Large-scale Renewable Generation Research Articles

Multi-objective Reactive Power Optimization of a Distribution Network based on Improved Quantum-behaved Particle Swarm Optimization

Background: Reactive power optimization (RPO) is crucial for distribution networks in the context of large-scale renewable distributed generation (RDG) access. Objective: To address the problems caused by the connection of RDG, an RPO model and an improved quantum-behaved particle swarm optimization (IQPSO) algorithm are proposed. Method: In this study, a dynamic S-type function is proposed as the objective function of the minimum active power loss, whereas an exponential function is proposed as the objective function of the minimum voltage deviation to establish an RPO objective function. The operating cost of distribution is considered as the third objective function. To address the RPO problem, a QPSO algorithm based on the ε-greedy strategy is proposed in this paper. ModifiedIEEE33 bus and IEEE69 bus systems were used to evaluate the proposed RPO method in simulations. Results: The simulation results reveal that the IQPSO algorithm obtains a better solution, and the proposed RPO model can considerably reduce active power loss, node voltage deviation, and distribution network operating costs. Conclusion: The RPO model and IQPSO algorithm proposed in this study provide a highperformance method to analyze and optimize reactive power management in distribution network.

An economic-environmental energy management system design for MT-HVDC networks via a semi-definite programming approximation with robust analysis

This research deals with the economic-environmental dispatch problem (EEDP) in multi-terminal high-voltage direct-current (MT-HVDC) systems by proposing a convex approximation based on semi-definite programming (SDP). The exact formulation of the EEDP corresponds to a non-convex, nonlinear programming problem due to the presence of a nonlinear quadratic constraint regarding the products between voltage variables. The thermal plants' economic and objective functions are modeled using typical quadratic functions. The SDP approach allows reaching a convex approximation that ensures the global optimal solution for each objective function independently or the construction of the optimal Pareto front via the weighting-factor optimization methodology. The proposed SDP approach also considers uncertainties in the demand and in the available power of renewable sources, which makes it robust. The main contribution of this research is a multi-period analysis that includes large-scale renewable generation sources and a robust analysis regarding demand and variations in renewable generation. Numerical results in two MT-HVDC systems demonstrate the effectiveness of the proposed SDP approach when compared to combinatorial optimization algorithms. All numerical simulations were carried out using the CVX convex disciplined tool, with the help of the SEDUMI and SDPT solvers, in the MATLAB programming environment.

Temporal Scenarios-based Assessment of Maximum Hosting Capacity of Renewable Generation in Distribution Systems Considering ANM Techniques

Abstract Incorporating large-scale renewable distributed generation (RDG) into the power grid introduces significant challenges associated with the indeterminacy of the power system, significantly impacting the comprehensive integration and efficient utilization of the distribution network. This underscores the imperative to investigate scientific evaluation methods and mechanisms for enhancing the RDG absorptive capacity within the distribution network. In light of the stochastic and temporal characteristics inherent to RDG, a novel approach for assessing the RDG hosting capacity within distribution networks is proposed, taking temporal scenarios into account. This method optimizes the RDG output probability distribution model based on its temporal fluctuation change rule, and constructs RDG temporal scenario model by using scenario reduction technology. Subsequently, a temporal scenarios-based RDG hosting capacity assessment model is established, taking into account active network management (ANM) techniques. The effectiveness of the proposed methodology is substantiated through simulation results conducted on a modified IEEE 33-bus system.

A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.

