Modeling of unforced demand response programs

Abstract

Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.

Multi‐agent decentralized microgrids planning considering long‐term demand response model

“…A key feature of SMGs is the use of DRPs to provide flexibility to energy system and thus, increase its efficiency. 26,27 Many studies have used DRPs in MG planning, some of which are reviewed in the following.…”

“…According to the above constraint, the amount of power generated by DGs, installed and new renewables, DRPs, and batteries in each SMG at any time t, in each scenario s, and in each bus must be greater than a certain percentage of its demand. This constraint is mentioned in Equation (26). The αα its coefficient indicates a certain percentage of demand that must be generated by SMG itself.…”

Optimal scheduling of DG and EV parking lots simultaneously with demand response based on self‐adjusted PSO and K‐means clustering

“…For these customers, an efficient impact on the power system is expected. They can improve the day-ahead system reliability and decrease total operational costs by voluntary management of load demands and DR. 2,3 On the other hand, there are a number of aspects of the contemporary power systems that make the V2G optimization inadequate for the charging/discharging of EVs. These characteristics include (i) The widespread utilization of renewable energy sources, which are uncertain and intermittent.…”

“…[30][31][32][33][34] The optimal charging management was investigated by Mkahl et al 35 Similar work was achieved by Bin-Humayd and Bhattacharya. 36 The parking coordination of EVs was investigated in the study by Faddel et al 37 The investigation of the above research [2][3][4][5][6][7][8][9][10][11][12][13][15][16][17] reveals that a constant elasticity matrix for a specific interval of time was used, which results in some incredibility. In the study by Srivastava et al, 14 regression methods generally need training, and the accuracy of the regression models depends on the number of available data.…”