The review of load scheduling model research based on demand response method

2013 ◽  
Author(s):  
Fan Yingdan ◽  
Ai Xin
Keyword(s):  
Demand Response ◽  
Load Scheduling ◽  
On Demand ◽  
Scheduling Model ◽  
Model Research ◽  
Response Method

Research on Demand Response Day-Ahead Scheduling Model for Multi-type Residential Customers

2021 ◽  
Author(s):  
Aiming Chen ◽  
Lu Shan ◽  
Jingtao Wang ◽  
Xiuxin Chen ◽  
Xiao Chen
Keyword(s):  
Demand Response ◽  
On Demand ◽  
Scheduling Model

scholarly journals Generation-Load Coordinative Scheduling considering the Demand-Response Uncertainty of Inverter Air Conditioners

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wei Hu ◽  
Jin Yang ◽  
Yi Wu ◽  
Weiguo Zhang ◽  
Xueming Li ◽  
...  
Keyword(s):  
Power System ◽  
Demand Response ◽  
Load Scheduling ◽  
Air Conditioners ◽  
Power Scheduling ◽  
Scheduling Model ◽  
Conditioning Equipment ◽  
The Cost ◽  
First Time

Inverter air conditioners (IACs) have gradually become the mainstream of resident air-conditioning equipment. Similar to traditional fixed-frequency air conditioners, IACs have the potential for demand response and load scheduling. However, the uncertainty of IACs is nonnegligible in generation-load scheduling. In this paper, the uncertain demand-response cost of IACs is studied for the first time. Meanwhile, based on the cost, a generation-load coordinative day-ahead scheduling model is proposed. In the scheduling, an IACs aggregator and traditional generators are coordinately dispatched to minimize the expected scheduling cost of the power system. The case study shows that the coordinative scheduling model can reduce the scheduling cost of the power system and encourage the IACs aggregator to improve their responsiveness or reduce their uncertainty, so as to improve the economy and reliability of power scheduling.

Two-stage load-scheduling model for the incentive-based demand response of industrial users considering load aggregators

2018 ◽  
Vol 12 (14) ◽  
pp. 3518-3526 ◽  
Author(s):  
Jingjing Zhang ◽  
Peng Zhang ◽  
Hongbin Wu ◽  
Xianjun Qi ◽  
Shihai Yang ◽  
...  
Keyword(s):  
Demand Response ◽  
Two Stage ◽  
Load Scheduling ◽  
Scheduling Model

scholarly journals A demand-response method to balance electric power-grids via HVAC systems using active energy-storage: Simulation and on-site experiment

2021 ◽  
Vol 7 ◽  
pp. 762-777
Author(s):  
Qinglong Meng ◽  
Yang Li ◽  
Xiaoxiao Ren ◽  
Chengyan Xiong ◽  
Wenqiang Wang ◽  
...  
Keyword(s):  
Energy Storage ◽  
Electric Power ◽  
Demand Response ◽  
Hvac Systems ◽  
Power Grids ◽  
Electric Power Grids ◽  
Response Method

scholarly journals Research on Demand Response Aggregators Participating in Power Market Based on Bi-level Optimization

2021 ◽  
Vol 781 (4) ◽  
pp. 042009
Author(s):  
Weijie Shen ◽  
Cheng Fang ◽  
Jiaxin Ma ◽  
Jialin Lin ◽  
Ming Zeng
Keyword(s):  
Demand Response ◽  
Power Market ◽  
On Demand

scholarly journals An Energy Management Optimization Method for Community Integrated Energy System Based on User Dominated Demand Side Response

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4398
Author(s):  
Yiqi Li ◽  
Jing Zhang ◽  
Zhoujun Ma ◽  
Yang Peng ◽  
Shuwen Zhao
Keyword(s):  
Energy Management ◽  
Demand Response ◽  
Value At Risk ◽  
Operating Cost ◽  
Optimization Method ◽  
Conditional Value At Risk ◽  
Demand Side ◽  
Scheduling Model ◽  
Management Optimization ◽  
Demand Side Response

With the development of integrated energy systems (IES), the traditional demand response technologies for single energy that do not take customer satisfaction into account have been unable to meet actual needs. Therefore, it is urgent to study the integrated demand response (IDR) technology for integrated energy, which considers consumers’ willingness to participate in IDR. This paper proposes an energy management optimization method for community IES based on user dominated demand side response (UDDSR). Firstly, the responsive power loads and thermal loads are modeled, and aggregated using UDDSR bidding optimization. Next, the community IES is modeled and an aggregated building thermal model is introduced to measure the temperature requirements of the entire community of users for heating. Then, a day-ahead scheduling model is proposed to realize the energy management optimization. Finally, a penalty mechanism is introduced to punish the participants causing imbalance response against the day-ahead IDR bids, and the conditional value-at-risk (CVaR) theory is introduced to enhance the robustness of the scheduling model under different prediction accuracies. The case study demonstrates that the proposed method can reduce the operating cost of the community under the premise of fully considering users’ willingness, and can complete the IDR request initiated by the power grid operator or the dispatching department.

scholarly journals A Multiobjective Scheduling Optimization Model for Multienergy Complementary System Integrated by Wind-Photovoltaic-Convention Gas Turbines considering Demand Response

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Zhang Lihui ◽  
Xin He ◽  
Ju Liwei
Keyword(s):  
Demand Response ◽  
Optimization Model ◽  
Gas Turbines ◽  
Risk Level ◽  
Objective Functions ◽  
Entropy Weight ◽  
Scheduling Optimization ◽  
Power Sources ◽  
Complementary System ◽  
Scheduling Model

To utilize the complementary feature of different power sources, wind power plant (WPP), and solar photovoltaic power (PV), convention gas turbines (CGT) and incentive-based demand response (IBDR) are integrated into a multienergy complementary system (MECS) with the implementation of price-based demand response (PBDR). Firstly, the power output model of WPP, PV, and CGT is constructed and the mathematical model of DR is presented. Then, a multiobjective scheduling model is proposed for MECS operation under the objective functions of the maximum economic benefit, the minimum abandoned energy, and the minimum risk level. Thirdly, the payoff table of objective functions is put forward for converting the multiobjective model into a single objective model by using entropy weight method to calculate weighting coefficients of different objective functions. Finally, the improved IEEE 30 bus system is taken as the simulation system with four simulation scenarios for comparatively analyzing the influence of PBDR and IBDR on MECS operation. The simulation results show the following: (1) The MECS fully utilized the complementarity of different power sources; CGT and IBDR can provide peaking service for WPP and PV to optimize overall system operation. (2) The proposed algorithm can solve the MECS multiobjective scheduling optimization model, and the system scheduling results in the comprehensive optimal mode can take into account different appeal. And the total revenue, abandoned energy capacity, and load fluctuation are, respectively, 108009.30¥, 11.62 MW h, and 9.74 MW. (3) PBDR and IBDR have significant synergistic optimization effects, which can promote the grid connection of WPP and PV. When they are both introduced, the peak-to-valley ratio of the load curve is 1.19, and the abandoned energy is 5.85 MW h. Therefore, the proposed MECS scheduling model and solution algorithm could provide the decision basis for decision makers based on their actual situation.

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