Real-Time Power Balancing in Electric Grids With Distributed Storage

Abstract

Abstract-Power balancing is crucial for the reliability of an electric power grid. In this paper, we consider an aggregator coordinating a group of distributed storage (DS) units to provide power balancing service to a power grid through charging or discharging. We present a real-time, distributed algorithm that enables the DS units to determine their own charging or discharging amounts. The algorithm accommodates a wide spectrum of vital system characteristics, including time-varying power imbalance amount and electricity price, finite battery size constraints, cost of using external energy sources, and battery degradation. We develop a modified Lyapunov optimization framework for real-time power balancing and provide a fast iterative method for distributed implementation. The two components interact through a novel cost cushion parameter that tunes the trade-off between system performance and convergence speed. We show analytically that the algorithm converges quickly and provides asymptotically optimal performance as the capacity of DS units increases. We further study through simulation the algorithm performance over a wide range of parameter values and demonstrate that it is highly competitive over a greedy alternative.

Distributed Real-Time Power Balancing in Renewable-Integrated Power Grids With Storage and Flexible Loads

“…On the other hand, if these CGs have heterogeneous characteristics and therefore cannot be combined into one, the proposed algorithm can still be used. In particular, in the original problem P1, we would have constraints (7) and (8) for each individual generator; the total output of the generators in (10) is M j=1 g j,t ; and the total cost of the generators is M j=1 C j (g i,t ). The resultant relaxed problem P2 would be similar to the current one, in which the ramping constraint (8) is removed for each individual CG.…”

“…Lyapunov optimization can be used to transform some long-term time-averaged constraints such as (2) into queue stability constraints, and to provide efficient real-time algorithms for complex dynamic systems. Unfortunately, the time-coupled constraints (5) and (8) are not time-averaged constraints, but are hard constraints required at each time slot. Therefore, the Lyapunov optimization framework cannot be directly applied.…”

“…In particular, at each time slot t, we solve an optimization problem that is the same as P3 except without the ramping constraint (8). Therefore, the resultant CG output may be infeasible to P1.…”

“…These works emphasize on various issues of the system in energy management (see Table I for a summary). For example, the authors of [5] and [6] consider supply side management by assuming that all loads are uncontrollable, the authors of [7] study demand side management by optimally scheduling noninterruptible and deferrable loads of individual users, and the authors of [4], [8], and [9] propose to employ energy storage to clear power imbalance. In some other works, the authors combine supply side and demand side managements [10], or supply side and storage managements [11], or demand side and storage managements [12]- [14].…”

“…Note that P3' is the same as P3 except without the ramping constraint (8). Denote the optimal objective values of P3' and P3 asf t and f * t , respectively, and denote an optimal solution of P3' and P3 asũ t and u * t , respectively.…”