Grid Imbalance Research Articles

A forecast-driven stochastic optimization method for proactive activation of manual reserves

Reducing operating balancing costs is paramount for an affordable transition towards renewable-dominated power systems. In European balancing markets, operating balancing costs are driven by the activation of automatic and manual frequency restoration reserves, respectively aFRR and mFRR. An inadequate combination of both products for resolving grid imbalances may result in economic inefficiencies where, e.g., saturated aFRR can lead to balancing price spikes. To avoid such situation, we propose a proactive activation policy of manual reserves, aiming at an optimal trade-off between aFRR and mFRR products via a stochastic optimization method. The tool is fed with 1-min time trajectories of system imbalances covering the next quarter hour. The one minute temporal granularity allows modeling the ramping phenomena of mFRR products, while keeping track of the faster activation of aFRR products. The proposed balancing energy activation methodology is tested on Belgian market data, which currently adopts a reactive balancing strategy. Ex-post comparisons of the proposed balancing strategy with a reactive one show that our methodology allows a decrease of the balancing activation costs. This is expected as early activation of mFRR, when appropriately provided, allows avoiding the activation of extremely high aFRR bids.

A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction

The increasing expansion of photovoltaic power generation leads to unpredictable fluctuations in electricity supply, which can potentially jeopardize the stability of the power grid and escalate the costs associated with grid imbalances. As a result, precise forecasts of photovoltaic power generation play a vital role in optimizing capacity deployment, enhancing consumption levels, improving planning strategies, and maintaining grid balance within systems characterized by significant penetration of solar energy. This paper proposes a structural adaptive grey seasonal model based on data reorganization. Solar photovoltaic power generation data typically exhibit seasonal fluctuations, which pose a challenge to existing prediction techniques. Therefore, this paper adopts the idea of data reorganization to eliminate the seasonal fluctuations of observations, and the adaptive accumulation operator can accurately simulate the change trend of the original data in different periods, overcoming the defect of insufficient adaptability of the traditional accumulation operator. Subsequently, the time trend items are incorporated into the model structure to identify the trend characteristics of system development, which can effectively explain the power generation trend of photovoltaic systems at different time periods and improve the prediction accuracy of the model. In addition, the compatibility and unbiased nature of the proposed model have been demonstrated to help us better perceive the model. The Grey Wolf Optimizer (GWO) is used to optimize the adaptive parameters of the model, endowing it with higher flexibility and stronger adaptability. In order to verify the effectiveness of the model, three practical cases (namely quarterly solar power generation in the United States, Japan, and Germany) were compared with existing econometric techniques, artificial neural networks, and grey prediction methods. The experimental results show that the new model outperforms other benchmark models in both simulation and prediction performance, and enjoys high robustness.

Machine Learning and Weather Model Combination for PV Production Forecasting

Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

Robust EMPC-Based Frequency-Adaptive Grid Voltage Sensorless Control for an LCL-Filtered Grid-Connected Inverter

A robust explicit model predictive control (EMPC)-based frequency-adaptive grid voltage sensorless control is developed for a grid-connected inverter (GCI) via a linear matrix inequality (LMI) approach under the model parametric uncertainties as well as distorted and imbalanced grid voltages. In order to ensure the quality of grid currents injected into the utility grid even when the system model parameters vary, the proposed control scheme is accomplished by an enhanced prediction model rather than the conventional prediction model obtained by fixed parameters. Furthermore, an LMI-based observer is integrated with the disturbance observer to improve the reference tracking performance and to reject disturbances. The proposed observer is employed for the grid frequency-adaptive control without the need for grid voltage sensors. The proposed current controller and observer employ the LMI scheme to maintain a stable and robust operation of the GCI. The discrete-time frequency response and pole-zero map analyses are utilized to examine the system performance including the stability and robustness against parametric uncertainties. Comprehensive simulation and experimental tests as well as theoretical analyses clearly validate the robustness of the proposed control scheme under various harsh test conditions with non-ideal and unexpected grid and system parametric uncertainties.

