Change of morphometric characteristics of Kaunas sea in the Kruonis PSHP influence zone

In this paper, analysis and evaluation of Kaunas Sea (the reservoir of Kaunas HEPP on the river Nemunas) shore and bottom deformations, which occurred during the last 27 years in the 6 km stretch at the mouth of Streva river, is carried out. For the investigations, data of bathymetric plan made in 1983 and results of bathymetric measurements performed in 2011 were used. By means of GIS software and technologies the numerical model of the relief of the investigated part of Kaunas Sea bottom and shore was worked out. By employing the model, 13 cross-sections along the traces of bathymetric measurements were made. By comparing bottom levels quantified in cross-sections in 1983 with bottom levels measured in 2011 it was found that morphometric characteristics of Kaunas Sea mostly changed during the PSHP construction period. Now, in the investigated zone of Kaunas Sea and in the reverse canal, more intensive are processes of silting and sedimentationVytauto Didžiojo universitetasŽemės ūkio akademij

Towards the Automated Extraction of Flexibilities from Electricity Time Series

Several recent and ongoing smart grid projects aim at incorporating more renewable energy sources (RES) into the energy production. Among them, the European MIRABEL project tackles this problem by managing flexibilities on energy demand and supply. Typically, this project assumes that some parts of the energy demand can be shifted when the RES production is sufficient, e.g., the washing machine can be turned on when the wind blows. To express these flexibilities, the project introduces the core-concept of flexoffer. Unfortunately, flex-offer data from the consumers is not yet available. Consequently, in order to test and evaluate the MIRABEL prototype, the flex-offers are extracted from the real world electricity consumption time series. In this work, we investigate, discuss, and experiment several ways to automatically capture flexibility within the electricity time series. Particularly, we show that incorporating domain knowledge, for instance, appliance information or appliance usage frequencies, can improve a lot the outcome of the flex-offer generation and, thus, the MIRABEL project global evaluation. 1

Poster: Uncertain FlexOffers, a scalable, uncertainty-Aware model for energy flexibility

As the usage of Renewable Energy Sources (RES) in electricity grids increases in popularity, energy flexibility has a crucial role. The most common weaknesses of current flexibility models are: i) being hard-coded for specific devices, ii) not scaling for long time horizons and many devices, iii) losing a lot of flexibility if the model is approximated, and iv) not considering the uncertainty affecting flexibility representations, which causes the model to capture too much excess flexibility when imbalance penalties are high. The FlexOffer (FO) model can perform approximations of flexibility with good accuracy across different devices, and scales well to long time horizons and many devices: this work extends FOs to uncertain FOs (UFOs), which keep the good properties while capturing uncertainty. We show that UFOs are very fast by performing optimization in under 5.27 seconds for a 24 hours time horizon, while exact models use more than 29.05 hours for even a 6 hours 15 minutes time horizon, making them totally infeasible in practice. UFOs can capture more flexibility than other uncertain models: UFOs considering energy dependencies can model flexibility without losses for a charging battery, and retain of the total flexibility for batteries and for EVs when imbalance penalties are high, compared to and respectively for other models. UFOs allow to aggregate up to 6000 loads for up to 96 time units while retaining of the total flexibility: exact models fail already for 330 loads or 21 time units.</p

Data management in the MIRABEL smart grid system

Nowadays, Renewable Energy Sources (RES) are attracting more and more interest. Thus, many countries aim to increase the share of green energy and have to face with several challenges (e.g., balancing, storage, pricing). In this paper, we address the balancing challenge and present the MIRABEL project which aims to prototype an Energy Data Management System (EDMS) which takes benefit of flexibilities to efficiently balance energy demand and supply. The EDMS consists of millions of heterogeneous nodes that each incorporates advanced components (e.g., aggregation, forecasting, scheduling, negotiation). We describe each of these components and their interaction. Preliminary experimental results confirm the feasibility of our EDMS