Operation Of Generating Units Research Articles

Hydrogen vs. conventional sector-coupling residential energy systems: An economic and environmental comparison based on multi-objective design optimization

Planning residential energy systems faces economic, environmental, and technical challenges, necessitating cost-effective and environmentally friendly solutions. Hydrogen technologies in districts have recently gained interest. However, their economic and environmental potential relative to conventional technologies remains largely unexplored. Therefore, we quantitatively compared the impacts of designing hydrogen-based and conventional sector-coupling systems on total costs and CO2 emissions.We developed a multi-objective optimization model for the design and operation of generation and storage units, aiming to minimize total costs and CO2 emissions. We created three energy system configurations for an exemplary German district, each involving photovoltaics, heat storage, and batteries, while varying the main heat supplier. The first configuration includes a gas combined heat and power unit, the second features a heat pump, and the third incorporates a fuel cell, an electrolyzer, and hydrogen storage.Our comparison of the Pareto fronts revealed that the hydrogen system’s cost is 83% higher than the gas-based system and 33% higher than the heat-pump-based system. Environmentally, the hydrogen system emits 11% less than the gas-based system but 47% more than the heat-pump-based system. In conclusion, hydrogen units offer environmental benefits through improved photovoltaic utilization but perform poorly economically due to high investment and operating costs.

Directional Adaptive Triboelectric Nanogenerator with Wind-Wave Synergy.

To enhance the adaptability and synergy of the triboelectric nanogenerator (TENG) in a wave environment, this paper introduces a directional adaptive triboelectric nanogenerator (DA-TENG) with wind-water synergistic action for wave energy collection. An innovative design combining a wind vane on the top and fan-shaped blade electrodes internally allows the DA-TENG to adjust its swinging direction adaptively to align with the direction of wave motion. The internal multiple power generation units work in coordination, effectively addressing the issues of low efficiency associated with spherical TENGs in capturing multidirectional wave energy. The DA-TENG demonstrates superior performance under various wind speed conditions, showcasing its practical application potential. Experimental results show that the DA-TENG, equipped with a single tail wind vane and a 700 g mass block, can achieve an output voltage, current, and charge of 374.97 V, 84.77 μA, and 622.69 nC under a mild wind environment. Its peak power density reaches 7.51 W m-3, enabling successful data transmission for a wireless temperature and humidity sensor and powering 248 light-emitting diodes (LEDs). This research expands the possibilities of omnidirectional wave energy collection and the collaborative operation of multiple power generation units, offering an effective method for powering low-power maritime devices.

Optimizing urban energy management: A strategic examination of smart grids and policy regulations

This research addresses the critical challenges in urban energy management, focusing on load forecasting and optimizing renewable energy sources considering policy regulation. A novel Hybrid Long Short-Term Memory-Artificial Neural Network (LSTM-ANN) learning technique is proposed for accurate load and renewable energy output power forecasting. The hybrid model leverages the strengths of LSTM and ANN, providing a robust solution for capturing temporal dependencies and complex patterns in the data. Additionally, the Bee Algorithm for Optimization (BAO) is employed to handle the nonlinearity and nonconvexity inherent in energy management problems. BAO optimally schedules the operation of various generation units, considering the diverse characteristics of gas turbines, photovoltaic panels, and wind turbines. The effectiveness of the proposed hybrid model and optimization algorithm is demonstrated through comprehensive comparisons, showcasing superior accuracy and convergence speed compared to traditional methods. This research contributes to the advancement of intelligent energy management systems, offering a reliable framework for optimizing renewable energy utilization in real-world applications.

Assessment of Spatial and Temporal Modeling on Greenhouse Gas Emissions From Electricity Generation

This paper highlights the importance of precise assessments of greenhouse gas (GHG) emissions associated with power generation for effective policy making in environmental sustainability. The current assessment approaches based on historical data or estimated generation using energy models may not accurately reflect the reality of future power systems due to the impact of spatial-temporal and techno-economic characteristics of generation mix and load demands. To address this, the paper presents a comprehensive methodology for accurately quantifying the geographical and temporal variations in GHG emissions associated with generating units’ operation, startup, and shutdown at an hourly resolution. The methodology is based on a detailed electricity model that considers various sources of generation, techno-economic, and spatial-temporal characteristics of system components. The study demonstrates the effectiveness of the methodology in quantifying GHG emissions in the IEEE RTS-GLMC system, with a focus on CO2, N2O, and CH4. The analysis reveals significant variations in GHG emissions among different generation buses and hours of the year, attributed to the high proportion of renewable energy in the generation mix. The paper emphasizes the inadequacy of examining marginal environmental impacts based on GHG emission intensity alone and suggests a more thorough analysis based on total GHG emissions generation. Finally, the paper emphasizes the crucial role of time-varying and marginal assessment techniques in identifying effective strategies for reducing GHG emissions in the electricity sector, including optimizing the operation and capacity of generation units, energy storage systems, and electric vehicles, including their locations.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs, especially in a microgrid, have caused many power system issues, such as control and operation of these networks, to be affected. The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid. In this paper, the optimum operation of distributed generation resources and heat and power storage in a microgrid, was performed based on real-time pricing through the proposed gray wolf optimization (GWO) algorithm to reduce the energy supply cost with the microgrid. Distributed generation resources such as solar panels, diesel generators with battery storage, and boiler thermal resources with thermal storage were used in the studied microgrid. Also, a combined heat and power (CHP) unit was used to produce thermal and electrical energy simultaneously. In the simulations, in addition to the gray wolf algorithm, some optimization algorithms have also been used. Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm. The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid, considering the demand response program.

Устойчивость работы электротехнических комплексов морских судов при изменении настроек регуляторов частоты дизель-генераторов

The article deals with the issue of ensuring the sustainable operation of ship electrical power systems. Experimental oscillograms of power exchange and in-phase oscillations during parallel operation of diesel generator units are presented. The results of mathematical modeling allow us to conclude that it is necessary to limit the change in the values and ratios of the transmission coefficients of the governor. It is proposed to limit the change in the transmission coefficients of the governor when eliminating power exchange oscillations.

A Comparative Analysis of Maximum Power Point Techniques for Solar Photovoltaic Systems

The characteristics of a PV (photovoltaic) module is non-linear and vary with nature. The tracking of maximum power point (MPP) at various atmospheric conditions is essential for the reliable operation of solar-integrated power generation units. This paper compares the most widely used maximum power point tracking (MPPT) techniques such as the perturb and observe method (P&O), incremental conductance method (INC), fuzzy logic controller method (FLC), neural network (NN) model, and adaptive neuro-fuzzy inference system method (ANFIS) with the modern approach of the hybrid method (neural network + P&O) for PV systems. The hybrid method combines the strength of the neural network and P&O in a single framework. The PV system is composed of a PV panel, converter, MPPT unit, and load modelled using MATLAB/Simulink. These methods differ in their characteristics such as convergence speed, ease of implementation, sensors used, cost, and range of efficiencies. Based on all these, performances are evaluated. In this analysis, the drawbacks of the methods are studied, and wastage of the panel’s available output energy is observed. The hybrid technique concedes a spontaneous recovery during dynamic changes in environmental conditions. The simulation results illustrate the improvements obtained by the hybrid method in comparison to other techniques.

Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources

Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model.

Improvement of the Distribution Systems Resilience via Operational Resources and Demand Response

This article presents a restoration approach for improving the resilience of electric distribution systems (EDSs) by taking advantage of several operational resources. In the proposed approach, the restoration process combines dynamic network reconfiguration, islanding operation of dispatchable distributed generation units, and the prepositioning and displacement of mobile emergency generation (MEG) units. The benefit of exploring a demand response (DR) program to improve the recoverability of the system is also taken into account. The proposed approach aims to separate the in-service and out-of-service parts of the system while maintaining the radiality of the grid. To assist the distribution system planner, the problem is formulated as a stochastic-scenario-based mixed-integer linear programming model, where uncertainties associated with PV-based generation and demand are captured. The objective function of the problem minimizes the amount of energy load shedding after a fault event as well as PV-based generating curtailment. To validate the proposed approach, adapted 33-bus and 83-bus EDSs are analyzed under different test conditions. Numerical results demonstrate the benefits of coordinating the dynamic network reconfiguration, the prepositioning and displacement of MEG units, and a DR program to improve the restoration process.

