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An optimization-based data-driven approach is proposed to identify the unknown location(s) of destabilizing faults and attacks in power systems. The analysis in this paper kicks in at the critical moment where the presence of destabilizing fault or attack is detected within the power system; therefore, there is an immediate need to identify the location(s) of the affected generators or loads in order to enable proper and effective post-detection measures. The proposed method works in frequency-domain. It does not require prior knowledge about the number of affected location(s). It is accurate in identifying the correct locations and also in preventing false alarms. It is computationally more efficient than its time-domain counterparts. Importantly, it is well-suited to be implemented in a hierarchical fashion, with applications such as in wide area monitoring systems. Various case studies on IEEE 9 and IEEE 39 bus test systems verified the performance of the proposed algorithms.

Modern power systems utilize automatic generation control (AGC) for real-time frequency regulation to ensure power balance of generation and consumption. Increasing penetration of intermittent renewables will introduce greater variability in generation, which in turn requires an even higher participation of AGC. In light of this, frequency regulation becomes a very critical service in modern power systems’ operations that has to be carried out with high economic efficiency. The use of demand response (DR) is being offered as a green, affordable alternative to conventional generation-based AGC. Although advances in wide-area monitoring and control technologies have made real-time control of DR a possibility, significant challenges remain for large-scale integration. In this paper, we propose a dynamic regulation market mechanism (DRMM) that allows both DR units and generators to bid for regulation service in real time, ensuring optimal allocation and reducing regulation service costs. Unlike similar proposals in the literature, the DRMM is practical to implement on a large power system with multiple areas. In particular, the DRMM requires modest communication rates on par with the existing AGC system and can explicitly accommodate constraints on DR energy deferment. We demonstrate the added benefit of the DRMM over the conventional AGC through simulations on a 3-area 900-bus power system and derive conditions for its stability.

This paper presents the design and performance analysis of Flower Pollination Algorithm (FPA) based Proportional Integral Derivative (PID) controllers for Automatic Generation Control (AGC) of an interconnected power system. A two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. A different kind of approach is made to design a multi-objective function which contains weighted performance functions such as ISE, IAE, ISTE, ITAE. These weights are the functions of system response. It is noticed that the dynamic performance of new objective optimized PID controller is better than the others mentioned in the literature. The objective function also includes performance response for various percentage of loads, so that obtained gain parameters are optimal for dynamic load conditions. Keywords: Flower pollination, Weights, Dynamic loading, Automatic generation control, Two area thermal system, Governor dead-band nonlinearity

This paper presents a new method for scheduling of battery storage systems for participation in frequency regulation and energy markets, simultaneously. Unknown automatic generation control signal of regulation market is modeled through robust optimization. In addition, the complex effect of participation in regulation market on battery’s lifespan is modeled through a dynamic procedure. For this purpose, a long-term optimization process is proposed in which, the short-term participation strategy defines battery’s lifespan. In order to prevent fast depreciation of battery due to frequent and deep charges/discharges, a new limiting method is introduced here, which would be useful for participation in regulation market. The proposed long-term model is linearized by implementation of Benders’ decomposition. Optimum values for limiting factors are determined in the master problem, while the daily operation strategies are decided by sub-problems. Finally, the applicability of the proposed method is investigated using an illustrative case study.

Solar power forecasting improvements are mainly evaluated by statistical and economic metrics, and the practical reliability benefits of these forecasting enhancements have not yet been well quantified. This paper aims to quantify reliability benefits from solar power forecasting improvements. To systematically analyze the relationship between solar power forecasting improvements and reliability performance in power system operations, an expected synthetic reliability (ESR) metric is proposed to integrate multiple state-of-the-art independent reliability metrics. The absolute value and standard deviation of area control errors (ACEs), and the North American Electric Reliability Corporation Control Performance Standard 2 (CPS2) score are calculated through a multi-timescale scheduling simulation, including the day-ahead unit commitment, real-time unit commitment, real-time economic dispatch, and automatic generation control sub-models. The absolute ACE in energy, CPS2 violations, CPS2 score, and standard deviation of the raw ACE are all calculated and combined as the ESR metric. Numerical simulations show that the reliability benefits of multi-timescale power system operations are significantly increased due to the improved solar power forecasts.

Motivated by an increase of renewable energy sources, we propose a distributed optimal load frequency control scheme achieving frequency regulation and economic dispatch. Based on an energy function of the power network, we derive an incremental passivity property for a well-known nonlinear structure preserving network model, differentiating between generator and load buses. Exploiting this property, we design distributed controllers that adjust the power generation. Notably, we explicitly include the turbine-governor dynamics, where first-order and the widely used second-order dynamics are analyzed in a unifying way. Due to the non-passive nature of the second-order turbine-governor dynamics, incorporating them is challenging, and we develop a suitable dissipation inequality for the interconnected generator and turbine-governor. This allows us to include the generator side more realistically in the stability analysis of optimal load frequency control than was previously possible.

