Intermittent Power Generation Research Articles

Industrial processes and the smart grid: overcoming the variability of renewables by using built-in process storage and intelligent control strategies

Manufacturers are facing pressure to reduce electricity costs. Onsite renewable energy generation may be a solution, but its high capital cost and intermittent power generation limit its use. Grid-responsive smart manufacturing could effectively incorporate renewables in industrial processes. This study integrates grid-responsive smart manufacturing with renewables on an industrial plant scale and demonstrates both a favourable economic and environmental outcome. A user-friendly decision-aid model for energy management is provided to manufacturers. A case study shows how solar panels, industrial batteries, smart pumping strategies, and various combinations of those elements can save on electricity costs. Dynamic simulation results demonstrate that grid-responsive smart manufacturing can effectively lower peak demand. The economic results show that grid-responsive smart manufacturing and renewables synergistically optimise cost reductions. The solar coupled with smart pumping scenario shows annual cost savings of $755,200, accounting for 4.6% of the total electricity cost. Smart pumping alone saves $371,900 annually with a 0.7-year payback period, demonstrating how the manufacturing sector can utilise its own processes in load shifting. This study supports that incorporating grid-responsive smart manufacturing with renewables can effectively reduce electricity costs and emissions for industry. Abbreviations: e: Equivalent; GHG: Greenhouse gas; PBP: Payback period; PV: Photovoltaics; SP: Setpoint; VFD: Variable speed drives

Research on Coordinated Control Strategy of Thermal Heating and Melting Depth of Steam Heating and Melting Salt Reservoir

The implementation of the upgrading of the national coal electric power unit has provided a clear proposal to promote the clean and low-carbon transformation of the power industry. With the power of large-scale intermittent renewable energy and power generation, the electric crew should be flexible enough to adjust resources to achieve a depth of 35% THA. This article aims to propose a heat extracting and heat storage system for fire power plants, to realize the coordinated control strategy of the deep peak, and to explore the coordinated control strategy of the steam–molten salt heat exchanger, molten salt and water exchanger, and the turbine’s main control. The simulation results reveal that the coordinated control of the steam–molten salt heat exchanger, molten salt and water heat exchanger, and steam turbine control could reduce the depth of the fire power unit by 10% THA. The output power response speed of the thermal power unit is enhanced by utilizing the heat turbine, which could effectively enhance the output power response speed of the thermal power unit and increase the output power response speed pertinent to 302.55 s by 75.60%.

Development of a reliable simulation framework for techno-economic analyses on green hydrogen production from wind farms using alkaline electrolyzers

The present study investigates the feasibility of coupling the intermittent electric power generation from a wind farm with alkaline electrolyzers to produce green hydrogen. A physically accurate model of commercial electrolytic modules has been first developed, accounting for conversion efficiency drop due to modules’ cool down, effects of shutdowns due to the intermittence of wind power, and voltage degradation over the working time frame. The model has been calibrated on real modules, for which industrial data were available. Three commercial module sizes have been considered, i.e., 1, 2 and 4 MW. As a second step, the model has been coupled with historical power datasets coming from a real wind farm, characterized by a nominal installed power of 13.8 MW. Finally, the model was implemented within a sizing algorithm to find the best combination between the actual wind farm power output and the electrolyzer capacity to reach the lowest Levelized Cost Of Hydrogen (LCOH) possible. To this end, realistic data for the capital cost of the whole system (wind farm and electrolyzers) have been considered, based on industrial data and market reports, as well as maintenance costs including both periodic replacements of degraded components and periodic maintenance. Simulations showed that, if the right sizing of the two systems is made, competitive hydrogen production costs can be achieved even with current technologies. Bigger modules are less flexible but, by now, considerably cheaper than smaller ones. A future economy of scale in alkaline electrolyzers is then needed to foster the diffusion of the technology.

