Customer-owned battery energy storage systems (BESS) have been used to reduce electricity costs of energy storage owners (ESOs) under a time-of-use (TOU) tariff in Korea. However, the current TOU tariff can unintentionally induce customer’s electricity usage to have a negative impact on power systems. This paper verifies the impact of different BESS operation strategies on power systems under a TOU tariff by analyzing the TOU tariff structure and the customer’s load pattern. First, several BESS operation strategies of ESO are proposed to reduce the electricity cost. In addition, a degradation cost calculation method for lithium ion batteries is considered for the ESO to determine the optimal BESS operation strategy that maximizes both electricity cost and annual investment cost. The optimal BESS operation strategy that maximizes ESO’s net benefit is illustrated by simulation using an urban railway load data from Namgwangju Station, Korea. The results show that BESS connected to urban railway loads can negative impact power system operation. This is due to the high BESS degradation costs and lack of incentive of differential rates in TOU tariff that can effectively induce proper demand response.

This paper proposes an algorithm for the cooperative operation of air conditioning facilities and the energy storage system (ESS) in railway stations to minimize electricity. Unlike traditional load patterns, load patterns of an urban railway station can peak where energy charge rates are not high. Due to this possibility, if applying the traditional peak-reduction algorithm to railway loads, energy changes can increase, resulting in higher electricity bills. Therefore, it is required to develop a new method for minimizing the sum of capacity charges and energy charges, which is a non-linear problem. To get a feasible solution for this problem, we suggest an algorithm that optimizes the facility operation through two optimizations (primary and secondary). This method is applied to the air-quality change model for operating air conditioning facilities as demand-response (DR) resources in railway stations. This algorithm makes it possible to estimate operable DR capacity every hour, rather than calculating the capacity of DR resources conservatively in advance. Finally, we perform a simulation for the application of the proposed method to the operation of DR resources and ESS together. The simulation shows that electricity bills become lowered, and the number of charging and discharging processes of ESS is also reduced.

This study developed a reinforcement learning-based energy management agent that controls the fine dust concentration by controlling facilities such as blowers and air conditioners to efficiently manage the fine dust concentration in the station. To this end, we formulated an optimization problem based on the Markov decision-making process and developed a model for predicting the concentration of fine dust in the station by training an artificial neural network (ANN) based on supervised learning to develop the transfer function. In addition to the prediction model, the optimal policy for controlling the blower and air conditioner according to the current state was obtained based on the ANN to which the Deep Q-Network (DQN) algorithm was applied. In the case study, it is confirmed that the ANN and DQN of the predictive model were trained based on the actual data of Nam-Gwangju Station to converge to the optimal policy. The comparison between the proposed method and conventional method shows that the proposed method can use less power consumption but achieved better performance on reducing fine dust concentration than the conventional method. In addition, by increasing the value of the ratio that represents the compensation due to the fine dust reduction, the learned agent achieved more reduction on the fine dust concentration by increasing the power consumption of the blower and air conditioner.

Electric power systems worldwide are receiving an increasing volume of wind power generation (WPG) because of environmental concerns and cost declines associated with technological innovation. To manage the uncertainty of WPG, a system operator must commit sufficient conventional generators to provide an appropriate reserve. At times, frequent start and stop operations are applied to certain generators, which incurs maintenance costs associated with thermal-mechanical fatigue. In this paper, we suggest a comprehensive approach to unit commitment (UC) that considers maintenance cost: the parameters of equivalent start (ES) and equivalent base load hours (EBHs) are adopted in the UC problem to determine optimal generation scheduling. A new formulation for the maintenance cost that can be readily combined with an existing mixed integer linear programming algorithm is presented. The effectiveness of the proposed UC method is verified through simulations based on an IEEE 118-bus test system. The simulation results show that considering maintenance cost in the UC problem effectively restricts frequent start and stop operation scheduling. Furthermore, the operating cost is reduced, the required reserve level is maintained, and the computational time is comparable with that of the conventional UC method.