In this paper, a novel hybrid chemical reaction optimisation and particle swarm optimisation (HCROPSO) optimised PI controller has been proposed for automatic generation control (AGC) problem. A two area reheat thermal-hydro system with non-linearities such as governor dead band (GDB), generation rate constraints (GRC) and boiler dynamics is considered. The parameters of PI controller are optimised employing HCRO-PSO technique. The superiority of the proposed approach is shown by comparing the results with PSO, CRO and fuzzy logic control (FLC). Improvement in system performance is obtained in terms of reduced settling time, overshoot and undershoot of frequency deviation and tie line power deviation with proposed controller. Investigation is performed with variation in inter rate and inertia weight parameters. Sensitivity analysis is performed by varying the system parameters and generation rate constraints from their nominal values. Analysis reveals that HCRO-PSO optimized PI gains obtained at nominal are quite robust and need not be reset for wide changes in system parameters. Keywords: Chemical reaction optimisation (CRO), Particle swarm optimisation (PSO), Governor dead band (GDB), Generation rate constraints (GRC)

© 2010-2012 IEEE. Real-Time charging strategies, in the context of vehicle to grid technology, are needed to enable the use of electric vehicle fleets batteries to provide ancillary services. In this paper, we develop tools to manage charging and discharging in a fleet to track an automatic generation control signal when aggregated. We propose a real-Time controller that considers bidirectional charging efficiency and extend it to study the effect of looking ahead when implementing model predictive control. Simulations show that the controller improves tracking error as compared with benchmark scheduling algorithms, as well as regulation capacity and battery cycling.

This paper proposes a lifelong learning (LL) based complementary generation control (CGC) of interconnected power grids with high-penetration renewable energy sources and electric vehicles (EVs). The wind farms (WFs), photovoltaic stations (PVs), and EVs are aggregated as a wide-area virtual power plant (WVPP) for automatic generation control (AGC), which can significantly accelerate the system response and reduce the regulation costs of balancing unexpected power mismatches between generation side and demand side. Under such framework, CGC is decomposed into a multi-layer generation command dispatch to rapidly compute an optimal solution. LL is first employed for the primary layer CGC between conventional power plants and a WVPP. Then, the secondary layer is implemented according to the ascending order of regulation costs of all reserve sources. Finally, the tertiary layer is accomplished by a coordinated control in each WFs or PVs, whereas an online optimization of EVs is adopted by considering the charging demands. The imitation learning is introduced to improve the learning efficiency of agents with transfer learning, thus an online optimization of CGC can be satisfied. Case studies are carried out to evaluate the performance of LL for multi-layer CGC of AGC on a practical Hainan power grid of southern China.

This paper illustrates the potential of commercial buildings to act as frequency reserves providers through an experimental demonstration conducted in a multi-zone university building. The proposed control methodology is presented in detail, including the control architecture, the controller design, model identification, and hardware description. Finally, the effectiveness of the presented approach is tested by means of simulations and experiments in a controlled environment.

With the increasing penetration of renewables in power system, frequency regulation is becoming a big challenge for conventional coal-fueled thermal generator units. To address this issue, more stringent criteria of automatic generation control (AGC) has been established by grid operators to regulate the generators. However, old-fashioned units can hardly meet the requirements and thus are subject to penalty. In this paper, an approach of using battery energy storage systems (BESS) for coordinated frequency regulation is proposed to improve the AGC performance of such generators. A demonstration project with a 2 MW BESS has been commissioned at Shijingshan Thermal Power Plant, Beijing. The basic principles, coordinated control strategy, technical performance as well as economic benefits have been presented and validated with field test results. As a pioneer of its kind in China, this project offers an excellent demo of utilizing BESS for improving AGC performance under the context of high renewable integration.

The design of frequency regulation services plays a vital role in automation and eventually reliable operation of power system at a satisfactory and stable level. Frequency response capability offered by wind plant is not same as the primary control capability of conventional plants, hence the integration of wind energy based generation at large scale has widespread impacts on power system stability and reliability. With the changing generation mix, modern electric power systems are facing a critical challenge in the real‐time balancing of demand and supply. This paper comprehensively reviews the various control functionalities available in wind energy systems for supporting frequency regulation at different levels of frequency control services starting from inertial control to the secondary control. An insight to new research challenges for better frequency control ancillary services in wind integrated system is also provided. Though wind-based ancillary services are still in research and development stage in most of the countries, future wind energy system participation is expected to contribute to enhanced market efficiency, improved system reliability and macro economic benefits to all stakeholders.

In this paper, we propose a novel control strategy for doubly fed wind turbine generators (DFWTG) in complex power systems to improve the primary frequency response and enhance low-frequency oscillation (LFO) damping of power systems. The main innovation in the new control scheme dwells in the novel control schemes for rotor side controller (RSC) of DFWTG. Weighted frequency deviations of local synchronous generator (SG) bus bars are utilized as input signals to a dedicated power system stabilizer, specifically designed for the RSC of DFWTG, with parameters optimized by particle swarm optimization. The newly devised RSC with conventional DFWTG control structure is capable of ameliorating primary frequency response of the power system. To eliminate the area control error, a secondary control scheme is incorporated, which makes use of the spinning reserve of selected SGs through automatic generation control. Tie-line power deviations are employed as control signals in both primary and secondary control schemes, on the purpose of further enhancing the primary and secondary frequency regulations and also maintaining the obligation of power transmissions among adjoining areas. Simulation results demonstrate the superiority of the proposed DFWTG control methods in enhancing primary frequency response and also suppressing LFOs of the power system over the conventional strategy.

This paper describes the demonstration and evaluation of stacking wind power time-shifting and regulation by a 1 MW/2 MWh battery energy storage system (BESS) within a 10 MW wind park. The BESS time-shifted 15.8 MWh of wind power to peak periods while responding to the automatic generation control (AGC) signals that provide regulation over the 24 day test period. Its fast response time and accurate output allowed the BESS to provide both services simultaneously. Limiting the charging to periods when wind power was available lowered the amount of time-shifting and AGC that could be provided. Some form of AGC was delivered from the BESS for 78.5% of the test period and, if it were remunerated at the PJM rate when the test was performed, it would have earned 8627 USD, with 97% from regulation and the rest from time-shifting wind power. Using PJM's performance template, the BESS averaged a performance score of 70.5% during hours when regulation was performed. This work adds to the growing empirical data that is informing models of and policy for grid storage.

