Residential task scheduling under dynamic pricing using the multiple knapsack method

Kumaraguruparan, N.; Sivaramakrishnan, H.; Sapatnekar, Sachin S.

doi:10.1109/isgt.2012.6175656

Cited by 45 publications

(27 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kempton and Montgomery proposed a "folk quantification" method [11] to quantify the importance of appliance use in homes. Kumaraguruparan et al formulated a scheduling problem based on the multiple knapsack problem, in which daily power-consuming tasks are allocated to time slots with different electricity bills to minimize total electricity bills, and made a simulation-based evaluation [12].…”

Section: Related Workmentioning

confidence: 99%

Energy-on-Demand system based on combinatorial optimization of appliance operation modes

Morimoto

2016

2016 13th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

View full text Add to dashboard Cite

In this paper, the author proposes an Energy-onDemand (EoD) system based on combinatorial optimization of appliance operation modes, and describes its implementation and evaluation. Recently, efficient usage of limited amount of electrical energy has been an important issue. EoD is a novel power network architecture of demand-side power management, whose objective is to intelligently manage power flows among power generations under the limitation of available power resource. In an EoD system, the importance of each appliance is explicitly parameterized, and the amount of power consumption of appliances is measured by power sensors. When total power consumption exceeds the limit of power resource, a power allocation manager deployed in the system decides the optimal power allocation to all the appliances based on their parameters, and controls the amount of power supplied to the appliances in a way that causes minimum undesired effect to quality-oflife (QoL) of users. Therefore, one of the most crucial factors in an EoD system is the strategy for deciding the optimal power allocation. From a mathematical viewpoint, the power allocation management in an EoD system can be considered as an optimization problem of appliance operation modes. In the developed system, power allocation is based on the multiple-choice knapsack problem (MCKP), a kind of combinatorial optimization problem. The system measures power consumption of appliances, computes the optimal power allocation based on an algorithm for the MCKP, and realizes computed power allocation by controlling IR-controllable appliances and mechanical relays.

show abstract

Section: Related Workmentioning

confidence: 99%

Energy-on-Demand system based on combinatorial optimization of appliance operation modes

Morimoto

2016

2016 13th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

View full text Add to dashboard Cite

show abstract

“…• Bill minimization: The objective of the problem is to minimize the overall daily costs of the user, also considering the gains derived from the energy sale [56]:…”

Section: Objective Functionsmentioning

confidence: 99%

“…In these cases, devices are scheduled also based on the availability of an intermittent electricity source (e.g., PV plants). In [56], a very simple problem is proposed to optimally schedule the operating times of appliances, while minimizing the electricity bill. In this work, both fixed and energy-based elastic devices are considered, and locally-generated power is included in the model, even if profits from selling renewable electricity to the energy market are not incorporated in the objective function.…”

Section: Deterministic Approach To Dsm For Individual Usersmentioning

confidence: 99%

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

2014

View full text Add to dashboard Cite

Abstract:The residential sector is currently one of the major contributors to the global energy balance. However, the energy demand of residential users has been so far largely uncontrollable and inelastic with respect to the power grid conditions. With the massive introduction of renewable energy sources and the large variations in energy flows, also the residential sector is required to provide some flexibility in energy use so as to contribute to the stability and efficiency of the electric system. To address this issue, demand management mechanisms can be used to optimally manage the energy resources of customers and their energy demand profiles. A very promising technique is represented by demand-side management (DSM), which consists in a proactive method aimed at making users energy-efficient in the long term. In this paper, we survey the most relevant studies on optimization methods for DSM of residential consumers. Specifically, we review the related literature according to three axes defining contrasting characteristics of the schemes proposed: DSM for individual users versus DSM for cooperative consumers, deterministic DSM versus stochastic DSM and day-ahead DSM versus real-time DSM. Based on this classification, we provide a big picture of the key features of different approaches and techniques and discuss future research directions.

show abstract

“…The relation between the key terms used in MKP and energy management system can be developed as in [44] and is given below, 1. m knapsacks = m time interval. 2. n objects = n appliances.…”

Section: Mkp In Energy Management Systemmentioning

confidence: 99%

Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

et al. 2017

View full text Add to dashboard Cite

Abstract:In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.

show abstract

Residential task scheduling under dynamic pricing using the multiple knapsack method

Cited by 45 publications

References 8 publications

Energy-on-Demand system based on combinatorial optimization of appliance operation modes

Energy-on-Demand system based on combinatorial optimization of appliance operation modes

Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey

Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid

Contact Info

Product

Resources

About