Grid-aware Scheduling and Control of Electric Vehicle Charging Stations for Dispatching Active Distribution Networks. Part-II: Intra-day and Experimental Validation (2024)

I Introduction

II Problem Statement

We consider an ADN with a generic topology (meshed or radial) hosting several EVCSs and other heterogeneous distributed energy resources (DERs) such as BESS, uncontrollable photovoltaic (PV) plants, and demand. The objective is to track a pre-determined power schedule (the dispatch plan) at the GCP of the ADN using the flexibility from the EVCSs and BESS. We propose a real-time model predictive control (RT-MPC)-based approach, which accounts for the short-term forecast of the uncontrollable injections and models of the controllable resources and tracks the dispatch plan accurately at a user-defined timescale.The proposed scheme is experimentally validated on a real-life ADN interfaced with two EVCSs, two BESSs, loads, and multiple distributed PV generation plants. The RT-MPC consists of several building blocks. They are schematically shown in Fig.1 and described below.

The link between each block is schematically shown in Fig.1. The grid and the weather measurements feed the RTSE, the forecaster, and the RT-MPC. Finally, RT-MPC sends the power setpoints to the actuation layer.

III Methods for Real-time Operation

The two important components of the real-time operation are the short-term forecasting of the stochastic resources and the real-time MPC problem. Each is described in the following subsections.

III-B Real-time Model Predictive Control

The objective of the RT-MPC is to control the power setpoints of the controllable resources to track the day-ahead dispatch plan every 5 minutes for the whole day.Before, describing the problem formulation, we define the following notations.

• •

  The dispatch setpoint to track is sampled at 5-minute resolution²²2It is consistent with day-ahead electricity market time resolution. and is denoted by $p^{disp}_{y}$, where the 5-minutes indices are denoted by $y=1,\dots,N-1$, $N=288$ for 24 hours in a day.

• •

  The time resolution of the real-time control is 30-seconds; the index for real-time control is denoted by $k=1,\dots,2880$ (i.e., 10 time indices per 5-minutes dispatch period).The first and the last 30-second³³3It is decided based on the time taken to solve RT-MPC problem and communication overheads. indices during the 5-minute interval are denoted, respectively, by $\underline{k}$ and $\bar{k}$ (i.e., $\underline{k}={\lfloor{\frac{k}{10}}\rfloor}\times 10$ and $\bar{k}=\underline{k}+10-1$).

• •

  The dispatch setpoint to be tracked at any time index $k$ is

  $\displaystyle\bar{p}^{disp}_{k}=p^{disp}_{\lfloor{\frac{k}{10}}\rfloor},$

  (1)

  where $\lfloor{.}\rfloor$ refers to the floor function.

• •

  The power measurements at the GCP, denoted by $p_{0,k}^{meas}$, are obtained andthe dispatch-energy error at time $k$ is computed by summing up the (i) uncovered-energy errors from time index $\underline{k}$ to $k-1$,

  $\displaystyle\hat{\epsilon}_{k}=\frac{30(k+1-\underline{k})}{3600}\sum_{j=$

  (2)

  and(ii) the anticipated error from $k$ to $\bar{k}$ given as

  $\displaystyle\epsilon_{k}=\frac{30(\bar{k}-k)}{3600}\sum_{j=k}^{\bar{k}}(\bar{$

  (3)

  Both energy-related errors $\epsilon_{k}$ and $\hat{\epsilon}_{k}$ are schematically shown in Fig.2.The implemented power at the GCP shown in light green color differs from the dispatch setpoint (shown in grey color) due to the inherent nature of uncertainty on the demand and generation, which is quantified by $\hat{\epsilon}_{k}$ in (2) to be compensated in the left-over MPC horizon period. A schematic representation of the timeahead setpoints computed by the MPC (until the end of the MPC horizon) is shown in red and expressed using decision variables by $\epsilon_{k}$ in (3). MPC optimizes EVCS and BESS setpoints and indirectly optimizes GCP power $p_{0,j}$ such that the sum $\epsilon_{k}+\hat{\epsilon}_{k}$ tend to reduce to zero by the end of the MPC horizon period.

The grid objective at a time index $k$ can be defined as minimizing the energy error incurred over a 5-minute horizon length with power setpoints actuated every 30seconds (i.e, the dispatch energy error incurred at the GCP from current timestep to the end of the 5 min period).

