Abstract
This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage (LV) distribution network for voltage management, energy arbitrage or peak load reduction. The methods compared include: a neural network (NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network (WNN) model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system (ANFIS) approach. The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge (SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic (PV) field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.
Highlights
Community battery systems are shared by a small group of energy consumers to reduce the peak power demands, provide energy arbitrage or control the network voltages [1, 2]
This paper will focus on the use of load forecasting to optimize the economic operation of community battery systems
The battery energy is the integral of power and minimum state of charge, SoCmin depends on the constant of integration in (3)
Summary
Community battery systems are shared by a small group of energy consumers to reduce the peak power demands, provide energy arbitrage or control the network voltages [1, 2]. This paper will focus on the use of load forecasting to optimize the economic operation of community battery systems. The use of a non-causal average is introduced as an optimum method of peak reduction. This process is inherently dependent upon a future knowledge of the network load and the need for load forecasting is introduced. The periodic nature of the daily or diurnal optimization is discussed and the Fourier series is introduced as a compact method of representing the battery state of charge. A generalized optimization method is introduced and some representative outcomes, based on data from the Perth Solar City trials, are presented
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
