A New Incentive-Based Optimization Scheme for Residential Community With Financial Trade-Offs

Ni, Zhen; Das, Avijit

doi:10.1109/access.2018.2872929

Cited by 28 publications

(9 citation statements)

References 41 publications

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“…Reference [18] divided the load into three categories and calculated the demand response cost respectively. Reference [19] designed three different reward schemes for different comfort level of users, but completed information is needed to calculate users' comfort level. In [20], a classification algorithm is employed to divide consumers into different categories, and a pricing model is formulated as a nonlinear programming problem, aiming to minimize the overall operation cost.…”

Section: A Literature Reviewmentioning

confidence: 99%

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Liu

et al. 2020

IEEE Access

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In demand response programs, load service entity (LSE) can aggregate user as an independent entity to participate in the day ahead energy market, and complete the response target through incentive in the next day. In order to reduce the incentive cost of LSE, a differentiated incentive mechanism that consider the differences in user response flexibility is proposed in this paper. Then, a user response behavior model is established by using the long short-term memory (LSTM) network, with aim of accurately predicting users' response. Subsequently, an optimization strategy combining particle swarm (PSO) and LSTM is proposed, so that the response target can be accurately completed with low cost. Simulation experiments verified that the cost of LSE is close to the theoretical minimum, and can be reduced by 20% compared with the optimal result under unified incentive mechanism. Moreover, it also verified that the proposed strategy has high response accuracy and good stability.

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Section: A Literature Reviewmentioning

confidence: 99%

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Liu

et al. 2020

IEEE Access

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“…Generally, most of the outcome of the application of GA in optimization is to increase the probability of consumer thermal comfort in naturally ventilated rooms [68] and air-conditioned rooms [69][70] in buildings in individual homes or a collective community [71]. Apart from natural or artificial ventilation, other considerations such as the control of daylight entering residential homes have been optimized by the use of genetic algorithm [72].…”

Section: Optimizationmentioning

confidence: 99%

Smart Review of the Application of Genetic Algorithm in Construction and Housing

Okagbue¹,

Peter²,

Akinola³

et al. 2020

IJATCSE

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Genetic algorithm (GA) is an example of evolutionary algorithms that are bio-inspired computational methods. GA has been applied to numerous fields. It has been applied in different aspects of construction and building but that is scarcely any review that documents it. The paper reviewed the application of GA in construction and building. It was revealed that energy management is the major area of application which are further subdivided into load scheduling, prediction, and optimization. Other nonenergy applications are pricing, environment, and construction design or real estate. The review presents research information to researchers. The information can assist in the optimization of construction processes which can reduce the construction time and costs, ensure optimal allocation and use of energy, prediction of energy demands and supply in houses and incorporation of sustainability in construction and management of real estate.

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“…In [35], a social welfare maximization model was proposed based on Markov decision process, considering the features matrix that describes the elasticity appliances available in the SH, as well as several state transfer functions to highlight the semi-elastic appliances. Moreover, in order to guarantee the RCs' privacy, the model was divided into two sub-problems, i.e., RCs and suppliers.…”

Section: Related Work Considering Management Of Controllable Applianmentioning

confidence: 99%

Analysis Application of Controllable Load Appliances Management in a Smart Home

et al. 2019

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The residential sector is one of the sectors with the highest rates of electricity consumption worldwide. For years, many studies have been presented in order to minimize energy consumption at the residential level. The idea of such studies is that the residential customer (RC) is the interested party of their own consumption. Moreover, the algorithms that have been developed to predict and manage the energy consumption, also analyze the behavior of the loads, with the objective of minimizing the energy costs, with good safety, robustness, and comfort levels. In the context of the smart house (SH), one of the objectives of smart grids (SGs) is to enable the RC, with home energy management systems (HEM), to actively participate, allowing for higher reliability at different levels. In this work, a new model that simulates the behavior of an SH, considering heating, ventilation and air conditioning (HVAC) and sanitarian water heater (SWH) devices, is presented. For this purpose, the proposed model considers realistic physical parameters of the SH, together with customer comfort, in order to mitigate the RC disinterest. The proposed model considers the electric vehicle (EV), a battery-based energy storage system (ESS), a micro production unit, and different types of tariffs that the RC might choose, aiming to maximize the benefits, and temporarily shifting the proposed loads.

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A New Incentive-Based Optimization Scheme for Residential Community With Financial Trade-Offs

Cited by 28 publications

References 41 publications

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Differentiated Incentive Strategy for Demand Response in Electric Market Considering the Difference in User Response Flexibility

Smart Review of the Application of Genetic Algorithm in Construction and Housing

Analysis Application of Controllable Load Appliances Management in a Smart Home

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