Incentive-based demand response (IBDR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncerta...
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Incentive-based demand response (IBDR) has been recognized as a powerful tool to mitigate supply-demand imbalance in electricity market. However, the complex uncertainties of consumers, including participation uncertainty and responsiveness uncertainty, have been a central challenge to implement IBDR programs. In this paper, a stochasticprogramming model for IBDR considering the complex uncertainties of consumers is proposed. The proposed model can effectively deals with the above two uncertainties. Besides, the model of energy storage unit (ESU) has been improved to cope with properly the deviation between total actual balancing power and required balancing power. Moreover, the model enhances the applicability of IBDR to be applicable to both curtailment IBDR programs and absorbing IBDR programs by adding dynamic parameters. The model is formulated as a bi-level stochastic programming problem based on uncertain programming theory, and corresponding equivalent model is also given to solve the problem effectively. Finally, simulation results verify merits of the proposed model in cutting down total cost of DRA, decreasing risk cost of DRA and reducing balancing power deviation caused by uncertainty of consumers.
Determination of an optimal departure schedule is the key issue for a successful staged emergency evacuation for reducing unnecessary network congestion and decreasing the evacuation clearance time. However, flow-rela...
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ISBN:
(纸本)9781457721977
Determination of an optimal departure schedule is the key issue for a successful staged emergency evacuation for reducing unnecessary network congestion and decreasing the evacuation clearance time. However, flow-related stochastic events, such accidents and short-term traffic breakdowns, bring numerous challenges for developing a reliable evacuation plan with an optimal departure schedule. In order to assist the decision makers for evaluating potential planning risks, this study proposes a modeling framework and a corresponding solution approach that incorporates flow-related accidents and breakdowns into a staged emergency evacuation model. A bi-level stochastic programming problem is formulated. In the upper level, we introduce planning risk by using Mean-Mean Absolute Deviation (Mean-MAD), which is a common measure employed for risk-averse planning. In the lower level, System Optimum Dynamic Traffic Assignment (SO-DTA) formulation, proposed in [16], is modified as a tool for quantifying the performance of the proposed multistage stochastic traffic assignment approach. The applicability of the proposed framework and solution approach is illustrated by a numerical example designed to depict salient features of the proposed risk-based planning methodology.
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