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In recent years, with the growing number of EV charging stations integrated into the grid, optimizing the aggregated EV load based on individual EV flexibility has drawn aggregators’ attention as a way to regulate the grid and provide grid services, such as day-ahead (DA) demand responses. Due to the forecast uncertainty of EV charging timings and charging energy demands, the actual delivered demand response is usually different from the DA bidding capacity, making it difficult for aggregators to profit from the energy market. This paper presents a two-layer online feedback control algorithm that exploits the EV flexibility with controlled EV charging timings and energy demands. Firstly, the offline model optimizes the EV dispatch considering demand charge management and energy market participation, and secondly, model predictive control is used in the online feedback model, which exploits the aggregated EV flexibility region by reducing the charging energy based on the pre-decided service level for demand response in real time (RT). The proposed algorithm is tested with one year of data for 51 EVs at a workplace charging site. The results show that with a 20% service level reduction in December 2022, the aggregated EV flexibility can be used to compensate for the cost of EV forecast errors and benefit from day-ahead energy market participation by USD 217. The proposed algorithm is proven to be economically practical and profitable.

Optimal dispatch is a major concern in the optimization of hybrid energy systems (HESs). Efficient and effective dispatch models that satisfy the load demand at the minimum net present cost (NPC) are crucial because of the high capital costs of renewable energy technologies. The dispatch algorithms native to hybrid optimization of multiple energy resources (HOMER) software, cycle-charging (CC) and load-following (LF), are powerful for modeling and optimizing HESs. In these control strategies, the decision to use fuel cell systems (FCs) or battery energy storage systems (BESs) at each time step is made based on the lowest cost choice. In addition, the simultaneous operation of a FC with a BES reduces the operating efficiency of the FC. These deficiencies can affect the optimal design of HESs. This study introduces a dispatch algorithm specifically designed to minimize the NPC by maximizing the usage of FCs over other components of HESs. The framework resolves the dispatch deficiencies of native HOMER dispatch algorithms. The MATLAB Version 2021a, Mathworks Inc., Natick, MA, USA Link feature in HOMER software was used to implement the proposed dispatch (PD) algorithm. The results show that the PD achieved cost savings of 4% compared to the CC and LF control dispatch strategies. Furthermore, FCs contributed approximately 23.7% of the total electricity production in the HES, which is more than that of CC (18.2%) and LF (18.6%). The developed model can be beneficial to engineers and stakeholders when optimizing HESs to achieve the minimum NPC and efficient energy management.

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings.