Tie-line Power Flow Research Articles

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals.

Enhancing Load Frequency Control of Interconnected Power System Using Hybrid PSO-AHA Optimizer

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

A techno-economic framework for optimizing multi-area power dispatch in microgrids with tie-line constraints

Fuel-fired power plants are the primary electricity sources in Saudi Arabia. This investigation aims to reduce the overall costs and emissions of electricity production from thermal units operated by the Shuqaiq Water and Electricity Company. It also considers practical thermoelectric constraints, such as power losses, ramp-rate constraints, and environmental optimization functions, to align with the sustainability goals of Saudi Vision 2030. Optimizing electric energy delivery is crucial within an energy control center. The focus of this work is to develop an advanced computational technique that reduces reliance on fossil fuels and minimizes the production cost of electricity, while also considering environmental emissions as an optimization function. An examination was conducted through two distinct practical case studies with power demand of 850 MW—one involving renewable sources and the other without—to assess the effectiveness of the hybrid crow search and JAYA optimization algorithm in optimizing performance. This system includes a multi-area setup with limitations on the tie-line power flow constraints. Additionally, the study also involved RETScreen modelling to assess the practical financial, emission, and benchmark analysis of a proposed integrated solar plant. This analyses offers a thorough assessment of the facility’s performance and reliability in practical situations. This study serves as a motivating factor for energy operators, stakeholders, governmental organizations, and grid administrators to invest resources in environmentally-friendly initiatives. The simulation results showed that the proposed approach successfully reduced the electricity cost by 2.29% and 3.70% in hybrid energy networks while effectively handling the system constraints. In addition, the analysis revealed that the optimization algorithm effectively decreased toxic emissions from power plants while also supporting the sustainability objectives of United Nations SDG-7 and Saudi Vision 2030 through the successful integration of renewable sources.

Frequency regulation in interconnected power system with DFIG participation under ABT-based various operating strategies

The implementation of various operating strategies based on the Availability Based Tariff (ABT) in a two-area interconnected power system with thermal and DFIGs in each area for frequency regulation is investigated in this paper. A suitable Generation Control Error (GCE) signal is needed to regulate frequency and tie-line power flow in an ABT-based operating strategy. This signal combines marginal generation cost and frequency-dependent unscheduled generation cost. A GCE signal generated in succession by three distinct operating strategies controls all thermal generators. To determine the best ABT-based operating strategy for the system under consideration, a simulation study is performed to compare and analyse the corresponding system responses. The operating strategy that utilises GCE, which is the difference between unscheduled interchange charges and marginal cost from their reference values, performed better than the other frequency regulation operating strategies. Furthermore, the simulation study compares traditional and identified ABT-based better control strategies.

Optimal AGC of a power system incorporated BESS-EHVAC/DC link using evolutionary techniques

This research paper reveals the design of controller with optimization techniques for automatic generation control (AGC) in multi-area power system incorporated with diverse energy sources. The diverse sources are conventional thermal, hydro and gas power station (THG). The frequency deviations should be remains constant in modern power systems. Optimization techniques used for proposed model for proportional integral (PI) controller which is tuned for AGC with diverse energy sources. The battery energy storage system (BESS) and extra high voltage alternating current/ direct current (EHVAC/DC) link investigated with proposed model. The proposed model has been tested with 1% step load perturbation (SLP) and MATLAB/Simulink verified their results. All the simulation results justified the area control error (ACE) with frequency deviation & tie-line power flow for area-1 and area-2. The comparative study done for PI controller with GA & PSO in the proposed model. It has shown its efficacy with proposed model including BESS and EHVAC/DC- link using GA and PSO.

