International Transactions on Electrical Energy Systems最新文献

In modern generation expansion planning of power systems, installing grid-connected renewable energy systems is preferred than thermal units due to their low generation cost and environmental pollution. However, the expanded power system must have ability to resist against any outages influenced on the frequency response of the system. So, several frequency-constrained expansion planning models are extracted to provide a reliable infrastructure to manage the frequency behavior. The main distinction of our model with others is considering the failure of grid-connected renewables in the expansion planning models. Furthermore, due to the lack of information about the uncertainty of malfunctions, a distributionally robust optimization approach is applied to the problem under several ambiguity radiuses. The results of implementing the proposed method on the IEEE RTS96 case show that increasing the penetration of malfunctioned units can lead to more investment on the thermal units to prevent frequency violation under any outage in the system. With increase of the Kullback–Leibler divergence from zero (stochastic) to 3 (robust), the cost of the robust model is increased about 0.02%. The model is designed for the deregulated market to increase the competition of market through maximizing their benefit and line congestion management with local marginal pricing techniques.

Power transmission using a voltage source converter- (VSC-) based high-voltage direct current (HVDC) system offers autonomous control of real and reactive power, constant DC voltage polarity, and bidirectional power flow. This helps to realize the multiterminal VSC-HVDC system and its integration into renewable energy sources to meet the growing power demand. However, there is a risk of higher voltages and currents during a DC line fault. The barrier to the advancements of VSC-MTDC systems is the nonavailability of commercial, higher-rated DC circuit breakers. This necessitates research on alternative methods of DC fault-clearing schemes with available technologies. In this direction, a superconducting fault current limiter (SCFCL) is an alternative option to mitigate the problems encountered in VSC-MTDC system operation. Because of this, there are not many VSC-MTDC systems available worldwide. This paper discusses different issues associated with the transient performance of the VSC-MTDC system. A representative case involving resistive SCFCL for DC line protection is presented. The simulations are carried out in the PSCAD/EMTDC platform.

A prevailing problem in power and energy subsystems is the smooth operation of electric energy systems. This work presents recent, efficient, and reliable evolutionary algorithm for solving the optimal power flow (OPF) analysis. All various practical complex equality and inequality constraints, namely, bus voltages, real powers of the generator buses, tap settings of the transformers and the reactive power generations, shunt compensation, and emission, are considered for the real-world scenario. Primary feature in a gas power plant that raises a lot of computational shortcomings with nonlinear structure in fuel cost is valve point effect. The existing research works have not factored the valve point effect and lack the accuracy in the fuel cost minimization and do not reflect the various practical complexities such as valve point and emission effects in the OPF problem formulation. This paper, for the first time, introduces modified OPF problem formulation incorporating valve point effect and applies covariance matrix adaptation-evolution strategy (CMA-ES) for solving the modified OPF problem. The algorithm is scrutinised and tested on a modified IEEE-30-bus platform for various OPF objectives such as cost minimization, transmission loss, and total voltage deviation, subjected to practical constraints. Load flow analysis has been carried out using the Newton–Raphson method. This work aims to lay the foundation in such a way that it can be applicable in a real-world scenario for any number of buses.

The Distributed Renewable Energy Sources (DRESs) integrate hybrid microgrid and prosumer activities that constitute a dynamic system characterized by unknown network parameters. The dynamic system faces challenges, such as intermittent power supply due to low inertia, renewable intermittence, plug-and-play prosumer activities, network topology variations, and a lack of constraint handling. These complexities pose significant issues in designing effective control for frequency regulation and consensus-based economic load dispatch (ELD) within DRES to meet varying load demands. To address the above challenges, this research employs a machine learning-based distributed multiagent consensus design that offers a rapid and robust approach, mitigating the limitations associated with the Distributed Average Integral (DAI) control design. The proposed multiagent scheme empowers the successful implementation of ELD and frequency regulation, accommodating the intermittent DRES, diverse network topologies, and the dynamic plug-and-play activities of prosumers. Moreover, an optimization-based DAI tuning model is introduced to overcome tuning limitations. Intelligent renewable energy agents are trained through machine learning-based regression models that use root mean square error metrics for performance evaluations. The intelligent agents employ DAI control to overcome inherent limitations. The effectiveness of the machine learning-based DAI is thoroughly evaluated using the DRES-based IEEE 14-bus hybrid microgrid system. The quantitative results prove its efficacy in addressing the complex challenges of integrated microgrid dynamics.

In this paper, DR programs are integrated with the unit commitment economic dispatch model for a single day to lower total operating costs for an insular microgrid. The proposed model takes into account the forecasting errors associated with wind, solar, and load demands. A new combined DR program is presented to enhance microgrid operation and financial effectiveness, benefiting microgrid consumers. The price elasticity and consumer profit are the foundation for DR modeling. The optimization problem is developed as mixed-integer nonlinear programming (MINLP) and solved using GAMS software. For the case study, an insular microgrid consisting of two microturbines, a wind turbine, solar photovoltaic, and battery storage is considered. Optimization is carried out under both with and without the DR program. The outcomes show that by implementing TOU and DLC DR programs, the operating cost is reduced by 13.55% and 9.68%, respectively. While consumers experience a financial loss in TOU-DR, they earn profit in DLC-DR. Therefore, a combination of the two, i.e., TOU + DLC DR, is proposed, reducing operating costs by 10.73% while increasing profit for users. The suggested approach benefits the microgrid operator as well as its users, encouraging the construction and operation of insular microgrids in rural or isolated areas.

