Abstract
As renewable penetration increases in microgrids (MGs), the use of battery energy storage systems (BESSs) has become indispensable for optimal MG operation. Although BESSs are advantageous for economic and stable MG operation, their life degradation should be considered for maximizing cost savings. This paper proposes an optimal BESS scheduling for MGs to solve the stochastic unit commitment problem, considering the uncertainties in renewables and load. Through the proposed BESS scheduling, the life degradation of BESSs is minimized, and MG operation becomes economically feasible. To address the aforementioned uncertainties, a scenario-based method was applied using Monte Carlo simulation and the K-means clustering algorithm for scenario generation and reduction, respectively. By implementing the rainflow-counting algorithm, the BESS charge/discharge state profile was obtained. To formulate the cycle aging stress function and examine the life cycle cost (LCC) of a BESS more realistically, the nonlinear cycle aging stress function was partially linearized. Benders decomposition was adopted for minimizing the BESS cycle aging, total operating cost, and LCC. To this end, the general problem was divided into a master problem and subproblems to consider uncertainties and optimize the BESS charging/discharging scheduling problem via parallel processing. To demonstrate the effectiveness and benefits of the proposed BESS optimal scheduling in MG operation, different case studies were analyzed. The simulation results confirmed the superiority and improved performance of the proposed scheduling.
Highlights
J where et,j is the energy stored in marginal cost segment j at time t; e j is the maximum amount of energy that can be stored in cycle depth segment j; Emin and Emax represent the minimum and maximum energy stored in the battery energy storage systems (BESSs), respectively; e0j is the initial amount of energy of segment j; and E f inal is the amount of energy stored at the end of the scheduling horizon
The parallel processing nature of Benders decomposition (BD) should be noted; these results indicate that the calculation speed can be improved as the number of uncertainties increases, and that a more realistic power system which readily matches the requirements of realistic day-ahead BESS
To minimize the total operation cost (TOC) of the MG, we focused on operation scheduling that minimizes the life degradation of BESSs
Summary
Microgrids (MGs) contain renewable energy sources (RESs) and loads that can operate in a controlled manner. Given that the RESs and loads in MGs are characterized by uncertainty, the MG operator performs stochastic unit commitment (SUC) as an alternative to deterministic. Optimal scheduling, including the state of charge (SOC) and charging/discharging cycle of the BESS, is essential to maximize the benefits while operating an MG. Owing to the current high price of BESSs, it is crucial to prolong the lifetime of the battery by appropriately managing its charging/discharging schedule. In the long run, complete economical exploitation of the battery will not be possible because of the replacement cost caused by shorter battery lifetimes This increases the life cycle cost (LCC) of the BESS. Battery characteristics should be considered in the operation scheduling of the SUC problem in MGs
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