Distributed battery energy storage systems operation framework for grid power levelling in the distribution networks

Abstract

The integration of battery energy storage systems (BESS) in the electrical grid is accelerating to mitigate the challenges associated with the rapid deployment of low carbon technologies (LCTs). This work investigates the ability of BESS to provide the power networks with important ancillary services such as peak shaving and grid power levelling through two case studies conducted in collaboration with Northern Ireland's distribution network operator. A powerful approach consisting of two strategies is developed to operate the BESS powerfully to enhance the operation of the distribution network. The first strategy is day‐ahead scheduling that aims to dispatch the distributed BESS to smooth the grid power and mitigate voltage and lines stresses. A powerful demand forecasting algorithm is utilised to efficiently apply the day‐ahead scheduling. The second strategy is a real‐time operation to flatten the grid power which can be used separately or to adjust the results obtained from the day‐ahead strategy against the forecasting errors. The proposed approach was validated using real measurements and applied to an 11 kV distribution network located in Northern Ireland, UK. The BESS expected revenues from the participation in different services available in the island of Ireland are quantified and the degradation is considered.

Stacking Battery Energy Storage Revenues in Future Distribution Networks

“…The cost-benefit analysis (CBA) results of these studies show that the sole participation of BESS in the I-SEM is not viable as the BESS cannot pay back the investment expenditures. This also has been shown in [7], where CBAs have been performed to evaluate the economic feasibility of BESS participating in individual services in the MV networks of Northern Ireland. Study [7] concludes that the BESS can be profitable only with enhanced transmission services.…”

Single electricity market forecasting and energy arbitrage maximization framework

“…For instance, in the island of Ireland, besides the SEM, the BESS can participate in the enhanced services provided by the Irish transmission system operator (TSO) through the DS3 programme [49], as well as providing flexibility and other services to the distribution system operator (DSO) through the FLEX or FESS projects [50,51]. Under these schemes, the expected BESS revenues were quantified previously in [11,17].…”

“…However, machine learning models can provide a long-term forecast with good accuracy compared to the classical methods which are crucial for planning applications such as energy markets. In addition, machine learning models have proved to outperform classical methods in predicting energy demand and prices as shown in [7,8,17]. Yet, machine learning methods require careful selection of the model predictors.…”

“…However, the previous studies did not consider the profitability nor the economic feasibility of deploying BESS under these services. In [17], the use of BESS to enhance the distribution network operation was introduced through a two‐stage methodology to determine the look‐ahead and real‐time setpoints of multiple BESS to flatten the grid power curve. Additionally, study [17] explored the expected profits and the economic feasibility under the sole participation in the available services in the island of Ireland.…”

Effective identification of distributed energy resources using smart meter net‐demand data

“…photovoltaic [PV] systems, battery energy storage systems and small‐scale wind turbines) and low‐carbon loads (i.e. electric vehicles [EV], electric heating and air conditioning units) has increased in the last decades [1]. This has been motivated by worldwide climate‐led policies to reduce CO 2 equivalent (CO 2 e) emissions [2], such as the Kyoto Protocol (1997), the Paris Agreement (2015) and the United Nations Sustainable Development Goals (2015).…”