A four-stage fast reliability assessment framework for renewables-dominated strong power systems with large-scale energy storage by temporal decoupling and contingencies filtering

Reliability assessment for renewables-dominated strong power systems presents significant challenges, primarily due to the overwhelming computational demands posed by large-scale renewable generation and energy storage. In response to the limitations and lack of scalability in traditional methods, this study introduces a novel four-stage fast reliability assessment framework tailored for renewables-dominated strong power systems. First and foremost, the pre-dispatch and temporal decoupling of energy storage serve as vital foundations for the fast assessment framework. Following the pre-dispatch model, an adaptive scenario clustering technique is proposed to eliminate redundant scenarios while retaining extreme ones. Consequently, a quick verification process for contingencies is established, employing DC optimal power flow in the worst-case scenario to identify potential load shedding. Then, all contingencies are filtered to only effective ones for reliability indices computation. Finally, a modified reliability calculation model with linear AC power flow is built using the filtered contingency set and reduced scenarios. The efficacy of this approach is validated through four numerical experiments, demonstrating impressive efficiency and computation feasibility. For accuracy, the proposed method shows a 3.7% average error in a modified IEEE RTS system, and for efficiency, the proposed method finishes N-2 analysis in 424 s for a 62-bus-212-line case as well as 4924 s for a 200-bus-490-line case.

Toward a physical Dodge City high voltage direct current (HVDC) electricity hub

A physical Dodge City high voltage direct current (HVDC) electricity commodity hub would foster the kind of risk transfer that would invite the full participation of the financial industry in an interstate transmission system essential to the speedy development of renewable electricity and complementary storage technologies. Obstacles lie in the way of such a vision, however. Chief among them, paradoxically, is the administrative system that has evolved since the late 1990 s to promote regional electricity markets. That system abstracts from investment risk and the nation’s uneven geography of renewable resources. It lodges a kind of monopoly planning and scheduling power in a small group of bureaucratic centralized system operators that mimics a command economy in their own distinct regions, not the kind of market economy that has traditionally driven investment in US interstate energy infrastructure. And yet, in dealing with the vexing transmission constraints faced by the renewable generating sector (the “queue”), the Federal Energy Regulatory Commission (FERC) continues to pursue ever greater authority for those regional transmission organizations (RTOs). Such FERC action regarding its RTOs in 2022–23 represents a highly evident case of path dependency—a problem—that state or federal policy makers wishing to speed the entry of large-scale renewable generation, and complementary storage technologies, will have find a way either to confront directly or to work around.

Design of Multi-Input Multi-Output Non-linear Model Predictive Control for Main Steam Temperature of Super Critical Boiler

Flexible operation of coal-fired power plants is becoming increasingly necessary for successful integration of large-scale renewable power generation into the power grid. The maximum ramp rate and the number of load cycles are generally limited by the thermal stress experienced by the boiler pressure parts, turbine metallurgy and creep and fatigue of critical thick-walled components Main steam temperature is a critical operating parameter that must be controlled within acceptable limits for safe operation. Main steam temperature deviation beyond acceptable limit has impact on boiler pressure parts and turbine material of construction due to creep and fatigue effect. Base load operating units do not require steep ramp rate and hence recommended ramping rates are kept low within the safe operating zone in comparison to the flexible operation of the units with wide range load change width. Thermal stresses are caused by the temperature changes inside the thick-walled components and turbine steam admission parameters. Hence, the quality of main steam temperature control plays a vital role in flexible operation of the coal fired units. Conventional cascaded PID temperature control loop architecture performs well at steady state condition within a limited variation of load change at low ramp rate but it acts slowly and performs poorly at transient operating conditions of flexible operation of the boiler turbine with wide range load variation and load cycle with high ramp rate and remains far from rated conditions. In this paper, a Multi-Input Multi-Output (MIMO) Non-linear Model Predictive Control (MPC) design for regulation of the main steam temperature of a Once-Through supercritical Boiler is proposed. The controller is based on a non-linear dynamic model which incorporates dynamics of the variables of interest. It has the capability to operate effectively across a wide load range while maintaining main steam temperature within acceptable limits. A notable advancement in this design of MPC is the incorporation of coal flow demand and feedwater flow demand as additional control inputs alongside primary and secondary spray flows. In simulation test cases, the MPC controller demonstrates satisfactory performance and computational efficiency.

