A Three-Stage Model to Manage Energy Communities, Share Benefits and Provide Local Grid Services

Abstract

This paper proposes a three-stage model for managing energy communities for local energy sharing and providing grid flexibility services to tackle local distribution grid constraints. The first stage addresses the minimization of each prosumer’s individual energy bill by optimizing the schedules of their flexible resources. The second stage optimizes the energy bill of the whole energy community by sharing the prosumers’ energy surplus internally and re-dispatching their batteries, while guaranteeing that each prosumer’s new energy bill is always be equal to or less than the bill that results for this prosumer from stage one. This collective optimization is designed to ensure an additional collective benefit, without loss for any community member. The third stage, which can be performed by the distribution system operator (DSO), aims to solve the local grid constraints by re-dispatching the flexible resources and, if still necessary, by curtailing local generation or consumption. Stage three minimizes the impact on the schedule obtained at previous stages by minimizing the loss of profit or utility for all prosumers, which are furthermore financially compensated accordingly. This paper describes how the settlement should be performed, including the allocation coefficients to be sent to the DSO to determine the self-consumed and supplied energies of each peer. Finally, some case studies allow an assessment of the performance of the proposed methodology. Results show, among other things, the potential benefits of allowing the allocation coefficients to take negative values to increase the retail market competition; the importance of stage one or, alternatively, the need for a fair internal price to avoid unfair collective benefit sharing among the community members; or how stage three can effectively contribute to grid constraint solving, profiting first from the existing flexible resources.

Integration of Intelligent Neighbourhood Grids to the German Distribution Grid: A Perspective

“…However, this approach implemented a ZIP linearisation and does not consider any multi-objective criteria for neighbourhood grids. A three-stage model to handle energy communities was proposed in [32,33] to provide flexibility services to the distribution grid. However, the implementation of two-stage or three-stage optimisation (also known as hierarchical strategy), differs from the objective of determining a bi-level optimisation approach, to consider the impacts between the grids [34].…”

A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services

“…For the remaining hours, the power for the EVs is zero (9). The constraint in (10) guarantees that at the end of the time slot in which the EVs are connected to the grid, the whole energy amount necessary to fully charge all the EVs has been charged, i.e., the SoC for every EV must be 100%.…”

“…[9] describes a pilot project of a residential VPP developed in Ireland, where ESS scheduling is addressed with different objective functions, like improving individual or common self-consumption, reducing electricity bills, or providing peak-shaving services. A recently published model [10] adds an extra optimization layer to modify the previous schedule of resources based on economic objectives in order to fulfill the constraints of the distribution grid. Notably, none of these works include the EV charging/discharging schedule.…”

“…• Both single-objective and multi-objective methods are used and compared to combine economic and technical targets. -OPF [9] -MILP [10] -MILP/OPF [11] EMS resource MILP [12] Backup storage Several heuristic [13] EMS resource OPF [14] EMS resource MILP [15] EMS resource MPC [16] Charging-V2G if possible None [17] States: charge/discharge/idle, no power modulation GWO [28] -GA [29] EMS resource GA/MILP [30] -GA/MOPSO [31] -GA [32] Charging-V2G only in overloaded hours GA [33] -GA [34] -GA Current Paper EMS resource GA…”