$\displaystyle f^{disp}_{k}=\epsilon_{k}+\hat{\epsilon}_{k}$

(4)

The MPC problem considers the constraints of the grid and that of the controllable resources. In the following subsections, we describe the grid constraints and controllable resource models.

III-B1 Grid constraints

Similar to the day-ahead stage (Part-I), the grid constraints modeled by a linearized power flow grid model. They are given below.

	$\displaystyle\underline{\mathbf{v}}\leq\mathbf{A}_{k}^{\mathbf{v}}\begin{$		(5c)
	$\displaystyle\mathbf{0}\leq\mathbf{A}_{k}^{\mathbf{i}}\begin{bmatrix}\mathbf{p$		(5f)
	$\displaystyle\begin{bmatrix}p_{0,k}\\q_{0,k}\end{bmatrix}=\mathbf{A}_{k}^{0}\begin{bmatrix}\mathbf{p}_{k}\\\mathbf{q}_{k}\end{bmatrix}+\mathbf{b}_{k}^{0};~{}(p_{0,k})^{2}+(q_{0,k})^{2}$		(5k)
Eq. (5c) constrains the nodal voltages ($\mathbf{v}\in\mathds{R}^{(n_{b}-1)}$, $n_{b}$ being number of nodes in the grid), by bounding them to upper and lower limits $[\underline{\mathbf{v}}~{}\bar{\mathbf{v}}]$. Eq.(5f) expresses the lines current magnitudes ($\mathbf{i}\in\mathds{R}^{n_{l}}$, $n_{l}$ being the number of lines) and bounds them to their respective ampacities $\bar{\mathbf{i}}$. Eq.5k expresses the GCP power and bounds it to the transformer capacity $S_{\text{max}}$.Note that $\mathbf{p}\in\mathds{R}^{(n_{b}-1)}$ and $\mathbf{q}\in\mathds{R}^{(n_{b}-1)}$ are the three-phase total nodal active and reactive controllable injections for all nodes except the slack node. $\mathbf{A}^{\mathbf{v}}\in\mathds{R}^{(n_{b}-1)\times 2(n_{b}-1)}$ and $\mathbf{b}^{\mathbf{v}}\in\mathds{R}^{(n_{b}-1)}$, $\mathbf{A}^{\mathbf{i}}\in\mathds{R}^{n_{l}\times 2(n_{b}-1)}$ and $\mathbf{b}^{\mathbf{i}}\in\mathds{R}^{n_{l}}$ are the coefficients derived from the voltage magnitude sensitivity coefficients and operating point (as described in [28]), $\mathbf{A}^{0}\in\mathds{R}^{2\times 2(n_{b}-1)}$ and $\mathbf{b}^{0}\in\mathds{R}^{2}$ being the coefficients corresponding to the GCP power.The power injections in (III-B1) consist of controllable and uncontrollable powers at the nodes for time index $k$ containing the contributions of all the resources. It is worth noting that it may contain several resources per node as well. It is given as
	$\displaystyle\mathbf{p}_{k}=\mathbf{\widehat{p}}^{\text{load}}_{k}-\mathbf{$		(5l)
	$\displaystyle\mathbf{q}_{k}=\mathbf{\widehat{q}}^{\text{load}}_{k}-\mathbf{$		(5m)

where $\mathbf{\widehat{p}}^{\text{load}}_{k}/\mathbf{\widehat{q}}^{\text{load}}_{k}$, $\mathbf{\widehat{p}}^{\text{pv}}_{k}/\mathbf{\widehat{q}}^{\text{pv}}_{k}$, $\mathbf{p}^{\text{bess}}_{k}/\mathbf{q}^{\text{bess}}_{k}$ and $\mathbf{p}^{\text{evcs}}_{k}/\mathbf{q}^{\text{evcs}}_{k}$ are the nodal active/reactive power injections corresponding to load, PV generation, BESS and EVCS, respectively.

III-B2 EVCS objectives and constraints

Let the EVCS be located at indices $i\in\mathcal{N}^{ev}$ where $\mathcal{N}^{ev}$ denotes the set of node indices where EVCSs are installed. Let each EVCS has multiple charging plugs indexed by $l=1,\dots,L_{i}$.Similar to the day-ahead formulation, the EVCS cost function has multiple objectives: the first term minimizes the worst-case difference between the SoC target and SoC that can be achieved at the end of the MPC horizon, the second term minimizes the EV battery wearing between subsequent time-steps [29] (i.e., $\left|p_{k+1,l,i}^{\text{evcs}}-p_{k,l,i}^{\text{evcs}}\right|$, where $p_{k,l,i}^{\text{evcs}}$ is the power demand at indices $k,l,i$).The EVCS objective at time index $k$ is given by

$\displaystyle\begin{aligned} f^{evcs}_{k}=&\frac{3600}{(\delta{t})L_{i}}\sum_{$

(6)

where $\delta t$ is the sampling time (i.e., 30sec).

