ד"ר עדי [עדי שריד] שריד

מדעים להיי-טק עמית הוראה

מחקר

Adi Sarid is a PhD student at the department of industrial engineering at Tel-Aviv university, working under the supervision of prof. Michal Tzur. He completed his MSc  in the department of statistics and operations research in Tel-Aviv university under the supervision of prof. Saharon Rosset, and BA in mathematics, statistics and operations research in the Israel Institute for Technology (Technion) in Haifa. Adi also holds a position at the Sarid Research institute, a privately (family owned) research firm. Previously he worked in the IDF in various operations research positions at the Navy and at the Planning Directorate.

His current academic research areas involve deterministic optimization techniques and mathematical modeling, mainly in the context of electrical (smart) grid infrastructure. Adi also has extensive knowledge in statistics and quantitative research methods.

The Incremental Electricity Network Design in a Smart Grid Environment

Technological advances in the last decade are influencing electrical grids as we know them today, and tremendous changes in generation and in demand in the not so distant future are expected: the grid is transforming into a Smart Grid, a term which, for example, refers to any one (or a combination) of the following aspects: the exchange and the use of real time consumer information, automated decision making on power supply, pricing and bidding on electricity, and the incorporation of new elements into the grid such as "smart homes". The latter includes, for example, privately owned generators, capacity to store energy, more control over energy requirements and ability to postpone requirements.

Another central transformation of the power grid revolves around the increased reliance and use of alternative and renewable energy sources to sustain the power grid. From the grid infrastructure planner's perspective, there is a need to accommodate for new sources of supply and of demand which vary significantly across different times of day, seasons and years to come. Some elements may be planned one year ahead (for example, small-scale "neighborhood" facilities). Other elements must be planned many years ahead (such as major generation facilities). The long term perspective is crucial to avoid unnecessary expense on the one hand and to prepare for increasing demand on the other. The examination of "fine resolution" of time (hours) or of the intricacies of different seasons contribute to optimally planning the infrastructure for peak hours, as well as for low times (e.g., supply of excess generation may cause damage to the grid's elements).

To illustrate a possible significant change that has the potential to influence the infrastructure quickly: assume a neighborhood which in just a few years' time changes its electrical consumption habits, by switching to electrical vehicles (EVs). The nature of EVs is that they are expected to require a lot of energy, at some bursts. With proper management, some of the energy requirements may be postponed to a time when the grid is not fully utilized. Understanding and planning the grid, within this delicate balance requires a model which combines a fine resolution of time, a planning horizon of years ahead, and a neighborhood grid's network representation. Similar examples can be given with other elements which will be incorporated into the grid's model, such as private distributed generation (DG). This can somewhat decrease the requirements from the main "classical" power generation facilities, but raise requirements within the transmission network relaying the generated electricity further.

The objective of my research is to provide tools for the optimal planning of infrastructure upgrade to support new characteristics of the Smart Grid.

The input of the problem consists of a representation of the existing grid containing facilities, consumers, and the transmission lines. Given additional information such as costs of transmission, facility capacities, costs of facility establishment or upgrade, and forecast of consumer demand, we are able to formulate a mixed integer linear program. The problem's solution includes establishment or upgrade of additional facilities or transmission lines and the flow of energy (electricity). We use constraints to make sure that physical properties of the problem are not violated, such as the conservation of flow, the maximal capacity of transmission lines, the generation limits of the facilities, and more. The objective is to minimize the total costs over time (of establishment, upgrade, generation and transmission).

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