Perovskite quantum dot-based tandem triboelectric-solar cell for boosting the efficiency and rain energy harvesting

Silicon (Si) solar cell efficiently convert solar energy into electricity under the sun illumination, but its inherent intermittent power generation due to diurnal and seasonal cycle has usually allowed for the requirement for alternative power sources. Owing to the abundance, diversity and availability, rain drop energy is one of the most promising green energy sources. Hence, hybrid energy harvesting could be a promising strategy of achieving preferential hybrid performances without sacrificing the advantages in individual devices. Herein, we proposed a hybrid solar and rain energy harvesting device featuring the integration of triboelectric nanogenerator (TENG) top-cell and a crystalline Si solar cell bottom-cell by laminating perovskite quantum dots-embedded polydimethylsiloxane composite film (PQDP) onto the Si solar cell, which endows enhanced electrical output performances under rainy days. To overcome the limitation of low external quantum efficiency (EQE) in the ultraviolet region of the Si solar cell, the PCE of the hybrid solar cell is significantly enhanced from 18.47% of bare Si solar cell to 20.16% owing to the anti-reflection effect of PQDP film and the down-conversion property of CsPbBr3 QDs doped with Ce3+ ions. Meanwhile, this hybrid solar cell presents excellent durability after continuous measurement over 7 days. This tandem hybrid solar cell provides a promising strategy to simultaneously scavenging the solar and rain energy and boosting the power conversion efficiency by introducing the perovskite QDs without affecting the efficiency of bare Si solar cell.

Towards a Power Production from 100% Renewables: The Italian Case Study

The need to reduce greenhouse gas emissions is driving many actions to decarbonize the most impactful sectors. Among these, the energy sector accounts for almost one third of emissions. Increasing the penetration of renewable energy in the energy mix could easily reduce the emissions of this sector. Theoretically, the target to aim for would be 100% renewable energy production. However, the variable nature of power production from photovoltaic and wind systems, which are expected to play a key role in the energy transition, may pose several limitations to the effective penetration of renewable energy. Many concerns arise when one considers the large diffusion of renewable energy that would be required to meet green targets, and the operating conditions of other systems in charge of compensating for renewable energy variations. This study aims to investigate the potential impact of an increase in the amount of renewable energy installed in a country, particularly in Italy. A simplified approach has been used, based on the assumption of knowing the hourly demand and power generation mix, and multiplying the intermittent power generation by a certain factor. Although not accurate, this approach allows the authors to highlight some critical aspects regarding the potential surplus of renewable energy and the operating conditions of other energy sources. The results of this study may provide a useful basis for a preliminary system evaluation, in particular to assess the feasibility of surplus recovery and the operability of residual generation systems. In addition, it may be easily replicated in other countries for similar estimations.

Local Power Distribution—A Review of Nanogrid Architectures, Control Strategies, and Converters

Environmental issues and the global need to extend sustainable access to electricity have fostered a huge amount of research in distributed generation by renewables. The challenges posed by the widespread deployment of distributed generation by renewables, such as intermittent power generation, low inertia, the need for energy storage, etc., call for the development of smart grids serving specific local areas or buildings, referred to as microgrids and nanogrids, respectively. This has led in the last decades to the proposal and actual implementation of a wide variety of system architectures and solutions, and along with that the issue of the power converters needed for interfacing the AC grid with DC micro- or nanogrids, and for DC regulation within the latter. This work offers an overview of the state of the art of research and application of nanogrid architectures, control strategies, and power converter topologies.

Enhanced Situational Awareness for DER-Rich Distribution Systems Under Pre- and Post-Outage

Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.

