# Dynamic Incentive Signaling through Advanced Metering Infrastructure Networks

Matthieu Silard, Anne-Cécile Orgerie, Nicolas Montavont, Georgios Papadopoulos

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Matthieu Silard, Anne-Cécile Orgerie, Nicolas Montavont, Georgios Papadopoulos. Dynamic Incentive Signaling through Advanced Metering Infrastructure Networks. SmartGridComm 2025 - IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, Sep 2025, Toronto, Canada. pp.1-5. [https://hal-05310150](https://hal-05310150)

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Submitted on 11 Oct 2025

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# Dynamic Incentive Signaling through Advanced Metering Infrastructure Networks

Matthieu Silard*, Anne-Cécile Orgerie*, Nicolas Montavont†, Georgios Z. Papadopoulos†

*Inria, CNRS, IRISA, University of Rennes, Rennes, France, email: {matthieu.silard, anne-cecile.orgerie} @irisa.fr

†IMT Atlantique, IRISA, Rennes, France, email: {nicolas.montavont, georgios.papadopoulos} @imt-atlantique.fr

**Abstract**—The massive integration of non-dispatchable Distributed Energy Resource (DER) and the growing electrification of uses, notably via Electric Vehicles (EVs), poses major challenges for Distribution System Operator (DSO). These evolutions are causing congestion on Medium Voltage (MV) networks, and increase uncertainty of production periods. To address these challenges without costly infrastructure investment, this article proposes an innovative demand management solution based on direct, intermediary-free communication between the DSO and users. Leveraging existing Advanced Metering Infrastructures (AMIs), this approach broadcasts dynamic incentive signals to Electric Vehicle Supply Equipments (EVSEs), which enables optimized management of energy flows, and thus provides a rapid response to network needs. Our solution has the advantage of using existing power and network infrastructures and does not require to gather private information. In addition, it allows users to choose whether or not to provide their devices as flexible loads. The results show significant potential for improving grid stability and reducing congestion, offering a cost-effective, scalable alternative for smart grids.

**Index Terms**—Smart Grid, Demand Response, Electric Vehicle, Smart Charging, Congestion Management, Non-Dispatchable DER, AMI, PLC

## I. INTRODUCTION

The rapid evolution of power grids across the world poses additional challenges for DSOs. Energy transition policies require the deployment of decentralized generation plants, which were not originally considered in the grid's dimensioning. The massive integration of non-dispatchable DERs, such as solar and wind power plants, alters the energy flows.

The integration of non-synchronous generation, which relies on inverters rather than traditional generators, reduces the rotational inertia provided to the grid, making it more susceptible to abrupt frequency deviations [1].

At the same time, the growing electrification of uses, particularly in the mobility sector with the adoption of EVs, is leading to a significant increase in electrical demand.

These trends are leading to new points of congestion on the distribution network, particularly at MV level. To avoid costly investment in electricity infrastructure expansion, DSOs are exploring Demand Response (DR) solutions. EVs, equipped with large batteries, represent both a challenge and an opportunity for grid stability. Parked EVs can share their battery as a flexible storage resource, if properly integrated into grid management strategies.

This work was funded by the French National Research Agency (ANR) under the project ANR EDEN4SG (ANR-22-CE05-0023).

<figure><img src="image_2.png" /><figcaption>Fig. 1. Classification of conventional DR methods [3].</figcaption></figure>

Since the 1960s, Power Line Communication (PLC) technologies, such as ripple control, have been used to control domestic equipment, like electric water heaters [2], thus helping to balance the grid. Extending this signaling to modern equipment, such as EVSEs, offers a significant potential for optimizing the management of large-scale energy flows.

The paper is organized as follows. Section II details the related work. Our approach for Dynamic Incentive Signaling (DIS) is described in Section III. Section IV introduces our case study. A simulation-based evaluation is provided in Section V. Some limitations concerning this work are discussed in Section VI. Section VII concludes this work and proposes future directions.

## II. RELATED WORK

Demand Response (DR) methods play a key role in managing congestion on power grids, particularly in a context of increasing integration of non-dispatchable DERs and electrification of usage. As illustrated in Figure 1 (adapted from [3]), DR approaches fall into two main categories as described in [4]: Incentive-based DR and Price-based DR.