A New Soft Computing Fuzzy Logic Frequency Regulation Scheme for Two Area Hybrid Power Systems

Modern renewable energy power system designs provide significant application benefits, but they also produce losses. The total generation, total load demand, and system losses must be balanced in order for this structured power system to operate reliably. The actual and reactive power balances are disturbed as a result of changes in load demand. System frequency and tie line interchange power deviate from their planned values as a result of this. A high system frequency deviation can cause the system to crash. In that case, multiple connect area systems use intelligent load frequency control techniques to deliver dependable and high-quality frequency and tie line power flow. Here, a standalone hybrid power system is taken into consideration, with generated power and frequency being controlled intelligently. In addition to the unpredictable nature of the wind, frequent adjustments in the load profile can produce sizeable and detrimental power variations. The output power of such renewable sources may fluctuate to the point that it causes significant frequency and voltage changes in the grid. An intelligent approach recently proposed to address the load frequency control (LFC) issue of an interconnected power system is known as fuzzy logic PID controller (FLPIDC). Standard proportional integral derivative (PID) controllers are used to control each section of the system.

A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow. The purpose of this paper is to demonstrate how optimising LFC in a two-area interconnected energy system with hydro, thermal plants, and a particle swarm optimization (PSO) method may improve power system stability and save revenue on power generation. A standard (PID) controller is used to control the system. The PSO optimization approach is utilised to determine the optimal gain values of the controllers kp, ki, and kd.

Multi-Area Load Frequency Control in Power System Integrated With Wind Farms Using Fuzzy Generalized Predictive Control Method

Frequency security is critical for the power systems’ stability and reliability. The integration of renewable energy resources brings new challenges to the frequency security of power systems. To provide a more robust control method for controlling the frequency and tie-line power flow of an interconnected power system integrated with wind farms (IPSWF), a load frequency control (LFC) controller of generalized predictive control (GPC) based on the Takagi-Sugeno (T–S) fuzzy model (fuzzy-GPC) is proposed in this paper. First, the T–S fuzzy model of the IPSWF is constructed. Then, the GPC is used to design the controller. Lastly, to validate the proposed scheme, an LFC strategy is established for a two-area interconnected power system integrated with a wind farm in power systems computer aided design (PSCAD). The control performance of proportional-integral, GPC, and fuzzy-GPC controller are compared under two faulty operating conditions. The simulation results show that the performance indicators and the dynamic behavior when the fuzzy-GPC controller is used are better than the performance when the PI controller and GPC controller are used. The proposed fuzzy-GPC used in the LFC of the IPSWF can regulate the frequency deviation and tie-line power deviation adaptively and achieve minimum frequency deviation and tie-line power deviation in a multi-area IPSWF.

Automatic Generation Control of Simplified Egyptian Power System Using Fractional Order PI Controller Based on PSO

In this Paper, Fraction Order (FO) PI controller is tested in order to find the optimized gains for Automatic Generation Control (AGC) controller by Particle Swarm Optimization (PSO) algorithm to represent the Simplified Egyptian Power System (SEPS) to achieve the development of power grid for the sustainable growth of Egypt. The mission of AGC is to return primary frequency regulation capability, bring back the frequency to its predefined set point in addition to reduce power fluctuation due to unplanned tie-line power flows among nearby control zones. The suggested controller is built using actual statistical records of SEPS for minimum and maximum loading situations of the SEPS in Winter and Summer of 2019-2020. The strength of the proposed controller is illustrated by implement the suggested controller and verify the outcomes of trip of the biggest generation unit in this Simplified Egyptian Power System (SEPS) on the grid frequency. The gains of fractional order FO-PI controller parameters such as proportional, integral, order of integrator (λ) are elevated by different Particle Swarm Optimization (PSO) and compared with another conventional supplementary Proportional Integral (PI) based on PSO also. The results display that the suggested FO-PI controller-built on PSO provides finest dynamic performance for a step load variation. The used software for gaining the results is MATLAB-Simulink.

A novel primary frequency control framework for multi-area power systems containing battery energy storage systems

Power system frequency response analysis is generally conducted on a single-area power system model due to fast and simple calculations. However, to obtain more accurate results of the system frequency dynamics, a frequency response analysis should be conducted on a multi-area power system model. In a multi-area power system, the frequency during disturbances can manifest itself differently in certain areas in the initial moments following a disturbance. As a result, generating units located in the distant areas contribute to the primary frequency control (PFC) with a certain time delay due to wave propagation. To overcome this issue, this paper introduces a novel method for a PFC mechanism. The main goal is to allow generators in remote areas to start changing the output power earlier, much before the wave propagates to that area. To achieve the desired result, the control of battery energy storage systems (BESS) during participating in PFC is modified to follow the tie-line power flow changes, while turbine generators of synchronous generators receive the frequency signal depending on the disturbance location area. To demonstrate the effectiveness of the proposed method, the results are obtained by simulating a three-area power system model.

