Scheduling of battery energy storages in the joint energy and reserve markets based on the static frequency of power system

By using the battery energy storage (BES) as a fast, reliable, and controllable resource, the system operator can compensate for power mismatches via changing the generation and consumption in discharging and charging modes. However, BES could decrease the inertia of the grid and endanger the security of the system. Therefore, system operators require a scheduling model that takes into account both security and economic issues. This paper presents a linear model for the optimal scheduling of synchronous generators and BESs in the joint energy and reserve markets, based on the constraints of primary and secondary frequency services. In the proposed model, the technical limitations of synchronous generators and BESs, the frequency limitation of the grid, rate of change of frequency (RoCoF) of generators, and the RoCoF of the grid are considered as constraints of the optimization problem. Accordingly, the optimal scheduling of the resources is determined in a way that ensures the security criteria of the system are not violated after the contingency. The effectiveness of the proposed strategy is demonstrated by four case studies. Simulation results show that increasing the battery capacity by 4.68% of the total capacity of the system reduces the total frequency reserves, and total costs of the system by 13.21 and 2.96%, respectively. Consequently, system operators can reduce total operating costs and provide adequate security by deploying BESs.The present work has received funding from the European Regional Development Fund (FEDER) through the Northern Regional Operational Program, under the PORTUGAL 2020 Partnership Agreement and the terms of the NORTE-45–2020–75 call - Support System for Scientific and Technological Research - "Structured R&D&I Projects" - Horizon Europe, within project RETINA (NORTE 01-0145-FEDER-000062), and CEEC IND/02887/2017. We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team.info:eu-repo/semantics/publishedVersio

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted

Improvement of Frequency Regulation in VSG-Based AC Microgrid via Adaptive Virtual Inertia

A virtual synchronous generator (VSG) control based on adaptive virtual inertia is proposed to improve dynamic frequency regulation of microgrid. When the system frequency deviates from the nominal steady-state value, the adaptive inertia control can exhibit a large inertia to slow the dynamic process and, thus, improve frequency nadir. And when the system frequency starts to return, a small inertia is shaped to accelerate system dynamics with a quick transient process. As a result, this flexible inertia property combines the merits of large inertia and small inertia, which contributes to the improvement of dynamic frequency response. The stability of the proposed algorithm is proved by Lyapunov stability theory, and the guidelines on the key control parameters are provided. Finally, both hardware-in-the-loop and experimental results demonstrate the effectiveness of the proposed control algorithm

Cooperative Control And Advanced Management Of Distributed Generators In A Smart Grid

Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced iii to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimize the losses. This distributed optimization is a gradient based technique and it is shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators