Efficient Operation of Modular Grid-Connected Battery Inverters for RES Integration

Grid-connected battery storage systems on megawatt-scale play an important role for the integration of renewable energies into electricity markets and grids. In reality, these systems consist of several batteries and inverters, which have a lower energy conversion efficiency in partial load operation. In renewable energy sources (RES) applications, however, battery systems are often operated at low power. The modularity of grid-connected battery storage systems thus allows improving system efficiency during operation. This contribution aims at quantifying the effect of segmenting the system into multiple battery-inverter subsystems on reducing operating losses. The analysis is based on a mixed-integer linear program that determines the system operation by minimizing operating losses. The analysis shows that systems with high modularity can meet a given schedule with lower losses. Increasing modularity from one to 32 subsystems can reduce operating losses by almost 40%. As the number of subsystems increases, the benefit of higher efficiency decreases. The resulting state of charge (SOC) pattern of the batteries is similar for the investigated systems, while the average SOC value is higher in highly modular systems

FACTS Devices Allocation to Congestion Alleviation Incorporating Voltage Dependence of Loads

This paper presents a novel optimization based methodology to allocate Flexible AC Transmission Systems (FACTS) devices in an attempt to improve the previously mentioned researches in this field. Static voltage stability enhancement, voltage profile improvement, line congestion alleviation, and FACTS devices investment cost reduction, have been considered, simultaneously, as objective functions. Therefore, multi-objective optimization without simplification has been used in this paper to find a logical solution to the allocation problem. The optimizations are carried out on the basis of location, size and type of FACTS devices. Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are utilized to achieve the determined objectives. The problem is formulated according to Sequential Quadratic Programming (SQP) problem in the first stage. This formulation is used to accurately evaluate static security margin with congestion alleviation constraint incorporating voltage dependence of loads in the presence of FACTS devices and estimated annual load profile. The best trade-off between conflicting objectives has been obtained through Genetic Algorithm (GA) based fuzzy multi-objective optimization approach, in the next stage. The IEEE 14-bus test system is selected to validate the allocated devices for all load-voltage characteristics determined by the proposed approach

Distribution expansion planning considering reliability and security of energy using modified PSO (Particle Swarm Optimization) algorithm

Distribution feeders and substations need to provide additional capacity to serve the growing electrical demand of customers without compromising the reliability of the electrical networks. Also, more control devices, such as DG (Distributed Generation) units are being integrated into distribution feeders. Distribution networks were not planned to host these intermittent generation units before construction of the systems. Therefore, additional distribution facilities are needed to be planned and prepared for the future growth of the electrical demand as well as the increase of network hosting capacity by DG units. This paper presents a multiobjective optimization algorithm for the MDEP (Multi-Stage Distribution Expansion Planning) in the presence of DGs using nonlinear formulations. The objective functions of the MDEP consist of minimization of costs, END (Energy-Not-Distributed), active power losses and voltage stability index based on SCC (Short Circuit Capacity). A MPSO (modified Particle Swarm Optimization) algorithm is developed and used for this multiobjective MDEP optimization. In the proposed MPSO algorithm, a new mutation method is implemented to improve the global searching ability and restrain the premature convergence to local minima. The effectiveness of the proposed method is tested on a typical 33-bus test system and results are presented

Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding

This paper proposes a novel model to cluster similar load consumption patterns and identify time periods with similar consumption levels. The model represents the customer's load pattern as an image and takes into account the load variation and uncertainty by using exponential intuitionistic fuzzy entropy. The advantage is that the proposed method can handle the uncertain nature of customer's load, by adding a hesitation index to the membership and non-membership functions. A multi-level representation of the load patterns is then provided by creating specific bands for the load pattern amplitudes using intuitionistic fuzzy divergence-based thresholding. The typical load pattern is then determined for each customer. In order to reduce the number of features to represent each load pattern with respect to the time-domain data, the discrete wavelet transform is used to extract some spectral features. To cope with the data representation with fuzzy rules, the fuzzy c-means is implemented as the clustering algorithm. The proposed approach also identifies the time periods associated to different load pattern levels, providing useful hints for demand side management policies. The proposed method has been tested on ninety low voltage distribution grid customers, and its superior effectiveness with respect to the classical k-means algorithm has been represented by showing the better values obtained for a set of clustering validity indicators. The combination of load pattern clusters and time periods associated with the segmented load pattern amplitudes provides exploitable information for the efficient design and implementation of innovative energy services such as demand response for different customer categories