This paper presents a new dynamic model and control approach for static synchronous series compensator (SSSC) to participate effectively in automatic generation control (AGC) of an interconnected deregulated power system. In doing so, a new mathematical formulation is extracted to present the participation of the SSSC in the tie-line power flow exchange. Besides, fractional order controllers (FOCs) are employed to design an effective SSSC damping controller. The effectiveness of the proposed SSSC-based damping controller in preparing an efficient AGC ancillary service is compared with its earlier model. Two heuristic algorithms of improved particle swarm optimization (IPSO) and modified group search optimization (MGSO) are compared to optimize the controller parameters. To achieve realistic results under a competitive scenario, a diverse-GENCOs multi-DISCOs power system with the physical nonlinear constraints, bilateral contracts, and pool-co transactions are taken into consideration, simultaneously. Dynamic simulation results reveal that the proposed FOC-based SSSC damping controller is superior to the earlier one to improve the restructured AGC performance. Comprehensive examinations are carried out under the un-contracted step, higher degree step, and random load demands which act as contract violation scenarios to validate the damping performance of the proposed controller. To demonstrate the robustness of the proposed control approach, sensitivity analysis is accomplished in a wide range of loading condition and system parameters.

This paper demonstrates the operation of a 1 MW/2 MWh grid-tied battery energy storage system (BESS) in a 10 MW wind R&D park for Automatic Generation Control (AGC) for 29 days. The efficiency and utilization of the BESS in the context of regulation and grid integration are examined. The response time for the BESS is as low as one second, which is faster than the current accepted practice of a conventional generator with governor control. Using PJM's performance template gives an average performance score of 93% while the storage is providing AGC. However, because the storage system only charges when there is sufficient wind energy and spent significant time in maintenance mode, the 29-day performance average is only 65%. The battery was able to carry out some mode of AGC for 64% of the test period. When energy costs and battery degradation are considered, utilizing the battery costs USD 19,000 over the 29-day period, whereas the potential income from AGC, charging only with wind power, was USD 9,037. This 29-day demonstration shows that batteries have fast response and can perform AGC, but within a wind farm AGC is unlikely to be suitable without changes in the tariff schemes.

This paper presents a detailed literature review on the issues and technical difficulties encountered in integrating large-scale photovoltaic (PV) systems to the grid and the role of generation dispatch in solving such challenges. It is observed that although there is a widespread adoption of stability (voltage, frequency and angle) strategies in dealing with intermittent nature of PV systems, generation dispatch techniques can equally be employed to overcome the challenges posed by large-scale PV systems. Hydro-electric generators equipped with automatic generation control and combustion turbines coupled to the grid are some of the best options in this regard. It is expected that utilities will not only fulfill their mandate of ensuring continuity of supply, but will also be able to cut costs through optimal generation dispatch. The greatest benefit is the self-reliance in terms of energy supply at all times, including night-time when ordinary photovoltaic systems are considered redundant.

In this paper, a risk-based security assessment methodology is presented, which allows the assessment of operational security of a power system’s future state under uncertainty deriving from varying topology scenarios (contingencies) and forecast errors (loads and renewable infeeds). The methodology models input uncertaintywith a copula function-based Monte–Carlo (MC) framework. Furthermore, it provides the highest level of accuracy on initiating causes of failures through an AC power flow (AC PF) framework. Finally, it achieves speed in solution by the combination of twomeasures of risk. A fast screening tool, based on severity functions, allows us to quickly screen the system for the most severe states. A detailed analysis tool, based on an AC optimal power flow (AC OPF) framework and the notion of lost load, provides additional valuable information, including remedial actions to steer away from severe system states. This paper presents results from the application of the methodology proving the necessity of such a framework. It is shown that not taking into account stochasticdependence through a proper MC setup seriously underestimates system risk and that an AC framework is needed, as voltage deviations are shown to often be initiators of system collapse.

The present aspect studies automatic generation control (AGC) of a multi-area multi-source test system with the effects of thyristor controlled series compensation (TCSC) and superconducting magnetic energy storage (SMES) units. The studied test system is a three-area power system model having thermal–thermal unit in area-1, thermal-hydro unit in area-2 and thermal-gas unit in area-3. The same is imposed by appropriate time delay, governor deadband, generation rate constraint nonlinearities. A nature-inspired optimization technique called moth-flame optimization algorithm is implemented for solving the constrained optimization parameters. The step load perturbation (SLP) and random SLP are used for the dynamic performance analysis. The designed controller based dynamic responses are also studied with wide perturbation in parametric values through robustness analysis. Analysis reveals that the proposed control strategy, considering SMES and TCSC units, improves the dynamical performances of system significantly in terms of settling time and overshoot against parametric uncertainties against a wide range of area load demands and disturbances even in the presence of system nonlinearities.

