The microgrid and demand response (DR) are important technologies for future power grids. Among the variety of microgrid operations, the multi-agent system (MAS) has attracted considerable attention. In a microgrid with MAS, the agents installed on the microgrid components operate optimally by communicating with each other. This paper proposes an operation algorithm for the individual agents of a test microgrid that consists of a battery energy storage system (BESS) and an intelligent load. A microgrid central controller to manage the microgrid can exchange information with each agent. The BESS agent performs scheduling for maximum benefit in response to the electricity price and BESS state of charge (SOC) through a fuzzy system. The intelligent load agent assumes that the industrial load performs scheduling for maximum benefit by calculating the hourly production cost. The agent operation algorithm includes a scheduling algorithm using day-ahead pricing in the DR program and a real-time operation algorithm for emergency situations using emergency demand response (EDR). The proposed algorithm and operation strategy were implemented both by a hardware-in-the-loop simulation test using OPAL-RT and an actual hardware test by connecting a new distribution simulator.

Recently, the penetration of electric vehicle (EV) has increased, and several motor companies have declared a paradigm shift to EVs. However, most existing studies focusing on EV charging scheduling in power system operations neglect the integrated operation of transportation and distribution systems. Because EVs affect both systems, it is necessary to consider the grid operation and the individual EV’s preferences (charging cost and time) simultaneously. In this study, an integrated power-transportation system structure and operation algorithms are developed to analyze the impact of EVs on the distribution system. In addition, this study proposes an optimal EV driving model that consists of two driving patterns: routine driving and long-distance driving. For routine driving patterns, the Markov chain model is implemented considering the mileage and conditions of individual EVs. Subsequently, long-distance driving patterns are developed to select the optimal route through reinforcement learning. In addition, the EV aggregator structure of EV charging stations can effectively predict local charge demands and perform charge control in real-time. An integrated power-transportation system is developed using the IEEE RBTS system and the integrated system architecture and algorithms are developed using MATLAB. It utilizes the ACOPPF function of MATPOWER to evaluate systematic stability and derive DLMP (Distributed Locational Marginal Price). This study conducts performance verification in various scenarios, confirming the effect of charging control of EV aggregators considering system stability, loss reduction, and cost reduction of the entire system.

There is a growing market for electric vehicles (EVs) in recent years. Due to this, many studies on electric vehicles are in progress and research on charging operations for EVs are especially active. Recent research trends on electric vehicle routes rely on the stochastic modelling of various factors such as convenience of a user’s point of view, charging station (CS), location, destination, and so on. In this paper, a charging control scheme for electric vehicles is proposed from the point of view of the system operators rather than the user. From a user’s point of view, the EV route can be set up directly, but it is difficult for the system operator to directly participate in the route of the EV. In this paper, a method is proposed to indirectly change the route of the EV by changing the charging cost through real-time dynamic pricing, in order to prevent risks in the system operation due to voltage fluctuations in the system. With dynamic pricing, the voltage of the system is kept within a stable range, and the EV user sets the route with an economic benefit. The proposed scheme is verified through Dijkstra’s algorithm and a control strategy via a simulation model using MATLAB.

Energy storage system (ESS) can play a positive role in the power system due to its ability to store, charge and discharge energy. Additionally, it can be installed in various capacities, so it can be used in the transmission and distribution system and even at home. In this paper, the proposed algorithm for economic optimal scheduling of ESS linked to transmission systems in the Korean electricity market is proposed and incorporated into the BESS (battery energy storage system) demonstration test center. The proposed algorithm considers the energy arbitrage operation through SMP (system marginal price) and operation considering the REC (renewable energy certification) weight of the connected wind farm and frequency regulation service. In addition, the proposed algorithm was developed so that the SOC (state-of-charge) of the ESS could be separated into two virtual SOCs to participate in different markets and generate revenue. The proposed algorithm was simulated and verified through Matlab and loaded into the demonstration system using the Matlab “Runtime” function.