System Inertia Constant Research Articles

Fast-tracking method of inertial constant based on system identification

Aiming at the problem of quantitative inertia evaluation of a new energy electric power system, the system inertia constant tracking method based on system identification is studied. The method is divided into two categories: non-recursive algorithm and recursive algorithm. The non-recursive algorithm uses a batch of data for batch processing to obtain the estimated value of the identification model parameters. The recursive algorithm is based on the estimated value of the model parameter at the previous moment and corrects the estimated value based on the new data currently obtained. From the perspective of the identification principle, the difference and internal relationship between the two in terms of calculation storage and identification speed are analyzed. The IEEE typical system is used to compare and verify the experimental examples. Theoretical analysis and experimental results show that the recursive algorithm has high identification accuracy, stable identification results and fast identification speed. It is suitable for the identification of objects with large numbers of nodes and complex structures, which is conducive to real-time monitoring and fast perception of the inertia constant of the new energy power system.

An instant inertia estimated and operation burden considered frequency regulation method for low inertia power systems

The application of virtual inertia (VI) has become a novel solution and new orientation for the frequency regulation of the high variable renewable energy (VRE) penetrated low inertia systems. How to provide a frequency regulation strategy for the system with VI is a key problem. Firstly, the power system is a multi inputs multi outputs (MIMO) system, in which the coupling characteristics of VI control and the primary and secondary frequency controls can’t be ignored. Secondly, the system inertia constant is time-varying and the estimation of it is crucial for gaining a satisfying regulation performance. This paper intends to provide a frequency regulation strategy for this problem. In this work, an Elman neural network (ENN) based inertia estimation with a deadband method is proposed for the identification of the system’s instant inertia. And the Laguerre function-based rate of change of frequency (ROCOF) constrained MIMO-MPC is constructed to provide the collaboration strategy. Then, the control parameters are tuned by the gravitational search algorithm (GSA) considering both the system performance indexes and the operation burden indexes. The proposed strategy is testified based on a three-area power system. The results prove not only the robustness of the controller but also the effectiveness of the ROCOF constraint, the operation burden index, and the instant inertia estimation method.

Participation of load aggregator in grid frequency stabilization with consideration of renewable energy resources integration

Alterations in the proportion of conventional generation to that of renewable energy source could be a future challenge for electric energy networks. Conventional power plants are being replaced by wind and solar energy. Solar PV plants and wind turbines lower the system inertia constant, causing the grid frequency to shift. Renewable energy generation plants that are integrated into the energy system may cause problems with frequency stability if adequate frequency control techniques are not applied. In this study, a Load Frequency Control framework based on aggregators is developed to improve the frequency response of the power system. It was demonstrated that the aggregator is capable of forecasting available flexibility for the day ahead contributing to the frequency control process. The aggregator is contributing to the grid frequency stabilization considering the price signal for minimizing energy costs, with an effect on end user’s energy bills reduction. For assessing the aggregator’s flexibility availability, the aggregator calculates the contributed power after minimizing its energy costs. The results show that it is possible to use DR technology to stabilize the grid frequency. With the loss of generation of 50 MW, the aggregators contribute mainly for the morning event with a contribution of about 4 MW followed by the night event with the contribution of approximately 3.5 MW. The largest contribution was obtained between 06:00 and 8:00 for the examined area with three aggregators, and the smallest contribution was obtained between 22:00 and 24:00. By considering the whole power system, with the integrated renewable energy resources in different periods, the findings demonstrate that even though the frequency decreases, it quickly returns to the usual operating limit and stabilizes itself back to the normal operating limits of 50 Hz as a result of the aggregator participation in the process of grid frequency stabilization.