Since the RT-MPC is solved with short MPC horizon of 5minutes, the SoC target needs to be updated for the end of 5minutes.This is done by interpolation using the current SoC, target SoC, and departure time. SoC target time index $k$ is given by

$\displaystyle\text{SoC}^{\text{Target}}_{l,i,\bar{k}}=\text{SoC}^{\text{evcs,$

(7)

where $\text{SoC}^{\text{evcs, meas}}_{l,i,k-1}$ is the measured SoC at time index $k-1$, $\text{SoC}^{\text{Target}}_{l,i,\bar{k}}$ the target SoC for time index $k$, $l-$th plug, $i-$th EVCS and the departure time index $k_{f}$.

The EVCS constraints consist in limiting on the EV state-of-charge ($\text{SoC}^{\text{evcs}}_{k}$) and are expressed as

	$\displaystyle 0\leq\text{SoC}^{\text{evcs}}_{k,l,i}=\text{SoC}^{\text{evcs}}_{$		(8a)
where ${E_{l,i}^{\text{max}}}$ is the energy capacity for EV connected to plug $l$ and node $i$.Another constraint limits the EVCS active power by the charger’s minimum and maximum admissible444The minimum and maximum admissible setpoint per plug is sent by the charger to the MPC controller. setpoint given by $p^{\text{evcs},\text{min}}_{k,l,i}$ and $p^{\text{evcs},\text{max}}_{k,l,i}$, respectively.
	$\displaystyle\mu_{k,l,i}~{}p^{\text{evcs},\text{min}}_{k,l,i}\leq p_{k,l,i}^{$		(8b)

where $\mu_{k,l,i}$ is a known boolean expressing whether, or not, an EV is connected to plug $l=1,...,L_{i}$. The reactive power from the EVCS is uncontrollable and null (i.e., $q_{k,l,i}^{\text{evcs}}=0,\forall k,l,i$).

III-B3 BESS Objectives and Constraints

The BESS is one of the controllable resources in the proposed dispatching framework. It can provide both active and reactive power regulation during real-time operation. Let $p^{\text{bess}}_{k,i},q^{\text{bess}}_{k,i}$ be the battery’s active and reactive power at time $k$ and node $i\in\mathcal{N}^{bess}$ where $\mathcal{N}^{bess}$ is the set of node indices where BESSs are installed.

For each BESS, the objective is to minimize its usage (i.e., absolute injections $|p^{\text{bess}}_{k,i}|$), to mitigate its cycling related aging. The objective is

$\displaystyle f^{bess}_{k}=\sum_{i\in\mathcal{N}^{bess}}\sum_{k=k}^{\bar{k}}|p$

(9)

It considers the constraints on the state-of-charge i.e., $\text{SoC}^{\text{bess}}_{k,i}$ of each battery ($i\in\mathcal{N}^{bess}$) is constrained by SoC limits [$\underline{\text{SoC}}^{\text{bess}}_{i},~{}\overline{\text{SoC}}^{\text{bess}$], ${\underline{\text{SoC}}^{\text{bess}}_{i}}/{\overline{\text{SoC}}^{\text{bess}$ are the minimum/maximum SoC bounds of battery $i$

	$\displaystyle{\underline{\text{SoC}}^{\text{bess}}_{i}}\leq\text{SoC}^{\text{$		(10a)
where $E^{\text{bess, max}}_{i}$ represent EV energy capacity.Moreover, the active and reactive power are bounded by converter ratings
	$\displaystyle(p^{\text{bess}}_{k,i})^{2}+(q^{\text{bess}}_{k,i})^{2}\leq({S}^{$		(10b)

where ${S}^{\text{bess,max}}_{i}$ is the rated power of the converter associated to the i-th battery. The circle constraint in (10b) is approximated by a set of piece-wise linear constraints as in [30].