A novel forecast-based operation strategy for residential PV-battery-flexible loads systems considering the flexibility of battery and loads

Leveraging the flexibility of static batteries and residential flexible loads is an effective way to reduce the impacts of intermittent renewable energy power generation on the utility grid. However, there is lack of proper operation strategies to manage these distributed energy resources. To address this problem, a novel forecasting and flexibility-based operation strategy was proposed for the photovoltaic-battery-flexible loads (PV-battery-FL) system of a single household. Compared with the traditional maximizing self-consumption and time-of-use strategies, the proposed strategy fully harnesses the flexibility of batteries and residential flexible loads, and is more grid-friendliness to the utility grid. To assess the performance of the PV-battery-FL system under the proposed strategy, a comprehensive evaluation framework was developed considering the economic and environmental performance, PV self-consumption, zero energy potential, and impacts on the utility grid. The effectiveness of the forecasting and flexibility-based strategy was validated by case studies given various scenarios. Results indicate that the proposed strategy performs better than the maximizing self-consumption and time-of-use strategies. Especially, the average ramping index reduction ratio of the forecasting and flexibility-based strategy was improved by 105.1% and 60.9%; the maximum power fed into the utility grid was decreased by 50.3%, compared with the maximizing self-consumption and time-of-use strategies for the PV-battery-FL system. When the feed-in limitation exists, the forecasting and flexibility-based strategy can also reduce the cut-off energy significantly. This study can provide guidance for the demand-side management of the battery and flexible loads of residential PV-battery-FL systems in practice.

Surface Modification of PAN-Derived Commercial Graphite Felts Using Deep Eutectic Solvents for their Application as Electrodes in All-Vanadium Redox Flow Batteries.

All-vanadium redox flow batteries are promising large-scale energy storage solutions to support intermittent power generation. Commercial graphite felts are among the most used materials as electrodes for these batteries due to their cheap price, high conductivity, and large surface area. However, these materials exhibit poor wettability and electrochemical activity towards vanadium redox reactions, which translates into overpotentials and lower efficiencies. Deep eutectic solvents (DES) are mixtures of Lewis acids and bases that exhibit lower melting points than their original components. Here, a DES composed of choline chloride and urea, and a DES composed of FeCl3 and NH4 Cl have been employed to modify the surface of graphite felts alongside a series of re-carbonization steps. The resulting materials were compared against pristine, thermally activated, and oxidatively activated graphite felts. Our results indicated that the treatments introduced new oxygen and nitrogen functionalities to the carbonaceous surface and increased the surface area, the degree of disorder and defects in the graphitic layers of the fibres. Cyclic voltammetry studies demonstrated higher electrochemical activity towards vanadium redox reactions and electrochemical impedance spectroscopy experiments showed the modified materials exhibited significantly lower charge transfer resistances. When tested in full cell configuration the electrode modified with the urea-based DES exhibited comparable coulombic efficiencies and superior energy storage capacity retention than the thermally oxidized felt used as benchmark, suggesting that the introduction of oxygen- and nitrogen-rich functional groups had a positive effect on the overall electrochemical performance of graphite felts.

Power Hardware-in-the-Loop (PHIL): A Review to Advance Smart Inverter-Based Grid-Edge Solutions

Over the past decade, the world’s electrical grid infrastructure has experienced rapid growth in the integration of grid-edge inverter-based distributed energy resources (DERs). This has led to operating concerns associated with reduced system inertia, stability and intermittent renewable power generation. However, advanced or “smart” inverters can provide grid services such as volt-VAR, frequency-Watt, and constant power factor capabilities to help sustain reliable grid and microgrid operations. To address the challenges and accelerate the benefits of smart inverter integration, new approaches are needed to test both the impacts of inverter-based resources (IBRs) on the grid as well as the impacts of changing grid conditions on the operation of IBRs. Power hardware-in-the-loop (PHIL) stands out as a strong testing solution, enabling a real-time simulated power system to be interfaced to hardware devices such as inverters which can be implemented to determine interactions between multiple inverters at multiple points of common coupling on the grid and microgrids. This paper presents a review of PHIL for grid and microgrid applications including recent advancements and requirements such as real-time simulators, hardware interfaces and communication and stability considerations. An illuminating case study is summarized followed by exemplary PHIL testbed developments around the world, concluding with a proposed research paradigm to advance the integration of smart grid-following and grid-forming inverters.

