Intermittent Power Generation Research Articles

A modern technique to manage energy profile in Iraq: virtual power plant (VPP)

A virtual power plant (VPP) comprises decentralized generation integrated with energy storage and intermittent loads. Such a plant is an independently-controlled single production unit for the electrical grid and market. Frequency changes and power supply interruption are among the several scenarios that lead to peak grid loading. Considering the material and financial challenges, it is necessary to determine an optimal resource scheduling solution; VPP can address these challenges. This study evaluated two different domestic-sector situations in Iraq. MATLAB Simulink was used to simulate a VPP test system comprising loads, energy storage technology, and distributed generation. An analysis of the economic aspects concerning the network and involved resources was performed. The outcomes indicated that home electricity bills reduced by up to 50%. Simulations suggest that using VPP is beneficial as against distribution generators (DG) systems relying solely on intermittent renewable power generation.

Process Analysis on Coal Fired Oxy-fuel Combustion Power Plant Combined with Power-to-Gas Plant

Power-to-Gas(PtG) is a promising technology that produces hydrogen by the electrochemical reaction in water electrolyzer to manage the intermittent power generation from renewable energy such as wind and solar. Water electrolyzer generates hydrogen and highly purified oxygen as a by-product. In this study, the process was examined in coal fired oxy-fuel combustion power plant and in PtG plant to use highly purified oxygen obtained from water electrolyzer. The power generation efficiency of a coal-fired oxy-fuel power plant supplied with oxygen through an air separation unit and that of a coal-fired oxy-fuel power plant supplied with oxygen in a polymer electrolyte membrane (PEM) water electrolysis process were compared. As a result of the analysis, in the case of the combined system of the coal-fired oxy-fuel power plant and the PtG plant, the net power generation and power generation efficiency were higher than the case of individual coal-fired oxy-fuel power plant supplied with oxygen through the air separation unit. The process efficiency is further improved when using biomass, fueling the oxy-fuel combustion boiler rather than coal and when steam-water is in supercritical conditions.

RELIABILITY OF SMART GRIDS WITH SMART ASSETS AND LARGE WIND FARMS

Uncertainties and vulnerabilities exist with extensive integration of renewable energy resources and high dependence of the smart grid system on information communication technology. This paper proposes an innovative four-state smart component model based on Markovian differential time dependent state probabilities concept to capture smart components' intelligent operational behaviour in a smart grid system and to assess detailed reliability performance of the entire system. The proposed approach consists of the four-state smart model of smart component behaviour, stochastic variation of wind power generation outputs, random load variations and quantification of the impacts using Monte Carlo simulation. Also, the approach quantitatively evaluates the impacts of smart components failure in the presence of intermittent power generation of wind farms. The approach is demonstrated by case studies. The results suggest that the four-state smart components model capture the intelligent operational behaviour comprehensively. Findings justify that the intermittent integration of wind power outputs and geographical locations can potentially impact the smart grid system reliability considerably.

Electrochemical Analysis of High-Performance Flow-Electrode Capacitive Mixing (F-CapMix) under Different Operating Conditions

Unlike fixed-electrode-based capacitive mixing (CapMix) cells that are characterized by intermittent power generation, flow-electrode capacitive mixing (F-CapMix) cells show continuous power generation characteristics owing to the continuous flow of the electrode in the unit cell. F-CapMix cells can be operated in two modes, depending on whether the feed water is fresh water or seawater. In this study, the F-CapMix cell was operated under the fresh-water-based flow-electrode/salt water mode (FFW) and the salt-water-based flow-electrode/fresh water mode (FSW). Under the FFW mode (∼0.175 W/m2), the F-CapMix cell showed a higher power density of 6.7% than under the FSW mode (∼0.164 W/m2). Additionally, impedance analysis based on various flow-electrode and feed water flow conditions under both the modes showed that the higher power density of the F-CapMix cell could be attributed to its lower non-ohmic resistance (i.e., lower charge transfer resistance via the ion-exchange membrane) in the FFW mode than in the FSW mode.

A Modified Control Method for Grid Connected Multiple Rooftop Solar Power Plants

This article proposes an enhanced control strategy for a microgrid consisting of multiple rooftop solar photovoltaic sources. These sources are of different power ratings and intermittent in nature. The microgrid is flexible to function either in islanded mode or in grid connected mode. It affects power management and hampers the power quality in microgrid. The proposed control scheme enhances the power quality of the microgrid by providing reactive power compensation to local loads. Thus, it reduces the burden on utility grid. The proposed control technique establishes a priority-based policy for reactive and active power compensation under local demand variance. A smooth and automated transition of microgrid between islanded and grid connected modes is achieved. This seamless transition of the system between two modes avoids the transient in the system. In islanded mode, the proposed control scheme ensures improved voltage regulation under dynamic load demand. It also ensures a minimum burden on the voltage source. Also, the proposed control scheme maintains the power factor of the system near to unity. The system performance is validated experimentally under a number of various operating conditions such as dynamic load variation, grid condition, automated transition, intermittent power generation in real time simulator.