Analysis and Control of Cascaded Energy Storage System for Energy Efficiency and Power Quality Improvement in Electrified Railways

Energy-efficient and grid-friendly railway power system is critical for the sustainable development of electrified railways. In this paper, a cascaded energy storage system (CESS) is investigated for energy efficiency and power quality improvement of the railway power system. First, the detailed operation principles of the CESS for multiple control objectives, including regenerative braking energy (RBE) utilization, reactive power compensation, and negative sequence current suppression, are analyzed. On this basis, a hierarchical power flow control strategy is developed for achieving the power flow management and control of multiple objectives for CESS. Specifically, a multi-objective power flow management strategy is designed in the system layer to allocate the power references for the CESS under sufficient and insufficient capacity scenarios. In the sufficient capacity scenario, the power references are directly obtained by the operation principles. In the insufficient capacity scenario, the power references are allocated through a three-stage optimization method considering the power shaving rate, grid power factor, and grid imbalance degree. Subsequently, the power references from the system layer are tracked in the converter layer to achieve power flow control. Finally, the effectiveness of the proposed hierarchical power flow control strategy is verified through experimental results. The results show that the proposed CESS can achieve superior performance on RBE utilization and power quality improvement under complex operating conditions in electrified railways.

Optimization of Grid Energy Balance Using Vehicle-to-Grid Network System

This paper proposes a methodological way to compensate for the imbalance between energy generation and consumption using a battery block from electric vehicles as an energy reservoir through the well-known vehicle-to-grid system (V2G). This method is based on a simulation process developed by the authors that takes into consideration the daily fluctuations in energy consumption as well as the power level generated by an energy source, either conventional, renewable, or hybrid. This study shows that for very large electric vehicle fleets, the system is rendered non-viable, since the remaining energy in the battery block that allows the electric vehicle to be usable during the daytime avoids having to compensate for the energy grid imbalance, only allowing it to cover a percentage of the energy imbalance, which the proposed methodology may optimize. The analysis of the proposed methodology also shows the viability of the system when being applied to a small fleet of electric vehicles, not only compensating for the energy imbalance but also preserving the required energy in the battery of the electric vehicle to make it run. This method allows for predicting the optimum size of an electric vehicle battery, which depends on the energy generation level, coverage factor of the energy imbalance, and size of the electric vehicle fleet.

Adaptive Control for Improved Virtual Synchronous Generator Under Imbalanced Grid Voltage

Adaptive Control for Improved Virtual Synchronous Generator Under Imbalanced Grid Voltage

Enhancing the reliability of probabilistic PV power forecasts using conformal prediction

The increasing integration of renewable energy, particularly solar photovoltaic (PV) power, presents challenges for power system operation. Accurate forecasts of renewable energy are both financially beneficial for electricity suppliers and necessary for grid operators to optimize operation and avoid grid imbalances. This paper proposes a forecasting framework to implement conformal prediction (CP) on top of point prediction models, which predict the PV power on a day-ahead basis, to quantify the uncertainty of those predictions. Simple and multiple linear regression, along with random forest regression, are used to construct the point predictions based on weather forecasts. Several variants of CP, including weighted CP, CP with k-nearest neighbors (KNN), CP with Mondrian binning, and conformal predictive systems, are built to transform the point predictions into rigorous uncertainty intervals or cumulative distribution functions to enhance reliability. The framework’s performance is evaluated using large datasets of weather predictions and PV power output in the Netherlands. Results indicate that CP combined with KNN and/or Mondrian binning after a linear regressor outperforms the corresponding linear quantile regressor. CP with KNN and Mondrian binning after using random forest regression demonstrates the most accurate probabilistic PV power forecasts, improving the weighted interval score by 14% compared to multiple linear quantile regression.

Small‐signal impedance modelling and stability analysis of doubly fed induction generator‐based wind turbines during asymmetrical weak grid condition

AbstractDoubly fed induction generator (DFIG)‐based wind turbines (WTs) that connected into weak power grid may lose their stability. However, the stability issue becomes more complex and has not been well addressed during the asymmetric grid scenario, especially when the negative sequence current response is required, according to the latest grid codes. To settle such an issue, a small‐signal impedance model of the DFIG‐based WT with negative‐sequence current control is established in this paper. Based on the proposed model, the interaction mechanism between the DFIG and the imbalanced grid is analysed. The influence of the main physical and control parameters on the system stability is then assessed in detail. It is found that improper design of the notch filters in the control loop may arouse insufficient decoupling between the positive and negative components, which can further deteriorate the system stability and robustness. To cope with the problem, the setting rule of the quality factor of the second‐order notch filter, considering its dynamic and static characteristics, is derived. Simulations and experiments are finally conducted to validate the correctness of the theoretical analysis.

