Power Balance Of Grid Research Articles

Sizing of energy storage for hybrid power plants aiming for turbine stress reduction under FCR-N requirements

This paper examines the storage sizing for hybrid power plants providing the Frequency Containment Reserve (FCR) for power grid balancing, where the hybridization of a turbine with energy storage system aims to decrease turbine stress under the most recent Nordic FCR requirements. This objective is addressed via the formulation of a hybrid plant optimal control problem, focusing on the achievable stress reduction as a function of storage energy capacity. The analysis of the optimization problem provided an analytical representation of the least upper bound for the best attainable turbine stress reduction, which was used to derive sizing recommendations for energy capacity. The obtained results imply that the hybrid plant’s control policy has a significant impact on the achievable stress reduction, as relatively small energy capacity can provide a high-stress reduction with “well–designed” control.

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Grid variability and value assessment of long-duration energy storage under rising photovoltaic penetration: Evidence from Japan

Using high-resolution grid power balance and market data, this work investigates the effects of rising solar photovoltaic generation on the variability of large-scale net grid load and spot prices, and conducts an analysis of the potential balancing profits of various grid-scale energy storage systems. Analysis results show the correlation between PV (photovoltaic) generation and electricity demand has been identified as a significant factor influencing spot price value. As PV penetration increases, the value of spot prices experiences a notable decline, with values declining to nearly zero when the share of hourly PV generation surpasses 70 %. The volatility of electricity spot prices has a substantial impact on utilization rates and economic profits of energy storage systems employed for grid energy balancing. Despite the lower roundtrip efficiency, reversible hydrogen storage system can offer positive benefits. The longer storage duration offers the advantage of capturing greater dispatch profits by exploiting price differences over extended periods. Considering the time-varying nature of spot prices, the rated power capacity of the storage system plays a significant role in determining its utilization rates. The larger rated charging and discharging ability of the storage system allows for more effective utilization of price fluctuations, resulting in greater profitability.

Prediction method for power fluctuations in cross regional consumption and transportation under the integration of new energy

Against the backdrop of the increasing development of the new energy industry, the volatility of new energy output poses significant challenges to regional power grid balance and energy absorption. Therefore, this article proposes a prediction method for cross regional transmission power fluctuations under new energy integration conditions. A comprehensive and representative sample dataset was constructed by comprehensively considering factors such as fluctuations in new energy output, capacity confidence, and peak shaving characteristic parameters, combined with numerical weather forecast data. Normalize the sample data to eliminate dimensional differences between parameters. The sparrow search algorithm is used to optimize the weights and thresholds of the double hidden layer BP neural network, effectively avoiding local optimization problems caused by over training. The experimental results show that this method has significant advantages in predicting power fluctuations in cross regional absorption and transportation of new energy, with a predicted power to power ratio of over 0.85.

Research and Governance of Power Quality of the Distribution Network Based on Large-Scale Distributed PV Access

The continuously increasing distributed photovoltaic installations have a great impact on power grid operation, which shows a trend of “local to global development and distribution network to the main network extension”. The situation puts forward higher requirements for power grid power balance, reactive power regulation, and power quality control. Based on the measured data of a regional power grid, this paper analyzes the new problems brought by the large-scale access of distributed photovoltaic to the power quality of distribution network from the aspects of voltage deviation, harmonics, three-phase imbalance, voltage fluctuation, low voltage ride through, and reactive power support in detail. Besides, it describes a series of management and control measures that the local government and grid companies can take to reduce the operation risk of the distribution network against these adverse effects. The purpose is to provide technical guidance for exploring the new mode of distributed photovoltaic grid connection service and regulation operation management in the whole county.

An Advance Control of Grid Integrated Wind Turbine Driven DFIG-Battery System with Grid Power Shaping Under Gust Wind Variation

There has been an increase in wind power penetration into the utility grid. The wind speed variations exhibit undesirable oscillations that influence power quality and system reliability. Therefore, the grid operation is challenging in this scenario. Battery energy storage system (BESS) smooths out wind power production and reduces fluctuations. However BESSs low power density and high capacity requirements to compensate for fluctuation limit its implementation for high-power applications. This paper introduces a wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) incorporating a vanadium redox flow battery (VRFB) in the DC-link, which supports grid power smoothing and power balance even under varying wind speed. Real-site wind data was used to develop a technique for designing the VRFB. The rotor side converter (RSC) employs an off-maximum power point tracking to keep the VRFB operation within the limit and deliver consistent power to the grid. An improved phase lock loop control is designed for DFIG that suppresses DC offset at the input and allows RSC and grid side converter (GSC) to perform appropriately even under grid disturbances. The GSC control is designed with an improved DC-link voltage control by adopting an anti-windup fractional-order proportional-integral (AWFOPI) controller, which can improve transient response. A sparrow search algorithm is adopted to tune the AWFOPI controller. Matlab Simulink is used to simulate the system. Test results show that the system performs well under various operating conditions and is satisfactory. It has also been tested on the OPAL-RT real-time simulator test bench.

Green Hydrogen as a Potential Solution for Reducing Carbon Emissions: A Review

Hydrogen is one of the types of energy discovered in recent decades, which is based on the electrolysis of water in order to separate hydrogen from oxygen. These include grey hydrogen, black hydrogen, blue hydrogen, yellow hydrogen, turquoise hydrogen, and green hydrogen. Generally, hydrogen can be extracted from a variety of sources, including fossil fuels and biomass, water, or a combination of the two. Green hydrogen has the potential to be a critical enabler of the global transition to sustainable energy and zero-emissions economies. Worldwide, there is unprecedented momentum to realize hydrogen's long-standing potential as a clean energy solution. Green hydrogen is a carbon-free fuel and the source of its production is water, and the production processes witness the separation of its molecules from its oxygen counterpart in the water by electricity generated from renewable energy sources such as wind and solar energy. Green hydrogen is one of the most important sources of clean energy, which may be why it is called green hydrogen. It is a clean source of energy, and its generation is based on renewable energy sources, so no carbon gases are released during its production. Green hydrogen produced by water electrolysis becomes a promising and tangible solution for the storage of excess energy for power generation and grid balancing, as well as the production of decarbonized fuel for transportation, heating, and other applications, as we shift away from fossil fuels and toward renewable energies. Green hydrogen is being produced in countries all over the world because it is one of the solutions to reducing carbon emissions, and it is clean, environmentally friendly energy that is derived from clean renewable energy. However, due to the combination of renewable generation and low-carbon fuels, projects for the production of green hydrogen are very expensive. The goal of this review is to highlight the various types of hydrogen, with a focus on the more practical green hydrogen.

Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventional input features used for wind speed prediction, such as wind speed, wind direction, air pressure, and air temperature, new features were identified through the feature importance of the random forest method, including wave height, air pressure difference, air-sea temperature difference, and hours and months, representing the periodic components of time series analysis. The air–sea temperature difference is proposed to replace the wind speed difference to represent atmosphere stability due to the availability and adequate accuracy of the data. A random forest and an artificial neural network model were created to investigate the effectiveness and generality of these new features. Both models are superior to persistence models and models using only conventional features. The random forest model outperformed all models. We believe that time-consuming and tune-required sophisticated models may also benefit from these new features.

Design of Space Efficient Electric Vehicle Charging Infrastructure Integration Impact on Power Grid Network

With an ever-increasing number of electric vehicles (EVs) on the roads, there is a high demand for EV charging infrastructure. The present charging infrastructure in the market requires a lot of space and sometimes leads to traffic congestion, increasing the risk of accidents and obstruction of emergency vehicles. As the current infrastructure requires ample space, the cost of setting up this charging infrastructure becomes very high in metropolitan cities. In addition, there are a lot of adverse effects on the power grid due to the integration of EVs. This paper discusses a space-efficient charging infrastructure and multi-agent system-based power grid balance to overcome these issues. The proposed multi-level EV charging station can save a lot of space and reduce traffic congestion as more vehicles can be accommodated in the space. Depending on the size, capacity, and type of multi-level vehicle charging system, it can serve as a reliable charging solution at sites with medium and high daily footfall. We integrated the EV charging station with IEEE 33 bus test system and analyzed the grid and charging stations. The proposed scheme is exhaustively tested by simulation in a discrete-time event simulator in MATLAB and analyzed with varying EV arrival rates, time periods, etc.

