Solar-plus-storage economics: What works where, and why?

McLaren, Joyce; Laws, Nick; Anderson, Kate; DiOrio, Nick; Miller, H.P.

doi:10.1016/j.tej.2019.01.006

Cited by 22 publications

(7 citation statements)

References 8 publications

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“…Lithium-ion batteries (LIBs) are emerging as a part of the comprehensive solution to climate change by assisting integration of renewable electricity into the grid. − Although LIBs are commercially successful, efforts have been made to improve their energy and power densities by replacing their graphitic anodes with lithium metal. − However, dendrites formed from uneven stripping and deposition of lithium ions have limited the adoption of lithium-metal batteries . It has been suggested that reducing the counteranion mobility and/or increasing the electrolyte shear modulus may alleviate the issue of dendrite formation.…”

Section: Introductionmentioning

confidence: 99%

Design of Polymer Blend Electrolytes through a Machine Learning Approach

et al. 2020

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We apply a machine learning (ML) technique to the multiobjective design of polymer blend electrolytes. In particular, we are interested in maximizing electrolyte performance measured by a combination of ionic transport (measured by ionic conductivity) and electrolyte mechanical properties (measured by viscosity) in a coarse-grained molecular dynamics framework. Recognizing the expense of evaluating each of these properties, we identify that the anionic mean-squared displacement and polymer relaxation time can serve as their proxies. By employing the ML approach known as Bayesian optimization, we identify a trade-off between ion transport and electrolyte mechanical properties as a function of varied design parameters, which include host molecular weight and polarity. Our results suggest that blend electrolytes whose hosts have unequal molecular weights, such as gel polymer electrolytes, rarely maximize electrolyte performance. Overall, our results suggest the potential of a framework to design highperformance electrolytes using a combination of molecular simulation and ML.

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Section: Introductionmentioning

confidence: 99%

Design of Polymer Blend Electrolytes through a Machine Learning Approach

et al. 2020

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“…Because energy storage systems can enhance the ability of the customer to increase DPV self-consumption and avoid retail electricity purchases, the retail tariff structure tends to have a more significant effect on overall DPVplus-storage customer economics than choosing between an NEM or net billing scheme (McLaren et al 2019).…”

Section: Retail Tariff Designmentioning

confidence: 99%

“…Thus, as with TOU rates, the implementation of demand charges has the potential to increase the economic attractiveness of DPV-plus-storage systems. McLaren et al 2019, for example, demonstrate that the presence of demand charges, particularly those greater than $10/kW, among commercial customers, contribute to larger bill savings for DPV-plus-storage systems relative to grid-tied DPV systems (McLaren et al 2019).…”

Section: Demand Chargesmentioning

confidence: 99%

An Overview of Behind-the-Meter Solar-Plus-Storage Program Design: With Considerations for India

Zinaman

Bowen

Aznar

2020

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“…Decentralised storage systems often refer to behind-themeter solar PV systems coupled with battery storage systems at consumer level, where the value resides in increasing selfconsumption to obtain a reduction in utility-bought energy, demand charges and the potential of a financial return from providing grid services [5]- [7]. Previous research studies in several geographical regions showed that the economic profitability of these systems highly depends on the availability of favourable incentives, tariffs and schemes [5], [6], [8], [9]. Nevertheless, the provision of grid services from smallscale prosumers is nowadays very limited due to the lack of supporting policy frameworks and the current operation of power markets conceived for larger generators in the MW range.…”

Section: Introductionmentioning

confidence: 99%

“…t,losstot(5) where P ch t,ef f is the effective charging power for a given time step t that results from the actual input power P t,in subtracting the total charging losses P ch t,losstot . The total VOLUME XX, 202X…”

mentioning

confidence: 99%

Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

Lopez‐Lorente¹,

Liu

Best

et al. 2021

IEEE Access

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Centralised, front-of-the-meter battery energy storage systems are an option to support and add flexibility to distribution networks with increasing distributed photovoltaic systems, which generate renewable energy locally and help decarbonise the power sector. However, the provision of specific services at distribution level remains under development for real applications in industry. To this end, this paper presents an exhaustive techno-economic analysis of the role of front-of-the-meter battery energy storage systems in primary distribution networks with presence of distributed PV covering: (i) the siting decision for storage systems using multi-objective genetic algorithm optimisation; (ii) the response when smart capabilities for PV inverters (e.g., volt-var control) are present; and (iii) the quantification of revenue streams and compensation schemes that would bring positive profitability when providing distributionspecific services based on the supply of real and reactive power. The performance of grid-level battery energy storage technology is evaluated in the IEEE 34-bus system particularised to the distribution code of Northern Ireland, UK. The techno-economic assessment covers one year of simulations at 1-minute resolution performed with the simulation tool EPRI OpenDSS and its Python communication interface. The results show that, besides the technical benefits of centralised battery storage systems, the economic compensation based on traditional transmission-level services is not sufficient for the profitability of these projects. Several ideas are explored to improve the profitability of these systems considering the local value and decarbonisation they add. The findings presented can direct system operators and regulators towards developing schemes to incentivise centralised battery energy storage projects in distribution networks in the context of distribution-level services.INDEX TERMS battery energy storage systems, distribution network services, distributed solar energy, flexibility services, photovoltaic power.

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Solar-plus-storage economics: What works where, and why?

Cited by 22 publications

References 8 publications

Design of Polymer Blend Electrolytes through a Machine Learning Approach

Design of Polymer Blend Electrolytes through a Machine Learning Approach

An Overview of Behind-the-Meter Solar-Plus-Storage Program Design: With Considerations for India

Techno-Economic Assessment of Grid-Level Battery Energy Storage Supporting Distributed Photovoltaic Power

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