The effect of vehicle-to-grid integration on power grid stability: A review

This article explores the promising potential of vehicle-to-grid (V2G) technology, which has attracted considerable interest due to its capacity to enhance power grid stability, decrease carbon emissions, and boost renewable energy utilization. The study begins by examining recent developments in V2G technology, then proceeds to discuss the challenges associated with integrating V2G into the power grid, and finally presents strategies to improve grid stability. Despite the potential of V2G technology, certain challenges must be addressed, including grid stress, complexities in load management, and grid imbalances. The article outlines various strategies to improve power grid stability, such as smart energy metering, time-of-use (TOU) schemes, rolling prediction decision frameworks, and an array of control approaches. Additionally, the research highlights the application of machine learning algorithms for efficient EV scheduling and power control units, voltage stabilization systems, and energy management units to ensure grid reliability. Future research should focus on developing scalable infrastructure solutions, establishing universal standards, designing optimization algorithms, and devising strategies to enhance battery life, to facilitate efficient V2G integration.

Discretized Discontinuous Modulation Strategy for Cascaded H-Bridge StatCom

This paper proposes a discontinuous modulation (DM) scheme for cascaded H-bridge (CHB) static compensators (StatComs) with star configuration. The proposed DM scheme guarantees zero steady-state active power in each converter phase-arm, even in the presence of severe grid imbalance conditions, thus remaining decoupled from the inter-phase capacitor voltage balancing controller. Moreover, the proposed DM scheme exploits the possibility of clamping to the zero-voltage level, thus providing a more flexible discontinuous operation than conventional DM methods. This involves lower zero-sequence-voltage injection requirements and lower switching losses. The proposed modulation scheme is experimentally corroborated and its performance under different grid voltage conditions is compared with traditional modulation schemes.

SOLAR ENERGY FORECASTING WITH DEEP LEARNING TECHNIQUE

The increasing reliance on renewable energy sources, such as solar power, necessitates accurate forecasting to ensure efficient grid integration and stability. This study explores the application of deep learning techniques, particularly deep neural networks (DNNs), in predicting solar irradiance and power output. By leveraging advanced algorithms and large datasets, this research aims to enhance the precision of solar energy forecasts, thereby optimizing grid management and resource allocation. Deep learning techniques, characterized by their ability to model complex nonlinear relationships, offer significant advantages over traditional statistical methods in forecasting solar energy. This study focuses on the development and evaluation of deep neural networks for predicting solar irradiance, which directly influences the power output of photovoltaic (PV) systems. The models are trained on extensive historical weather data, satellite imagery, and real-time solar output measurements to capture temporal and spatial variations in solar energy. Key components of the research include data preprocessing to handle missing values and noise, feature extraction to identify relevant patterns, and model training using various architectures of DNNs such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The study also employs ensemble learning techniques to combine predictions from multiple models, further enhancing forecast accuracy. Results demonstrate that deep neural networks significantly outperform traditional forecasting methods in terms of accuracy and reliability. The improved forecasts enable better scheduling of energy generation and distribution, reducing reliance on fossil fuels and minimizing grid imbalances. Additionally, accurate solar energy predictions support the efficient integration of solar power into the grid, enhancing overall system stability and reducing operational costs. The study also addresses challenges in implementing deep learning models, such as computational requirements and the need for large, high-quality datasets. Solutions include leveraging cloud computing resources and developing standardized data collection protocols to facilitate broader application and scalability of the models. Application of deep learning techniques in solar energy forecasting represents a promising advancement for the renewable energy sector. By improving the accuracy of solar irradiance and power output predictions, deep neural networks contribute to more reliable grid integration and efficient energy management. This research advocates for the continued exploration and adoption of advanced machine learning methods to support the transition to a sustainable energy future. Keywords: Deep Learning Techniques; Solar Energy; Forecasting; Grid Integration; Power Output.

