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Title: Space-time dynamics of electricity markets incentivize technology decentralization
We study economic incentives provided by space-time dynamics of day-ahead and real-time electricity markets. Specifically, we seek to analyze to what extent such dynamics promote decentralization of technologies for generation, consumption, and storage (which is essential to obtain a more flexible power grid). Incentives for decentralization are also of relevance given recent interest in the deployment of small-scale modular technologies (e.g., modular ammonia and biogas production systems). Our analysis is based on an asset placement problem that seeks to find optimal 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 profit variances. We also construct a more sophisticated placement formulation that captures different risk metrics and constraints on types of technologies to systematically analyze trade-offs in expected profit and risk. Our analysis reveals that space-time market dynamics provide significant incentives for decentralization and strategic asset placement but that full mitigation of risk is only possible through simultaneous investment in generation and loads (which can be achieved using batteries or microgrids).
Authors:
;
Award ID(s):
1748516
Publication Date:
NSF-PAR ID:
10145099
Journal Name:
Computers chemical engineering
ISSN:
0098-1354
Sponsoring Org:
National Science Foundation
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