# 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”. |

## References

- Pulcherio, M.C.; Renjit, A.A.; Illindala, M.S.; Khalsa, A.S.; Eto, J.H.; Klapp, D.A.; Lasseter, R.H. Evaluation of Control Methods to Prevent Collapse of a Mixed-Source Microgrid. IEEE Trans. Ind. Appl.
**2016**, 52, 4566–4576. [Google Scholar] [CrossRef] - Meng, L.; Sanseverino, E.R.; Luna, A.; Dragicevic, T.; Vasquez, J.C.; Guerrero, J.M. Microgrid supervisory controllers and energy management systems: A literature review. Renew. Sustain. Energy Rev.
**2016**, 60, 1263–1273. [Google Scholar] [CrossRef] - Amasyali, K.; El-Gohary, N.M. A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev.
**2018**, 81, 1192–1205. [Google Scholar] [CrossRef] - Jiang, Q.; Xue, M.; Geng, G. Energy Management of Microgrid in Grid-Connected and Stand-Alone Modes. IEEE Trans. Power Syst.
**2013**, 28, 3380–3389. [Google Scholar] [CrossRef] - Levron, Y.; Guerrero, J.M.; Beck, Y. Optimal Power Flow in Microgrids With Energy Storage. IEEE Trans. Power Syst.
**2013**, 28, 3226–3234. [Google Scholar] [CrossRef] [Green Version] - Malysz, P.; Sirouspour, S.; Emadi, A. An Optimal Energy Storage Control Strategy for Grid-connected Microgrids. IEEE Trans. Smart Grid
**2014**, 5, 1785–1796. [Google Scholar] [CrossRef] - Olivares, D.E.; Cañizares, C.A.; Kazerani, M. A Centralized Energy Management System for Isolated Microgrids. IEEE Trans. Smart Grid
**2014**, 5, 1864–1875. [Google Scholar] [CrossRef] - Chalise, S.; Tonkoski, R. Day ahead schedule of remote microgrids with renewable energy sources considering battery lifetime. In Proceedings of the 11th IEEE/IAS International Conference on Industry Applications, Juiz de Fora, Brazil, 7–10 December 2014; pp. 1–5. [Google Scholar] [CrossRef]
- Klanšek, U. A comparison between MILP and MINLP approaches to optimal solution of Nonlinear Discrete Transportation Problem. Transport
**2015**, 30, 135–144. [Google Scholar] [CrossRef] [Green Version] - Pegueroles-Queralt, J.; Igualada-Gonzalez, L.; Corchero-Garcia, C.; Cruz-Zambrano, M.; del Rosario-Calaf, G. Coordination of control and energy management methods for microgrid systems. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Conference Europe, Instabul Turkey, 12–15 October 2014; pp. 1–6. [Google Scholar]
- Kanwar, A.; Rodríguez, D.I.H.; von Appen, J.; Braun, M. A Comparative Study of Optimization-and Rule-Based Control for Microgrid Operation. In Proceedings of the 2015 Power and Energy Student Summit (PESS), Dortmund, Germany, 13–14 January 2015; pp. 1–6. [Google Scholar]
- Graditi, G.; Silvestre, M.L.D.; Gallea, R.; Sanseverino, E.R. Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids. IEEE Trans. Ind. Inform.
**2015**, 11, 271–280. [Google Scholar] [CrossRef] - Wierzbowski, M.; Lyzwa, W.; Musial, I. MILP model for long-term energy mix planning with consideration of power system reserves. Appl. Energy
**2016**, 169, 93–111. [Google Scholar] [CrossRef] [Green Version] - Kaur, J.; Sood, Y.R.; Shrivastava, R. A two-layer optimization approach for renewable energy management of green microgrid in deregulated power sector. J. Renew. Sustain. Energy
**2017**, 9, 065905. [Google Scholar] [CrossRef] - Choi, S.; Min, S. Optimal Scheduling and Operation of the ESS for Prosumer Market Environment in Grid-Connected Industrial Complex. IEEE Trans. Ind. Appl.
**2018**, 54, 1949–1957. [Google Scholar] [CrossRef] - Li, Y.; Zhao, T.; Wang, P.; Gooi, H.B.; Ding, Z.; Li, K.; Yan, W. Flexible scheduling of microgrid with uncertainties considering expectation and robustness. IEEE Trans. Ind. Appl.
**2017**, 54, 3009–3018. [Google Scholar] [CrossRef] - Paul, T.G.; Hossain, S.J.; Ghosh, S.; Mandal, P.; Kamalasadan, S. A Quadratic Programming Based Optimal Power and Battery Dispatch for Grid-Connected Microgrid. IEEE Trans. Ind. Appl.