### A. Incentive-based DR

Described in [3], incentive-based DR programs financially reward consumers who reduce their electricity consumption at the request of the supplier, usually during peak periods or critical events. These programs include Direct load control [5], which allows the supplier to turn off certain appliances such as air conditioners; Load curtailment programs, where consumers
are paid to reduce their consumption; Demand bidding and Emergency reduction programs are for large consumers [6] and helps during major power grid disruptions. Participants receive payments or rebates, but may be penalized for non-compliance in some cases.

These programs are particularly effective in managing peak demand and avoiding overloads, while providing economic benefits to participants and improving the stability of the electrical system. However, they come with limitations: Direct Control reduces consumer flexibility and freedom by imposing centralized load management, while other approach such as Demand Bidding require active consumer participation, often incompatible with real-time responsiveness.

## B. Price-based DR

Price-based DR programs encourage consumers to adjust their electricity consumption based on variable prices that reflect the cost of supplying electricity at different times. These programs use dynamic pricing structures, such as Time-of-use [7], Real-time pricing [3], Critical peak pricing [8] or Inclining block rate, to encourage a reduction or shift in consumption during periods of high demand or high costs. For example, a higher price during peak hours (2 p.m. to 7 p.m.) encourages consumers to use energy-intensive appliances during off-peak hours, thereby reducing pressure on the grid.

These programs promote grid balance, lower overall costs, and reduce emissions by limiting the use of polluting power plants, while offering consumers the opportunity to save on their bills. On the other hand, while effective in inducing behavioral changes through price variations, they can lead to price inequality depending on location and limited user acceptability when faced with frequent price fluctuations.

# III. PROPOSED SOLUTION: DYNAMIC INCENTIVE SIGNALING (DIS)

## A. Overview

We propose a DIS solution that lies between Incentive-based and Price-based approaches, using direct communication between the DSO and consumers. The existing PLC infrastructure is used to broadcast dynamic incentive signals to EVSE. This method enables real-time, bidirectional adjustments to demand, customized to the specific location of each Low Voltage (LV) network, to manage congestion and stabilize the grid. Smart devices using this signaling, such as Vehicle to Grid (V2G) EVs, automatically adjust their electricity consumption or injection, forming a Virtual Power Plant (VPP). This solution offers a cost-effective and scalable approach that relies on user autonomy. An aggregator can potentially use this signaling to adjust its traditional DR program, thus providing a financial incentive to consumers.

## B. The Dynamic Incentive Value (DIV)

To determine the DIV, the DSO can leverage multiple metrics, each assigned with distinct weights based on their relative importance. These metrics may include, for instance, the network frequency deviation (±50 Hz), the voltage deviation

at a specific point on the MV network (±20 kV), the output of non-dispatchable DERs, as well as line and transformer load rates.

In our case, the load rate of a transformer is identical to the load rate of the line feeding it, both being rated for the same maximum power. Furthermore, we focus specifically on line congestion to compute the DIV, accounting for both forward and reverse power flows. This indirectly takes into account the production of the local non-dispatchable DER. The MV network can be modeled as a tree as in Figure 2, with the root as the primary substation (HV/MV transformer) and the leaves as the LV substations (MV/LV). Each edge $e$ in the tree represents a bidirectional MV line, with its maximum capacity $I_{max,e}$ and a current capacity $I_e$ determined by the OPF solver. The load rate of each edge is:

$$ \text{Congestion}_e = \max \left( -1, \min \left( 1, \frac{I_e}{I_{\text{max},e}} \right) \right) $$

where $\text{Congestion}_e = 1$ indicates maximum forward flow (from root to leaf), and $\text{Congestion}_e = -1$ indicates maximum reverse flow.

For each leaf $d$, a unique $DIV_d$ is broadcasted within each district's AMI network based on the path $P_d$ from the root to $d$. A positive value encourages consumption, while a negative value encourages consumption reduction or even injection if possible (using V2G). Each DIV is the weighted average of the incentives associated with each metric, as defined by:

$$ \text{DIV}_d = - \frac{\sum_{e \in P_d} w_e \cdot \text{Congestion}_e}{\sum_{e \in P_d} w_e} $$

where $w_e \ge 0$ with $\sum_{e=1}^n w_e > 0$.