VSC-HVDC control without PLL for unbalanced grid

When unbalanced faults such as single-phase voltage drop occur in the power grid, VSC-HVDC will fail or become unstable due to sudden changes in amplitude, phase, frequency, etc. of the power grid voltage. Therefore, the role of PLL in a non-ideal power grid is particularly important, but often PLL needs to make a compromise between dynamic response and system stability. Therefore, this paper proposes a free-PLL control strategy under non ideal power grid. First, according to the power transmission model of VSC-HVDC under power grid imbalance, the control command required by the inverter is given, which is determined in the environment free-PLL. Therefore, PLL is not required to retrace the phase of the grid voltage. In addition, in order to overcome the impact of the sudden change of power grid frequency on the free-PLL strategy, a frequency locking strategy is proposed in the free-PLL environment, the response time is about 1 grid cycle. Finally, in order to verify the effectiveness and correctness of the proposed strategy, the VSC-HVDC system’s active power transmission stability is guaranteed when the grid voltage encounters different degrees of single-phase sags.

Direct Arm Energy Control of the Modular Multilevel Matrix Converter

Modular multilevel matrix converter is a type of converter used in medium-voltage ac-ac power conversion. Controlling the energy content of the converter arms is a prerequisite for a proper converter operation, and at the same time a challenging control task. So far, there are several approaches presented in the literature that successfully deal with the challenge. However, almost all of them fail when it comes to the simplicity of implementation, and ease of understanding by control engineers and researchers entering the field. This paper extends the direct arm energy control concept, already introduced for the class of modular multilevel converters, to the modular multilevel matrix converter. Presented control approach is characterized by an intuitive and simple implementation, ability to control the arm energies to arbitrary values, and stable operation under all operating conditions, including grid imbalances. Validity of the control concept was demonstrated using an in-house-developed hardware-in-the-loop simulator of the modular multilevel matrix converter, with control algorithms being deployed to the industrial-grade controller.

Hydrogen Fuel Cell Hybrid Electric Vehicles: the Impact of Commercial Vehicle Fleets on the Penetration of Renewable Energy Sources

The transport sector is one of the most significant contributors to greenhouse gas (GHG) emissions, with the road segment accounting for large part of the emissions and requiring immediate action to address the increasing issues of climate change. To this aim, the international community is promoting technological alternatives to fossil fuel-based powertrains, thus favoring the increase in electric vehicle (EVs) penetration. However, to be sustainable the electrification of mobility requires a large share of energy produced by renewable sources to support the decarbonization process. The simultaneous increase in volatility for both the energy demand (due to EVs diffusion) and production (due to the Renewable Energy Sources RES growth), has a significant impact on the reliability of the electricity grid infrastructure. The growth of RES penetration brings an energy production increase that may not synchronized with the demand evolution, possibly leading to a grid imbalance. To this aim, the storing potential of green hydrogen, produced from RES during energy surplus periods, could allow better exploitation of the rising installed RES capacity. The proposed study aims to analyze the benefits that can arise from introducing Fuel Cell Hybrid Electric Vehicles (FCHEVs) in a commercial electric fleet in terms of environmental and grid resilience perspectives. FCHEVs are electric vehicles propelled by a hybrid powertrain, coupling electric battery packs and a fuel cell stack as energy sources; here, hydrogen is used as an energy vector stored in high-pressurized tanks. Starting from a reference scenario of a fleet composed of 10 EVs, the number of FCHEVs is progressively increased to assess their impact on the fleet performance. A set of mission scenarios for the commercial vehicles is obtained through the Markov Chain method, starting from accurate GPS data. These different scenarios are used in a numerical model to optimally plan the fleet scheduling and their configuration in terms of vehicles associated with specific missions. Results show a reduction of up to 57 % in grid fluctuations (estimated through its standard deviation) and a decrease from 77 % to 82 % in CO2 emissions concerning traditional vehicles increasing the FCHEV penetration considering the current Italian energy scenario.