The Influence of Renewable Energy Sources and Electric Energy Storages on Grid Power Balances

In recent decades, increasingly more attention has been paid in our country and abroad to the use of renewable energy sources (RES). However, such factors as nonuniform distribution of RESs over the country territory and stochastic nature of their availability in time impede their wide-scale application. For RESs to be used on a wider scale, relevant regulatory and technical documents stipulating the electric power system designing should be developed, and a number of technical problems should be solved, e.g., those relating to the need of smoothing the electricity generation nonuniformity. In this study, distribution power grids containing RESs (wind and solar power plants) and electric energy storages are considered. The aim of the study is to develop proposals for a procedure of integrating RESs and energy storages into grids. The study results are supposed to be used in developing software and hardware solutions for control of an energy storage and predict the electricity consumption and electricity generation by RESs in power grids. In the study, mathematical modeling (computation experiment) methods were used for development of energy storage and RES models that take into account climatic factors (wind velocity, solar irradiance, and temperature) in certain geographical locations received from open data bases. For studying the grid power balance reliability, the operation of RESs within a generalized distribution grid is considered. The grid contains an equivalent centralized power system source, a distribution network, consumer loads, various types of RESs (wind and solar power plants), and a centralized electric energy storage. The power outputs produced by wind and solar power plants for a day (based on hourly averaged meteorological conditions) and month are analyzed for a number of regions in Russia; the annual amount of electricity generated by them is estimated, and the effect of smoothing the generation schedules due to combination of various types of RES and use of electric energy storages is evaluated. Universal models of RESs and electric energy storages for electric grid load flow analysis are suggested; the application of the developed models for power balance analysis of grids containing RESs and energy storages is illustrated; the key stages of introducing RESs and energy storage devices are identified and described, and tasks for which electrical operation modes have to be numerically analyzed are formulated.

Metal Supported Electrolysis Cells

Solid oxide electrolyser (SOE) technology can become a key player in energy systems, with increasing shares of electricity from fluctuating sources such as wind and solar, contributing to power grid balance and energy storage as well as providing green fuels for transportation. Most mature SOE configurations are electrolyte supported or fuel electrode supported. Metal supported SOE cell configurations are an interesting concept for decreasing costs and increasing robustness. The present study compares fuel electrode supported and metal supported cells in terms of performance and durability under SOE conditions. Special emphasis was on medium temperature operating conditions of 650 °C. Metal supported cells, fabricated using ceramic processing methods, showed a better performance compared to state-of-the-art (SoA) cells with Ni/YSZ fuel electrode supported configuration, fabricated by tape casting and screen printing, under steam electrolysis conditions at 700 and 650 °C. The area specific cell resistance (ASR) was lower by ca. 20% for the metal supported cell in 50% H2O in H2 vs. air at 650 °C. Furthermore, the metal supported cells showed a stable performance—even a slight activation—during long-term steam electrolysis tests over 500 h at 650 °C and −0.25 and −0.5 A/cm2, while the SoA reference cell degraded with 13%/1000 h under the same conditions.

Short-Term Three-Phase Load Prediction With Advanced Metering Infrastructure Data in Smart Solar Microgrid Based Convolution Neural Network Bidirectional Gated Recurrent Unit

The collaboration development between the power supply, solar power, and storage batteries is a formidable commission in the smart solar microgrid. The efficient power management integrated into a smart solar microgrid is required to address these problems. The deep learning predictive tools are utilized to forecast the maximum building’s three-phase load power in the short term. In this project, historical data, from the solar module, battery module, grid data, and climate data, are collected by integrated advanced metering infrastructure in a smart solar microgrid in Taiwan. These historical data are utilized in predicting the building’s three-phase load power in the smart solar microgrid. This project proposed the hybrid model in the short-term three-phase loads forecasting based on convolution neural network bidirectional gated recurrent unit (CNN-Bi-GRU). The CNN-Bi-GRU utilizes the continuous-time sliding window, which is extracted features and reshaped into vectors by CNN layers. The hyperparameter optimization is utilized to construct the highest performance structure of the CNN-Bi-GRU model. The CNN-Bi-GRU performance is compared and evaluated with other state-of-arts models, which are the recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM), and Bi-GRU models. The experiment results prove that the forecasting accuracy could be effectively improved by a hybrid CNN-Bi-GRU model with an appropriate window size of sequential historical data. According to research knowledge, this is the first work to predict the building’s three-phase load power using multiple source data from advanced metering infrastructures (AMI) and deep learning CNN-Bi-GRU in the smart solar microgrid. The CNN-Bi-GRU model is successfully integrated into the efficient power management, which maintains the adequate battery storage level, solar power, and grid power balance under different weather conditions and working requirements of the building’s three-phase load power.

