# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
The small-scale distributed energy resources can participate in the wholesale electricity market with the VPP concept. The correlated operation of several distributed energy resources implies surplus profit for the VPP. A profit-sharing scheme was developed for the combination of DERs. This scheme can be applied to the VPP dispatching center for reserve markets and energy trading [9]. Although a profit-sharing scheme of distributed energy resources was developed, the authors did not consider the constraints of the DG and energy storage system, which effects the generated power quality. A real-time control strategy was developed for active power dispatch among the units. The optimization problem was solved with the interior-point approach by considering the controllable DG capacity constraints to obtain the balance between demand and generation [10]. The security issues of the future energy supply and the concern about the environment's health have led the research activities on alternative renewable energy systems. The VPP consists of renewable generation such as solar and wind, which are stochastic in nature. Hence, to maintain the grid security, these sources should be combined with dispatchable generation. An economic operation-based load-dispatching algorithm was developed which consists of a forecasting algorithm by the combination of the cascade-forward neural network, empirical mode decomposition and linear model [11]. The main policy of European energy is to supply green energy in an efficient manner. Therefore, distributed generation technologies such as Renewable Energy Source (RES)-based generation with combined heat and power coupled to district heating (CHP-DH) are largely installed. The optimal bidding strategy was investigated for a VPP that uses CHP-DH for meeting the uncertainties of renewable generation. Two-stage stochastic programming was used for modeling the uncertainties [12]. The efficient operation of the VPP is based on the framework of profit maximization. A bi-level optimization model was developed for maximizing the profit of the VPP. This bi-level method was formulated as a mixed-integer linear programming problem and the operation model was verified with the IEEE-118 bus power system [13]. Even if the optimal energy management was developed in this paper using profit maximization, the market strategies of the VPP operation were not considered. Therefore, a bi-level optimal scheduling model was developed which combines both the real-time and day-ahead markets. The hourly scheduling strategy of the VPP was developed for the day-ahead market in the first stage. The second stage dealt with the real-time market operation, where the cost imbalance was minimized. The particle swarm optimization algorithm and commercial solver were used to solve the two-stage scheduling problem [14]. A day-ahead scheduling framework was presented for a VPP, which consists of a mix of generation units in terms of a wind power plant, synchronous distributed generation and storage facilities such as a small pumped storage plant (PSP) and electric vehicles (EVs). The uncertainties of the EV owner behavior, wind generation and market prices were considered using the point estimate method [15]. An optimal day-ahead schedule was developed using a modified scenario-based decision-making method for thermal and electrical energy sources. The uncertainties of intermittent renewable power generation, electrical demand and the market prices were also considered [16]. Optimal scheduling of DERs was presented for a VPP in many studies [17; 18; 19; 20; 21]. All of these contribute to solving the prevailing problem of integration of renewable energy sources along with the cost optimization of the VPP. The use of hybrid energy resources is increasing day by day due to the intermittency of renewable generation and the present economic condition. The optimal scheduling of electric and gas energy resources was developed for a smart home which was provided with a fuel cell-based micro-CHP system.