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Integrating the cold load pickup effect of reserve supplying demand response resource in social cost minimization based system scheduling

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  • Behrangrad, Mahdi
  • Sugihara, Hideharu
  • Funaki, Tsuyoshi

Abstract

Expansion of smart grids and aggregator business facilitates the utilization of reserve supplying demand response (RSDR) resources. One of the loads that are increasingly used for reserve provision is air-conditioning load (ACL) that have cold load pickup (CLPU) or “payback” characteristics. With larger scale utilization of RSDR resources, as an effect of increasing DR aggregation business, CLPU characteristics of ACL can affect system optimal operation. Actual utilization time and duration of RSDR resources are probabilistic and affected by system scheduling and contingency occurrence. Therefore the CLPU effect of RSDR resources is probabilistic. This creates extra burden on the system reliability maintenance that should be considered from social cost minimization point of view. This complexity is addressed in this paper by modeling the extra expected load not supplied (ELNS) that the probabilistic CLPU of RSDR can impose on system. Then the aggregated RSDR resources, with CLPU characteristics, are integrated into day-ahead simultaneous system scheduling with the objective function of social cost minimization. This study showed that CLPU can have considerable effects on system scheduling and RSDR effectiveness. The proposed method of this paper proved to be useful for reducing the negative effects of CLPU while using RSDR resources.

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  • Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2012. "Integrating the cold load pickup effect of reserve supplying demand response resource in social cost minimization based system scheduling," Energy, Elsevier, vol. 45(1), pages 1034-1041.
    • Handle: RePEc:eee:energy:v:45:y:2012:i:1:p:1034-1041
    DOI: 10.1016/j.energy.2012.06.040
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    References listed on IDEAS

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    Cited by:

    1. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    2. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    3. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).

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