Multi-Objective Optimization for Customer Flexibility Management to Mitigate Consecutive Days of Power Supply Shortages

Extreme weather such as heatwaves increases the probability of power system supply shortages, thus necessitating enhanced customer flexibility in instances of limited generation-side resources. This paper proposes an optimization model for managing customer flexibility to tackle multiple consecutive days of power supply shortages. Firstly, it constructs a customer flexibility management framework, considering power supply shortages under extreme heatwave conditions. Then, a multi-objective optimization is built for the customer flexibility management center to minimize the customer flexibility management costs and impacts on industrial customers' production. In this model, the impact index and customer uncertainty updating methods are proposed for managing customer flexibility over consecutive days based on exponential smoothing and Bayesian inference methods. A combined Tchebycheff decomposition and the analytic hierarchy process (AHP)-entropy weight method is constructed to tackle the impact of subjective and objective factors on decision-making. Finally, industrial customers in a city of Zhejiang province, China are used for case studies and the result shows that the proposed model can help customer flexibility management centers reduce and delay the power supply shortages during consecutive days of heatwaves.