Unit Commitment in Smart Grids Considering Emission and Energy Storage Systems

The problem of unit commitment (UC) or in other words, the issue of placing the units in orbit is a very important optimization problem, the exact solution of which can lead to a significant reduction in operation costs. In general, UCchr('39')s goal is to minimize the total cost of production units by considering the constraints of the units and the system during the intended period. In fact, UC is divided into two issues. First, there is the issue of determining the on or off mode of production units for each hour, which is usually 24 hours, and the second is the distribution of economic dispatch between production units. Considering both problems at the same time will complicate the solution. It should also be noted that the difficulty of the problem also increases in proportion to the number of production units as well as the constraints considered. As mentioned, unit cost is typically the primary goal of minimizing UC issues. This paper considers smart grids, one of whose goals is reducing environmental pollution in addition to reducing costs. Therefore, this paper considers UC problem-solving in smart grids by considering the pollution of production units, so a multi-objective function is selected for minimization. On the other hand, with the introduction of smart grids, the existence of energy storage systems (ESS) in the network is also considered. This paper suggests optimal charging and discharging of energy storage systems, too. Another issue modeled in this paper is the issue of demand response in smart grids. Demand response program modeling allows grid demands to be shifted within the specified range at different times of the day. The proposed model is a mixed integer linear programming model (MILP) that has been tested to validate the performance of the proposed model, i.e., 4-unit and 10-unit systems with storage resources. To solve the proposed model, a powerful Gurobi solver is used. This solver also guarantees optimal global solutions and can get optimal solutions in the shortest time. In the end, the simulation results showed that modeling the problem of optimizing the charge and discharge of the energy storage system, along with the problem of demand-response management in the issue of unit participation, is very effective in reducing operating costs and reducing pollution. On the other hand, due to the linear nature of the problem, the proposed model can be solved in larger dimensions of the power systems.