# Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components
## 1 Introduction  
Microgrids are used in electric power grids to manage a localized group of power sources and loads that can operate in both connecting and disconnecting to the traditional main power grids [1; 2; 3]. In association with the growth of renewable energy-based generation systems (REGs), this concept has been attracting attention as one of the most realistic sustainable power grids in terms of efficient use of REGs. Actually, microgrids bring the possibility of grid independence to consumers by improving/keeping the efficiency, reliability, and quality in power supply and demand management in which components of the microgrids can be operated appropriately. Since the early 2000s, extensive research and development has been in progress to figure out efficient solutions for microgrid operations [4]. Furthermore, demonstrative field tests for microgrids have been actively promoted around the world [5; 6; 7]. There are two types of microgrid components: controllable and uncontrollable components. The former includes controllable generation systems (CGs), energy storage systems (ESSs), and controllable loads (CLs). A part of the controllable components (e.g., electric vehicles (EVs)), change their attributes depending on the judgement of whether the main grids accept reverse power flow from them or not (accepted: ESS, not accepted: CL). On the other hand, electrical loads in consumer-side and variable REGs (VREGs) (e.g., solar photovoltaic generation systems (PVs) and wind power generation systems(WTs)) are in the latter. Since the VREGs, whose outputs strongly depend on the weather condition, take a significant portion of electrical power sources of microgrids, it becomes very difficult to manage the power supply and demand with few adverse effects on the main power grids [1; 2; 3; 4; 5; 6; 7; 8; 9]. If microgrid operators cannot maintain the balance of supply and demand, the resulting electricity surplus/shortage must be compensated by trading electricity with an extra payment to the main grids, called imbalance penalty. These are reasons why an operation scheduling method for the microgrids is crucially required. Focusing on operation of the CGs, the operation scheduling problem in the microgrids is formulated as a mixed-integer programming (MIP) problem that combines optimization problems of the unit commitment (UC) and the economic load dispatch (ELD). This problem is essentially the same as scheduling problems for thermal power generation units [10; 11; 12; 13; 14; 15; 16], and, therefore, their solution algorithms are applicable. Branch-and-bound (BB) [17; 18] and dynamic programming (DP) [19; 20] have been traditionally utilized in thermal power generation scheduling. Meanwhile, intelligent optimization-based techniques have also been involved in solving the problems. Evolutionary programming (EP) [21], genetic algorithms (GAs) [22], simulated annealing (SA) [23; 24], tabu search (TS) [25; 26], and particle swarm optimization (PSO) [27; 28] have been employed in them. Although various traditional and intelligent algorithms have been applied, there has been no established solution until now. Moreover, practical operation scheduling in the microgrids becomes more complicated. This is because installation of the other controllable components brings new optimization variables representing charging/discharging operations of the ESSs and charging operation of the CLs, and their influences cannot be neglected from the microgrid operations. That is, it is necessary to determine the states of all optimization variables simultaneously from the viewpoint of efficient operation of the microgrids [3; 26; 27; 29; 30; 31; 32; 33]. Under these circumstances, first, the authors formulate a problem framework to obtain a coordinated operation schedule of the microgrid components. In the problem framework, trading electricity with the main grids is introduced in the optimization target to provide operational alternatives for the microgrid operators.