Optimal Utilization of Renewable Energies in Low Voltage (LV) Power Distribution Systems
The increasing amount of distributed renewable energy resources (DER) installed in low voltage power distribution grids (LVDG) imposes new challenges on the grid operation. Load flows will not be predictable anymore and a monitored and managed grid operation will become necessary. Many DER as well as new types of electric loads increase the risk of critical load flows. As the extension of grid capacity is costly and inefficient, new control algorithms are needed to manage the distributed power production and consumption within LVDG, aiming at maximum usage of DER while keeping grid parameters within their limits.Therefore, a utility-based optimization is proposed, a concept that is common practice in com-munication networks, and applied to the problem of determining optimal power generation of DER and consumption of controllable loads within an LVDG. Optimality is understood as maximum utility experienced by producers and consumers. While the optimization algorithm itself shall be ignorant of constraints on grid currents and voltages, it is closely coupled to a corrective power flow module that identifies critical situations and uses sensitivity analysis to propose corrections on single units’ power generation or consumption.At first, a solution of the static problem is implemented at a central place, meaning that power level bounds and producer/consumer utility functions remain constant during the optimization process. This shall demonstrate the applicability of the concept.The final goal is to develop a distributed optimization algorithm under dynamically changing pa-rameters. It shall run at a high update rate, applying incremental power level changes towards the optimum in each time step. A state estimation in distribution grids is employed for identification of the actual grid state. The high update frequency presumably allows for linearized system modelling. The resulting influences on convergence and stability of the optimization algorithm are main questions to be addressed. The distributed nature, which is also well known from communication networks, is expected to increase robustness and ensure scalability while limiting computing demands on single devices. These properties are crucial for reliable and economically efficient operation of a dynamically changing LVDG. At the same time, the distributed approach requires extensive exchange of information between the participants, putting a high burden on the communication network. The performance of the communication network therefore has a crucial impact on the performance of the LVDG optimization algorithm. For the evaluation of the applicability of the distributed algorithm and for the investigation of the overall performance of the proposed utility-based approach, a model of the communication networks is established. This model mainly focuses on the delay distribution functions using mathematical models such as Signal Flow Graphs, Stochastic Network Calculus and Queueing Theory.