QFS-CNN for sub-synchronous oscillation source location based on dissipating energy flow

Sub-synchronous oscillation (SSO) caused by large-scale renewable energy generations can threaten the safe and reliable operation of power systems. In this paper, a dissipating energy-based quaternion feature set convolutional neural network (QFS-CNN) method is proposed to locate the SSO sources quickly and accurately, providing the foundation for isolation of the SSO sources from power systems in time. Firstly, aiming at the difficulty in feature extraction of SSO sources due to the poor observability of power system, a temporal and spatial feature extraction method based on oscillation energy is proposed to characterize the SSO sources information in the form of images with limited measurement data. The temporal and spatial feature images are extracted based on the variation of oscillation energy in time and the direction of energy flow power in space, respectively. Then, the correlation between the oscillation energy and the SSO sources location can be revealed by the feature images with less calculation and storage space. Secondly, a QFS-CNN method based on temporal and spatial feature images for SSO sources location is proposed to address the insufficient labeled data of SSO. The QFS data augmentation technique increases the feature set via meaningful recombination of the existing labeled feature images. Then, the augmentative feature set is used to ensure the model training accuracy of QFS-CNN to improve the location accuracy of SSO sources. Finally, the proposed method is demonstrated and evaluated by a modified IEEE 39-bus wind power generation system. Simulation results show that the method has high accuracy and stronger anti-noise ability in the case of poor system observability and few sample data.

A hydrogen supply-chain model powering Australian isolated communities

This article proposes a supply chain-based green hydrogen microgrid modelling for a number of remote Australian communities. Green hydrogen can be used as an emissions-free fuel source for electricity generation in places where large-scale renewable energy production is impossible due to land availability, population, or government regulations. This research focuses on the Torres Strait Island communities in northern Australia, where the transition from diesel to renewable electricity generation is difficult due to very limited land availability on most islands. Due to geographical constraints, low population and smaller electrical load, the green hydrogen needs to be sourced from somewhere else. This research presents a green hydrogen supply chain model that leverages the land availability of one island to produce hydrogen to supply other island communities. In addition, this research presents a model of producing and transporting green hydrogen while supplying cheaper electricity to the communities at focus. The study has used a transitional scenario planning approach and the HOMER simulation platform to find the least-cost solution. Based on the results, a levelised cost of energy range of AU$0.42 and AU$0.44 was found. With the help of a green hydrogen supply chain, CO2 emissions at the selected sites could be cut by 90 %. This study can be used as a guide for small clustered communities that could not support or justify large-scale renewable generation facilities but need more opportunities to install renewable generation.

Siting and sizing of energy storage for renewable generation utilization with multi-stage dispatch under uncertainty: A tri-level model and decomposition approach

For grids suffering from large-scale renewable generation curtailment, the reasonable allocation of energy storage can smooth renewable generation fluctuation for better utilization. This paper analyzes the optimal non-profit planning of energy storage in a grid rich in renewable generation from the perspective of third-party investors. First of all, to model the prediction errors of wind and solar output, a multi-stage scenario tree of intraday uncertainty is established to describe the stochastic output of wind and photovoltaics. In addition, scenario reduction techniques and nonanticipativity constraints are adopted to realize multi-stage optimization. Second, aimed to improve social welfare with an acceptable cost, a tri-level optimization model is established to describe storage siting and sizing, storage dispatch, and market clearing respectively to reach a high level of renewable generation utilization. Furthermore, Karush–Kuhn–Tucker conditions and linearization techniques are used to reform the tri-level model into a linear bi-level one. Third, a decomposition algorithm and a differential cut are proposed to solve the bi-level problem. The optimum is gradually approached via iterating by adding differential cuts and integer cuts. Finally, the model and method are verified based on a region of the HRP-38 system. The results show that storage investment aimed at social welfare can effectively achieve a much higher goal of renewable generation utilization than individual profit-seeking storage investment.

