Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userOccupancy-Driven Stochastic Decision Framework for Ranking Commercial Building Loads
55
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
For effective integration of building operations into the evolving demand response programs of the power grid, real-time decisions concerning the use of building appliances for grid services must excel on multiple criteria, ranging from the added value to occupants comfort to the quality of the grid services. In this paper, we present a data-driven decision-support framework to dynamically rank load control alternatives in a commercial building, addressing the needs of multiple decision criteria (e.g. occupant comfort, grid service quality) under uncertainties in occupancy patterns. We adopt a stochastic multi-criteria decision algorithm recently applied to prioritize residential on/off loads, and extend it to i) complex load control decisions (e.g. dimming of lights, changing zone temperature set-points) in a commercial building; and ii) systematic integration of zonal occupancy patterns to accurately identify short-term grid service opportunities. We evaluate the performance of the framework for curtailment of air-conditioning, lighting, and plug-loads in a multi-zone office building for a range of design choices. With the help of a prototype system that integrates an interactive textit{Data Analytics and Visualization} frontend, we demonstrate a way for the building operators to monitor the flexibility in energy consumption and to develop trust in the decision recommendations by interpreting the rationale behind the ranking.
rate research
Read More
Demand flexibility is increasingly important for power grids. Careful coordination of thermostatically controlled loads (TCLs) can modulate energy demand, decrease operating costs, and increase grid resiliency. We propose a novel distributed control framework for the Coordination Of HeterOgeneous Residential Thermostatically controlled loads (COHORT). COHORT is a practical, scalable, and versatile solution that coordinates a population of TCLs to jointly optimize a grid-level objective, while satisfying each TCLs end-use requirements and operational constraints. To achieve that, we decompose the grid-scale problem into subproblems and coordinate their solutions to find the global optimum using the alternating direction method of multipliers (ADMM). The TCLs local problems are distributed to and computed in parallel at each TCL, making COHORT highly scalable and privacy-preserving. While each TCL poses combinatorial and non-convex constraints, we characterize these constraints as a convex set through relaxation, thereby making COHORT computationally viable over long planning horizons. After coordination, each TCL is responsible for its own control and tracks the agreed-upon power trajectory with its preferred strategy. In this work, we translate continuous power back to discrete on/off actuation, using pulse width modulation. COHORT is generalizable to a wide range of grid objectives, which we demonstrate through three distinct use cases: generation following, minimizing ramping, and peak load curtailment. In a notable experiment, we validated our approach through a hardware-in-the-loop simulation, including a real-world air conditioner (AC) controlled via a smart thermostat, and simulated instances of ACs modeled after real-world data traces. During the 15-day experimental period, COHORT reduced daily peak loads by an average of 12.5% and maintained comfortable temperatures.
Building energy management is one of the core problems in modern power grids to reduce energy consumption while ensuring occupants comfort. However, the building energy management system (BEMS) is now facing more challenges and uncertainties with the increasing penetration of renewables and complicated interactions between humans and buildings. Classical model predictive control (MPC) has shown its capacity to reduce building energy consumption, but it suffers from labor-intensive modelling and complex on-line control optimization. Recently, with the growing accessibility to the building control and automation data, data-driven solutions have attracted more research interest. This paper presents a compact review of the recent advances in data-driven MPC and reinforcement learning based control methods for BEMS. The main challenges in these approaches and insights on the selection of a control method are discussed.
Thermostatically controlled loads such as refrigerators are exceptionally suitable as a flexible demand resource. This paper derives a decentralised load control algorithm for refrigerators. It is adapted from an existing continuous time control approach, with the aim to achieve low computational complexity and an ability to handle discrete time steps of variable length -- desirable features for embedding in appliances and high-throughput simulations. Simulation results of large populations of heterogeneous appliances illustrate the accurate aggregate control of power consumption and high computational efficiency. Tracking accuracy is quantified as a function of population size and time step size, and correlations in the tracking error are investigated. The controller is shown to be robust to errors in model specification and to sudden perturbations in the form of random refrigerator door openings.
Despite their potential of increasing operational efficiency, transparency, and safety, the use of Localization and Tracking Systems (LTSs) in warehouse environments remains seldom. One reason is the lack of market transparency and stakeholders trust in the systems performance as a consequence of poor use of Test and Evaluation (T&E) methods and transferability of the obtained T&E results. The T&E 4Log (Test and Evaluation for Logistics) Framework was developed to examine how the transferability of T&E results to practical scenarios in warehouse environments can be increased. Conventional T&E approaches are integrated and extended under consideration of the warehouse environment, logistics applications, and domain-specific requirements, into an application-driven T&E framework. The application of the proposed framework in standard and application-dependent test cases leads to a set of performance criteria and corresponding application-specific requirements. This enables a well-founded identification of suitable LTSs for given warehouse applications. The T&E 4Log Framework was implemented at the Institute for Technical Logistics (ITL) and validated by T&E of a reflector-based Light Detection and Ranging (LiDAR) LTS, a contour-based LiDAR LTS, and an Ultra-Wideband (UWB) LTS for the exemplary applications Automated Pallet Booking, Goods Tracking, and Autonomous Forklift Navigation.
Demand flexibility is increasingly important for power grids, in light of growing penetration of renewable generation. Careful coordination of thermostatically controlled loads (TCLs) can potentially modulate energy demand, decrease operating costs, and increase grid resiliency. However, it is challenging to control a heterogeneous population of TCLs: the control problem has a large state action space; each TCL has unique and complex dynamics; and multiple system-level objectives need to be optimized simultaneously. To address these challenges, we propose a distributed control solution, which consists of a central load aggregator that optimizes system-level objectives and building-level controllers that track the load profiles planned by the aggregator. To optimize our agents policies, we draw inspirations from both reinforcement learning (RL) and model predictive control. Specifically, the aggregator is updated with an evolutionary strategy, which was recently demonstrated to be a competitive and scalable alternative to more sophisticated RL algorithms and enables policy updates independent of the building-level controllers. We evaluate our proposed approach across four climate zones in four nine-building clusters, using the newly-introduced CityLearn simulation environment. Our approach achieved an average reduction of 16.8% in the environment cost compared to the benchmark rule-based controller.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
