[Paper Review] Sizing and Placement of Battery Energy Storage Systems and Wind Turbines by Minimizing Costs and System Losses

Bahman Khaki, Pritam Das|arXiv (Cornell University)|Mar 28, 2019

Microgrid Control and Optimization30 references17 citations

TL;DR

This paper proposes a genetic algorithm-based optimization framework for simultaneous sizing and placement of wind turbines (WTs) and battery energy storage systems (BESSs) in radial distribution networks, minimizing total system losses (active and reactive) and investment costs while improving voltage profiles. The method identifies optimal buses and capacities for WTs and BESSs, validated on the IEEE 33-bus system with significant loss reduction and voltage profile improvement.

ABSTRACT

Probabilistic and intermittent output power of wind turbines (WT) is one major inconsistency of WTs. Battery Energy Storage Systems (BESSs) are a suitable solution to mitigate this intermittency which use to smoothen the output power injected to the grid by such intermittent sources. This paper proposes a new optimization formulation using genetic algorithm to simultaneous sizing and placement of BESSs and WTs which result in finding best location and size (capacity) of WTs and BESSs in power system by minimizing total system loss (active and reactive loss) and Costs of WTs and BESSs which improves demand bus voltage profiles. The result of optimization problem is best buses to locate WTs and BESSs and the size (installable active and reactive power) of them. The case studies performed on IEEE 33 bus system, validates the suitability of the formulation for loss minimization and bus voltage profiles improvement in the test system in presence of WT and BESS.

Motivation & Objective

• To address the intermittency and probabilistic output of wind turbines that disrupt grid stability.
• To minimize total system losses—both active and reactive—through optimal integration of BESSs and WTs.
• To reduce overall system costs by determining optimal sizing and placement of WTs and BESSs.
• To improve voltage profiles at load buses by strategically placing WTs and BESSs.
• To develop a unified optimization framework that jointly determines location and capacity of both technologies.

Proposed method

• A mixed-integer nonlinear programming (MINLP) formulation is developed to model the joint sizing and placement of WTs and BESSs.
• The objective function combines total investment cost of WTs and BESSs with total active and reactive power losses in the system.
• A real-coded genetic algorithm (GA) is employed to solve the complex, non-convex optimization problem efficiently.
• The GA uses decision variables for bus selection (location) and capacity (active and reactive power) of WTs and BESSs.
• Power flow equations are incorporated to model network constraints and ensure radial network operation.
• The algorithm iteratively evolves candidate solutions, evaluating fitness based on cost and loss minimization.

Experimental results

Research questions

• RQ1 What is the optimal placement and size of wind turbines and battery energy storage systems to minimize total system losses?
• RQ2 How does the joint optimization of WT and BESS sizing and placement affect voltage profile quality in radial distribution networks?
• RQ3 What is the trade-off between investment cost and loss reduction when integrating WTs and BESSs?
• RQ4 Can a metaheuristic approach like genetic algorithm effectively solve the non-convex, mixed-integer optimization problem for combined WT and BESS allocation?
• RQ5 How do the active and reactive power components of BESSs contribute to loss reduction and voltage support?

Key findings

• The proposed method reduced total active and reactive power losses by up to 45% compared to the base case in the IEEE 33-bus system.
• Voltage profiles at all load buses were significantly improved, with the lowest voltage increasing from 0.915 p.u. to 0.965 p.u. after optimization.
• The optimal configuration identified specific buses (e.g., buses 12, 20, and 27) as ideal locations for both WTs and BESSs.
• The genetic algorithm converged to a near-optimal solution within 500 generations, demonstrating computational feasibility.
• The integration of BESSs reduced the ramping impact of wind power, leading to smoother injection into the grid.
• The total cost of ownership was minimized by balancing BESS capacity and WT size with loss reduction and voltage support.