Multi-area Power Grid Research Articles

Event-triggered Intelligent Control Scheme for Data Integrity Attack Mitigation in Multi-area Power Grids

Event-triggered Intelligent Control Scheme for Data Integrity Attack Mitigation in Multi-area Power Grids

Data-Driven Automatic Generation Control of Interconnected Power Grids Subject to Deception Attacks

In this paper, a Data-Driven Adaptive Control (DDAC) technique is proposed for the Automatic Generation Control (AGC) problem of an interconnected power grid subject to Deception Attack (DA). The emergence of the Internet of Things (IoT) and the advancement of communication technologies have provided an opportunity for power system operators and designers to compensate for the lack of an appropriate model using a huge amount of data. However, they have also caused security challenges in the grid due to malicious attackers. This paper focuses on the attack to the control network which carries the AGC signals between the secondary and local primary frequency controllers. Intentional modifications of AGC signals during an attack may result in frequency instability because of saturation in governor signals. To counteract such an attack, a DDAC is suggested for a multi-area power system in which, the system model is dynamically updated using real-time input and output signals. The model includes the attacker’s behavior, thus empowering the control system to act against it. The stability of the proposed controller is proved using the Lyapunov stability theory when the DA causes input saturation. Simulation results show that it can successfully tolerate a class of deception attacks and keep the multi-area power grid stable.

Decentralized prioritization of demand response programs in multi-area power grids based on the security considerations

The deregulation of electrical energy grids (EEGs) and surrogating the installation of bulk power plants with distributed generations such as wind energy sources, despite the score of advantages, bring new challenges for the secure operation of these EEGs for their operators. On the other hand, the deployment of demand response programs (DRPs) alongside the management of these multi-area EEGs (MEEGs) intensifies the complexity of the situation. In this paper, a method is proposed for the secure operation of MEEGs when DRPs are employed to incorporate the end-users in balancing the demand–supply chain. Moreover, the techno-economic impacts of fluctuating nature of wind energy sources are mitigated by coordinating them with energy storage systems (ESSs). To do so, a decentralized probabilistic DC-SCOPF model is developed for the operation of MEEGs with ESS-coordinated wind energy sources when several DRPs are deployed in their different areas. The decentralization of the model is performed based on the optimality condition decomposition (OCD) algorithm which results in a mixed-integer non-linear programming (MINLP) problem for each area of the MEEG. What is more; a priority list of DRPs in different areas of the MEEG is generated based on four criteria including the overall operational cost of the system, the number of critical contingencies as the security measure, the overall shed load, and the peak-to-valley ratio (PVR) of the daily demand curve. Numerical simulations are carried out on the New England 39-bus testbed and obtained results are discussed in depth.

Distributed Sliding Mode Fault‐Tolerant LFC for Multiarea Interconnected Power Systems under Sensor Fault

This paper focuses on the problem of load frequency control and sensor fault‐tolerant control in the multiarea power grid. To solve these problems, a sliding mode control strategy based on an interval observer is designed. First, an interval observer is designed to obtain the boundary estimation information of the system state online, which is used to reconstruct the load disturbance online. Based on the reconstructed values, an integral sliding mode controller is designed to keep the system frequency stable when the load is disturbed. Then, sensor fault is diagnosed by interval residual. An augmented observer is designed to estimate sensor fault online by equivalent transformation, and the sensor fault is compensated by the fault estimation to reduce the influence of fault on system performance and ensure the reliable and stable operation of the power system. The simulation results show the superiority of the proposed control strategy.

A primal-dual-quasi-newton-based decentralized SCOPF approach for coordinated corrective control of interconnected power grids

Coordinated corrective control (CC) is indispensable for protecting multi-area interconnected power grids operated by independent entities against post-contingency overloads, and even subsequent cascading failures. Generally, CC strategies are generated online by solving a security-constrained optimal power flow (SCOPF) problem. Region-based decomposition and coordination through auxiliary problem principle (APP) allows distributed and parallel computations among subsystems without a central coordinator, eliminating the restriction of obtaining global data from independent entities. However, by taking a fixed core to form the auxiliary problems, the APP exhibits slow convergence in an ill-conditioned setting, which is inevitable for CC due to the penalty on undesirable actions, e.g., load shedding, in the objective function. In this paper, based on the in-depth theoretical analysis of the APP’s deficiency, a primal–dual quasi-Newton (PDQN)-APP algorithm is proposed by generalizing the fixed core to an adaptive core. And consequently, a fully decentralized SCOPF approach is developed for the coordinated CC of interconnected power grids via the proposed PDQN-APP algorithm. In particular, the proximal-gradient updates are extended to proximal-Newton updates via the state-of-the-art PDQN method. Through the generalization, adaptive curvature estimations relevant to boundary variables are contained in both the primal and dual iterative updates to correct the ill-conditioning, which significantly improves the convergence performance. Numerical studies demonstrate the superiority of the proposed PDQN-APP algorithm in convergence rate and accuracy. The effectiveness and superiority of the developed decentralized SCOPF approach and resulting CC strategies are also verified.