Till this moment, the model of interconnected power systems in the automatic generation control (AGC) loops relies only on the synchronous generating units. In today's world, a high level of penetration of renewable energy sources (RES) is integrated to the power grids. This paper presents a novel power system model, which includes both conventional generating units and RES for studying the AGC problem of such systems. The control strategy in the AGC loops is based on the proportional-integral-derivative (PID) controller, which is optimally designed by the whale optimization algorithm (WOA). It represents a great challenge to this controller to deal with the RES uncertainties. The effectiveness of the WOA-based PID controller is compared with other computation evolutionary algorithms-based PID controller. The system performance is evaluated under different operating conditions. For achieving a realistic study, 1) real wind speed data that extracted from Zafarana location in Egypt are used, 2) solar irradiation and temperature data that extracted from a field test are implemented, and 3) an irregular wave energy condition is applied. The validity of the control strategy is verified using the simulation results, which are carried out using MATLAB environment.

Summary A new algorithm called moth-flame optimization (MFO) algorithm is proposed to optimize a dual-mode controller (DMC) for a multiarea hybrid interconnected power system. Initially, a 2-area nonreheat system is considered. The optimum gains of DMC and proportional-integral controller are optimized using the MFO algorithm. The superiority of the proposed approach is established while comparing the results with genetic algorithm, bacterial forging optimization algorithm, differential evolution, and hybrid bacterial forging optimization algorithm particle swarm optimization for the same system. The proposed approach is further extended to 2 unequal areas of a 6-unit hybrid-sources interconnected power system. The optimum gain of DMC and sliding mode controller (SMC) is optimized with MFO algorithm. The performance of an MFO tuned DMC is compared with particle swarm optimization and genetic algorithm tuned DMC, MFO tuned SMC, and teaching-learning–based optimization optimized SMC for the same system. Furthermore, robustness analysis is performed by varying the system parameters from their nominal values. It is observed that the optimum gains obtained for nominal condition need not be reset for a wide variation in system parameters.

The use of synchrophasor networks consisting of phasor measurement units (PMUs) makes it possible to monitor, analyzes, and control the electric power grid in real-time. PMU measurements of frequencies, currents, voltages, and phase angles are transmitted to system control centers through synchrophasor networks. Delayed or missing measurements from PMUs in closed-loop applications could lead to power system instability. Although the use of virtual private network (VPN) tunnels eliminates many security vulnerabilities, VPNs are still vulnerable to denial of service (DoS) attack that exploits a side-channel vulnerability. In this paper, the authors detail their analysis of DoS attack on a two-area four machine power system with a utility-scale photovoltaic (PV) plant. Automatic generation control (AGC) is used to implement a tie-line bias control in one of the areas. The impact of PMU packet dropping on AGC operation and countermeasures are presented in this paper. Cellular computational network (CCN) prediction of PMU data is used to implement a virtual synchrophasor network (VSN). The data from VSN is used by the AGC when the PMU packets are disrupted by DoS attacks. Real-time experimental results show the CCN based VSN effectively inferred the missing data and mitigated the negative impacts of DoS attacks.

This article presents automatic generation control of an interconnected combined cycle gas turbine (CCGT) incorporated unequal area thermal system under restructured environment. Investigations have been carried out with two generating companies and two distribution companies in each area. Area 1 includes CCGT-thermal system and area 2 includes thermal–thermal system. Both areas are employed with appropriate generation rate constraint (GRC) and governor dead band in thermal systems. Integral minus proportional derivative with first-order filter effect (I-PDF) controller is used as secondary controller whose promising performance is compared with proportional, proportional integral and proportional integral derivative controller. The controller gains and other parameters are optimised using stochastic fractal search (SFS) algorithm. Sensitivity analysis is performed with change in system loading, system parameters such as inertia constant, governor and turbine time constant and random load disturbances to evaluate the robustness of proposed SFS optimised I-PDF controller. The simulation results with different set of contractual agreement postulated by DISCO participation matrix and its violation illustrates the effectiveness of SFS optimised I-PDF controller. Additionally, the system under restructured regime is investigated with combinatorial increment in GRC and delayed controller action for appropriate use and maintenance of the proposed controller.

This paper presents a risk-based approach for evaluating the participation strategy of a battery storage system in multiple markets. Simultaneous offering in day-ahead energy, spinning reserve, and regulation markets is considered in this paper. The uncertainties considered include predicted market prices as well as energy deployment in spinning reserve and regulation markets. A new nonprobabilistic model is introduced in this paper to handle the uncertain nature of spinning reserve and regulation markets. Robust optimization is implemented to model these uncertain parameters and manage their related risk. The proposed risk-based model is a max–min problem, which is converted to its equivalent ordinary maximization problem using duality theory. The presented model is linearized by implementing strong duality theory. Finally, the proposed method is tested and verified using an illustrative case study.

This paper is focused on design and application of proportional integral derivative (PID) controller for deregulated multiarea automatic generation control (AGC) scheme utilizing the imperialist competitive algorithm (ICA) under various electricity market transactions. A multiarea hydro thermal 75-bus real power system is considered as a test system for deregulated AGC scheme. Simulation study results demonstrate the effective performance of controller on various load disturbance scenarios. The performance of ICA-tuned PID controller is also compared with genetic algorithm (GA)-tuned PID controller. The comparative results show that ICA–PID controller has higher convergence rate, smaller settling time, reduced oscillations than GA-tuned PID controller, i.e. reaching to better solution. Robustness of ICA–PID controller is also checked to determine its response towards system parameters uncertainties through sensitivity analysis. It is seen that the performance of PID controller tuned by ICA algorit...

