# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref. No.** & **Contribution** & **Techniques** & **Remarks** \\ \hline \multirow{6}{*}{[38]} & A basic scheduling model was presented for & & \\  & maximizing the operating revenue without & & \\  & considering uncertainty. The scheduling model & & \\  & was developed for a virtual energy plant which & & \\  & consists of photovoltaic power generation (PV), & & \\  & wind power plant, combined heat and power & & \\  & generation (CHP), solar collectors, electric boiler. & & \\ \hline \multirow{6}{*}{[39]} & An optimal scheduling model was developed for a & & \\  & smart distribution network (SDN) with electric & & \\  & vehicle. This model was used to minimize the & & \\  & operational cost of SDN and emission reduction. & & \\ \hline \multirow{6}{*}{[40]} & The operational model was developed for optimal & & \\  & day-ahead thermal and electrical scheduling of & & \\  & VPP. & & \\  & & & \\ \hline \multirow{6}{*}{[41]} & Optimal scheduling was developed with & & \\  & two-stage robust stochastic programming model. & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{[42]} & A local day-ahead energy market (LDEM) was & & \\  & modeled for the optimal operation of a distributed & & \\  & network. & & \\ \hline \multirow{6}{*}{[43]} & The optimal scheduling of distributed energy & & \\  & resources was developed in this paper. & & \\  & & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{[44]} & Optimal energy management was developed for & & \\  & VPP in this paper. & & \\ \hline \multirow{6}{*}{[45]} & A risk-based two-stage stochastic optimization & & \\  & framework was developed to address the optimal & & \\  & energy management of VPP. & & \\  & & & \\  & & & \\ \hline \multirow{6}{*}{[46]} & Customer adoption model was utilized as & & \\  & decision-making tool. & & \\ \hline \multirow{6}{*}{[47]} & Optimal 3-D path finding. & & \\  & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Literature survey of Virtual Power Plant. Many research works are available with optimal scheduling of generation in the VPP network [44; 45; 46]. The grid integration of the VPP was presented with different approaches [54]. Moreover, a combination of stochastic and robust optimization methods was developed [51] in order to increase the profit of the VPP in the unified electricity market. The authors achieved good results, but the convergence time was long and complex calculation was required for most methods. In addition, the market strategies of VPP operation were not considered in most of the literature. In order to bridge the research gap, the optimal dispatch strategy is presented for the day-ahead market using the beetle antenna search algorithm. This algorithm is based on the searching behavior of the longhorn beetles. It incorporates the function of the antenna of the beetle and random searching behavior of the beetle. It is proved to have good performance in solving optimization problems with the advantages of tuning a few parameters and high speed. This algorithm contains a wide searching range with higher accuracy and can maintain less time complexity for multiple mechanisms. Even though there are several advantages, the algorithm has the shortcomings of low precision and fast reaching in local optimum for solving complex problems. The contribution of the paper is as follows:  
* The novelty of this work is the integration of microgrids under the virtual power plant environment and the optimal dispatch of the power to achieve maximum profit. * The beetle antenna search algorithm is proposed to solve the problem of the optimal dispatch strategy of the virtual power plant in the day-ahead market environment. * An empirical study of different parameter settings of the BAS algorithm is performed and demonstrates achieving near global optima solutions. * Three different scenarios are considered to check the effectiveness of the proposed method with other methods available in the literature.