# An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids

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## Abstract

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## 1. Introduction

- Rule-based strategies: decisions are taken based on a predefined response pattern to a set of monitored conditions (e.g., net demand, storage state of charge);
- Predictive optimization: scheduling decisions are the variables of an optimization problem, defined over a given future temporal window;

## 2. Problem Description

#### Comparability of Scheduling Approaches

- the magnitude of forecast errors;
- how uncertainty was addressed in the problem formulation;
- the quality of the correction algorithm implemented.

## 3. System Modelling

#### 3.1. Dispatchable Generators

#### 3.2. Renewable Generators

#### 3.3. Energy Storage System

#### 3.4. Electrical Load

#### 3.5. Spinning Reserve Requirements

## 4. Scheduling Strategies

#### 4.1. Milp Scheduling Optimization

#### 4.2. Evolutionary Scheduling Optimization

#### 4.3. Load Following and Cycle Charge

#### 4.4. Tunable Rule-Based Heuristic Strategy

- If $\overline{So{E}_{t}^{Th}}\ge \underline{So{E}_{t}^{Th}}$, the strategy is equivalent to the traditional CC, with the amplitude of the ESS cycles set by the value of the thresholds. In particular, if ${\overline{SoE}}_{t}^{Th}>{\widehat{SoE}}_{max}^{ESS}$ (maximum allowable SoE) or $\underline{So{E}_{t}^{Th}}<{\widehat{SoE}}_{min}^{ESS}$ (minimum allowable SoE), the corresponding prioritization mode switch, defined by the the change of variable H that prioritize programmable generators over storages, will not be allowed. In other words, in these conditions, the periodic cycling of the ESS, specific to this control strategy, will be forbidden.
- If $0.5\underline{So{E}_{Th}}\le \overline{So{E}_{t}^{Th}}\le \underline{So{E}_{t}^{Th}}$, the strategy is equivalent to the traditional load following (LF): the battery is always prioritized with respect to programmable generators and recharged only if RES production is exceeding demand, while the generators supply positive net loads.
- If $\overline{So{E}_{t}^{Th}}\le \underline{So{E}_{t}^{Th}}$, the energy content of the battery is stabilized, following the net load with the generators without discharging the storage. Precisely for this reason, this operating scenario has been classified as SoC Preservation (SP).

## 5. Results

#### 5.1. Case Study Description

#### 5.2. Rule-Based Heuristic Structure Optimization Results

#### 5.3. Dispatch Results Comparison

## 6. Conclusions

- Significantly outperforms the standard rule-based logics LF and CC that constitute its building blocks by identifying the optimal combination of operating modes during different hours of the day that can minimize operating costs;
- Outperforms the direct use of EAs to tackle the optimal scheduling problem, showing how a dispatch strategy structure based on physical considerations can reduce the number of optimization variables and guide the EA towards better decisions
- Shows comparable results with respect to a perfect foresight MILP, defining the global optimum for the daily operating cost.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Abbreviations

CC | Cycle Charge strategy |

DE-HS | Differential Evolutionary - Harmony Search |

EA | Evolutionary optimization Algorithms |

ESS | Energy Storage System |

GA | Genetic Algorithm |

GOA | Grasshopper Optimization Algorithm |

PLC | Programmable Logic Controllers |

LF | Load Following strategy |

MILP | Mixed Integer Linear Programming |

MPP | Maximum Power Point |

NSGA-II | Non-Dominated Sorted Genetic Algorithm-II |

PSO | Particle Swarm Optimization |

RES | Renewable Energy Resources |

SNO | Social Network Optimization |

SoC | State of Charge |

SoE | State of Energy |

SP | SoC preservation strategy |

SSA | Salp Swarm Algorithm |

TRBH | Tunable Rule-Based Heuristic |

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**Figure 6.**Daily operating cost [€] yielded by the TRBH approach with one intra-day window, as a function of the SoE thresholds.

**Figure 7.**Number of losses in the comparison between the average final value obtained by DE, BBO, SGA, PSO, and SNO.

**Figure 8.**Mean, best, and worst daily operating cost attained by the optimal tuning of TRBH over 20 instances, as a function of the number of intra-day intervals.

**Figure 9.**TRBH average percentage cost increase compared to the MILP solution as a function of the number of intra-day intervals.

**Figure 10.**Distance from minimum operating cost of the scheduling approaches, in the days associated with the best performance of each method.

**Figure 11.**Comparison of dispatch profiles yielded by the MILP formulation and the optimal tuning of TRBH12, defined by the SoE thresholds shown in the bottom graph.

**Table 1.**Average gap attained by each method over all simulated days of operation. The 10th and 90th percentiles are also shown, to give an idea of the reliability of the scheduling approach in operating days with different characteristics.

Strategy | ||||
---|---|---|---|---|

TRBH | SNO | LF | CC | |

10th perc. | 1.43% | 3.72% | 10.79% | 6.71 % |

Average | 2.39% | 5.31% | 12.45% | 10.46% |

90th perc. | 3.41% | 6.25% | 13.99% | 17.21% |

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**MDPI and ACS Style**

Moretti, L.; Meraldi, L.; Niccolai, A.; Manzolini, G.; Leva, S. An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids. *Electronics* **2021**, *10*, 1162.
https://doi.org/10.3390/electronics10101162

**AMA Style**

Moretti L, Meraldi L, Niccolai A, Manzolini G, Leva S. An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids. *Electronics*. 2021; 10(10):1162.
https://doi.org/10.3390/electronics10101162

**Chicago/Turabian Style**

Moretti, Luca, Lorenzo Meraldi, Alessandro Niccolai, Giampaolo Manzolini, and Sonia Leva. 2021. "An Innovative Tunable Rule-Based Strategy for the Predictive Management of Hybrid Microgrids" *Electronics* 10, no. 10: 1162.
https://doi.org/10.3390/electronics10101162