A Two-stage Optimization Method for Storage Coordinated with Thermal Power Unit in AGC

Modeling of battery energy storage systems for AGC performance analysis in wind power systems

Maintaining the balance between real-time generation and load is one of the important tasks of power system. Automatic generation control (AGC) is an important part of power grid frequency control, so grid-connected thermal power units must have good AGC performance. Many factors may cause the AGC regulation performance of generating units to fail to meet the requirements, such as unreasonable system design, imperfect control strategy, poor equipment characteristics and so on. This paper introduces the performance index of thermal power units participating in AGC regulation and analyses the influence of flow characteristics of steam turbine valves on AGC performance. By optimizing the steam turbine valve management function, the performance can be effectively improved. The effectiveness of this method is verified by its implementation on a typical 300 MW thermal power unit.

In the process of energy storage clusters participating in automatic generation control (AGC) of power systems, reasonable command power allocation is the key to effective execution of frequency regulation commands. Currently, the traditional AGC command tracking energy storage control strategy is to allocate according to the proportion of energy storage rated power. Due to the difference in power configuration of energy storage capacity in energy storage clusters, this will cause the state of charge (SOC) of small capacity energy storage power stations to reach the boundary first, resulting in limited output and command tracking errors, affecting the frequency regulation effect. This article proposes an energy storage cluster frequency modulation power optimization strategy that takes into account command tracking errors to address the above issues. Firstly, considering the basic process of automatic power generation control, a collaborative control architecture was designed for energy storage clusters to participate in AGC regulation; Then, a multi type energy storage regulation target considering the state of charge of energy storage was constructed. The model construction took into account the constraints of instruction power allocation and energy storage operation. Finally, the Lagrange multiplier method was used to solve the problem and obtain the output allocation of energy storage. The proposed strategy has been verified through simulation analysis to ensure the effective execution of AGC instructions and reduce the instruction tracking error of energy storage clusters participating in AGC regulation.

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

In order to improve the automatic generation control (AGC) performance of thermal generators, this paper presents a stochastic model predictive control (SMPC) approach for a battery/flywheel hybrid energy storage system (HESS) to distribute power. The approach combines an adaptive Markov chain for power demand prediction of HESS, a scenario tree generation and model predictive control strategy. To develop an effective prediction model to deal with the randomness of a thermal generator in response to AGC command, a Markov chain is used to describe the randomness of the HESS power demand, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> information is used to adapt to the fluctuation of the AGC command. A scenario tree generation approach is proposed to make better use of the Markov probability matrix. Based on these efforts, an SMPC approach is proposed for HESS energy management. Simulation results show that the regulation performance of the proposed approach outperforms conventional approaches, and that its performance is close to the model predictive control strategy with prescient information of the future power demand. In addition, compared with other approaches such as rule-based strategies, it does less damage to the battery and yields higher annual average net benefits in the whole life cycle.

The dynamic performance of automatic generation control (AGC) primarily relies upon the performance index criteria (PIC) being used to optimize the supplementary controller gains. Most of the previous AGC studies were focused on developing various intelligent controllers and meta-heuristic algorithms to improve the system dynamic performance of the AGC system. However, no significant efforts were made to formulate effective PICs in order to acquire better supplementary control and improved AGC dynamic performance. In this paper, a maiden attempt has been made and new hybrid-peak-area-based PICs have been proposed for the optimal tuning of supplementary controller gains. The proposed PICs have been implemented upon single area as well as standard New England IEEE 39 bus system. Sensitivity analysis has also been carried out to demonstrate the superiority of the proposed PICs for wide changes in AGC power system model parameters. The proposed PICs demonstrate better dynamic performance in comparison to existing PICs in terms of overshoot, subsequent oscillations, and settling time.

