Reinforcement Learning Based Energy Storage Units Scheduling Considering Point of Common Coupling Constraint

In future low voltage grids, with multiple inverter interfaced sources connected, voltage regulation may become a necessary task. The potential exists for inverter interfaced sources to be deployed to regulate the voltage at the point of common coupling (PCC) of each inverter interfaced sources. The PCC voltage regulation is attainable with inverter interfaced sources by dynamically controlling the amount of reactive power injected to the power distribution grid by individual systems. In the current research, a closed-loop controller is proposed to regulate the PCC voltage of a solar photovoltaic (PV) system that is connected to a single-phase power distribution feeder (with R to X ratio greater than 1). The plant model of the PCC voltage controller of the PV system is derived considering both reactance and resistance of the network to which the PV system is connected. Three different compensators are evaluated to identify a suitable compensator for the closed-loop PCC voltage controller to regulate the PCC voltage at a given reference voltage. Simulation studies and experimental verification confirm that the theoretical approach taken to derive the control plant model of the PCC voltage controller is accurate and the procedure that is followed to design the controller is robust. The control design procedures illustrated in the current research leads to a PCC voltage control system with acceptable dynamic and steady state performance.

Microgrids are the main structures of a new model for electric power systems. These structures allow the participation of distributed generation, electric vehicles, and energy storage in the utility grid. Among the microgrid topologies, the hybrid AC / DC stands out. In this paper, the implementation of a solid state transformer (SST) using the dual active bridge (DAB) structure is used to build up a hybrid microgrid integrating photovoltaic generation, energy storage systems, AC and DC loads. The operation of the hybrid microgrid is analyzed for different scenarios of load and generation. Results show that the proposed SST structure is able to deal with microgrid bidirectional power flow, ensuring DC-links voltage regulation and high quality energy on the point of common coupling despite unbalanced currents on microgrid loads.

The voltage of point of common coupling (PCC) connects industrial distribution networks to the utility grid. Asymmetric sags often occur at PCC due to large load changes. To tackle this challenge, the energy storage system (ESS) is employed to improve the voltage stability of industrial distribution networks. However, there is lack of effective control methods to improve the stability of grid since the unstable energy control rate of ESS. Passivity-based control (PBC), which makes the unstable objects and energy storage resources into a passive system, is applied to improve the voltage stability. PBC is an energy-based approach, which aims to render the closed-loop system with a given storage function. In this paper, a PBC strategy based on euler-lagrange (EL) model is proposed for industrial distribution networks under asymmetrical utility grid voltage sag conditions. According to the passive control theory, the passivity based controller is designed by a new positive/negative sequence damping injection method. The power distribution of parallel-connected multiple inverters has been investigated to achieve coordinated operation. Simulation results verify the effectiveness of the proposed control strategy, such as faster response, good stability, and better voltage support performance.

Multi-stall fast charging stations are often thought to require megawatt-range grid connections. The power consumption profile of such stations results in high cost penalties due to monthly power peaks and expensive linkage fees. A local energy storage system (ESS) can be used to address peak power demands. However, no appropriate sizing method is available to match specific constraints, such as the contracted power available from the grid and the projected recharging demand. A stochastic distribution of charging events was used in this paper to model power demand profiles at the station, with a one minute resolution. Based on 100 simulated months, we propose an optimum number of charging points, and we developed an algorithm to return the required local ESS capacity as a function of the available grid connection. The role of ESSs in the range of 100 kWh to 1 MWh was studied for all stations with up to 2000 charging events per week. The relevance of ESS implementation was assessed along three parameters: the number of charging points, the available grid connection, and the ESS capacity. This work opens new possibilities for multi-stall charging station deployment in locations with limited access to the medium voltage grid, and provides sizing guidelines for effective ESSs implementation. In addition, it helps build business cases for charging station operators in regions with high demand charges.

The variability of large scale wind power generation at high penetration levels has a significant impact on the secure and economic operation of onshore power systems. Grid bottlenecks, high gradients and wasting of wind energy can be avoided using local Energy Storage Systems (ESS) with dedicated power and capacity. The storage type considered in this study is an adiabatic compressed air energy storage (ACAES). This paper presents a comparison of different ESS applications aiming both to increase the profit of the power plant operator in selling the electricity into the electricity market and to support the intermittent electricity production of the wind park. This contribution presents the investigation methodology and discusses computation results for a region in Germany.

