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This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors.The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

Micro grids are expected to be one of the most realistic energy systems for efficient use of renewable energy sources with few adverse effects on the main electric power grids. However, it is difficult to maintain the supply-and-demand balance because distributed renewable energy generation units (DREGs), such as photovoltaic generation systems and wind turbine generation systems, generate a significant portion of electrical energy in the micro grids. Therefore, an operation planning method is needed considering the uncertainty in weather prediction in order to ensure stable micro grid operations. This paper presents an optimization method for operation plans of controllable generators in micro grids that copes with the uncertainty of DREG outputs. In the proposed method, the optimal operation plans are determined by, depending on the problem conditions, either an enumeration method or Tabu Search with preprocessing. Numerical simulations were carried out for a micro grid model in order to verify the usefulness of the proposed method. In the simulations, the daily operation plan and the modified half-hourly one were determined by the proposed method. As a result, we could obtain the optimal plans which had enough reserve margins for coping with the fluctuations caused by DREGs and demand. C⃝ 2014 Wiley Periodicals, Inc. Electr Eng Jpn, 190(4): 56-65, 2015; Published online in Wiley Online Library (wileyonlinelibrary.com).

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The growth in penetration of photovoltaic generation units (PVs) has brought new power management ideas, which achieve more profitable operation, to Power Producer-Suppliers (PPSs). The expected profit for the PPSs will improve if they appropriately operate their controllable generators and sell the generated electricity to contracted customers and Power Exchanges together with the output of Megawatt-Solar Power Plants (MSPPs). Moreover, we can expect that the profitable cooperation between the PPSs and the MSPPs decreases difficulties in the supplydemand balancing operation for the main power grids. However, it is necessary that the PPSs treat the uncertainty in output prediction of PVs carefully. This is because there is a risk for them to pay a heavy imbalance penalty. This paper presents a problem framework and its solution to make the optimal power management plan for the PPSs in consideration with the electricity procurement from the MSPPs. The validity of the authors' proposal is verified through numerical simulations and discussions of their results.