Optimal Planning Approaches under Various Seasonal Variations across an Active Distribution Grid Encapsulating Large-Scale Electrical Vehicle Fleets and Renewable Generation

With the emergence of the smart grid, the distribution network is facing various problems, such as power limitations, voltage uncertainty, and many others. Apart from the power sector, the growth of electric vehicles (EVs) is leading to a rising power demand. These problems can potentially lead to blackouts. This paper presents three meta-heuristic techniques: grey wolf optimization (GWO), whale optimization algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric vehicle charging stations (EVCSs). The aim of implementing these techniques is to optimize allocation of renewable energy distributed generation (RE-DG) for reducing active power losses, reactive power losses, and total voltage deviation, and to improve the voltage stability index in radial distribution networks (RDNs). MATLAB 2022a was used for the simulation of meta-heuristic techniques. The proposed techniques were implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs while considering seasonal variations and uncertainty modeling. The results validate the efficiency of meta-heuristic techniques with a substantial reduction in active power loss, reactive power loss, and an improvement in the voltage profile with optimal allocation across all considered scenarios.

Battery state of charge based improved adaptive droop control for power management of a microgrid having large scale renewable generation

Power management in a standalone microgrid having large scale renewable generation is generally based upon droop control strategies. However, the renewable sources such as photovoltaics (PV), which are interfaced with microgrid through power electronic converters have low inertia. Because of low inertia, the transient power variation during switching events like change in load may be very large and may result into variation in voltage and frequency beyond permissible limits. In this paper an adaptive scheme for power management of a standalone microgrid is proposed, which is based on available state of charge (SoC) of the batteries to mitigate these issues. The proposed battery SoC based adaptive droop control for power management allows smooth transition of distributed energy resources (DERs) from maximum power point tracking (MPPT) mode to adaptive droop mode and vice versa. The transient behaviour is handled with the help of batteries and supercapacitor (SC), which is further improved with the help of fuzzy logic based proportional integral controller (FLC-PI). A significant drop in SC power during switching events is obtained with FLC-PI. The test microgrid is composed of multiple PV generation at different locations as primary source of generation, multiple batteries energy storage system (ESS) at different locations, fuel cell (FC), supercapacitor (SC) and a dynamic load.

Impact of hybrid electrical energy storage system on realistic deregulated power system having large-scale renewable generation

Frequency stabilization in the deregulated power system (DPS) becomes more significant because of increased complexities and uncertainties due to higher penetration of renewable energy sources (RESs) among other operational challenges of the system. Variable generation due to RESs and frequent load deviations impact the supply and demand balance which causes huge frequency deviations in the system. The issues associated with the grid due to RES can be effectively tackled by maintaining power balance using different types of energy storage and advanced control mechanism. This article evaluates the suitability of hybrid electrical energy storage (HEES) and a novel (1 + TDF)-PI cascade controller to tackle the frequency regulation issues of the two-area DPS. The DPS has a power generation capacity of 2000 MW in each area integrated with RESssuch as geothermal power plant (GPP) and realistic dish-sterling solar thermal system (RDSTS). To resemble the practical operational situation of the DPS, several contract transaction scenarios and nonlinearities have been considered and examined. The HEES is a combination of a Super-Capacitor (SC) and redox flow battery (RFB). The SC is very fast responding and it can counter immediate variations in the power whereas, the RFB system can counter large variations of the power as needed in the system. The supremacy of the proposed controller has been confirmed by equating its performance with other well-known controllers. Compared to the state-of-the-art approaches, with the proposed controller smaller setting time has been achieved i.e. 10.72 s and 10.579 s for area-1 and area-2 frequency and 1.25 s for tie-lie power under the BBT scenario. Moreover, the system performances and capabilities to handle system nonlinearities have been prominently enriched due to precise control and a quick response provided through the proposed HEES.