: Seamless integration of distributed/renewable energy into a smart MicroGrid architecture is a hot topic of research for both public and defense applications. It is the DoD's intention to develop a more cost efficient and reliable power architecture on the battlefield. A smart MicroGrid seamlessly integrates an array of locally installed, distributed power sources and couples them using advanced control schemes to improve the reliability at the sources' point of common coupling. These islanded grids must be capable of powering a wide range of conventional and advanced loads including climate control systems, lights, communication systems, and high pulsed power systems such as radars, high power microwave (HPM) weapons, or electromagnetic launchers among others. The latter types of loads draw rapid high power bursts of energy from the grid and are typically powered using their own dedicated power supplies. The impact this type of loading has on the central DC or AC bus of a MicroGrid must be studied and it was the intent of this research to better understand how operation of these advanced loads impacts the quality of the voltage on the main bus of a MicroGrid.

Considerable increase in distributed power generation system (DPGS) has been marked in the literature during the last few years. Some DPGS are interconnected with its corresponding load to from a micro grid where most of the loads are fed from renewable sources. Power quality has dragged the attention of many power system engineers as it affects the performance of generation, transmission, distribution utilities. Generation of harmonics at the point of common coupling is a serious issue as it affects the performance of other loads connected to the same point of coupling. Production of harmonics due to various sources must be limited to its standard value for improvement of power quality. This paper will describe about the concept of active power filter for mitigating the harmonic produced at the point of common coupling. The experimental validation of a concept is carried out through MATLAB/SIMULINK model as it universally accepted. The performance of the system is realised based on total harmonic distortion, power factor level of improvement and cost wise reduction which correspond to the annual saving from consumer side as well as the generation side.

In the context of microgrids, most of the research has concentrated on developing new control strategies for integration of DG sources into the power grid and seamless transition techniques between grid-connected and islanded modes. However, a few research work has focused on the power control at the point of common coupling (PCC) between utility grid and microgrid. This paper presents alternative connection scheme and control method at PCC between the utility grid and microgrid. A series voltage source converter (S-VSC) which emulates the operation of a series H-bridge has been introduced at PCC to control the active power exchange. This connection scheme gives the advantage that it has low losses and cost compared to other configurations beside that it is less bulky. The main utility grid interacts only with the S-VSC instead of the whole microgrid generating units. In order to make the active power controllable, this connection scheme provides a controlled voltage that can change the microgrid DGs voltage. The voltage provided by the S-VSC is in agreement with the voltage based droop control of the microgrid DGs. Thus, the DG units will spontaneously be adopted with changes of S-VSC voltage. The effectiveness of the proposed connection scheme and control strategy was validated through simulations using SimPower Systems toolbox of Matlab/Simulink.

The integration of distributed generators (DGs), electric storage system (ESS), distributed electric loads and the utility grid through the point of common coupling is called microgrid (MG) .The coordinated operation of MG and the main utility grid with variable load demand is controlled by using microgrid control center (MGCC). MGCC methodology is introduced to regulate power flow for each source depends on the outputs commands from MG energy management center (MGEMC). This paper is focused only on developing an efficient and fast MGCC based on power control mode taking into account the outputs commands from MGEMC without considering its strategy. The MG in this paper is assumed to be interconnected to the main utility gird, and can purchase some power from utility grid at off-peak hours or when the production of the MG is insufficient to meet the load demand. On the other hand, there is a daily income to the MG when the generated power exceeds the load demand during on-peak hours. The proposed MGCC and electrical power system is simulated using Matlab/Simulink.

Renewable energy sources (wind turbine and photovoltaic system) are connected to the smart grid to promote the grid power, but the output of these sources is changed due to the sunlight and wind speed variations. Power storage system has the ability to reduce variations in a power system. Battery energy storage system (BESS) and superconducting magnetic energy storage system (SMES) are good solutions for this problem. The storage unit is connected to a power system at the point of common coupling and is able to absorb/store both active and reactive powers from this system and inject them into the power system in the peak demand periods. A control strategy based on proportional–integrative–derivative (PID) and model predictive controller (MPC) are used to control (SMES/BESS) to enhance the transient performance of a smart grid. The proposed algorithm has been tested on standard IEEE 5‐bus system connected to wind turbine distributed generator, non‐linear loads, and storage device (BESS/SMES) to verify the superiority of the presented method. The simulation results show that the performance of SMES with PID is more efficient than BESS with PID, but they have nearly the same output when MPC control strategy is used.