The weight $w_e$ for each edge $e \in P_d$ is defined based on the absolute load rate:

$$ w_e = \begin{cases} 1 & \text{if } |\text{Congestion}_e| \ge 1, \\ 10(1 - |\text{Congestion}_e|) & \text{if } 0.9 < |\text{Congestion}_e| < 1, \\ 0 & \text{otherwise.} \end{cases} $$

For load between -90% and 90% (negative values indicating reverse flow from excess generation), no specific incentive is applied, allowing consideration for other metrics not addressed in this study. For load exceeding ±90%, the DIV adjusts linearly, reaching -1 at maximum forward flow congestion to discourage consumption, or increasing to 1 at maximum reverse flow congestion to encourage consumption and absorb excess production. This approach ensures that only critically congested edges ($|\text{Load}_e| > 0.9$) contribute to the incentive, with their influence decreasing linearly as $|\text{Load}_e|$ approaches 0.9.

## C. Charging Algorithms

The *Uncontrolled Charging* algorithm is used as a reference to evaluate the performance of the smart charging approach. This algorithm consists of charging the EV at a constant maximum power, which corresponds to the maximum power
output of the EVSE. Unlike smart charging methods, it does not take into account the external network conditions provided by the DIV. The algorithm works as follows: the EV starts charging as soon as time t reaches the charging start time $t_{start}$, and continues at full power until the state of charge SoC(t) reaches the state of charge desired by the user SoC<sub>desired</sub>. The charging power $P(t)$ at time t is defined as follows:

$$P(t) = \begin{cases} P_{max} & \text{if } t \ge t_{start} \text{ and SoC}(t) < SoC_{desired}, \\ 0 & \text{otherwise.} \end{cases}$$

This algorithm ignores the DIV received by the smart meter, which indicates whether consumption should be encouraged (positive value) or reduced (negative value). As a result, *Uncontrolled Charging* can increase network congestion, particularly during periods of high demand. This approach is used as a basis for comparison in the study to demonstrate the benefits of the *DIV-aware* algorithm, which adjusts charging power according to the received incentives.

The *Nominal Charging* algorithm represents an intermediate approach in our study. Like the *Uncontrolled Charging* algorithm, it ignores the DIV incentive signal received by the smart meter. However, unlike the latter, *Nominal Charging* spreads charging evenly over the entire available period, thereby reducing charging power. Charging power $P(t)$ at time t is defined as follows:

$$P(t) = Battery_{capacity} \cdot \frac{SoC_{desired} - SoC_{start}}{t_{end} - t_{start}}$$

This approach limits peak demand compared with *Uncontrolled Charging*, but remains insensitive to grid conditions. The *Uncontrolled DIV-aware* algorithm is an intermediate version which integrates DIV incentive signals while relying on the logic of the *Uncontrolled Charging* algorithm. With this approach, the electric vehicle starts charging at maximum power, as in *Uncontrolled Charging*. However, if the DIV signal becomes negative, indicating a need to reduce consumption, the charging power is reduced. In other cases, charging power gradually increases until a new negative signal is encountered. Inspired by the classical TCP (Transmission Control Protocol) congestion-avoidance algorithm used in Internet networks [9], this algorithm seeks to maximize charging power while avoiding network congestion.

The *DIV-aware* algorithm is proposed as a smart charging solution that uses DIV to modulate EV charging while respecting grid constraints. Unlike the *Uncontrolled Charging* algorithm, this algorithm dynamically adjusts charging power according to network conditions and the time available before the vehicle departure time.

First, we calculate $P(t_{start})$ by spreading the charge over the period available between the current time and the departure time, in order to avoid consumption peaks.

$$P(t_{start}) = Battery_{capacity} \cdot \frac{SoC_{desired} - SoC(t)}{t_{end} - t}$$

$$P(t) = P(t - 1) \cdot (1 + DIV_d), \text{ with } DIV_d \in [-1; 1]$$

We keep the previous conditions $t \ge t_{start}$ and SoC(t) < SoC<sub>desired</sub>, which are not shown here for lack of space.