An energy storage resource (ESR) has outstanding ramping capability, but its limited energy disables the provision of regulation service around the clock. As a comparison, a conventional generator (CG) is not restricted by the released energy, but the ramp rate is limited. In this paper, a method is proposed to evaluate the effectiveness of ESRs providing frequency regulation service in a single-area system. We measure the performance of frequency regulation by the standard deviation of system frequency excursions, and define the regulation requirement of an isolated power system as the minimum regulation capacity which satisfies the desired regulation performance. By analyzing the regulation requirements under different combinations of regulation resources, we can quantitatively compare the effectiveness of CGs and ESRs. Case studies show that ESRs can reduce regulation requirements, indicating that they are more effective than CGs in frequency regulation. However, they become less effective and even outperformed by CGs when they constitute a larger portion of the system regulation capacity.

The paper deals with the discrete data automatic generation control (AGC) of two area hydrothermal power system operating at different loading conditions. The power system load varies considerably throughout the day and accordingly, thermal and hydropower systems are scheduled to operate at different loading conditions. It is found that steam as well as hydro turbine time constants vary as the power system loading varies. In earlier AGC studies, these parameters are considered to be constant, irrespective of power system loadings. This paper studies the dynamic performance of hydrothermal power system considering the variation of these turbine time constants along with the nominal loading of power system. The studies have been conducted for linear as well as nonlinear models of speed governor and hydroturbine models recommended by the IEEE committee. It is also discovered that conventional empirical formula-based hydraulic governor settings recommended by the IEEE working group are completely unacceptable for hydrothermal power system. A maiden attempt is made to optimize the hydraulic governor settings simultaneously along with the controller gains using metaheuristic algorithm. The comparison of system dynamic responses reveal that optimization-based hydrogovernor settings give the better dynamic performance in case of linear as well as nonlinear models of hydro system, and therefore are strongly recommended over the conventional settings. The paper also deals with the optimum selection of sampling periods for discrete data AGC operation of hydrothermal systems.

This paper describes an integrated operational simulation tool that combines various stochastic unit commitment and economic dispatch models together that consider stochastic loads and variable generation at multiple operational timescales. The tool includes four distinct configurable sub-models within: day-ahead security-constrained unit commitment (SCUC), real-time SCUC, real-time security-constrained economic dispatch (SCED), and automatic generation control (AGC). The unit commitment and dispatch sub-models within can be configured to meet multiple load and variable generation (VG) scenarios with configurable first stage and second-stage decisions determined where first-stage decisions are passed on and second-stage decisions are later determined by other sub-models in a continuous manner. The progressive hedging algorithm (PHA) is applied to solve the stochastic models to maintain the computational tractability of the proposed models. Comparative case studies, considering various configurations of stochastic and deterministic sub-models are conducted in low wind and high wind penetration scenarios to highlight the advantages of the stochastic programming during different decision-making processes. The effectiveness of the proposed method is evaluated with sensitivity tests using both economic and short-term reliability metrics to provide a broader view of its impact at different timescales and decision-making processes.

In present aspect, automatic generation control (AGC) of deregulated power system with thyristor controlled series compensator (TCSC) device is investigated. The objective is to discuss bilateral power transaction issue with the TCSC effect. A deregulated two-area power system model having two thermal units in each control area is considered for this act. A quasi-oppositional harmony search (QOHS) algorithm is being applied for the constrained optimization problem. Three cases, commonly studied in deregulation, are discussed for the effectiveness of the proposed technique. Further, sensitivity analysis is studied by varying the test system parameters up to ±25% from their rated values. The obtained simulation plots are analytically discussed with the calculation of oscillatory modes, transient details and the studied performance indices. Sugeno fuzzy logic control technique is also investigated to the studied test system. The simulation results show that the proposed QOHS based TCSC controller is quite effective in deregulated environment.

This paper proposes to use a hybrid Stochastic Fractal Search (SFS) and Local Unimodal Sampling (LUS) based multistage Proportional Integral Derivative (PID) controller consisting of Proportional Derivative controller with derivative Filter (PDF) plus (1 + Proportional Integral) for Automatic Generation Control (AGC) of power systems. Initially, a single area multi-source power system consisting of thermal hydro and gas power plants is considered and parameters of Integral (I) controller is optimized by Stochastic Fractal Search (SFS) algorithm. The superiority of SFS algorithm over some recently proposed approaches such as optimal control, Differential Evolution (DE) and Teaching Learning Based Optimization (TLBO) is demonstrated. To improve the system performance further, LUS is subsequently employed. The study is further extended for different controllers like PID, and proposed multistage PID controller and the superiority of multistage PID controller over conventional PID controller structure is demonstrated. The study is further extended to a two-area six unit multi-source interconnected power system and the superiority of proposed approach over, TLBO and optimal control is demonstrated. Finally the study is extended to a three unequal area system power system with appropriate nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB) and time delay. From the analysis, it is found that hybrid SFS–LUS algorithm is superior to the original SFS algorithm and substantial improvement in system performance are realized with proposed multistage PID controller over conventional PID controller structure.

This paper emphasizes the development of control strategy for inter-area oscillation suppression for a unified two-area hydro–thermal deregulated power system. A proportional derivative-type fuzzy logic controller with integral (PDFLC+I) controller was proposed for automatic generation control. Further comparisons among conventional integral controller, proportional integral derivative controller, and PDFLC+I are carried out, where the PDFLC+I controller is optimized by four different optimization techniques namely, algorithm, ant colony optimization, classical particle swarm optimization, and adaptive particle swarm optimization. In PDFLC+I controller optimization, scaling parameters of controllers are optimized. A comparative study shows that the proposed PDFLC+I controller has a better dynamic response following a step load change for the combination of PoolCo and bilateral contract-type transaction in deregulated environment. Proposed controller performance has also been examined for ±30% vari...

