(623b) Multi-Stage Dynamic Optimization-Based Scheduling & Demand Side Management of Hybrid Renewable Energy Systems Under Uncertainties

Majority of the existing works on scheduling and demand side management of hybrid renewable energy systems focus on offline methods that generally neglect forecasting errors of both supply and demand sides [1] [2]. The highly intermittent nature of renewable energy sources and the consumer demands can make this offline strategy highly challenging and inaccurate. Although various stochastic optimization-based techniques are discussed in literature to deal with these uncertainties [3], these methods generally work on probabilistic model-based approaches that reflect the possible uncertainties, which can often become less realistic. In this work, a multi-stage real-time scheduling and demand side management of a hybrid renewable energy based microgrid in the presence of uncertainties is proposed. The main objectives of deploying this methodology are to minimize the operational cost and carbon emissions of the microgrid together with managing the electricity usage patterns in order to address both the supply and demand side uncertainties in real-time.

In the first stage, a model predictive control (MPC)-based dynamic optimization is designed at a slower time scale to determine the optimal energy mix schedule of the available energy sources. From this optimal schedule, only the schedule associated with the current time step is shared with the second stage. In the second stage, in order to address the highly intermittent characteristics of uncertain renewable energy sources and the abrupt load demand fluctuations, a faster-time scale real-time control (RTC) algorithm [4] is designed with real-time measurements of the uncertain parameters. Using this information and the scheduling plan received from the first stage, the RTC algorithm calculates the compensation power required by the microgrid and the required charge/discharge levels of the energy storage component and accordingly adjusts the energy distribution profiles of these energy sources and the charge/discharge patterns of the energy storage component.

Effective interaction and information exchange between the two stages with varying time-scales can capture the real-time updated information and can achieve better performance and increased grid stability in the presence of uncertainties. Compared to the single layer steady state optimization-based scheduling algorithm [5], this methodology can enhance the performance of the system load characteristics by maintaining a balance between the supply and demand sides of the microgrid together with achieving an optimal operational cost and associated carbon emissions. For demonstrating the effectiveness of this approach, a case study of a generalized industrial facility equipped with three main energy sources viz. solar PV, waste to energy (WTE) and electricity grid together with a battery storage unit (which acts as a flexible energy component) is considered.

References

[1] S. Misra, P.S. Pravin, Gudi, R.D., Bhartiya, S., "Integration of supply and demand side management using renewable power sources: Application on an Air Separation Plant", Industrial & Engineering Chemistry Research 60 (9), 2021, 3670-3686.

[2] L. Gan, P. Jiang, B. Lev, X. Zhou, Balancing of supply and demand of renewable energy power system: A review and bibliometric analysis, Sustainable Futures 2, 2020, 100013.

[3] H. J. Kim, R. Sioshansi, A. J. Conejo, Benefits of stochastic optimization for scheduling energy storage in wholesale electricity markets, Journal of Modern Power Systems and Clean Energy 9 (1) (2021) 181–189.

[4] L. Barelli, G. Bidini, D. Ciupageanu, A. Micangeli, P. Ottaviano, D. Pelosi, Real time power management strategy for hybrid energy storage systems coupled with variable energy sources in power smoothing applications, Energy Reports 7, 2021, 2872–2882.

[5] P.S. Pravin, S. Misra, Bhartiya, S., Gudi, R.D., "A Reactive Scheduling and Control framework for Integration of Renewable Energy Sources with a Reformer-based Fuel Cell system and an Energy Storage Device", Journal of Process Control (87), 2020, 147-165.