# OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration

^{*}

## Abstract

**:**

## 1. Introduction

Ref No. | Objective | Technique | MG Mode | DERs | Implementation | Schedule |
---|---|---|---|---|---|---|

[4] | EM - min cost | MILP | Islanded Grid Connected | PV, Wind Turbines, Fuel Cells, Micro Turbines, Diesel Generators, ESS | Simulations | DA RT |

[5] | EM - min losses | Dynamic Programming | Grid Connected | Wind Turbines, ESS, PV, ESS | Simulation | DA |

[6] | EM - min cost | MILP | Grid Connected | PV, Wind Turbines, ESS | Simulations | DA |

[7] | EM | MILP/NLP | Isolated | PV, Wind Turbines, Fuel Cells, Micro Turbines, Diesel Generators, ESS | Simulations | DA |

[8] | multi-objective | MILP | Isolated | PV, Diesel Generators, ESS | Simulations | DA |

[10] | min cost (operation) | MILP | Grid Connected | PV, Wind Turbines, ESS | Simulations | DA |

[11] | min cost (operation) | MILP | Grid Connected | PV, Wind Turbines, ESS | Simulations | DA |

[12] | min losses + min fuel consumption | Heuristic | Grid Connected | PV, Micro Turbines | Simulations | DA |

[14] | Social benefit max | Quadratic Programming | Grid Connected | PV, Wind Turbines, Fuel Cells, Micro Turbines, Pico Hydel ESS | Simulations | DA RT |

[15] | min cost (operation) | Quadratic Programming | Grid Connected | PV, Wind Turbines, ESS | Simulations | DA RT |

[16] | min cost (operation) | Stochastic/Robust optimisation | Grid Connected | PV, Wind Turbines, Diesel Generators, ESS | Simulations | DA |

[17] | min cost (operation) | Quadratic Programming | Grid Connected | PV, ESS | Simulations | DA |

[18] | min cost | ITSP and WCVaR | Grid Connected | PV, Wind Turbines, Micro Turbines, CHP, ESS | Simulations | DA |

[20] | EM | Heuristic | Grid Connected | PV, Wind Turbines, Fuel Cells, Diesel Generators, ESS | Simulations | DA |

[21] | min cost (operation) | MILP | Isolated | Heavy/Light fuel units, PV, Wind Turbines, ESS | Simulations | DA |

[22] | min cost (operation) | MILP | Islanded | PV, Wind Turbines, Diesel Generators, ESS | Simulations | DA Intra-DA |

[23] | min cost—max ESS utilisation efficiency | MILP | Gird Connected | PV, Wind Turbines, Micro Turbines, Diesel Generators, ESS | Simulations | HA MA |

[24] | min cost | Dynamic Programming | Grid Connected | PV, ESS | HIL | DA |

[25] | min cost (operation) | Rule-based | Isolated | PV, Fuel Cells | HIL | DA |

[26,27] | min cost (operation) | LP/MILP | Islanded Grid Connected | PV, Wind Turbines, ESS | HIL | DA |

[30] | min cost (operation) | MILP | Islanded Grid Connected | PV, Wind Turbines, Fuel Cells, ESS | HIL | DA |

[32] | min cost | MILP | Isolated | PV, Wind Turbines, microCHP, ESS | Real-life Prototype | DA |

[33] | min cost (operation + emissions) | Rule-based | Isolated | PV, Diesel Generators | Simulations | Yearly |

[34] | min cost (operation + emissions) | NLP/Sequential Quadratic Programming | Islanded Grid Connected | PV, Wind Turbines, Micro Turbines, CHP, ESS | HIL | DA |

[36] | EM - min cost | Rule-based | Grid Connected | PV, Wind Turbines, Micro Turbines, ESS | Simulations + HIL | DA RT |

[39] | min cost (operation) | Stochastic Programming | Grid Connected | PV, Wind Turbines, Micro Turbines, ESS | Simulations | DA |

[37,48] | min cost (operation) | Heuristic | Grid Connected | PV, Wind Turbines, CCHP, ESS | Simulations | DA RT |

[38] | max profit | two-stage Stochastic Programming | Grid Connected | PV, Wind Turbines, ESS | Simulations | DA |

[40] | max power production | MINLP | Grid Connected | PV, ESS | Simulations + HIL | DA RT |

[41,51] | min cost (operation + extension of hybrid ESS lifetime) | MILP | Isolated | PV, Diesel Generators, ESS | Simulations + HIL | DA |

[43] | min fuel consumption + min cost (operation) | LP/MILP | Isolated | PV, Diesel Generators, ESS | Simulations | DA |

[44] | min cost (operation) | Heuristic | Isolated | PV, Wind Turbines, Diesel Generators, ESS | Simulations | DA |

[45] | min cost (operation) | Heuristic | Islanded Grid Connected | PV, Wind Turbines, HT, Fuel Cells, ESS, GT | Simulation | DA |

