Robust bidding strategy of interconnected multi-carrier systems in the electricity markets under the uncertainty in electricity load

This paper presents a robust bi-level model for optimal bidding of interconnected multi-carrier systems (MCSs) in the wholesale electricity markets under the uncertainty in electricity load. The proposed model implements a robust optimization method (ROM) to model the risk-based bidding of interconnected MCSs under the uncertainty in load in the upper-level problem while including the energy and reserve markets in the lower-level problem. The studied MSCs include combined heat and power (CHP) units, boilers, central air conditioning (CAC) systems, and battery storage units to provide local energy and make the MCSs' operator capable of participating in the reserve market to gain profit. ROM models the electricity demand and provides different bidding strategies (i.e., risk-seeking, risk-neutral, and risk-averse) for the participation of MCSs' operator in the energy and reserve markets. The studied bi-level model is converted to a single-level mixed-integer linear programming problem using the Karush-Kuhn-Tucker conditions and strong duality and then solved using the CPLEX solver of the General Algebraic Modeling System. Numerical experiments reveal that the interconnection of MSCs, along with the integration of storage units, enables the MSCs' operator to control the uncertainty in the electricity load and increase the energy efficiency in order to optimally bid in the day-ahead energy and reserve markets. Results show that by enabling the MCSs to locally trade energy, the total operation cost of MCSs in the risk-seeking, risk-neutral, and risk-averse modes decreases by 5.65%, 4.28%, and 3.49%. Moreover, with the integration of battery storage systems into the interconnected operation of MCSs, the total operation cost of systems in the risk-seeking, risk-neutral, and risk-averse modes decreases by 7.65%, 6.99%, and 6.40%, respectively.