A time-series dynamic optimization model for distributed photovoltaic capacity planning considering the coupling of capacity and sales price

Abstract

In the context of increasing photovoltaic penetration and reducing midday sales electricity price, whether users' willingness to invest in new distributed photovoltaics (DPV) will be reduced remains to be studied. Considering that the purpose of the user's installment of DPV is to reduce the cost of electricity, and recognizing the significant impact of investment cost and user psychology on investment willingness. Firstly, technological progress and herd mentality are taken into account, and a time-series dynamic robust optimization model for the capacity planning of DPV is constructed with the objective of minimizing user electricity cost. Secondly, a residential community is used as case study to carry out model verification. The results show that the new capacity of DPV initially grows rapidly at an average rate of 11.58 %, and then slowly declines at an average rate of 1.77 %, confirming our hypothesis. Finally, through an in-depth analysis of the impact mechanisms of dynamic electricity price changes and user consumption behavior on DPV deployment capacity, we have uncovered the underlying reasons for the decline in new installations. Based on the research findings, this study proposes policy recommendations for promoting sustainable development of DPV through reasonable regulation of electricity price reduction rates.