Abstract
With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.
Highlights
In recent years, many electric power systems throughout the world have undergone massive changes due to restructuring in the power industry, with the aim of propelling this industry from a non-competitive integrated structure toward a competitive framework, resulting in substantial alterations in the operation and planning of vertically integrated electrical energy systems [1,2]
The profit-based unit commitment (PBUC) is a sophisticated, non-linear, and mixed-integer optimization problem determining the optimum commitment status of units, as well as the production scheduling of committed power plants, with the goal of maximizing the generation companies (GENCOs)’s profit while meeting the commitment and scheduling constraints mentioned for the unit commitment (UC) problem. Another difference between UC and PBUC problems is concerned with the power balance constraint, which is considered as a soft constraint in a deregulated environment, meaning that GENCOs can maximize their own profits without any obligation to satisfy the load demand
In case 2, the day-ahead scheduling problem of the GENCO is addressed in the presence of compressed air energy storage (CAES) units
Summary
Many electric power systems throughout the world have undergone massive changes due to restructuring in the power industry, with the aim of propelling this industry from a non-competitive integrated structure toward a competitive framework, resulting in substantial alterations in the operation and planning of vertically integrated electrical energy systems [1,2]. In the current study, a novel model for the PBUC problem considering CAES as well as CSP units is presented to determine the optimal short-term operation scheduling of a price-taker GENCO participating in a day-ahead and pool-based electricity market. The proposed framework allows the construction of the optimal offering curves of a GENCO composed of three types of production facilities, consisting of conventional fossil-fuel-based thermal power plants, CAES systems, and CSP units. Providing a profit-maximization model to obtain the GENCO’s optimal day-ahead scheduling strategy in the presence of CAES as well as CSP; Determining the charge and discharge of CAES to maximize the GENCO’s profit; Integration of the TES system with the CSP plant, enabling it to operate independently from the instantaneous solar radiation; Investigating the impacts of CAES and CSP units on the operation scheduling of the conventional thermal units; Using the CPLEX solver to solve the PBUC problem for a GENCO in the presence of conventional thermal units, CAES, and CSP.
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