Impact of Dynamic Thermal Rating on optimal siting and sizing of energy storage systems under Renewable Portfolio Standards requirements

Abstract

Renewable Portfolio Standards (RPS), enforced as part of environmental regulations, are taking precedence worldwide to promote investments in renewable energy sources (RES). From the grid perspective, one of the bottle-neck factors that limits large-scale RES integration is the transmission network congestion. Dynamic Thermal Rating (DTR) is a smart grid technology that utilizes line condition and real-time weather conditions to alleviate network congestion by exploiting the transmission network inherent flexibility. Due to the intermittency of the RES, energy storage systems (ESS) have emerged as an essential component of power system that stores the excess generated energy by RES and pumps it back into the network at peak hours. However, despite the benefits of these technologies, the coordination of these two technologies with RPS regulations are not studied together. This paper presents a co-optimized model to evaluate the role of DTR in optimal siting and sizing of ESS units in connection with RPS requirements. The modeling framework co-optimizes bulk ESS expansion and DTR investment planning subject to constraints on RPS regulations and is formulated as a two-stage stochastic mixed integer linear programming (MILP) optimization problem. The model utilizes DTR to overcome the problem of network congestion and ESS to mitigate RES curtailment by saving the extra generated energy in non-peak hours. The efficacy of the proposed model in realizing impact of the DTR on ESS (location, energy capacity and power rating) in achieving high RPS targets is investigated on the modified IEEE 24-Bus system. Results demonstrate that DTR complements ESS under different RPS constraints by reducing the total system cost and ESS size significantly. • A stochastic MILP model is presented to model DTR implementation and ESS sizing simultaneously. • The model incorporates RPS policies and demand side management with various limits. • The impact of the DTR on investment cost, location and size of ESS units are discussed. • We find that the co-optimized model is noteworthy to study the power systems with high RPS targets.