A joint energy and reserve scheduling framework based on network reliability using smart grids applications

Abstract

Summary This paper presents a new method based on a probabilistic scheduling reserve for smart grids, which include renewable energy sources (RESs), thermal units, and energy storage systems (ESSs). The method is divided into two main parts. The first part is based on spinning reserve to overcome the load‐generation imbalance in the network, while the second part is related to the spinning reserve for the outage of generation units. The total expected energy not supplied (TEENS) is the reliability criterion that determines the required spinning reserve. In the proposed formulation, generation units, the demand response (DR) program, and ESSs are capable of providing both types of reserves. The proposed method is evaluated using The Institute of Electrical and Electronics Engineers (IEEE) standard 30‐bus and IEEE reliability test networks. The suggested structure has been implemented by General Algebraic Modeling System (GAMS), and the results indicate the appropriate performance of the proposed method.

An optimal probabilistic spinning reserve quantification scheme considering frequency dynamic response in smart power environment

“…The probability distribution function is categorized into five segments and the amount of the probability for each segment is calculated to produce the required scenarios. [32][33][34][35] 3.4 | Demand side management modeling (load shifting program)…”

A novel decentralized method based on the system engineering concept for reliability‐security constraint unit commitment in restructured power environment

“…59,60 The uncertainty of the forecasted load is mainly modeled by the Gaussian probability distribution function. [61][62][63] To provide a finite number of load modes in 24-hours, the distribution function will be sampled. In the proposed approach, the Gaussian distribution is taken into account for the forecasting error.…”

“…Most of the article has been presented with different methods of load forecasting 59,60 . The uncertainty of the forecasted load is mainly modeled by the Gaussian probability distribution function 61‐63 . To provide a finite number of load modes in 24‐hours, the distribution function will be sampled.…”

“…In the first hour, scenarios are randomly generated. And then, highly probable and different scenarios are selected by the Kantorovich approach 63 (Figure 5). The probability of each scenario can generate a scenario for the next hour based on the following equation: …”

Strategic equilibrium of economic dispatch in smart grid with a bi‐level game approach

“…The economic dispatch problem (EDP) of the smart grid has been of considerable interest to the power system academia in recent years [1]. Some of them focus on the wind power that is different from classical dispatch due to its uncertainty and intermittence [2–4], while others focus on the joint dispatch of renewable energy sources, thermal units, energy storage systems, and carbon capture plants [5–7]. The operating cost of the unit is reduced after adopting economic dispatch, and the revenue of the utility company is greatly improved.…”