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A slot-based energy storage decision-making approach for optimal Off-Grid telecommunication operator

Youssef Ait El Mahjoub, Jean-Michel Fourneau

TL;DR

Problem: Off-Grid telecoms must ensure network reliability while monetizing surplus energy under realistic weather-driven variability. Approach: a slot-based DTMDP with non-stationary energy arrivals and PV failures, optimizing energy release/sale subject to battery capacity and deadlines, solved efficiently by exploiting Robertazzi-type graph structure. Contributions: a structured, scalable solution with exact evaluation, data-driven policy analysis across cities/months, and clear metrics (Release/Delay/Lost) to quantify performance. Impact: enables location-aware deployment decisions and provides a practical framework for energy-aware management of off-grid telecom infrastructure.

Abstract

This paper proposes a slot-based energy storage approach for decision-making in the context of an Off-Grid telecommunication operator. We consider network systems powered by solar panels, where harvest energy is stored in a battery that can also be sold when fully charged. To reflect real-world conditions, we account for non-stationary energy arrivals and service demands that depend on the time of day, as well as the failure states of PV panel. The network operator we model faces two conflicting objectives: maintaining the operation of its infrastructure and selling (or supplying to other networks) surplus energy from fully charged batteries. To address these challenges, we developed a slot-based Markov Decision Process (MDP) model that incorporates positive rewards for energy sales, as well as penalties for energy loss and battery depletion. This slot-based MDP follows a specific structure we have previously proven to be efficient in terms of computational performance and accuracy. From this model, we derive the optimal policy that balances these conflicting objectives and maximizes the average reward function. Additionally, we present results comparing different cities and months, which the operator can consider when deploying its infrastructure to maximize rewards based on location-specific energy availability and seasonal variations.

A slot-based energy storage decision-making approach for optimal Off-Grid telecommunication operator

TL;DR

Problem: Off-Grid telecoms must ensure network reliability while monetizing surplus energy under realistic weather-driven variability. Approach: a slot-based DTMDP with non-stationary energy arrivals and PV failures, optimizing energy release/sale subject to battery capacity and deadlines, solved efficiently by exploiting Robertazzi-type graph structure. Contributions: a structured, scalable solution with exact evaluation, data-driven policy analysis across cities/months, and clear metrics (Release/Delay/Lost) to quantify performance. Impact: enables location-aware deployment decisions and provides a practical framework for energy-aware management of off-grid telecom infrastructure.

Abstract

This paper proposes a slot-based energy storage approach for decision-making in the context of an Off-Grid telecommunication operator. We consider network systems powered by solar panels, where harvest energy is stored in a battery that can also be sold when fully charged. To reflect real-world conditions, we account for non-stationary energy arrivals and service demands that depend on the time of day, as well as the failure states of PV panel. The network operator we model faces two conflicting objectives: maintaining the operation of its infrastructure and selling (or supplying to other networks) surplus energy from fully charged batteries. To address these challenges, we developed a slot-based Markov Decision Process (MDP) model that incorporates positive rewards for energy sales, as well as penalties for energy loss and battery depletion. This slot-based MDP follows a specific structure we have previously proven to be efficient in terms of computational performance and accuracy. From this model, we derive the optimal policy that balances these conflicting objectives and maximizes the average reward function. Additionally, we present results comparing different cities and months, which the operator can consider when deploying its infrastructure to maximize rewards based on location-specific energy availability and seasonal variations.

Paper Structure

This paper contains 16 sections, 26 equations, 10 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Battery Filling Markov Chain Model
  3. The state
  4. The transitions
  5. The graph structure
  6. Battery Filling Markovian Decision Process
  7. MDP formulation : states and actions
  8. MDP formulation : probabilities per action
  9. MDP formulation : instant rewards
  10. MDP resolution : average reward criteria
  11. MDP resolution : optimal policy measures
  12. Numerical results
  13. Algorithms comparison
  14. Application : optimal policy analysis
  15. Application : cities comparison
  16. ...and 1 more sections

Figures (10)

  • Figure 1: Average number of Energy Packets produced by a solar panel. Barcelona, August NREL.
  • Figure 2: Distribution of Data Packets arrival i.e. service demands. Erlangs-based workload Cisco07.
  • Figure 3: Toy example filling process with parameters $t_0=9h$, $T=12h$, $F=3$, $C=3$, and $\forall h \in \{t_0,\dots, T\}$, ${\cal{A}_H} = \{0, 1, 2\}$.
  • Figure 4: System representation
  • Figure 5: Energy Packets distribution $\mathcal{A}_{12h}$, $\mathcal{A}_{13h}$ ... $\mathcal{A}_{17h}$. Barcelona, August.
  • ...and 5 more figures

Theorems & Definitions (5)

  • proof
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