Extreme learning machine–radial basis function neural network–based state-of-charge estimation of lithium-ion batteries assisted with fiber Bragg grating sensor measurements

Lithium-ion batteries have been widely used in power systems for accepting large-scale renewable energy generations and in the transport sector to facilitate vehicle decarbonization, the accuracy of state-of-charge (SOC) estimation is extremely crucial for the battery systems to operate safely and reliably. Many approaches have been developed for battery SOC estimation based on the measurable electrical signals. However, the electrical measurements are usually corrupted by sensor noise and are also limited by insulation, corrosion, and electromagnetic compatibility. To address these problems, fiber Bragg grating (FBG) sensors are used to replace thermocouple sensors, the battery SOC therefore can be estimated based on the measurement of the strain information. In order to train the SOC estimation model for lithium-ion batteries, an FBG sensor–based battery SOC estimator, namely, ELM-RBFNN, is developed using the radial basis function neural network (RBFNN) identified by extreme learning machine (ELM). According to the ELM principle, the ELM-RBFNN model randomly selects the radial basis function (RBF) centers and determines the parameters of the hidden-layer nodes without any manual adjustment. The ELM-RBFNN estimation model has the advantages of fast learning speed, global optimization, and excellent generalization. Experimental results confirm that compared to the linear estimator and the polynomial estimator, the developed SOC estimator is able to produce more accurate results.

Recent progresses in state estimation of lithium-ion battery energy storage systems: A review

Battery storage has been widely used in integrating large-scale renewable generations and in transport decarbonization. For battery systems to operate safely and reliably, the accuracy of state estimation is extremely crucial in battery management system (BMS). However, due to the complex working conditions and electrochemical reactions, it is difficult to accurately estimate the internal state of batteries. This survey focuses on categorizing and reviewing some of the most recent estimation methods for internal states, including state of charge (SOC), state of health (SOH) and internal temperature, of which internal temperature estimation methods have been rarely reviewed in the existing literature. The main advantages and disadvantages of these methods are compared, with a focus on the new sensing technologies for internal state estimations. Then, the future prospects of battery state estimation technologies are discussed.

Energy reliability enhancement of a data center/wind hybrid DC network using superconducting magnetic energy storage

The progressive penetrations of sensitive renewables and DC loads have presented a formidable challenge to the DC energy reliability. This paper proposes a new solution using series-connected interline superconducting magnetic energy storage (SCI-SMES) to implement the simultaneous transient energy management and load protection of DC doubly-fed induction generator (DC-DFIG)/internet data center (IDC) composited medium-voltage direct-current (MVDC) network. The SCI-SMES mainly consists of two current source converters (CSCs) sharing a common SMES link. It can implement the transient energy conversion and interaction between sensitive sources and loads in two individual DC feeders, even they are with different voltage and current levels. To prove the above-mentioned functions, a kW-class experimental SCI-SMES prototype is established. For actual renewable energy scenarios, six different cases of power flow and energy interaction between DC-DFIG and IDC are presented in this paper, and a MW-class with SCI-SMES-integrated hybrid DC network is built in simulation for verification. A techno-economic assessment, considering capital investment, operating cost, and financial losses of voltage sags, shows that the total investment payback period is in the range of 5.44–18.41 years, which proves the SCI-SMES has both economic and technical advantages in large-scale data centers and renewable generation sectors.

Real-Time Battery Temperature Monitoring Using FBG Sensors: A Data-Driven Calibration Method

Battery storage has an important role to play in integrating large-scale renewable power generations and in transport decarbonization. Real-time monitoring of battery temperature profiles is indispensable for battery safety management. Due to the advantages of small size, resistance to corrosion, immunity to electromagnetic interference, and multiplexing, fiber Bragg-grating (FBG) sensing has received substantial interest in recent years for battery temperature measurement. However, traditional temperature calibration for FBG sensors often requires a high-standard reference and causes the sensors to fail to be consistent during the calibration or recalibration processes. To tackle the challenges, an ensemble data-driven calibration method is developed in this article for FBG sensors. The calibration model consists of a linear part and a nonlinear part. First, the fuzzy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${C}$ </tex-math></inline-formula> -means (FCM) algorithm is used to extract the linear relationship between the measured FBG wavelength shift and temperature variation. Then, the empirical mode decomposition (EMD) technique is used to classify the intrinsic mode functions (IMFs) and the remainder of the unmodeled nonlinear information. The unmodeled nonlinear information is further compensated using battery state of charge (SOC) and cycle number information. The experimental results confirm that the proposed temperature calibration method achieves desirable accuracy and reliability, with both the mean absolute error (MAE) and root mean square error (RMSE) being around 0.2 °C, respectively. Compared with the traditional temperature calibration method, the proposed approach can be used online in real-life applications.

