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Advanced Energy Management Protocols for Battery Allocation in Various Environmental Conditions

This document explores innovative energy management protocols aimed at optimizing resource allocation for different types of Battery-Assisted Technology (CBAT). It covers strategies related to energy efficiency, energy sourcing (indoor and outdoor), and optimal battery sizing. We also discuss the 'straight-line' solution through stochastic sampling and consider energy input variability across environments. Our goal is to develop algorithms—both smart and simple—that can enhance system performance by reliably predicting energy outputs and managing capacity effectively, ensuring robustness in dynamic conditions.

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Advanced Energy Management Protocols for Battery Allocation in Various Environmental Conditions

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  1. Goal: energy-management /resource-allocation protocols for EnHants CBAT large CBAT small CBAT large CBAT small ‘straight line’ solution Stochastic sampling ‘straight line’ solution • What we are brainstorming • What we are working on right now

  2. Comments on battery size • Per day energy with: • 1% efficiency • 10cm^2 solar cell • Dim indoor environments: 0.1 - 0.3 J • Bright indoor environments: 1-4 J • Outdoor environments: 100- 300 J • Capacity ~60% of daily input • Allows for a `straight-line’ solution • Capacity ~2-4x daily input • Can function well with smart algorithms • Capacity ~10x daily input • Functions well with very simple algorithms

  3. Stochastic case: CBAT large • Capacity 2-4 times the daily energy supply • Exact battery state at a particular time of day not of a great importance • Solution developed for a constrained case an overkill for when the battery is not too high/ too low • Can assume individual days are i.i.d and have a dynamic programming solution • Days are not i.i.d. • Computationally complex • Extensions to more than one device become even more complex

  4. Stochastic case: CBAT small • Difference between the input and the prediction is not dramatic • Using simple adjustments, do not get a good solution

  5. Idea for the stochastic case: Sampling The problem In our case δ represents the vector of harvested energy Assume we have probability P on Δ The method Choosing δ(1),…,δ(N) according to P and solving the problem

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