## IV. CASE STUDY

We focus on a given case study to illustrate our DIS approach that we simulate using real traces. Our analysis is based on the xKy dataset from 2024 [10], which collects minute-by-minute data from smart meters in French households, enabling us to simulate specific consumption profiles including equipment such water heaters.

We use the 2015 IEEE European Low Voltage Test Feeder (ELVTF) dataset [11] for our analysis, which models the 400V LV power network of a European residential district of 55 houses, integrating cable characteristics for a typical representation of the French network. Since we need to represent the MV network, we use three instances of the ELVTF network connected as shown in Figure 2 as districts A, B and C.

Our non-dispatchable DER is a solar power plant. Thus, its peak production takes place during the summer period, and this is why our study simulates a summer scenario. However, residential household load profiles, provided by the xKy dataset [10], represent winter consumption patterns, a time of year when French households show a higher electricity demand due to the intensive use of electric heating. This contradiction attenuates the phenomenon of congestion on line D, as high winter demand reduces the reverse flow that would otherwise be more pronounced in summer. Indeed, if summer load profiles were used, the reverse flow caused by solar generation would be significantly amplified. To generate the solar power plant's production profile, we use the PVGIS web application based on the SARAH3 dataset [12]. This dataset provides high-precision solar radiation data with a resolution of 5 km² across Europe and Africa, and a better representation of local climatic variations than ground-based interpolations, validated by an average absolute deviation of 8 W/m².

<figure><img src="image_3.png" /><figcaption>Fig. 2. Representation of the considered power grid.</figcaption></figure>
<table><caption>TABLE I
PROPERTIES OF ALUMINUM CABLES USED IN THE MV NETWORK</caption><thead><tr><th>Cables</th><th>A, B, C</th><th>D</th><th>E</th></tr></thead><tbody><tr><td>Apparent Power</td><td>220 kVA</td><td>440 kVA</td><td>880 kVA</td></tr><tr><td>Max Current</td><td>7.06 A</td><td>14.11 A</td><td>28.23 A</td></tr><tr><td>Cross-Section</td><td>1.8 mm²</td><td>3.6 mm²</td><td>7.1 mm²</td></tr><tr><td>Resistance</td><td>15.67 Ω/km</td><td>7.83 Ω/km</td><td>3.97 Ω/km</td></tr><tr><td>Reactance</td><td></td><td>0.3 Ω/km</td><td></td></tr><tr><td>Capacitance</td><td></td><td>7 nF/km</td><td></td></tr><tr><td>Length</td><td></td><td>2 km</td><td></td></tr><tr><td>Voltage Drop</td><td></td><td>&lt; 1.95%</td><td></td></tr></tbody></table>

The 90kV High Voltage (HV) (in red on Fig. 2) connects to the transport network. The 20kV MV network is in green and the 400V LV networks, the residential areas, are in blue. Each district follows the ELVTF topology shown on Figure 3.

Given the small size of the studied MV network, specially dimensioned cables had to be created to match the scale of the system:

$$I_{\max} = \frac{P_{(kVA)}}{\sqrt{3} \cdot V_{LL} \cdot PF} \qquad (1)$$

where the current $I_{\max}$ (in A) corresponds to the “Max Current” values presented in Table I, calculated based on the apparent power $P_{(kVA)}$ (in kilovolt-amperes). The line-to-line voltage ($V_{LL}$) is specified as 20 kV, representing the typical voltage in the French MV three-phase distribution network. The power factor (PF) is assumed to be 0.9, a common value in power systems. The apparent power $P_{(kVA)}$ of lines supplying district transformers is defined as one-third of the sum of the installed powers across all houses served by the cable, expressed as:

$$P_{(kVA)} = \frac{\sum_{i=1}^{N} P_{\text{installed},i}}{3}$$

where $P_{\text{installed},i}$ is the installed apparent power (in kVA) for the i-th house, and N is the total number of houses served by the cable. This is representative of the dimensioning of a French MV network.

The ELVTF dataset establishes the characteristics of the electrical network without EVs, so we need to add them to study their impact. Three neighborhoods are selected with distinct EV penetration rates: 70 % in district A, 40 % in district B and 90 % in district C. These values are chosen to reflect the diversity of urban contexts and visualize the variations in incentive signals according to EV adoption levels.

