Area Control Error Research Articles

Application of load frequency control method to a multi-microgrid with energy storage system

Active power and frequency control reflecting the stability of network operation is referred to as load frequency control (LFC). The area control error (ACE) is a combinatorial model between the tie-line in the interconnected system and its frequency deviations. The ACE is forced to be close to zero to achieve generation and load balance in network control and network balancing. The present research proposes introducing an energy storage system (ESS) in the control loop to regulate the frequency when the network is under an unstable condition or where the average frequency between the different areas connected is different to zero. The proposed method is developed to coordinate the different modes (charging and discharging) of the storage system. The storage parameters are unknown constants and are calculated online with a modified heuristic and directional derivative objective. The system consists of two tie-lines connected between the two areas, and each one is made up of a synchronous generator, renewable energy sources, ESS and load. The optimal frequency deviation in the areas and tie problem was modelled into a control problem and solved using the MPC. The dynamic model of ESS was modelled as a control bahaviour to inject energy at any time of the control horizon to balance the electrical network. Modified control of the ESS by a new approach used to estimate uncertainties parameters of the interconnected system. The effectiveness of the frequency control strategies is measured through simulation assessment.

Flexible selection framework for secondary frequency regulation units based on learning optimisation method

Grid connections of new energy sources have increased system frequency fluctuations, which requires secondary frequency regulation(SFR) to ensure the safe and stable operation of the system. SFR balances the power deviation between generation and consumption in real time by changing the setpoints of the units. In this paper, we propose a dynamic area control error (ACE) threshold value-based method to rationalise unit selection for SFR tasks. Each conventional unit or virtual generation unit has an independent threshold to aid dispatchers in determining the response priority of each unit to SFR tasks relative to scheduled tasks. In this manner, power generation units can switch their control mode flexibly and track different setpoints using automatic generation control(AGC) and load control technology. We designed a threshold solver based on a sequential decision framework using the deep reinforcement learning method. The solver can dynamically update thresholds based on the real-time system operational state such that the selected units can better meet the current dispatching needs. In the experiment, we tested the performance of the solver in several typical scenarios; the proposed method was found to be effective in maintaining the frequency stability of the system, with lower operation costs.

Frequency Regulation Model of Bulk Power Systems With Energy Storage

This paper presents a Frequency Regulation (FR) model of a large interconnected power system including Energy Storage Systems (ESSs) such as Battery Energy Storage Systems (BESSs) and Flywheel Energy Storage Systems (FESSs), considering all relevant stages in the frequency control process. Communication delays are considered in the transmission of the signals in the FR control loop and ESSs, and their State of Charge (SoC) management model is considered. The system, ESSs and SoC components are modelled in detail from a FR perspective. The model is validated using real system and ESSs data, based on a practical transient stability model of the North American Eastern Interconnection (NAEI), and the results show that the proposed model accurately represents the FR process of a large interconnected power network including ESS, and can be used for long-term FR studies. The impact of communication delays and SoC management of ESS facilities in the Area Control Error (ACE) is also studied and discussed, as well as the computational efficiency of the proposed FR model.

Synergistic balancing control for low-inertia power systems with high PV penetration: Tibet as a case study

This paper proposes a novel synergistic balancing control scheme for low-inertia power systems with high photovoltaic (PV) penetration in Tibet power grid (TPG). The mechanism and dispatching principle of choosing the suitable area control error (ACE) control mode for TPG are analyzed., An adaptive control mode switching method is proposed to meet the different control demands for the dynamically changing topologies. Then, we propose a new coordinated control scheme to dispatch hydropower generators and PV power stations. A set of optimum control objectives considering balancing control performance, regulation mileage of conventional generators, and power curtailment of PV are first presented. The control objectives are decomposed and simplified by step-by-step approximation and linearization, which are easily extended to be applied to real-world automatic generation control (AGC) systems. The simulation results show the effectiveness of the proposed solutions for synergistic balancing control by using the equivalent TPG test system. The equivalent system includes 63 generators with total power equal to 1618.5 MW and 51 PV power stations over 1100 MW. In addition, the actual operation cases present that the coordinated control between conventional and renewable generation dispatched by AGC has the basic conditions in the actual operation of the power grids, which will play a more significant role with the rapid increase of renewable generation sources in the future.

Optimal linear quadratic Gaussian control based frequency regulation with communication delays in power system

<p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p>

Observer-based dynamic event-triggered [formula omitted] LFC for power systems under actuator saturation and deception attack

This paper focuses on the observer-based H∞ load frequency control (LFC) of multi-area power systems with actuator saturation and deception attack. Firstly, a state observer is constructed to estimate the unmeasurable states and a time-delay model with the area control error is established. Secondly, a dynamic event triggering mechanism is developed, where dynamic parameter and a novel error-related exponential term are introduced to reduce the communication burden further in the network. Thirdly, considering time delays, data-packet losses and stochastic deception attack, a resilient LFC controller of power systems based on actuator saturation is designed and the security of the system is guaranteed with the condition of H∞ mean-square exponential stability. Sufficient conditions to co-design the event-triggered scheme, observer and desired controller gain are derived by Lyapunov functional analysis approach. Finally, simulation example is given to demonstrate the efficiency of the developed method.

