Stacked value streams of hybrid energy storage systems in prosumer microgrids

Abstract

Purpose Storage systems are deemed to be unable to provide revenue commensurate with the resources invested in them, thus discouraging their incorporation within power networks. In prosumer microgrids, storage systems can provide revenue from reduced grid consumption, energy arbitraging or when serving as back-up power. This study aims to examine stacking these revenue streams with the aim of making storage systems financially viable for inclusion in prosumer microgrids. Design/methodology/approach With the aim of reducing self-consumption and maximising revenue, the prosumer microgrid incorporating hybrid energy storage systems (HESS) and solar PV power is solved using the CPLEX solver of the Advanced Interactive Multidimensional Modeling Software (AIMMS). The financial analysis of the results is carried out to provide the payback periods of different system configurations of the prosumer microgrid. Findings The findings reveal that the payback period of the three HESS when minimising grid expenses during self-consumption alone and when compared with stacked revenue streams shows an improvement from 4.8–11.2 years to 2.4–6.6 years. With stacked HESS revenues, the supercapacitor-lithium ion battery HESS gave the shortest payback period of 2.31 years when solar PV power is at 75% penetration level. Originality/value Existing literature has considered revenue streams of storage systems at the electrical power transmission and distribution levels, but not for prosumer microgrids in particular. This study has captured these benefits and verified the profitability of stacking revenue from HESS to prosumer microgrids, using a case study.

Stacking Revenues From Flexible DERs in Multi-Scale Markets Using Tri-Level Optimization

“…Future works will assess the market participation of the FSP considering only its partial access to the market information. Thermal characteristics of the HVAC systems are modelled by a first order discrete temperature model in (16). Let τ b,t be the temperature of the building, τ out b,t be the ambient temperature, p he b,t , p co b,t be the heating and cooling power, R b , C b , be thermal constants and η he b , η co b are efficiencies of the heating and cooling [44].…”

“…Authors in [15] use Particle Swarm Optimization to co-optimize BESS size along wind systems to maximise profits. Reference [16] stacks revenues streams for BESSs in microgrids, with the aim of making them financially viable using linear programming. Long term BESSs bidding strategy in day-ahead and frequency markets is investigated at national scale by [17].…”