EVs characteristics, such as arrival time and initial SoC level, are selected randomly from discrete intervals. These intervals are defined based on the annual EV user survey conducted by Enedis, France's leading DSO [13]. This data guarantees a realistic representation of user behavior. What's more, the EVs modeled in our network are representative of the French fleet, thanks to registration statistics collected by AAA Data. This approach enables us to study the effectiveness of our demand management solution in a variety of scenarios, taking into account the specific dynamics of each neighborhood and their impact on network stability.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Use case topology using three ELVTF residential district [11].</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 4. DIV for different load scenarios across the three districts.</figcaption></figure>

## V. PERFORMANCE EVALUATION

Simulations are performed with a one-minute time step, assuming that the incentive signals are calculated and acknowledged by the EVs in less than a minute. For this, we use the Python module PandaPower [14], which integrates Optimal Power Flow (OPF) solvers based on the MATLAB package MATPOWER.

Figure 4 shows four graphs illustrating the evolution of Dynamic Incentive Value (DIV) during the simulation. The top graph displays the non-dispatchable DER generation in kW, highlighting significant power losses due to passing clouds, with a peak of 700kW occurring around midday. The
subsequent three graphs present the DIV propagated across residential districts A, B, and C, each featuring two curves: a red line for *Uncontrolled Charging*, and a green line for *DIV-aware Charging*. The *Nominal Charging* and *Uncontrolled DIV-aware* algorithms are not shown here, as they do not contribute to overall readability.

For both charging algorithms, we observe reverse flow congestion during peak photovoltaic production periods, encouraging districts B and C to consume. District A, which is not downstream of the DER, has no positive incentive signal, since it cannot help to reduce congestion. *Uncontrolled Charging* introduces considerable loads on the network, adding congestion to MV/LV substations, resulting in negative incentive signals. In contrast, the *DIV-aware* strategy mitigates forward flow congestion by distributing the charging evenly and effectively reduces reverse flow congestion, as further evidenced in Figure 6.

<figure><img src="image_6.png" /><figcaption>Fig. 5. EVs charging profiles for different algorithms</figcaption></figure>

Figure 5 shows the normalized charging profiles of the 165 EVs, with the x-axis representing the normalized charging period and the y-axis indicating the normalized SoC. On the top left, in red, charging profiles for *Uncontrolled Charging* show a constant charge at 3.6 kW until the desired SoC is reached. EVs with smaller-capacity batteries tend to slightly exceed their desired SoC, due to the fact that charging is stopped only after this threshold has been exceeded.

The orange curve, corresponding to the *Nominal Charging* algorithm, shows that all charging profiles are overlapping. This homogeneity is due to the fact that EVs determine their charging power according to the parameters $SoC_{desired}$ and $t_{end}$, distributing the charge evenly over the available period,

<figure><img src="image_7.png" /><figcaption>Fig. 6. Line D Utilization Over Time: Forward and Reverse Flow Dynamics.</figcaption></figure>

regardless of grid conditions.

In contrast, the purple curve, representing the *Uncontrolled DIV-aware* algorithm, shows dynamic adaptation to the DIV. EVs reduce their power below $P_{max}$ when DIV is negative, indicating a need to limit consumption to avoid congestion. When DIV equal zero, they increase power to return to $P_{max}$. This behavior reflects better responsiveness to network constraints than *Nominal Charging*, although further optimization is required for maximum efficiency.

On the bottom right, in green, the profiles of the *DIV-aware* algorithm reveal a distribution of charging over the entire available period. As long as the DIV signal remains zero, indicating the absence of network congestion, vehicles maintain constant charging power. However, when EVs in districts B and C receive a positive incentive signal, they increase their charging power, dynamically readjusting their base power to reach the desired SoC at the end of the period. On the other hand, vehicles in district A, whose incentive signal remains zero, do not modify their behavior. In this way, vehicles in districts B and C help to relieve network congestion during periods of high production, while ensuring that all vehicles reach their required charging level at the end of the charging period.