Multi-Objective Energy Management of a Smart Home in Real Time Environment

In home energy management, the occupants schedule the operating appliances to achieve lowest optimal energy cost with minimum discomfort. Smart home energy management turns out to be challenging due to the penetration of rooftop intermittent solar power generations. This article develops a multi-objective and real time smart residential load management and hence energy management topology considering variation in rooftop solar power generation with variation in price of distribution grid energy. Primarily, the optimization problem is established as a time average stochastic optimization formulation and then simplified to mixed integer linear programming (MILP) using Lyapunov optimization. The energy cost minimization objective is added with ocular and thermal discomfort minimization which also lowers the discomfort level for occupants due to actions caused by real time load management. The home loads are managed by an Internet-of-Things (IoT) based controller which also alerts the occupants for any incipient malfunction in the household loads. The proposed control technique is authenticated on a real-life smart residential data with simulations and experiments. The resultant controller can thus optimally control the smart residential loads in real time.

Research on optimal operation model of multi-energy complementary energy system considering multi-type peak shaving costs

The vigorous development of clean energy is an important way to achieve “Carbon peak, Carbon neutral”, but the inherent intermittent and uncontrollable power generation characteristics of clean energy such as scenery and other comprehensive factors make the energy consumption problem more prominent, and the multi-energy complementary can solve the energy consumption problem more effectively. However, the current study of multi-energy complementary operation only considers the fuel cost, load shedding and wind and solar abandonment cost, and does not consider enough the peaking cost of thermal power units and the life loss of batteries. To address these problems, a multi-energy complementary energy system operation optimization model considering multiple types of peaking costs is established, and the peaking costs of regulating units are modeled more finely and solved by a dynamic inertia weighted particle swarm algorithm. The results demonstrate that the addition of energy storage system can effectively reduce the total operating cost of the system under the condition of large-scale clean energy grid connection, and the addition of creeping cost and low load operating cost can reflect the cost of conventional units more realistically, which can better provide the dispatching strategy for the decision of multi-energy complementary operation of power system.

A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin

With the growing percentage of the intermittent renewable power generation, the energy system is under increasing pressure in balancing the supply and the demand. As a major part of urban energy consumptions, buildings can provide considerable regulation flexibility for the energy system by actively managing their energy demands. For the building energy flexibility (BEF) provided by thermostatically controlled loads (TCL), its dispatch performance is vulnerable to the building thermal parameter errors, and in some cases, occupants need to provide the critical information related to the indoor temperature state and the occupancy state to the energy management system outside buildings, which decreases the availability of the BEF and raises privacy concerns. For these issues in the BEF utilization, this paper proposes a data-model fusion dispatch strategy based on the digital twin (DT). The proposed strategy is capable of parameter fault tolerance and privacy protection by combining the model-free advantage of the data-driven method with the analytical optimization advantage of the model-driven method. Firstly, a DT-based BEF dispatch framework is proposed. Secondly, the building DT is established by combining the building thermal dynamics (BTD) data-driven model and the TCL operation mechanism model. And the building response deduction is carried out based on the DT. Finally, under the rolling optimization framework, the data-model fusion dispatch strategy is devised by uniting the DT deduction and the optimization constructed by the BTD mechanism model, in which the multi-dimensional modeling of the BTD is carried out from the state dimension and the energy dimension. The simulation results show that the optimization result can reach 98.4% of the ideal result under the scenario with 15% parameter random error, and 98.3% of the ideal result under the scenario with 15% random state noise injection.

