Efficiently solving DSM problems: Are we there yet?
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With an increasing amount of renewable energy generation, the scheme of supply following demand is no longer viable. As a consequence, aggregating entities (e.g., utilities, service providers) have to find new ways to balance demand and supply in order to guarantee an economic and environmental friendly operation of the energy grid. An approach recently extensively studied is the concept of duration-deadline jointly differentiated energy services that elicits temporal flexibility of the demand side. This paper considers different mathematical models that can be used to solve this demand side management problem applied to an electric vehicle charging use case. A classically applied approach (referred to as classic approach) uses a three-dimensional allocation matrix whereas a specially designed approach for this problem class (referred to as multiple deadline approach) uses majorization theory to answer the questions of adequacy and adequacy gap. These approaches are compared in regard to their time to create and time to solve the optimization problem as well as their sensitivity towards an increasing number of customer, deadline, and scenarios of renewable power generation. The results show that computation time of the classic approach is strongly influenced by the number of scenarios and customers whereas computation time of the multiple deadline approach is strongly influenced by the number of deadlines and scenarios. Neither of the approaches can be described as superior to the other as both react differently to input data. Furthermore, the results show that for a large-scale implementation both approaches must be improved in their complexity to ensure a continuous operation.
KeywordsDemand side management Computational analysis Electric vehicles Load shifting Mathematical model Duration-deadline jointly differentiated energy services
- 1.Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: AAAI-94 workshop on knowledge discovery in databases, vol 10, pp 359–370Google Scholar
- 2.BMWi (2016) Informationsportal erneuerbare energien. http://www.erneuerbare-energien.de/EE/Navigation/DE/Technologien/Windenergie-auf-See/Ziele/ziele.html
- 3.Chen W, Qiu L, Varaiya P (2015) Duration-deadline jointly differentiated energy services. In: 54th annual conference on decision and control (CDC) pp 7220–7225Google Scholar
- 5.Clement K, Haesen E, Driesen J (2009) Coordinated charging of multiple plug-in hybrid electric vehicles in residential distribution grids. In: Power systems conference and exposition, pp 1–7Google Scholar
- 6.Gan L, Topcu U, Low S (2007) Optimal decentralized protocols for electric vehicle charging. IEEE Trans Power Syst 6(1):1–10Google Scholar
- 8.Meyn S, Negrete-Pincetic M, Wang G, Kowli A, Shafieepoorfard E (2010) The value of volatile resources in electricity markets. In: 49th IEEE conference on decision and control, pp 1029–1036Google Scholar
- 9.Negrete-Pincetic M, Nayyar A, Poolla K, Salah F, Varaiya P (2016) Rate-constrained energy services in electricity. doi: 10.1109/TSG.2016.2623275
- 12.Yu R, Zhong W, Xie S, Yuen C, Gjessing S, Zhang Y (2016) Balancing Power Demand through EV Mobility in Vehicle-to-Grid Mobile Energy Networks. IEEE Trans Ind Inf 3203(12.1):79–90, doi: 10.1109/TII.2015.2494884