Robust Peak-Shaving for a Neighborhood with Electric Vehicles

Abstract

Demand Side Management (DSM) is a popular approach for grid-aware peak-shaving. The most commonly used DSM methods either have no look ahead feature and risk deploying flexibility too early, or they plan ahead using predictions, which are in general not very reliable. To counter this, a DSM approach is presented that does not rely on detailed power predictions, but only uses a few easy to predict characteristics. By using these characteristics alone, near optimal results can be achieved for electric vehicle (EV) charging, and a bound on the maximal relative deviation is given. This result is extended to an algorithm that controls a group of EVs such that a transformer peak is avoided, while simultaneously keeping the individual house profiles as flat as possible to avoid cable overloading and for improved power quality. This approach is evaluated using different data sets to compare the results with the state-of-the-art research. The evaluation shows that the presented approach is capable of peak-shaving at the transformer level, while keeping the voltages well within legal bounds, keeping the cable load low and obtaining low losses. Further advantages of the methodology are a low communication overhead, low computational requirements and ease of implementation.

On the value of device flexibility in smart grid applications

“…Devices such as batteries and EVs can often be deployed without predictions, or the decisions are robust against prediction errors. More precisely, for maximizing self-consumption they even do not need predictions of the expected house profile since they can apply a greedy strategy: charge when there is abundant PV, otherwise discharge to fully compensate for the house consumption, and for EVs peak shaving algorithms that are robust against prediction errors exist [9]. In contrast, most of the currently existing white goods are uninterruptible, and therefore they cannot use this strategy because after starting the device, this decision cannot be undone and therefore they do need good predictions to make an optimal decision.…”

“…In practice, such information is hard to obtain, hence the start times cannot be chosen optimally and the value of flexibility decreases. Some other devices, e.g., EVs [9], suffer less from this problem since they can respond faster to changing circumstances.…”

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

“…The authors in [18,19] investigated impacts of different charging schemes in distribution networks and developed various control algorithms (e.g., energy shifting [20], load profile smoothing [21], valley filling [22], peak-shaving [23], and loss minimization [24]) to address grid issues. In addition, an adaptive control algorithm based on predefined voltage and current sensitivity is proposed in [25] to mitigate grid constraints violations.…”

Minimization of Load Variance in Power Grids—Investigation on Optimal Vehicle-to-Grid Scheduling

“…Various optimal V2G scheduling strategies have been introduced in the literature to minimize the power grid load variance [48,49]. This concept is well known for its effectiveness in reducing power grid operation losses [50].…”