Most studies undertaken on energy usage in buildings have shown that energy utilization is widely influenced by occupancy presence and occupants’ activities relative to the indoor environment, which may be widely dependent on weather conditions and user behaviors. However, the core drawback that has negated the proficient estimation of energy is the modelling of occupant behavior relative to energy use. Occupants’ behavior is a complex phenomenon and has a dynamic nature influenced by numerous internal, individual, and circumstantial factors. This research proposes a computational intelligence-based model for household electricity usage profile development as impacted by core input variables—household activities, household financial status, and occupancy presence. The incorporation of these variables and their adaptiveness is expected to address and resolve unpredictability or nonlinearity concerns, thus allowing for adept energy usage estimation. The model addresses issues unresolved in many other studies, such as occupancy determination (deduction) and the impact on energy consumption. The performance precision of this approach has been demonstrated by trend series analysis, demand analysis, and correlation analysis. Based on the performance indicators including mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE), the model has shown proficient predictive output with respect to the metered (actual) energy usage data. The proposed model, compared to actual data, showed that average MAPE values for the respective day standard, morning peak, and night peak demand period (TOUs) are 2.8%, 1.88%, and 0.31% for all income groups, respectively. The aptitude to improve on energy prediction and evaluation accuracy, especially in these periods, makes it a highly suited tool for demand-side management, power generation, and distribution planning activity. This will translate into power system reliability, reduce operation cost (lowest cost), and reduce greenhouse emissions (environmental pollution), thereby cumulating into sustainable cities.

DC microgrids are getting more attention because majority of the renewable energy sources generate DC output voltage and also modern gadgets require DC voltage for its operation. In this work, high gain SEPIC (HGSC) topology is derived from switched inductor voltage boosting cell (SIVBC). The HGSC converter provides continuous source current due to SIVBC and high conversion ratio and achieves maximum efficiency of 97.88% when compared with the existing SEPIC topology. The operating modes, conversion ratio expression, power loss distribution, voltage drop, current stress of the semiconductor devices, and efficiency are also analysed. In DC microgrids, the HGSC intends to track the peak power from solar PV array. An incremental conductance algorithm is employed to track the peak power of the solar PV modules. The power flow in the microgrid system is analysed by employing synchronous reference frame theory-based current controller. In order to validate the theoretical concepts of the HGSC converter, the hardware model is developed for the load rating of 1,000 W/380 V output voltage.

As power systems become more complex and uncertain, low-voltage distribution networks face numerous challenges, including three-phase imbalances caused by asymmetrical loads and distributed energy resources. We propose a robust stochastic optimization (RSO)-based optimal power flow (OPF) control method for three-phase four-wire low-voltage distribution networks that consider uncertainty to address these issues. Using historical data and deep learning classification methods, the proposed method simulates optimal system behaviour without requiring communication infrastructure. The simulation results verify that the proposed method effectively controls the voltage and current amplitude while minimizing the operational cost and three-phase imbalance within acceptable limits. The proposed method shows promise for managing uncertainties and optimizing performance in low-voltage distribution networks.

An optimal configuration method for the combined heat and power (CHP) system considering demand response is proposed to scientifically and reasonably configure the parameters of the additional heat source and reduce unnecessary investment and construction costs. First, additional heat sources and demand response are utilized to decouple power generation and heating supply, enhancing the flexibility of the CHP system. Second, a multiobjective optimization configuration model of the CHP system is established, taking the system’s comprehensive satisfaction, economic cost, and wind power consumption capacity as the objectives, the unit output, and the capacity parameters of the additional heat source as the decision variables. Furthermore, an improved memetic algorithm (IMA) combined with a hierarchical sequence method is designed to solve the optimization model characterized by multiple objectives, hierarchical levels, and nonlinearity. The hierarchical sequence method solves problems sequentially based on the importance of optimization goals, ensuring the satisfaction of the configuration scheme. The IMA employs adaptive crossover and mutation probabilities, enhancing the algorithm’s convergence and quality. Finally, case analysis demonstrates that the CHP system achieves the best benefits when heat storage tanks and electric boilers are configured simultaneously. Moreover, compared to the MA, IPSO, and IABC algorithms, the IMA algorithm reduces the average economic cost by approximately 5.11%, 2.70%, and 8.43%, respectively.

This paper proposes a multiobjective hybrid Taguchi genetic algorithm (HTGA) to optimize the speed range of a yokeless axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. HTGA combines Taguchi’s local optimization with the global optimization of traditional genetic algorithms (GAs), facilitating faster and more accurate solutions. The Taguchi method is employed to generate offspring individuals within GA; it inherits parameter characteristics from stronger offspring, saving considerable computation time. The objective is to achieve a motor with low cogging torque, high average torque, and an expanded speed range in the field weakening area. Various parameters of the motor, such as the split ratio, stator axial length, pole angles, PM arc, and number of conductors per slot, are selected as optimization variables. The optimization constraints include the field-weakening rate, saliency rate, cogging torque, and average torque. The optimized motor parameters are determined, and the speed range before and after optimization is evaluated. Cosimulation analysis using a 3-D finite element method (FEM) is performed under no-load and full-load conditions to compare the motor’s speed regulation range. The optimized motor exhibits a maximum speed that is almost 1.5 times higher than the initial design, with improvements of 11.3% in average torque and 9% in cogging torque. Experimental results compared to 3-D FEM simulations demonstrate the superior performance of the optimized motor in terms of speed, torque, power, and efficiency.