As wind energy generation becomes more prevalent in some regions, there is increased demand for wind power plants to provide ancillary services, which are essential for grid reliability. This paper compares two different wind turbine control methodologies to provide active power control (APC) ancillary services, which include derating or curtailing power generation, providing automatic generation control (AGC), and providing primary frequency control (PFC). The torque APC controller provides all power control through the power electronics whereas the pitch APC controller uses the blade pitch actuators as the primary means of power control. These controllers are simulated under various wind conditions with different derating set points and AGC participation levels. The metrics used to compare their performance are the damage equivalent loads (DELs) induced on the structural components and AGC performance metrics, which are used to determine the payments for AGC services by system operators in the United States. The simulation results show that derating the turbine reduces structural loads for both control methods, with the APC pitch control providing larger reductions in DELs, lower AGC performance scores, and higher root-mean-square pitch rates. Providing AGC increases the structural loads when compared to only derating the turbine, but even the AGC DELs are generally lower than those of the baseline control system. The torque APC control methodology also allows for more sustained PFC responses under certain derating conditions.

The battery energy storage (BES) is very promising to be used for improving the performance of automatic generation control (AGC) in power system by offering fast active power compensation. However, the improper parameters of controller in AGC system may cause an unstable frequency problem. This paper presents an optimal controller design method based on bacteria foraging algorithm (BFA) for AGC system with BES. A two-area reheat thermal system is considered to be equipped with the proportional plus integral (PI) controllers. The BFA technique is employed to search for the optimum controller parameters by minimizing the integral of time multiply absolute error (ITAE) index. The performance of the proposed BFA tuning controller has been evaluated with that of the controllers tuned by particle swarm optimization (PSO) and genetic algorithm (GA). Simulation results emphasize the performance of AGC system with BES and demonstrate the superiority of the proposed BFA tuning controller compared to the optimized controller based on PSO and GA.

An analysis on automatic generation control (AGC) performance of an interconnected two-area hydro–hydro-power system model is presented in this paper subjected to dynamic control of damped oscillations in the presence of thyristor-controlled series compensation (TCSC) and the superconducting magnetic energy storage (SMES) unit. In real time, the load profile characteristics are un-deterministic in nature. Therefore, the current article studies a diverse prospective of area load profiles [such as step load perturbation (SLP), random SLP and sinusoidal load perturbation] in view of AGC performance analysis. The present work is to improve the dynamic responses and to pursue their significances in damping oscillation after the addition of a fast-acting TCSC (as a damping controller) in area-1, whereas SMES unit is installed in area-2 (to provide large values of energy instantaneously). In the present prospect, a new control strategy based on Taylor theorem is implemented to modify the TCSC controller as well as the two-stage phase-compensating blocks are cascaded to both the TCSC and the SMES to improve the phase lag of the system. In this paper, a nature-inspired optimization paradigm [called moth-flame optimization (MFO) algorithm] is utilized to design the controller gains. Additionally, the robustness of the designed controller is investigated in the event of loaded condition and model parameter uncertainties through sensitivity analysis. Analytically, eigenvalues, performance indices values and transient details are presented in support of the designed MFO–TCSC–SMES controller. The obtained simulation results are compared to genetic algorithm (GA)-based designed GA–TCSC–SMES controller to show the optimizing performance of the MFO algorithm in the controller design. The simulation results showed that after the addition of a TCSC–SMES unit in the studied power system model, in addition to eliminating damped oscillations, the settling times of frequency and tie-line power flow are considerably reduced.

With the real‐time changes of wind speed and operating conditions, it is a challenge to fully tap the active power regulation ability and improve the control performance of automatic generation control (AGC) in a wind farm (WF). The essence of tapping the active power regulation ability is to realise the coordination and complementarity of each wind turbine's (WT's) dynamic adjustment performance (DAP). To address this, a novel data mining method is developed to derive the internal relations between WTs’ output power and pitch angle, impeller speed and pitch angle during the power adjustment process, and a unified mechanism model is established to describe DAP of WTs. Based on the discovered relationship between WTs’ DAP and its operating states, an active power distribution algorithm and a dynamic interval control method are proposed. Then, an active power dynamic interval control strategy that has been implemented using Java script in MyEclipse for WFs is further developed. The control strategy has been tested and applied in a 50 MW WF in northwest China. The preliminary results showed that the control strategy has improved the rapidity and accuracy of AGC in the WF.

This paper illustrates the application of fuzzy logic FL) to automatic generation control (AGC) of a two area thermal power system and compares its performance with genetic algorithm (GA) based PI controller. The PI controller parameters are redesigned by the application of fuzzy logic. To verify the performance of the fuzzy based AGC, the task of tuning PI parameters is reformulated as an optimization problem for the application of GA. A comparison between the proposed fuzzy logic and GA based controllers, in terms of three time domain performance indices are presented.

