Abstract
With the integration of huge renewable energy sources (RESs) into active distribution networks, how to address the uncertainty outputs of RESs for the day-ahead volt/var control (VVC) is a significant challenge. This paper presents a chance constrained mixed integer second order cone model to handle the nodal power uncertainties and nonlinear branch flow equations. A direct and fast scenario generation method is proposed by employing the group division method and the seven-step probability distribution model of RESs outputs. An efficient and accurate solution method which only uses few larger probability level scenarios instead of all reserved scenarios is also proposed. Numerical simulations on the IEEE 69 standard system show the superiority of the proposed algorithm over traditional Monte Carlo sampling (MCS)-based methods.
Highlights
Day-ahead volt/var control (VVC) in active distribution networks is a primary control strategy aiming at energy loss minimization while respecting operational and physical constraints by periodically controlling slow and discrete devices like switchable shunt capacitors (SSCs) and on-load tap changer (OLTC) transforms, and fast and continuous devices like inverter reactive power of renewable energy sources (RESs) [1]
With the increasing number of RESs integrated into active distribution networks, the day-ahead VVC problem is converted from a deterministic optimization problem [8], [9] to an uncertain one [10]
The numerical results reveal that the proposed algorithm has better performance such as high efficiency and easy to be tackled compared to the traditional Monte Carlo sampling (MCS)-based methods
Summary
Day-ahead volt/var control (VVC) in active distribution networks is a primary control strategy aiming at energy loss minimization while respecting operational and physical constraints by periodically controlling slow and discrete devices like switchable shunt capacitors (SSCs) and on-load tap changer (OLTC) transforms, and fast and continuous devices like inverter reactive power of renewable energy sources (RESs) [1]. (2) The key scenarios are generated by employing the group division method and seven-step probability distribution model of RES outputs. To generate scenarios which reflect different schedules, the group division method and seven-step probability distribution model of RES outputs are applied in this paper. C. A DIRECT AND FAST SCENARIO GENERATION METHOD By using seven outputs of the RESs shown in Fig., we can decide seven control schemes separately for capacitors in each group since each group can be optimized separately. A DIRECT AND FAST SCENARIO GENERATION METHOD By using seven outputs of the RESs shown in Fig., we can decide seven control schemes separately for capacitors in each group since each group can be optimized separately Some of these seven control schemes may be the same because the decision variables are discrete. Since QGsj,h is a continuous decision variable in practice, only binary decision variables zhk and whm which correspond to the schedule of OLTC and capacitor are regarded as final decision variables
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