Date: Wednesday 27 March 2019
Time: 14:30 - 16:00
Location / Room: Room 6
Chair:
Davide Quaglia, University of Verona, IT
Co-Chair:
Massimo Poncino, Politecnico di Torino, IT
In this session, three approaches to optimizing smart grid and photovoltaic systems are presented, targeting cost, efficiency, and privacy.
| Time | Label | Presentation Title Authors |
|---|---|---|
| 14:30 | 7.6.1 | COST/PRIVACY CO-OPTIMIZATION IN SMART ENERGY GRIDS Speaker: Alma Proebstl, Technical University of Munich, DE Authors: Alma Proebstl1, Sangyoung Park2, Sebastian Steinhorst1 and Samarjit Chakraborty3 1Technical University of Munich, DE; 2Technische Universität München, DE; 3TU Munich, DE Abstract The smart grid features real-time monitoring of electricity usage such that it can control the generation and distribution of electricity as well as utilize dynamic pricing in response to the demands. Smart metering systems continuously monitor the electricity usage of customers, and report it back to the Utility Provider (UP). This raises privacy concerns regarding the undesired exposure of human activity and time-of-use of home appliances. Photovoltaics (PV) and a residential Electrical Energy Storage (EES) have proven to be effective in mitigating the privacy concerns. However, this comes at several costs: Installation of PV and EES, its subsequent aging and the possibly increased electricity cost. We quantify the trade-off between privacy exposure and financial costs by formulating a stochastic dynamic programming problem. Our analysis shows that i) there is a quantifiable trade-off between the financial cost and privacy leakage, ii) proper control of the system is crucial for both metrics, iii) a strategy solely focusing on privacy results in high financial costs, and iv) that for a typical residential setting, the costs for a trade-off solution lie in the range of 600 US$-1700 US$. Load flattening is also known as peak shaving and we propose to split costs among UP and user due to the mutual benefit. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 15:00 | 7.6.2 | A LOW-COMPLEXITY FRAMEWORK FOR DISTRIBUTED ENERGY MARKET TARGETING SMART-GRID Speaker: Kostas Siozios, Dept. of Physics, Aristotle University of Thessaloniki, GR Authors: Kostas Siozios and Stylianos Siskos, Department of Physics, Aristotle University of Thessaloniki, GR Abstract With the increasing connection of distributed energy resources, traditional energy consumers are becoming prosumers, who can both dissipate and generate energy in a smart-grid environment. This enables the wide adoption of dynamic pricing environment, where demand and price forecasting for determining prices and scheduling loads are applied. Throughout this paper we propose a Peer-to-Peer (P2P) platform, as well as a light-weighted decision-making mechanism based on game theory to support the energy trading. Experimental results based on real data validate the efficiency of proposed framework, as it achieves considerable reduction to the energy cost (on average 87%) as compared to the corresponding cost from the main-grid. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 15:30 | 7.6.3 | IRRADIANCE-DRIVEN PARTIAL RECONFIGURATION OF PV PANELS Speaker: Enrico Macii, Politecnico di Torino, IT Authors: Daniele Jahier Pagliari, Sara Vinco, Enrico Macii and Massimo Poncino, Politecnico di Torino, IT Abstract Adaptive reconfiguration of a photo-voltaic (PV) panel by means of a switch network is a well-known approach to tackle shading issues dynamically and with a reasonable cost. Most of these approaches assume however that the entire panel is reconfigurable, resulting in high installation costs due to the large wiring overhead required by this solution. In this work we propose an architecture in which only a portion of the panel is reconfigurable, while minimizing the loss in the extracted power with respect to a fully reconfigurable solution. The key feature of our approach is the use of environmental (irradiance and temperature) data to determine the reconfigurable subset at design time. Simulation results show that, by reconfiguring only about 70% of the panel, it is possible to achieve a 20-45% power increase with respect to a static topology, while losing less than 1-5% power with respect to full reconfiguration. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:01 | IP3-17, 358 | MACHINE-LEARNING-DRIVEN MATRIX ORDERING FOR POWER GRID ANALYSIS Speaker: Wenjian Yu, Tsinghua University, CN Authors: Ganqu Cui1, Wenjian Yu1, Xin Li2, Zhiyu Zeng3 and Ben Gu3 1Tsinghua University, CN; 2Duke University, US; 3Cadence Design Systems, Inc., US Abstract A machine-learning-driven approach for matrix ordering is proposed for power grid analysis based on domain decomposition. It utilizes support vector machine or artificial neural network to learn a classifier to automatically choose the optimal ordering algorithm, thereby reducing the expense of solving the subdomain equations. Based on the feature selection considering sparse matrix properties, the proposed method achieves superior efficiency in runtime and memory usage over conventional methods, as demonstrated by industrial test cases. Download Paper (PDF; Only available from the DATE venue WiFi) |
| 16:00 | End of session Coffee Break in Exhibition Area
On all conference days (Tuesday to Thursday), coffee and tea will be served during the coffee breaks at the below-mentioned times in the exhibition area. Lunch Breaks (Lunch Area)On all conference days (Tuesday to Thursday), a seated lunch (lunch buffet) will be offered in the Lunch Area to fully registered conference delegates only. There will be badge control at the entrance to the lunch break area. Tuesday, March 26, 2019
Wednesday, March 27, 2019
Thursday, March 28, 2019
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