This paper proposes a decentralized Second Order Sliding Mode (SOSM) control strategy for Automatic Generation Control (AGC) in power networks, where frequency regulation is achieved, and power flows are controlled towards their desired values. This work considers a power network partitioned into control areas, where each area is modelled by an equivalent generator including second order turbine-governor dynamics, and where the areas are nonlinearly coupled through the power flows. Asymptotic convergence to the desired state is established by constraining the state of the power network on a suitable sliding manifold. This is designed relying on stability considerations made on the basis of an incremental energy (storage) function. Simulation results confirm the effectiveness of the proposed control approach.

This paper addresses the concept of a bias injection cyber-attack on the load frequency control loop of a single-area power plant. The system evolves in the discrete-time domain and a convex and compact set of polyhedral state constraints represents a valid domain of safe operation under the effect of a stabilizing output-feedback controller. Whenever the safety constraints are violated, an alarm alerts the control center to a security breach. An attacker gains access to frequency sensor measurements and corrupts the data transferred to the automatic generation control unit. The objective of the adversary is to drive the electrical frequency to a safety-critical steady-state value without triggering an alarm. An analysis based on robust invariance concepts assesses the vulnerability of the system on this particular scenario and determines the maximal impact that such an attack can have without activating an alarm. Simulation studies highlight the effect of the cyber-attack on the physical plant.

In the power demand side, responsive loads can provide fast regulation and ancillary services as reserve capacities in interconnected power systems. This paper presents a distributed pinning demand side control (DSC) strategy for coordinating multiple load aggregators, i.e., aggregated responsive loads, to provide frequency regulation services. Specifically, a leader-following communication protocol is considered for the load aggregators in which there is a centralized pinner (leader) and multiple load aggregators (followers). The regulation objective is generated from the pinner and only shared with a small fraction of load aggregators. Moreover, a multi-step algorithm is proposed to determine the control gains in the DSC, which not only guarantees the stability of the close-loop system, but also restrains the plant disturbance. Furthermore, the distributed pinning DSC algorithm is integrated into the traditional centralized proportional-integral-based automatic generation control (AGC) framework, which has formed the coupled secondary frequency control structure. It has been shown that the total power mismatch in each control area is shared with both AGC units and load aggregators, and the system frequency can be improved by considering the distributed pinning DSC for load aggregators. Finally, simulation results are provided to demonstrate the effectiveness of the proposed coupled frequency control strategy.

With high renewable power penetration, uncertainty and intermittence of renewable sources become major concerns of power system planning and operation. Large and fast wind speed change may cause great variation of active power generation, which may lead to system stability and reliability problems. The response speeds of the committed conventional generators (CGs) for frequency regulations are critical for system reliable and stable operation. The slow response of the committed CGs may result in power shortage or surplus, which may affect system frequency. This paper proposes a technique to evaluate operational reliability and efficiency problems of power systems with high wind power penetration from frequency aspect. Energy unnecessarily consumed and less supplied during system frequency control processes are modeled in detail. The reliability and economic indexes are formulated with considering system dynamic frequency control processes. The IEEE-RTS79 is used to verify the proposed models and method.

This study proposes an artificial emotional reinforcement learning (ERL) controller for automatic generation control (AGC) of large-scale interconnected power grids. In the scheme of ERL, the agent consists of two parts, a mechanical logical part and a humanistic emotional part, which essentially develop the control strategies of the agent. These two parts in proposed controller are introduced by reinforcement learning (RL) and artificial emotion (AE), respectively. The ERL controller can generate different control strategies depending on the operating scenarios, by highly integrating AE functions, which are quadratic function, exponential function, and linear function, respectively, with the elements of RL, such as action, learning rate, and reward function. The effectiveness of ERL controller with nine control strategies has been demonstrated considering AGC on a two-area load frequency control power system and China Southern Power Grid (CSG) power system. Results of simulation show the superior performance of ERL over that of proportional-integral control and four RL techniques.

This study proposes a new distributed networked control (DNC) scheme and its stability analysis framework for automatic generation control in networked interconnected power systems with participation of wind turbine. It is assumed that the remote control signals are measured at locations away from the control site and exchanged among a non-ideal communication network with both time-varying delays and random packet dropouts. First, a model is proposed for large-scale DNC system consisting of subsystems, in which the states of each subsystem have their own time-varying delay and there are also delay and packet dropouts in their interconnection links. Then, a linear matrix inequality (LMI)-based method is proposed to design the distributed controller for better system performance. For this, a new Lyapunov–Krasovskii function is developed to conclude some LMI-based delay-independent theorem for designing control law. To evaluate the proposed method, a multi-area power system with participation of wind turbine is considered. Simulation results show the capability of the proposed approach to enhance the performance of networked power system in the presence of load perturbation among a non-ideal communication network with both time-varying delays and random packet dropouts.