Design of structured control policy for shared energy storage in residential community: A stochastic optimization approach

For energy storage shared by multiple residential consumers who are using electricity based on time-varying price and equipped with solar photovoltaic panels, this study is motivated to design an efficient control policy that allows individual consumers to determine operational decisions to realize economic and feasible energy sharing. Although shared energy storage has been considered a promising and practical solution for sharing energy, a proper control policy is required for realizing the expected benefits and advantages of energy sharing via shared energy storage because of the stochastic nature of fluctuated electricity demand load, intermittent solar power generation, and time-varying electricity price in addition to the complex dynamics existing in consumers’ behavior. This study intends to design a structured control policy that is uniquely designed to allow consumers to share energy with energy cost-saving and less solar power spillage. We develop a mathematical optimization model that can be formulated to efficiently find the designed control policy. Numerical experiments are conducted by simulating the derived control policy using real historical data comprising 14 residential houses in New York and 11 residential houses in Texas. Through the numerical experiments, we validate the feasibility and evaluate the performance of the proposed control policy to demonstrate the practicality. The results of the numerical experiments show that the proposed control policy enables consumers to cost-efficiently share energy while efficiently utilizing solar power generation to meet their electricity demand load without the excessive use of electricity from the grid to save energy costs.

A cost-oriented resource scheduling of a solar-powered microgrid by using the hybrid crow and pattern search algorithm

The tendency towards more and more employing renewable energies, particularly at the distribution system level and the residential sector has significantly increased. The growing penetration level of distributed generation (DG) units based on renewable energies has brought several challenges into power system operation and management. Thus, developing and proposing efficient energy management systems is regarded as a critical task in the area of power system operation. This issue is more highlighted in microgrids (MGs), that are mainly based on DG units. Accordingly, a day-ahead energy management system is proposed, which is operating in the grid-connected mode and equipped with a photovoltaic (PV) panel, a wind turbine (WT), and a microturbine (MT), besides the energy storage system. A detailed mathematical model was used for the PV panel and different weather circumstances and their impacts on the solar power generation and MG day-ahead resource scheduling problem are investigated. Afterward, a hybrid optimization algorithm, obtained by combining the crow search algorithm and pattern search method, known as (HCS-PS) has been proposed to deal with the problem for a 24-h scheduling period. The results, obtained from simulation show that the suggested HCS-PS method is capable of resulting in more superior solutions compared to some other well-established optimization algorithms. Moreover, the uncertainties of the problem due to the intermittent renewable power generation, load demand, and market price, have been successfully handled by deploying a scenario-based stochastic optimization technique.

Effective virtual inertia control using inverter optimization method in renewable energy generation

The high-level penetration of intermittent renewable power generation may limit power system inertia, resulting in system frequency instability in increasing power converter-based energy sources. To resolve this problem, virtual inertia control using distributed gray wolf optimization (DGWO) method in a synchronous generator is simulated under a distinct output fluctuation condition. First, the DGWO algorithm was established to achieve a local and global balance solution, and standard test functions were employed to verify the model convergence. Second, the key parameters that determine the effect of the virtual inertia controller in the power grid were analyzed. A DGWO-based optimization strategy to stabilize inertia was also developed. Finally, simulation results using step and random loads under a high permeability level are provided to verify the effectiveness of the proposed model. In the step load disturbance, the system can recover from the disturbance point to the stable point after 3 s under the regulation of the proposed control strategy, which is reduced by 18 s compared with the traditional control method. In the random load test, it takes only 12 s, 63 s less than the traditional one. Accordingly, the power system frequency can be stabilized more quickly from a disturbance state to a stable stage.