Fractional order phase lead-lag controller (FOPLC) is a generalization of classical phase lead-lag controller (CPLC) using fractional calculus. Nowadays, fractional order controllers (FOC) have found wide applications in various aspects of power system control and operation. In this paper, FOPLC are used in structure of flexible ac transmission system (FACTS) based frequency stabilizer to enhance the dynamic performance of automatic generation control (AGC) system. So, Thyristor controlled series capacitor (TCSC) based damping controller with FOPLC structure is proposed in coordination with secondary integral load frequency controller (LFC). The controller is optimized via an improved particle swarm optimization (IPSO) algorithm. The dynamic performance of the proposed controller is compared with CPLC using eigenvalue analysis and time domain simulations. An interconnected multi-source realistic power system with TCSC in series with the tie-line is investigated considering the nonlinearity effects of generation rate constraint (GRC) and governor dead band (GDB). Simulation results reveal that FOPLC based TCSC damping controller achieves superior dynamic performance under various load perturbation patterns in terms of decreased performance index, settling time and amplitude of oscillations. Furthermore, the robustness of the proposed controller has been tested for large uncertainties in system parameter and loading condition.

In this work, an attempt has been made to understand the dynamic performance of Automatic Generation Control (AGC) of three-area reheat-thermal power system. For more realistic study, Generation Rate Constraint (GRC) has been taken into account in all areas. Firstly, the Genetic Algorithm has been executed to obtain the optimal gains of PID regulator for the design of secondary control loop. Then, Interline Power Flow Controller (IPFC) unit has been placed with the AC tie line. Finally, the obtained results demonstrate the performance of IPFC unit.

As the penetration of renewable energy sources (RESs) in the grid increases, the requirements for Automatic Generation Control (AGC) frequency regulation and reserve services should also be adjusted. RESs have great potential to provide auxiliary services, however, RESs also face the problem of revenue trade-off between participation in the electricity energy market and the auxiliary frequency regulation service market. Opportunity cost can help decision-makers make a reasonable allocation of resources and maximize the theoretical benefits. In this context, an opportunity cost calculation model for RESs in delivering AGC service is provided in this paper. The calculation process of the opportunity cost for RESs to provide AGC auxiliary services is proposed in this paper. To analysis the opportunity cost of the RES plants providing frequency adjustment service, the modified IEEE 6-bus system with two thermal power plants, two wind power plants, and one photovoltaic power plant, is adopted in this paper. Firstly, the clearing results of the electric market in the system is adopted. Then, the opportunity cost of the RESs with AGC is calculated. Finally, by comparing the opportunity cost per unit of capacity of frequency adjustment service and the AGC compensation price in the North Grid of China and the Northwest Grid of China, the optimal decision of AGC participation factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a$</tex> can be obtained. Case studies reveal that the marginal opportunity cost of AGC capacity for RESs increase with the growth of the AGC capacity. As a result, total revenue of the RESs reaches the maximum value at an appropriate ratio of AGC capacity, and this ratio is the optimal decision.

When sudden insufficient power supply occurs that will cause frequency drop quickly, and this could lead to a complete network collapse. Automatic generation control (AGC) is one of the important auxiliary services that play an important role in satisfying the large load changes or other disturbances, maintaining system frequency stability, keeping tie-line transaction security and enhancing the safety of the power system. The system must have a certain level of AGC frequency regulation reserve (FRR) to ensure system security. Furthermore, different AGC unit has different aspects, such as response rate, capacity, price and location that will have different influences on frequency security, the safety of transmission lines and system operating economy. So, Independent System operator (ISO) must evaluate different AGC effects, purchase and dispatch AGC FRR in scientific way. This paper provides the scientific bases for calculating necessary AGC FRR, guiding the operator to dispatch and select AGC FRR, allocating them among AGC FRR provider to provide emergent FRR following a generation deficiency. The optimal is the maximization of the utility's social welfare. Simulation results of IEEE 30-bus 6-machine test system illuminate the proposed methodology.