Stochastic energy procurement of large electricity consumer considering photovoltaic, wind-turbine, micro-turbines, energy storage system in the presence of demand response program

Abstract In this paper, Stochastic synchronization of the Wind and Solar Energy Using Energy Storage system based on real-time pricing in the Day Ahead-Market Along with taking advantage of the potential of Demand Response programming, has been analyzed. Since renewables energies, loads and prices are uncertain, and planning is based on real-time pricing, the optimal biding proposition considers the wind power, solar system, and energy storage system. Uncertainty is addressed to solve the bidding strategy in a day-ahead market for optimal wind and PV power and optimal charging for energy storage. Batteries are the most promising device to compensate for the fluctuations of wind and photovoltaic power plants to mitigate their uncertainty. In general, using MILP is a suitable approach to address uncertainty as long as a linear formulation is acceptable for modeling either with continuous variables or integer ones. By setting some scenarios to formulate market prices, imbalance of energy, wind and solar system, the uncertainty problems could be easily solved by MILP solver. The model created enables the retailer to realize the potentials of the demand response program and exploit high technical and economic advantages. To ensure fair prices, a set of regulating constraints is considered for sales prices imposed by the regulation committees. A model is presented to optimize the electricity trading strategy in the electricity market, considering the uncertainty in the wholesale market price and the demand level. The retailer considered in this paper is a distribution company that is the owner and operator of the networks and operates under real-time pricing regulations. To model demand response, the elasticity coefficient is used. The proposed solution is implemented on a standard 144-bus sample network using a nonlinear integer programming method. The presented method results provide helpful and valuable information based on the optimal method proposed by the retailers considering the Demand response program and real-time pricing (RTP) system.

Download Full-text

Cooperative control framework of the wind turbine generators and the compressed air energy storage system for efficient frequency regulation support

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2021.106844 ◽

2021 ◽

Vol 130 ◽

pp. 106844

Author(s):

Said I. Abouzeid ◽

Yufeng Guo ◽

Hao-Chun Zhang

Keyword(s):

Energy Storage ◽

Wind Turbine ◽

Cooperative Control ◽

Storage System ◽

Energy Storage System ◽

Compressed Air ◽

Frequency Regulation ◽

Turbine Generators ◽

Wind Turbine Generators ◽

Control Framework

Download Full-text

Cooperative Operation Schedules of Energy Storage System and Demand Response Resources Considering Urban Railway Load Characteristic under a Time-of-Use Tariff

Journal of Electrical Engineering and Technology ◽

10.1007/s42835-021-00670-3 ◽

2021 ◽

Vol 16 (3) ◽

pp. 1273-1284

Author(s):

Hye Ji Kim ◽

Hosung Jung ◽

Young Jun Ko ◽

Eun Su Chae ◽

Hyo Jin Kim ◽

...

Keyword(s):

Energy Storage ◽

Demand Response ◽

Air Conditioning ◽

Linear Problem ◽

Storage System ◽

Energy Charge ◽

Energy Storage System ◽

Peak Reduction ◽

Railway Stations ◽

Charging And Discharging Processes

AbstractThis paper proposes an algorithm for the cooperative operation of air conditioning facilities and the energy storage system (ESS) in railway stations to minimize electricity. Unlike traditional load patterns, load patterns of an urban railway station can peak where energy charge rates are not high. Due to this possibility, if applying the traditional peak-reduction algorithm to railway loads, energy changes can increase, resulting in higher electricity bills. Therefore, it is required to develop a new method for minimizing the sum of capacity charges and energy charges, which is a non-linear problem. To get a feasible solution for this problem, we suggest an algorithm that optimizes the facility operation through two optimizations (primary and secondary). This method is applied to the air-quality change model for operating air conditioning facilities as demand-response (DR) resources in railway stations. This algorithm makes it possible to estimate operable DR capacity every hour, rather than calculating the capacity of DR resources conservatively in advance. Finally, we perform a simulation for the application of the proposed method to the operation of DR resources and ESS together. The simulation shows that electricity bills become lowered, and the number of charging and discharging processes of ESS is also reduced.

Download Full-text