Comparing Optimization Strategies in Local Electricity Markets

Abstract

Energy communities have emerged as a new way of exploiting and incentivizing local energy production based on local Peer-to-Peer (P2P) trading and battery flexibility. Local electricity markets with storage units can possibly lead to grid upgrades deferral by reducing and controlling peak demand, and achieve higher levels of self-sufficiency. This paper presents three different system strategies to understand the value of local electricity markets. The first strategy aims at reducing the electricity and grid-related costs for each consumer and prosumer within a community separately. The second control strategy, called the centralized strategy, looks at the consumers and prosumers as a community and aims at reducing the costs for the whole community. None of the aforementioned control system strategies take into account the peak power demand seen from the grid. To study the relationship between the total cost of electricity and the peak power demand, the third case focuses on a multi-objective optimization (MOO) model aiming to reduce the electricity and grid-related costs and to reduce the peak demand from the grid, simultaneously. Results show that the centralized optimization strategy gives the lowest total costs for the community compared to the decentralized strategy. It does also reduce the amount of energy consumed from the main grid. The results from the MOO show that for the case studies there is a dependency between total electricity costs and peak power. Also, a small increase in cost can reduce the peak power demand by a significant amount.