Rebate Auction Mechanisms for Bidirectional Grid Balancing Using Cloud Workload Migrations

Increasing the power-grid’s flexibility is essential for expanding the integration of renewable energy sources in modern power grids. This work presents a new rebate auction framework that allows power grids to use cloud datacenters as managed loads to provide upward/downward grid flexibility. Since the energy consumption of datacenters is proportional to their computational workload, this paper presents a rebate auction framework that can induce cloud workload migrations between datacenters to correct energy imbalances. Unlike existing datacenter-based power grid balancing approaches that have only focused on providing downward flexibility and only considered owner-operated datacenters, the proposed framework provides bidirectional flexibility and accommodates both owner-operated and public cloud datacenters. Thus, providing a general framework for creating workload migrations between datacenters to balance the power grid. Because workloads on public cloud datacenters are managed by end-users, the proposed framework uses monetary incentives (auctioned rebates) to encourage large-scale end-users to migrate their cloud workloads between datacenters to correct energy imbalances. The use of monetary rewards as an incentive hides the complexity of grid-balancing from auction participants, who only participate in the auction to lower their cost, while grid-balancing happens as a result of workload migrations. This paper presents and compares two auction implementations under the proposed framework, a strategy-proof implementation that guarantees truthful bidding as a dominant strategy, but has NP-hard computational complexity, and an alternative implementation that does not guarantee truthful bidding, but has polynomial time complexity. Simulation results show that the proposed framework is effective in incentivizing cloud workload migrations to achieve the grid balancing goal and provides positive utility to all participants.

Control of inverters in standalone and grid-connected microgrid using different control strategies

PurposeThe share of renewable energy sources (RESs) in the power system is increasing day by day. The RESs are intermittent, therefore maintaining the grid stability and power balance is very difficult. The purpose of this paper is to control the inverters in microgrid using different control strategies to maintain the system stability and power balance.Design/methodology/approachIn this paper, different control strategies are implemented to the voltage source converter (VSC) to get the desired performance. The DQ control is a basic control strategy that is inherently present in the droop and virtual synchronous machine (VSM) control strategies. The droop and VSM control strategies are inspired by the conventional synchronous machine (SM). The main objective of this work is to design and implement the three aforementioned control strategies in microgrid.FindingsThe significant contributions of this work are: the detailed implementation of DQ control, droop control and VSM control strategies for VSC in both grid-connected mode and standalone mode is presented; the MATLAB/Simulink simulation results and comparative studies of the three aforementioned controllers are introduced first time in the proposed work; and the opal-RT digital real-time simulation results of the proposed VSM control show the superiority in transient response compared to the droop control strategy.Research limitations/implicationsIn the power system, the power electronic-based power allowed by VSM is dominated by the conventional power which is generated from the traditional SM, and then the issues related to stability still need advance study. There are some differences between the SM and VSM characteristics, so the integration of VSM with the existing system still needs further study. Economical operation of VSM with hybrid storage is also one of the future scopes of this work.Originality/valueThe significant contributions of this work are: the detailed implementation of DQ control, droop control and VSM control strategies for VSC in both grid-connected mode and standalone mode is presented; the MATLAB/Simulink simulation results and comparative studies of the three aforementioned controllers are introduced first time in the proposed work; and the opal-RT digital real-time simulation results of the proposed VSM control show the superiority in transient response compared to the droop control strategy.

Моделювання розвитку електроенергетичної системи України для обґрунтування Другого національно визначеного внеску щодо обмеження викидів парникових газів

Ukraine is obligated to adopt Nationally Determined Contribution to the Paris Agreement (NDC2) in 2021 for a period up to 2030 as a percent of 1990, requiring correct projection of green-houses gases (GHG) emissions first of all in key categories, one of that is heat and electricity production by thermal power plants (TPP) and combined heat and electricity production (CHP) which are burning organic fuel. The Ministry of environment protection and natural resources of Ukraine (MEP) proposed GHG reduction of 65% compared to 1990 which is about 3.2% lower compared to 2018, and there is a very ambitious goal taking into account desired rapid Ukrainian economic growth. To assess the reliability of this proposal the appropriate modeling GHG emission reduction from TPP and CHP was made and presented in the article. The modelling was made taking into account methodological approaches of TIMES-Ukraine modeling system, which is the basic tool for assessment of GHG reduction potential in Ukraine for MEP, and also alternative one used in BACS-RVE modeling tool, which was used for calculations for "Generation Adequacy report" which was developed by Ukrainian transmission system operator "Ukrenergo". The analysis of the modeling results showed that neglecting in the methodology used in TIMES-Ukraine the operation modes of generation and required levels of power grid balance reliability indicators does not allow fully adequately project power system development. The modeling results using BACS-RVE modeling tool showed that the reference case scenario of generation development (according to the TIMES-Ukraine) could be feasible under the condition that GHG emission tax should not be less than 100 EUR per tonne of CO2eqv, and potentially will cause the increase of electricity price. Another important result obtained using BACS-RVE is that GHG emission level for 2030 is higher compared to the reference case scenario, hence additional modeling and discussion of results are required to ensure well-grounded GHG emission projection which will allow reviewing NDC obligation for the power sector of Ukraine. Keywords: power system, greenhouse gases, modeling, load profiles, grid balance reliability

