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The paper proposes a modified Bat algorithm (MBA) for searching optimal solutions of Economic dispatch of combined heat and power generation (CHPGED) with the heat and power generation from three different types of units consisting of pure power generation units, pure heat generation units and cogeneration units. The CHPGED problem becomes complicated and big challenge to optimization tools since it considers both heat and power generation from cogeneration units. Thus, we apply MBA method with the purpose of enhancing high quality solution search ability as well as search speed of conventional Bat algorithm (BA). This proposed approach is established based on three modifications on BA. The first is the adaptive frequency adjustment, the second is the optimal range of updated velocity, and the third is the retained condition of a good solution with objective to ameliorate the search performance of traditional BA. The effectiveness of the proposed approach is evaluated by testing on 7, 24, and 48 units systems and IEEE 14-bus system and comparing results with BA together with other existing methods. As a result, it can conclude that the proposed MBA method is a favorable meta-heuristic algorithm for solving CHPGED problem.

To achieve economic and environmental benefit for the stand-alone microgrid consisting of diesel generators, wind turbine generators, photovoltaic generation system and lead-acid batteries, a multi-objective stochastic optimal planning method and a stochastic chance-constrained programming model are presented. In the model, the optimal objective is to simultaneously minimise the total net present cost and carbon dioxide emission in life cycle; the type and capacity of distributed generation units have been selected as the optimal variables; the loss of capacity is adopted as probability index constraint; the coordinated operation strategies between diesel generators and battery, the multi-unit operation constraints of diesel generators and the reserve capacity have been considered in the hard-circle operation strategy. Considering the uncertainties of wind speed, clearness index and load demand, Markov process transition probability matrix is adopted to synthesise those time series data. Optimal planning for an island microgrid system has been carried out by the planning system for microgrid (PSMG), a self-developed optimal planning software based on the multi-objective stochastic optimal planning method for stand-alone microgrid system.

Distributed generation units (DGUs) as auxiliary sources of power generation can play an effective role in meeting the load consumption of the distribution network, also have positive effects such as reducing loss and improving voltage. Moreover, capacitors by reactive power compensation produce positive effects similar to DGUs in the distribution networks. The idea of joint operation of DGUs and shunt capacitors (SCs) in the presence of demand response program (DRP) to derive maximum benefits from their installation is proposed in this paper. The time of use (TOU) mechanism is used as one of the demand response programs (DRPs) to alter the consumption pattern of subscribers and improve the performance of the distribution system. Objective functions include minimization of energy loss, operational cost, and energy not supplied (ENS). In general, the problem of determining the optimal capacity of DGUs and SCs is complex due to the demand variation. Also, considering the effect of uncertainty sources complicate the optimization problem. Hence, a modified shuffled frog leaping algorithm (MSFLA) is proposed to overcome the complexities of this problem. The proposed approach is tested on two 95, and 136-node test networks, and the results are compared with other evolutionary algorithms. According to the obtained results, after using the proposed approach in determining the optimal capacity of DGUs and SCs in the first system, the amount of energy loss, operational cost and ENS dropped by 11, 25.5 and 5% compared to baseline values. After applying the TOU mechanism in allocation of DGUs and SCs simultaneously in the second system by proposed method, the values obtained for the mentioned objectives reduced by 29, 65 and 7% compared to initial values.

DC microgrids (DCMGs) integrate and coordinate various DC distribution generation units including various renewable energy sources and battery storage systems, and have been used in satellites, the International Space Station, telecom power stations, computer power supplies, electric aircraft, and electric ships. However, the presence of constant power loads (CPLs) can cause instability in DCMGs. Thus, this paper reviews the stabilization techniques that can resolve instability caused by CPLs, as well as various parameters of CPLs, such as bandwidth, and the frequency of the CPLs that can stabilize the DCMGs. It also discusses recent trends and future work in finding stability limits using the parameters of CPLs. It should be useful for directing research towards appropriate mathematical and experimental approaches for the stability of DCMGs with CPLs.

The development of the electric power industry needs to be understood against the current backdrop of the transition to technological platforms facilitating the adoption of smart grids. Smart grids can be made up of separate clusters (microgrids) consisting of power consumers, power grids, and distributed generation (DG) units. To improve energy efficiency, DC microgrids can be integrated into smart grids to deliver power to consumers within a building (or several buildings) and at the sites of C&I facilities. It is advisable to carry out integrations of DC and AC microgrids with DG and energy storage units on the basis of power routers used to couple grids of different voltage classes. This study outlines a computer model of power router-based integration of DC and AC microgrids with distributed generation and energy storage units. The model was developed in the MATLAB environment. The paper also features the results of a study of the proposed methods as applied to voltage control under normal and emergency operating conditions of a DC and AC distribution grid.

Fault detection in a Direct Current (DC) microgrid with multiple interconnections of distributed generation units (DGUs) is an interesting topic of research. The occurrence of any sensor fault in the DC microgrid should be detected immediately by the fault detection network to achieve an overall stable performance of the system. This work focuses on sensor fault diagnosis of voltage and current sensors in interconnected DGUs of the microgrid. Two separate higher order sliding mode observers (HOSM) based on model dynamics are designed to estimate the voltage and current and generate the residuals for detecting the faulty sensors in DGUs. Multiplicative single and multiple sensor faults are considered in voltage and current sensors. By appropriate selection of threshold, single and multiple sensor fault detection strategies are formulated. A hierarchical controller is designed to ensure equal sharing of current among the DGUs of the DC microgrid and stabilize the system. Simulations are performed to validate the proposed approach for various configurations of the DC microgrid under various load and off noise conditions.

