Abstract
This paper presents control relationships between the low voltage distribution grid and flexibilities in a peer-to-peer local energy community using a stratified control strategy. With the increase in a diverse set of distributed energy resources and the next generation of loads such as electric storage, vehicles and heat pumps, it is paramount to maintain them optimally to guarantee grid security and supply continuity. Local energy communities are being introduced and gaining traction in recent years to drive the local production, distribution, consumption and trading of energy. The control scheme presented in this paper involves a stratified controller with grid and flexibility layers. The grid controller consists of a three-phase unbalanced optimal power flow using the holomorphic embedding load flow method wrapped around a genetic algorithm and various flexibility controllers, using three-phase unbalanced model predictive control. The control scheme generates active and reactive power set-points at points of common couplings where flexibilities are connected. The grid controller’s optimal power flow can introduce additional grid support functionalities to further increase grid stability. Flexibility controllers are recommended to actively track the obtained set-points from the grid controller, to ensure system-level optimization. Blockchain enables this control scheme by providing appropriate data exchange between the layers. This scheme is applied to a real low voltage rural grid in Austria, and the result analysis is presented.
Highlights
In recent years, local energy communities (LECs) are gaining interest in Europe and the world by introducing new regulations for its formation, operation and control
Introduction is due to increased distributed renewable energy sources (DERs) and new loads such as electric vehicles, storage and heat pumps, hereafter referred to as nextgen loads, in low voltage distribution grids
optimal power flow (OPF) is run and optimal PQ set-points are generated at certain critical buses where smart buildings are connected; These optimal set-points are written into the blockchain; end Algorithm 2: Control actions performed at flexibility level controller
Summary
Local energy communities (LECs) are gaining interest in Europe and the world by introducing new regulations for its formation, operation and control. Models describing the low voltage distribution grids are based on the transmission system’s supervisory control and data acquisition systems at the transmission level This host functions like load (LF) and optimal power flow (OPF), using models that are single phased. In [27], a multi-time scale and stage optimization method is proposed to control flexibilities such as air conditioning, heating and ventilation systems, and plug-in hybrid electric vehicles are presented It uses a constrained stochastic optimization algorithm using MPC to minimize costs, peak power and consumer comfort. To overcome the limitations presented in the literature, the authors present a novel stratified control scheme with grid and flexibility layers The former consists of three-phase unbalanced optimal power flow using the holomorphic embedding load flow method (HELM) and the latter, three-phase unbalanced model predictive control. Optimal scheduling of PQ set-points at critical buses, where smart buildings are connected and model predictive control results from flexibilities to the reference optimal schedules from the grid controller (see Section 7)
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