A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price

Lu, Qing; Zhang, Yufeng

doi:10.1016/j.energy.2021.122504

Cited by 35 publications

(6 citation statements)

References 25 publications

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“…Another study proposes a multi-objective optimization methodology in individual neighborhoods to maximize consumer thermal comfort, reduce demand, and minimize battery operational costs [6]. The paper [7] develops a multi-objective optimization model that balances user satisfaction with various demand response types in a dynamic electricity pricing environment, for optimizing efficient energy consumption across different user categories. A multiobjective optimization is developed in [8] to enhance economic efficiency and reliability in smart integrated energy systems while addressing demand responses and consumer comfort.…”

Section: A Occupants Thermal Comfortmentioning

confidence: 99%

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Nematirad,

Ardehali,

Khorsandi

et al. 2024

IEEE Access

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Residential consumers can optimize their participation in demand response programs (DRPs) using home energy management systems (HEMS). By automatically adjusting air conditioning (AC) setpoints and shifting certain appliances to off-peak hours, HEMS can lead to significant cost reductions. While HEMS aims to adjust AC temperature setpoints, it is important to consider the occupants thermal comfort. This study aims to develop a multi-objective model for the application of DRPs in a smart residential house. The objectives of the model are to achieve (a) reduction in electrical load demand, (b) adjustment in thermal comfort temperature setpoints, and (c) minimization of consumer costs subject to the related constraints. Determining occupancy status through HEMS provides more economic benefits and thermal comfort for consumers. However, traditional methods such as direct occupancy monitoring are often costly, inaccurate, and can intrusively collect data on residents' activities, locations, and routines, compromising their privacy. To tackle these challenges, this study introduces advanced forecasting algorithms such as random forest, light gradient boosting machine, and multilayer perception artificial neural networks to predict occupancy by utilizing indirect data sources, such as energy consumption patterns. This approach enables the prediction of residential presence without direct monitoring. However, inherent uncertainty associated with predicted parameters can compromise the effectiveness of DRPs, and potentially lead to non-optimal energy savings, jeopardizing consumer comfort, and even system instability. To address these uncertainties, this study integrates robust counterpart optimization techniques, augmented with uncertainty budgets to control uncertainty variation making them less conservative. Simulations show uncertainty increases costs by 36% and reduces AC temperature setpoints. Further, implementing DRPs reduces demand by shifting some appliances to off-peak hours and lowers costs by 13.2%.

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Section: A Occupants Thermal Comfortmentioning

confidence: 99%

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Nematirad,

Ardehali,

Khorsandi

et al. 2024

IEEE Access

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“…The analysis method is based on the assumption that the electricity purchase costs of large consumers obey a normal distribution, and the expectation and variance of the electricity purchase costs are calculated based on D(C) = E C 2 − E 2 (C). The entropy value is calculated using Equation ( 14), and the entropy value and the average value of the electricity purchase cost of large consumers can be substituted into Equation (14) to obtain the large consumer portfolio optimization model based on expected utility-entropy:…”

Section: Expected Utility-entropy-based Power Purchase Transaction Mo...mentioning

confidence: 99%

“…Li Fe [3] established a two-layer game optimization model for RPS-driven green and thermal power suppliers' bidding and large consumer direct electricity purchase, and solved the coupled optimization decision problem of multi-power suppliers bidding and multi-large consumer direct electricity purchase transactions. Lu Qing [14] constructed a non-cooperative Stackelberg model based on game theory to study the demand response characteristics of multiple types of consumers according to the principles of consumer psychology. The impacts of grid load fluctuations on the benefits of electric companies and the satisfaction of consumers with electricity consumption were quantified, the Nash equilibrium solution of the model was obtained by the NSGA-II algorithm, and the sensitivity analysis of the correlation coefficients was carried out.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of the Double Game Model for Direct Purchase of Electricity by Large Consumers under Consideration of Risk Factors

Yu,

Zhang,

Cui

et al. 2024

Energies

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With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers’ contract bidding and large consumers’ electricity purchase strategy in the medium- and long-term contract transactions.

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“…And an example analysis was conducted with residential electricity consumption in 12 Chinese provinces to verify that the optimized tariff is beneficial to the reduction of residential electricity bills and peak electricity consumption. Lu and Zhang (2022) investigated customers' electricity consumption satisfaction based on consumer psychology principles and constructed a time-of-use price optimization model considering multiple objectives based on this. Duman et al (2023) investigated a new technology, home energy management systems (HEMSs), to influence the demand response potential of residents in an area.…”

Section: Literature Reviewmentioning

confidence: 99%

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

Pan,

Liu

2023

MEQ

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PurposeOn the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.Design/methodology/approachFirst, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.FindingsCompared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.Originality/valueThis paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.HighlightsThe stability of electricity consumption is important to the stable operation of the grid.An improved FP-growth algorithm is employed to explore the influencing factors.The improved algorithm enables the mining of rules containing specific attribute labels.Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

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A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price

Cited by 35 publications

References 25 publications

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

Construction and Application of the Double Game Model for Direct Purchase of Electricity by Large Consumers under Consideration of Risk Factors

Mining research on correlation factors of residential electricity stability based on improved FP-growth algorithm

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