Hierarchical, Decentralized, or Hybrid? A Systematic Review of Control Architectures for Distributed Energy Resources
Abstract
This paper presents a structured review of the scholarship on control architectures for distributed energy resources, read through a socio-technical lens that treats engineering performance and social outcomes as a single, interdependent concern rather than as separate questions to be addressed in sequence. The review organizes a fragmented body of work that spans engineering, economics, development studies, and energy justice, and it draws these traditions together around a common question, namely how technical capability and social legitimacy jointly determine whether interventions endure in resource-dependent and underserved regions. Particular attention is given to control topology as an implicit governance model that must align with ownership and trust structures, which the review identifies as a recurring but inadequately measured determinant of outcomes. The contribution is conceptual and synthetic: this paper clarifies the state of knowledge, exposes the gap between the recognized importance of social factors and the field's limited progress in measuring them, and sets out a conceptual agenda for integrated evaluation oriented toward inclusive and economically sustainable energy transition
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.