Resilience promotion of active distribution grids under high penetration of renewables using flexible controllers

Flexible voltage controllers (FVCs), such as voltage regulation transformers (VRTs) and shunt capacitor banks (SCBs), can play a significant role in enhancing the resilience of active distribution systemS (ADS) equipped with large-scale renewable distributed generation. In this paper, a comprehensive resilience response framework is presented, which provides preventive and emergency actions both before and after hurricane events. In this study, a preventive action identifies the on/off state of dispatchable distributed generation (DDG) units and discrete settings of FVCs for both before and after a hurricane event, whereas an emergency action includes the redispatch of DDG unit, load curtailment, and voltage regulation after a hurricane event. The core of the proposed framework is a trilevel max–min robust optimization (MMRO) model, which enhances the resilience of the ADS operation problem against multistage extreme N − k contingencies and renewable power generation uncertainty. The proposed MMRO model is formulated as a mixed-integer convex programming model by relaxing nonconvex AC power flow equations into mixed-integer second-order cone relaxation (MISOCR) equations. Finally, to test the effectiveness of the proposed trilevel MMRO model, we conduct experiments on a modified IEEE-33/69 bus distribution system. The numerical results show that the proposed trilevel MMRO model is an effective model for enhancing the resilience of ADSs.

Impacts of integration of very large‐scale photovoltaic power plants on rotor angle and frequency stability of power system

The increase in penetration of large-scale renewable generation significantly affects power system characteristics. Thus, it is necessary to assess the steady state and dynamic performance of a power system for different installing scenarios of renewable power plants such as photovoltaic (PV) ones. The paper specifically investigates the impacts of very large-scale photovoltaic (VLS-PV) generation on the power system dynamic performance including rotor-angle and frequency stabilities. For this purpose, a detailed equivalent dynamic model for a multi-unit VLS-PV system and corresponding controllers are developed. The paper also proposes a comprehensive set of replacement scenarios comprising different PV penetration, location, and voltage control strategies as well as simulating various large disturbances. To investigate the impacts of VLS-PV generation on dynamic system performance, a study framework for frequency and rotor-angle stability involving the modal and transient analysis is presented as well. By using the VLS-PV detailed model along with other dynamic components of a power system such as synchronous generators, automatic voltage regulators (AVRs), and governors, the stabilities studying instructions are employed for the IEEE 39-bus and 118-bus test systems.

Discrete-Time State-Space Construction Method for SSO Analysis of Renewable Power Generation Integrated AC/DC Hybrid System

Construction of the linearized state-space model is the key step of eigenvalue analysis for SSO study of power systems with large-scale renewable power generation (RPG) integrated. This paper proposes a novel method to construct the state matrix of the AC/DC hybrid power system with RPG within the discrete-time domain. The continuous-time state-space model of each AC/DC component as well as RPG units in the power system, is discretized together with their corresponding control. The discretized equation of each component is then represented by an equivalent circuit consisting of parallel historical current and conductance. According to the power system configuration, the discrete-time equivalent circuit of the whole AC/DC hybrid system is established, and the nodal analysis method is applied for the construction of the discrete-time state-space model of the power system. In this way, the state matrix can be acquired much more conveniently than that of continuous-time state-space method, which needs topology analysis to find independent state variables of the network from the proper tree. The proposed method has been applied in the eigenvalue analysis for the SSO study of a case system adapted from the AC/DC hybrid transmission system in northwest China. Eigenvalue results are in high accordance with time-domain simulation results in PSCAD/EMTDC.