[46] | min embodied energy + min LPSP | NLP/Sequential Quadratic Programming | Grid Connected | PV, Wind Turbines, ESS | Simulations | hours-ahead |

[47] | min cost (operation + emission) | Heuristic | Grid Connected | ESS | Simulations | hours-ahead |

[49] | min cost | Heuristic | Grid Connected | PV, Wind Turbines, Micro Turbines, ESS | Simulations + HIL | DA RT |

[52] | max profit | MILP | Grid Connected | PV, ESS | Simulations + HIL | DA RT |

## 2. Materials and Methods

#### 2.1. Optimisation Problem Formulation

#### 2.1.1. Virtual Distributed Energy Resources

#### 2.1.2. Adjusted Unit Commitment Problem Formulation

#### 2.2. System Architecture

#### 2.2.1. Optimal Scheduling Engine for Microgrids

#### 2.2.2. Real-Time Validation and Application of Optimal Schedule

#### 2.2.3. Load and PV Forecasting Engines

## 3. Experimental Validation

#### 3.1. Experimental Setup

#### 3.1.1. Microgrid Infrastructure

#### 3.1.2. Local Customisation—Web Services

#### 3.1.3. Experimental Scenarios

**Baseline Operation**: A fully automatic state, without any optimisation scheme applied, aiming at maximising PV generation and keeping batteries fully charged.**Scenario A—Optimal Day-Ahead Scheduling (“Opt Mode”)**: Right before midnight, OSEM creates the day-ahead optimal schedule as described in Section 2.2.1. Within the day and if the scheduling is applicable, the RT-VAOS follows that schedule and sends commands every minute to each asset with the appropriate setpoints.**Scenario B—Adaptive Optimal Day-Ahead Scheduling (“reOpt Mode”)**: As in Scenario A, OSEM creates the day-ahead schedule for the examined day. RT-VAOS monitors the status of MG DERs. When an out-of-limits deviation occurs between actual and forecasted load, or PV generation, RT-VAOS triggers a recalculation of the schedule, for the remainder of the day (see Section 2.2.2).

#### 3.2. Experimental Results

_{A}” (%) is defined as the relative error of actually achieved to expected daily profits, with respect to baseline daily costs (9). As observed, when the MG operated in Opt mode, this KPI is always negative, meaning that it is impossible to reach the initially set goal since time slots are being “lost” when the schedule is disregarded (MG-AUTO). On the contrary, on certain days, reOpt mode may lead to positive values of the MG

_{A}KPI, meaning that the actually achieved daily profit was eventually greater than what originally predicted, a fact justified by the improved accuracy of both short-term forecasts as this derives from their re-calibration upon real-time measurements. This observation highlights the superiority of reOpt mode over the Opt mode. This statement is further strengthened by the analysis of daily cost reductions relative to the baseline daily costs (Figure 8b). Indeed, in both Opt and reOpt modes, the overall MG cost is less than what it would be without any optimisation. Specifically, Opt mode leads to approximately 5.5% daily cost reduction, whereas by employing reOpt mode, cost reduction fluctuates between 6% and 13%, with an average value of 9%. Additionally, for all Scenario B days, the daily cost reduction that would be reached if Opt mode was applied is calculated for comparison reasons. For example, during the demonstrated day with Scenario B, the daily profit was reached €0.198 (7.11% relative profit), while if Scenario A was applied on the same day, the daily profit would be equal to €0.09 (3.25% relative profit). Generally, as seen in Figure 8b, reOpt mode leads to 1.5–2 times greater relative cost reductions compared to Opt mode.

## 4. Conclusions

## Author Contributions

## Funding

^{o}731268), and the General Secretariat for Research and Innovation of Greece through the 3DMicroGrid project via the ERANETMED initiative (identification: ERANETMED-energy-11-286).

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

## Abbreviations

T | Total duration of the optimisation horizon as number of t steps. |

${r}_{t}$ | Time resolution considered, given the time slot duration in minutes divided by 60. |

$\overrightarrow{{j}_{T}}$ | Auxiliary unit vector T-sized, respectively. |

${\mathbf{E}}_{S}\left(t\right)$ | Energy setpoints for N Energy Storage Systems (ESSs) sized $2\times N$ (in Wh). |

$\overrightarrow{{E}_{S}^{+}}\left(t\right)$, $\overrightarrow{{E}_{S}^{-}}\left(t\right)$ | Energy setpoint N-sized vectors for the charging/discharging vDERs of N ESSs (in Wh). |

$\overrightarrow{{E}_{S}}\left(0\right)$ | The initially stored energy of the batteries (N-sized vector in Wh). |

$\overrightarrow{{E}_{S}^{min}}$, $\overrightarrow{{E}_{S}^{max}}$ | ESS minimum/maximum stored energy (N-sized vectors in Wh). |

