PV-Wind-Battery Hybrid Power Systems: Power Management Control Strategy Using P2D Battery Model

Abstract

A standalone hybrid power system (SHPS) using renewable energy resources (RERs) like wind, solar, hydro and biomass can be installed at any remote location where power grid extension is either not possible or too expensive. However, these RERs have drawbacks like unpredictable nature, intermittency, dependence on location and mismatch of generated power with load demand. To overcome the above limitations SHPSs are often integrated with battery energy storage system (BESS) [1]. Lithium-ion batteries (LiBs) are one of the most popular and emerging options for energy storage for SHPSs [2]. The financial feasibility of the SHPSs depends on the operation and performance of LiBs which is one of the most expensive components. Improperly designed BESS leads to a high life cycle cost or failure of the system to provide a reliable power supply and hence the power management and control in the SHPS is an active area of research. It is essential to manage the power flow between the load, the power generated by RERs and the battery to ensure the power stability in the system and guarantee continuous power supply to the end user [3]. Till now various researchers successfully implemented empirical/equivalent circuit models (ECM), which are less reliable under dynamic operating conditions, to design battery management systems and control algorithms due to the ease of their implementation in the control and operation of SHPSs [4, 5]. We present the use of physics-based battery (pseudo-2D) models representing the transport and kinetic processes that take place in the LiBs [6] which provides better control and prediction of battery performance to design robust power management control strategy for the SHPS consisting of PV-wind based generators integrated with Li-ion battery. The wind velocity is negatively correlated to solar insolation [7] and therefore we consider the solar-wind combination to utilize their complementary nature. This framework comprises of PV panels, wind turbines, Maximum Power Point Tracking (MPPT) controller, power electronics, and LiBs. A single-diode equivalent circuit-based model is used to model a solar cell. A differential algebraic equation based MPPT algorithm is implemented to track the maximum power point of the solar array [8]. The power output from the wind turbine generator is predicted using wind speed and speed characteristics of the turbine. The power management strategy guarantees 0% excess output power production, and ensuring no energy is transferred to the dump load. A case study will be presented for a year-long real-world data which would consider the seasonal variability in load demand and power generation. The load demand and power generated (by RERs) and power supplied by the battery for a sample 7-day data is shown in Figure 1. These models are used to study the effects of RERs and load demand uncertainties and variations in the operating conditions of hybrid systems. Further, we explore the possibilities of using a physics-based battery model for a Li-ion battery in designing optimal BESS capacity that minimizes capacity fade and thermal degradation.