Long-term forecast of local electrical demand and evaluation of future impacts on the electricity distribution network

Abstract

Enedis (France's main electricity DSO) and Mines Paris Tech are working on a new method called MOSAIC that aims at assessing the impact of long term local development projects on the electrical grid. The MOSAIC method uses a bottom-up simulation tool able to determine the current and future consumption and production load curve of an area. The consumption and production simulators are based on a multi-level data model that makes it possible to run a simulation even if some data are missing. The simulation parameters are calibrated by comparing the simulated load curves with observations on MV feeders (for the consumption simulator) and on 100 renewable energy producers (PV and WP). Once the simulators are calibrated for the current situation, the load curve of each development scenario of the area is estimated (the evolution scenarios are proposed by the local government, based on infeed growing, building projects …). Enedis and Mines Paris Tech are now working on linking the load curve simulators with more traditional electrical study tools. From the simulated load curve, we estimate the most likely maximal power of the grid infrastructures and use these values in the load flow tool called ERABLE to evaluate the impact of the scenarios on the grid. Initial tests were successful and this method will be further developed in 2017 on ten experimentation projects in France. Each project will help to develop new functionalities such as integrating Demand Response or electrical vehicle infrastructures.

Multinodal load forecasting for distribution systems using a fuzzy-artmap neural network

“…For example, in [28], the authors propose a methodology for predicting the electrical current in several transmission lines based on a statistical method. Reference [29] presents a system called MOSAIC that uses a bottom-up simulation tool to determine the current and future consumption and production load curve in an area of the electrical system. Reference [30] introduces the basic principles of an ANN with its trainingphase executed by a backpropagation method [4] for predicting the short-term load of a distribution network.…”

Evolution of electrical distribution grid sizing considering self-consumption of local renewable production

“…a distribution station, can handle this new client. To evaluate the distribution grid's capacity to receive a new load, Enedis [3] defines dimensioning events that the grid needs to be able to resist. In this paper, we do not consider voltage issues and only consider the three following sizing events:…”

“…The description of the different household usages is defined by statistical distributions, thus making it possible to generate different consumption scenarios. The method for generating input data for MOSAIC per MV feeder is described in [3]. Different temperature and irradiation data are used to generate multiple scenarios, combining consumer behaviour and weather conditions in order to provide inputs for MOSAIC.…”

“…Moreover, as MOSAIC is a bottom-up method, it can be used to simulate the load at a smaller scale, such as LV feeders, which are not monitored by Enedis. This downscaling approach using MOSAIC is described and illustrated in [3]. Load curves are simulated using 8 years of meteorological data.…”

Abnormal behaviour analysis algorithm for electricity consumption based on density clustering

“…With the development of smart grid, China has cumulatively installed hundreds of millions of smart meters [1]. The large‐scale real‐time electricity consumption information collection through smart meters is very important to the rapid development of electricity big data technology [2, 3]. The electricity consumption data collected by the electricity company potentially contains information about the user electricity consumption, and there is a certain correlation between these electricity consumption data [4–6].…”