Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging

As the infiltration rate of renewable energy generation (REG) is increasing, the power balance of power systems needs to be maintained urgently. Electric vehicle (EV) as a widely distributed demand-side flexibility resource can be utilized to address the supply-demand balance of the power system. Current EV charging management strategies do not consider user-side demand flexibility and cannot accurately guide users to charge. To address the problem that EV charging demand cannot be accurately directed, this study proposes a fractional-order long-term price guidance mechanism (FLPGM) for EV charging with a bidirectional prediction based on an attention mechanism (AM). The FLPGM combines an AM with a bidirectional gated recurrent unit (BiGRU) and a long-short term memory (LSTM) and applies a high-precision fractional-order stochastic dynamic differentiation method that considers the effects of community health index, EV types, commuting distance, holidays, and power status on charging demand. FLPGM can equate customer charging demand with REG and reduce the cost of charging to customers, resulting in a balance between supply and demand in a long-term electricity market environment. In this study, FLPGM is applied to an electric vehicle charging model (EVCM) for experiments. The test results show that: the charging load of EVs under the FLPGM matches the energy supply by 98.85%; the EVCM under the FLPGM has a charging cost of only 67.61% of the cost in the natural state and can save 79.93% of the potential savings; the EVCM under the FLPGM maintains the balance between supply and demand while reducing charging costs for users, maximizing the benefits for all parties.