“Optimizing the Coordinated Control of Distributed Generation, Storage and Demand Response based on Decision Trees”

Discussion Abstract

The questions regarding the provision of firm capacity by Distributed Generation (DG) as also the coordinated control of its de-loading, so that it can mitigate over-frequency phenomena, may be answered by incorporating DG under the Virtual Power Plant (VPP) paradigm. A VPP represents an abstract organizational concept of generation, storage and demand units that can cater for the procurement of ancillary services at Power System (PS) level, while ensuring optimality for the units involved. Taking into account the stochastic nature of PS load demand, as also of a great number of DG units based on Renewable Energy Sources, the uncertainty implied requires for solutions that (i) are characterized by flexibility, (ii) may not be optimal, but represent the best available approach, and (iii) can be realized in a short-time-ahead horizon, so that they exploit the most current information.

To this end, binary decision trees (DT) based on the Shannon entropy metric of information gain are used. Although the progress concerning DTs has been vast and in-depth, there are features of the aforementioned version of the tool that have not been clearly identified and exploited, previously. Thus, a DT-based methodology is suggested which can either re-dispatch the assets of a VPP, so that they cover for a considerable loss of power and, thus, provide firm capacity, or reduce the total output of the VPP, in order to support the mitigation of some over-frequency in the PS. In both cases, the optimality of the dispatching is ensured regardless of the stochastic nature of the VPP components, while the hour-ahead horizon of the realization exploits the current data. The time tedious process of generating the learning set of the DT can be minimized by splitting the burden among the microprocessors of the units of the VPP.

During the talk, the focus will be on (without being limited to) the characteristics of the binary DTs as a tool which can approach an optimization problem from the viewpoint of actual applicability; especially, when emergency (contingency) measures are required. Based on experience, a more generic discussion on the Smart Grid concepts and their deployment in modern PSs may follow and is encouraged.

Additional info on the recent work and publications concerning the talk can be found in http://panay1ot1s.com/

Short CV

I received my Diploma and Phd in Electrical and Computer Engineering (ECE) from the National Technical University of Athens (NTUA), Greece, in November 2007 and January 2015, respectively. I have been involved in R&D projects in collaboration with native and European research institutes, industries and universities in the field of renewable energy sources, microgrids and energy efficiency. I have offered teaching assistance in the Power System Analysis courses of ECE NTUA and was involved as a lecturer on Smart Grid applications in the Energy Academy of the Ios-Aegean Energy Agency. Since 2006 I am involved in technical consulting to the sectors of photovoltaic investments and sustainability in Greece from various posts. In 2014 I was a Research Fellow on Microgrids at the University of Greenwich, in collaboration with Arup. Since February 2016 I am Postdoctoral Research Associate at Carnegie Mellon University, in the framework of the SHINES program of R&D projects.  My research interests lie in the field of renewable sources integration, virtual power plants, microgrids and application of artificial intelligence to power system management and control. I am a member of the Power & Energy, Industrial Electronics, Computational Intelligence and Computer IEEE societies and a registered member of the Technical Chamber of Greece.