A Unit Commitment Model with Demand Response for the Integration of Renewable Energies

The output of renewable energy fluctuates significantly depending on weather conditions. We develop a unit commitment model to analyze requirements of the forecast output and its error for renewable energies. Our model obtains the time series for the operational state of thermal power plants that would maximize the profits of an electric power utility by taking into account both the forecast of output its error for renewable energies and the demand response of consumers. We consider a power system consisting of thermal power plants, photovoltaic systems (PV), and wind farms and analyze the effect of the forecast error on the operation cost and reserves. We confirm that the operation cost was increases with the forecast error. The effect of a sudden decrease in wind power is also analyzed. More thermal power plants need to be operated to generate power to absorb this sudden decrease in wind power. The increase in the number of operating thermal power plants within a short period does not affect the total operation cost significantly; however the substitution of thermal power plants by wind farms or PV systems is not expected to be very high. Finally, the effects of the demand response in the case of a sudden decrease in wind power are analyzed. We confirm that the number of operating thermal power plants is reduced by the demand response. A power utility has to continue thermal power plants for ensuring supply-demand balance; some of these plants can be decommissioned after installing a large number of wind farms or PV systems, if the demand response is applied using an appropriate price structure.Comment: submitted to 2012 IEEE Power & Energy Society General Meetin

再生可能エネルギー発電の大量導入時の蓄電池の負荷周波数制御への適用の効果評価に関する研究

学位の種別: 論文博士審査委員会委員 : (主査)東京大学教授 松橋 隆治, 東京大学特任教授 谷口 治人, 東京大学准教授 馬場 旬平, 東京大学教授 横山 明彦, 東京大学教授 吉田 好邦University of Tokyo(東京大学

Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions

The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%

Cross-correlation of output fluctuation and system-balancing cost in photovoltaic integration

The authors analysed the cross-correlation of photovoltaic (PV) output fluctuation for the actual PV output time series data in both the Tokyo area and the whole of Japan using the principal component analysis with the random matrix theory. Based on the obtained cross-correlation coefficients, the forecast error for PV output was estimated with/without considering the cross-correlations. Then the operation schedule of thermal plants is calculated to integrate PV output using the proposed unit commitment model with the estimated forecast error. The system-balancing cost of PV system was also estimated with or without demand response. Finally, validity of the concept of ‘local production for local consumption of renewable energy’ and alternative policy implications were discussed

Integration of Electric Vehicles into the Electric Power System Based on Results of Road Traffic Census

We propose a model for the integration of electric vehicles (EVs) into the grid power system in Japan. The potential of the switchover from conventional vehicles to EVs and the incurred charging loads for the EV fleet were evaluated based on the results of a Japanese road traffic census. Furthermore, an EV battery operation model was incorporated into the production cost analysis model, which is capable of determining the optimal electricity supply and demand, considering the existing interconnector power flows. The potential economic and environmental contributions of EV charge and discharge controls, with the ultimate goal of realizing the introduction of a massive renewable energy source in the future, were also evaluated. We found that EVs can greatly contribute to expanding the use of renewable energy and reducing system cost by charging and discharging not only at the owner’s home but also at his/her workplace

Marginal Value of Vehicle-to-Grid Ancillary Service in a Power System with Variable Renewable Energy Penetration and Grid Side Flexibility

Regulating the frequencies of power grids by controlling electric vehicle charging and discharging, known as vehicle-to-grid (V2G) ancillary services, is a promising and profitable means of providing flexibility that integrates variable renewable energy (VRE) into traditional power systems. However, the ancillary services market is a niche, and the scale, saturation, and time-dependency are unclear when assuming future changes in the power system structure. We studied the marginal value of V2G ancillary services as a balancing capacity of the power system operation on the load-frequency control (LFC) timescale and evaluated the reasonable maximum capacity of the LFC provided by V2G. As a case study, we assumed that the Japanese power system would be used under various VRE penetration scenarios and considered the limited availability time of V2G, based on the daily commuter cycle. The power system operation was modeled by considering pumped storage, interconnection lines, and thermal power–partial load operations. The results show that the marginal value of V2G was greater during the daytime than overnight, and the maximum cost saving (USD 705.6/EV/year) occurred during the daytime under the high-VRE scenario. Improving the value and size of V2G ancillary services required coordination with energy storage and excess VRE generation

Study on development of EV charging services coupled with power system conditions using IoT technology

Deployment of electric vehicles (EVs) has been accelerated in many countries. However, for further deployment of EVs and their contribution to electrical power systems, various new services need to be implemented related to EVs and their charging, called “Place of Use” (PoU) services. The combined menu of the charging services has been proposed which bundles EV charging fees with home electricity bills. Settlement will be needed for such combined menu. This paper proposes the combined menu of EV charging with settlement of electricity. Technical feasibility of the settlement has been tested by EV charging testbed with the IoT-HUB technology. IoT-HUB is the virtual infrastructure which interconnects various connected devices and application by using “drivers” for each device. This paper also proposes a forecast method of EV charging demand for the settlement after the charging starts. The proposed method has reduced the forecast error of total charged energy compared to the simple method, but some of the forecast error has remained because of the variability of the charging time at the final step of state of charge