Yuzuru Ueda

Spatiotemporally Multiresolutional Optimization Toward Supply-Demand-Storage Balancing Under PV Prediction Uncertainty

Large-scale penetration of photovoltaic (PV) power generators and storage batteries is expected in recently constructed power systems. For the realization of smart energy management, we need to make an appropriate day-ahead schedule of power generation and battery charge cycles based on the prediction of demand and PV power generation, which inevitably involves nontrivial prediction errors. With this background, a novel framework is proposed to maintain the balance among the total amounts of power generation, demand, and battery charging power with explicit consideration of the prediction uncertainty, assuming that consumer storage batteries are not directly controllable by a supplier. The proposed framework consists of the following three steps: 1) the day-ahead scheduling of the total amount of generation power and battery charging power; 2) the day-ahead scheduling of utility energy consumption requests to individual consumers, which aim to regulate battery charging cycles on the consumer side; and 3) the incentive-based management of the entire power system on the day of interest. In this paper, we especially focus on the day-ahead scheduling problems in steps 1) and 2), and show that they can be analyzed in a manner originating from spatiotemporal aggregation. Finally, we demonstrate the validity of the proposed framework through numerical verification of the power system management.

The Competitiveness of Continuous Monitoring of Residential PV Systems: A Model and Insights From the Japanese Market

Residential photovoltaic (PV) systems installed worldwide have kept growing in number over the last two decades and represent a predominant component of the PV market in a number of countries including Japan. The status quo in the maintenance of these systems is based on periodic inspections (PIs), with a few PV systems manufacturers and maintenance service providers now starting to offer services based on continuous monitoring (CM). Profitability of CM versus PI has been widely studied in the literature, with respect to conventional power systems as well as wind turbines. No such studies exist on residential PV systems, particularly from the viewpoint of the service provider. Our work addresses this gap. We first develop a stochastic model of a residential PV installation, accounting for its degradation and failures. We then develop a model of the costs and benefits for both the system owner and the maintenance service provider, under both PI and CM. We conclude by analyzing the profitability of the two maintenance schemes using real data from the Japanese market.

Real time estimation of PV output in distribution systems based on smart meters and irradiance measurement

In Japan, the capacity of photovoltaic (PV) system installed in residential areas is increasing dramatically since the launch of feed-in-tariff (FIT) in 2012. When a large amount of PV systems are installed in distribution systems, areal PV power output needs to be estimated in real time to prevent an overload when recovering from a power system accident. This paper proposes a method to estimate areal PV power output by using smart meters, a pyranometer, and power flow sensors. Simulation results show that the RMSE of the proposed method is less than 5% of the total system capacity and that the proposed method can estimate at a good accuracy throughout the year.

Verification of consumer's benefits for different area ratio of PV array and solar thermal water heater considering regional characteristics

Along with the advancement in mass introduction of solar photovoltaic (PV) systems to residential sector, power supply and demand balance adjustment becomes difficult due to increased reverse power flow to the power grid. As a countermeasure to this problem, we focused on the combination of the PV array and the solar thermal water heater (STWH). Thus, a part of the solar energy that consumers had flown to the power grid as the reverse power flow from the PV will be utilized in the form of heat. In this paper, considering that the performance of PV and STWH are dependent on weather conditions, we simulated the operations of consumers equipments while changing the area ratio of PV and STWH on the roof in the 10 regions of Japan with different weather conditions. As a result, the area ratio, which the forward and reverse power flow is the smallest, was obtained. In addition, we performed the simulations respect to economic efficiency of the STWH in six scenarios at two power buying prices rate and the three stages of power selling prices (power selling prices of current and expected in future). We could confirm an improvement in economic efficiency through the introduction of the STWH along with the reduction of the power selling price.

Real-time estimation of areal PV output power using sunshine duration and smart meters

In order to stably operate electric power systems under mass integration of photovoltaic systems in distribution systems, distribution system operators have to grasp the total output power of photovoltaic systems in real-time. In this paper, a method to estimate areal photovoltaic output power in real-time using sunshine duration and smart meter measurement is proposed. The proposed method was able to accurately estimate the moving average of areal photovoltaic output power with root mean square error at 3.7% of the total system capacity when using sunshine duration on-site, and 5.2% when using sunshine duration measured at a site 12km away from the estimating area.