Controllers in some power system problems are required to satisfy different performance objectives, which could be conflicting with one another. Therefore in the process of their gain tuning, when the problem is formulated within an optimization framework, it becomes necessary to achieve multiple objectives with a method multi-objective optimization method. This work presents a new method of multi-objective optimization method to optimize several controller parameters. The problem deals with optimization of controllers of doubly fed induction generator modeled for frequency regulation in an interconnected two-area wind power integrated thermal power system. The gains of integral controller of automatic generation control loop and the proportional and derivative controllers of doubly fed induction generator inertial control loop are optimized in a coordinated manner by employing a multi-objective non-dominated sorting based Cuckoo search algorithm. The algorithm is formed by synthesizing the parallel searching abilities of Cuckoo search algorithm (CSA) with the non dominated sorting methodology adopted in Non dominated sorting genetic algorithm (NSGA-II). Based on the set of selected instances, the algorithm termed as non dominated sorting Cuckoo search (NSCS), exhibits better efficiency of optimization compared to the NSGA-II, CSA, genetic algorithm, and particle swarm optimization. The performance of the designed controller is further compared with the performance obtained with a modified version of NSCS, which includes the method of archiving in it. The designed set of controllers perform robustly even with the variation in disturbances, parameter and operating conditions in the system.

This study investigates the dynamic participation of wind power plants (WPPs) in the automatic generation control (AGC) task. The pre-specified model of wind farm in the AGC studies has been used. It is shown that operating of WPPs at the command mode results in a significant improvement in the frequency behaviour of power system due to their faster response. However, the WPPs may change their operation mode from the command mode to the maximum power point mode according to the wind speed conditions and load variations. This reduces the improvement in the frequency response. In this condition, the shortage in the wind power should be compensated by the conventional units. Thus, the share of different units becomes uncertain (but bounded) when the WPPs participate in the AGC task. To address this challenge, this study provides the robust stable frequency operation of power system under uncertain shares of different units in the AGC task using the Edge theorem. Simulations are performed on an IEEE 39-bus test system connected to some wind farms in different buses. Results confirm the robust stability of the underlying power system, and hence, the effective capability of the wind farms to participate in the AGC task.

This work presents the performance evaluation of different commonly used objective functions in load frequency control (LFC)/automatic generation control (AGC) of single/multi-area non-reheat thermal power systems. The commonly used objective functions in LFC/AGC of power system are integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The objective functions are used to tune proportional–integral–derivative (PID) controller values in single-area, two-area, three-area and four-area power systems with one percent Step Load Perturbation (1% SLP) in area 1. The gain values of proportional controller gain $$({K}_{\mathrm{p}})$$ , integral controller gain $$({K}_{\mathrm{i}})$$ and derivative controller gain $$({K}_{\mathrm{d}})$$ values are tuned by using artificial intelligence (AI)-based ant colony optimization (ACO) technique with aforementioned different objective functions. The cumulative performance of the investigated power systems with different objective function-based ACO-PID controller response reveals that the objective function performance is varied with the increase in the power system size. The performance of power systems is measured by considering time domain specification analysis, namely the settling time, undershoot and peak overshoot. The results established that the objective functions performance is diverse based on the power system size. In addition, the ITSE-based PID controller response guarantees minimum peak undershoot in all power system’s responses compared to ISE-, ITAE- and IAE-based controller response.

Redox flow batteries (RFB) showcase extremely long charge-discharge life cycle and outstanding quick response to alleviate the oscillations under sudden disturbances in power system. The assimilation of energy storage units in power system can curtail the oscillations energetically by contributing fast active power compensation. Hence, in this paper, RFB are tried to integrate into multi-source power systems and their efficacy in boosting automatic generation control (AGC) performance is executed. A new fractional order fuzzy PID (FOFPID) controller is employed to enhance the system performance. FOFPID controller parameters are optimized maidenly by utilizing imperialist competitive algorithm (ICA). The controller is designed and implemented on more realistic single/two-area multi-source hydrothermal gas system with/without RFB. The potency of FOFPID is established by contrasting its responses with optimal and lately developed hSFS-PS, DE, TLBO and IPSO optimized controllers. To check the efficacy of the approach, the study is further conducted under the presence of important non-linearites like GDB and GRC. Analysis of results reveals that the advocated approach with/without RFB improves the system performance significantly in comparison to the prevalent methods. The robustness of the method is demonstrated against wide variations in system parameters under the presence/absence of RFB/GDB-GRC and at higher and variable step load demands.

The electrical energy system size is increasing and getting complex day by day with rising trends to integrate renewable energy sources like wind power in order to deliver the economic energy to the modern and developing society. However, the rising trends of wind energy generation and involvement raises serious concern for matching the generations with load demand and hence to maintain the standards of automatic generation control (AGC). Therefore, the power system needs robust design techniques for such type of complex system. This paper discusses the robust methodology for AGC of conventional system with sufficient contribution from doubly fed induction generator (DFIG) in control areas. The robust AGC are applied on the above said complex system and the application results are compared with control design based on optimal theory in terms of system stability and the achieved responses to validate the influence of the proposed technique.

This paper describes the integrated grid modeling system (IGMS), a novel electric power system modeling platform for integrated transmission-distribution analysis that co-simulates off-the-shelf tools on high performance computing platforms to offer unprecedented resolution from independent system operator (ISO) markets down to appliances and other end uses. Specifically, the system simultaneously models hundreds or thousands of distribution systems in co-simulation with detailed ISO markets and automatic generator control-level reserve deployment. IGMS uses a new message passing interface-based hierarchical co-simulation framework to connect existing sub-domain models. Our initial efforts integrate open-source tools for wholesale markets, bulk ac power flow, and full-featured distribution systems including physics-based end-use and distributed generation models (many instances of GridLAB-D). The modular IGMS framework enables tool substitution and additions for multi-domain analyses. This paper describes the IGMS tool, characterizes its performance, and demonstrates the impacts of the coupled simulations for analyzing high-penetration solar photovoltaic and price responsive load scenarios.