A Neuro-Predictive Controller Scheme for Integration of a Basic Wind Energy Generation Unit with an Electrical Power System

Developing control methods that have the ability to preserve the stability and optimum operation of a wind energy generation unit connected to power systems constitutes an essential area of recent research in power systems control. The present work investigates a novel control of a wind energy system connected to a power system through a static VAR compensator (SVC). This advanced control is constructed via integration between the model predictive control (MPC) and an artificial neural network (ANN) to collect all of their advantages. The conventional MPC needs a high computational effort, or it can cause difficulties in implementation. These difficulties can be eliminated by using Laguerre-based MPC (LMPC). The ANN has high performance in optimization and modeling, but it is limited in improving dynamic performance. Conversely, MPC operation improves dynamic performance. The integration between ANN and LMPC increases the ability of the Neuro-MPC (LMPC-ANN) control system to conduct smooth tracking, overshoot reduction, optimization, and modeling. The new control scheme has strong, robust properties. Additionally, it can be applied to uncertainties and disturbances which result from high levels of wind speed variation. For comparison purposes, the performance of the studied system is estimated at different levels of wind speed based on different strategies, which are ANN only, Conventional MPC strategy, MPC-LQG strategy, ANN- LQG strategy, and the proposed control. This comparison proved the superiority of the proposed controller (LMPC-ANN) for improving the dynamic response where it mitigates wind fluctuation effects while maintaining the power generated and generator terminal voltage at optimum values.

Rapid Quantitation of Coal Proximate Analysis by Using Laser-Induced Breakdown Spectroscopy

Proximate analysis of coal is of great significance to ensure the safe and economic operation of coal-fired and biomass-fired power generation units. Laser-induced breakdown spectroscopy (LIBS) assisted by chemometric methods could realize the prediction of coal proximate analysis rapidly, which makes up for the shortcomings of the traditional method. In this paper, three quantitative models were proposed to predict the proximate analysis of coal, including principal component regression (PCR), artificial neural networks (ANNs), and principal component analysis coupled with ANN (PCA-ANN). Three model evaluation indicators, such as the coefficient of determination (R2), root-mean-square error of cross-validation (RMSECV), and mean square error (MSE), were applied to measure the accuracy and stability of the models. The most accurate and stable prediction of coal proximate analysis was achieved by PCR, of which the average R2, RMSECV, and MSE values were 0.9944, 0.39%, and 0.21, respectively. Although the R2 values of ANN and PCA-ANN were greater than 0.9, the higher RMSECV and MSE values indicated that ANN and PCA-ANN were inferior to PCR. Compared with the other two models, PCR could not only achieve accurate prediction, but also shorten the modeling time.

Spinning reserve stochastic model of compressed air energy storage in day-ahead joint energy and reserve market using information gap decision theory method

Regarding numerous benefits of the compressed air energy storage (CAES) in the utility level, these devices can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources and demands. This paper proposes a two-stage mathematical optimization model for optimally day ahead operation of generation units as well as CAESs in energy and reserve markets in a stochastic way. The features of presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the reserve of CAES for providing capability at each hour by six operation modes; (b) considering the limitations related to the state of charge in the CAESs. It is clear that there is an intrinsic deviation between predicted and actual uncertainty variables in a power system. This paper presents a stochastic optimal operation model on the basis of information gap decision theory together with risk averse strategy in order to overcome this information gap and to help independent system operator. As demand response program, the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed-integer nonlinear problem, which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system demonstrates the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours, lessens the total reserve by 22.75 MW and increases the operation cost by $438.26.

Power Hardware-In-the-Loop Approach for Autonomous Power Generation System Analysis

The article presents the Power Hardware-In-the-Loop (PHIL) dynamic model of a synchronous generator of 125 kVA for autonomous power generation system analysis. This type of system is typically composed of electrical energy sources in the form of several diesel generator units with synchronous machines, the main distribution switchboard and different loads. In modern power distribution systems, the proposed power management strategies are typically aimed at the minimization of fuel consumption by maintaining the operation of diesel generator units at peak efficiency. In order to design and test such a system in conditions as close as possible to the real operating conditions, without constructing an actual power distribution system, a PHIL model in the form of a power inverter that emulates the behaviour of a real synchronous generator is proposed. The PHIL model was prepared in the MATLAB/Simulink environment, compiled to the C language and fed into a 150 kVA bidirectional DC/AC commercial-grade converter driven by a HIL real-time simulation control unit. Experimental research was performed in the LINTE2 laboratory of the Gdańsk University of Technology (Poland), where the PHIL emulator was developed. The proposed model was validated by comparing the output voltages and currents as well as an excitation current with the measurements performed on the 125 kVA synchronous generator. The obtained results proved satisfactory compliance of the PHIL model with its real counterpart.