Optimization of residential PV and water heating system's configration and operation using Multi-Objective Particle Swarm Optimization

Massive PV introduction induces difficult power supply-demand balance adjustment. As one of solutions, a PV system and a Solar Hot-Water Supply System which is a hybrid system of a Solar Thermal Water Heater and a Heat Pump Water Heater is combined. In optimization of these configuration, it is necessary to consider the optimization of multiple objectives such as cost and energy efficiency. However, it is difficult to satisfy these objectives simultaneously since these are trade-offs. In this paper, we conducted to find pareto optimal solutions by Multi-Objective Particle Swarm Optimization which is a kind of an evolutionary computing. To facilitate the solution search within the constraints conditions, we gave a flag to the excess and run-out of the heat storage amount. Thereby, we could find quasi-optimal solutions according to each objective, and conducted a search twice for solutions by Multi-Objective Particle Swarm Optimization with fixed configuration to find better solutions with respect to operation. As a result, the evaluation values of all objective functions are improved.

The impact of training period for forecasting clearness index and insolation using support vector regression

Forecasting insolation of the next day is expected as a method to deal with photovoltaic power fluctuation. In general, extending the training period as long as possible is desirable for the training of a machine learning. However, annual variation of global horizontal insolation could affect the accuracy of insolation forecast. Thus, the objective of this study is to forecast clearness index and to investigating the forecast error of forecast models with various training periods. The proposed clearness index forecast model uses temperature, absolute humidity, cloudiness at each level from GPV-MSM, and air mass as input data into support vector regression. Also, the model was divided into three models depending on the solar height. Results showed that root mean square error, mean absolute error, and mean bias error were better compared to the insolation forecast. Consequently, the clearness index forecast model is preferable to eliminate a negative effect of annual variation.

Real-time estimation of areal output power of photovoltaics under Japanese smart grid system

As the amount of photovoltaic systems in distribution system increases, the need to grasp areal photovoltaic output power increases. Assuming Japanese smart grid system, this paper proposes a method to estimate areal photovoltaic output power of distribution system in real-time using smart meters, a pyranometer, and IT switches. By using 30 minute integrated areal photovoltaic output energy obtained by smart meters, the proposed method can perform estimation accurately throughout the year. Also, to track fast fluctuations of photovoltaic output power, the proposed method estimates low and high frequency components separately. To apply frequency classification in real-time, the proposed method uses a frequency classification method using a sliding window and linear fit. Simulations were performed using real measured data, and the proposed method was able to improve a previously proposed method and estimate areal photovoltaic output power with root mean square error less than 4% of the system capacity.

Characteristics evaluation of various types of PV modules in Japan and U.S.

National demonstration projects for characteristics evaluation of various types of photovoltaic (PV) modules were carried out in both Japan and United States (U.S.). Each type of the PV modules is evaluated from the viewpoint of performance ratio and degradation. The PV module characteristics are also compared between in Japan and U.S. site for an impact assessment using climate condition differences and the PV module types as parameters. Consequently, the performance ratio and degradation showed different characteristics among the PV types while they are almost similar between the two sites.

Development of Simple Estimation Model for Aggregated Residential Load by using Temperature Data in Multi-Region

With spread of photovoltaic (PV) system in power systems, it will become more difficult to keep supply-demand balance of the power in multi-region by control of only the system side. Cooperation between the centralized energy management and distributed energy management is important for improving the supply-demand balance. For this purpose, it is necessary to accurately forecast the load and the PV power generation in a region. However, forecasting of residential load is very difficult because of steep fluctuations. In addition, we cannot obtain every residential load data. So we need to develop a basic model that forecasts the load of multi-regions without using actual measured data in all of the regions. In this paper, we analyzed the main component of the load to create a load estimation model. The load response associated with temperature fluctuations was modeled per time resolution of one hour by the main component analysis. We also estimated aggregated residential loads of multi-region. On the other hand, PV power generation was chosen from a similar day based on irradiance and temperature of multi-region. As a result, a dataset for large-scale power system control simulation was made, and a stable power supply from the system side will be possible by demand response (DR) using demand-side storage batteries.