LMP computation at DG buses in radial distribution system

Abstract

Purpose The purpose of this paper is to estimate the locational marginal price (LMP) at each distributed generation (DG) bus based on DG unit contribution in loss reduction. This LMP value can be used by distribution company (DISCO) to control private DG owners and operate network optimally in terms of active power loss. Design/methodology/approach This paper proposes proportional nucleolus game theory (PNGT)-based iterative method to compute LMP at each DG unit. In this algorithm, PNGT has been used to identify the share of each DG unit in loss reduction. New mathematical modeling has been incorporated in the proposed algorithm to compute incentives being given to each DG owner. Findings The findings of this paper are that the LMP and reactive power price values for each DG unit were computed by the proposed method for the first time. Network can be operated with less loss and zero DISCO’s extra benefit, which is essential in deregulated environment. Fair competition has been maintained among private DG owners using the proposed method. Originality/value PNGT has been used for the first time for computation of LMP in distribution system based on loss reduction. Incentives to each DG unit has have been computed based on financial savings of DISCO due to loss reduction. Share of active and reactive power generation of each DG unit on change in active power loss of network due to that DG unit has been computed with new mathematical modeling. The proposed method provides LMP value to each DG unit in such a way that the network will be operated with less loss.

Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models

“…Active power load was predicted in [17] based on load data of last four hours. Active power load was estimated in [18][19][20] based on load data for the last four hours and load data at the same hour for the last two days.…”

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

“…Authors have developed an ANN architecture in Shaloudegi et al 22 to predict load-based loads on the same system over the last 4 hours. Veeramsetty et al 23,24 have developed a new ANN architecture to predict the load of an electrical utility at a given hour of the day based on the load data for the last 4 hours and the load data over the last 2 days at the time of prediction. An ANN model is developed in Veeramsetty et al 25 to predict load at a 33-/11-kV substation at a given hour of the day based on load at the previous three hours and load at the time of prediction but for the last 4 days.…”

Probabilistic locational marginal price computation in radial distribution system based on active power loss reduction

“…Similarly sensitivity of DG units reactive power generation at bus 'b' on the APL has been computed using (13) and 14for lagging and leading power factor DGs, respectively. The loss sensitivity factors, which were utilised for computing the probabilistic LMP at each DG bus have been validated in [25] ∂P…”