Search

Optimal power flow (OPF) is a crucial issue to maintain the reliable operation of power systems. However, achieving this objective is not easy, especially when renewable energy sources (RESs) are penetrated into the power system due to their uncertainty nature. This paper provides an optimal solution for the power flow problem including two different types of RESs based on a marine predator algorithm (MPA). The OPF model used in this paper has three different types of energy resources (thermal, wind, and solar). The output power from wind or solar generator has two probabilities either underestimation or overestimation consequently. These two probabilities have been translated into the objective function by two extra costs, penalty cost, and reserve cost, respectively. To check the validity of the proposed algorithm, it is applied to a modified IEEE-30 and IEEE-57 bus systems. The obtained results are compared with some recent optimization methods. The results show the effectiveness of marine predator algorithm in providing the optimal solution for the power flow problem with maintaining the power system constraints inviolate. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the allocation of a hybrid system that includes PV‐DG and DSTATCOM. The planning problem considers the variations of load demand and solar irradiance under deterministic and probabilistic conditions. An efficient optimization algorithm called MPA is implemented to assign the optimal placement and ratings of the hybrid PV‐DG and DSTATCOM. The considered objective function is a multi‐objective function that includes the annual cost reduction, improvement of voltage profiles, and system stability improvement. The assessment is accomplished with the inclusion of a single and two‐hybrid system on a large 94‐bus system. For validating the effectiveness of the MPA, the yielded results are compared with the PSO, which is considered a commonly used algorithm. In deterministic conditions, the hourly variations of the load demand and solar radiation are considered in four yearly seasons, while in probabilistic conditions, 3 years of hourly historical data of solar irradiance and load demand are utilized to describe the uncertainties of the load demand and solar irradiance. The simulation results demonstrate that the optimal inclusion of a single‐hybrid PV‐DG and DSTATCOM system can enhance the system's technical performance (the voltage profile, the voltage stability) and enhance the economic scheme (total annual cost reduction). In addition, the inclusion of two‐hybrid systems is superior compared with the inclusion of a single‐hybrid system in terms of the considered objective functions, as well as the proposed technique is more efficient for solving the allocation problem of the hybrid system. [ABSTRACT FROM AUTHOR]

Summary: Quasi Z‐source inverter (qZSI) is a single‐stage high gain buck‐boost converter suitable for various applications like renewable energy sources, power electronic drives, and power supply systems. Controller design for qZSI should ensure simultaneous control of multiple objectives such as the DC capacitor voltage, source inductor current, and load current. While controllers dealing with multiple objectives, obtaining a good steady‐state and transient performance with low control complexity is essential. Fortunately, Model Predictive Control (MPC) is emerging as an effective alternative that can address all the above‐said issues simultaneously. However, some of the severe problems to be addressed with the conventional MPC algorithms developed for qZSI are applying a single vector per sampling duration, higher computational requirements, and lack of analytic procedure to handle weight factors. This paper presents a control algorithm with features of weight factor elimination and AC load current ripple reduction by using a modified double vector predictive control approach with fewer computational requirements. The principle of inclusion of zero vector, weight factor elimination, and a detailed calculation for duty ratio are included. As the converter operates at a fixed frequency, significant ripple reduction for DC current, link voltage, and load current ripple have been achieved. The proposed MPC requires only ≈81% of computations needed for the conventional MPC. Methodological comparison has been provided to present the superiority of the proposed MPC compared to appropriate conventional MPCs available in the literature. [ABSTRACT FROM AUTHOR]

Summary: It is known that the demand for renewable energy systems during recent years has increased significantly, especially for wind and photovoltaic systems. The continuous improvement of the efficiency and performance of these systems is important to their growth, particularly in terms of harvesting the maximum power. Many types of methods and techniques are available for tracking the maximum power point, the most important of which is the concept of artificial intelligence. Artificial intelligence (eg, neural networks and fuzzy neural networks) has great potential to handle complex systems or those that have non‐linear parameters and are influenced by environmental conditions. In this paper, artificial intelligence is used to predict the optimal reference values for wind turbines and photovoltaic panels used in maximum power point tracking (MPPT). Neural networks were used in the wind turbine system, while fuzzy neural networks were used in the photovoltaic systems. The networks were trained in real‐time using an incremental training mode based on the back‐propagation algorithm. The proposed system was designed practically based on the microcontroller and has been tested, and the results are extracted in real‐time. The results have proven their effectiveness and conformity with the required optimal values. [ABSTRACT FROM AUTHOR]

Summary: Enhancing distribution system operation accomplished with the integration of renewable energy resources (RERs) has several technical, economical, and environmental dimensions. In this regard, this paper presents an optimal integration procedure of Distributed Generation (DG) based on Photovoltaic panel (PV) and Distribution Static Compensator (DSTATCOM) in Electrical Distribution System (EDS). The proposed procedure is formulated as a multi‐objective function (MOF). The considered objectives that reflect the technical, economic, and environmental issues, are Active Power Loss Level (APLL), Short Circuit Level (SCL), Voltage Deviation Level (VDL), Net Saving Level (NSL), and Environmental Pollution Reduction Level (EPRL). The proposed procedure investigates several hybrid optimization methods that combine the firefly algorithm (FA) with various acceleration coefficients PSO algorithms to improve the overall solution quality of the hybrid algorithms compared with the individual algorithms. To prove the capability of the proposed procedure, four different cases are tested on IEEE 33‐bus and 69‐bus EDSs. Added to that, the proposed algorithms are extended to practical Algerian EDS in Adrar City 205‐bus. Results obtained by the hybrid FA‐SCAC‐PSO algorithm showed that the simultaneous allocation of multiple DG and DSTATCOM in all standard and practical test systems significantly reduces the loss and enhances the voltage profile. An energy‐efficient analysis to proceed for different cases studied based on the best hybrid FA‐SCAC‐PSO algorithm to reach the best value of MOF compared to other algorithms, moreover the capability to achieve the optimal allocation of DG and DSTATCOM by maintaining the voltages profile within the permissible limit, whatever the variation of load. Significant technical economic and environmental achievements are found for different case studies especially in the existence of DGs and DSTATCOM devices. [ABSTRACT FROM AUTHOR]

Summary: Conventionally, voltage profile are regulated by controllers that are not operational flexible to the system dynamism and challenges offered by the integration of renewable energy sources. Consequently, there is a need for the adaptive coordinating mechanism as an interface between the passive network and the new active network. This will provide a platform for the smooth integration of distributed energy resources in most developing countries where traditional network is still operational. This paper proposes an algorithm for real‐time multiperiod adaptive compensation of network reactive power support based on hierarchical structure and decentralized layered multiagent system voltage coordinating scheme. A multiperiod voltage control algorithm replicates the daily load flow evaluation aiming at ameliorating voltage fluctuation over a 24‐hour simulation period. The algorithm applies the decentralized voltage control mechanism to monitor the network parameters and dynamically evaluate the capacity of the capacitor banks (CBs) to be injected for voltage compensation. This mitigates the challenges associated with under and over reactive power compensation experienced in the day‐ahead programming and manual switching operation. The algorithm works in conjunction with an on‐load tap changing transformer (OLTC) and energy storage system (ESS). The ESS complements the OLTC during switching delay, dip in its output root mean square and voltage fluctuation as network loading condition varies. The effectiveness of the proposed method is verified on standard IEEE 33 and 118‐bus distribution systems. The results show an improved voltage performance and reduction in stress/complexity associated with load and renewable generation forecast in manual switching operations. [ABSTRACT FROM AUTHOR]