# Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing

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## Abstract

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## 1. Introduction

- To avoid increasing simulation processing, incorporate a battery model that does not include the simulation degradation process. However, it is essential to emphasize that the battery model used in the simulation is realistic enough to provide accurate results in the management of the purchase and sale of electricity, quality of voltage levels, and frequency in the bars;
- Consider the variable and uncertain nature of PV generation, the unpredictable inputs and outputs of loads at the distribution level, and the price of electricity in the dynamic scenario for a daily operation;
- Present simultaneous solutions for multiple desired objectives, with the minimization of daily operating costs, the improvement of the use of generated PV energy, and the improvement in the use of the battery, keeping it within its operational limits;
- Present suggestions for prediction algorithms, with ANN training being carried out using data from the next day’s schedule and control and operation simulations for the purchase/sale of electricity in MG in a dynamic pricing scenario. With these algorithms, achieving an adequate balance between control accuracy to provide electrical power quality indices and low computational effort to develop an efficient and sustainable EMS is possible;
- Apply a method of short-term nature in the forecast horizon for making decisions on the purchase or sale of electrical energy.

- It is developed in ANN with a non-linear autoregressive technique formulated and trained for forecasting the variables of load, PV-generated power, and electricity price. The controller uses the FL code to make decisions regarding purchasing or selling electricity. In this way, the proposed resolution algorithms can be tuned and implemented to effectively deal with the restrictions associated with the particularities of the MG;
- Dynamic operation. These algorithms are suitable for making real-time EMS control decisions.

## 2. Challenges of Implementing Modern and Intelligent Grids

“MGs are electricity distribution systems containing distributed loads and energy resources (such as distributed generators, storage devices or controllable loads) that can be operated in a controlled and coordinated manner, both while connected to the main power grid and while islanded.”

- Stability: The operation of the MG can result in voltage and frequency oscillations, compromising the grid’s stability in the transition from connected to islanded control modes [16]. Therefore, in order to effectively solve these challenges, it is important to guarantee the adjustments in a precise way for the operation of the MGs [8,17].
- Low inertia: MGs do not have inherent stability and can face several instabilities caused many times in situations of grid formation without the installation of SG and high concentrations of NDRS in conditions to operate in island mode. To avoid these problems, implement BESSs with the appropriate controls or reinstall the SG [7,18].
- Uncertainties: MGs are subject to variations caused by environmental conditions. Therefore, forecasting methods are implemented to ensure accuracy in meeting demand about present generations and decisions regarding the purchase and sale of electricity, according to prices [17].
- Coordination between entities: Because there are several challenges from factors such as energy balancing, failure rates of equipment installed in the MG, variations in installed loads, uncertain generations and dependent on renewable sources, and weather forecasts, it is extremely important to use communication compatibility between components installed in the MG [14].

#### The MG Control

- The MG system must address the challenges above to ensure reliability and economy;
- It is essential to ensure a smooth transition between operating modes;
- Mains connected or island operating modes are desirable for the system, including voltage and frequency regulation;
- To achieve the goals of decreasing MG operating costs, it is necessary to develop and implement advanced control strategies that meet the specific requirements of MG, allowing efficient and optimized operation in different operating conditions.

- Centralized control: A central controller sends signals to each controllable agent based on data from the MG components and the external grid, as shown in Figure 2a.
- Decentralized control: In this configuration, local control of each MG unit is carried out independently, without exchanging information with other units, except for a few lead agents who transmit and receive information through a center. This is represented in Figure 2b.
- Distributed control: When local controllers use a communication grid to exchange information and seek a cooperative solution to the general control problem, we have a distributed approach, as shown in Figure 2c.
- Hierarchical control: This approach seeks a balance between a fully centralized and fully decentralized control architecture. It involves implementing a hierarchical control scheme in which centralized and decentralized methods can be used at each level of the hierarchy [16,17,19,20]. These different control architectures offer flexibility in MG management, allowing adaptation to the specific needs of each system. Choosing the appropriate control approach will depend on the MG characteristics, performance requirements, and operational constraints.

**Figure 2.**A comprehensive view of control architectures covering centralized, decentralized, and distributed configurations [21].

- Regarding the interfacing of the converters present in the MG, the primary control is decentralized and consists of controllers located in the energy converters. This control layer is responsible for functions that provide fast response, power sharing, and detection for island operating conditions;
- Being slower than the speed of the primary control layer, the secondary control aims to correct steady-state deviations, correcting the frequency and voltage levels according to the reference levels programmed in the primary controller. Thus, this layer must synchronize and exchange energy with the main grid;
- At a high level and dedicated to evaluating the long-term operation of the MG, tertiary control is considered the “top layer”. This layer, through intelligence, introduces the advance in the MG operation, being able to consider optimization, resources, demand forecasts, and adaptations to environmental conditions.

## 3. Aspects Related to the EMS and the MG

#### 3.1. The MG and the Current Energy System

#### 3.2. The EMS of MG

- Loads;
- PV generation;
- Electricity prices.

#### 3.3. Energy Management Methods

#### 3.4. The Heuristic Method

- Facility;
- Computational processing speed.

#### 3.5. Developed Heuristic Algorithm

#### 3.6. The AI Method

#### 3.6.1. The ANN with the Non-Linear Autoregressive Model for Predicting the Variables

_{i};

^{®}Neural Network Time Series application is used with the non-linear autoregressive network (NAR) model for predicting the variables function, which allows visualizing and training dynamic neural networks to solve autoregressive network problems non-linear and non-linear autoregressive with exogenous non-linear time series. The definition of the percentage divisions of the data of the sets:

- Training (70%);
- Validation (15%);
- Test (15%).

