Deep Learning Based Muti-Objective Reactive Power Optimization of Distribution Network with PV and EVs

Abstract

With the high penetration of photovoltaic (PV) and electric vehicle (EV) charging and replacement power stations connected to the distribution network, problems such as the increase of line loss and voltage deviation of the distribution network are becoming increasingly prominent. The application of traditional reactive power compensation devices and the change of transformer taps has struggled to meet the needs of reactive power optimization of the distribution network. It is urgent to present new reactive power regulation methods which have a vital impact on the safe operation and cost control of the power grid. Hence, the idea that applying the reactive power regulation potential of PV and EV is proposed to reduce the pressure of reactive power optimization in the distribution network. This paper establishes the reactive power regulation models of PV and EV, and their own dynamic evaluation methods of reactive power adjustable capacity are put forward. The model proposed above is optimized via five different algorithms and approximated through the deep learning when the optimization objective is only set as line loss and voltage deviation. Simulation results show that the prediction of deep learning has an incredible ability to fit the Pareto front that the intelligent algorithms obtain in practical application.

Voltage Optimization in PV-Rich Distribution Networks—A Review

“…In [77], the authors used both computational intelligence methods and analytical methods. In [55], five multi-objective evolution algorithms (MOEAs), named promising-region-based evolutionary many-objective algorithm (PREA), strength Pareto evolutionary algorithm 2 (SPEA 2), nondominated sorting genetic algorithm II (NSGA-II), nondominated sorting genetic algorithm III (NSGA-III), and two-phase framework (ToP), are used to determine the reactive power capacity of PVs and EVs. The results obtained by MOEAs are used to train a deep deconvolution neural network (DDNN) to solve the problem of voltage deviation and loss minimization.…”

“…In addition to voltage optimization, the following objectives also appear: (i) power loss minimization [53][54][55], (ii) on load tap changer (OLTC) switching operation minimization [56], (iii) PV cost minimization [38], (iv) reactive power injection/absorption minimization [57], (v) active power curtailment (APC) minimization [58], (vi) cost of purchased energy minimization [59], (vii) peak shaving minimization [59], and (viii) security margin index (SMI) minimization [59]. The mathematical expressions of the commonly used objectives are given in Table 2.…”

“…Objectives Variables [65] single-objective min VD PV inverter reactive power, OLTC [38] multi-objective min losses, min VD, min VUF, min PV generation cost, min PV APC cost PV inverter reactive power [66] multi-objective min losses, min VD, improvement VSI PV inverter reactive power and static compensator [53,54,[67][68][69] multi-objective min losses, min VD PV inverter reactive power [70] multi-objective min VD, min losses PV inverter reactive power, CBs, and OLTC [71] single-objective min VUF PV inverter active and reactive power, power injected by TS [72] single-objective min VD PV inverter reactive power [73] multi-objective min losses, min cost of APC and generated/consumed reactive power, min VD PV inverter reactive power [74] multi-objective min VD, min voltage unbalance PV inverter reactive power, OLTC, VR, and CB [75] multi-objective min losses, min VD, min VUF PV inverter reactive power [32] single-objective min VUF PV inverter reactive power [76] single-objective min VD PV inverter reactive power, OLTC [77] multi-objective min losses, min VD, min control action of OLTC and SC PV inverter reactive power, OLTC, SC [78] single-objective min VD PV inverter reactive power, OLTC [79] multi-objective min VD, min losses PV inverter reactive power, OLTC, and SC [57] multi-objective min VD, min losses, min reactive power injection, and absorption PV inverter reactive power [80] multi-objective min VD, min losses PV inverter reactive power, OLTC [81] single-objective min VD PV inverter reactive power, OLTC, and VR [58] multi-objective min VD, min losses, min APC PV inverter reactive power, OLTC and CB [55] multi-objective min VD, min losses PV and EV inverter reactive power, the compensation device [56] multi-objective min VD, min OLTC tap operation PV inverter reactive power, OLTC [82] single-objective min VD PV inverter reactive power, charge/discharge rate of ESS [83] multi-objective min losses, min VUF PV inverter reactive power [34] multi-objective min cost, min losses, min cost associated with active power s...…”

Voltage Optimization in Active Distribution Networks—Utilizing Analytical and Computational Approaches in High Renewable Energy Penetration Environments

“…The algorithm's effectiveness is demonstrated through a case study on the South Korean distribution system, confirming significant improvement in system performance through optimized smart inverter settings. The author in [81] addresses the challenges of increased line losses and voltage deviations in distribution networks due to high PV and EV penetration. The study proposes new reactive power regulation methods, utilizing the potential of PVs and EVs, to alleviate these challenges.…”

The Actual Exactness of a Fast RMS Correction during Abrupt Voltage Change

“…Among the suggested solutions, some are based on the usage of electrical storage [ 1 , 2 , 3 , 4 ], and some of them apply power flow dispatch at generating facilities [ 5 , 6 ]. Other solutions are based on the utilization of reactive power equipment [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Considering the influence of the high costs of high-voltage storage, reactive power usage as the voltage regulation model may be preferable among other methods.…”