A multi-objective optimization model for the quantification of flexibility in a large business park

Demand-side management (DSM) is an effective way to strengthen the present power system's reliability and security with increasing penetration of renewable energy generations. With the fusion of information technology, present-day loads are getting smarter with their ability to modulate the power and control their switching operations in response to signals. The benefits get multiplied when flexibility is planned for a cluster of consumers having a similar load profile. In this paper, a framework based on mixed-integer linear programming (MILP) is developed to quantify flexibility in a large business park with little historical time series data access. The proposed mathematical model considers smart loads such as heat pumps, electric vehicle (EV) charging stations, a centralized energy storage system and renewable energy sources such as photovoltaic power. The quantification of flexibility is cast as a bi-objective optimization problem, which is solved by approximating the set of Pareto-efficient solutions using the epsilon-constraint method. Based on the developed optimization model, numerical simulations across one year with a time step of one hour are performed. The projected yearly monetary saving ranges from 1.5% to 30.8 %, and maximum peak shavings range from 9.6% to 61.4% for different capacities of centralized energy storage.