#### 3.6.2. FL for Decision Making—Model Architecture

- Energy demand forecast;
- Forecast of electricity price values;
- Forecast of PV-generated power;
- BESS SoC.

#### 3.6.3. The Fuzzy Ruleset

## 4. The Adopted Model and Results

#### 4.1. Variables, Information, Predictions, and Programs Used

^{®}R2019b, and for the vectorization of the images, the Coreldraw

^{®}program was used.

#### 4.1.1. The CIGRÉ 14-Bar

#### 4.1.2. The Parameters

#### 4.1.3. The Data

- Clear sky: Refers to a sky without significant clouds or with few scattered clouds. In this condition, solar irradiance reaches the Earth’s surface without major obstructions, resulting in high irradiation levels to increase the generated PV power;
- Partially cloudy sky: This condition occurs when clouds cover part of the sky while other areas remain unobstructed. Solar irradiance can vary depending on the amount and density of the clouds with a decrease in the generated PV power.

#### 4.1.4. The Modules for Forecasting the Variables

#### 4.2. Case Study Scenarios

- Case 1: Heuristic method for the clear sky;
- Case 2: Proposed method for the clear sky;
- Case 3: Heuristic method for the partially cloudy sky;
- Case 4: Proposed method for the partially cloudy sky.

#### 4.2.1. Cases 1 and 3 of Reference Applied the Heuristic Method for Precise and Partially Cloudy Sky Conditions

#### 4.2.2. Cases 2 and 4, Proposed Methods Applied for Clear and Partially Cloudy Sky Conditions

## 5. Analysis of the Results of the Four Cases

#### 5.1. Discussion of Cases 1 and 3 with the Heuristic Method

#### 5.2. Discussion of Cases 2 and 4 with the Proposed Method

#### 5.3. Comparison between the Heuristic and Proposed Algorithms

- Heuristic method;
- Proposed method with AI;
- Without using BESSs.

## 6. Conclusions

- Minimizes the energy bought at peak hours;
- Maximizes self-consumption of locally made photovoltaic energy;
- Created adequate service to the battery by holding it within its limitations and reducing its degradation.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

## Appendix B

## Appendix C

**Figure A9.**Battery model used [46].

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**Figure 1.**Traditional electrical systems compared to the current electrical system. In (

**a**) a traditional electricity distribution system is presented, and letter (

**b**) a future electricity distribution system is presented.

**Figure 3.**Organization (hierarchy) in layers of the control system for an MG [23].

**Figure 4.**Example of a basic MG-AC composed of generators, BESSs, power electronic converters, and loads.

**Figure 5.**MG power management functions [30].

**Figure 7.**The illustration of the positive sign convention for energy flows in the MG [37].

**Figure 10.**The methodology of realization of the proposed model in FL [45].

**Figure 11.**Adopted example of Mandani-type fuzzy inference with four input modules and one output module (drawn figure inspired by Matlab’s Fuzzy Logic Toolbox in Matlab R2019b software).

**Figure 13.**PV generator power data. (

**a**) The situation with the sky clear, and (

**b**) the partially cloudy sky.

**Figure 16.**Modules for forecasting demand variables (kW), prices (R$/kWh), and PV-generated power (kW). And modules input into the FL controller (blocks modeled for Simulink).

**Figure 18.**Results of the heuristic reference case for the simulation period with partially cloudy sky day.

**Figure 19.**Results of the case with the proposed model for the simulation period with a clear-sky day.

**Figure 20.**Results of the case with the proposed model for the simulation period with partially cloudy skies.

SoC | EEPF | FOD | PVg | Output |
---|---|---|---|---|

Low | Low | Low | Low | Low |

Low | Low | Low | High | Low |

Low | Low | High | Low | Low |

Low | Low | High | High | Low |

Low | High | Low | Low | High |

Low | High | Low | High | High |

Low | High | High | Low | Medium |

Low | High | High | High | High |

High | Low | Low | Low | Low |

High | Low | Low | High | Low |

High | Low | High | Low | Low |

High | Low | High | High | Low |

High | High | Low | Low | High |

High | High | Low | High | High |

High | High | High | Low | High |

High | High | High | High | High |

Parameter | Value |
---|---|

MG voltage (Vrms) | 12,470 |

Nominal frequency (Hz) | 60 |

Battery type | Lithium-ion |

Rated power (kW) | 400 |

Rated capacity (kWh) | 2500 |

Overall system efficiency (%) | 95.5 |

Upper charge limits (%) | 80 |

Lower charge limits (%) | 20 |

SoC to recharge (%) | 11 |

Initial state of charge (0–100%) | 50 |

Initial active Cmds (kW) | 400 |

Maximum PV power (kW) | 250 |

Simulation type | Phasor |

Cost in Simulations | Value (EUR) |
---|---|

EMS cost with heuristic method | 1046.6575 |

EMS cost with AI method | 781.2258 |

Cost in Simulations | Value (EUR) |
---|---|

EMS cost with heuristic method | 1343.4049 |

EMS cost with AI method | 1101.0664 |

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## Share and Cite

**MDPI and ACS Style**

Alves, G.H.; Guimarães, G.C.; Moura, F.A.M.
Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing. *Energies* **2023**, *16*, 5262.
https://doi.org/10.3390/en16145262

**AMA Style**

Alves GH, Guimarães GC, Moura FAM.
Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing. *Energies*. 2023; 16(14):5262.
https://doi.org/10.3390/en16145262

**Chicago/Turabian Style**

Alves, Guilherme Henrique, Geraldo Caixeta Guimarães, and Fabricio Augusto Matheus Moura.
2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing" *Energies* 16, no. 14: 5262.
https://doi.org/10.3390/en16145262