Section: New Results

Energy Optimization and Smart Grids Simulation

Large-scale decentralized photovoltaic (PV) generators are currently being installed in many low-voltage distribution networks. Without grid reinforcements or production curtailment, they might create current and/or voltage issues. In [13], [45], we consider the use the advanced metering infrastructure (AMI) as the basis for PV generation control. We show that the advanced metering infrastructure may be used to infer some knowledge about the underlying network, and we show how this knowledge can be used by a simple feed-forward controller to curtail the solar production efficiently.

We developed a environment for co-simulating electrical networks, telecommunication networks and online learning algorithms [3]. One the outputs of this work was to allow us to perform realistic numerical simulations of active distribution networks. We used this simulator to compare our proposed controller with two other controller structures: open-loop, and feedback P (U) and Q(U). We demonstrate that our feed-forward controller –that requires no prior knowledge of the underlying electrical network– brings significant performance improvements as it can effectively suppress over-voltage and over-current while requiring low energy curtailment. This method can be implemented at low cost and require no specific information about the network on which it is deployed.

Finally, we study demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to rearrange their consumption in order to reduce production costs. Such mechanisms are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users. In in[4], we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the different mechanisms and their sensitivity to forecast errors. At a high level, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than those suggested by previous studies, but that the gap reduces when the population's flexibility increases.