A REVIEW OF NON-CLASSICAL LOAD FREQUECNCY CONTROL (LFC) SCHEMESFOR MULTI-AREA INTERCONNECTED POWER SYSTEMS (MAIPS)

Load frequency control (LFC) is a critical factor in the design of an electrical power system. To obtain and sustain a steady state operation of a power system for the delivery of quality power, generated power must at all times equal the load demand plus the losses. To achieve this state a Load frequency control system also known as Automatic Generation Control system is employed in an interconnected power system. The load-frequency control (LFC) is used to maintain the balance between load and generation in each control area as well as maintain the tie-line power flow in a multi area interconnected power system (MAIPS). The main goal of LFC is to minimize the transient deviations and steady state error to zero in advance. In this paper, a literature review of some non-classical LFC schemes employed by researchers for multi area interconnected power system (MAIPS), using different techniques is presented, some advantage sand drawbacks of the reviewed schemes highlighted, comparison of schemes stated.

Distributed optimal dispatching method for smart distribution network considering effective interaction of source-network-load-storage flexible resources

Smart distribution networks (SDNs) can integrate the flexible resources from source-network-load-storage (SNLS) to cope with the fluctuation due to a high proportion of distributed generation (DG). However, such SNLS resources are characterized by complex coupling relationships; their control authority may belong to different stakeholders. Challenged by the above, the laminar flow structure from the communication field is introduced for distributed optimal dispatching in SDNs. A day-ahead laminar dispatching method considering the effective interaction of SNLS resources is proposed. First, the applicability of the laminar flow structure is analyzed. An upper-layer dispatching model for the SDN and a lower-layer dispatching model for users with DG are established. Then, by introducing intermediate variables, the lower dispatching model is transformed into a quadratic programming problem and the upper dispatching model is transformed into a second-order cone relaxation programming problem. Selecting the tie-line power flow as the exchanged information in the boundary, the upper- and lower-layer models are alternately solved until the convergence criterion is met. Finally, an improved IEEE 33-bus system is experimentally analyzed. We find that the SNLS flexible resource dispatching scheme can be obtained with only a few iterations, and the DG consumption can be significantly improved.

Stabilization of frequency in Multi-Microgrid system using barnacle mating Optimizer-based cascade controllers

This paper presents a new control strategy in multi-microgrid system for supressing the variations of frequency and of tie-line power flow due to intermittent nature of renewable sources and frequently varying load demands. A novel cascade combination of fractional order-based integral derivative and proportional integral derivative with filter called CFOID-FOPIDN controller is utilized as secondary controller for automatic generation control. In this work, diverse sources such as photovoltaic, wind, diesel engine, fuel-cell, electrical vehicles and battery energy storage systems are considered for investigations under various uncertainties conditions. A newly developed barnacle mating optimizer is presented to tune the CFOID-FOPIDN for diminishing the frequency and tie-line power changes. Moreover, the amalgamation of redox flow batteries and modern flexible alternating current transmission system device like distributed power flow controller is integrated into the system for ameliorating the dynamic stability of the system. The tested outcomes show that the barnacle mating optimizer-based CFOID-FOPIDN controller with the combination of distributed power flow controller and redox flow batteries exhibits low fluctuation rate for frequency (5.6 s for area-1 and 5.5 s for area 2) and tie-line power (6.2 s). Finally, the robustness of the CFOID-FOPIDN is validated under three operating conditions, such as step load perturbations, change in wind speed, irradiance, and change in both wind and irradiance levels.

