Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

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.

Multi-Objective Profit-Based Unit Commitment with Renewable Energy and Energy Storage Units Using a Modified Optimization Method

“…Reddya et al proposed a direct search optimization (DSO) method to solve the PBUC problem, considering large-scale renewable energy resources such as wind and photovoltaics in a 10-unit system [49]. For a price-taker GENCO that owns traditional thermal power facilities as well as concentrating solar power (CSP) and compressed air energy storage (CAES) units, an effective methodology for achieving optimal offering curves has been proposed [50]. A literature survey of the PBUC problem shows that most studies have not considered emission concerns in solving the PBUC problem, and they have solved the problem based on the sum of the maximum profits of the power generation units.…”

Modified Genetic Algorithm for the Profit-Based Unit Commitment Problem in Competitive Electricity Market

“…Therefore, the first time slot in the Φ rel c list has the highest energy price and a priority in selling energy. Additionally, the minimum and maximum hourly amounts of active power that can be consumed and generated by the storage system are stored as per Equations ( 37)- (40).…”

“…Additionally, stored power contributes to the reduction of upkeep costs associated with these facilities [39]. The profitability of Compressed Air Energy Storage (CAES) systems for Generation Companies (GENCOs) has been recognized [40]. This feasibility arises from the capacity of these companies to acquire energy from the market during periods of low demand when prices are favorable and store it in their compressed air storage systems.…”

“…Additionally, the authors incorporate Conditional Value-at-Risk in the formulation for risk management in CAES scheduling, and the problem is formulated as a Mixed-Integer Linear Programming (MILP) problem. In [40], a methodology is presented to solve the PBUC problem for a generation company participating in the Day-Ahead market, owning thermal units, concentrated solar power (CSP) units, and CAES. The adopted formulation is a Mixed-Integer Linear Programming (MILP) problem, solved using commercial solvers.…”

“…Table 3 provides the parameters for the compressed air energy storage systems. The parameters for the first system were obtained from references [40], while the parameters for the other systems were defined arbitrarily. It is worth noting that the second system has a higher generation capacity, but its initial storage is close to the minimum storage limit.…”