III-B4 Final MPC Formulation

The final MPC problem at time index $k$ is formulated as

	$\displaystyle\underset{\mathbf{p}_{k}^{\text{evcs}},\mathbf{p}_{k}^{\text{bess$		(11a)
	$\displaystyle\text{subject to}~{}~{}~{}~{}\eqref{eq:grid_cons},\eqref{eq:evcs_$		(11b)

As it can be observed, the problem is linear, thanks to the linearized grid model and piece-wise linear approximation of the capability curves (5k) and (10b). Therefore, the problem can be solved efficiently by any LP solver.

It may appear that the RT-MPC formulation in (III-B4) is similar to the day-ahead problem from the Part-I paper. However, the key difference are listed below:

• •

  The RT-MPC formulation is designed to achieve real-time tracking of the dispatch schedule decided by the day-ahead stage. It is formulated as model predictive control to account for the real-time variations of the prosumption, whereas the day-ahead stage is formulated as scenario-based stochastic optimization scheme.

• •

  The RT-MPC optimization problem is much faster to solve (below 30 seconds), whereas the day-ahead formulation can take a few hours to solve.

IV Experimental Validation

In this section, we present the experimental validation of the described RT-MPC on a real-life ADN. In particular, we first describe the hardware setup, the monitoring, and communication infrastructure, and then present the experimental validation results for different day types.

IV-A Experimental Infrastructure

The experimental validation is performed on the setup described in Part-I of the paper, shown in Fig.3. The detailed description of the resources and the grid is included in Part-I of the paper.

For the experimental validation of the RT-MPC, we consider two BESS and two EVCS as controllable resources. Other resources such as PV plants and buildings are treated as uncontrollable. Each resource used in the experiment is described below.

• •

  BESS: the ADN hosts two different BESS with capacities of 25kW/25KWh (BESS1) and 150kW/300kWh (BESS2). Both BESS are based on the Lithium Titanate Oxide (LTO) and are rated for 15,000 cycles (of 1C/1C charge/discharge rates and 100% depth of discharge). BESS1 consists of 13 modules, and BESS2 consists of 9 parallel racks where each rack consists of 15 modules. Each module is composed by 20s3p cells. A view of BESS1 and BESS 2 is shown in Fig.4b and 4a.

• •

  EVCS: the ADN hosts two different charging stations EVCS1 and EVCS2 as shown in Fig.5. EVCS1 is a level-3 DC fast charging station and has a peak power capacity of 172kW. It hosts 5 plugs but only 2 plugs can be operational at the same time. EVCS2 is a level-2 AC charger with a 32 kWp capacity. It hosts 3 plugs, but only 2 plugs can be operational at the same time. The capacity per plug is reported in TableI. EVCS plugs, depending on their type, use different communication protocols. For example, the CHAdeMO plugs use the DCMS protocol from EvTec [27]. Type-2 plugs use IEC-61852 [31].

  Resources	Labels	Ratings
  Electric Vehicle	EVCS1	1 $\times$ Type-2 Plug - 43kWp
  Charging Station	(5plugs)	1 $\times$ Type-2 Plug - 22kWp
  	Level-3	2 $\times$ CCS DC Plug - 150kWp
  		1 $\times$ CHAdeMO Plug - 150kWp
  	EVCS2	2 $\times$ Type-2 Plug - 22kWp
  	(3plugs)	1 $\times$ CHAdeMO Plug - 10kWp
  	Level-3

  Furthermore, as explained in Sec. III-C of [32, 33], power-to-current lookup tables are needed to enable explicit active power control of level-2 plugs. Recall that level-2 plugs are controlled through an analog pulsed signal that dictates to the EV the RMS value of the maximum per-phase current it can consume. As a result, power-to-current lookup tables were precomputed.For more details on the characterization of the current-power lookup table, the reader can refer to [33].

• •

  PV: The grid hosts 3 different PV plants, PV1 and PV2 are rooftop PV rated at 13kWp and 16kWp, respectively. PV3 is on a building facade and is rated at 13.2kWp. All these power generation assets are considered uncontrollable in the experiment and are operated at their maximum power potential (i.e., at the MPPT). The three PV plants are shown in Fig.6.

• •

  Demand: it refers to the electricity demand corresponding to the building shown in Fig.6. The consumption is split over two transformers: L1-ELLA (i.e., first load) and L2-ELLB (i.e., second load) associated to transformers A and B, respectively and rated at 20kW and 5kW, respectively.

IV-B Situational Awareness Infrastructure

The experimental setup is equipped with a dedicated monitoring and communication infrastructure enabling real-time state estimation (RTSE), and information of the resources state such as BESS SoC, current setpoints, EVCS power etc. In the following, we briefly describe them.

