A Unified Framework for Integrated Energy Systems Optimization With Day-Ahead and Real-Time Coordination

The operational phase of integrated energy systems (IESs), installed in distribution systems, is increasingly confronted with uncertainties in electrical consumption and output power of variable energy resource (VRE) (e.g., solar photovoltaics). One common approach to mitigate this challenge is to implement day-ahead (DA) scheduling based on cleared prices and purchased power. While this method is effective to some extent, it may not always provide the level of accuracy required. Another viable option is the application of incentive-based demand-response program (I-DRP), where adjustments to energy usage are made in return for a fixed tariff. Nonetheless, this method could result in monopolistic decision-making within an expanding energy industry, albeit assisting in alleviating the barrier. The complexity doubles as the nonlinear formulation of economic objective function is considered in the optimization stage. Hence, the optimality of the solution is a matter of concern. To address these challenges, this research introduces a two-stage coordinated strategy (TSCS) that combines accurate short-term forecasting of uncertainties and I-DRP with regulations of DA and real-time markets to identify the best possible offers and improves the operational efficiency of the IES. By conducting simulations on the IEEE 85-bus test system and comparing the results of the proposed algorithm with those of stochastic programming with fixed reward and penalty rates, our study demonstrates the reliability and accuracy of TSCS, highlighting the flexibility of offers in terms of customer’s participation in the optimal operation of the IES.