The Load Frequency Control by Adaptive High Order Sliding Mode Control Strategy

Sliding mode control (SMC) has the attractiveness of robustness to the system model uncertainties and disturbance, which has wide applications in power system, power electronics, and vehicle suspension system. However, it also has a drawback of chattering. To decrease the chattering phenomenon, this study develops a novel adaptive high-order sliding mode control (HOSMC), which has the benefit of parameterizable unmodeled dynamics compared with the HOSMC method. The proposed method can be employed for load frequency control with nonlinearities. The simulations are based on two and three areas of the power system with load frequency control. From the simulation results, it is apparent that the frequency deviation and ACE (area control error) converge to zero when using the proposed method. In addition, the simulation results validate the robustness of the proposed method in the case of parameters change. The oscillation and chattering can be attenuated more by the raised adaptive HOSMC compared to the super-twisting (ST) algorithm, which verifies the advantages of the proposed adaptive HOSMC scheme.

Efficient Load Frequency Control of Renewable Integrated Power System: A Twin Delayed DDPG-Based Deep Reinforcement Learning Approach

Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning (RL) agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given two-area interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error (IAE) of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random load-generation disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations.

A Multi-Stage Stochastic Risk Assessment With Markovian Representation of Renewable Power

Probabilistic forecasts provide a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">distribution</i> of possible outputs and so can capture the uncertainty and variability of Variable Renewable Energy (VRE). However, taking advantage of uncertainty information has practical challenges that make it difficult to integrate probabilistic forecasting into control room decision-making. This paper proposes a novel use-case for probabilistic forecasts by incorporating them into the hour-ahead operations for situational awareness via a risk-averse multi-stage stochastic program. We employ a Markovian representation of the probabilistic forecasts that enables the formulation of the multi-stage problem and avoids a scenario generation phase. We test the model on a realistically sized system to assess risk and showcase the capability of using probabilistic renewable forecast as input to produce probabilistic output forecasts of future system states. The results show that the model can capture time consistency in the reserves and Area Control Error (ACE) forecast. The solution times are adequate for risk profiling in hour-ahead timescales.

Regulation Signal Design and Fast Frequency Control With Energy Storage Systems

This paper presents a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{2}$</tex-math></inline-formula> filter design procedure to optimally split the Frequency Regulation (FR) signal between conventional and fast regulating Energy Storage System (ESS) assets, considering typical Communication Delays (CDs). The filter is then integrated into a previously validated FR model of the Ontario Power System (OPS) including Battery and Flywheel ESSs, which is used to analyze the impact of these ESSs, CDs, and limited regulation capacity in the FR process in a real system. The proposed methodology to split the FR signal is also compared with the existing FR process, with the results showing that the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{2}$</tex-math></inline-formula> filter design and signal splitting strategy can improve the FR process performance significantly, in terms of reducing the Area Control Error (ACE) signal, and thus reduce the need for regulation capacity.

Rank-Sum-Weight Method Based Systematic Determination of Weights for Controller Tuning for Automatic Generation Control

The design and performance evaluation of a grey wolf optimizer (GWO) aided rank-sum-weight method based proportional-integral-derivative regulator with derivative filter (PIDn) for automatic generation control (AGC) of two-area interconnected power systems are presented in this research. The derivative gain filter is used to lessen the impacts of noise in the input signal. Sub-objectives based on integral of time multiplied square error (ITSE) of frequency deviations, tie-line power deviation, and area-control errors (ACEs) are used to formulate the objective function for adjusting regulator settings. A single overall objective function is formed by combining these sub-objectives. ITSEs of two areas, ITSEs of tie-line power deviation, and ITSEs of ACEs of two areas comprise up the overall objective function. In the control design, the weights in the overall objective function are used to evaluate relative significance of each sub-objective. In contrast to previous techniques, where weights are either considered equal by ignoring the relative relevance of sub-objectives or selected randomly, the weights in this article are obtained using the rank-sum-weight method systematically. Using the GWO algorithm, the overall objective function is minimized. For six different circumstances including different load disturbances in interconnected areas, the effectiveness of the proposed GWO aided rank-sum-weight method based controller is examined. The performance of the GWO-tuned controller is also compared to those of other controllers tuned using the differential evolution, elephant herding optimization, Nelder-Mead simplex, membrane computing, and Luus-Jaakola algorithms. Time domain specifications are tabulated for each of the six circumstances. The findings are also plotted to demonstrate the frequency and tie line power fluctuations. A statistical analysis is also performed in order to assess the overall efficacy of the suggested controller.

