恒温控制负荷在停电后恢复供电时会对电力需求产生显著影响，这一现象被称为冷负荷启动（cold load pick-up，CLPU）。主动管理CLPU正成为配电系统运营商推迟网络升级、加快停电后用户复电速度的重要工具。主动管理CLPU所需的一项核心功能是对其进行多尺度预测。智能计量设备的广泛部署也为数据驱动的负荷建模与预测开辟了新机遇。本文提出了一种利用带时间戳的局部负荷测量值，在用户侧估计CLPU的方法。该方法采用自回归积分滑动平均（Auto-Regressive Integrated Moving Average，ARIMA）模型，对停电期间的短期损失能耗进行预测。预测以小时为单位开展，用于估计持续时间最长达数小时的停电后潜在需要恢复的电量。此外，为适配不断变化的用户行为与天气情况，模型阶数会动态调整。本文基于50户居民用户为期一年的实际智能电表测量值开展了仿真实验，仿真结果通过居民负荷物理建模得到验证，且与基于ARIMA的预测结果吻合度较高。研究还将该方法的准确率与执行速度和长短期记忆网络（Long Short Term Memory Network，LSTM）、霍尔特-温特斯指数平滑（Holt-Winters Exponential Smoothing，HWES）等其他主流时间序列预测方法进行了对比，结果表明基于ARIMA的预测在准确率和计算速度两方面均表现更优。
英文摘要：
Thermostatically-controlled loads have a significant impact on electricity demand after service is restored following an outage, a phenomenon known as cold load pick-up (CLPU). Active management of CLPU is becoming an essential tool for distribution system operators who seek to defer network upgrades and speed up post-outage customer restoration. One key functionality needed for actively managing CLPU is its forecast at various scales. The widespread deployment of smart metering devices is also opening up new opportunities for data-driven load modeling and forecast. In this paper, we propose an approach for customer-side estimation of CLPU using time-stamped local load measurements. The proposed method uses Auto-Regressive Integrated Moving Average (ARIMA) modeling for short-term foregone energy consumption forecast during an outage. Forecasts are made on an hourly basis to estimate the energy to potentially recover after outages lasting up to several hours. Moreover, to account for changing customer behavior and weather, the model order is adjusted dynamically. Simulation results based on actual smart meter measurements are presented for 50 residential customers over the duration of one year. These results are validated using physical modeling of residential loads and are shown to match well the ARIMA-based forecasts. Additionally, accuracy and execution speed has been compared with other state-of-the-art approaches for time-series forecasting including Long Short Term Memory Network (LSTM) and Holt-Winters Exponential Smoothing (HWES). ARIMA-based forecast is found to offer superior performance both in terms of accuracy and computation speed.
Nowadays, attitudes towards electricity customers have been changed, so that they are no longer considered static players. The customers behavior identification is vital for establishing modern power systems. This paper utilizes the customers risk aversion coefficient to model their consumption behavior. Through the coefficients, the value of electricity consumption is measured employing the concept of the utility function. The utility function is an essential concept in microeconomics that measures customers preferences and interprets how a rational consumer would make consumption decisions. …
Demand response involves system operators using incentives to modulate electricity consumption during peak hours or when faced with an incidental supply shortage. However, system operators typically have imperfect information about their customers' baselines, that is, their consumption had the incentive been absent. The standard approach to estimate the reduction in a customer's electricity consumption then is to estimate their counterfactual baseline. However, this approach is not robust to estimation errors or strategic exploitation by the customers and can potentially lead to overpayments t…
Demand response (DR) for smart grids, which intends to balance the required power demand with the available supply resources, has been gaining widespread attention. The growing demand for electricity has presented new opportunities for residential load scheduling systems to improve energy consumption by shifting or curtailing the demand required with respect to price change or emergency cases. In this paper, a dynamic residential load scheduling system (DRLS) is proposed for optimal scheduling of household appliances on the basis of an adaptive consumption level (CL) pricing scheme (ACLPS). Th…
Electric Vehicle (EV) penetration and renewable energies enables synergies between energy supply, vehicle users, and the mobility sector. However, also new issues arise for car manufacturers: During charging and discharging of EV batteries a degradation (battery aging) occurs that correlates with a value depreciation of the entire EV. As EV users' satisfaction depends on reliable and value-stable products, car manufacturers offer charging assistants for simplified and sustainable EV usage by considering individual customer needs and battery aging. Hitherto models to quantify battery aging have…
Fraunhofer Institute for Telecommunications, HHI（弗劳恩霍夫电信研究所）
As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing agent, which sends price signals to the customers and contributes to shifting the customer demand to periods of high renewable energy generation. We propose an implementation of a pricing agent with a reinforcement learning approach where the environment is represented by the customers, the electricity generation utilities and the weather conditions.
Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the "range anxiety" of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers …
Electricity usage is a major portion of utility bills and the best place to start lowering them. An effective home energy management approach is introduced to decrease customers' electricity bills by determining the optimal appliance scheduling under hourly pricing based demand response (DR) strategies. The proposed approach specifically addresses consumer comfort through acceptable appliance deferral times, within which their consumption can be shifted from the normal schedules. Electric vehicles (EVs), with vehicle to grid (V2G) and vehicle-to-home (V2H) abilities, Energy storage systems (ES…