Mobile Off-Grid Energy Generation Unit for Temporary Energy Supply

Temporary structures are being extensively used by emergency services (rescue, disaster relief, military response units), and other end-users requiring temporary mobile off-grid energy solutions for different purposes (event organization, vacation homes, summer camps, etc.). Yet energy systems for these purposes largely remain fossil-based (such as diesel generators). Although such energy systems are inexpensive, they are carbon intensive and inefficient. This study presents a methodology of simulating temporary shelter with access to an energy supply system through a mobile energy unit with renewable (PV) power supply systems to ensure on-site electricity production, as well as heating/cooling and ventilation. Digital modeling simulations have been performed for a simulated temporary shelter in different climate conditions incorporating different combinations of electricity generation systems with a fossil fuel-based solution and a PV system, using TRNSYS software. Study results show that the operation of a mobile energy generation unit can operate HVAC systems and generate electricity for temporary shelter occupants in off-grid solutions. The modeling results show that the use of a mobile energy generation unit can significantly reduce diesel consumption in temporary shelters from 54% annually (in Riga, Latvia) to 96 % annually (in Jerusalem, Israel). Furthermore, the output of PV-generated electricity is higher (in most cases) than the consumed electricity amount.

Shafting Torsional Vibration Analysis of 1000 MW Unit under Electrical Short-Circuit Fault

Taking a 1000 MW turbine generator as the research object, the short-circuit fault in electrical disturbance is analyzed. Since it is very difficult to carry out fault analysis experiments and research on actual systems, simulation analysis is one of the more effective means of electrical fault diagnosis; the simulation’s results approach the actual behavior of the system and are ideal tools for power system analysis, and can provide an empirical basis for practical applications. The short-circuit fault model of the SIMULINK power system is built to analyze the two types of faults of generator terminals short-circuit and power grid short-circuit. The impact load spectrum, fault current and speed fluctuation between low-voltage rotors were extracted and analyzed. The conclusion is that the impact value of electromagnetic torque at the generator terminal is greater than that on the power grid side. The impact value of a two-phase short-circuit at the generator terminal is the largest, and that of a three-phase short-circuit on the power grid side is the smallest. The transient impulse current of a three-phase short-circuit at any fault point is greater than that of a two-phase short-circuit; the impulse current of the grid side short-circuit is much greater than that of the generator terminal short-circuit; the speed fluctuation and fluctuation difference caused by the three-phase short-circuit in the grid side are the largest. The alternating frequency of the transient electromagnetic force of the four kinds of faults avoids the natural frequency of the torsional vibration of the shaft system, and the torsional resonance of the shaft system in the time domain of the short-circuit fault will not appear. However, after the fault is removed, the residual small fluctuation torque in the system has a potential impact on the rotor system. This research shows an analysis of the structural integrity and safe operation of turbine generator units after a short-circuit fault, which can not only be applied to engineering practice, but also provide a theoretical basis for subsequent research.

Reliability and reserve in day ahead joint energy and reserve market stochastic scheduling in presence of compressed air energy storage

Compressed air energy storages (CAESs) have many advantages in the utility level as an emerging technology and thus, they can be taken into account in energy and reserve markets. One of the most important features of CAES is its fast response ability, which makes it an attractive option to alleviate the uncertainties of renewable energy resources (RERs) and demands. This paper proposes a two-stage mathematical optimization model for optimally day operation of generation units as well as CAESs in energy and reserve market in a stochastic way. The features of the presented reserve model of CAESs are as follows: (a) considering two constraints in order to model the CAES reserve for providing capability at every hour through six operation modes; (b) considering the limitations related to the state of charge (SOC) in the CAESs. Moreover, the generator reliability is not generally considered during scheduling thermal generation units. Therefore, a model to take into account generator reliability in the scheduling problem is presented by this paper. Regarding the stochastic behavior of some variables in the power system such as demands and RERs, this paper presents a stochastic optimal operation model on the basis of information gap decision theory (IGDT) together with risk averse (RA) strategy in order to overcome this information gap and to help independent system operator (ISO). As demand response program (DRP), the curtailed demand is considered for enhancing the market flexibility. The proposed model is formulated as a mixed integer nonlinear problem (MINLP), which is solved by CPLEX solver of the GAMS software. Employing the presented model in the 6-bus test system shows the efficacy of the proposed model. Simulation results show that considering restrictions on reserve deliverability across multiple hours lessens the total reserve by 21.34 MW and increases the operation cost by $434.54. Moreover, considering the reliability index rises total operation costs by almost 10%.

