7.6 Optimization of Smart Energy Systems

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Date: Wednesday, March 27, 2019
Time: 14:30 - 16:00
Location / Room: Room 6

Chair:
Davide Quaglia, University of Verona, IT, Contact Davide Quaglia

Co-Chair:
Massimo Poncino, Politecnico di Torino, IT, Contact Massimo Poncino

In this session, three approaches to optimizing smart grid and photovoltaic systems are presented, targeting cost, efficiency, and privacy.

TimeLabelPresentation Title
Authors
14:307.6.1COST/PRIVACY CO-OPTIMIZATION IN SMART ENERGY GRIDS
Speaker:
Alma Proebstl, Technical University of Munich, DE
Authors:
Alma Proebstl, Sangyoung Park, Sebastian Steinhorst and Samarjit Chakraborty, Technical University of 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.
15:007.6.2A 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.
15:307.6.3IRRADIANCE-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.
16:00IP3-18, 549OPTIMAL DESIGN AND MANAGEMENT OF A HYBRID ENERGY STORAGE SYSTEM
Authors:
Eugene Kim1, Liang He2, Youngmoon Lee1 and Kang Shin3
1University of Michigan, US; 2University of Colorado Denver, US; 3,
Abstract
Electric vehicles (EVs) are powered by a large number of battery cells, which must be managed effectively to deliver the required power/energy during their warranty period. An EV's operation requires large and fluctuating power from its battery pack, but its battery cells have only limited tolerance to (dis)charge stress, accelerating battery degradation. Moreover, battery cells have different (dis)charge stresses depending on their physical positions in the battery pack, causing different degradation rates and thus the unbalanced State-of-Health (SoH)/State-of-Charge (SoC). To remedy this problem, we design, implement and evaluate a novel energy storage system with energy buffers and an SoC-balancing circuit, to extend both the battery life and EV's operation-time. We first design a hybrid energy storage system that supplies the EV's representative power requirement efficiently. We then develop an optimal power distribution to minimize the energy consumption of the EV and the stress of its battery cells. Our prototype-based experimentation has shown the proposed system to reduce discharge/charge stress by about 21.8 %, thus leading to lifetime extension while balancing cells' SoC.
16:01IP3-19, 358MACHINE-LEARNING-DRIVEN MATRIX ORDERING FOR POWER GRID ANALYSIS
Speaker:
Wenjian Yu, Tsinghua University, CN
Authors:
Ganqu Cui1, Wenjian Yu2, Xin Li3, Zhiyu Zeng4 and Ben Gu4
1Tsinghua Univ., CN; 2Tsinghua University, CN; 3Duke University, US; 4Cadence 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.
16:00End of session
Coffee Break in Exhibition Area



Coffee Breaks in the 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

  • Coffee Break 10:30 - 11:30
  • Lunch Break 13:00 - 14:30
  • Awards Presentation and Keynote Lecture in ""TBD"" 13:50 - 14:20
  • Coffee Break 16:00 - 17:00

Wednesday, March 27, 2019

  • Coffee Break 10:00 - 11:00
  • Lunch Break 12:30 - 14:30
  • Awards Presentation and Keynote Lecture in ""TBD"" 13:30 - 14:20
  • Coffee Break 16:00 - 17:00

Thursday, March 28, 2019

  • Coffee Break 10:00 - 11:00
  • Lunch Break 12:30 - 14:00
  • Keynote Lecture in ""TBD"" 13:20 - 13:50
  • Coffee Break 15:30 - 16:00