# Energy Management of Virtual Power Plant Considering Distributed Generation Sizing and Pricing
## 1 Introduction
In [15], a mathematical model of complex integer linear programming is proposed to minimize the cost of smart home power. This home has a controlled and equipped storage and renewable resource. Linear approaches like linear programming (LP) category for steady-state analysis of distribution systems are getting more and more imperative due to the penetration of DERs [16]. A mixed integer linear programming (MILP) based approach for determining optimal number, type, and location of automation devices such as remotely controlled circuit breakers/reclosers, sectionalizing switches, remotely supervised fault passage indicators, has been presented in [17]. According to the concept of responsive loads, a smart grid, including responsive loads, DERs, batteries, and electric vehicles, is presented in [18]. Congestions in the lines are eliminated using the load shifting. Uncertainties in the price of electrical energy and the amount of new energy sources are demonstrated by a limited number of scenarios. The issue of energy management is modeled by mixed integer linear programming method. The impact of using the smart grid on the energy management system has been reviewed in [19]. In [20; 21], a linear programming problem for energy management of multi-carrier energy systems has been presented. Utilizing intelligent network technology will help to manage the operation cost and reliability. This can result in minimum daily energy consumption, increasing the hourly limit and reducing the amount of energy consumed. The uncertainty in the price of electrical energy is exhibited by a robust optimization method in [22; 23]. With the development of the model presented in [19], the new energy management system for a VPP is extracted. The plant includes a set of responsive loads, distributed discharges equipment, wind farms, and storage systems connected through a grid. Uncertainties in the price of electrical energy and the production of wind farms have been solved by robust optimization. Linear programming and direct optimal load flow have been used to implement the energy management algorithm. In [24], the impact of forecasting accuracy and technical constraints on the energy management of VPP with and without the presence of smart grid has been investigated. The constraints of the storage system, stochastic generation of distributed resources, energy transfer constraints, and responsive loads are considered in this model. In order to model the uncertainties in electrical energy prices and generation of solar stations, point prediction method has been used [25]. In this study, the possibility of contingencies in power management of the VPPs has not been discussed. In this paper, the contributions are listed as below:  
* In this manuscript, with the development of the VPP model, an optimal formulation is proposed to manage the VPPs energy scheduling. * The point prediction method is used to model non-deterministic variables with uncertainties and PSO methodology is proposed to solve the energy management concerns. * Taking into account that the possibility of contingency occurrence and equipping the microgrid with the necessary instruments to monitor the situation quickly, the impact of those phenomena in a smart microgrid is investigated on the management process. * Proposing a deterministic model for VPP energy management to monitor the contingencies of microgrid. * The sizing and siting of VPP components are investigated under different scenarios with reliability evaluation.