Day-Ahead Optimal Dispatch for Active Distribution Network Considering Probability Model of Controllable Distributed Generation

Jianxi Wang,Yonghui Sun,Shida Zhang,Pengpeng Wu,Rabea Jamil Mahfoud,Xinye Du

doi:10.3389/fenrg.2021.814850

Jianxi Wang, Yonghui Sun + Show 4 more

Open Access

https://doi.org/10.3389/fenrg.2021.814850

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Abstract

In this paper, the probabilistic model of the controllable distributed generation in active distribution network is developed and applied to the daily stochastic optimal dispatch. The probabilistic characteristics of photovoltaic power generation system with active control capability are explored, and the relationship between the reference value of active power and its cumulative distribution function and mean value is obtained. The active power probability model of wind power generation system is improved according to the actual wind speed power curve. By fully utilizing the inverter capacity and coordinating active power, the reactive power of distributed generation is actively controlled under the constraint of power factor. Then considering the chance constraints, a daily optimal scheduling model for active distribution network with the goal of minimizing the operating cost of distribution network is developed, and the constraints that can calculate the charge and discharge times of the energy storage system are designed. The chance constrained programming is solved by the heuristic method, and the deterministic optimization steps are solved by the second-order cone programming method, respectively. The probabilistic power flow method based on stochastic response surface method is utilized to test chance constraints. Finally, the modified IEEE33 node distribution system example shows that the obtained models and algorithms are correct and can meet the requirements of safe and economic operation.

Highlights

The distribution network (DN) is the last part of the power system where the electricity is delivered to the end users, it shoulders the whole task of the final consumption of electricity
2) According to the optimization scheme, combined with the probability model of load demand and PV output, the cumulative distribution function (CDF) of voltage is calculated by probabilistic power flow (PPF) to judge whether it meets the chance constraints
The above examples illustrate that controlling adjustable devices is enough when the access capacity of the DG is small, and the active power adjustment of PV system is close to zero; the stochastic optimization can be realized at a small cost

Summary

INTRODUCTION

The distribution network (DN) is the last part of the power system where the electricity is delivered to the end users, it shoulders the whole task of the final consumption of electricity. A constraint that can limit the times of state changes (in charge or discharge) in a dispatch cycle for energy storage system is proposed. 2) According to the optimization scheme, combined with the probability model of load demand and PV output, the CDF of voltage is calculated by probabilistic power flow (PPF) to judge whether it meets the chance constraints. The above examples illustrate that controlling adjustable devices is enough when the access capacity of the DG is small, and the active power adjustment of PV system is close to zero; the stochastic optimization can be realized at a small cost. When the access capacity of PV system is large, it is necessary to avoid the risk of probability limit by PV power curtailment, which would substantially increase operating costs by the compensation fee of PV power curtailment

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT