Frequency Regulation Resources Research Articles

Coordinated control strategy for improving frequency security of LCC[sbnd]HVDC sending-end system with large-scale wind power

To improve the frequency security of the LCCHVDC sending-end system with high penetration of wind power system and achieve optimal utilization of frequency regulation resources, a cooperative control strategy involving wind power and LCCHVDC in frequency regulation is proposed. Firstly, under a variety of scenarios, the power support requirements of LCCHVDC sending-end system are analyzed according to the power regulation margin of various frequency regulation resources and the disturbance power, and a multi-time scale frequency regulation control strategy for DFIG based wind farms is proposed, which combines the advantages of variable speed control and variable pitch angel control. And a cooperative control strategy of DFIG and LCCHVDC considering the frequency regulation deadband is proposed. Then, based on the maximum power regulation margin of synchronous generators and wind farms, the key control parameters of relative control links are designed to enable them autonomously adjust the active power according to various scenarios to involve in inertia response and primary frequency regulation task. Finally, the effectiveness of the control strategy is verified by the simulation model of LCCHVDC sending-end system with large-scale wind power, which significantly improves the frequency security and stability.

Smart Transformer-Assisted Frequency Control Mechanism for RES Penetrated Power Systems Considering Metaheuristic-Based Secondary Controller

Abstract: The gradual replacement of conventional generators with variable renewable energy sources (RES) will reduce their online frequency regulation (FR) resources and degrade their overall frequency control capabilities. Although various inertia emulation methods exist, shaping load consumption is considered a more effective strategy during emergency conditions than under-frequency load shedding. Managing loads following frequency excursions can support grid stability owing to rapid power response. In this context, a Smart Transformer (ST)-based FR framework for a RES-penetrated power system is studied in this paper. The ST, with its distinctive features, effectively shapes the load profile through online load sensitivity identification-based control, aiding in the stabilization of grid frequency. This paper also proposes a tilt integral second-order double derivative (TIDD2) controller for a secondary loop whose parameters are optimized using the Learner Performance-based Behavior (LPB) algorithm. A thorough investigation reveals that the response from ST controlling the voltage-dependent load in the presence of TIDD2 controllers can greatly enhance system performance by damping oscillations and peak deviations. In addition, the performance of Proportional–Integral–Derivative and TIDD2 considering ST in the primary loop is compared to delineate the robustness of the LPB-based TIDD2 controller. It is found that the proposed control scheme offers greater controllability and flexibility, enhancing the system’s dynamic performance.

An Integrated Frequency Regulation Method Based on System Frequency Security Posture

As the share of renewable energy in a grid increases, the grid’s frequency support capability weakens, and the spatial distribution of grid frequency becomes more pronounced. As a result, control strategies based on system frequency consistency and traditional frequency regulation dominated by synchronous machines are becoming increasingly inadequate for meeting the frequency regulation requirements of new-type power systems. To enhance the system’s frequency support capability, it is imperative to fully utilize the frequency regulation resources within the power system. To address this issue, this paper first introduces a system frequency security posture assessment method that accounts for the spatial distribution characteristics of the grid. Subsequently, a parameter optimization method for diverse frequency regulation resources is proposed in conjunction with the proposed comprehensive evaluation method for frequency regulation. Next, using a renewable energy feeder regional grid as an example, an integrated frequency regulation method based on the system frequency security posture is presented. Finally, the frequency regulation performance and economic costs of different frequency regulation methods are analyzed under various operating scenarios and disturbances using a model based on actual data from the renewable energy feeder regional grid. The simulation and index calculation results demonstrate that the method proposed in this paper effectively enhances the system’s frequency support capability, reduces the frequency disparity between different nodes within the grid, and maintains high economic performance.

Bio-inspired distributed load frequency control in Islanded Microgrids: A multi-agent deep reinforcement learning approach

To prevent errors in load frequency control (LFC) decision-making in an islanded microgrid and reduce the waste of frequency regulation resources, a fully distributed “starfish” load frequency control method is proposed. For this method, a novel deep reinforcement learning algorithm called the distributed decomposed multirole multiagent deep deterministic policy gradient (DDMR-MADDPG) algorithm is introduced. This algorithm treats each unit as an agent, enabling centralized training of all the agents. Through the coordination and control of multiple agents, a global optimal strategy can be obtained. During online application, the algorithm employs a distributed implementation strategy, enabling each unit to make decisions autonomously. This decision-making strategy is akin to the distributed neural network of a starfish, and it can fundamentally improve the efficiency and correctness of decision-making. In addition, it employs multiple techniques, including value function decomposition, multirole learning, imitation learning, and curriculum learning approaches. The performance of the proposed method compared with 24 existing methods is demonstrated on a simulated LFC model of the Zhuzhou Island microgrid in China.

The design of an improved index system for frequency control in China Southern Power Grid

Introduction: In order to dispatch frequency regulation resources in regional power grids efficiently and promote the development of spot markets, China Southern Power Grid (CSG) established the unified frequency regulation control area. However, the existing regional control performance standards (CPS) for evaluating the performance of frequency control in bulk power systems is no longer suitable for the unified frequency control mode.Method: This paper proposed an innovative frequency control performance standard, named tertiary control performance standards (TCPS) and used Gaussian mixture model (GMM) and folded normal distribution (FND) to describe the distributions of frequency deviations and power deviations of tie line.Result and Discussion: Based on these probability models, the parameters of the proposed TCPS can be determined optimized. Finally, case studies were carried out with the practical data from CSG and indicated that the parameters of proposed TCPS index could be calculated and improved the control performance for the real time power balance of the regional power grid with spot markets.

Frequency Control and Optimal Operation of Low-Inertia Power Systems With HVDC and Renewable Energy: A Review

With the integration of large-scale renewable energy sources (RES) and high voltage direct current (HVDC) based inter-regional transmission, modern power systems are facing low-inertia issues and poor frequency regulation capabilities. This paper conducts a comprehensive review on frequency control and optimal operation methods in this context. First, the frequency characteristic of a low-inertia power system is analyzed covering the whole frequency response process. Secondly, the impacts of RES and HVDC integration on system inertia and frequency performance are studied, and the system frequency characteristics considering RES and HVDC are analyzed. Then, the frequency response modes of various frequency regulation resources are compared from the perspective of “source-grid-load-storage”, including their basic control principles and frequency control strategies. Further, from the operation dispatch perspective, the frequency security-constrained power system optimal operation methods are reviewed. Finally, future research trends in this area are discussed.

A Reinforcement Learning Based Coordinated but Differentiated Load Frequency Control Method With Heterogeneous Frequency Regulation Resources

As the diversification of frequency regulation resources, it is increasingly important to find a load frequency control (LFC) scheme that can effectively coordinate different resources. This paper proposes a coordinated but differentiated LFC control method that utilizes heterogeneous frequency regulation resources with their characteristics fully considered. The optimal LFC models for the single area and multi-area power systems are formulated as partially observable Markov decision process model (POMDP) and partially observable Markov game (POMG) considering the accessibility of system information. A deep reinforcement learning method called recurrent proximal policy optimization is leveraged to solve the POMDP and POMG models. Simulations based on historical data demonstrate that the proposed control scheme can better coordinate different resources and achieve superior control performance.

Power grid frequency regulation strategy of hybrid energy storage considering efficiency evaluation

With the rapid expansion of new energy, there is an urgent need to enhance the frequency stability of the power system. The energy storage (ES) stations make it possible effectively. However, the frequency regulation (FR) demand distribution ignores the influence caused by various resources with different characteristics in traditional strategies. Considering efficiency evaluation, an FR strategy is established to better utilize the advantages and complementarity of various ESs and traditional power units (TPUs). The strategy consists of two interacting modules. The power rolling distribution module optimizes the FR demand to the TPUs and ES stations with the minimum cost first. Then, it optimizes the demand of an ES station to its ES units based on the results of the efficiency evaluation module. The efficiency evaluation module assesses the comprehensive efficiency in cost, revenue, and performance by the EWM-based TOPSIS using the real-time status of different ES units from the power optimization module. A regional grid with a TPU and a hybrid ES station is used to validate the effectiveness of the proposed strategy. The results show that the FR resources are stimulated to improve their performance, and thus, the frequency performance of the system is improved by the proposed strategy.

