# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
The real coded genetic algorithm was used for attaining the optimal operational cost of the smart home [22]. A new technical-economic dispatch model of the VPP was presented to maximize the profit. The technical-economic dispatch model was developed as a mixed-integer optimization problem. The model was applied to real data from an irrigation system [23]. The stochastic, interval and robust optimization requires complex calculation. Thus, the deterministic and interval optimizations are combined to solve the VPP's dispatch problem [24]. Optimal dispatch of power to the consumers and the grid is necessary for the VPP operation. To maintain the optimal operation, the active power dispatch strategy of the VPP component such as a large-scale wind farm should balance the solver reliability and tracking accuracy. The conventional proportional distribution (PD) strategy for a large-scale wind farm is not adequate for balancing the conflict of the regulation flexibility and the tracking facility. A novel hierarchical active power dispatch strategy was proposed for a larger-scale wind farm based on the fuzzy c-means clustering algorithm and model predictive control method. The developed strategy shows the advantages of eliminating dynamic tracking errors, increasing the distribution flexibility of [25]. Stochastic dispatch in the optimization of a gas-electric VPP (GVPP) was developed which consists of photovoltaic power generation, a wind power plant and a gas turbine as the generating plant [26]. To reduce the losses and generation cost, and to improve the voltage profile in an isolated microgrid, a joint active and reactive power dispatch strategy was developed [27]. The demand side management was accomplished with dynamic economic dispatch (DED) which integrates new modeling for the demand, dynamic load dispatch and advanced metering. The big data technology was adopted to store a huge market and plant data [28]. A bi-level multi-time scale scheduling method was presented to solve the optimal scheduling problem of a multiple-operator virtual power plant. The internal electricity price formation-based bidding equilibrium was presented at the upper level, and the lower level consists of the multi-time scale optimal scheduling method. This method can improve the application range of the virtual power plant along with the reduction in the uncertainty on dispatching results [29]. The power forecasting model was established with a neural network and the clustering algorithm [30]. An optimal dispatch strategy was formulated for the unified electricity market using the fruit fly optimization algorithm. The dispatch strategy aims to earn the maximum profit which includes the retail price, load demand and market price [31]. The multi-objective optimization problem was solved with the fruit fly optimization algorithm [32; 33]. The environmental economic dispatch problem was solved with the enhanced non-dominated sorting (ESFOA)-based fruit fly optimization algorithm [34]. The improved fruit fly optimization algorithm was used to solve the non-linear complex optimization problem [35; 36]. The load uncertainties were considered and investigated in the virtual power plant environment [37; 38]. The detailed literature survey on virtual power plant is presented in the Table 1.