IV-B1 Communication Infrastructure, Centralized Server and Data-Logging

ADN sensing and connected resources communicate over a dedicated IPv4 communication network.Fig.7 shows the schematic of the communication network. It connects different workstations (WS1-WS6) hosting the RTSE, data server, day-ahead dispatcher, RT-MPC and the actuator. WS1 collects the PMU packets and hosts a phasor data concentrator (PDC) [34]; the data is then used to estimate the state of the power grid in real-time each 20ms. A discrete Kalman filter-based state estimator processes the measurements [35] and provides the estimates of the voltage and current phasors of all the nodes and lines with a total latency of less than 80ms with respect to the UTC-GPS time tag of the PMU measurements. Details on the RTSE can be found in [35, 36]. WS2 implements a SQL-based (influxdb ⁵⁵5https://www.influxdata.com) database to log all the time-series during the experiments. WS3 computes the day-ahead forecasts and dispatch plan (formulation reported in Part-I paper). WS4 runs the real-time MPC, and computes short-term forecasts for different resources. WS5 and WS6 run the BESS and EVCS softwares, respectively to implement the power setpoints computed by the RT-MPC.The workstations, the PMUs and meteobox are connected by a switch.

IV-B2 Time-tagged measurement infrastructure

As discussed before, the network is monitored by several PMUs that provide real-time information of the grid states (coupled to a RTSE) and a meteobox that provides real-time information on GHI and air temperature. These measurements are also stored in the data-server as historical measurements, which are used for day-ahead and short-term forecasting. The measurement units are briefly described below.

• •

  PMU(s): are used to provide time-synchronised and time-tagged measurements at 50 frames per second. An example of the installed PMU is shown in Fig.8a. The PMUs are implemented on National Instrument Compact RIO consisting of a GPS module and current/voltage acquisition modules. More details on the specification of the components are in [37]

• •

  Meteobox: is installed to get measurements of GHI, air- and PV-panel- temperatures. It provides real-time measurements with a sampling rate of 1sec (including communication latency). Fig8b shows the meteobox installed on the site; each meteobox consists of a pyranometer (to sense the GHI), two temperature sensors, and a power supply. More details on the specification can be found in [38].

IV-C Dataflow

Fig.9 shows the sequence of operations and communication flows during the real-time stage.The real-time stage starts at 00:00 local time. First, we retrieve the current power dispatch setpoint $\bar{p}_{k}^{disp}$. Then, the grid measurements are obtained and used to infer the actual system state (used to formulate the LOPF). It then updates the resources states (such of BESS and EV SoC). Then, the short-term forecasts of the uncontrollable resources are updated. All these information is then used for solving RT-MPC in (III-B4). The optimized power setpoints are then sent to EVCS and BESS actuators. This cycle is repeated every 30sec until 23:59:30 local time.

IV-D Experimental results

The experimental validation was performed for several days during the week. For the sake of brevity, we present results for two notable days of experiments. The days are characterised by different weather conditions and number of EV charging sessions. The results are presented when there is no control of both BESS and EVCS, referred to as “without control” and when BESS and EVCS are both controlled, referred to as “with control”.

IV-D1 Day 1

It corresponds to a weekday and is characterised by cloudy irradiance conditions. The day shows intermittent PV production and has several EV charging sessions.

Figure10 and 11 show the experimental results obtained for day-1. Figure10a shows the dispatch plan in shaded green (from day-ahead formulation described in Part-I) and realized power at the GCP with and without⁶⁶6Since each experiment day is unique with respect to the solar irradiance, number and energy demand of EV charging sessions, it is impossible to redo the same experiments in “without control” mode. Therefore, we obtain the plot “without control” by removing the contribution of the BESS and re-running the AC load flow with the rest of the injections. control (as shown in black and red color, respectively).Figure10b shows the power injections and the SoC from the controllable battery BESS1. Figure 10c-10d shows the EV demand (with control) and the EV SoC of the connected cars at the EVCS1 and EVCS2, respectively. In these figures, the target SoC is shown in red, and the SoC is shown in black. Figure 10e shows the uncontrollable demand (at nodes B20 and B21) and Fig.10f shows the PV generation (at nodes B14 and B16).