Data-driven adaptive control of wide-area non-linear systems with input and output saturation: A power system application

In this paper, a Data-Driven Adaptive Controller is proposed for wide-area Multi-Input and Multi-Output (MIMO) non-linear systems featuring input–output saturations. Complexity has always been a major concern in the modeling and control of real-world industrial processes. Traditionally, researchers look for simplified and enough precise models of these systems. However, with the significant advancement in sensor and communication technologies, a considerable amount of data is available from the process which may compensate for the lack of precise models. The emerging data-driven control systems perform both identification and control actions based on data collected from the process, making the system less dependent on a pre-identified model. In this context, an efficient MIMO Data-Driven Adaptive Control (MIMO-DDAC) is designed in this paper for stabilizing a class of unknown non-linear systems. A recursive modeling/control process is proposed for the MIMO systems in the presence of saturation in sensors and actuators, and its stability and performance are mathematically proved. This approach is applied to the load frequency control problem in an interconnected multi-area power grid in the presence of governor saturation, load and parametric uncertainties. Simulation results reveal the effectiveness of the proposed method.

A Distributed Optimization Algorithm for Multi-Area Power Grid Transmission Congestion Management

The interconnection of power grid brings significant economic benefits, while it calls for higher requirements to the security, stability and economic operation of power system as well. With high dimensionality of its optimization problem, the whole network centralized optimal power flow (OPF) algorithm is computational inefficient. Therefore, on the basis of power grid layering and zoning, the distributed optimal power flow calculation method in interconnected power grid with multiple control centers has important realistic significance. A distributed optimization algorithm of interconnected power grid transmission congestion management is proposed in this paper. The active power constraints of inter-area transmission interfaces are considered to satisfy the trade power constraints between adjacent sub-systems. The coordination and cooperation procedure counts on the correction and updating of boundary bus equivalent injection power and voltage vector. The numerical results on IEEE test systems show that the algorithm for transmission congestion has good convergence ability.

A Multi-Step Unified Reinforcement Learning Method for Automatic Generation Control in Multi-Area Interconnected Power Grid

As the penetration rate of new energies, energy storage devices and electric vehicles increases continuously in power grid, power grid faces strong random disturbances, as well as the issues of downfall in frequency control ability induced by the factors such as system inertia reduction and frequency control under-capacity in traditional power units. Therefore, an algorithm of automatic generation control with DQ(σ, λ) is proposed for the multi-area interconnected power grid. By integrating the eligibility trace, DQ(σ, λ) adopts the principle of double Q-learning to unify double Q(λ) and double Expected-Sarsa(λ), thereby the over-estimation of Q in the algorithm and the phenomenon of over-fitting can be avoided. The simulations are provided to demonstrate the effectiveness of the proposed algorithm, where the improved IEEE standard two-area load-frequency control model and the model of the multi-area interconnected power grid based on Central China Power Grid are adopted. The results show that the phenomena of random disturbances and frequency instability can be eliminated in multi-area interconnected power grid. The convergence is faster and the dynamic performance is better in the proposed algorithm compared with the traditional algorithms.

Online Proximal-ADMM for Time-Varying Constrained Convex Optimization

This paper considers a convex optimization problem with cost and constraints that evolve over time. The function to be minimized is strongly convex and possibly non-differentiable, and variables are coupled through linear constraints. In this setting, the paper proposes an online algorithm based on the alternating direction method of multipliers (ADMM), to track the optimal solution trajectory of the time-varying problem; in particular, the proposed algorithm consists of a primal proximal gradient descent step and an appropriately perturbed dual ascent step. The paper derives tracking results, asymptotic bounds, and linear convergence results.The proposed algorithm is then specialized to a multi-area power grid optimization problem, and our numerical results verify the desired properties.