$\overrightarrow{{E}_{G}}\left(t\right)$ | Energy setpoint ($[{E}_{G}^{-}\left(t\right)$ ${E}_{G}^{+}\left(t\right)]\top \left(t\right)$) for the import/export at MG PCC (2-sized vector in Wh). |

$\overrightarrow{{E}_{G}^{min}}$, $\overrightarrow{{E}_{G}^{max}}$ | Minimum/maximum allowed energy exchange with the distribution network (T-sized vectors in W). |

$\overrightarrow{{E}_{PV}}\left(t\right)$ | Energy setpoints (${E}_{P{V}_{i}}\left(t\right)$, $i\in [1,M]$) for the M PV modules of the MG (M-sized vectors in Wh). |

$\overrightarrow{{E}_{PV}^{max}}\left(t\right)$ | Maximum Power Point of M PV modules (M-sized vector in Wh). |

${E}_{L}\left(t\right)$ | MG load demand (in Wh). |

${\beta}_{S}\left(t\right)$ | Index of the N ESS operation ($2\times N$). |

$\overrightarrow{{\beta}_{S}^{+}}\left(t\right)$, $\overrightarrow{{\beta}_{S}^{-}}\left(t\right)$ | Operation Index (0: not operating, 1:operating) for the N charging/discharging vDERs (N-sized vectors). |

$\overrightarrow{{\beta}_{G}}\left(t\right)$ | Binary 2-sized vector for the operation of importing/exporting ($\left[{\beta}_{G}^{-}\left(t\right)\phantom{\rule{3.33333pt}{0ex}}{\beta}_{G}^{+}\left(t\right)\right]\top \left(t\right)$) vDERs. |

$\overrightarrow{{j}_{S}}$, $\overrightarrow{{j}_{G}}$, $\overrightarrow{{j}_{PV}}$ | Auxiliary unit vectors, N/2/M-sized, respectively. |

$\overrightarrow{LCO{E}_{S}}$, $\overrightarrow{LCO{E}_{PV}}$ | LCOE values for the N ESSs and M PV modules (in €/Wh) N/M-sized, respectively. |

$\overrightarrow{{R}_{C}^{+}}$, $\overrightarrow{{R}_{C}^{-}}$ | Charge/discharge C-Rates N-sized vectors, for N ESSs. |

$\overrightarrow{Cap}$ | Nominal N-sized vector capacity for N MG ESSs (in Wh). |

$\overrightarrow{DoD}$ | N-sized vector for the Depth-of-Discharge for N MG ESSs (in %). |

$\overrightarrow{RTP}\left(t\right)$ | Import ($RTP{\left(t\right)}^{-}$)/export ($RTP{\left(t\right)}^{+}$) real-time pricing vector ($T\times 2$-sized vector in €/Wh). |

${\Pi}_{e}$ | Expected profit by the microgrid throughout T optimisation horizon at the beginning of the scheduling (in €). |

${\Pi}_{a}$ | Achieved profit by the microgrid throughout T optimisation horizon after the scheduling horizon has passed (in €). |

$M{G}_{A}$ | KPI “Microgrid Achievement”. |

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Opt Dates | 15/7 | 16/7 | 17/7 | Mean Values | |||
---|---|---|---|---|---|---|---|

MAE Load | 0.192 | 0.232 | 0.379 | 0.268 | |||

MAE PV | 0.092 | 0.265 | 0.523 | 0.293 | |||

reOpt Dates | 19/7 | 22/7 | 23/7 | 25/7 | 26/7 | 29/7 | Mean Values |

MAE Load | 0.153 | 0.126 | 0.124 | 0.114 | 0.131 | 0.097 | 0.124 |

MAE PV | 0.111 | 0.049 | 0.139 | 0.122 | 0.125 | 0.101 | 0.107 |

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## Share and Cite

**MDPI and ACS Style**

Bintoudi, A.D.; Zyglakis, L.; Tsolakis, A.C.; Gkaidatzis, P.A.; Tryferidis, A.; Ioannidis, D.; Tzovaras, D.
OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration. *Energies* **2021**, *14*, 2752.
https://doi.org/10.3390/en14102752

**AMA Style**

Bintoudi AD, Zyglakis L, Tsolakis AC, Gkaidatzis PA, Tryferidis A, Ioannidis D, Tzovaras D.
OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration. *Energies*. 2021; 14(10):2752.
https://doi.org/10.3390/en14102752

**Chicago/Turabian Style**

Bintoudi, Angelina D., Lampros Zyglakis, Apostolos C. Tsolakis, Paschalis A. Gkaidatzis, Athanasios Tryferidis, Dimosthenis Ioannidis, and Dimitrios Tzovaras.
2021. "OptiMEMS: An Adaptive Lightweight Optimal Microgrid Energy Management System Based on the Novel Virtual Distributed Energy Resources in Real-Life Demonstration" *Energies* 14, no. 10: 2752.
https://doi.org/10.3390/en14102752