Multi-objective residential load dispatch based on comprehensive demand response potential and multi-dimensional user comfort

Wang, Tonghe; Wang, Jing; Zhao, Yuming; Chen, Jiongcong

doi:10.1016/j.epsr.2023.109331

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…They employed a gradient projection method for solving it, aiming to maximize the social welfare of participants in real-time demand response schemes in smart grids. Wang et al [33] presented a multi-objective optimal scheduling strategy for residential load resources, with objectives including enhancing user comfort, user income, and load aggregator revenue. Considering various disaster resilience potentials and residential user participation willingness, they constructed a comprehensive evaluation system for user DR potentials.…”

Section: Demand Response Research Statusmentioning

confidence: 99%

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Xu,

Liu,

Cui

et al. 2024

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With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations.

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Section: Demand Response Research Statusmentioning

confidence: 99%

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Xu,

Liu,

Cui

et al. 2024

Information

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“…Wang Y. et al [22] established a DR index system and used the index system to classify user response potential levels. Wang T. et al [23] established a formula for user DR potential based on electricity consumption patterns, equipment usage frequency, and electricity comfort. Giannelos S. et al [24] provided a new stochastic multi-stage planning model that expresses user DR potential as a probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

Zhao,

Wu,

Zhou

et al. 2024

Sustainability

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In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%.

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Smart communities - Design of integrated energy packages considering incentive integrated demand response and optimization of coupled electricity-gas-cooling-heat and hydrogen systems

Shi

Vasquez

Guerrero

et al. 2023

International Journal of Hydrogen Energy

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Multi-objective residential load dispatch based on comprehensive demand response potential and multi-dimensional user comfort

Cited by 11 publications

References 33 publications

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives

Smart communities - Design of integrated energy packages considering incentive integrated demand response and optimization of coupled electricity-gas-cooling-heat and hydrogen systems

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