Location Of Photovoltaic Systems Research Articles

A modified Runge–Kutta optimization for optimal photovoltaic and battery storage allocation under uncertainty and load variation

The interest in incorporating environmentally friendly and renewable sources of energy, like photovoltaic (PV) technology, into electricity grids has grown significantly. These sources offer benefits, such as reduced power losses and improved voltage stability. To optimize these advantages, it is essential to determine optimal placement and management of these energy resources. This paper proposes an Improved RUNge–Kutta optimizer (IRUN) for allocating PV-based distributed generations (DGs) and Battery Energy Storage (BES) in distribution networks. IRUN utilizes three strategies to avoid local optima and enhance exploration and exploitation phases: a non-linear operator for smoother transitions, a Chaotic Local Search for thorough exploration, and diverse solution updates for refinement. The efficacy of IRUN is evaluated using 10 benchmark functions from the CEC’20 test suite, followed by statistical analysis. Next, IRUN is used to optimize the allocation of PVDG and BES to minimize energy losses in two standard IEEE distribution networks. The optimization problem is divided into two stages. In the first stage, the optimal size and the location of PV systems are calculated to meet peak load demand. In the second stage, considering time-varying load demand and intermittent PV generation, effective energy management of BES is employed. The effectiveness of IRUN is compared against the original RUN and other well-known optimization algorithms through simulation results. The comprehensive analysis demonstrates that IRUN outperforms the compared algorithms, making it a leading solution for optimizing PV distributed generation and BES allocation in distribution networks and the results show that the energy loss reduction reaches 63.54% and 68.19% when using PVand BES in IEEE 33-bus and IEEE 69 bus respectively.

Optimal location and size of photovoltaic systems in high voltage transmission power networks

This paper presents Water Cycle Algorithm (WCA) to solve the Optimal Power Flow (OPF) problem with and without renewable energy sources. WCA has been tested on IEEE 30, 57, and 118-bus transmission networks to find minimum fuel cost. Main parameters of WCA have been set to different values for surveying the real performance and the best settings of the parameters have led to better results than those of previous methods. In addition, the best appropriate parameters have also supported WCA in finding the best location of a Photovoltaic system (PVS) in the first network. WCA could reach less power loss than two other metaheuristics, especially reaching much less power loss than the case without PVS. Consequently, WCA with the most appropriate parameter settings is really a powerful search method in terms of quick convergence speed and high-quality global optimum for the OPF problem with large-scale power networks.

Optimal Location and Sizing of Photovoltaic Systems in Order to Reduce Power Losses and Voltage Drops in the Distribution Grid

The paper presents a method for the optimal allocation and sizing of photovoltaic systems in order to improve power quality in distribution grids. The proposed method is based on the Genetic Algorithm Matlab Toolbox application. The obtained results show that Genetic Algorithm is more accurate and convergence speed is higher than the algorithm used in DIGSILENT/Power factory software. In this way, the defined capacity and location of photovoltaic systems were checked through the performed simulations in DIGSILENT/Power factory software. The simulations were carried out with real data obtained from the Kosovo Distribution Grid. The results from the application of the proposed method showed reduced power losses and voltage drops in the distribution grid confirming the method validity.

Spatio-Temporal Analysis and Forecasting of Distributed PV Systems Diffusion: A Case Study of Shanghai Using a Data-Driven Approach

In recent years, distributed photovoltaic (PV) systems have witnessed rapid development worldwide. Nevertheless, the diffusion of distributed PV systems in a specific region is still indefinite and hard to predict, which bring uncertainties to the planning and operation of electricity distribution network. This paper investigates the diffusion tendency and forecasting approach of distributed PV systems from macro- and micro-aspects. Macroscopic analysis includes spatial clustering of PV systems and quantitative analysis of PV adoption drivers in the time-dimension. Shanghai, Pudong in China is studied in this paper to offer some insights. Analysis reveals that the capacity and location of PV systems are clustered. These clusters continuously spread to the surrounding with changes of size and location, under the impact of internal and external factors. This indicates that diffusion of PV systems can be simulated by a cellular automation model. For microscopic analysis, a data-driven forecasting approach of PV diffusion is proposed based on cellular automation. Analysis shows that the developing state of PV cells can be forecasted based on multi-source datasets. Besides, statistical distribution of newly installed PV capacity per cell tends to be stable, so that it can also be considered as a predictor of distributed PV systems diffusion.

Micro Static Var Compensator for Over-Voltage Control in Distribution Networks with High Penetration of Rooftop Photovoltaic Systems

A micro static var compensator (µSVC) is introduced in this work to prevent the over-voltage problems in radial distribution networks with high number of rooftop photovoltaic (PV) connections. The µSVC is aimed to use in the PV system that has the fixed-power factor inverter, which cannot provide the active voltage controllability. The µSVC is a small shunt compensator installed parallel with the PV system and providing the automatic reactive power support to deal with the dynamic voltage variations at the point of common coupling. Two reactive power control methods, Q(P) and Q(V), can be employed into each µSVC depending on the location of PV systems. Moreover, the coordinated reactive power control among µSVCs, without communication system requirement, is presented for enhancing the Volt-Var controllability to the group of PV systems located in the same feeder. The dynamic voltage control performances are examined on simulation in DIgSILENT PowerFactory software. The results showed that the proposed control method can mitigate the rise of voltage level sufficiently.