three markets. • There is a bi-directional volatility spillover between the coal market and the new energy stock market. • The optimal weight is 82.65% for new energy stock and 17.35% for coal. • The optimal hedge ratio is 17.34% for new energy stock and 49.98% for coal. [ABSTRACT FROM AUTHOR]