Controlling the Mitigating Impacts of Communication Delay on Load Frequency Control with an Adaptive Method

Abstract: Load Frequency Control is one of the most essential frequency management technologies in modern power systems (LFC). When employing LFC over a vast region, communication latency is unavoidable. A delay might not only affect system performance but also cause system instability. An alternate design strategy for constructing delay compensators for LFC in one or more control areas utilising an AFPI controller and ANFIS is proposed in this paper. For one-area LFC, a sufficient and required condition for designing a delay compensator is described. It is demonstrated that for multi-area LFC with Area Control Errors (ACEs), each control area can have its own delay controller designed as if it were a one-area system if the index of coupling among the areas is less than the small gain theorem's threshold value. The effectiveness of the proposed technique is validated by simulation experiments on LFCs with communication delays in one and multiple interconnected areas with and without time variable delays.

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.

Interval Type-2 Fuzzy Logic Control-Based Frequency Control of Hybrid Power System Using DMGS of PI Controller

The load frequency control of a microgrid is one of the emerging areas due to the changes in demand and supply in power system. So the controllers’ implementation must be changed accordingly. This paper proposes an interval type-2 fuzzy logic-based, dual-mode gain scheduling (DMGS) of the proportional and integral controller in which the gains of the PI controller werescheduled through the dynamic selector. This proposed controller was implemented ina hybrid microgrid power system in which nonconventional energy sources wereadded to each area of the conventional power plant, which madethe system much more prone to frequency variations. The controller was designed for three areas, consisting of a photovoltaic (PV) system, a wind power system, a fuel cell and a diesel engine/hydropower generator in which the generation rate constraint (GRC) was considered as a nonlinearity. The proposed power system was investigated under various load conditions in the MATLAB/SIMULINK environment. A comparative evaluation of changes in frequency, tie-line power fluctuations and variations in area control errors for the test system showed the effectiveness of the current approach over simple fuzzy PI and a conventional PI-controlling approach.

From Hierarchical Control to Flexible Interactive Electricity Services: A Path to Decarbonization

We propose to conceptualise electric energy systems as complex dynamical systems using physically intuitive multilayered energy modelling as the basis for systematic diverse technology integration, and control in on-line operations. It is shown that such modelling exhibits unique structure which comes from the conservation of instantaneous power (P) and of instantaneous reactive power ( _Q), (interaction variables (intVar)) at the interfaces of subsystems. The intVars are used as a means to model and control the interactive zoomed-out inter-modular (inter-area, inter-component) system dynamics. Control co-design can then be pursued using these models so that the primary control shapes intVars of its own module by using its own lowlevel detailed technology-specific model and intVar info exchange with the neighbours. As a result, we describe how the proposed approach can be used to support orderly evolution from today’s hierarchical control to a platform enabling flexible interactive protocols for electricity services. The potential for practical use of the proposed concepts is far-reaching and transparent. All that needs to be conceived is that intVar characterising any intelligent Balancing Authority (iBA) is a generalisation of today’s Area Control Error (ACE) characterising net energy balance of a Balancing Authority (BA). An iBA can be any subsystem with its own sub-objectives, such as distributed energy resources (DERs) comprising customers and grid forming microgrids; distribution systems; transmission systems; Independent System Operators (ISOs); and, ultimately, electric energy markets within large interconnection. Several industry problems are described as particular sub-problems of general interactive electricity services. These formulations help one compare models and assumptions used as part of current solutions, and propose enhanced solutions. Most generally, feasibility and stability conditions can be introduced for ensuring feasible power flow solutions, regulated frequency and voltage and orderly power exchange across the iBAs.

Distributed optimal-tracking control to coordinate the optimization of active distribution networks with automatic generation control

To the shortage of automatic generation control (AGC) resources in power systems with high proportions of renewable generators, this paper proposes an optimal-tracking control strategy to integrate the optimization of active distribution networks (ADNs) into AGC. Different ADNs can coordinate to absorb the power fluctuations beyond AGC adjustment ranges in a distributed way. First, we build a time-varying model to integrate the optimization of ADNs into AGC, which gives the power allocation mechanism among different ADNs and AGC units in real time. Second, we design a distributed strategy to continuously drive the operation of ADNs to the solutions of the time-varying model using the optimal-tracking sensitivities calculated based on the feedback of measurements. In this strategy, the measurements of net load powers are calculated using the integral of the area control error in AGC; moreover, the optimal-tracking sensitivities are decomposed into several items of which each one only relates to the inner information of the transmission network or an ADN, which is deduced according to the physical laws of power flow of transmission and distribution networks. Based on the decomposition, each ADN can form the optimal-tracking sensitivities only based on its inner information and some coordination information from the transmission network. Thus, the distributed optimal-tracking controller is achieved. Finally, the effectiveness of the proposed control strategy is verified by case studies: ADNs are regulated to effectively restrain the excessive frequency deviations, caused by the sharp fluctuations of renewable generator outputs, with meeting the operational constraints of ADNs (e.g. the constraints on voltages) in real time.