Considering the adequate energy capacity and lower price of retired electric vehicle traction batteries, the second-life battery energy storage system (SLBESS) is of great significance for active distribution network (ADN) to enhance sustainable energy accommodation and decrease investment capital costs. This paper proposes a comprehensive assessment approach to quantify the operation performance of SLBES in the ADN. A multi-level comprehensive assessment framework is established, which incorporates both the operation benefits and electrical features. As for the SLBES operation benefits, the amount and volatility of exchanging power between the ADN and utility grid are adopted. Moreover, the voltage volatility, three-phase unbalance and harmonic content at the point of common coupling are taken as the main quantitative indices of electrical features. Furthermore, the analytic hierarchy process method is utilized to determine the relative weights of intra-level indices, and then the comprehensive assessment index is obtained via the linear weighted summing of sub-level quantitative indices orderly. A case study is conducted to demonstrate the effectiveness of the proposed assessment approach.

The intermittent injection of power and the nature of power electronic devices used for photovoltaic (PV) systems can affect the power quality (PQ) of the grid to which they are connected. This study proposes to quantify and evaluate the impact of PV injection on the PQ of a low-voltage (LV) network by applying a statistical analysis through hypothesis testing for the mean comparison of populations of parameters with and without a PV system. The effects of PV power injection and load demand at the point of common coupling on PQ are monitored. The methodology includes the selection and monitoring of PQ, the use of a matrix for classification of data with similar load and PV power injection conditions, and the application of the Wilcoxon rank sum test. This methodology was applied to evaluate the impact of a 9.8 kWp PV system on the PQ of an LV network.

The transition to sustainable and intelligent energy systems has intensified the development of smart microgrids, which offer decentralized, resilient, and efficient power solutions. This review critically examines the integration of Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) into smart microgrid platforms, focusing on their role in optimizing sustainable energy management. Traditional energy management systems often struggle to adapt to the dynamic nature of modern energy demands, renewable energy intermittency, and grid complexity. AI-driven solutions, particularly DRL, provide adaptive, autonomous, and data-driven mechanisms for real-time decision-making and predictive control within microgrids. DRL, by learning optimal policies through interaction with the environment, is capable of handling multi-objective problems, including demand-response optimization, energy storage control, load forecasting, and distributed generation scheduling. This paper synthesizes recent advancements and applications of DRL algorithms such as Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO) in smart microgrids. It also explores hybrid models that combine DRL with other AI techniques, such as fuzzy logic and neural networks, to improve performance under uncertainty and nonlinearity. Furthermore, the review evaluates benchmark testbeds, simulation tools, and real-time platforms used to implement and validate these intelligent systems. Challenges such as high computational costs, model generalizability, real-time implementation issues, and cybersecurity vulnerabilities are discussed. The study also highlights recent research trends emphasizing decentralized control, edge computing, and federated learning to enhance scalability and privacy in DRL-based microgrid applications. Future directions suggest the need for explainable AI (XAI), robust training environments, and standardization to facilitate the wider adoption of DRL in real-world microgrid systems. The integration of DRL into smart microgrids represents a transformative shift toward resilient, efficient, and sustainable energy ecosystems. This review offers a comprehensive understanding of how AI and DRL are revolutionizing energy management, providing a foundation for future research and practical deployment in smart energy infrastructures.

Microgrids open up a new arena of power generation, distribution, and consumption. Microgrids have the ability to deliver reliable and clean power. It enhances grid reliability through its ability to isolate from the utility grid under any abnormal conditions and work in standalone mode, then reconnect and synchronize again with the grid without load interruption. The growing installation of renewable energy resources creates higher interactions between transmission and distribution systems, which increases fears of the impact of power electronics devices on the stability and power quality metrics of the utility grid. Such studies require complete modeling and simulation of the power electronics interfacing microgrids with the power system. This paper presents a co-simulation framework for a microgrid connection with a transmission system. The framework uses Power System Simulator for Engineering (PSS/E) as a power system analysis tool and electromagnetic transient simulator (PSCAD) which is used to model the microgrid system and its control. The paper also investigates the impact of a large-scale microgrid on the power quality of the grid network. The total harmonic distortion (THD) in the current and voltage at the point of common coupling (PCC) is investigated and the obtained results are compared with the limits specified by the international standards. The co-simulation results are aimed to show that the inclusion of Distributed Energy Resources (DERs) in the transmission system works within the allowed limits and standards.

Decentralized robust operation of the unbalanced microgrids in distribution networks: A convex relaxation approach