Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty

Abstract

A large-scale renewable-based sustainable power system requires multifaced techno-economic optimization and energy penetration. Due to the volatile and non-periodic nature of renewable energy, the uncertainty of renewables combined with load uncertainties significantly impacts the operational efficiency of renewable integration. The complexities in balancing demand, generation, and maintaining system reliability have introduced new challenges in the current distribution system. Most of the associated challenges can be effectively reduced by using a battery energy storage system (BESS) and the right techniques for handling uncertainties. In this paper, a distributionally robust optimization (DRO) technique with a linear decision rule is formulated for the unit commitment (UC) framework for optimal scheduling of a distribution network that consists of a wind farm, solar PV, a distributed generator (DG), and BESS. To cut the energy cost per unit, BESS plays an important role by storing energy at an off-peak time for on-peak-time use with relatively lower prices. For the all-time minimum overall systems cost, the distribution system requires an optimal size of the BESS to be connected to provide optimal scheduling of DGs. Three case studies are formulated using an IEEE 14 bus system (converted from MW to kW to match the BESS size available in the market) and solved with the proposed distributionally robust optimization technique to achieve the maximum operating point with an optimal capacity of BESS, i.e., wind, solar and hybrid. Each case study has its own optimal 30-min interval schedule for DGs along with the optimal capacity of BESS. The cost comparison with and without BESS and its impact on the start-up and shut down of DGs is reported with all the dynamic economic dispatch results, including the battery’s state-of-charge profile. The proposed technique can handle the uncertainties in renewables and allows economical energy dispatch and optimal BESS sizing with comparatively lower computational processing and complexities.

Stability Study of Grid-Connected Power System for Wind Farms Considering Power Control

“…Rectifiers and inverters are the electrical mechanisms that aim in improving the electronic circuits for a better flow of current. They rectify the power supply and make the programming stable for wind farms [15].…”

Power Flow Optimization Strategy of Distribution Network with Source and Load Storage Considering Period Clustering

“…In this paper, the elbow rule is adopted to ensure the accuracy of k value selection. When Euclidean distance is used as the metric, K-means takes the sum of squares for error (SSE) (also called the degree of distortion) as the target to measure the clustering quality, and its calculation formula is shown in Equation (19).…”

Adaptive Design of Solar-Powered Energy Systems Based on Daily Clearness State Evolution

“…Among these, photovoltaic (PV) systems have emerged as a leading technology, with rapidly decreasing costs and maturing auxiliary devices contributing to explosive growth in the global installed capacity [5][6][7]. However, the inherent uncertainty of solar power poses significant challenges to the photovoltaicbased HESs, particularly in the optimal design phase, when the predictive information is hard to get, making it challenging to achieve an optimal configuration that balances load demand, minimizes cost, and meets other performance indices [8][9][10]. It is necessary to identify a reasonable method for the HES optimization design to quantitatively analyze the uncertainty of solar radiation.…”