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The adoption of grid-connected electric vehicles (GEVs) brings a bright prospect for promoting renewable energy. An efficient vehicle-to-grid (V2G) scheduling scheme that can deal with renewable energy volatility and protect vehicle batteries from fast aging is indispensable to enable this benefit. This paper develops a novel V2G scheduling method for consuming local renewable energy in microgrids by using a mixed learning framework. It is the first attempt to integrate battery protective targets in GEVs charging management in renewable energy systems. Battery safeguard strategies are derived via an offline soft-run scheduling process, where V2G management is modeled as a constrained optimization problem based on estimated microgrid and GEVs states. Meanwhile, an online V2G regulator is built to facilitate the real-time scheduling of GEVs' charging. The extreme learning machine (ELM) algorithm is used to train the established online regulator by learning rules from soft-run strategies. The online charging coordination of GEVs is realized by the ELM regulator based on real-time sampled microgrid frequency. The effectiveness of the developed models is verified on a UK microgrid with actual energy generation and consumption data. This work can effectively enable V2G to promote local renewable energy with battery aging mitigated, thus economically benefiting EV owns and microgrid operators, and facilitating decarbonization at low costs.

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This paper attempts to disclose the law of land use variation in the Northeast China Tiger and Leopard National Park (NCTLNP), and provide theoretical basis for eco-environment protection of the national park in future. The data on land use variation in four phases (i.e., 1995, 2005, 2015, and 2018) were selected for analysis based on the geographical information system (GIS). The variation and transfer features of land use were quantified, with the aid of single land use dynamic degree, comprehensive land use dynamic degree, and land use transfer matrix. The results show that: (1) In 1995-2008, the main land types in the NCTLNP were forest, grassland, and cultivated land, which took up more than 90% of the total area; the grassland area increased, while the areas of cultivated land and forest declined; forest was the land use with the largest transfer-out area (523.59 km2), about 55.29% of the total transfer-out area in the study area; (2) In the sample period, NCTLNP witnessed significant transfers between land uses; the transfers mostly occurred between forest, grassland, and cultivated land; forest transfers were observed in every county and city; the transfer of forest to grassland mainly concentrated in Dongning City. The research results lay the basis for building up a stereo eco-environment monitoring network in the study area, and provide the research direction for eco-environment protection in the NCTLNP.

The long-term uncertainty of multi-energy demand poses significant challenges to the coordinated pricing of multiple energy systems (MES). This paper proposes an integrated network pricing methodology for MES based on the long-run-incremental cost (LRIC) to recover network investment costs, affecting the siting and sizing of future distributed energy resources (DERs) and incentivizing the efficient utilization of MES. The stochasticity of multi-energy demand growth is captured by the Geometric Brownian Motion (GBM)-based model. Then, it is integrated with a system operation model to minimize operation costs, considering low-carbon targets and flexible demand. Thereafter, the kernel density estimation (KDE) method is used to perform the probabilistic optimal energy flow (POEF) to obtain energy flows under uncertain load conditions. Based on the probability density functions (PDFs) of energy flows, an LRIC-based network pricing model is designed, where Tail Value at Risk (TVaR) is used to model the risks of loading levels of branches and pipelines. The performance of the proposed methodology is validated on a typical MES. The proposed pricing method can stimulate cost-effective planning and utilization of MES infrastructures under long-term uncertainty, thus helping reduce low-carbon transition costs.

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