Demand response is one of the key technologies in energy management systems, and is defined as the changes in electricity usage of end-use consumers that occur as a result of changing the electricity price, the incentive, and so on. In this paper, a design method for demand response is proposed based on model predictive control. Model predictive control is a control method using prediction by a mathematical model, which is used for controlling systems with constraints. Because constraints are considered in the design of demand response, it is appropriate to utilize model predictive control. First, the model predictive-based demand response problem is formulated. By solving this problem, demand response is performed based on the actual power consumption. Next, constraints on the peak shift effect are imposed, and an event-triggered mechanism is introduced. Third, the effects of demand response are theoretically analyzed. Finally, the economic value of the proposed approach is evaluated by numerical simulations. The proposed approach provides us a fundamental result for the design of demand response and aggregators such as retailers.

In demand response based on contracts with consumers, some participants have the potential to default on providing their scheduled negawatt energy due to demand-side fluctuations. Thus, the detection of defaulting participants is an important function of the aggregator. In particular, it is preferable to detect them with limited information and not by real-time continuous metering because of communication costs and social acceptance. This paper addresses the problem of detecting defaulting participants in contract-based demand response, provided that the aggregator can inspect the total negawatt energy and the negawatt energies of a limited number of participants via smart meters. By focusing on the property that only a few participants are defaulting under their contracts, we propose a detection method based on sparse reconstruction, i.e., reconstructing a sparse vector from a small number of scalar equations. The proposed method is iterative, and each iteration improves the sparse reconstruction by including the inspection data from the previous iteration. It is theoretically guaranteed that the proposed method derives the exact solution under practical conditions. Finally, direct load control is incorporated into the detection method to eliminate defaulting participants.

While the number of private electrical facilities is increasing, there are not enough security personnel to perform the security work. In this paper, we propose a random forest model for predicting leakage current alarms in order to improve the efficiency of electrical safety operations. A random forest was created using periodic inspection data, alarm data, and meteorological data as explanatory variables, and generalization performance was evaluated by OOB-based F-measure. In order to obtain the highest performance, a grid search was performed to optimize the hyperparameters. As a result, it was possible to achieve alarm prediction with a certain level of performance. In addition, the optimal hyperparameters were found by grid search, and the F-measure was improved.

In 2021, it is scheduled for the creation of the balancing capacity market that can procurement and trade more flexible adjustment power than before, and the detailed design of the system is currently underway. As the number of providers of adjustment power increases as a result of marketization and broadening of the area, not only the reduction of procurement costs but also transparency and fairness of procurement are expected. In this paper, we propose the method to analysis scenarios of the balancing market based on Unit Commitment (UC) that considers the range of adjustment power secured in the regulation capacity market and quantitatively evaluate the effect of the adjustment power on the UC.