Sinan Cai

The penetration rate of renewable energy source generation in power systems continues to increase in recent years. However, due to the intermittent nature of renewable energy source (RES) generation, more frequency regulation resources with faster response time and larger capacities are required in the system in order to maintain the system frequency stability. Electric vehicles (EVs) can provide frequency regulation capacities to the system with their batteries when idle, but first they need to

This paper proposes a novel criterion for evaluating electricity price forecasting results for demand-side responses (DRs) who participate in the electricity market. Generally, the DR needs to predict the market price and arrange its bidding and operation schedule according to the forecast result. The mean-square-error (MSE) or the R-squared coefficient is used for evaluating the forecast result conventionally. However, it is shown in this paper that a forecast result with a good MSE or R-square

This paper proposes a novel electricity price forecast method for demand responses (DRs) who participate in the electricity market. The conventional forecast methods, especially machine learning-based methods, tend to model the featured training data by minimizing the mean-square-error (MSE). In such methods, the loss function is defined to be the MSE. However, due to the characteristics of the DRs, a forecast result with a good MSE value is not necessarily more beneficial for the DRs in a dereg

The introduction of a large amount of renewable energy to the power system is causing system frequency stability issues due to the randomness of its generation. Electric vehicles (EVs) are regarded as one of the most prospective solutions for system frequency regulation. EVs, when aggregated, can join the ancillary market for frequency regulation as the demand-side response. However, the communication delay of the control signal could degrade the performance of EVs and reduce the payback. This p

With the establishment of the Japan Electric Power eXchange market (JEPX) and the Replacement Reserve market (RR), the demand-side response (DR) is encouraged to participate in daily system operation with traditional generation resources. Since electricity markets are generally day-ahead or hour-ahead auction markets where the actual price is announced after the bidding and clearing process, the DR owner needs to predict the market price in advance for the resources' optimal scheduling to maximi

As Japan’s electricity market continues to liberalize and renewable energy adoption accelerates, self-wheeling - the practice of transmitting electricity from privately owned generation sites to distant consumption points - is gaining attention. However, forecast errors in photovoltaic (PV) generation can lead to self-wheeling imbalance, undermining not only power system stability but also the cost-effectiveness of self-wheeling schemes. This study investigates the impact of PV forecast on imbal

This paper proposes a novel profit-oriented criterion for evaluating electricity price forecasts used by demand-side response (DR) participants in electricity markets. Typically, DR participants must predict market prices and schedule their bids and operations accordingly. Previous research has shown that a profit-oriented evaluation, focused on accurately identifying price peaks and dips, offers greater economic benefits for DR participants than conventional metrics such as mean-squared error (