# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework  
[PERSON]  
1 Department of Electrical and Electronics Engineering, Hindustan Institute of Technology and Science, No. 1, OMR Road, Chennai 603103, India; [EMAIL_ADDRESS] (P:P:); [EMAIL_ADDRESS] (P.A.K. ); [EMAIL_ADDRESS] (D.K.R.) 2 School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9 JT, UK; [PERSON]RESS] (S.J.A. ); [PERSON]ESS] (S.O.) 3 Renewable Energy Lab, Department of Communication and Networks, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia; [EMAIL_ADDRESS] (Clean and Resilient Energy Systems Laboratory (CARES), Texas A & M University, Galveston, TX 77553, USA (Clean and Resilient Energy Systems Laboratory, Central Queensland University, Rockhampton, QLD 4701, Australia (Clean and Resilient Energy Systems Laboratory, Texas A & M University, Galveston, TX 77553, USA  
Various aspects of VPPs and the microgrid scheduling problem such as solving methods, modeling techniques and uncertainty were discussed in [1]. The VPP integrates various distributed energy resources (DERs), Energy Storage System (ESS), small conventional power plants and controllable loads, so the bidding strategy within the market is necessary to conduct analyses. The strategic bidding equilibrium of virtual power plants in a joint regulation and energy market was analyzed. A bi-level mathematical program was developed for maximizing the profit as well as social welfare maximization of each producer. The bi-level problem was formulated as a mixed-integer linear programming model and the power transmission distribution factors (PTDF) were used for modeling the transmission constraints [2]. The power system can operate most efficiently by optimal energy management. Demand response can play an important role for managing the sources optimally. A detailed review was presented based on the different aggregation technologies of the VPP and developments of demand side resources [3]. The VPP integrates a wide variety of distributed generation resources such as photovoltaic, wind and hydro power plants and can enable the small sources to participate in the wholesale electricity market. The VPP operation can be maximized by optimizing the economic benefits of the system [4]. A fully distributed approach of the VPP was presented for maximizing the economic benefits subjected to line transmission limits, power balance constraints and the local constraints for the distributed energy resources. A distributed VPP dispatch algorithm was developed for this purpose, in which the centralized control system is not needed [5]. The integration of renewable generation such as photovoltaic and wind is highly volatile and leads to the problem of grid security. The control of the generation in a VPP pool leads to reconsidering the dynamic constraints, which makes the system more complex. A time domain simulation was presented for improving the power quality of the VPP, which consists of photovoltaic power, wind power and pumped storage integrated with an islanded grid including a thermal power plant [6]. The VPP aggregates several small-size generators which are situated close to the loads. The quality of power production by the VPP can be improved by optimizing the profit. A VPP model was considered which consists of two Distributed Generator (DGs) loads and a microgrid. The maximization of profit was formulated as a non-linear maximization problem with constraints. GAMS software was used to solve the optimization problem [7]. The VPP can operate with maximum efficiency when its generations are at the utmost condition. The small-scale wind turbine is of great interest in the VPP. To achieve the maximum energy from the wind turbine, two modeless methods were designed such as intelligent and non-linear strategies based on the HCS method. Fuzzy logic control (FLC) was used to implement intelligent Maximum Power Point Tracking (MPPT) due to its ability to cope with the problem related to the conventional methods [8].