A novel Traversal-and-Pruning algorithm for household load scheduling

Wang, Chengshan; Zhou, Yue; Wang, Jidong; Peng, Pei-Yuan

doi:10.1016/j.apenergy.2012.09.010

Cited by 46 publications

(18 citation statements)

References 24 publications

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“…Firstly, in case of emergent use of EV's owners, a lower limit of SoC is considered (2). Secondly, for the sake of safe operation of the gird, an upper limit on total output of EVs at each hour should be stipulated (4). Thirdly, now that EV may not be connected to the grid all the 24 h, it is sensible to set a time range limit when EV is available for the system operator (5).…”

Section: A Smart Grid Environmentmentioning

confidence: 99%

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Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Zhang

Dai

et al. 2016

IEEE Trans. Smart Grid

152

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With the development of smart grid, demand-side resources (DSR) will play an increasingly important role in the power balance of supply and demand. In addition, the requirement of a low-carbon smart grid means some policy backgrounds, such as carbon emissions trading (CET), should not be ignored. Under these circumstances, it is a good idea to construct a novel unit commitment (UC) model. This paper proposes a model that not only takes advantage of various resources on the demand side, such as electric vehicles, demand response, and distributed generation, but also reflects the effects of CET on generation schedule. Then, an improved particle swarm optimization (IPSO) algorithm is applied to solve the problem. In numerical studies, we analyze the impacts of DSR and CET on the results of UC, respectively. In addition, two meaningful experiments are conducted to study the approaches to allocate emission quotas and the effects of price transmission mechanism. Index Terms-Carbon emission quotas, carbon emissions trading (CET), demand response (DR), distributed generation (DG), electric vehicle (EV), improved particle swarm optimization (IPSO), smart grid, unit commitment (UC). NOMENCLATURE Index bIndex of bus. i Index of generating unit. j Index of electric vehicle (EV). t Index of hour. Variables and Functions DGa t Distributed generation (DG) used by its owners at time t. DGb t Output of grid-connected DG at time t. DGC Total cost (TC) of DG. DRC TC of demand response (DR). Emission of unit i at time t. E V2G,t Emission of vehicle-to-grid (V2G) at time t. FC i Fuel cost of unit i. I i,t On/off status of unit i at time t. Iter Current number of iteration. pc Probability of crossover. P i,t Output of unit i at time t. pm Probability of mutation. SC i,t Start-up cost of unit i at time t. SoC t,j State of charge of EV j at time t. TC TC of unit commitment (UC). V2GC TC of V2G. V2G t Output of V2G at time t. V2G t,j Output of V2G of EV j at time t. X on i,t , X off i,t Duration of continuously on/off of unit i at time t. η t Penetration rate of DG at time t.

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Section: A Smart Grid Environmentmentioning

confidence: 99%

“…V2G and its impact on the cost and emission of power system are studied on basis of UC model in [1]- [3]. The significance and feasibility of DR and its role in supply-demand schedule are examined in [4]- [6]. Economical operation of DG and chance-constrained schedule of active network with DG are researched [7]- [9].…”

Section: Index Bmentioning

confidence: 99%

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Zhang

Dai

et al. 2016

IEEE Trans. Smart Grid

152

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“…The states of the DEWH are the decision variables in the model, which should be solved for consumers as guiding schedules for the following day. Mathematically, the DEWH load scheduling can be formulated as [12]:…”

Section: The Domestic Electric Water Heaters (Dewh) Load Scheduling Pmentioning

confidence: 99%

A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties

et al. 2017

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Abstract:In this paper, a robust optimization strategy is developed to handle the uncertainties for domestic electric water heater load scheduling. At first, the uncertain parameters, including hot water demand and ambient temperature, are described as the intervals, and are further divided into different robust levels in order to control the degree of the conservatism. Based on this, traditional load scheduling problem is rebuilt by bringing the intervals and robust levels into the constraints, and are thus transformed into the equivalent deterministic optimization problem, which can be solved by existing tools. Simulation results demonstrate that the schedules obtained under different robust levels are of complete robustness. Furthermore, in order to offer users the most optimal robust level, the trade-off between the electricity bill and conservatism degree are also discussed.

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“…Wang et al [22] presented an algorithm based on mixed integer nonlinear programming to schedule the residential loads in relation to the use of the devices. The proposed system was able to schedule residential loads and reduce the electricity costs and is based on the characteristics of the thermal dynamics of thermostatically controlled loads by combining the operations of the algorithm and the particularities of the appliance commitment problem.…”

Section: Introductionmentioning

confidence: 99%

Towards the Handling Demand Response Optimization Model for Home Appliances

et al. 2018

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Abstract:The Demand Response (DR) is used by public electric utilities to encourage consumers to change their consumption profiles to improve the reliability and efficiency of the electric power system (EPS) and at the same time to minimize the electricity costs for the final consumers. Normally, DR optimization models only aim to reduce the energy consumption and reduce the final cost. However, this disregards the needs of the consumer. Therefore, proposals which appear excellent in theory are usually impracticable and non-commercial. This paper proposes a real-time Demand Response (DR) optimization model to minimize the electricity costs associated with consumption without compromising the satisfaction or comfort of residential consumers. The proposed DR here considered the different home appliance categories and level of consumer satisfaction for the new load scheduling of the appliances and is much more comprehensive than the other models analyzed. Moreover, it can be applied in any country, under any energy scenario. This model was developed as a nonlinear programming problem subject to a set of constraints. An energy consumption analysis of 10 families for 2015 from five geographic and climatic regions of Brazil was carried out. A computational validation of the model was performed using a genetic algorithm (GA) to determine the programming of residential devices for the time horizon. The computational simulations showed a decrease in the cost of the electricity. Moreover, the results showed that there was no impairment to consumer satisfaction and comfort due to the scheduling of appliances.

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A novel Traversal-and-Pruning algorithm for household load scheduling

Cited by 46 publications

References 24 publications

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

Unit Commitment Model in Smart Grid Environment Considering Carbon Emissions Trading

A Robust Optimization Strategy for Domestic Electric Water Heater Load Scheduling under Uncertainties

Towards the Handling Demand Response Optimization Model for Home Appliances

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