The nature of combining energy storage applications for residential battery technology

Abstract Batteries are expected to play an important role in the transition to decarbonised energy systems by enabling the further penetration of renewable energy technologies while assuring grid stability. However, their hitherto high capital costs is a key barrier for their further deployment. In order to improve their economic viability, batteries could provide several applications offering revenues. The techno-economic evaluation of batteries simultaneously serving several applications has proven to be challenging due to the trade-offs between energy and power applications. Focusing on residential batteries, we develop an optimisation method for designing optimal value propositions and we test it for four different applications both individually and jointly: PV self-consumption, demand load-shifting, avoidance of PV curtailment and demand peak shaving. Our results show that the combination of all applications currently helps batteries to get closer to profitability, from a net present value (NPV) per unit of capital expenditure (CAPEX) of −0.63 ± 0.04 for PV self-consumption only to −0.36 ± 0.10, with the combination of demand peak-shaving and PV self-consumption adding most value (0.21 ± 0.04). We also find that the annual household’s electricity consumption determines the value of energy storage applications. The proposed method allow us to classify storage applications as complementary and substitutive depending on whether their combined application increases their economic attractiveness or not. These results thus offer valuable insight for stakeholders interested in the deployment of energy storage in combination with energy efficiency, heat pumps and electric vehicles such as consumers, utility companies and policy makers.

[1]  E. Nieuwlaar,et al.  Introduction to Energy Analysis , 2008 .

[2]  P. Lombardi,et al.  Sharing economy as a new business model for energy storage systems , 2017 .

[3]  Peng Sun,et al.  A comparative study of feed-in tariff and renewable portfolio standard policy in renewable energy industry , 2015 .

[4]  Stuart A. Norman,et al.  Optimum community energy storage system for demand load shifting , 2016 .

[5]  Erin Baker,et al.  Evaluating energy storage technologies for wind power integration , 2012 .

[6]  Daniel Nilsson,et al.  Photovoltaic self-consumption in buildings : A review , 2015 .

[7]  M. Webber,et al.  The impacts of storing solar energy in the home to reduce reliance on the utility , 2017, Nature Energy.

[8]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[9]  Wouter L. Schram,et al.  Photovoltaic systems coupled with batteries that are optimally sized for household self-consumption: Assessment of peak shaving potential , 2018, Applied Energy.

[10]  Marcelo Gradella Villalva,et al.  Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays , 2009, IEEE Transactions on Power Electronics.

[11]  T. Schmidt,et al.  Limiting the public cost of stationary battery deployment by combining applications , 2016, Nature Energy.

[12]  Dirk Uwe Sauer,et al.  Scientific measuring and evaluation program for photovoltaic battery systems(WMEP PV-speicher) , 2015 .

[13]  Marta C. González,et al.  Community energy storage: A smart choice for the smart grid? , 2018 .

[14]  P. Mulheran,et al.  Towards an objective method to compare energy storage technologies: development and validation of a model to determine the upper boundary of revenue available from electrical price arbitrage , 2012 .

[15]  Hendrik Kondziella,et al.  Assessing the influence of the temporal resolution of electrical load and PV generation profiles on self-consumption and sizing of PV-battery systems , 2016 .

[16]  Marta C. González,et al.  Projecting battery adoption in the prosumer era , 2018 .

[17]  Filip Johnsson,et al.  Solar photovoltaic-battery systems in Swedish households – Self-consumption and self-sufficiency , 2016 .

[18]  Martin Kumar Patel,et al.  Optimizing PV and grid charging in combined applications to improve the profitability of residential batteries , 2017 .

[19]  G.B.M.A. Litjens,et al.  Economic benefits of combining self-consumption enhancement with frequency restoration reserves provision by photovoltaic-battery systems , 2018, Applied Energy.

[20]  Marko Aunedi,et al.  Whole-Systems Assessment of the Value of Energy Storage in Low-Carbon Electricity Systems , 2014, IEEE Transactions on Smart Grid.

[21]  Martin Kumar Patel,et al.  An interdisciplinary review of energy storage for communities: Challenges and perspectives , 2017 .

[22]  Mark Gillott,et al.  Optimum community energy storage for renewable energy and demand load management , 2017 .

[23]  Dirk Uwe Sauer,et al.  Comparison of different operation strategies for PV battery home storage systems including forecast-based operation strategies , 2018, Applied Energy.

[24]  Martin Kumar Patel,et al.  Effect of tariffs on the performance and economic benefits of PV-coupled battery systems , 2016 .

[25]  T. Schmidt,et al.  The economic viability of battery storage for residential solar photovoltaic systems – A review and a simulation model , 2014 .

[26]  Nicholas Jenkins,et al.  Optimal battery storage operation for PV systems with tariff incentives , 2017 .

[27]  Audun Botterud,et al.  The value of energy storage in decarbonizing the electricity sector , 2016 .

[28]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[29]  David Parra Mendoza Optimum community energy storage for end user applications , 2014 .

[30]  E. Telaretti,et al.  An analysis of feed’in tariffs for solar PV in six representative countries of the European Union , 2014 .