Due to different viewpoints, procedures, limitations, and objectives, the scheduling problem of distributed energy resources (DERs) is a very important issue in power systems. This problem can be solved by considering different frameworks. Microgrids and Virtual Power Plants (VPPs) are two famous and suitable concepts by which this problem is solved within their frameworks. Each of these two solutions has its own special significance and may be employed for different purposes. Therefore, it is necessary to assess and review papers and literature in this field. In this paper, the scheduling problem of DERs is studied from various aspects such as modeling techniques, solving methods, reliability, emission, uncertainty, stability, demand response (DR), and multi-objective standpoint in the microgrid and VPP frameworks. This review enables researchers with different points of view to look for possible applications in the area of microgrid and VPP scheduling.

A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems

Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II

Accurate runoff forecasting plays an important role in management and utilization of water resources. This paper investigates the accuracy of hybrid long short-term memory neural network and ant lion optimizer model (LSTM–ALO) in prediction of monthly runoff. As the parameters of long short-term memory neural network (LSTM) have influence on the prediction performance, the parameters of the LSTM are calibrated by using ant lion optimizer. Then the selection of suitable input variables of the LSTM–ALO is discussed for monthly runoff forecasting. Finally, we decompose root mean square error into three parts, which can help us better understanding the origin of differences between the observed and predicted runoff. To test the merits of the LSTM–ALO for monthly runoff forecasting, other models are employed to compare with the LSTM–ALO. The scatter-plots and box-plots are adopted for evaluating the performance of all models. In the case study, simulation results with the historical monthly runoff of the Astor River Basin show that the LSTM–ALO model has higher accuracy than that of other models. Therefore, the proposed LSTM–ALO provides an effective method for monthly runoff forecasting.

Monthly runoff forecasting based on LSTM–ALO model

Multi-objective dynamic economic emission dispatch of electric power generation integrated with game theory based demand response programs

Optimal operation scheduling of wind power integrated with compressed air energy storage (CAES)

Improved gravitational search algorithm for unit commitment considering uncertainty of wind power

Improved gravitational search algorithm for parameter identification of water turbine regulation system

This paper proposes a new gravitational search algorithm to solve the unit commitment (UC) problem, which is integrated binary gravitational search algorithm (BGSA) with the Lambda-iteration method. The proposed method is enhanced by priority list based on the unit characteristics and heuristic search strategies to repair the spinning reserve and minimum up/down time constraints. Furthermore, local mutation strategies are applied to improve the performance of BGSA. The implementation of the proposed method for UC problem consists of three stages. Firstly, the BGSA based on priority list is applied for solution unit scheduling when neglecting minimum up/down time constraints. Secondly, heuristic search strategies are used to handle minimum up/down time constraints and decommit excess spinning reserve units. Thirdly, local mutation strategies are raised to avoid premature convergence of the algorithm and prevent it from trapping into local optima. Finally, Lambda-iteration method is adopted to solve economic load dispatch based on the obtained unit schedule. The feasibility and effectiveness of the proposed method is verified by the systems with the number of units in the range of 10-100 and the results are compared with those of other methods reported in literatures. The results clearly show that the proposed method gives better quality solutions than other methods.

Hao Tian

Papers

Design of a fractional order PID controller for hydraulic turbine regulating system using chaotic non-dominated sorting genetic algorithm II

Improved gravitational search algorithm for unit commitment considering uncertainty of wind power

Improved gravitational search algorithm for parameter identification of water turbine regulation system

A new approach for unit commitment problem via binary gravitational search algorithm

Multi-objective optimization of short-term hydrothermal scheduling using non-dominated sorting gravitational search algorithm with chaotic mutation