Development of Frequency-watt Function Model of Smart Inverter by Power Flow Calculation with Frequency Fluctuation

Tamai, Masahiko; Komatsu, Toshimitsu; Yamane, Keisuke; Orihara, Dai; Iioka, Daisuke; Aoto, Yuki; Hashimoto, Jun; Goda, Tadahiro; Otani, Kenji

doi:10.1541/ieejpes.139.99

Cited by 5 publications

(8 citation statements)

References 1 publication

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“…Therefore, we applied the proposed static model in ref. [30] to calculate the frequency. In general power flow calculations, the voltage and phase of each bus bar are calculated based on the assumption that supply and demand coincide.…”

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

“…In ref. [30], the power flow equation used in the power flow calculation considering frequency fluctuation is the same as the general power flow calculation, a power equation expressed using an admittance matrix and node voltage. However, when calculating the active power injected into each node, the frequency component is considered an unknown parameter, and the active power generated by the frequency variation is added.…”

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

“…However, the supply and demand imbalance is calculated as a frequency fluctuation in ref. [30]. This paper uses Equation (1) with the BESS node as a slack node and Equation (2) for the PV-BESS system.…”

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

“…On the other hand, off‐grid in this study cannot use slack nodes because the supply source is renewable energy. Therefore, this study refers to the method [30], which calculates the supply–demand mismatch as a frequency fluctuation. In ref.…”

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

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Frequency regulation of off‐grid system with battery energy storage system using deep Q‐network

Takayama

Sawabe

Ishigame

et al. 2022

The Journal of Engineering

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This paper proposes a model-free decision algorithm for battery energy storage system (BESS) charging/discharging using deep reinforcement learning (DRL) to regulate off-grid frequency fluctuation. This method is novel since the frequency regulation problem is cast in an off-grid system to a deep Q-network framework, which avoids directly solving a specific optimization model or model-based control. The advantage of the proposed method is that the agents learn to comprehensively make short-term forecasts of demand and PV output, analyse trends in forecast errors, and create control signals within the framework of DRL. A reward function with two criteria, frequency target value and frequency constraint value, guides the agent to suppress the frequency fluctuation by adjusting the weight coefficient. The effectiveness of the proposed method is verified using computational simulations with the off-grid system model and actual power consumption data. The proposed method enables controlling the BESS to satisfy the balance of supply and demand on the off-grid, thus realizing a control method that does not require predictive information. Moreover, the frequency control can be achieved more accurately in a short computation time than the prediction-based method using the seasonal auto-regressive integrated moving average.

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Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

Section: Frequency Fluctuation Calculationmentioning

confidence: 99%

See 2 more Smart Citations

Frequency regulation of off‐grid system with battery energy storage system using deep Q‐network

Takayama

Sawabe

Ishigame

et al. 2022

The Journal of Engineering

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“…6 Smart inverters may be provided with fault ride through (FRT) functions with respect to voltage and frequency as well as with active/reactive power control depending on voltage and frequency. 7,8 In Japan, simulation tools are developed to analyze effects of smart inverters on distribution networks 9 ; besides, practical development and demonstration tests of smart inverters are conducted. 10 In California, Hawaii and other regions of the United States where PV is massively introduced, some functions of smart inverters have been already included in grid codes.…”

Section: Introductionmentioning

confidence: 99%

Determination method of Volt‐Var and Volt‐Watt curve for smart inverters applying optimization of active/reactive power allocation for each inverter

Yamane

Orihara

Iioka

et al. 2019

Electrical Engineering Japan

Self Cite

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In recent years, the smart inverter that is an advanced inverter has been investigated as a solution to voltage problem in the distribution network in which a lot of photovoltaic(PV)s are introduced Among multiple functions of the smart inverter, voltage‐reactive power control (Volt‐Var) and voltage‐active power control (Volt‐Watt) have been attracted attention because of their effectiveness. However, the problem is how to determine optimal curves setting of Volt‐Var and Volt‐Watt. In this paper, we suggest the determination method of optimal Volt‐Var and Volt‐Watt curves, and verify the effectiveness of the optimal curves on distribution network.

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Purely resistive load estimation for three‐phase inverter by using pseudo random binary sequence signal

Minami

Morinaka

2023

Electrical Engineering Japan

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Development of Frequency-watt Function Model of Smart Inverter by Power Flow Calculation with Frequency Fluctuation

Cited by 5 publications

References 1 publication

Frequency regulation of off‐grid system with battery energy storage system using deep Q‐network

Frequency regulation of off‐grid system with battery energy storage system using deep Q‐network

Determination method of Volt‐Var and Volt‐Watt curve for smart inverters applying optimization of active/reactive power allocation for each inverter

Purely resistive load estimation for three‐phase inverter by using pseudo random binary sequence signal

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