Optimal Allocation of Spinning Reserves in Interconnected Energy Systems with Demand Response Using a Bivariate Wind Prediction Model

Abstract

The proposed study presents a novel probabilistic method for optimal allocation of spinning reserves taking into consideration load, wind and solar forecast errors, inter-zonal spinning reserve trading, and demand response provided by consumers as a single framework. The model considers the system contingencies due to random generator outages as well as the uncertainties caused by load and renewable energy forecast errors. The study utilizes a novel approach to model wind speed and its direction using the bivariate parametric model. The proposed model is applied to the IEEE two-area reliability test system (RTS) to analyze the influence of inter-zonal power generation and demand response (DR) on the adequacy and economic efficiency of energy systems. In addition, the study analyzed the effect of the bivariate wind prediction model on obtained results. The results demonstrate that the presence of inter-zonal capacity in ancillary service markets reduce the total expected energy not supplied (EENS) by 81.66%, while inclusion of a DR program results in an additional 1.76% reduction of EENS. Finally, the proposed bivariate wind prediction model showed a 0.27% reduction in spinning reserve requirements, compared to the univariate model.

Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

“…The "price" component of the price-quantity offers provided by DRPs include both the energy and capacity cost, while in the "quantity" component, DRPs specify the maximum amount of load reduction, which must be greater than or equal to the minimum amount specified by ISO, and the maximum time of this reduction. The derivation of DRP's cost-function is done using linear approximation, in the same way as it was done for conventional capacity (Bapin et al, 2019).…”

Parçacık sürü optimizasyonu temelli ultra hızlı yenilenebilir enerji kaynağı optimizasyon aracı tasarımı

“…Maliyet fonksiyonu olarak enerji maliyeti(COE), seviyelendirilmiş enerji maliyeti (LCOE), toplam yıllık maliyet (TAC), toplam maliyet (TC), Net Bugün ki Değer (NPV) parametreleri birçok araştırmacı tarafından kullanılmıştır [8]. Güvenilirlik parametresi olarak; güç kaynağı olasılığı kaybı (LPSP), Beklenen Sağlanmayan Enerji (EENS) [9,10], Yük Kaybı Olasılığı (LOLP), Beklenen yük kaybı (LOLE), Beklenen Enerji Kaybı (LOEE), Eşdeğer Kayıp faktörü (ELF), Güç Eksikliği Tedarik Olasılığı (DPSP), Yenilenebilir Enerji Oranı (REF), Temin Edilmeyen enerji (ENS), Enerji Endeksi Oranı (EIR), Güç Kaynağı Eksikliği (DPS), Yük Kaybı Olasılığı (LLP) [11], Yük Kaybı (LOL), Beklenen Hizmet Almamış Enerji (EUE), parametreleri kullanılmaktadır. Güvenilirlik parametresi olarak birçok araştırmacı güç kaynağı olasılığı kaybı (LPSP), Göreceli Fazla Yük Üretimi (REPG)parametresini tercih etmektedir.…”