Economic impact of photovoltaic power forecast error on power system operation in Japan

Udagawa, Yasuo; Ogimoto, Kazuhiko; Silva, Joao Gari da; Júnior, Fonsêca; Ohtake, Hideaki; Fukutome, Suguru

doi:10.1109/ptc.2017.7981080

Cited by 6 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Operation costs are calculated through demand‐supply simulations using UC and EDC expressed by mixed integer programming, with reference to literature 5,6 . Simulations deal with day‐ahead scheduling and on‐the‐day operation; at the stage of day‐ahead scheduling, reserve must be provided based on PV forecast errors.…”

Section: Methods Of Analysismentioning

confidence: 99%

“…In this context, the present article estimates EV usage based on the past data and estimates the necessary operating reserve based on the trends in PV forecast errors; in addition, the article evaluates the effect of system operation cost reduction due to system flexibility provided by EVs. In calculation of system operation, we use a UC model that can secure required operating reserve determined based on PV output forecast errors and an economic dispatch control (EDC) model assuming actual operation, with reference to literature 5,6 . Besides, for the purpose of intended evaluation, both models are added with operating reserve due to EV charge/discharge and V2G as well as load frequency control (LFC) coordination.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Economic evaluation of charging/discharging control of electric vehicles as system flexibility considering control participation rate

Yoshioka

Asano

Bando

2020

Electrical Engineering Japan

View full text Add to dashboard Cite

In Japan, installed capacity of variable renewable generation (VRG) is rapidly increasing. Through the efforts to make VRG into one of main power resource, vehicle-togrid (V2G) is considered as an effective source of flexibility, which is concerned to lack under large amount of VRG. In this study, we analyze the cost reduction effect of using V2G as system flexibility through calculation of day-ahead unit commitment (UC) in Kyushu area. To consider uncertainty of day-ahead UC, we calculate UC simulation with operation reserve requirement considering day-ahead forecast error of photovoltaic generation, followed by economic dispatch simulation using actual photovoltaic generation. V2G as LFC reduces operation cost especially when demand is low, even with 180 000 electric vehicles (EVs). Cost reduction per EV is about 20-80 JPY/day with 180 000 EVs, which saturates if more EV participates in the control. V2G as operation reserve reduces discharge from EV for about 0.5-1.5 kWh/day/EV, which results in reducing degradation of EV battery.

show abstract

Section: Methods Of Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Economic evaluation of charging/discharging control of electric vehicles as system flexibility considering control participation rate

Yoshioka

Asano

Bando

2020

Electrical Engineering Japan

View full text Add to dashboard Cite

show abstract

“…Third, macroeconomic growth rate is generally forecasted based on models or the synthetic index released by National Bureau of Economic Research (NBER) [20][21][22]. Udagawa et al [23] combined static Probit model and Monte-Carlo (MC) simulation to analyze the long-and short-term spreads, and forecasted the turning point of US economic recession. Shapot, and Shapot [24] presented an unconstrained vector autoregressive model, which overcomes the large forecast errors of large macroeconomic models induced by the lack of empirical evidences and theoretical supports.…”

Section: Introductionmentioning

confidence: 99%

An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast

Wang¹

2020

RIA

View full text Add to dashboard Cite

The statistics and cyclical swings of macroeconomics are necessary for exploring the internal laws and features of the market economy. To realize intelligent and efficient macroeconomic forecast, this paper puts forward a macroeconomic forecast model based on improved long short-term memory (LSTM) neural network. Firstly, a scientific evaluation index system (EIS) was constructed for macroeconomy. The correlation between indices was measured by Spearman correlation coefficient, and the index data were preprocessed by interpolating the missing items and converting low-frequency series into high-frequency series. Next, the corresponding mixed frequency dataset was constructed, followed by the derivation of the state space equation. Then, the LSTM neutral network was optimized by the Kalman filter or macroeconomic forecast. The effectiveness of the proposed forecast method was verified through experiments. The research results lay a theoretical basis for the application of LSTM in financial forecasts.

show abstract

Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely Large Photovoltaic Generation

Udawalpola

Masuta

Ohtake

et al. 2019

2019 IEEE Milan PowerTech

View full text Add to dashboard Cite

Economic impact of photovoltaic power forecast error on power system operation in Japan

Cited by 6 publications

References 5 publications

Economic evaluation of charging/discharging control of electric vehicles as system flexibility considering control participation rate

Economic evaluation of charging/discharging control of electric vehicles as system flexibility considering control participation rate

An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast

Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely Large Photovoltaic Generation

Contact Info

Product

Resources

About