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The variability and intermittency of renewable energy and power load bring great pressure to the dispatch of Micro-Grid, especially intra-day dispatch. In order to reduce the intra-day dispatch pressure, this paper proposes a data-driven two-stage day-ahead dispatch model for islanded Micro-Grid. The first stage dispatch model considers multiple demand responses, and applies phase space reconstruction and machine learning to predict renewable energy output and power load. Multi-objective particle swarm optimization algorithm is used to solve the first stage dispatch model. For the obtained Pareto Front, weight multiple objectives by entropy weight method to get the optimal solution. Since the deviation between the predicted value and the actual value can lead to renewable energy curtailment or load loss, the role of the second stage is to predict the renewable energy curtailment and load loss after the first stage dispatch. Firstly, extreme gradient boosting is used to predict when renewable energy curtailment and load loss occur. Secondly, extreme learning machine is used to predict the amount of renewable energy curtailment and load loss at the corresponding time points. Finally, linear programming and mixed integer linear programming are used to solve the second stage dispatch model. By comparative cases analysis, simulation results show that the enhancement of demand response to system efficiency is at the cost of increasing dispatch cost and reducing reliability. In contrast, the proposed two-stage dispatch method using regulation reserve capacity not only reduces dispatch cost, but also improves system efficiency and reliability.

As the global energy crisis and environmental pollution problems are becoming more and more serious, the microgrid (MG), as an effective supplementary means of large power grid, has gradually become the focus of attention. With the gradual maturity of MG technology and deepen practice of engineering application, the economic dispatching of MG has become the key issue in the field of MGs. This paper analyzes the status quo of domestic and international researches on MG economic dispatch from multiple perspectives, such as dynamic and static economic dispatch, demand side response, energy management and multi-MGs joint dispatch. It points out that the economic dispatching of micro-grid still has some problems to be further studied. For instance, the imperfect of uncertainty researches on renewable energy (RE) and load demand (LD) and the immaturity of research on MG economic dispatch. Finally, a summary and prospect for economic dispatch research is concluded.

This work introduces the dynamic economic dispatch of the micro-grid considering the uncertainty of the renewable energy. Firstly, this paper applies scenario analysis method to deal with the uncertainty of the renewable energy. After scenario generation and scenario reduction, the most representative scenario is selected. Secondly, the integrated optimal dispatching model of the micro-grid is established on the basis of considering the economy and the environmental friendliness. Finally, a case study is carried out in selected scenarios, using differential evolution algorithm to solve the integrated optimal dispatching model of micro-grid.

Based on the operation characteristics of each dispatch unit, a multi-objective hierarchical Microgrid (MG) economic dispatch strategy with load level, source-load level, and source-grid-load level is proposed in this paper. The objective functions considered are to minimize each dispatching unit’s comprehensive operating cost (COC), reduce the power fluctuation between the MG and the main grid connect line, and decrease the remaining net load of the MG after dispatch by way of energy storage (ES) and clean energy. Firstly, the load level takes electric vehicles (EVs) as a means of controlling load to regulate the MG’s load fluctuation using its energy storage characteristics under time-of-use (TOU) price. Then, in order to minimize the remaining net load of the MG and the COC of the ES unit through Multiobjective Particle Swarm Optimization (MPSO), the source-load level adopts clean energy and ES units to absorb the optimized load from the load level. Finally, the remaining net load is absorbed by the main grid and diesel engines (DE), and the remaining clean energy is sold to the main grid to gain benefits at the source-grid-load level. Ultimately, the proposed strategy is simulated and analyzed with a specific example and compared with the EVs’ disorderly charging operation and MG isolated grid operation, which verifies the strategy’s scientificity and effectiveness.