Optimal allocation of battery energy storage systems to enhance system performance and reliability in unbalanced distribution networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Optimal sizing design and operation of electrical and thermal energy storage systems in smart buildings

Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller

An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks

The allocation of grid-scale energy storage systems (ESSs) can play a significant role in solving distribution network issues and improving overall network performance. This paper presents a strategy for optimal allocation and sizing of distributed ESSs through P and Q injection by the ESSs to a distribution network. The investigation is carried out in a renewable-penetrated (wind and solar) medium voltage IEEE-33 bus distribution network for two different scenarios: (1) using a uniform ESS size and (2) using non-uniform ESS sizes. DIgSILENT PowerFactory is used for system modeling and testing, and simulation events are automated using Python scripting. A hybrid meta-heuristic optimization algorithm such as the fitness-scaled chaotic artificial bee colony algorithm is applied to optimize parameters of the objective function. The artificial bee colony algorithm is also applied to justify the results attained from the fitness-scaled chaotic artificial bee colony algorithm. A performance comparison, in relation to proposed PQ injection approach with previously applied P injection technique, is presented. The obtained results suggest that the proposed PQ injection-based ESS placement strategy performs better than the P injection-based approach, which can significantly improve distribution network performance by minimizing voltage deviation, power losses, and line loading

Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks

The placement of grid-scale energy storage systems (ESSs) can have a significant impact on the level of performance improvements of distribution networks. This paper proposes a strategy for optimal allocation of distributed ESSs in distribution networks to simultaneously minimize voltage deviation, flickers, power losses, and line loading. The optimal ESS allocation is investigated through the PQ injection (considering a variable power factor on the dispatch of ESSs) and the results are compared in terms of performance and power quality improvements. An IEEE-33 bus distribution system (medium voltage), having a high influence of renewable (wind and solar) distributed generation, is used as the test network. The overall investigation is conducted for two distinct scenarios: (1) applying a uniform ESS size and (2) applying non-uniform ESS sizes. DIgSILENT PowerFactory is used for developing, analyzing, and testing the system models. The fitness-scaled chaotic artificial bee colony optimization algorithm (a hybrid meta-heuristic technique) is applied to optimize parameters of the objective function. A Python script is used to automate simulation events in PowerFactory. The optimization results are verified through the application of the conventional artificial bee colony algorithm. Detailed simulation results imply that the proposed ESS allocation technique can successfully minimize voltage deviation, flicker disturbance, line loading, and power losses, and thereby significantly improve performance and power quality of a distribution network