The prosumer market allows prosumers to sell their energy surplus to consumers. The prosumer should offer the amount of energy to sell and its unit price to the contracted consumers while economically operating their system. This paper presents optimal operations and business strategies to maximize the prosumer's benefit by utilizing an energy storage system and ensuring a contract with residential consumers under a progressive pricing policy, where electricity unit price increases with the amount of monthly electricity consumption. By the proposed method, a prosumer under time-of-use pricing scheme stores abundant renewable energy or utility energy at a low price and uses it during a highprice period. Moreover, the proposed optimization can determine the amount of energy and the unit price that the prosumer will offer as a contract in a way that gives consumers strong motivation for the contract; the contract can eventually alleviate consumers' electricity rates by avoiding a high-price zone. For optimization, a quadratic objective problem with quadratic constraints is formulated, and the interiorpoint algorithm with the Hessian function is used. This study investigates the effectiveness of the proposed method not only under the various penetration rates of renewables but in consideration of uncertainties of renewables and loads. Based on actual field data from Jeju Island of South Korea for 30 days, numerical simulations were performed, and the results indicate that the prosumer's operating costs were reduced by about 12%, simultaneously offering a smaller contract price to the consumer. The Hessian function of the Lagrangian reduced the processing time for the optimization by a maximum of 98.3%. Finally, the ensemble forecast method generating multiple statistical scenarios was tested to address the uncertainty of renewables, showing that the uncertainty had no impact on the contract price and energy.

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.

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A power transformer is an essential device for stable and reliable power transfer to customers. Therefore, accurate modeling of transformers is required for simulation-based analysis with the model. The paper proposes an efficient and straightforward parameter estimation of power transformers based on sweep frequency response analysis (SFRA) test data. The method first develops a transformer model consisting of repetitive RLC sections and mutual inductances and then aligns the simulated SFRA curve with the measured one by adjusting parameters. Note that this adjustment is based on individual parameter impacts on the SFRA curve. After aligning the two curves, the final transformer model can be obtained. In this paper, actual single-phase, three-winding transformer model parameters were estimated based on field SFRA data, showing that SFRA curves simulated from the estimated model are consistent with the measured data.

The complexity and uncertainty of the distribution system are increasing as the connection of distributed power sources using solar or wind energy is rapidly increasing, and digital loads are expanding. As these complexity and uncertainty keep increasing the investment cost for distribution facilities, optimal distribution planning becomes a matter of greater focus. This paper analyzed the existing mid-to-long-term load forecasting method for KEPCO’s distribution planning and proposed a mid- to long-term load forecasting method based on ensemble learning. After selecting optimal input variables required for the load forecasting model through correlation analysis, individual forecasting models were selected, which enabled the derivation of the optimal combination of ensemble load forecast models. This paper additionally offered an improved load forecasting model that considers the characteristics of each distribution line for enhancing the mid- to long-term distribution line load forecasting process for distribution planning. The study verified the performance of the proposed method by comparing forecasting values with actual values.