Buildings-to-Grid Integration Framework

Taha, Ahmad F.; Gatsis, Nikolaos; Dong, Bing; Pipri, Ankur; Li, Zhaoxuan

doi:10.1109/tsg.2017.2761861

Cited by 47 publications

(23 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, and appropriate matrices A t , B GR , and B LD . For the full derivation of the system parameters for a similar model without wind-power integration, the reader is referred to [17,21,25]. The element of vector Ψ(δ(t)) ∈ R n t for bus k is defined as ψ k = ∑ j∈T n k b kj sin(δ k (t) − δ j (t)).…”

Section: Wind Integrated Tso Model Dynamicsmentioning

confidence: 99%

“…Traditional resistance and capacitor (RC) networks are widely used to model thermodynamics of building envelopes [15]. Both highly detailed models with variables for many zones, e.g., [26][27][28], and low order models with a single lumped zone, e.g., [17,18], have been proposed in the literature. As the envisioned purpose of the framework is to integrate large clusters of buildings into the grid, a low order thermal building model is most appropriate, considering the resulting computational complexity.…”

Section: Building Thermal Comfort Modelmentioning

confidence: 99%

“…The potential of using building HVAC control as a source of ancillary services for the grid is shown by [15,16]. In [17,18], the authors develop the so-called Buildings-to-Grid (BtG) integration framework, which couples buildings and grid dynamics explicitly. Similarly, refs.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

2020

View full text Add to dashboard Cite

We present a Buildings-to-Grid (BtG) integration framework with intermittent wind-power generation and demand flexibility management provided by buildings. First, we extend the existing BtG models by introducing uncertain wind-power generation and reformulating the interactions between the Transmission System Operator (TSO), Distribution System Operators (DSO), and buildings. We then develop a unified BtG control framework to deal with forecast errors in the wind power, by considering ancillary services from both reserves and demand-side flexibility. The resulting framework is formulated as a finite-horizon stochastic model predictive control (MPC) problem, which is generally hard to solve due to the unknown distribution of the wind-power generation. To overcome this limitation, we present a tractable robust reformulation, together with probabilistic feasibility guarantees. We demonstrate that the proposed demand flexibility management can substitute the traditional reserve scheduling services in power systems with high levels of uncertain generation. Moreover, we show that this change does not jeopardize the stability of the grid or violate thermal comfort constraints of buildings. We finally provide a large-scale Monte Carlo simulation study to confirm the impact of achievements.

show abstract

Section: Wind Integrated Tso Model Dynamicsmentioning

confidence: 99%

Section: Building Thermal Comfort Modelmentioning

confidence: 99%

See 1 more Smart Citation

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

2020

View full text Add to dashboard Cite

show abstract

“…In addition, buildings do not explicitly coordinate with each other when controlling flexible loads. The bang-bang controller [59] does not optimally control the HVAC because the control input is fixed at the maximum level when the HVAC is turned on [32]. Thus, to make a fair comparison, a multi-state control model similar to [60] is adopted here.…”

Section: Evaluation Of the Proposed Level-1 Aggregation Algorithmmentioning

confidence: 99%

“…In [31], the particle swarm optimization is performed to determine the operation strategies for all loads under the building cluster. Regarding the building to grid integration frameworks in both [32] and [33], all buildings under a transmission network node are regarded as a cluster during optimization. However, this method is unsuitable and inaccurate for the situation where loads are distributed in a large distribution network.…”

mentioning

confidence: 99%

Coordinating the operations of smart buildings in smart grids

Liu

Wang

et al. 2018

Applied Energy

Self Cite

View full text Add to dashboard Cite

With big thermal storage capacity and controllable loads such as the heating ventilation and air conditioning systems, buildings have great potential in providing demand response services to the smart grid. However, uncoordinated energy management of a large number of buildings in a distribution feeder can push power distribution systems into the emergency states where operating constraints are not completely satisfied. In this paper, we propose a bi-level building load aggregation methodology to coordinate the operations of heterogeneous smart buildings of a distribution feeder. The proposed methodology not only reduces the electricity costs of buildings but also guarantees that all the distribution operating constraints such as the distribution line thermal limit, phase imbalance, and transformer capacity limit are satisfied.

show abstract

Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids

Badings

Hartmanns

Jansen

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

We study a smart grid with wind power and battery storage. Traditionally, day-ahead planning aims to balance demand and wind power, yet actual wind conditions often deviate from forecasts. Shortterm flexibility in storage and generation fills potential gaps, planned on a minutes time scale for 30-60 min horizons. Finding the optimal flexibility deployment requires solving a semi-infinite non-convex stochastic program, which is generally intractable to do exactly. Previous approaches rely on sampling, yet such critical problems call for rigorous approaches with stronger guarantees. Our method employs probabilistic model checking techniques. First, we cast the problem as a continuous-space Markov decision process with discretized control, for which an optimal deployment strategy minimizes the expected grid frequency deviation. To mitigate state space explosion, we exploit specific structural properties of the model to implement an iterative exploration method that reuses precomputed values as wind data is updated. Our experiments show the method's feasibility and versatility across grid configurations and time scales.

show abstract

Buildings-to-Grid Integration Framework

Cited by 47 publications

References 29 publications

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Demand Flexibility Management for Buildings-to-Grid Integration with Uncertain Generation

Coordinating the operations of smart buildings in smart grids

Balancing Wind and Batteries: Towards Predictive Verification of Smart Grids

Contact Info

Product

Resources

About