A Determination Method for the Optimal Operation of Controllable Generators in Micro Grids That Copes with Unstable Outputs of Renewable Energy Generation

Abstract

Micro grids are expected to be one of the most realistic energy systems for efficient use of renewable energy sources with few adverse effects on the main electric power grids. However, it is difficult to maintain the supply-and-demand balance because distributed renewable energy generation units (DREGs), such as photovoltaic generation systems and wind turbine generation systems, generate a significant portion of electrical energy in the micro grids. Therefore, an operation planning method is needed considering the uncertainty in weather prediction in order to ensure stable micro grid operations. This paper presents an optimization method for operation plans of controllable generators in micro grids that copes with the uncertainty of DREG outputs. In the proposed method, the optimal operation plans are determined by, depending on the problem conditions, either an enumeration method or Tabu Search with preprocessing. Numerical simulations were carried out for a micro grid model in order to verify the usefulness of the proposed method. In the simulations, the daily operation plan and the modified half-hourly one were determined by the proposed method. As a result, we could obtain the optimal plans which had enough reserve margins for coping with the fluctuations caused by DREGs and demand. C⃝ 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 190(4): 56-65, 2015; Published online in Wiley Online Library (wileyonlinelibrary.com).

A study on electric power management for power producer-suppliers utilizing output of megawatt-solar power plants

“…[11] When all the following conditions are satisfied, we can regard the target optimization problem as a static hierarchical optimization problem: This is because the optimization variables at time t (u t and g t ) are independent of their values at time…”

Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components

“…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.…”

“…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].…”

“…As a result of problem reformulation, a combined algorithm of binary particle swarm optimization (BPSO) and quadratic programming (QP) can be applied in the solution. Unlike intelligent algorithms whose solutions much depend on choices of the initial solution and random number sequences [21][22][23][24][25][26][27][28]33], the proposed solution can restrain its dependency and, thus, provide us with more stable solutions. Finally, validity of the problem framework and usefulness of its solution method are verified through numerical simulations and a discussion of their results.…”

“…Detailed definitions of the variables are shown in Section 2.2. The operation scheduling problem in the microgrids is generally formulated to determine a set of start-up/shut-down timing and output shares of component 1, charging/discharging states of component 2, and charging states of component 3 in response to the forecasted values of net load on each time interval [26,27,30,33]. The net load is calculated by the sum of the forecasted values of electricity consumption of component 4 and output of component 5.…”