ABSTRACT
This study explores and quantifies the social costs and benefits of grid-scale electrical energy storage (EES) projects in Great Britain. The case study for this paper is the Smarter Network Storage project, a 6 MW/10 MWh lithium battery placed at the Leighton Buzzard Primary substation to meet growing local peak demand requirements. This study analyses both the locational and system-wide benefits to grid-scale EES, determines the realistic combination of those social benefits, and juxtaposes them against the social costs across the useful lifecycle of the battery to determine the techno-economic performance. Risk and uncertainty from the benefit streams, cost elements, battery lifespan, and discount rate are incorporated into a Monte Carlo simulation. Using this framework, society can be guided to cost-effectively invest in EES as a grid modernization asset to facilitate the transition to a reliable, affordable, and clean power system.
7. Conclusions
The social cost benefit analysis provides a strong framework to assess whether the regulatory regime should encourage more investment in grid-scale EES. We commend this approach to regulators and those assessing the public benefit of grid scale EES. Our approach draws attention to the fact that positive (or negative) private NPVs for such storage projects may not be accurately reflecting their true costs and benefits from the point of view of society.
This framework accounts for both the market and non-market benefits from the perspective of society and juxtaposes them with the social costs, thereby capturing insights into economic development, equity, and efficiency. Transfer payments between agents within society are removed from the analysis to provide a project appraisal that truly represents the net value to society. Through the Kaldor-Hicks criterion, a positive NPV of the grid-scale EES investment improves the state of society overall.
It is also concluded that a Monte Carlo simulation should be paired with the social cost benefit analysis when incorporating the risk and uncertainty of future benefit and cost streams of grid-scale EES. Rather than providing deterministic values, stochastic modelling incorporates the many real-world variables that affect the net present value of a project. For a stochastic sensitivity analysis, Monte Carlo simulations are helpful because statistical distributions can be applied to the benefit and cost streams.
