Laurynas Siksnys

A Location Privacy Aware Friend Locator

A location-based service called friend-locator notifies a user if the user is geographically close to any of the users friends. Services of this kind are getting increasingly popular due to the penetration of GPS in mobile phones, but existing commercial friend-locator services require users to trade their location privacy for quality of service, limiting the attractiveness of the services. The challenge is to develop a communication-efficient solution such that (i) it detects proximity between a user and the users friends, (ii) any other party is not allowed to infer the location of the user, and (iii) users have flexible choices of their proximity detection distances. To address this challenge, we develop a client-server solution for proximity detection based on an encrypted, grid-based mapping of locations. Experimental results show that our solution is indeed efficient and scalable to a large number of users.

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.

Towards Integrated Data Analytics: Time Series Forecasting in DBMS

Integrating sophisticated statistical methods into database management systems is gaining more and more attention in research and industry in order to be able to cope with increasing data volume and increasing complexity of the analytical algorithms. One important statistical method is time series forecasting, which is crucial for decision making processes in many domains. The deep integration of time series forecasting offers additional advanced functionalities within a DBMS. More importantly, however, it allows for optimizations that improve the efficiency, consistency, and transparency of the overall forecasting process. To enable efficient integrated forecasting, we propose to enhance the traditional 3-layer ANSI/SPARC architecture of a DBMS with forecasting functionalities. This article gives a general overview of our proposed enhancements and presents how forecast queries can be processed using an example from the energy data management domain. We conclude with open research topics and challenges that arise in this area.

Aggregating and Disaggregating Flexibility Objects

In many scientific and commercial domains, we encounter flexibility objects, i.e., objects with explicit flexibilities in a time and an amount dimension (e.g., energy or product amount). Applications of flexibility objects require novel and efficient techniques capable of handling large amounts of such objects while preserving flexibility. Hence, this paper formally defines the concept of flexibility objects (flex-objects) and provides a novel and efficient solution for aggregating and disaggregating flex-objects. Out of the broad range of possible applications, this paper will focus on smart grid energy data management and discuss strategies for aggregation and disaggregation of flex-objects while retaining flexibility. This paperfurther extends these approaches beyond flex-objects originating from energy consumption by additionally considering flex-objects originating from energy production and aiming at energy balancing during aggregation. In more detail, this paper considers the complete life cycle of flex-objects: aggregation, disaggregation, associated requirements, efficient incremental computation, and balance aggregation techniques. Extensive experiments based on real-world data from the energy domain show that the proposed solutions provide good performance while satisfying the strict requirements.

Arrowhead compliant virtual market of energy

Industrial processes use energy to transform raw materials and intermediate goods into final products. Many efforts have been done on the minimization of energy costs in industrial plants. Apart from working on “how” an industrial process is implemented, it is possible to reduce the energy costs by focusing on “when” it is performed. Although, some manufacturing plants (e.g. refining or petrochemical plants) can be inflexible with respect to time due to interdependencies in processes that must be respected for performance and safety reasons, there are other industrial segments, such as alumina plants or discrete manufacturing, with more degrees of flexibility. These manufacturing plants can consider a more flexible scheduling of the most energy-intensive processes in response to dynamic prices and overall condition of the electricity market. In this scenario, requests for energy can be encoded by means of a formal structure called flex-offers, then aggregated (joining several flex-offers into a bigger one) and sent to the market, scheduled, disaggregated and transformed into consumption plans, and eventually, into production schedules for given industrial plant. In this paper, we describe the flex-offer concept and how it can be applied to industrial and home automation scenarios. The architecture proposed in this paper aims to be adaptable to multiples scenarios (industrial, home and building automation, etc.), thus providing the foundations for different concept implementations using multiple technologies or supporting various kinds of devices.