(Student Battery Slam Best Presentation Award Winner) Combining Experimentation and Computation for Accelerated Understanding of Electrode Morphology in Redox Flow Batteries

Redox flow batteries (RFBs) hold promise as an electrochemical intermediary between intermittent renewable power generation and the grid.1 Within the RFB architecture, porous electrodes are an integral component, contributing to a myriad functions, such as providing surface sites for redox reactions, dispersing electrolyte throughout the void matrix, and shuttling electrons through its conductive network.2 Carbonaceous materials, such as cloths, papers, and felts, are commonly used in state-of-the-art RFB embodiments, despite their tailored morphologies in polymer electrolyte fuel cells. Consequently, these media engender suboptimal performance in RFBs, necessitating advancement of electrode structures specifically designed for flow battery operation.While there have been previous systematic studies of RFB electrode structure/function relationships,3,4 most have been limited by manufacturing constraints, restricting the explorable design space. However, recent computational efforts have enabled broad sweeping property analyses,5 leveraging both data science and machine learning algorithms to identify key parameter values. These modeling and statistical pursuits, combined with advanced cell diagnostics, provide a platform for high-throughput material screening in the furtherance of electrode engineering.In this presentation, we highlight some of the computational tools we have developed for simulated analyses of electrode functionalities in RFBs. We then present experimental tools for in situ and operando electrode benchmarking, which can also be simulated with the aforementioned computation toolkit. Finally, we conclude by demonstrating how this combined platform can be used across a wide variety of electrode domains, revealing performance comparisons and micron-scale designs to direct manufacturing avenues.AcknowledgmentsThis work was funded by the Joint Center for Energy Storage Research, an Energy Innovation Hub of the U.S. Department of Energy, Office of Science, Basic Energy (De-AC02-06CH11357). K.M.T. recognizes additional support from the U.S. NSF Graduate Research Fellowship (1122374).References A. Z. Weber et al., J. Appl. Electrochem, 41, 1137 (2011).K. J. Kim et al., J. Mater. Chem. A, 3, 16913–16933 (2015).A. Forner-Cuenca, E. E. Penn, A. M. Oliveira, and F. R. Brushett, J. Electrochem. Soc., 166, A2230–A2241 (2019).K. M. Tenny, A. Forner-Cuenca, Y.-M. Chiang, and F. R. Brushett, J. Electrochem. En. Conv. Stor, 17 (2020).Z. Cheng et al., Applied Energy, 279, 115530 (2020).

Multiple time‐scale economic dispatching strategy for commercial building with virtual energy storage under demand response mechanism

In recent years, commercial building technology integrating renewable energy power generation has developed rapidly. However, the stochastic, intermittent renewable energy power generation and the error of load forecasting of a commercial building (CB) are easy to cause frequent fluctuation of tie-line power, which brings challenges to the economic and safe operation of CB. In order to reduce the impact of renewable energy and load uncertainty on CB, and improve the power stabilization ability of CB, this paper proposes a multiple time-scale (MTS) economic dispatch strategy for CB with building virtual energy storage system (VESS) and vehicle to building (V2B) under demand response mechanism. Firstly, taking CB as terminal load carriers, a new heat balance model based on the R-C network is proposed, and the charging and discharging process of building VESS is analyzed. Secondly, by analyzing the travel trip law and charging and discharging behavior of EVs, an interactive control strategy between EVs and CB is proposed, that is, the control strategy of V2B. Then, Day-ahead optimal economic dispatch mode with 1 hour as the time interval is established with the objective of minimizing the daily operation cost and considering the user temperature comfort factor of CB. Based on the Day-ahead dispatching plan, the coordinated scheduling of VESS and V2B on the long time scale (15 minutes) and short time scale (1 minute) is used to establish a MTS rolling optimization model in Intra-hour adjustment stage. This model is used to modify the Day-ahead plan and stabilize the tie-line power fluctuation. Finally, an example shows that the proposed Day-ahead dispatching model can reduce the daily operating cost, and the Intra-hour adjustment model can stabilize the tie-line power fluctuation on MTS.

Intelligent Control Strategy to Enhance Power Smoothing of Renewable based Microgrid with Hybrid Energy Storage

A stand-alone renewable based microgrid (MG) performance with a hybrid energy storage system has been examined in this work. Stand-alone MG system mainly consists of a solar photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind system. The hybrid energy storage system is based on Ni-Metal- Hydride (NiMH) battery and a supercapacitor (SC). The paper's primary goal is to propose an artificial neural network (ANN) based control strategy for charging/discharging control of Ni-Metal- Hydride battery & supercapacitor. The proposed maximum power tracking techniques (MPPT) include perturb and observe (P& O) algorithm for solar PV system while optimum torque (OT) MPPT for PMSG based wind turbine. The ANN-based control mechanism can maintain the DC bus voltage constant and trigger the supercapacitor to limit the battery current when the battery charging/ discharging current reached its threshold value. The proposed model responds quickly to intermittent nature PV-wind power generation or load power variation.