Integration of storage and thermal demand response to unlock flexibility in district multi-energy systems

Optimal operation of generation units is crucial when looking for reduction in energy consumption and carbon emissions in multi-energy systems (i.e. multiple generation sources, energy networks and storages). This work proposes an innovative optimization approach that can be applied to energy systems composed by multiple small units for the production and conversion of electricity, heating and cooling. The optimization is conducted acting on the operation of the production units, the capacity and operation of thermal storage units and the application of demand side management to the thermal network. The optimization procedure is based on a two-level approach, combining a genetic algorithm and a linear programming approach and including a physical model of the district heating network. Multiple scenarios corresponding with typical days are considered. An application to a realistic system, which is optimized assuming an economic objective function, is performed. Results show that thermal storage installation can reduce costs of about 1.5 %, while its integration with demand-side management leads to a cost reduction up to 4 % and allows reducing the storage size.

Capacity Configuration Optimization of Micro-grid System basedon Quantum Particle Swarm Algorithm

The optimized configuration of wind/photovoltaic/storage micro-grid system capacity can realize multi-energy complementation and improve the stability and economy of grid-connected operation of power generation units. In this paper, the capacity of each core component of the micro-grid system under different combination paths such as Wind-PV power generation, battery energy storage, hydrogen production by electrolysis, and fuel cell power generation are optimized and economically analyzed. Taking the FCFF (Free Cash Flow of Firm) net present value maximization of the system running for 20 years as the objective function, considering the impact of energy shortage rate and dynamic electricity price, an operation research optimization model is established and intelligent algorithms are used to solve the model. The model can flexibly realize capacity optimization under different micro-grid combination paths, and it can prevent the solution result from falling into the local optimum through the design of quantum particle swarm algorithm. We analyzed the optimization results in terms of economic benefits, social benefits, and environmental benefits, and further analyzed the annual power generation status of the system and the operation status of the electrolysis hydrogen production system. The calculation example shows that under the current technical conditions, the micro-grid system composed of wind and solar power generation, electrochemical energy storage, and hydrogen production by electrolysis has better economic, social and environmental benefits than other models.

Impact of generator start-up lead times on short-term scheduling with high shares of renewables

To cope with the variability and uncertainty introduced by, i.a., intermittent renewable energy sources, the flexible planning and operation of generation units is crucial. Their reaction time is constrained by the lead times on the start-up decisions, whereas the demand for flexibility and operating reserves depends on the market clearing frequency. The start-up lead times are limited by the operator’s tolerance for increased maintenance on the asset, which should be reflected in short-term scheduling models. To study this interaction between the market clearing frequency and the start-up capabilities of combined-cycle gas turbines, we develop a unit commitment model. The model considers multiple start-up trajectories and the scheduling decisions in joint energy-operating reserve and balancing markets. The uncertainty on wind power forecasts is presented via wind power forecast updates generated by a dedicated data-driven tool. Leveraging this model, we investigate the interaction between (i) the frequency of wind power forecast updates, linked to the market clearing frequency, (ii) cost-optimal operating reserve volumes and (iii) combined-cycle gas turbine start-up decisions. Results show that, in general, higher market clearing frequencies lead to lower operating costs, driven by decreasing volumes of operating reserves and facilitated by the fast start-up capabilities of combined-cycle gas turbines.

Flexible Multi-Energy Scheduling Scheme for Data Center to Facilitate Wind Power Integration

Due to the intermittent and uncontrollable nature of wind resources and inflexible operation of conventional generation units, they present challenges for the power system to integrate more wind power. With its unique flexibility on the demand side, the data center can be considered as an effective solution to relieve wind curtailment. Moreover, with the help of waste heat recovery module, the data center can reduce the utilization of conventional thermal units especially in the residential heating sector which increases the flexibility of system operation and facilitates more renewable integration. In this paper, a flexible workload management and resource scheduling model are proposed to achieve a multi-energy co-optimization for data center and enhance the integration of wind power. A two-stage stochastic programming model is formulated to address the uncertainties involved in this process. The proposed model is examined by a simulative data center microgrid and the numerical results demonstrate its effectiveness and robustness.