MIRABEL DW: managing complex energy data in a smart grid

In the MIRABEL project, a data management system for a smart grid is developed to enable smarter scheduling of energy consumption such that, e.g., charging of car batteries is done during night when there is an overcapacity of green energy from windmills etc. Energy can then be requested by means of flex-offers which define flexibility with respect to time, amount, and/or price. In this paper, we describe MIRABEL DW, a data warehouse (DW) for the management of the large amounts of complex energy data in MIRABEL. We present a unified schema that can manage data both at the level of the entire electricity network and at the level of individual nodes, such as a single consumer node. The schema has a number of complexities compared to typical DW schemas. These include facts about facts and composed non-atomic facts and unified handling of different kinds of flex-offers and time series. We also discuss alternative data modeling strategies and present typical queries from the energy domain and a performance study.

Aggregating and disaggregating flexibility objects

Flexibility objects, objects with flexibilities in time and amount dimensions (e.g., energy or product amount), occur in many scientific and commercial domains. Managing such objects with existing DBMSs is infeasible due to the complexity, data volume, and complex functionality needed, so a new kind of flexibility database is needed. This paper is the first to consider flexibility databases. It formally defines the concept of flexibility objects (flex-objects), and provide a novel and efficient solution for aggregating and disaggregating flex-objects. This is important for a range of applications, including smart grid energy management. The paper considers the grouping of flex-objects, alternatives for computing aggregates, the disaggregation process, their associated requirements, as well as efficient incremental computation. Extensive experiments based on data from a real-world energy domain project show that the proposed solution provides good performance while still satisfying the strict requirements.

Dependency-based FlexOffers: scalable management of flexible loads with dependencies

Smart grid actors such as aggregators need scalable yet simple and powerful ways to aggregate, optimize, and disaggregate large collections of flexible loads (e.g., from heat-pumps and electric vehicles) based on models of flexible loads, e.g., state-space models. Based on system- and user-specific variables and constraints, e.g., power or temperature bounds, such models specify dependencies between system inputs, states, and energy amounts consumed/produced at discrete time intervals. Traditional approaches, using exact or simple approximate models, do not scale well, introduce errors, or unacceptably reduce the flexibility (solution space) when total energy needs to be optimized for many time intervals while respecting a large number of model constraints. To mitigate these problems, we propose the so-called dependency-based flexoffer (DFO) -- a low-complexity generalized model that allows efficiently approximating various exact models of both individual and aggregated loads while retaining most of the flexibility. We propose algorithms for generating DFOs as inner and outer approximations of the exact models. Additionally, we provide efficient algorithms for aggregating DFO instances and disaggregating energy series while respecting all DFO constraints and ensuring energy balance. An extensive experimental evaluation with thermostatic (heat-pump) and storage-like (battery) loads shows that DFOs offer a good trade-off between performance and flexibility when a large number of flexible loads need to be aggregated and/or optimized.

Generation and Evaluation of Flex-Offers from Flexible Electrical Devices

There exists an immense potential in utilizing the demand reduction and shifting potential (flexibility) of household devices to confront the challenges of intermittent Renewable Energy Sources. However, a widely accepted general flexibility extraction and evaluation process is missing. This paper proposes a generalized Flex-offer Generation and Evaluation Process (FOGEP) that extract flexibility from wet-devices (e.g. dishwashers), electric vehicles, and heat pumps and capture it in a unified model, a so-called flex-offer. The proposed process analyses the past consumption behavior of a device to automatically capture flexibility in its usage. It utilizes two device-level forecasting techniques and algorithms to capture various attributes and temporal patterns required for flexibility extraction. Further, the paper evaluates the performance of FOGEP regarding the accuracy of the extracted flexibility and performs an economic assessment to identify the device-specific best market to trade flexibility. The experimental results, based on real-world measurement data, show that household devices have up to 32% of reduction and 15 hours of shifting flexibility in their energy demands. Further, FOGEP can extract flexibility with up to 98% accuracy. The flexibilities can provide up to 51% and 11% savings in the spot and regulating market for Balance Responsible Party (BRP) and/or consumer, respectively.

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 flex-offer. 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.