Figure 6 shows the load percentage of line D throughout the simulation, where positive values indicate forward flow and negative values reverse flow. As observed in Figure 4, reverse flow congestion is noticeable during the day in the no EVs scenario (without any EVs), due to non-dispatchable DER generation. With the *Uncontrolled Charging* algorithm, EVs start charging as soon as they arrive, causing a forward flow congestion on the D line (and on the B and C lines, not shown here), with a load percentage exceeding 100%. This congestion is cleared around 4 a.m., when vehicles in districts B and C complete their charging, thereby joining the curve of the no-EVs scenario. On the other hand, the *DIV-aware* charging algorithm spreads the EVs' load over the available period, avoiding congestion when vehicles arrive. During the day, when EVs in districts B and C receive a positive incentive signal, they increase their charging power, thus removing reverse flow congestion. As a result, Table II shows that the cumulative reverse flow overload time of 66
<table><caption>TABLE II
ANALYSIS OF CONGESTION LENGTH ON LINE D (IN MINUTES).</caption><thead><tr><th rowspan="2">Algorithms</th><th colspan="2">Reverse Flow</th><th colspan="2">Forward Flow</th></tr><tr><th>Total congestion</th><th>Longest congestion</th><th>Total congestion</th><th>Longest congestion</th></tr></thead><tbody><tr><td>No EVs</td><td>66</td><td>16</td><td>0</td><td>0</td></tr><tr><td>Uncontrolled</td><td>66</td><td>16</td><td>92</td><td>46</td></tr><tr><td>Uncontrolled DIV-aware</td><td>66</td><td>16</td><td>0</td><td>0</td></tr><tr><td>Nominal</td><td>39</td><td>8</td><td>0</td><td>0</td></tr><tr><td>DIV-aware</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>

minutes on line D is eliminated, while no additional congestion is added to the direct flow, demonstrating the effectiveness of the DIV-aware strategy in keeping congestion at an acceptable level.

## VI. DISCUSSION AND LIMITATIONS

While the DIS method offers a promising approach for managing bidirectional congestion in distribution networks, several aspects warrant further discussion and highlight potential limitations.

Firstly, our solution relies on the percentage of electrical line utilization provided by the Power Flow (PF) Solver to determine the DIV. However, alternative metrics from the solver could also be leveraged to enhance the method's robustness. For instance, the voltage level of a line may approach its tolerated limits even if the line is not overloaded. In such cases, reducing the DIV could help lower the voltage, thereby mitigating potential stability issues. This suggests that the DIV could be designed to account for a broader range of constraints identified by the solver, such as voltage stability or reactive power limits, offering a more comprehensive approach to network management.

Another limitation concerns the assumptions underlying the sizing of non-dispatchable DERs in our study. We considered an installed capacity $E = D+B+C$, which may overestimate the installable capacity in practice, potentially closer to D. Such an overestimation could impact the generalizability of our findings, as the network's response to congestion might differ under a lower installed capacity. To address this potential critique, we can consider another case study in which a component downstream of the non-dispatchable DER - such as a district transformer or an overhead line - trips, requiring load redistribution to other districts in order to avoid overloading the upstream network. This scenario would test the DIS method's adaptability to varying network conditions and DER capacities, further validating its effectiveness in diverse operational contexts. These considerations highlight the need for additional work to refine the DIS method and ensure its robustness across a wider range of network configurations and DER deployment scenarios.

## VII. CONCLUSIONS AND PERSPECTIVES

In this paper, we propose a solution that combines a new demand response technique with the advanced metering infrastructure already deployed. We have demonstrated the

effectiveness of a smart charging algorithm in its ability to respond to incentive signals through simulation-based analysis using real traces.

To further validate and enhance the DIS method, we propose several research directions. First, we plan to conduct simulations to evaluate the broadcasting of the incentive signal through the AMI network, which in France relies on PLC technology. These simulations will determine the feasible frequency of DIV propagation—assumed every minute or faster—and assess latency under various channel conditions to ensure effective real-time congestion management. Second, we aim to expand our simulations by modeling a larger and more complex electrical network, incorporating a broader range of flexible loads. To reflect greater consumer diversity, we will include additional load profiles, such as those from office districts alongside residential areas. This will allow us to analyze the DIS method's adaptability across different times of the day, particularly as EVs transition between locations, providing insights into its scalability and robustness for widespread adoption in modern smart grids.

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