This article presents automatic generation control (AGC) of an interconnected two-area hybrid thermal system with additional power generation from dish-Stirling solar thermal system (DSTS) and wind turbine system (WTS). Each area is equipped with Integral (I), Proportional-Integral (PI), and Proportional-Integral-Derivative (PID) as secondary controllers to regulate power output of thermal system and maintain the power balance. This facilitates maintaining the frequency close to its nominal value. Biogeography-based optimization (BBO) technique is used for simultaneous optimization of controller parameters. System dynamics are evaluated with step load perturbation and random load perturbation in Area1. The performance of PID is found improved in comparison to I and PI controller in terms of settling time, peak deviation and magnitude of oscillation. Investigation shows that BBO optimized PID controller parameters obtained at nominal conditions are robust enough and not necessary to optimize for wide changes in renewable energy sources, area capacity ratio, system loading, and inertia constant (H). Thus, integration of DSTS and WTS for AGC of interconnected system is safely attributed.

Quickly getting back the synchronism of a disturbed interconnected multi area power system due to variations in loading condition is recognized as prominent issue related to automatic generation control (AGC). In this regard, AGC system based on fuzzy logic, i.e., so-called FLAGC can introduce an effectual performance to suppress the dynamic oscillations of tie-line power exchanges and frequency in multi-area interconnected power system. Apart from that, simultaneous coordination scheme based on particle swarm optimization (PSO) along with real coded genetic algorithm (RCGA) is suggested to coordinate FLAGCs of the all areas. To clarify the high efficiency of aforementioned strategy, two different interconnected multi area power systems, i.e., three-area hydro-thermal power system and five-area thermal power system have been taken into account for relevant studies. The potency of this strategy has been thoroughly dealt with by considering the step load perturbation (SLP) in both the under study power systems. To sum up, the simulation results have plainly revealed dynamic performance of FLAGC as compared with conventional AGC (CAGC) in each power system in order to damp out the power system oscillations.

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming U+0028 CCSNLP U+0029, and then transformed into a deterministic nonlinear programming U+0028 NLP U+0029. To tackle this NLP problem, a three-stage framework consists of particle swarm optimization U+0028 PSO U+0029, sequential quadratic programming U+0028 SQP U+0029 and Monte Carlo simulation U+0028 MCS U+0029 is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach.

The area-level coordination of regulation capacity by automatic generation control (AGC) is an effective approach to mitigate wind power fluctuations in interconnected power system with high wind penetration. However, the applicability of this method is restricted by the existing AGC strategy. For this reason, power systems in China are experiencing serious wind curtailments to meet the power balance. In this study, a distributed coordination AGC strategy based on regulation marginal cost (RMC) is proposed for coordinating regulation capacity in different areas to remove frequency deviations. Detailed regulating characteristics of the AGC strategy in the existing power systems are discussed. Based on the above analysis, a distributed coordination RMC controller is designed to meet the proposed multi-area coordination rule using a partial primal-dual sub-gradient algorithm. In this proposed controller, the coordination signal containing RMC is exchanged with adjacent controllers through communication networks. The simulation results prove that the distributed coordination RMC strategy can economically coordinate the regulation capacity of an interconnected power system and satisfy the frequency control performance standard.

A Modified Grey Wolf Optimization (MGWO) based cascade PI-PD controller is suggested in this paper for Automatic Generation Control (AGC) of power systems in presence of Plug in Electric Vehicles (PEV). The modification in original Grey Wolf Optimization (GWO) algorithm is introduced by strategy which maintains a proper balance between exploration and exploitation stages of the algorithm and gives more importance to the fittest wolves to find the new position of grey wolves during the iterations. Proposed algorithm is first tested using four bench mark test functions and compared with original GWO, Differential Evolution (DE), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO) to show its superiority. The proposed technique is then used to tune various conventional controllers in a single area three-unit power system consisting of thermal hydro and gas power plants for AGC. The superiority of proposed MGWO algorithm over some recently proposed approaches has been demonstrated. In the next step, different controllers like PI, PID, and cascaded PI-PD controller are taken and Plug in Electric Vehicles (PEVs) are assumed. The proposed approach is also extended to a two-area six-unit power system. Lastly, a five unequal area nonlinear power system with PEVs and dissimilar cascade PI-PD controller in each area is considered and proposed MGWO technique is employed to optimize the controller parameters in presence of nonlinearities like rate constraint of units, dead zone of governor and communication delay. It is observed that PEVs contribute in the AGC to control system frequency.

This article presents a hybrid PSO with Levy flight algorithm (LFPSO) for optimization of the PID controllers and employed in automatic generation control (AGC) of nonlinear power system. The superiority of the proposed LFPSO approach has been demonstrated with comparing to recently published Lozi map-based chaotic optimization algorithm (LCOA) and Particle swarm optimization to solve load-frequency control (LFC) problem. It is found that the proposed LFPSO method has robust dynamic behavior in terms of settling times, overshoots and undershoots by varying the system parameters and loading conditions from their nominal values as well as size and locations of disturbance. Secondly, a three-area thermal power system is considered with nonlinear as Generation Rate Constraints (GRC) and outperforms to the results of Bacteria Foraging algorithm based integral controller as well as hybrid Differential Evolution and Particle Swarm Optimization based fuzzy PID controller for the similar power system. Finally, the proficiency of the proposed controller is also verified by random load patterns.