(324g) Space-Time Dynamics of Electricity Markets Incentivize Technology Decentralization

Decentralization of electricity generation, consumption (loads), and storage is an on-going trend in the power industry [1]. From the perspective of an independent system operator (ISO), decentralization is desirable as it can provide spatial flexibility to control network flows and to overcome limited transmission infrastructure [2]. In addition, large centralized facilities can become liabilities during extreme weather or attack events [3]. Another issue associated with large centralized facilities is that they provide limited investment flexibility, which is often desirable to mitigate risks in electricity prices and policy [4]. The need to mitigate such investment risks is promoting the development and deployment of smaller-scale (modular) technologies [5][6]. On the other hand, it is well-known that large centralized facilities benefit from economies of scale and thus a complex trade-off between expected revenue and risk exist.

In this work, we propose a computational framework for analyzing economic incentives provided by space-time dynamics of electricity prices. Our framework is based on a technology placement formulation that seeks to find optimal placement locations for generators and loads in the network that minimize profit risk. We show that an unconstrained version of this problem can be cast as an eigenvalue problem. Under this representation, optimal network allocations are eigenvectors of the space-time price covariance matrix while the eigenvalues are the associated revenue variances. Consequently, risk analysis can be performed by using principal component analysis (PCA) techniques. Analysis using CAISO market data for 2015 reveals that there exists a large number of placement strategies that completely eliminate risk. We construct a constrained placement problem that captures constraints on the types of technologies. Unfortunately, for the ISO-scale data sets of interest, this problem is a mixed-integer quadratic programming problem that is intractable with current solvers. We thus propose to use the mean absolute deviation as an alternative risk measure to obtain a more scalable (but still challenging) mixed-integer linear program. Our analysis reveals that complete mitigation of revenue risk is only possible by simultaneous investment in decentralized generation and loads (which can also be achieved by using batteries or hybrid systems such microgrids). We thus conclude that space-time market dynamics indeed provide incentives for strategic diversification and placement of technologies.

References:

[1] Buchholz, S., 2010. Future Manufacturing Approaches in the Chemical and Pharmaceutical Industry. Chem. Eng. Process., 49(10), pp. 993.

[2] Kim, K., Yang, F., Zavala, V. M. and Chien A. A., 2017. Data centers as dispatchable loads to harness stranded power. IEEE Transactions on Sustainable Energy, 8(1), pp. 208–218.

[3] Lier, S., and Grnewald, M., 2011. Net present value analysis of modular chemical production plants. Chemical Engineering Technology, 34(5), pp. 809–816.

[4] Palys, M. J., Allman, A. and Daoutidis, P., 2018. Exploring the benefits of modular renewable-powered ammonia production: A supply chain optimization study. Industrial & Engineering Chemistry Research.

[5] C. Guo, C., Lu, G., Li, D., Wu, H., Zhang, X. and Shi, Y., 2009. Bcube: A high performance, server-centric network architecture for modular data centers. ACM SIGCOMM Computer Communication Review, 39(4), pp: 63–74.

[6] Kim, Y.-h., Park, L. K., Yiacoumi, S. and Tsouris, C., 2017. Modular chemical process intensification: a review. Annual review of chemical and biomolecular engineering, 8, pp: 359–380.