Small modular reactors for nuclear‐renewable synergies: Prospects and impediments

Conventional energy systems in both developed and developing economies are currently run by fossil-fueled power plants. Many of them are ageing and are now being considered for the replacement to reduce the carbon intensity in electricity generation. One of the main routes to achieve this goal is by scaling-up the deployment of the renewable energy system (RES). However, the increasing share of variable RES tends to affect the electrical grid operation. Hence, the need for reliable and flexible energy sources has emerged to cope with the variability in electricity and energy markets. Small modular reactors (SMRs) are emerging as alternatives to baseload fossil fuel systems and retiring large nuclear plants, primarily due to their small capacity and less-capital intensive characteristics. The intermittency of power generation in the grid with a higher share of RES can be coped with in synergetic and effective way by adopting SMRs that form a nuclear-renewable synergy in fulfilling the electricity demand. Furthermore, SMRs are designed to operate in load-following mode adjusting the power output as demand of electricity fluctuates. SMRs feature modularity intended to enable enhanced constructability and phased-deployment, adapting to the increased share of renewable energy in a distributed energy system, as energy demand increases. This paper will highlight the main outcomes of miscellaneous recent studies on the integrated energy systems carried out by international experts under the aegis of the International Atomic Energy Agency. The final publication of the study discusses the potential synergy of SMRs with RES for electric power production, as well as for other clean-energy applications including seawater desalination, district heating, and hydrogen production.

A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids

Ensuring the power balance and reliability of power grids is an increasing challenge due to the increasing involvement of intermittent renewable power generations. The use of existing heating, ventilation and air-conditioning (HVAC) systems in buildings has attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. However, the energy flexibility of buildings is not consistent, and the capacity available for frequency regulation service changes over time due to the changes of working conditions. In this study, a hierarchical optimal control strategy, consisting of a regulation bidding controller and a power use following controller, is proposed. It optimizes the power use baseline and regulation capacity, and controls HVAC systems to provide qualified frequency regulation service, considering the tradeoff between financial reward (regulation capacity) and thermal comfort while satisfying the operating constraints of HVAC systems. The proposed control strategy is validated on a simulation test platform. Results show that the strategy can maximize the use of regulation capacity provided by HVAC systems while ensuring the indoor environment control quality under a given guarantee rate.

Techno-economic and environmental evaluation of grid-connected and off-grid hybrid intermittent power generation systems: A case study of a mild humid subtropical climate zone in China

In order to promote the development of green buildings, this paper presents a technical, economic, and environmental evaluation of a residential building powered by hybrid intermittent generation systems in a mild humid subtropical climate zone in China. The technical, economic, and environmental mathematical models of hybrid systems are addressed. This study selected Guiyang city, which is a typical mild humid climate zone. The results revealed that the 30 kW grid-connected system for the building was the most economical with a net present cost of $ 28,041 and cost of energy of 0.069 $/kWh, whereas this was the least environmentally friendly form of power generation, emitting a maximum amount of CO2 of 26,609 kg/yr. From an economic and environmental perspective, grid/photovoltaic (PV)/wind hybrid systems in on-grid systems may be a better choice for supplying power to buildings in Guiyang. If the extension of the power grid is not feasible, off-grid PV/battery hybrid systems consisting of 115 kW PV units, 80 battery units, and a 30 kW power converter, are more suitable for supplying power to the building. Furthermore, the results indicated that wind power is not suitable for supplying power to buildings in Guiyang, mainly due to relatively low wind speeds.

Analysis of Power Grid Voltage Stability With High Penetration of Solar PV Systems

Grid integration of solar photovoltaic (PV) systems has been escalating in recent years, with two main motivations: reducing greenhouse gas (GHG) emission and minimizing energy cost. However, the intermittent nature of solar PV generated power can significantly affect the grid voltage stability. Therefore, intermittent solar PV power generation and uncertainties associated with load demand are required to be accounted for to gain a holistic understanding on power grid voltage stability with the high penetration of PV energy sources. This article presents a framework for power grid voltage stability analysis considering uncertainties associated with PV power generation and load demand using Monte Carlo simulation. Commonly used voltage stability indicators such as critical eigenvalue, line loss, reactive power margin have been considered in the proposed framework. The proposed methodology has been verified by analyzing the voltage stability of the IEEE 14 bus test system, with the high penetration of PV energy sources and considering uncertainties associated with load demand. The results provide a clear insight into the voltage stability of power grid with different penetration levels of PV energy sources into the power grid.