Fuzzy model predictive control for frequency regulation of temporary microgrids during load restoration

AbstractTemporary microgrids containing a high proportion of distributed renewable generations can offer great benefits during the recovery stage after large‐scale power system blackout, by picking up critical load and speeding up the entire grid recovery process. To solve the frequency security problem of temporary microgrids faced in the load restoration process, this paper proposes an adaptive fuzzy model predictive control (adaptive fuzzy‐MPC) strategy to obtain effective frequency regulation to suit the special characteristics of this process. First, the load restoration plan within a short time frame is regarded as the feedforward information and assists in the establishment of the MPC model. Then, considering the uncertainty of renewable distributed generations, the regulation resources are cooperated by an adaptive fuzzy controller which provides the weights of different regulation resources adopted in MPC. Specifically, the base weights of the energy storage system and renewable distributed generations are obtained from fuzzy rules, and then the weight for each regulation resource is modified by the designed adjustment rules. Simulation results illustrate that the proposed strategy can efficiently improve frequency performance while making various frequency regulation resources with different regulation capacities cooperate properly, which can further accelerate the load restoration progress and enhance system resilience.

Distributed Cooperative AGC Method for New Power System With Heterogeneous Frequency Regulation Resources

The inverter-interfaced renewable energy is increasing in future new power systems, both the system rotary inertia and frequency regulation capacity of traditional units are decreasing and becoming increasingly insufficient. It is urgent to explore various types of frequency regulation resources, especially for inverter-interfaced units that show fast response speed. The heterogeneous frequency regulation resources generally have different system models, capacities, and response speeds, which challenges the automatic generation control (AGC). In this paper, to make the heterogeneous frequency regulation resources provide improved frequency regulation performance, a novel distributed cooperative AGC method is proposed. Firstly, a distributed consensus-based area control error (ACE) discovery algorithm is proposed. Then, each frequency regulation unit can participate in the frequency regulation with an individual PI controller based on the discovered ACE information. Finally, by controlling the low-speed AGC units to generate more power, it can release the power capacity of the high-speed AGC units to prepare for the following round of AGC services. Furthermore, the communication delays are also considered for the proposed distributed AGC algorithm design. The improved performance of the proposed AGC method is verified based on the simulation study of a two-area power system with five different kinds of frequency regulation units.

Impact of electric vehicles fast frequency regulation and charging strategies on grid frequency stability

With the gradual reduction of grid inertia, system sensitivity to load unbalances is increasing, compromising frequency stability. To face this problem, faster frequency regulation resources are required. In this context, vehicle-to-grid (V2G) is one of the concepts that is suggested for this purpose. When electric vehicles (EVs) are parked, their batteries can be used as distributed energy storage systems (EESs), injecting and absorbing power faster than conventional generation units (CGUs). The goal of this paper is to analyse the impact of fast frequency regulation of EVs in a small-scale grid, under sudden load variations. In this paper, the effect of charging strategies (CSs) is addressed, as they have a direct impact on the magnitude of the EV fleet regulation. In this way, different CSs can be used to achieve symmetrical up and down regulation, or to maximize one of them. The effect of the EV fleet dynamics in the frequency transient is also studied. The speed of response, and hence, the frequency transient, are influenced by these dynamics. The focus of the analysis will be the communication delay between the EV fleet and the central controller.

Control Strategy and Performance Analysis of Electrochemical Energy Storage Station Participating in Power System Frequency Regulation: A Case Study of the Jiangsu Power Grid

Electrochemical energy storage stations (EESSs) have been demonstrated as a promising solution to mitigate power imbalances by participating in peak shaving, load frequency control (LFC), etc. This paper mainly analyzes the effectiveness and advantages of control strategies for eight EESSs with a total capacity of 101 MW/202 MWh in the automatic generation control (AGC) in the power system of the Jiangsu power grid. Firstly, an adaptive tracking strategy for electricity quantity that considers the state of charge (SOC) of EESSs is proposed to calculate the baseline power of the EESS participating in AGC. This strategy can simultaneously coordinate different time scale application requirements, such as peak shaving and LFC. Then, an adaptive strategy for regulation requirement allocation among AGC control groups with EESSs that considers different area regulation requirements (ARRs) is proposed to calculate the regulation power of EESS participating in AGC. This strategy can realize the balance transfer of fast and slow regulation capacity, ensure the complementary advantages of various frequency regulation resources and improve the dynamic regulation performance of the control area. Finally, the proposed strategy is validated via a test system to confirm its effectiveness and advantages, as well as via a quantitative analysis on the improvement of the control performance standard (CPS) of the Jiangsu power grid with the participation of EESSs in AGC.

