An Incentive-Based Demand Response (DR) Model Considering Composited DR Resources

Yu, Mengmeng; Hong, Seung Ho; Ding, Yuemin; Ye, Xun

doi:10.1109/tie.2018.2826454

Cited by 117 publications

(39 citation statements)

References 31 publications

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“…Considering the economic characteristics of users, [13] established a consumer's cost function using quadratic function, and then develop a joint online learning and pricing algorithm, to obtain the appropriate price for all the consumers in each time slot. Reference [14] used Stackelberg game theory to analyze the user's decision in demand response, and also used the quadratic function to establish the user's cost function. Reference [15] analyzed the impact of incentive-based demand response on microgrid operation, and [16] implemented incentive-based demand response by establishing stochastic energy cost function in microgrid.…”

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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“…In [24], the authors propose a DR scheme based on hour-ahead Real Time Pricing (RTP) for industrial facilities aiming to forecast unknown future prices through artificial neural network based price forecasting model to support global cost optimization and production targets. A resource trading framework aiming to minimize the cost and ensuring the coordination between decision makers using the game theory is proposed in [25]. The authors are proposing a model containing the grid operator from one side, the industrial customers linked to it directly and the smaller and mid-size consumers aggregated through the aggregators.…”

Section: Pricing Schemesmentioning

confidence: 99%

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

et al. 2020

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Industrial Customers are dispersed at various levels of the electrical network and fed together with other customers' categories in a distributed environment. Optimizing industrial processes in the presence of other customers' categories supplied by the same infrastructure is a challenging issue. Existing studies have analyzed the effect of different Industrial Demand Response Programs on the distribution network, which also supplies other customers' categories. They show the need for improving the distribution performance although multiple demand response programs have been suggested for this purpose. In this paper, a new approach is presented considering an optimal synchronized process among all consumers' categories. It shows that the balance between generation and demand is maintained, the customer satisfaction is guaranteed, the profit is maximized and the cost is minimized for all customers. Various time constraints set by different industry productions are considered in the optimization process. Fairness problems, multiple pricing schemes and formulation for the same are elaborated. The method is validated through a simulation on Matlab using K-Means Clustering and multi-objective particle swarm optimization (MOPSO) along with data prediction. INDEX TERMS Multiple demand response programs, MOPSO, group method of data handling (GMDH), Kmeans, clustering, smart grid, industrial customers.

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“…Some scholars have conducted further study on DR. For example, in Reference [20], a fuzzy inference system for considering the customer load profile attributes in DR bids was presented, and the impact of load profiling attributes on the DR exchange mechanism was investigated. In References [21,22], a commitment model based on incentive-based considering different types of DR resources was proposed. "Source-load-energy storage" coordination is an important method to improve the system operation economy and the accommodation rate of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

Wang

et al. 2019

Energies

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In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.

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An Incentive-Based Demand Response (DR) Model Considering Composited DR Resources

Cited by 117 publications

References 31 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

Industrial Loads Used as Virtual Resources for a Cost-Effective Optimized Power Distribution

Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response

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