(569f) Adaptive Energy Management in Renewable Energy Smart Grids Using the Power Grand Composite Curves Approach

To address the intermittent nature of largely unpredictable environmental phenomena, smart grids transform renewable energy sources (RES) into dependable power flows by simultaneous utilisation of different types of conversion and storage equipment (e.g. fuel cells, electrolysers, accumulators, etc.). The resulting infrastructures combine multiple subsystems that need to operate efficiently while satisfying power demands with minimum contribution of non-RES components. This is generally approached through the development of power management strategies (PMS) [1] typically accounting for decisions regarding the appropriate instance to activate/ deactivate different subsystems, the duration and conditions of their operation, the amount/type of energy carrier to use (e.g. power or hydrogen) and so forth. The participation in grids of numerous subsystems of heterogeneous technical features results in a large number of potential PMS. The selection of the appropriate PMS is a key requirement to enable optimum grid operation. This is a non-trivial task that requires intense effort to review, analyse and eventually exploit the complex interactions observed among the subsystems. Due to this complexity conventional systems mainly utilize a pre-specified PMS which is repeated throughout the cyclic system operation. This is clearly inefficient and results in energy wasting or increased non-RES power utilization because the system remains unresponsive to variability.

The recently proposed power pinch analysis [2] represents a method which allows the investigation of complex energy systems based on the systemic identification of insights pointing towards optimum operating decisions. A major advantage of the method is its implementation in the form of intuitive and easy to develop graphical interfaces (e.g. grand composite curves vs. time and so forth), while the underlying principles may also be combined with rigorous mathematical tools (e.g. flexible models combined with optimization algorithms). The current work discusses the implementation of the power grand composite curves method (PGCC) in the analysis of smart grids and addresses several issues including the simultaneous appearance of multiple operating limits, pinch points and operating targets that need to be satisfied. Furthermore, a novel approach to identify the optimum PMS in RES-based smart-grids using the PGCC to adaptively adjust the system operation in short-term power requirements is proposed. The main aim is to identify the optimum PMS within recurrent subsequent time intervals in order to minimize external non-RES utilization. This is approached by a) using an estimated PGCC to target the optimum power requirements for a predefined future prediction time horizon and b) then adjusting the system PMS in the current time-interval in order to meet the identified target within the prediction horizon. First the system PGCC is estimated for a future time horizon, indicating the external non-RES input required to satisfy the expected power demands. An appropriate shift in the PGCC sets a target of the minimum power inventory needed by the end of the current interval to completely avoid the use of non-RES power input during the desired time horizon. The development of this power inventory is then achieved through the selection of the most suitable PMS that best matches this target. The proposed method is presented within a formal mathematical framework supporting a systematic algorithm to select the optimum PMS within different time intervals in year-round operation. Pinch analysis is also formally associated with a generic model considering numerous potential structural and temporal interactions observed in smart grids and transforming them into a pool of reasonable PMS from where the optimum is selected.

The proposed method is illustrated through implementation on a hybrid smart grid considering multiple RES power generation and storage options (i.e. photovoltaics, wind generators, batteries, electrolyzer, fuel cell, intermediate and final hydrogen storage tanks, a compressor and a diesel generator) that is currently in operation in Greece. Numerous PMS [1] are considered through a generic and inclusive model able to capture different operating realizations in view of external environmental variability. Results indicate considerable performance improvements due to adaptive PMS utilization.

 

[1] D. Giaouris, A. I. Papadopoulos, C. Ziogou, D. Ipsakis, S. Voutetakis, S. Papadopoulou, P. Seferlis, F. Stergiopoulos, C. Elmasides, 2013, Performance investigation of a hybrid renewable power generation and storage system using systemic power management models, Energy, 61, 621-635 

[2] Wan Alwi S.R., Rozali N.E.M., Manan Z.A., Klemeš J.J., 2012, A process integration targeting method for hybrid power systems, Energy, 44, 6-10.