Recently, there has been a lot of attention on how to enhance the management capabilities of distributed electricity production and demand aggregation by Virtual Power Plants (VPPs), which poses the challenge of leveraging their ability to participate in electricity markets to maximize operating profits. This paper analyzes the scheduling problem in fully distributed controlled VPP and proposes a two-level game model. In this model, the Stackelberg game is used to describe the interaction between Distributed Energy Resources (DER) and Energy Management System (EMS). On the other hand, the competition between DERs is formulated by the Cournot game. EMS sets its electric rate as single-leader, and DERs generate the optimal generation capacity accordingly as multi-follower. Then, we analyze and obtain the relationship between the demand and the resource supply to achieve market equilibrium on maximizing the utility of EMS and DERs. Simulation results show the analysis’s correctness and the proposed strategies’ performance improvement.

A two-tier game model based scheduling strategy for Virtual Power Plants

Virtual power plants (VPP) aggregate large numbers of direct-controllable resources and indirect-incentive resources. Because of the great difference in the incentive interaction characteristics of IIRs, it brings technical challenges for VPP to participate in the refined operation of grid demand response (DR). This paper designs a VPP full-category aggregated resources (AR) to participate in power grid DR, through analyzing the VPP ARs interactive features of power grid DR, in order to establish uncertain models of AR for DR participation, and put forward optimized price incentive strategy based on chance-constrained programming for VPPs participating in demand response. The proposed strategy adapts to the difference of price incentive level of different ARs, accurately guides the ARs to participate in the DR, ensuring VPPs to meet overall response targets, and maximizing VPP global benefits of participating in the demand response. The simulation example results demonstrate the effectiveness of the proposed strategy.

Optimal Price Incentive Strategy Based on Chance-constrained Programming for Virtual Power Plants Participating in Demand Response

Considering the uncertainty of renewable energy generation and the market transaction risk in the day ahead market of virtual power plants, an optimal scheduling model of virtual power plant in the day ahead market was presented. Firstly, this study modeled and analyzed the uncertainty of wind and photovoltaic power generation fluctuations in the virtual power plant. In addition, based on the conditional value at risk theory and the day ahead market clearing rules, the optimal scheduling model of virtual power plants in the day ahead market was established aiming at minimizing the implementation deviation and maximizing the operating income. Finally, the transaction data of a typical region was selected for case study to verify the effectiveness and the feasibility of the optimal scheduling strategy of virtual power plant in day ahead market transaction.

https://iopscience.iop.org/article/10.1088/1742-6596/2354/1/012011/pdf

Optimal Scheduling Research for Day Ahead Market Transaction of Virtual Power Plant Considering Uncertainty and CVaR

This paper proposes a novel undisturbed switching strategy based on gain self-regulation for wind turbine generation system (WTGS) to restrain extra power fluctuations and mechanical loads caused during the controller switching process between full-load region and partial-load region. The switching characteristics near rated wind speed is firstly analyzed, and theoretical foundation of gain regulation is discussed. Then the gain self-regulation method based on Fourier transform is presented to optimize the switching performance. The simulation results based on FAST reveal that, the proposed switching strategy reduces output power fluctuations and mechanical loads significantly, which is also friendly to power grid and wind turbine devices.

Zhenan Xu

Papers

Optimal Price Incentive Strategy Based on Chance-constrained Programming for Virtual Power Plants Participating in Demand Response

Optimal Scheduling Research for Day Ahead Market Transaction of Virtual Power Plant Considering Uncertainty and CVaR

Undisturbed Switching Strategy Based on Gain Self-regulation for Wind Turbine Generation System