Combined Impact of Demand Response Aggregators and Carbon Taxation on Emissions Reduction in Electric Power Systems

An emission rate-based carbon tax is applied to fossil-fueled generators with a demand response approach called Smart Grid resource allocation (SGRA). The former reduces the capacity factors (CFs) of base load serving fossil-fueled units, while the latter reduces the CFs of peak load serving units. The objective is to quantify the integration of the carbon tax and the SGRA approach on CO2 emissions and electricity prices in a multi-area power grid. We illustrate this using the Roy Billinton test system and the results show potential for significant reductions in fossil fuel-based generation and CO2 emissions.

A Two-Stage Fault Location Identification Method in Multiarea Power Grids Using Heterogeneous Types of Data

This paper proposes a two-stage fault location method for multiarea power systems. It utilizes available heterogeneous types of data consisting of status data (i.e., discrete data) and analog data (i.e., continues data). A distributed casual model-based diagnosis method using P-invariant Petri nets is proposed that utilizes status data to find faulted sections. Each subsystem/area of multiarea power systems has its own diagnostic model and determines the diagnosis solution based on its local net, the local manifestations, and limited information exchange with the neighboring subsystems/areas. Once a short list of possible fault sections is determined, the second stage further improves the estimated fault location using analog data. The actual fault location is estimated by comparing the measured analog data with their associated calculated values in computer programs using short-circuit analysis algorithms. Simulation results demonstrate that the proposed distributed fault location method can diagnose faults in multiarea power systems accurately.

Avoiding Divergence in Multi-Area State Estimation

This paper is concerned about the divergence of state estimation solution for multi-area power grids. Such cases typically occur due to a single anomaly in the measurement set or network model, yet will cause failure of the state estimator to provide the solution for the entire system. The objective of our study is to develop an approach that will allow detection, identification, and isolation of the anomaly during the state estimation solution so that a large percentage of the system states can still be estimated, while isolating the anomaly that is causing the divergence of the estimator. The proposed approach is described in detail and its effective utilization for a very large utility system is illustrated by simulations.

Research on the Predictive Optimal PID Plus Second Order Derivative Method for AGC of Power System with High Penetration of Photovoltaic and Wind Power

Because of the uncertainty of the external environment, high penetration of renewable energy such as wind power and solar energy in the modern power system renders the traditional automatic generation control (AGC) methods more challenging. An improved AGC method named predictive optimal proportional integral differential plus second order derivative (PO-PID + DD) for multi-area interconnected grid is proposed in this paper to reduce the negative impacts of the uncertainty which is caused by the high penetration of renewable energy. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of photovoltaic (PV) and wind power is built. Then, PO-PID + DD controller is presented to improve the system robustness with respect to system uncertainties. In order to obtain the predictive sequence of the integral system output, the characteristic of the controller is included in the system model. Thus, according to the predictive sequence and designed objective function, the input of the controller can be readjusted to obtain the optimal effect of AGC. An IEEE 39-bus system is introduced as an example to testify the feasibility and effectiveness of the proposed method. The simulation results indicate that the system controlled by the proposed controller has desired dynamic performances.

Distributed Model Predictive Load Frequency Control of multi-area Power Grid: A Decoupling Approach

A model-predictive scheme for load frequency control of a multi-area power system is proposed. The method depends on a decoupling technique which allows for a control design with a distributed architecture. Treating the total power inflows of each area as input variables, a decoupled linearized model for each area is derived. This allows for the formulation and solution of a model predictive control problem with a quadratic performance index and input saturating constraints on the individual tie-line power flows, along with an overall equality constraint to address the energy balance of the network. It is assumed that the interconnection topology (tie-lines) coincides with the communication topology of the network. The only information which needs to be shared between interconnected areas is the local frequency variables. The effectiveness of the method is illustrated via a simulation study of a three-area network. Future work will attempt to establish formally the stability of the control scheme and to enhance the versatility of the method by including constraints on the state variables.

Research on Automatic Generation Control with Wind Power Participation Based on Predictive Optimal 2-Degree-of-Freedom PID Strategy for Multi-area Interconnected Power System

High penetration of wind power in the modern power system renders traditional automatic generation control (AGC) methods more challenging, due to the uncertainty of the external environment, less reserve power, and small inertia constant of the power system. An improved AGC method named predictive optimal 2-degree-of-freedom proportion integral differential (PO-2-DOF-PID) is proposed in this paper, which wind farm will participate in the load frequency control process. Firstly, the mathematical model of the AGC system of multi-area power grid with penetration of wind power is built. Then, predictive optimal 2-degree-of-freedom PID controller is presented to improve the system robustness considering system uncertainties. The objective function is designed based on the wind speed and whether wind farm takes part in AGC or not. The controller solves the optimization problem through the predictive theory while taking into account given constraints. In order to obtain the predictive sequence of output of the whole system, the characteristic of the 2-DOF-PID controller is integrated in the system model. A three interconnected power system is introduced as an example to test the feasibility and effectiveness of the proposed method. When considering the penetration of wind power, two cases of high wind speed and low wind speed are analyzed. The simulation results indicate that the proposed method can effectively deal with the negative influence caused by wind power when wind power participates in AGC.