Make or buy: IT-based decision support for grid imbalance settlement in smarter electricity networks

Decision (support) systems are a particularly important type of information system to energy informatics. A key challenge in energy informatics is that electricity supply must be in balance with demand at all times. More volatile renewable energy sources increase the relevance of electricity network balancing, i.e., imbalance settlement. Typically, electricity distribution network operators bought balancing power from external service providers (Buy option). Interestingly, however, more local energy resources help smarter electricity networks develop a Make option, as in our real-world evaluation. Choosing the better decision alternative within the relevant timeframes challenges human decision-making capabilities. Therefore, this research proposes a model-based decision system to improve the operators’ decisions concerning Make or Buy under various levels of data quality represented by availability, granularity, and timeliness. The study reports savings up to 40% of costs for imbalance settlement supporting ambitious development efforts by the municipality we study in our real-world evaluation.

A mixed-integer convex approximation for optimal load redistribution in bipolar DC networks with multiple constant power terminals

This paper proposes a mixed-integer convex model for optimal load-balancing in bipolar DC networks while considering multiple constant power terminals. The proposed convex model combines the Branch and Cut method with interior point optimization to solve the problem of optimal load balancing in bipolar DC networks. Additionally, the proposed convex model guarantees that global optimum of the problem is found, which ensures minimal power losses in the bipolar DC distribution grid branches, as the total monopolar load consumption has been balanced at the substation's terminals. In addition, an optimal load balancing improves the voltage profiles due to current redistribution between the positive and negative poles. Numerical results in the 21- and 85-bus test feeders and a comparison with three metaheuristic techniques show the effectiveness of the proposed convex model in reducing the total grid imbalance while minimizing the power losses and improving the voltage profiles.

Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation.

Control Techniques for CRM-Based High-Frequency Soft-Switching Three-Phase Inverter Under Unbalanced Grid Conditions

This article presents control techniques for the critical conduction mode(CRM) based high-frequency soft-switching three-phase inverter under unbalanced grid conditions. The soft-switching technique with silicon carbide (SiC) power devices allows the inverter to operate at hundreds of kHz with high efficiency and high power density. To extend the soft-switching technique to more practical cases, behaviors of the inverter under unbalanced grid conditions, specifically voltage sag, are investigated. During the voltage sag, the ac voltage at the grid becomes unbalanced. This requires unbalanced ac current reference to avoid output active power oscillation. With the unbalanced grid voltage and the ac current reference, the inverter loses the soft-switching capability. Continuous conduction mode (CCM) operation occurs where discontinuous conduction mode (DCM) operation is intended leading to large turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> loss. To eliminate the CCM issue, two improved modulation techniques are proposed by using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> -time extension in the CRM phase and by skipping pulses in the DCM phase. This article discusses how the CCM operation arises and details the proposed control methods. The performance of the proposed control is validated experimentally under a variety of grid imbalance cases with an SiC-based three-phase inverter prototype.

Compensation of Unbalanced Low-Voltage Grids Using a Photovoltaic Generation System with a Dual Four-Leg, Two-Level Inverter

In this paper, a grid-connected photovoltaic (PV) generation system is proposed with the purpose of providing support to low-voltage grids, namely through the elimination or attenuation of the grid imbalances. This compensation must consider the load types, which can be either linear or non-linear, and whether the reactive power and current harmonics generated by the non-linear loads need to be compensated in addition to the unbalanced active power. This must be well considered, since the compensation of all aspects requires oversized PV inverters. Thus, the different unbalanced compensation schemes are addressed. Several schemes for the generation of the inverter current references taking into consideration the compensation and load type are presented. For this PV generation system, a dual four-leg, two-level inverter is proposed. It provides full unbalanced compensation owing to the fourth leg of the inverter and also extends the AC voltage, which is important when this compensation is required. To control this inverter, a control scheme for the inverter that considers several compensation factors is proposed. A vector voltage modulator associated with the controller is another aspect that is addressed in the paper. This modulator considers the balance between the DC voltages of the inverters. Several compensation schemes are verified through computational tests. The results validate the effectiveness of the proposed PV generation system.