Reducing Neighborhood Peak Loads with implicit Peer-to-Peer energy trading under Subscribed Capacity tariffs
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Increased power demand is a growing problem for distribution system operators (DSO) capable of causing unwanted and expensive grid upgrades. Descending prices for flexible resources and power generation such as house batteries, electric vehicles (EV) and photovoltaic (PV) cells allow for consumers to have a more active role in the energy system and possibly help avoid these expensive upgrades. We developed a mixed integer linear programming (MILP) optimization model performed on a small neighborhood consisting of 30 consumers with different amounts of flexible resources to test grid tariffs impact on power peak reduction. We simulate four different case studies, and the results show an 11% decrease in peak power import during scarcity hours and a more stable import when implicit peer-to-peer (P2P) energy trading is enabled under a subscription based tariff structure. The main conclusion from this study is that there is a clear potential in local electricity markets and capacity based grid tariff structures, especially when metered at neighborhood level.