**2018**, 54, 1793–1805. [Google Scholar] [CrossRef] - Ji, L.; Huang, G.; Xie, Y.; Zhou, Y.; Zhou, J. Robust cost-risk tradeoff for day-ahead schedule optimization in residential microgrid system under worst-case conditional value-at-risk consideration. Energy
**2018**, 153, 324–337. [Google Scholar] [CrossRef] - Sharma, R.; Mudaliyar, S.; Mishra, S. Power management and economic load dispatch based control of hybrid PV-battery-diesel standalone AC system. In Proceedings of the 2018 IEEMA Engineer Infinite Conference (eTechNxT), New Delhi, India, 13–14 March 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Abdolrasol, M.G.M.; Hannan, M.A.; Mohamed, A.; Amiruldin, U.A.U.; Abidin, I.B.Z.; Uddin, M.N. An Optimal Scheduling Controller for Virtual Power Plant and Microgrid Integration Using the Binary Backtracking Search Algorithm. IEEE Trans. Ind. Appl.
**2018**, 54, 2834–2844. [Google Scholar] [CrossRef] - Psarros, G.N.; Karamanou, E.G.; Papathanassiou, S.A. Feasibility Analysis of Centralized Storage Facilities in Isolated Grids. IEEE Trans. Sustain. Energy
**2018**, 9, 1822–1832. [Google Scholar] [CrossRef] - Qiu, H.; Gu, W.; Xu, Y.; Zhao, B. Multi-Time-Scale Rolling Optimal Dispatch for AC/DC Hybrid Microgrids With Day-Ahead Distributionally Robust Scheduling. IEEE Trans. Softw. Eng.
**2019**, 10, 1653–1663. [Google Scholar] [CrossRef] - Yang, F.; Feng, X.; Li, Z. Advanced Microgrid Energy Management System for Future Sustainable and Resilient Power Grid. IEEE Trans. Ind. Appl.
**2019**, 55, 7251–7260. [Google Scholar] [CrossRef] - Riffonneau, Y.; Bacha, S.; Barruel, F.; Ploix, S. Optimal Power Flow Management for Grid Connected PV Systems With Batteries. IEEE Trans. Sustain. Energy
**2011**, 2, 309–320. [Google Scholar] [CrossRef] - Cingoz, F.; Elrayyah, A.; Sozer, Y. Optimized Resource Management for PV-Fuel-Cell-Based Microgrids Using Load Characterizations. IEEE Trans. Ind. Appl.
**2016**, 52, 1723–1735. [Google Scholar] [CrossRef] - Luna, A.C.; Diaz, N.L.; Graells, M.; Vasquez, J.C.; Guerrero, J.M. Mixed-Integer-Linear-Programming-Based Energy Management System for Hybrid PV-Wind-Battery Microgrids: Modeling, Design, and Experimental Verification. IEEE Trans. Power Electron.
**2017**, 32, 2769–2783. [Google Scholar] [CrossRef] [Green Version] - Luna, A.C.; Meng, L.; Diaz, N.L.; Graells, M.; Vasquez, J.C.; Guerrero, J.M. Online Energy Management Systems for Microgrids: Experimental Validation and Assessment Framework. IEEE Trans. Power Electron.
**2018**, 33, 2201–2215. [Google Scholar] [CrossRef] - Yi, Z.; Dong, W.; Etemadi, A.H. A Unified Control and Power Management Scheme for PV-Battery-Based Hybrid Microgrids for Both Grid-Connected and Islanded Modes. IEEE Trans. Smart Grid
**2018**, 9, 5975–5985. [Google Scholar] [CrossRef] - Kotsampopoulos, P.; Lagos, D.; Hatziargyriou, N.; Faruque, M.O.; Lauss, G.; Nzimako, O.; Forsyth, P.; Steurer, M.; Ponci, F.; Monti, A.; et al. A Benchmark System for Hardware-in-the-Loop Testing of Distributed Energy Resources. IEEE Power Energy Technol. Syst. J.