Experimental study and parametric analysis for primary frequency regulation of hydropower unit

With the integration of new large-scale energy sources into grids, power system stability is facing huge challenges. The primary frequency regulation of hydropower generators is an important regulation method for grid frequency stability and power balance. It can effectively suppress the grid frequency fluctuations caused by the high proportion of new energy sources, and it plays a very important role in maintaining the stability of the power system. The regulation parameters of the primary frequency regulation of a hydropower unit have a significant impact on its regulation capabilities and performance indices. The experimental analysis of the effects of the regulation parameters is crucial for the safe and stable operation of a hydropower unit. A frequency regulation parameter test on a hydropower station was conducted in this study to determine the effects of the regulation parameters on the performance of the primary frequency regulation. This provides a reference for the selection of the regulation parameters of a hydropower unit.

몬테카를로 기반 풍력발전단지 손실 저감을 위한 실시간 시뮬레이션 연구

The expansion of renewable energy in electrical system has become a major issue regarding the impact of energy consumption. In the case of wind power plants, a frequent output variation has been caused due to its environmental dependent characteristics which can lead the power curtailment process in order to match power balance of main power grid. The curtailment process of a large-scale wind farm causes loss not only electrical extraction but also mechanical loads. By considering the curtailment issue, the study for minimizing system loss of power plant is required in terms of operation efficiency. This paper proposes an algorithm to achieve flexible control during real power curtailment process in a wind farm considering wake effect. An algorithm based on the Monte Carlo was derived for finding appropriate weight factors based on electrical loss in wind farm arrays. The weight factors has been composed through realistic wind speed analysis from wake effect. A real wind farm model was established by RSCAD and implemented for case studies. A curtailment signal was imposed to verify the effects with composed real-time simulation.

Optimal Linear Controller for Minimizing DC Voltage Oscillations in MMC-Based Offshore Multiterminal HVDC Grids

The paper aims at minimizing DC voltage oscillations in offshore multiterminal high-voltage direct current (HVDC) grids based on modular multilevel converters (MMCs). The DC voltage stability is a crucial factor in multiterminal HVDC networks since it is associated with the grid power balance. Furthermore, DC voltage oscillations can cause the propagation of significant disturbances to the interconnected AC grids. This paper proposes an optimal control technique based on semidefinite programming to improve the DC voltage stability margins under the worst-case perturbation scenario. A centralized optimal linear feedback controller is introduced to achieve this goal while ensuring compliance with the control inputs’ and state variables’ constraints. Furthermore, the methodology is adapted to develop a decentralized optimal linear feedback controller with naturally decoupled constraints on the control inputs and state variables. It is shown that the proposed centralized and decentralized optimal linear controllers can minimize the DC voltage oscillations under the worst-case perturbation scenario in the presence or absence of the droop control gain. The performance of these controllers is verified via eigenvalue stability analysis and time-domain simulations of the MMC-based four-terminal HVDC test grid. Finally, a DC voltage oscillation index is introduced as a potential decision-support criterion. Its applicability is exemplified by identifying, among several options, the HVDC link that gives minimum DC voltage oscillations between independent point-to-point networks while considering the wind intermittency effect.

Wind power short-term prediction over mountain area using a high-resolution WRF model

Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.

Modelling of Wax Actuators in Underfloor Heating Manifolds

Finding sources for power grid balancing has become increasingly important with more renewables used for production. In buildings, heat pumps could be utilized among other electrical appliances. The heat pumps would work at full power to balance the overproduction in the grid. However, short-term grid flexibility announces the consumption need up to 5 minutes in advance, which can prove a problem to control. When there is no current energy need in the building, all valves are closed. That means that when a heat pump with overridden control starts working at the maximum frequency, its full power heats up the local circuit very fast, especially when there is no storage tank. Whether the heat pump overheats and cannot be used for balancing the grid or the whole system opens for heating depends on the regulating valves and their opening speed. For underfloor heating systems, the valve opening speed is slower than for other systems as wax actuators are used. This paper focuses on how to model these wax actuators and determine the opening time to provide input for further studies on flexibility. A physical and a linear segment model are parameterized and the results show that the wax actuator fully opens the valve in six minutes.