CAPEX vs FLEX: The optimal investment mix to integrate decentralized electricity production

With the increased popularity of decentralized production, the network faces congestion. Therefore, to avoid damaging over-voltage, solar panels are disconnected from the distribution grid. We identify this disconnection as a negative externality linked to the total installed capacity in a given line. Ultimately, this disconnection could limit higher penetration of decentralized production in the system. Possible solutions are more self-consumption, network reinforcement or expansion (or Capex), and investment in flexibility resources (or Flex). There are a few studies that focus on the trade-off between capex and flex as an efficient solution to increase grid hosting capacity. The research questions are, what is the optimal mix of investment between capex and flex ? Does the market offer sufficient incentives to invest in both capex and flex ? What are the instruments available to promote the efficient investment ? We address this trade-off issue using an agent-based modelling approach, taking into account the externality between prosumers. The model is characterized by several prosumers, a single retailer and a DSO overseeing the entire economy. We introduce the externality through a probability of non-disconnection, which is a function of capex and flex. By comparing the optimal mix of Capex and Flex with the decentralized outcome, we show that, due to the externality, they do not coincide. Market-based Flex is lower compared to optimal level, and Capex too high. Then, we look at solutions to restore the first best such as tariff distortion and flex subsidization. We find that, we can encourage prosumers to exchange less with the grid by increasing variable tariffs for prosumers. Additionally, we can incentivize prosumers to invest in flex and increase their self-consumption with subsidies, but there are difficulties in implementation due to information asymmetry. Thus, flex subsidies improve the outcome but do not achieve the first best due to heterogeneity.