A Data-Driven Under Frequency Load Shedding Scheme in Power Systems

This paper presents a measurement-based under-frequency load shedding scheme that considers time delays, measurement uncertainties, and communication network faults. Using a moving first-degree polynomial function, an iterative approach to estimate the frequency nadir and the associated frequency nadir time is proposed. Then, a novel method to simultaneously estimate the power imbalance and the system inertia constant is developed. According to an analytical basis, a base system is selected from a set of pre-defined base systems to guarantee accurate power imbalance estimation. Finally, a mathematical index is investigated to calculate a threshold for the generation-load power imbalance beyond which loads should be shed. Further, a systematic approach for specifying the amount of load to be shed in each transmission bus of the system is proposed. The effectiveness of the proposed methodologies is examined on both simulated and actual measured data from 9 historical power system disturbances in a national power grid.

Estimation of Rwanda's Power System Inertia as Input for Long-term Dynamic Frequency Response Regulation Planning

Changes in the quota between conventional generation and renewable energy sources constitute major challenges that the modern power systems have to encounter. Conventional power plants are replaced by renewable generation (e.g. wind turbines, photovoltaics) that contribute to the reduction of power system inertia. This may introduce frequency stability issues because frequency is affected by the amount of system inertia, along with the response of controllable frequency reserves and the amount of power imbalance. Therefore, the estimation and analysis of power system inertia and the frequency response assessment is essential to ensure power system stability and security. This paper proposes a novel method to estimates the inertia constant for three different periods in future, namely, 2025, 2035 and 2050 based on the produced future energy scenarios (FES) for Rwandan's power system. In addition, the paper evaluates the frequency response dynamics for each scenario. Results show that the highest progression in renewable energy resources penetration resulted to a larger reduction in the system inertia constant (from 7.2 in control area 1 to 3.83s in control area 3) and the largest frequency drop was observed during the high progression scenario in the year 2050 where the PV and imported power penetration was expected to reach more than 30% of the total installed capacity.

Online Estimation of Power System Inertia Constant Under Normal Operating Conditions

An online estimation method for the power system inertia constant under normal operating conditions is proposed. First of all, a dynamic model relating the active power to the bus frequency at the generation node is identified in the frequency domain using ambient data measured with the phasor measurement units (PMUs). Then, the inertia constant at the generation node is extracted from the unit step response of the identified model in the time domain using the swing equation. Finally, with the sliding window method and the exponential smoothing method, the estimated inertia constant is updated in real-time. Compared to the conventional methods using large disturbance data or field test data, the proposed method can estimate the inertia constant under normal operating conditions, and therefore, can provide the tracking trajectory of the power system inertia constant in real-time. The effectiveness of the proposed method is validated in the IEEE 39-bus system. The results show that the relative error of the identified inertia constant is below 5% and the identified inertia constant can be updated within 1s.

Salp swarm algorithm-based model predictive controller for frequency regulation of solar integrated power system

Global analysis of power system markets project frequency regulation as one of the most profitable ancillary services. It is associated with second-to-second balance of load and frequency within a control area and acquires a principal role in enabling power interchanges while offering better conditions for electricity exchange. In the light of the above, a novel control strategy, namely salp swarm algorithm (SSA)-based model predictive controller, is proposed for frequency regulation of an unequal two-area realistic power system, incorporating solar thermal power plant and conventional thermal plant. Governor dead band, generation rate constraint and transport delay are considered in each control area. Over the past few years, model predictive controller (MPC) has come forward as a prediction-based control strategy for stabilizing dynamical systems while considering non-linearities, system uncertainties and constraints. The MPC parameters are optimized using SSA. The performance of the proposed approach is validated by comparing the dynamic time responses of SSA-optimized MPC with the other SSA-optimized conventional controllers, namely PID, FOPID and cascade PIDN-FOPID controller. The simulation result analysis shows that the proposed optimal MPC outpaces the conventional controllers with respect to peak overshoot, undershoot and settling time of the time responses. A comparative study of various objective functions indicates that, as compared to other indices, integral square error is better for the considered test system. Further, sensitivity analysis reveals the robustiousness of MPC parameters obtained at nominal values and hence is not required to be retuned, against variations in system loading and inertia constant.