As the penetration of renewable power generation units connected to the grid increases, high power quality and flexible power regulation have raised much concern. This study proposes a competitive model predictive control strategy for inverters in renewable power generation applications. The controller uses the system model to predict the system behaviour in each sampling interval for each voltage vector, and the most appropriate vector is then chosen according to an optimisation criterion. In islanded mode, the control objectives of the cost function are the α and β components of the voltage so that stable voltage for the local loads can be established. In addition, a fast re-synchronisation scheme is introduced to achieve smooth grid connection. After connected to the grid, a new prediction scheme is developed to fulfill flexible active and reactive power regulation. Furthermore, a switching frequency reduction scheme is presented to reduce switching losses, which are especially significant when considering efficiency for renewable power generations. The effectiveness of the proposed control strategy was tested by simulation using MATLAB/Simulink and experimentally validated on a laboratory prototype.

Conservation voltage reduction (CVR) is a potentially effective and efficient technique for inertia synthesis and frequency support in modern grids comprising power electronics (PE)-based components, aiming to improve dynamic stability. However, due to the complexities of PE-based grids, implementing the CVR methods cannot be performed using traditional techniques as in conventional power systems. Further, quantifying the CVR impacts in modern grids, while focusing on dynamic time scales, is critical, consequently making the traditional methods deficient. This is an important issue as CVR utilization/quantification depends on grid conditions and CVR applications. Considering these concerns, this work offers a thorough analysis of CVR applications, implementation, and quantification strategies, including data-driven AI-based methods in PE-based modern grids. To assess the CVR applications from a new perspective, aiming to choose the proper implementation and quantification techniques, they are divided into categories depending on various time scales. CVR implementation methods are categorized into techniques applied to PE-based grids and islanded microgrids (MGs) where different control systems are adopted. Additionally, to address the evaluation issues in modern grids, CVR quantification techniques, including machine learning- and deep learning-based techniques and online perturbation-based methods are evaluated and divided based on the CVR application. Concerns with the further utilizing and measuring of CVR impacts in modern power systems are discussed in the future trends section, where new research areas are suggested.

In the power system, the economic dispatch (ED) problem is the key issue, while fossil fuels cause environmental pollution. The allocation of power generation is included in the actual economic load dispatch issue of power generation for reducing the operating cost. This creates the economic load dispatch issue, a large-scale, highly nonlinear controlled optimization issue. The major issue in the power systems is the loss, fuel cost, and emission. The existing algorithms can optimize the parameters mentioned above, but it is not much better. Hence, this paper presents the multi-objective multi-verse optimization (MOMVO) for the dynamic load dispatch problem. The dynamic load dispatch issue is evaluated by cost and emission evaluation with hybrid renewable energy Sources (RES). Here, the proposed algorithm generates the thermal, photovoltaic (PV) and wind power values, reducing the cost and emission values. The unit’s power generation is the foremost aim of dynamic economic load dispatch (DELD) to meet the load demand while sustaining various operational constrictions; the generation’s total cost is reduced. The MVO algorithm is applied to nonlinear DELD issues and is a reliable and robust optimization algorithm. The introduced scheme is implemented and tested over three test systems, such as 6, 10, and 11 generating units. The implementation is performed on the MATLAB R2016a platform, and the performance results are evaluated based on with and without valve-point loading (VPL). Finally, VPL produced better solutions than the without VPL case.

With the large-scale integration of photovoltaic power plants—comprising power electronic devices—into power systems, electromagnetic transient simulation has become a key tool for ensuring power system security and stability. The accuracy of photovoltaic unit controller parameters is crucial for the reliability of such simulations. However, as the issue of sub/super-synchronous oscillations becomes increasingly prominent, existing parameter identification methods are primarily based on high/low voltage ride-through characteristics. This limits the applicability of the identification results to specific scenarios and lacks targeted simulation and parameter identification research for sub/super-synchronous oscillations. To address this gap, this study proposes a mathematical model tailored for sub/super-synchronous oscillations and performs sensitivity analysis of converter control parameters to identify dominant parameters across different frequency bands. A frequency-segmented parameter identification method is introduced, capable of fast convergence without relying on a specific optimization algorithm. Finally, the proposed method’s identification results are compared with actual values, voltage ride-through-based identification, particle swarm optimization results, and results under uncertain conditions. It was found that, compared with traditional identification methods, the proposed method reduced the maximum identification error from 7.67% to 4.3% and the identification time from 2 h to 1 h. The maximum identification error of other intelligent algorithms was 5%, with a difference of less than 1% compared to the proposed method. The identified parameters were applied under conditions of strong irradiation (1000 W/m2), weak irradiation (300 W/m2), rapidly varying oscillation frequency, and constant oscillation frequency, and the output characteristics were all close to those of the original parameters. The effectiveness and superiority of the proposed method have been validated, along with its broad applicability to different intelligent algorithms and its robustness under uncertain conditions such as environmental variations and grid frequency fluctuations.