Theses: Distributed Demand-Side Optimization in the Smart Grid


The modern power grid is facing major challenges in the transition to a low-carbon energy
sector. The growing energy demand and environmental concerns require carefully revisiting how
electricity is generated, transmitted, and consumed, with an eye to the integration of renewable
energy sources. The envisioned smart grid is expected to address such issues by introducing
advanced information, control, and communication technologies into the energy infrastructure.
In this context, demand-side management (DSM) makes the end users responsible for improving
the eciency, reliability and sustainability of the power system: this opens up unprecedented
possibilities for optimizing the energy usage and cost at di erent levels of the network.
The design of DSM techniques has been extensively discussed in the literature in the last
decade, although the performance of these methods has been scarcely investigated from the
analytical point of view. In this thesis, we consider the demand-side of the electrical network as
a multiuser system composed of coupled active consumers with DSM capabilities and we propose
a general framework for analyzing and solving demand-side management problems. Since
centralized solution methods are too demanding in most practical applications due to their inherent
computational complexity and communication overhead, we focus on developing ecient
distributed algorithms, with particular emphasis on crucial issues such as convergence speed,
information exchange, scalability, and privacy. In this respect, we provide a rigorous theoretical
analysis of the conditions ensuring the existence of optimal solutions and the convergence of the
proposed algorithms.
Among the plethora of DSM methods, energy consumption scheduling (ECS) programs allow
to modify the user's demand pro le by rescheduling
exible loads to o -peak hours. On the other
hand, incorporating dispatchable distributed generation (DG) and distributed storage (DS) into
the demand-side of the network has been shown to be equally successful in diminishing the peak-to-average ratio of the demand curve, plus overcoming the limitations in terms users' inconvenience
introduced by ECS. Quite surprisingly, while the literature has mostly concentrated
on ECS techniques, DSM approaches based on dispatchable DG and DS have not attracted the
deserved attention despite their load-shaping potential and their capacity to facilitate the integration
of renewable sources. In this dissertation, we ll this gap and devise accurate DSM
models to study the impact of dispatchable DG and DS at the level of the end users and on the
whole electricity infrastructure.
With this objective in mind, we tackle several DSM scenarios, starting from a deterministic
day-ahead optimization with local constraints and culminating with a stochastic day-ahead
optimization combined with real-time adjustments under both local and global requirements.
Each task is complemented by de ning appropriate network and pricing models that enable
the implementation of the DSM paradigm in realistic energy market environments. In this regard,
we design both user-oriented and holistic-based DSM optimization frameworks, which
are respectively applicable to competitive and externally regulated market scenarios. Numerical
results are reported to corroborate the presented distributed schemes. On the one hand, the
users' electricity expenditures are consistently reduced, which encourages their active and voluntary
participation in the proposed DSM programs; on the other hand, this results in a lower
generation costs and enhances the robustness of the whole grid.

Full document
 | Slides

©UPC Universitat Politècnica de Catalunya
Signal Processing and Communications group
Powered by Joomla!.