Optimal dispatching control of EV aggregators for load frequency control with high efficiency of EV utilization

Maintaining frequency stability in a power system with a large scale of renewable energy resources (RES) requires extra frequency regulation resources due to the intermittent generation of RES. As one of the most promising solutions, electric vehicles (EVs) have faster response characteristics and can provide considerable regulation capacity when aggregated. When participating in load frequency control (LFC), the EV aggregator needs to dispatch the LFC signal into every single EV. However, since the aggregated EVs have different travel profiles and state-of-charge (SOC), it is challenging for the aggregator to decide the dispatching method so that both regulation requirements and users’ transport usage can be best satisfied. In this paper, an optimal dispatching control is designed for the EV aggregator. With the proposed control scheme, the EV aggregator can provide the regulation capacity to the system while ensuring that each individual EV will have enough SOC before the next trip. Compared with the optimal dispatching methods in existing literature, the proposed control operates at a faster time-step and allows EV aggregators to utilize EVs more efficiently, therefore more capacity payment from the market can be obtained by the EV aggregators. Simulation is performed in Matlab and Simulink to examine the performance and the effectiveness of the proposed dispatching controller.

A variable parameter control method for charging pile load aggregator to participate in the fast frequency market

Abstract With the access of large-scale new energy sources, fast frequency regulation resources of the power system are scarce. A variable parameter control method for charging pile load aggregator to participate in the fast frequency market is proposed in this paper. It is that charging pile load aggregator actively adjusts the active power according to the frequency of the local power system, and the response speed is within 0-2 seconds. In addition, the variable parameter control method proposed in this paper specifically controls the power of charging pile according to the speed and amplitude of frequency drop, which has strong pertinence and simple control scheme, and further improves the frequency stability of the power grid.

Frequency control strategy for coordinated energy storage system and flexible load in isolated power system

The isolated power system has a simple structure with small inertia and no support from the large-scale power system, so the frequency stability problem is more prominent. A frequency control method based on coordinated control of flexible loads (FL) and energy storage systems (ESS) is proposed in this paper. The ESS adopts the droop control considering the state of charge (SOC) to quickly respond to the system frequency deviation and provide fast frequency support. Based on the load power–voltage sensitivity, the FL participates in frequency control by adjusting the load voltage and thus the load demand. Based on the two frequency regulation resources, the active–reactive cooperative control strategy is proposed to adopt the fast response characteristics of ESS to cooperate with the synchronous unit to prioritize frequency regulation and enable the FL reactive control strategy when the system disturbance is larger than the primary frequency regulation capacity of the synchronous unit. The effectiveness of the proposed control strategy for isolated power systems is verified by building a modified IEEE33 node islanding system simulation in the real-time digital simulator (RTDS).

A multi-agent deep reinforcement learning-based “Octopus” cooperative load frequency control for an interconnected grid with various renewable units

With the aim of improving the frequency regulation performance of load frequency control (LFC) in an interconnected power system as well as reducing wastage of frequency regulation resources, an “octopus” cooperative load frequency control (OC-LFC) method is proposed. This method addresses the secondary frequency regulation coordination problem affecting different areas, as well as the coordination problem arising between the controller and power distributor within the same area. In addition, for this framework a tracking-explorer-based distributed multi-agent deep deterministic policy gradient (TED-MADDPG) algorithm is proposed. This algorithm imitates the edge-cloud cooperation framework, akin to an octopus. In the performance-based frequency regulation market, the controller and power distributor in each area are regarded as independent agents, which output the total generated power command and the generated power commands of different units. In the proposed method, centralized training and distributed implementation are employed so that a global optimal decision with higher robustness can be obtained. Furthermore, this algorithm also employs a tracking-explorer strategy which incorporate multiple learning techniques and in turn enhance the robustness of the proposed method. The effectiveness of the proposed method is demonstrated using a China Southern Grid (CSG) four-area load frequency control model.