Investigation on stability of delayed T-S fuzzy interconnected systems via decentralized memory-based sampled-data control and validation through interconnected power systems with DFIG-based wind turbines

This study investigates the stability of nonlinear interconnected systems (ISs) with time-delays by designing a decentralized memory-based sampled-data control and it’s an application to power system with doubly fed induction generator (DFIG)-based wind turbines (WTs). Initially, a nonlinear delayed IS is expressed as a delayed Takagi-Sugeno fuzzy ISs based on fuzzy IF-THEN membership rules. Following that, a decentralized sampled-data controller with a memory parameter is designed to ensure the stabilization of the considered ISs. Through constructing Lyapunov-Krasovskii functional by involving the information about the time-delays and sampling periods, delay-dependent stability and stabilization conditions are derived in terms of linear matrix inequalities. The sufficient conditions are guaranteed the asymptotic stability of the closed-loop ISs under a prescribed level of H∞ performance via a decentralized controller. To validate the derived theoretical sufficient conditions, a nonlinear interconnected power systems with DFIG-based WTs is modeled in which a time-delay is considered in the area control error (ACE). Finally, simulation results of the two area ISs are provided to illustrate the effectiveness of the derived theoretical conditions under the designed controller.

Distributed control of thermostatically controlled load aggregators in multi‐area power systems

In modern power systems, the high penetration of renewable energy challenges system frequency regulation and stability. In such conditions, the demand-side loads can be aggregated and applied for power system frequency regulation. In this study, a dual-level distributed control framework is proposed for thermostatically controlled load (TCL) aggregators in multi-area load frequency control. In the higher control level, the leader of TCL aggregators in each control area is controlled by local and neighbouring area control errors. In the lower control level, multiple TCL aggregators are coupled via a leader-follower consensus control protocol to track the power reference from the area leader. As a result, the dual-level distributed controlled TCL aggregators can operate together with synchronous generators for frequency regulation in multi-area power systems. The proposed method is validated in a three-area power system under various cyber-physical conditions, including contingency and normal operation conditions, as well as communication failure and delay conditions.

Fault-tolerant load frequency control for DFIG-based interconnected wind power systems

This paper investigates the fault-tolerant load frequency control (LFC) design of interconnected wind power systems. For this, a doubly-fed induction generator (DFIG)-based wind farm is integrated into the interconnected power systems. Distinct from the existing works, the communication time-delay is taken into the area control error (ACE) signals of interconnected wind power systems. Also, compared with the traditional LFC scheme, a fault-tolerant H∞ LFC scheme is presented with known and unknown actuator fault against load disturbances and wind speed fluctuations. Then, the asymptotic stability conditions are derived in the linear matrix inequalities (LMIs) form by utilizing the Lyapunov–Krasovskii functional (LKF) method and Wirtinger-based inequality technique under the designed LFC scheme with H∞ performance. Finally, numerical examples are given to validate the applicability and superiority of the designed LFC scheme.

Evaluation of the Simultaneous Operation of the Mechanisms for Cross-Border Interchange and Activation of the Regulating Reserves

This article examines the mechanisms for cross-border interchange of the regulating reserves (RRs), i.e., the imbalance-netting process (INP) and the cross-border activation of the RRs (CBRR). Both mechanisms are an additional service of frequency restoration reserves in the power system and connect different control areas (CAs) via virtual tie-lines to release RRs and reduce balancing energy. The primary objective of the INP is to net the demand for RRs between the cooperating CAs with different signs of interchange power variation. In contrast, the primary objective of the CBRR is to activate the RRs in the cooperating CAs with matching signs of interchange power variation. In this way, the ancillary services market and the European balancing system should be improved. However, both the INP and CBRR include a frequency term and thus impact the frequency response of the cooperating CAs. Therefore, the impact of the simultaneous operation of the INP and CBRR on the load-frequency control (LFC) and performance is comprehensively evaluated with dynamic simulations of a three-CA testing system, which no previous studies investigated before. In addition, a function for correction power adjustment is proposed to prevent the undesirable simultaneous activation of the INP and CBRR. In this way, area control error (ACE) and scheduled control power are decreased since undesired correction is prevented. The dynamic simulations confirmed that the simultaneous operation of the INP and CBRR reduced the balancing energy and decreased the unintended exchange of energy. Consequently, the LFC and performance were improved in this way. However, the impact of the INP and CBRR on the frequency quality has no unambiguous conclusions.