Integrating Energy Storage in Electricity Distribution Networks

Abstract

Electricity generation combined with its transmission and distribution form the majority of an electric utility's recurring operating costs. These costs are determined, not only by the aggregate energy generated, but also by the maximum instantaneous peak power demand required over time. Prior work proposes using energy storage devices to reduce these costs by periodically releasing energy to lower the electric grid's peak demand. However, prior work generally considers only a single storage technology employed at a single level of the electric grid's hierarchy. In this paper, we examine the efficacy of employing different combinations of storage technologies at different levels of the grid's distribution hierarchy. We present an optimization framework for modeling the primary characteristics that dictate the lifetime cost of many prominent energy storage technologies. Our framework captures the important tradeoffs in placing different technologies at different levels of the distribution hierarchy with the goal of minimizing a utility's operating costs. We evaluate our framework using real smart meter data from 5000 customers of a local electric utility. We show that by employing hybrid storage technologies at multiple levels of the distribution hierarchy, utilities can reduce their daily operating costs due to distributing electricity by up to 12%.

Analyzing Distribution Transformers at City Scale and the Impact of EVs and Storage

“…In addition, the use of energy storage has been studied in the context of load shifting, where energy storage charges itself during periods of excess generation (or o-peak pricing periods) and discharges when the demand is high [11,12,20,32]. Similarly, prior work has proposed algorithms to shave peaks at both the individual home or the grid level [28,30,31]. Again, our work is complementary, as this work does not study the use of peakshaving techniques to mitigate the impact of EVs on distribution edge transformers at city-scale.…”

“…At the same time, the emergence of the smart electric grid has resulted in new technologies for more exible demand-side load management and load mitigation in the grid. In particular, gridlevel energy storage is emerging as a key technology for supporting future smart grids, since it can smooth out uctuations from intermittent renewable energy sources, such as solar and wind, as well as enable grid optimizations, such as shaving peak loads and serving as backup power to reduce outage durations [28,30,31]. Interestingly, grid-level energy storage can also be used to mitigate the impact of EV loads on distribution transformers.…”

Joint Investment and Operation of Microgrid

“…Moreover, we observe that the actual cost of the operator C o (Q ω , e ω s , e ω w ) is a non-increasing function with respect to e ω s and e ω w . Thus we have the following theorem: (25) Based on the minimized actual operating cost (25) in period-2, we write the worst-case overall cost minimizing problem in period-1 as RP1: Robust optimization for overall cost minimization min αs,αw,αe…”

“…where SOC ω,t evolves with charging and discharging of the battery according to (8). It is shown in ( 9) that SOC ω,t is bounded between SOC min and SOC max , which are lower and upper bounds [25] for the level of energy storage in percentage, respectively. For example, we can set SOC max as 100%, which means the battery can be charged to reach its full capacity.…”

“…(25) Based on the minimized actual operating cost (25) in period-2, we write the worst-case overall cost minimizing problem in period-1 as RP1: Robust optimization for overall cost minimization min αs,αw,αe…”