Frequency Regulation From Distributed Energy Resource Using Cloud-Edge Collaborations Under Wireless Environments

The increasing integration of variable renewable energy increases the demand for a power system’s frequency regulation resources. The decreasing share of controllable power generators makes the frequency regulation resources insufficient. Simultaneously, distributed energy resources (DER) on the distribution network side, such as storage devices, electric vehicles, and controllable building energy systems, have excellent potential to provide frequency support. However, power system dispatchers cannot control these resources in the same manner as conventional power sources. These sources have power output uncertainties and cannot directly access power system communication networks. This paper proposes a cloud-edge collaboration and wireless communication coordination framework to facilitate DER frequency regulations. The cloud balances the power uncertainty of controllability in edges together with computing burden, wire, and wireless communication loads. Also, the edges adjust storage’s power outputs to achieve frequency regulations and covering PV power fluctuations. A numerical case study demonstrates the effectiveness of the proposed method. The solution holds 95.5% frequency regulation adjustable power capacity of theoretical maximum on average. Meanwhile, the computing costs reduce by 36.3%, and wire communication only operates in 0.35% of a day as a supplement (reduces by 99.65%).

Distributed deep reinforcement learning for integrated generation‐control and power‐dispatch of interconnected power grid with various renewable units

To mismatch various power disturbances of interconnected power grid, an integrated generation-control and power-dispatch (IGCPD) for performance-based frequency regulation market is proposed. Unlike the conventional automatic generation control with an independent controller and a power distributor, IGCPD can significantly improve the dynamic control performance via combining these two parts into an integrated decision agent. The IGCPD eliminates the waste of frequency regulation resources and repeated regulation of the units caused by the mismatch of the controller and power distributor. In addition, by setting the reward function reasonably, IGCPD can save regulatable capacity of fast units for regulation in the next large-scale disturbance. In order to enhance the decision ability, a swarm intelligence-based deep deterministic policy gradient (SI-DDPG) algorithm is proposed to acquire the control knowledge and implement a high-quality decision for this agent. Moreover, the swarm intelligence-based training sample can achieve a high learning robustness for SI-DDPG which solve the problem of low robustness in conventional deep reinforcement learning. The proposed technique is verified on an England two-area load frequency control model and the west China two-area LFC model. IGCPD improves the area control error control performance and reduce the frequency regulation mileage payment of the interconnected power grid.

Uncertainty modeling of wind power frequency regulation potential considering distributed characteristics of forecast errors

Large-scale integration of wind power generation decreases the equivalent inertia of a power system, and thus makes frequency stability control challenging. However, given the irregular, nonlinear, and non-stationary characteristics of wind power, significant challenges arise in making wind power generation participate in system frequency regulation. Hence, it is important to explore wind power frequency regulation potential and its uncertainty. This paper proposes an innovative uncertainty modeling method based on mixed skew generalized error distribution for wind power frequency regulation potential. The mapping relationship between wind speed and the associated frequency regulation potential is established, and key parameters of the wind turbine model are identified to predict the wind power frequency regulation potential. Furthermore, the prediction error distribution of the frequency regulation potential is obtained from the mixed skew model. Because of the characteristics of error partition, the error distribution model and predicted values at different wind speed sections are summarized to generate the uncertainty interval of wind power frequency regulation potential. Numerical experiments demonstrate that the proposed model outperforms other state-of-the-art contrastive models in terms of the refined degree of fitting error distribution characteristics. The proposed model only requires the wind speed prediction sequence to accurately model the uncertainty interval. This should be of great significance for rationally optimizing system frequency regulation resources and reducing redundant backup.

Data-Driven Distributionally Robust Optimization for Real-Time Economic Dispatch Considering Secondary Frequency Regulation Cost

With the large-scale integration of renewable power generation, frequency regulation resources (FRRs) are required to have larger capacities and faster ramp rates, which increases the cost of the frequency regulation ancillary service. Therefore, it is necessary to consider the frequency regulation cost and constraint along with real-time economic dispatch (RTED). In this article, a data-driven distributionally robust optimization (DRO) method for RTED considering automatic generation control (AGC) is proposed. First, a Copula-based AGC signal model is developed to reflect the correlations among the AGC signal, load power and renewable generation variations. Secondly, samples of the AGC signal are taken from its conditional probability distribution under the forecasted load power and renewable generation variations. Thirdly, a distributionally robust RTED model considering the frequency regulation cost and constraint is built and transformed into a linear programming problem by leveraging the Wasserstein metric-based DRO technique. Simulation results show that the proposed method can reduce the total cost of power generation and frequency regulation.