Improved UFLS with consideration of power deficit during shedding process and flexible load selection

This study presents an improved under-frequency load shedding (UFLS) scheme that can detect power deficit during the shedding process and accordingly adjust the amount of load shedding. This is achieved by continuous monitoring of the overshooting signal of the second frequency derivative of the centre of inertia. Once detected, an equivalent system inertia constant is estimated in order to quantify the new power deficit. The scheme is also equipped with an optimisation algorithm to determine the best combination of loads that is close to the amount of power deficit, which minimises frequency overshoot/undershoot. The optimisation technique selected for this work is based on particle swarm optimisation. The performance of the proposed UFLS scheme was validated using a modified IEEE 33 bus with two mini-hydro generators and one full converter wind turbine. The system and the proposed UFLS was modelled and simulated in PSCAD/EMTDC software. The results confirmed that the proposed scheme is capable of shedding loads with minimum undershoot/overshoot, and detect and estimate a new power deficit during load shedding. The results reported by the proposed scheme proved to be significantly better than those reported by conventional and adaptive load shedding schemes.

Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF) problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC) of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs.

Inertial and primary frequency response model of variable‐speed wind turbines

Increase of converter-connected renewable power generation such as variable-speed wind turbines (VSWTs) decreases the system inertia constant, which reduces the frequency stability of the power system. Thus, it will be necessary to include these renewables in the inertial response and primary frequency control (PFC) of future power systems. In this study, the participation of VSWTs in frequency support is studied. First, the importance of inertial response and PFC is discussed. Then, the linearised model of VSWT with inertial and primary frequency response capabilities is presented and compared against the non-linear model. Finally, this model is integrated in a system frequency response model that consists of various power plants. Different operational scenarios are simulated to investigate the impact of wind turbines with and without emulated inertia and primary frequency response on frequency stability.

Automatic generation control of a multi area hydrothermal system using reinforced learning neural network controller

This paper deals with automatic generation control (AGC) of a three unequal area hydrothermal system. Reheat turbines in thermal areas and electric governor in hydro area are considered. Appropriate generation rate constraints are considered in the areas. Bacterial foraging (BF) technique is used to simultaneously optimize the integral gains ( K Ii ) and speed regulation parameter ( R i ) keeping frequency bias fixed at frequency response characteristics. The integral controller in this case is termed as BFIC. The performance of a multilayer perception neural network (MLPNN) controller using reinforcement learning is evaluated for the system. In this reinforcement learning, the weights are dynamically adjusted online using backpropagation algorithm with error being the area control error (ACE). The performance of the MLPNN controller is compared with that of BFIC. Also, the performance of MLPNN controller over a wide range of system loading conditions and step load perturbations is compared with BFIC. Investigations clearly reveal the superior performance of MLPNN controller over BFIC. Sensitivity analysis subject to wide changes in system loading, inertia constant ( H) and size and location of step load perturbation is carried out to investigate the robustness of the controller with the optimum K Ii and R i obtained at nominal condition.

Estimation of power system inertia constant and capacity of spinning-reserve support generators using measured frequency transients

A procedure for estimating the inertia constant M(=2H) of a power system and total on-line capacity of spinning-reserve support generators, using transients of the frequency measured at an event such as a generator load rejection test, is presented. A polynomial approximation with respect to time is applied to the waveform of the transients in estimating the inertia constant, and a simple model based on the idea of average system frequency is assumed in estimating the capacity of the generators. Results of the estimation using the transients at 10 events show that the inertia constant of the 60 Hz power system of Japan is around 14 to 18 seconds in the system load base, and the capacity of the spinning-reserve support generators is 20 to 40% of the system load. The proposed procedure is expected to be tested by Kansai Electric Power Company with increased number of events. This effort will contribute to estimate and evaluate the dynamic behavior of the system frequency in loss of generation or load.

Switching Transients in Static Slip-Energy Recovery Drive

This paper deals with the study of starting transients of a static slip-energy recovery drive. The non-linear equations of the system have been simulated on a digital computer and solved by the application of Runga-Kutta method. The effects of firing angle, load, system inertia and filter time constant on transient torques and speeds following a switching operation have been investigated.