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    Design of an Automatic Demand-Side Management System Based on Evolutionary Algorithms

    Galvan, Edgar and Taylor, Adam and Clarke, Siobhan and Cahill, Vinny (2014) Design of an Automatic Demand-Side Management System Based on Evolutionary Algorithms. Proceedings of the 29th Annual ACM Symposium on Applied Computing. pp. 525-530.

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    Demand-Side Management (DSM) refers to programs that aim to control the energy consumption at the customer side of the meter. Different techniques have been proposed to achieve this. Perhaps the most popular techniques are those based on smart pricing (e.g., critical-peak pricing, real-time pricing). The idea, in a nutshell, is to encourage end users to shift their load consumption based on the price at a particular time (e.g., the higher the price, the less number of electric appliances are expected to be turned on). Motivated by these techniques (e.g., a strong positive correlation between the number of appliances being used and the electricity cost), we propose the use of an stochastic evolutionary-based optimisation technique, Evolutionary Algorithms, to automatically generate optimal, or nearly optimal, solutions that represent schedules to charge a number of electric vehicles (EVs) with two goals: (a) that each EV is as fully charged as possible at time of departure, and (b) to avoid charging them at the same time, whenever possible (e.g., load reduction at the transformer level). Instead of using a price signal to shift load consumption, we achieve this by considering what all the EVs might do at a particular time, rather than considering an interaction between an utility company and its user, as normally adopted in DSM programs. We argue that exploiting the interaction of these EVs is crucial at achieving excellent results because it carries the notion of smart pricing (e.g., balance energy usage), which is highly popular in DSM systems. Thus, the main contribution of this work is the notion of load shifting, borrowed from smart pricing methods, implemented in an evolutionary-based algorithm to automatically generate optimal solutions. To test our proposed approach, we used a dynamic scenario, where the state of charge of each EV is different for every day of our 28 days testing period. The results obtained by our proposed approach are highly encouraging in both: EVs being almost fully charged at time of the departure and the transformer load being reduced as a result of avoiding turning on the EVs at the same time.

    Item Type: Article
    Keywords: Evolutionary Algorithms; Bio-inspired Techniques; Smart Grids; Demand-Side Management Systems;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15364
    Identification Number:
    Depositing User: Edgar Galvan
    Date Deposited: 31 Jan 2022 15:04
    Journal or Publication Title: Proceedings of the 29th Annual ACM Symposium on Applied Computing
    Publisher: ACM Digital Library
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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