Performance analysis of induction heated-porous thermochemical energy storage for heat applications in power systems

Power flexibility with fast and long-duration heat storage systems is crucial in modern power systems to meet the increasing cooling and heating demand and reduce the gap between renewable intermittent power generation and power demand. The hereby study analyzes the thermal and electrical performances of induction heated-porous thermochemical energy storage for heat applications into microgrids. The induction heating model is built with Maxwell equations for fast induction heat generation and the Surface-to-Surface (S2S) model, P1 approximation and Brinkmann model are adopted for induced and diffused heat transfer in the fluid phase and porous heat storage medium. The effects of the conductive plates’ orientation and location and the coil’s location are investigated. Furthermore, the effect of operating and structural parameters in terms of the electrical power supplied to the coil and the heat transfer coefficient of the coil’s cooling system and reactor insulation are sufficiently examined. Finally, essential parameters for the thermochemical material selection are assessed. It is found that the model with the highest magnitude of the induced magnetic flux passing through the conductive plates has outperformed at 107.57 % the worst model among the developed reactors. Moreover, the temperature distribution of the reactor resulted from the supplied power to the homogenized multi-turn coil and the heating rate at the heat storage material significantly increases with the supplied power. However, increasing the power supplied to the coil leads to higher conductive and convective heat loss resulting in less efficient electrical to heat conversion performance. Thermochemical materials with low emissivity should be preferred as the emissivity could lead to a 13.7 % improvement in the energy efficiency of the reactor. Implementing the proposed model in a microgrid for cooling and heating load improved the energy cost and self-consumption by 31.7 and 8 %, respectively.

LSTM Recurrent Neural Network-Based Frequency Control Enhancement of the Power System with Electric Vehicles and Demand Management

Due to the unpredictable and stochastic nature of renewables, current power networks confront operational issues as renewable energy sources are more widely used. Frequency stability of modern power systems has been considerably harmed by fast and unpredictable power variations generated by intermittent power generation sources and flexible loads. The main objective of the power system frequency control is to ensure the generation demand balance at all times. In reality, obtaining precise estimates of the imbalance of power in both transmission and distribution systems is challenging, especially when renewable energy penetration is high. Electric vehicles have become a viable tool to reduce the occasional impact of renewable energy sources engaged in frequency regulation mainly because of vehicle-to-grid technologies and the quick output power management of EV batteries. The rapid response of EVs enhances the effectiveness of the LFC system significantly. This research work investigates a deep learning strategy based on a long short-term memory recurrent neural network to identify active power fluctuations in real-time. The new approach assesses power fluctuations from a real-time observed frequency signal precisely and quickly. The observed power fluctuations can be used as a control reference, allowing automatic generation control to maintain better system frequency and ensure optimum generation cost with the use of demand management techniques. To validate the suggested method and compare it with several classical methods, a realistic model of the Indian power system integrated with distributed generation technology is used. The simulation results clearly indicate the importance of power fluctuation identification as well as the benefits of the proposed strategy. The results clearly show a considerable improvement in response performance indices, as the maximum peak overshoot was decreased by 21.25% to 51.2%, and settling time was lowered by about 23.34% to 65.40% for the suggested control technique compared to other controllers.

Coordinated integration of distributed energy resources in unit commitment

Distributed energy resources, such as electric vehicles and roof-top solar panels have become increasingly popular and essential components of power distribution systems. However, their growth poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, increased peak demand, and unexpected frequency/voltage fluctuation. To tackle these challenges, we developed a vehicle-to-grid model, which incorporated dynamic electric vehicles usages, such as driving times, driving distance, and charging/discharging locations. The machine learning method of principal component analysis and XGBoost were used to develop the solar energy prediction model. We also developed a novel unit commitment model to coordinate and aggregate the distributed energy resources in power generation economic dispatch. Electric vehicle charging was used as elastic demand and electric vehicle discharging was used as power generation sources. The solar energy was considered as a negative load. Simulation results showed that uncontrolled electric vehicle charging and solar energy could negatively impact the power systems. For example, the load ramping rate was increased by 384%. Simulation results also showed that the proposed models could mitigate the negative impact and improve energy efficiency. For example, the peak demand, load ramping rate and average generation cost were reduced by 21%, 79%, and 27%.

