Tabu search for the optimization of household energy consumption

Ha, Long; Ploix, Stéphane; Zamaï, Éric; Jacomino, Mireille

doi:10.1109/iri.2006.252393

Cited by 74 publications

(42 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in [36] the authors discuss how a local EMS can be designed to optimize the size and operation of an ESS, minimizing the effect of aging and replacement costs. Other heuristic techniques proposed are for example particle swarm optimization [37] and metaheuristic Tabu search [38].…”

Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

View full text Add to dashboard Cite

This paper presents a two-level control scheme for the energy management of an electricity prosumer node equipped with controllable loads, local generation, and storage devices. The main control objective is to optimize the prosumer's energy bill by means of intelligent load shifting and storage control. A generalized tariff model including both volumetric and capacity components is considered, and user preferences as well as all technical constraints are respected. Simulations based on real household consumption data acquired with a sampling period of 1 s are discussed. The proposed control scheme bestows the prosumer node with the flexibility needed to support smart grid use cases such as bill optimization (i.e., local energy trading), control of the profile at the point of connection with the grid, demand response, and reaction to main supply faults (e.g., islanding operation), etc.

show abstract

Section: Related Workmentioning

confidence: 99%

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Liberati¹,

Giorgio

2017

Energies

View full text Add to dashboard Cite

show abstract

“…Authors of [6] proposed a three-layer control mechanism and use Tabu search to find a feasible solution. In [7], particle swarm optimization is used to find the optimal solution for coordinately scheduling multiple energy resources.…”

Section: B Related Workmentioning

confidence: 99%

Modeling and stochastic control for Home Energy Management

McLaughlin

Jia

et al. 2012

2012 IEEE Power and Energy Society General Meeting

View full text Add to dashboard Cite

Abstract-The problem of modeling and control for Home Energy Management (HEM) is considered. A first order thermal dynamic model is considered and its parameters are extracted using real measurements over a period of three summer months. The identified model is validated using separate data sets. The extracted model shows certain nonstationarity and non-Gaussianity. However, local approximations using a stationary model are shown to have relatively small modeling and prediction errors. The extracted model is then used for developing a multi-scale multi-stage stochastic optimization framework for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. A two time scale Model Predictive Control (MPC) strategy is proposed that minimizes the discomfort level subject to power and budget constraints: at the slow time scale, a power budget is allocated across different appliances at the hourly level; at the fast time scale, sensor measurements are used for the scheduling and control of different loads. Using parameters extracted from the real data, the proposed approach is compared with the simple rule based control strategy typically used in HVAC controllers.

show abstract

“…Authors of [2] proposed a three-layer control mechanism and use Tabu search to find a feasible solution. In [3], particle swarm optimization is used to find the optimal solution for coordinately scheduling multiple energy resources.…”

Section: B Related Workmentioning

confidence: 99%

Multi-scale stochastic optimization for Home Energy Management

Jia

Murphy-Hoye

et al. 2011

2011 4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

View full text Add to dashboard Cite

Abstract-The problem of scheduling and control of appliances for Home Energy Management (HEM) is considered. A multitime scale and multi-stage stochastic optimization framework is proposed for the control of the Heating, Ventilation, and Air Conditioning (HVAC) unit, the charging of Plug-in Hybrid Electric Vehicle (PHEV), and the scheduling of deferrable load such as washer/dryer operations. Formulated as a constrained stochastic optimization that incorporates thermal dynamics, temperature measurements, and the real time pricing signal, a model predictive control algorithm is proposed that minimizes customer's discomfort level subject to cost and peak power constraints.

show abstract

Tabu search for the optimization of household energy consumption

Cited by 74 publications

References 9 publications

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node

Modeling and stochastic control for Home Energy Management

Multi-scale stochastic optimization for Home Energy Management

Contact Info

Product

Resources

About