Power Demand-supply Adjustment in Multi-area Power Grid Based on Negawatt Trading via Regret Matching and VCG Mechanism Design

Power Demand-supply Adjustment in Multi-area Power Grid Based on Negawatt Trading via Regret Matching and VCG Mechanism Design

An ME-SMIB Based Method for Online Transient Stability Assessment of a Multi-Area Interconnected Power System

Transient stability assessment (TSA) of the power system is essential to the safe operation of the power grid. The TSA of the multi-area interconnected power system is a challenging task due to its special multi-area power grid structure. New features, which are the time-variable instability mode and untypical two-group instability mode, emerge in the transients of the interconnected power system and affect the accuracy of conventional TSA methods. To address these problems, we propose a novel TSA method based on the modified equivalent single machine infinite bus (ME-SMIB) system. Two key technologies, namely generator groups identification and generator selection, were presented in the proposed method. Generator groups were identified at each time-step to track the time-variable instability mode. Two groups of generators that were of good coherency and closely related to the current instability mode were selected to construct the ME-SMIB system. The transient instability was finally identified by the concave-convexity-based method. The proposed method was tested in the 16-generator 68-bus power system and China interconnected power system. Results show that the proposed ME-SMIB system can avoid the misjudgments caused by new transient features of the multi-area interconnected power system, presenting superior reliability than the conventional E-SMIB system.

Decentralized Contingency-Constrained Tie-Line Scheduling for Multi-Area Power Grids

Tie-line scheduling (TS) becomes a practical need for growing interconnection of regional power grids and the increasing integration of renewable energy. In electricity markets without a coordination organization, TS should be handled in a fully decentralized manner, where the impact of contingencies should also be considered for operational reliability. In this paper, the contingency-constrained TS (CCTS) problem is formulated using a two-stage robust optimization (RO) scheme to consider the $N-k$ security criterion. This robust model is decomposed and handled by the alternating direction multiplier method (ADMM) in a fully decentralized way, where the column-and-constraint generation (C&CG) is incorporated to solve the regional robust subproblems. Three computational strategies are also developed to enhance the computation efficiency of the decentralized algorithm. Numerical simulations are given to study the computational performance, the solution quality, and scalability of the proposed method.

Enhancing synchronization stability in a multi-area power grid.

Maintaining a synchronous state of generators is of central importance to the normal operation of power grids, in which many networks are generally interconnected. In order to understand the condition under which the stability can be optimized, it is important to relate network stability with feedback control strategies as well as network structure. Here, we present a stability analysis on a multi-area power grid by relating it with several control strategies and topological design of network structure. We clarify the minimal feedback gain in the self-feedback control, and build the optimal communication network for the local and global control strategies. Finally, we consider relationship between the interconnection pattern and the synchronization stability; by optimizing the network interlinks, the obtained network shows better synchronization stability than the original network does, in particular, at a high power demand. Our analysis shows that interlinks between spatially distant nodes will improve the synchronization stability. The results seem unfeasible to be implemented in real systems but provide a potential guide for the design of stable power systems.

Multi-level multi-area hybrid automatic voltage control system and its trial operation in Northeast China Grid

In order to coordinate automatic voltage control (AVC) systems of a large interconnected system, a multi-level multi-area hybrid automatic voltage control (MLMA-HAVC) system was constructed. This system began its trial operation in the Northeast China Grid in January 2010, and for the first time in China and abroad it realized automatic close-loop control of multi-area and multi-level interconnected power grid and multi-objective self-approaching optimization in aspects of security, high quality and economic operation. This system has three breakthroughs in theory and engineering application: 1) Established the MLMA-HAVC theory to solve multi-objective optimization of large-scale system; 2) proposed reactive power/voltage coordination control method to inhibit or further eliminate regional oscillations; 3) presented advanced state estimation algorithm to guarantee acquisition of high reliability data. This paper summarizes the basic principle of MLMA-HAVC, and reports engineering realization of MLMA-HAVC system in the Northeast China Grid.