Scheduling of Dynamic Electric Loads Using Energy Storage and Short Term Power Forecasting

TL;DR

This paper presents a versatile optimization method for scheduling electrical loads using short-term power forecasts and energy storage, enabling real-time control of dynamic loads with constraints.

Contribution

It introduces a novel parallel enumeration approach that efficiently schedules loads with dynamic demands and minimum on/off times based on short-term forecasts.

Findings

Effective scheduling with short-term forecasts demonstrated in simulations.

Limited complexity allows real-time operation with many loads.

Versatile approach accommodates dynamic load profiles and constraints.

Abstract

In this paper we formulate an optimization approach to schedule electrical loads given a short term prediction of time-varying power production and the ability to store only a limited amount of electrical energy. The proposed approach is unique and versatile as it allows scheduling of electrical loads that each have their own dynamic power demand during on/off switching, while also allowing the specification of minimum on/off times for each loads separately. The optimization approach is formulated as a parallel enumeration of all possible on/off times of the electrical loads using a moving time approach in which only a short term power production forecast is needed, while at the same time taking into account constraints on electrical energy storage and power delivery of a battery system. It is shown that the complexity of the optimization (number of enumerations) is limited by the number of data points in the short term power production forecast and the minimum on/off time of the electrical loads. The limited complexity along with parallel enumeration allows real-time operational scheduling of a large number of loads. The simulation results shown in this paper illustrate that relatively short term power forecast profiles can be used to effectively schedule dynamic loads with various dynamic load profiles.