Approximations made in day-ahead markets can result in suboptimal or even infeasible schedules for generating units and inaccurate predictions of actual costs and wind curtailment. Here we compare different optimal models of day-ahead markets based on unit commitment (UC) formulations, especially energyvs. power-based UC; excluding or including startup and shutdown trajectories; and determinist...

This paper presents a solution to Optimal Unit commitment (UC) of thermal units based on a evolutionary algorithm known as Genetic algorithm (GA). In this proposed method of GA for the UC problem, the scheduling variables are coded as integers, so that the minimum up/down time constraints can be handled directly. To verify the performance of the proposed algorithm, it is applied to systems with...

When facing real world planning problems, standard planners are often inadequate and enhancement of the current techniques are required. In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances invo...

Hastening the power industry toward smart operation juxtaposed with the unrivaled restructuring and privatization agendas, some of the ubiquitous smart grid advantages are glanced more and more. Recently, the vehicle to grid (V2G) technology, as one of these beneficial aspects, has found a worldwide attention due to its important advantages. The V2G technology can raise the system operation eff...

• Soft constraints are exploited within the Surrogate Lagrangian Relaxation framework. Synchronous parallel algorithm utilizes multiple cores to improve performance. Near-optimal solutions for sub-hourly unit commitment based on Polish system. Unit (UC) is an important problem solved a daily basis strict time limit. While hourly UC currently used, they may not be flexible enough accommodate gro...

Ant colony optimization (ACO) was inspired by the observation of natural behavior of real ants’ pheromone trail formation and foraging. Ant colony optimization ismore suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes evolving ant col...

Ant Colony Optimization is more suitable for combinatorial optimization problems. ACO is successfully applied to the traveling salesman problem, and multistage decision making of ACO has an edge over other conventional methods. In this paper, the authors propose the Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) f...

7 The increasing penetration of uncertain generation such as wind and solar in power systems imposes new challenges to the Unit Commitment (UC) problem, one of the most critical tasks in power systems operations. The two most common approaches to address these challenges — stochastic and robust optimization — have drawbacks that restrict their application to real-world systems. This paper demon...

The growing uncertainty associated with the increasing penetration of wind and solar power generation has presented new challenges to the operation of large-scale electric power systems. Motivated by these challenges, we present a multistage adaptive robust optimization model for the most critical daily operational problem of power systems, namely the unit commitment (UC) problem, in the situat...

This paper focuses on the state-of-the-art of unit commitment (UC) and economic dispatch (ED) algorithms suitable for Italian electricity market. In view spread renewable energy systems (RES), desired UC algorithm should be able to properly consider uncertainty affecting key input variables into formulation problem, as well different capabilities dispatched power plants provide ancillary servic...