Khalid Abdulla

Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation

Energy storage systems have the potential to deliver value in multiple ways, and these must be traded off against one another. An operational strategy that aims to maximize the returned value of such a system can often be significantly improved with the use of forecasting — of demand, generation, and pricing — but consideration of battery degradation is important too. This paper proposes a stochastic dynamic programming approach to optimally operate an energy storage system across a receding horizon. The method operates an energy storage asset to deliver maximal lifetime value, by using available forecasts and by applying a multi-factor battery degradation model that takes into account operational impacts on system degradation. Applying the method to a dataset of a residential Australian customer base demonstrates that an optimally operated system returns a lifetime value which is 160% more, on average, than that of the same system operated using a set-point-based method applied in many settings today.

Selecting an optimal combination of storage & transmission assets with a non-dispatchable electricity supply

The majority of renewable energy sources are non-dispatchable, meaning that it is not possible to control when and how much power they produce. For non-dispatchable renewable energy sources to meet a greater proportion of global electricity demand, the industry must develop and implement strategies that directly address the intermittency challenge. This paper considers electrical storage and transmission assets as alternative means of matching non-dispatchable generation with non-deferrable demand. It seeks an optimal combination of storage and transmission assets for a simplified representation of Australian population centres, assuming that demand is met entirely with solar PV generation. This problem is solved using a Mixed Integer Linear Program. Under the baseline assumptions it is found that the optimal (lowest cost) solution has significant quantities of storage in all load centres, as well as transmission assets installed over large distances. The storage selected was 10-15% Li-ion batteries by energy; with the remainder being pumped hydro storage.

Integrating Data-Driven Forecasting and Optimization to Improve the Operation of Distributed Energy Storage

Distributed energy technologies, such as residential energy storage, embedded generation, and microgrids, are likely to play an increasing role in future energy systems. Getting the most value from these distributed assets is often dependent on the ability to optimize their operation in a distributed manner. This distributed optimization, in turn, calls for effective short-term forecasts of the output of small-scale generating assets, and the demand of small-scale aggregations of users. This paper introduces the integration of data-driven forecasting and operational optimization methods into a single model, avoiding the need to explicitly produce forecasts. The method is tested against two empirical energy storage operational optimization problems, the minimization of peak energy drawn by a small aggregation of customers, and the minimization of the energy costs of a collection of households which have rooftop PV systems. The integrated forecasting and operational optimization approach performs well at the peak demand minimization problem for intermediate-sized aggregations (50 residential customers or more), while an approach with separate forecasting and optimization performed better on the energy cost minimization problem. These results suggest that the integrated approach can be effective in applications where (i) forecasting difficulty is intermediate, and (ii) the exact operational optimization formulation can be well approximated by a data-driven model trained on a small fraction of the available forecast training data.