A robust controlling and management of load with minimum frequency and voltage deviation in network employing genetic algorithm

In this work optimal autonomous controlling of frequency and voltage deviation of the power system network has been presented. The frequency deviations and the voltage fluctuations are assessed with conventional and genetic algorithm method. Initial model of power system network has been developed which is based on Tie line power flow. There were several unknown parameters observed in the objective functions of conventional tie line power method. These unknown parameters were trained with genetic algorithm. The genetic algorithm consist of three major steps: reproduction, crossover, and mutations. The suitable value of frequency and voltage deviations are obtained for various loading conditions. But due loading conditions, high value of transient or peak overshoot and settling time were attained for frequency and voltage variations. It is observed that optimal minimum value of peak overshoot and settling time for frequency and voltage deviations are attained with genetic algorithm in comparison to conventional methods.

Load-Frequency Control of Three-Area Interconnected Power Systems with Renewable Energy Sources Using Novel PSO~PID-Like Fuzzy Logic Controllers

Modern era power systems may include not only traditional primary energy sources like hydro or thermal energy, but also a variety of Renewable Energy (RE) sources such as solar and/or wind power. This leads to the complexity of the electrical networks related to their design and construction as well as system stability and control issues. Considered to be one of the most crucial control issues, Load Frequency Control (LFC), must be continuously improved in order to ensure the control goals. For an interconnected power system, the control purposes are to maintain the net frequency at nominal value, e.g. 50 or 60Hz as well as to ensure that tie-line power flows are stable at scheduled values. This work proposes a novel LFC strategy applying Particle Swarm Optimization (PSO) ~ PID – like fuzzy logic–based controllers. PSO is one of the most effective optimization techniques. It is used to optimally determine four scaling factors for each LFC proposed in this study. A three-area power network consisting of a hydraulic station, a non-reheat plant, and a reheat unit along with RE sources such as wind and solar power are taken into consideration. The control performance of the proposed control strategy is compared to those of existing controllers, i.e. Genetic Algorithm (GA), Bacteria Foraging Optimization Algorithm (BFOA), Fractional Order-PID (FPID), and fuzzy logic-based PI controllers for the same interconnected power grid model with various case studies of load changes along with nonlinearities and different RE source conditions. Simulation results implemented in MATLAB/Simulink demonstrate the feasibility and applicability of the proposed control strategy.

An Adaptive Controller Design using Duelist Optimization Algorithm for an Interconnected Power System

A Controller is generally considered as a continuous and discrete mode of execution by a huge sample period that may result in degenerated dynamic performance or system instability. Nowadays, the maximum penetration in thermal, wind, hydropower systems has decreased the power system inertia that leads to rapid frequency response and higher frequency deviation followed by contingencies and requires rapid load frequency control. The goal of load frequency control (LFC) is to achieve zero steady-state errors in frequency deviations and minimize unscheduled tie-line power flows among the interconnected areas. The study of the literature reveals that a lot of research has been carried out in this area to achieve the desired objectives using different approaches. This manuscript proposes an optimization algorithm called Duelist Optimization Algorithm (DOA) in a three area interconnected power system consisting of thermal, wind, and hydro generating systems. The proposed system introduces an adaptive PID fuzzy controller whose parameters are optimized by the DOA algorithm. The Duelist Optimization algorithm is used to optimally tune the parameters of the controller in order to keep the system frequency deviation within the threshold limit, and maintain the power balance among the control areas during load variations. The proposed method is simulated in MATLAB / Simulink environment for the estimation of its performance and then compared with some of the existing techniques such as Artificial Bee Colony (ABC) optimization algorithm, Bacteria Foraging Optimization (BFO), and Particle Swarm Optimization (PSO) algorithm. The simulation result established the suprerioty of the proposed method over the other methods.

Wind Diesel Hybrid System with Pitch Angle Controller

A wind diesel hybrid system is an autonomous electricity generating system using wind turbines with diesel generators to obtain a maximum contribution by the intermittent wind resource to the total power produced, while providing continuous high quality electric power. The main goal is to control load frequency and reduce fuel consumption enabling reduced system operating costs and environmental impacts. Frequency control in a power system is done to maintain an adequate balance between the consumed and generated active powers, so that the frequency remains within acceptable limits around the nominal frequency. Load–frequency control (LFC) is of importance in electric power system design and operation. The objective of the LFC in an interconnected power system is to maintain the frequency of each area within limits and to keep tie-line power flows within some pre-specified tolerances by adjusting the MW outputs of the generators so as to accommodate fluctuating load demands.