Optimal scheduling of flexible thermal power plants with lifetime enhancement under uncertainty

• The proposed method can limit equipment damage in different scenarios. • Damage can be reduced 40% with a revenue loss of less than 1%. • Different damage mechanisms in the equipment can be included in the method. • Variable uncertainty in power demand and price can be included. • Adequate selection of material design temperature reduces deterioration. Renewable energy sources have been the focal point to decarbonise the power sector. The large deployment of these intermittent power generation units requires mechanisms to balance the grid. Thermal power plants can provide this service by increasing the number of start-ups, shut-downs, and intraday ramps at the expense of higher deterioration in critical equipment, including high-pressure steam drums, turbine rotors and blades, and high-temperature heat exchangers and pipes. This work proposes a method to formulate the power generation scheduling of thermal power plants as a stochastic optimisation problem with limitations on the maximum damage in critical components. This method models the uncertainty associated with intermittent power generation from renewable sources with a scenario-tree whilst computing the deterioration of the equipment in each scenario to limit the maximum damage. Scheduling of a flexible natural gas combined cycle demonstrated how this methodology can reduce the deterioration of the superheating heat exchanger of the power plant with minimum detriment in power generation and revenue. Furthermore, the effect of the design temperature of the material on the total damage was analysed for a broad range of temperatures and operating profiles, showing how adequate selection of design temperature can reduce the deterioration of the equipment and enhance its lifetime.

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

Summary Given increased urgency to transition the global economy to net-zero CO2 emission, governments and industry have increased focus on decarbonizing hard-to-abate sectors, including steel making, which contributes roughly 6% of global CO2 emission and 8% of energy-related emission (including power consumption emission). This paper reviews current global iron and steel production and assesses available decarbonization technologies, including hydrogen injection, solid biomass substitution, zero-C electricity substitution, carbon capture and storage (CCS) retrofit, and combinations of these decarbonization approaches. Blast furnace-basic oxygen furnace (BF-BOF) dominates production (71%) and is particularly stubborn to any decarbonization technology. Direct reduced iron to electric arc furnace (DRI-EAF) production is 5% and growing, it appears to have better decarbonization potential to move toward net-zero. Secondary steel production using mainly steel scrap in electric arc furnace (EAF-scrap) is 24% of global production and has both the lowest energy consumption and is technically simplest to decarbonize through electrification but is limited in market share to recycled steel capacity. Of the options assessed, blue hydrogen, carbon neutral biomass, and CCS appear to have the lowest cost and highest technical maturity. However, no single approach today can deliver deep decarbonization to the iron and steel industry and all approaches lead to substantial production cost increase. No uniform ideal solution exists, and different geographies, infrastructure, and economies will determine the local optimum solution with viability and cost. Policy measures will be required to provide financial incentives for decarbonization and to avoid unwelcome outcomes such as emissions leakage or job loss.

Hydropower Pricing and Energy Management Strategies for Promoting Accommodation of Intermittent Renewable Energy Power Generation

Hydropower Pricing and Energy Management Strategies for Promoting Accommodation of Intermittent Renewable Energy Power Generation

Research on Network Loss of Distributed Energy Integration into Power System

At present, the integration of clean energy into the grid has led to excessive network losses. This paper proposes a method for optimizing clean energy power generation parameters. In view of the random characteristics of intermittent renewable energy power generation such as wind power and photovoltaic power generation, the maximum likelihood estimation method is used to optimize the clean energy power generation model parameters. Then, a P-Q equation based on the integration of clean energy into the system was established. Finally, the simulation verified that the optimized power generation model effectively reduces the grid loss.

Design, thermodynamic, and wind assessments of a compressed air energy storage (CAES) integrated with two adjacent wind farms: A case study at Abhar and Kahak sites, Iran

Wind speed fluctuation at wind farms leads to intermittent and unstable power generation with diverse amplitudes and frequencies. Compressed air energy storage (CAES) is an energy storage technology which not only copes with the stochastic power output of wind farms, but it also assists in peak shaving and provision of other ancillary grid services. In this paper, a CAES facility is proposed for two adjacent wind farms, Abhar and Kahak sites in Iran, with a total nominal power of 162.5 MW. To assess site peak profiles and storage potential, annual thermodynamic and wind assessments are carried out predominantly for the three critical months of the year requiring mitigation of electricity scarcity (July, August, and September). Results indicate that the sustained wind speed in July at both the Abhar and Kahak sites is higher than the other two critical months. Around 93, 74 and 60 MW stored power in the CAES facility are added to the grid during 5 h of peak demand in July, August and September months with round trip efficiencies of 52, 47, and 43%, respectively.