**2018**, 5, 94–103. [Google Scholar] [CrossRef] - Morais, H.; Kádár, P.; Faria, P.; Vale, Z.A.; Khodr, H. Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew. Energy
**2010**, 35, 151–156. [Google Scholar] [CrossRef] [Green Version] - Barnes, M.; Kondoh, J.; Asano, H.; Oyarzabal, J.; Ventakaramanan, G.; Lasseter, R.; Hatziargyriou, N.; Green, T. Real-world microgrids-an overview. In Proceedings of the 2007 IEEE International Conference on System of Systems Engineering, San Antonio, TX, USA, 16–18 April 2007; pp. 1–8. [Google Scholar]
- Silvente, J.; Graells, M.; Espuña, A.; Salas, P. An optimization model for the management of energy supply and demand in smart grids. In Proceedings of the 2012 IEEE International Energy Conference and Exhibition (ENERGYCON), Florence, Italy, 9–12 September 2012; pp. 368–373. [Google Scholar] [CrossRef]
- Pell, S.; Turcotte, D.; Colgate, G.; Swingler, A. Nemiah Valley Photovoltaic-Diesel Mini-Grid: System Performance and Fuel Saving Based on One Year of Monitored Data. IEEE Trans. Sustain. Energy
**2012**, 3, 167–175. [Google Scholar] [CrossRef] - Aluisio, B.; Dicorato, M.; Forte, G.; Trovato, M. An optimization procedure for MG day-ahead operation in the presence of CHP facilities. Sustain. Energy Grids Netw.
**2017**, 11, 34–45. [Google Scholar] [CrossRef] - Lai, K.; Illindala, M.S.; Haj-ahmed, M.A. Comprehensive protection strategy for an islanded microgrid using intelligent relays. In Proceedings of the 2015 IEEE Industry Applications Society Annual Meeting, Addison, TX, USA, 18–22 October 2015; pp. 1–11. [Google Scholar] [CrossRef]
- Marzband, M.; Sumper, A.; Ruiz-Álvarez, A.; Domínguez-García, J.L.; Tomoiagă, B. Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets. Appl. Energy
**2013**, 106, 365–376. [Google Scholar] [CrossRef] - Bao, Z.; Zhou, Q.; Yang, Z.; Yang, Q.; Xu, L.; Wu, T. A Multi Time-Scale & Multi Energy-Type Coordinated Microgrid Scheduling Solution—Part I: Model & Methodology. IEEE Trans. Power Syst.
**2015**, 30, 2257–2266. [Google Scholar] [CrossRef] - Nguyen, D.T.; Le, L.B. Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics. IEEE Trans. Smart Grid
**2014**, 5, 1608–1620. [Google Scholar] [CrossRef] - Ebrahimi, M.R.; Amjady, N. Adaptive robust optimization framework for day-ahead microgrid scheduling. Int. J. Electr. Power Energy Syst.
**2019**, 107, 213–223. [Google Scholar] [CrossRef] - Conte, F.; Massucco, S.; Saviozzi, M.; Silvestro, F. A Stochastic Optimization Method for Planning & Real-Time Control of Integrated PV-Storage Systems: Design & Experimental Validation. IEEE Trans. Softw. Eng.
**2018**, 9, 1188–1197. [Google Scholar] [CrossRef] - Oriti, G.; Julian, A.L.; Anglani, N.; Hernandez, G.D. Novel Economic Analysis to Design the Energy Storage Control System of a Remote Islanded Microgrid. IEEE Trans. Ind. Appl.
**2018**, 54, 6332–6342. [Google Scholar] [CrossRef] - Fathima, A.H.; Palanisamy, K. Optimization in microgrids with hybrid energy systems—A review. Renew. Sustain. Energy Rev.
**2015**, 45, 431–446. [Google Scholar] [CrossRef] - Dolara, A.; Grimaccia, F.; Magistrati, G.; Marchegiani, G. Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming. Energies
**2017**, 10, 241. [Google Scholar] [CrossRef] - Vrettos, E.I.; Papathanassiou, S.A. Operating Policy and Optimal Sizing of a High Penetration RES-BESS System for Small Isolated Grids. IEEE Trans. Energy Convers.
**2011**, 26, 744–756. [Google Scholar] [CrossRef] - Liao, G. The optimal economic dispatch of smart Microgrid including Distributed Generation. In Proceedings of the 2013 International Symposium on Next-Generation Electronics, Kaohsiung, Taiwan, 25–26 February 2013; pp. 473–477. [Google Scholar]
- Abbes, D.; Martinez, A.; Champenois, G. Eco-design optimisation of an autonomous hybrid wind-photovoltaic system with battery storage. IET Renew. Power Gener.
**2012**, 6, 358–371. [Google Scholar] [CrossRef] - Conti, S.; Nicolosi, R.; Rizzo, S.A.; Zeineldin, H.H. Optimal Dispatching of Distributed Generators and Storage Systems for MV Islanded Microgrids. IEEE Trans. Power Deliv.