Privacy-preserving federated learning for residential short-term load forecasting

With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting.

Investigation of internal flow characteristics by a Thoma number in the turbine mode of a Pump–Turbine model under high flow rate

The pump–turbine unit used in pumped-storage power generation plays a role in backing up and adjusting the power system in a flexible power generation facility, providing stability in response to renewable energy power grids with intermittent power generation. Owing to changes in the operating conditions of flexible power generation facilities, pump–turbine units have been operated continuously in off-design conditions. At the high flow rates in off-design conditions, undesirable flow with vortex rope is caused in the draft tube, which leads to operating system instability. Meanwhile, when operating at high flow rates in the turbine mode of the pump–turbine unit, the Thoma number must be considered for relatively stable operations due to cavitation phenomena. Thus, to expand stable operating ranges through the understanding of high flow rates, unsteady internal flow and pressure phenomena were investigated depending on the Thoma number in the turbine mode of the pump–turbine model through analyses with laboratory-scale pump–turbine model experiments and unsteady-state numerical analyses via systematic quantitative and qualitative comparisons. There was no difference in flow characteristics at the runner and draft tube areas based on the Thoma number at the best efficiency point. Nevertheless, torch-shaped visible vortex ropes developed and differences in flow characteristics at the draft tube appeared based on Thoma number under high flow rates.

Control of Single-Phase Distributed PV-Battery Microgrid for Smooth Mode Transition With Improved Power Quality

In this article, multiple parallel photovoltaic (PV) inverters based microgrid is developed to enhance the reliability and accessibility of electricity in remote areas. A self-adaptive control strategy is presented for a microgrid to ensure a seamless transition between the islanded mode (IAM) and grid-connected mode (GCM) of operation with a proportional power sharing. This microgrid consists of two solar PV inverters based on local distributed generation (DG) units. Each PV-inverter unit feeds an active power to support local loads and the power demand of each local load is shared between respective DG units and microgrid. The PV-battery-interfaced main unit with a self-adaptive control strategy in parallel with a PV array supported unit-2 is considered to operate in the IAM and GCM of operation. The control strategy achieves transient free operation during load disturbances, power variation at the PV array side, and off-grid operation. Moreover, the microgrid ensures high-quality performance at the point of common coupling and provides the seamless transition between GCM and IAM to guarantee a continuous power to critical loads. The microgrid simulation and experimental results are presented under various operating conditions, such as load variation, intermittent power generation, and automated mode transition between GCM and IAM.

Comparative Analysis of a DC-microgrid Incorporating Hybrid Battery/Supercapacitor Storage System Addressing Pulse Load

Renewable energy sources (RESs) have become the primary power source of microgrids with many merits. However, RESs depend on the climate (i.e., wind speed and solar irradiance), leading to intermittent power generation. Moreover, some of the particular loads, such as the pulse load, severely impact the microgrid performance. Therefore, energy storage systems (ESSs) are considered a vital solution to improve microgrid performance and overcome microgrid challenges. This paper presents a comparative analysis of the performance of a DC-microgrid incorporating a battery storage system (BSS) and a hybrid battery/supercapacitors storage system (B-SCSS). The study addressed the fluctuations of solar radiation and wind velocity on the system performance in the presence of both energy storage technologies. In addition, the sudden load disturbance is also investigated with particular attention to the pulse load. The overall microgrid components are designed and controlled based on the PI controller to enhance the system's stability. The photovoltaic and wind energy are utilized with maximum power tracking (MPPT) to get the full benefits from RESs. The B-SCSS is proposed to maintain the DC-bus voltage, load power, and frequency constant during various disturbance events. The obtained results using Matlab software prove the effectiveness of the proposed method.