Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility

Abstract

Abstract-This paper describes a robust optimization approach to minimize the total cost of supplying a remote telecommunication station exclusively by renewable energy sources (RES). Due to intermittent nature of RES, such as photovoltaic (PV) panels and small wind turbines, they are normally supported by a central energy storage system (ESS), consisting of a battery and a fuel cell. The optimization is carried out as a robust mixed-integer linear program (RMILP), and results in different optimal solutions, depending on budgets of uncertainty, each of which yields different RES and storage capacities. These solutions are then tested against a set of possible outcomes, thus simulating the future operation of the system. Since battery cycling is inevitable in this application, an algorithm that counts the number of cycles and associated depths of discharges (DoD) is applied to the optimization results. The annual capacity reduction that results from these cycles is calculated for two types of battery technologies, i.e. valve-regulated lead-acid (VRLA) and lithium-ion (Li-Ion), and treated as an additional cost. Finally, all associated costs are added up and the ideal configuration is proposed.Index Terms-Autonomous power facility, batteries, energy storage system, renewable energy sources, robust mixed-integer linear programming. (t) electricity discharged form the battery (kW), NOMENCLATUREwind turbine generation (kW),curtailed RES output, n hyd number of hydrogen storage replacements,consumed hydrogen (l), x ch bat (t) binary variable equal to 1 if battery is being charged at time period t, and 0 otherwise, x dis bat (t) binary variable equal to 1 if battery is being discharged at time period t, and 0 otherwise.

Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life

“…The operational cost is proportional to the amount of energy change in storage, as derived in (22). β t indicates the average energy consumed in regulation up or down within hour t for 1-MW committed regulation capacity, as determined by the generated RegD signal.…”

“…Reference [22] calculates a battery's capacity reduction from its cycles. Some papers derive formulations on the relation between battery cycle life and DOD using different fitting techniques.…”

“…Some papers derive formulations on the relation between battery cycle life and DOD using different fitting techniques. Reference [20] uses a fourth-order polynomial function, whereas [21] and [22] used an exponential function. Another widely used function is the power function [14]- [16], which also fits the data in [12] and [13] very well.…”

“…Regardless of the specific cycle life models, a rainflow counting algorithm is commonly used to calculate a battery's lifetime, as referenced in [13], [14], [22], and [23]. Most papers above apply this algorithm for static evaluation with fixed operating strategies.…”

Optimization and Analysis of a Hybrid Energy Storage System in a Small-Scale Standalone Microgrid for Remote Area Power Supply (RAPS)

“…ESSs can not only provide voltage and frequency support in the form of a main power supply to maintain system stability, but also have many advantages such as environmental-friendliness, bidirectional flow of energy, and continuously adjustable charge-discharge power features. Therefore, ESSs have become an important part of such standalone microgrids [3][4][5].…”