**2012**, 27, 1243–1251. [Google Scholar] [CrossRef] - Bao, Z.; Zhou, Q.; Yang, Z.; Yang, Q.; Xu, L.; Wu, T. A Multi Time-Scale & Multi Energy-Type Coordinated Microgrid Scheduling Solution—Part II: Optimization Algorithm & Case Studies. IEEE Trans. Power Syst.
**2015**, 30, 2267–2277. [Google Scholar] [CrossRef] - Marzband, M.; Yousefnejad, E.; Sumper, A.; Domínguez-García, J.L. Real time experimental implementation of optimum energy management system in standalone Microgrid by using multi-layer ant colony optimization. Int. J. Electr. Power Energy Syst.
**2016**, 75, 265–274. [Google Scholar] [CrossRef] [Green Version] - Mirjalili, S. Evolutionary Algorithms and Neural Networks: Theory and Applications; Springer: Cham, Switzerland, 2019; pp. 87–104. [Google Scholar] [CrossRef]
- Anglani, N.; Oriti, G.; Colombini, M. Optimized Energy Management System to Reduce Fuel Consumption in Remote Military Microgrids. IEEE Trans. Ind. Appl.
**2017**, 53, 5777–5785. [Google Scholar] [CrossRef] - Conte, F.; D’Agostino, F.; Pongiglione, P.; Saviozzi, M.; Silvestro, F. Mixed-Integer Algorithm for Optimal Dispatch of Integrated PV-Storage Systems. IEEE Trans. Ind. Appl.
**2019**, 55, 238–247. [Google Scholar] [CrossRef] - AhmadiAhangar, R.; Rosin, A.; Niaki, A.N.; Palu, I.; Korõtko, T. A review on real-time simulation and analysis methods of microgrids. Int. Trans. Electr. Energy Syst.
**2019**, 29, e12106. [Google Scholar] [CrossRef] [Green Version] - Digital Innovation Hubs. Smart Specialisation Platform. Available online: https://s3platform.jrc.ec.europa.eu/digital-innovation-hubs-tool (accessed on 7 September 2020).
- Van Ackooij, W.; Danti Lopez, I.; Frangioni, A.; Lacalandra, F.; Tahanan, M. Large-scale unit commitment under uncertainty: An updated literature survey. Ann. Oper. Res.
**2018**, 271, 11–85. [Google Scholar] [CrossRef] [Green Version] - Lotfi, H.; Khodaei, A. Levelized cost of energy calculations for microgrids. In Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17–21 July 2016; pp. 1–5. [Google Scholar] [CrossRef]
- Mitchell, S.; Consulting, S.M.; Dunning, I. PuLP: A Linear Programming Toolkit for Python. Available online: http://www.optimization-online.org/DB_FILE/2011/09/3178.pdf (accessed on 1 September 2020).
- Forrest, J.; Lougee-Heimer, R. COIN-OR Branch and Cut Solver. Available online: https://projects.coin-or.org/Cbc (accessed on 1 September 2020).
- Chen, T.; Guestrin, C. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 785–794. [Google Scholar] [CrossRef] [Green Version]
- Xue, P.; Jiang, Y.; Zhou, Z.; Chen, X.; Fang, X.; Liu, J. Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms. Energy
**2019**, 188, 116085. [Google Scholar] [CrossRef] - Timplalexis, C.; Bezas, N.; Bintoudi, D.A.; Zyglakis, L.; Pavlopoulos, V.; Tsolakis, C.A.; Krinidis, S.; Tzovaras, D. A hybrid physical/statistical day-ahead direct PV forecasting engine. In Proceedings of the Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MEDPOWER 2020), Pafos, Cyprus, 9–11 November 2020. [Google Scholar]
- Salamanis, A.I.; Xanthopoulou, G.; Bezas, N.; Timplalexis, C.; Bintoudi, A.D.; Zyglakis, L.; Tsolakis, A.C.; Ioannidis, D.; Kehagias, D.; Tzovaras, D. Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting. Energies
**2020**, 13, 5978. [Google Scholar] [CrossRef] - Holmgren, W.F.; Hansen, C.W.; Mikofski, M.A. pvlib python: A python package for modeling solar energy systems. J. Open Source Softw.
**2018**, 3, 884. [Google Scholar] [CrossRef] [Green Version] - Tsolakis, A.C.; Bintoudi, A.D.; Zyglakis, L.; Zikos, S.; Timplalexis, C.; Bezas, N.; Kitsikoudis, K.; Ioannidis, D.; Tzovaras, D. Design and Real-life Deployment of a Smart Nanogrid: A Greek Case Study. In Proceedings of the 2020 IEEE International Conference on Power and Energy (PECon), Penang, Malaysia, 7–8 December 2020; pp. 321–326. [Google Scholar]

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