One can observe from Figure 10a, that the dispatch plan is tracked with high fidelity thanks to the power injected from the controllable BESS and curtailment actions from EVCSs. From the plot, it can also be observed that the variation in the generation at the PV plants is well compensated by the battery storage. In Figure 10b, the BESS SoC stays within the imposed SoC constraint of 20% to 90%. Figure10c-10d shows the target SoC of the EVs, and in most of cases, EV users meet their target SoC.

MPC	Day 1			Day 2
	RMSE	AEE	MAE	RMSE	AEE	MAE
	(kW)	(kWh)	(kW)	(kW)	(kWh)	(kW)
No Control	28.7	137.9	441.7	19.1	91.9	327.4
RT-MPC	0.7	5.9	8.5	0.5	2.9	1.5
(EVCS+BESS)

Table II shows different metrics to quantify the dispatch error with and without control. It shows the RMSE error, max absolute error (MAE), and Absolute Energy Error (AEE) of the dispatch over the day. AEE is defined as the absolute sum of the dispatch tracking error over the whole day. From the comparison, it is clear that the RT control manages to track with high accuracy, exhibiting low RMSE and MAE. The real-time control manages to reduce error metrics more than tenfold. Moreover, RMSE, AEE and MAE are reduced by 2.4%, 4.2% and 1.9% respectively, compared to without control, proving the effectiveness of the proposed scheme.

We also show the cumulative distribution function (CDF) of the dispatch tracking error (averaged over the dispatch period of 5 minutes) with and without real-time control in Fig.11. As it can be observed, the dispatch error with control is always concentrated around zero.

IV-D2 Day 2

It corresponds to a weekend day and is characterized by rainy weather conditions, so it exhibits low irradiance, leading to low PV generation and relatively low demand compared to the weekday. It also has less EV charging sessions.

Again, we show the active power realization at the GCP with and without control. It is shown in Figure12a. One can observe that the dispatch plan is tracked well, thanks to the power regulation provided by the controllable batteries, as shown in Figure12b, and the curtailment action of EVCS1 as shown in Figure12c.As this day corresponds to a rainy day (resulting in less PV generation), the peak power of the dispatch plan is higher than the one on day 1. On this day, there are no sessions on the EVCS2 (therefore, the graph is not shown). Indeed, that charging station belongs to the office’s private space, which is unoccupied during the weekend. There are some charging sessions on the EVCS1 but less than on day 1 (due to weekend day). All sessions met their targets.

Furthermore, Figure13 shows the CDF of the dispatch error with and without control and it can be concluded that the real-time control achieves a very good accuracy in the dispatch tracking. The same can be observed by the metrics shown in TableII, where it is observed that the RMSE, AEE and MAE are reduced to 2.6%, 3.2% and 0.5%, respectively, compared to without control.

IV-D3 Multi-day

To demonstrate the effectiveness of the dispatching scheme, we ran the control of the BESS for four contiguous days shown in Fig.14. Figure14a shows the dispatch plan and the measured GCP power withand without the control scheme. In Fig.14b, we show the SoC evolution of BESS1 during the four days. The power at the GCP follows the dispatch plan and keeps the BESS SoC within comfortable bounds so that dispatching is facilitated on the next day.

V Conclusion

Part-II of the paper presented the intra-day stage of the dispatching framework proposed in the Part-I paper. More specifically, it proposed and experimentally validated a real-time model predictive control for tracking a pre-defined power profile, the dispatch plan, at the grid connection point of an ADN by controlling flexible resources. The MPC control scheme is designed to control flexible resources such as EVCS and BESS to provide the mismatch between the dispatch plan and realization during the day. The dispatch plan is provided by the day-ahead scheduling formulation from Part-I. The MPC accounted for the grid constraints via a linearized power flow to obtain a tractable formulation. It considered the stochasticity of the uncontrollable generation and demand via short-term forecasting schemes.

The MPC scheme was experimentally validated on a real-life ADN at the EPFL’s Distributed Electrical Systems Laboratory, which hosts two EVCSs and two BESSs as flexible resources and heterogeneous uncontrollable resources such as PV plants and office buildings. The RT-MPC was designed to run every 30 seconds with 5minutes MPC horizon time to track the dispatch plan on a 5-minutes time resolution.

The experimental validation carried out for several contiguous days proved the effectiveness of the MPC algorithm. The elaborated results presented for two distinct days showed good dispatch tracking accuracy. It has been observed that the metrics on dispatch tracking error such as root-mean-square-error, absolute energy error and maximum absolute error are reduced by factors ranging 38-41, 23-32, and 52-218, respectively by using the proposed MPC control scheme.

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