Adaptive LFC Incorporating Modified Virtual Rotor to Regulate Frequency and Tie-Line Power Flow in Multi-Area Microgrids

This research investigates a new coordination strategy for both isolated single-area and interconnected multi-area microgrids (MGs) using a modified virtual rotor-based derivative technique supported with Jaya optimizer based on balloon effect modulation (BE). Accordingly, the main concept of BE is to assist the classic Jaya to be more sensitive and trackable in the event of disturbances, as well as to provide optimum integral gain value on the secondary frequency controller adaptively for both suggested MGs. The proposed modified virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs considering full participation of the inverter-based energy storage systems. The proposed virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs to emulate the reduction in system inertia and the enhanced damping properties. Several nonlinearities were proposed in this work such as a dead band of governor, generation rate constraints, and communication time-delay are considered within the dynamic model of the suggested MGs. In addition, the proposed design of multi-area MGs takes the interval time-varying communication delays into account for stability conditions. In this study, A comparative study using unimodal (i.e., Sphere) and multimodal (i.e., Rastrigin) benchmark test functions are conducted to validate the proposed direct adaptive Jaya-based BE. Furthermore, Wilcoxon’s rank-signed non-parametric statistical test using a pairwise comparison was performed at a 5 % risk level to judge whether the proposed algorithm output varies from those of the other algorithms in a statistically significant manner. Thence, the superiority and effectiveness of the proposed method have also been verified against a variety of other metaheuristics optimization techniques, including classic electro-search, particle swarm, multi-objective seagull, and Jaya optimizers. In addition, an operative performance is assessed against the conventional integral controller, coefficient diagram method, and classic Jaya with/without virtual inertia. The final findings emphasize the superiority of the proposed direct adaptive Jaya-based BE supported by a modified virtual rotor and state better performance and stability compared to existing controllers.

Marine predators algorithm for load frequency control of modern interconnected power systems including renewable energy sources and energy storage units

Load frequency control (LFC) serves a critical purpose in modern power systems, controlling system frequency and tie-line power flow. Nowadays, the power systems are integrated with an effective contribution of renewable energy sources (RESs). This paper broaches a modern power grid paradigm including conventional generators considering non-linearities, in addition to RESs and energy storage (ES) units for the study of LFC issue. Three forms of RESs are involved in the study, which are wind, photovoltaic and solar thermal. Besides, the system paradigm contains two types of ES units which are superconducting magnetic energy storage (SMES) and battery energy storage (BES). The controllers used for the system model analysis are proportional-integral-derivative (PID) controllers. The optimum design of the controller is established using a novel optimization approach which is a Marine Predators Algorithm (MPA). To certify the realistic credibility of the proposed algorithm, real data are imported to the RESs. The efficacy of the applied MPA-tuned controller is contrasted with that tuned by other competing evolutionary algorithms. The simulation outcomes demonstrate the credibility of the suggested control scheme. Also, the results ensure the role of ES units in enhancing the time-domain transient responses. The simulation findings are acquired through MATLAB framework.

Real-time optimization of active distribution networks with distributed energy resources participating in frequency regulation

Dispatchable distributed energy resources (DERs) in distribution networks are envisioned to aid frequency regulation for transmission systems. In this paper, a real-time optimal dispatch framework for DERs in distribution networks is designed to offer frequency regulation services simultaneously. Different from the existing research that distribution networks track uniaxially the predetermined auxiliary-services commands of the transmission system, here we regard transmission system frequency regulation as a black box and learn the parameters of its proxy satisfaction function from the perspective of DER optimization. To solve such a special optimization problem with control performance feedback, first we employ Gaussian processes to learn the satisfaction function, and, especially, build pertinent upper confidence bounds to achieve the optimal provision of ancillary services. Next, the primal-dual gradient projection process is embedded into the Gaussian process upper confidence bound algorithm to pursue the optimal DER dispatch. Accordingly, the output powers of DERs can be controlled in real-time: in disaggregate mode, they meet the goal of the distribution network; in aggregate mode, they provide a more satisfactory tie-line power flow to the transmission system. Simulations for illustrative systems are provided to validate the approach.