# A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services

^{*}

## Abstract

**:**

## 1. Introduction

- A novel method based on GA has been designed, formulated, and tested for the EMS of a residential VPP that includes loads, renewable generation, ESS, and EVs. The proposed model is suitable for VPPs with limited resources.
- Although the reduction in the electricity bill is the main objective, technical support for the distribution grid is also considered. The literature has hardly examined AS provision from VPPs and energy communities to the distribution grid, even though this is an important challenge under the current energy transition circumstances.
- EVs are considered storage systems with a V2G strategy, respecting the time restriction and the availability of stored energy for mobility purposes. The previous literature has not examined the use of EVs as an EMS resource along with other ESSs to optimize energy costs and provide ASs at a residential level.
- Both single-objective and multi-objective methods are used and compared to combine economic and technical targets.

## 2. Case Study

_{nom}) was 24 kWh, with a maximum charge/discharge power of ±6 kW and a SoC range of 20–100%. The ESS sizing was limited in accordance with Spanish self-consumption remuneration as well. Batteries represent a high percentage of the investment required for self-consumption. Indeed, if only economic advantages were pursued, some studies discourage investment in storage systems until costs decrease [39].

## 3. Optimal Day-Ahead Schedule of Storage and Electric Vehicles

^{®}was used to handle constraints in this paper. Figure 4 presents a flow chart of the GA used.

_{EV}, where nh

_{EV}represented the number of hours any EV was connected to the grid and available for charging. Only a time slot during the night was considered for EV scheduling. Other papers open the EV schedule to other time slots during the day [11]. However, this situation may not be realistic in residential environments, as users are usually at home during the night, and both energy prices and network tariffs are usually lower during these hours. Accordingly, it is difficult to motivate EV owners to plug in their EVs at home during the day to participate in VPP management.

_{1}in (2) aims to minimize the power interchanged with the grid. It is useful to maximize SC and SS without considering prices, for example, in a TVPP devoted to supporting the distribution grid. A CVPP requires economic incentives for final users to be motivated to participate in the VPP. In this kind of VPP, objective function f

_{2}in (3) is more appropriate, as it aims to minimize the VPP electricity bill. Constraints for the ESS are shown in (4)–(6).

_{ESSmax}is considered, and both positive (charge) and negative (discharge) power values are allowed. Meanwhile, (5) delimits the allowed SoC range according to manufacturer recommendations, and (6) guarantees that the difference in SoC between the beginning and the end of the day is lower than 10%. Other works [8] propose equal values for these SoC values in order to complete a daily charge/discharge cycle. However, this constraint reduces the flexibility of the model. A maximum difference of 10% prevents the ESS from being completely charged or discharged at the beginning of the next day, with a certain degree of flexibility. The ESS SoC is calculated as in (7), considering both charge and discharge efficiency rates.

_{grid}to perform peak shaving as a means of grid congestion prevention (11).

## 4. Results and Discussion of the Optimization

^{−12}have been programmed for the GA as the stop criterion. The default values suggested by Matlab

^{®}support have been selected for the remaining GA parameters.

- P
_{ESSmax}= 6 kW - SoC
_{lo}= 20%; SoC_{hi}= 100% - SoC
_{in}= 50% - η
_{ch}= η_{di}= 0.95 - P
_{EVmax}= 7.4 kW - n
_{EV}= 4 - nh
_{EV}= 8 (0–8 h) - E
_{EVtotal}= 30 kWh - P
_{peak}= 10 kW

- Case 1: Minimization of Power Interchanged with the Grid

_{1}in (2) and the constraints (4)–(6) and (8)–(10) are used.

- Case 2: Minimization of the Electricity Bill

_{2}in (3) and constraints (4)–(6) and (8)–(10) are used.

- Case 3: Minimization of the Electricity Bill Subjected to Grid Congestion Constraints

- Case 4: Multi-Objective Optimization

_{1}(2) and f

_{2}(3) and the constraints of Cases 1 and 2 are used. Figure 5 shows the Pareto front obtained in the process. Each point in the Pareto front represents one possible solution for the problem. The fitness value of both objective functions for each solution is depicted. The marked point of the Pareto front is considered a good trade-off solution, as it provides a low fitness value for both objective functions without prioritizing either of them.

_{ESSmax}and the distance to the extreme values of the SoC. As an ESS converter is a fully controllable bidirectional device, the power range at each hour can be used to face uncertainty in generation or demand forecasting and to provide other ASs to the distribution grid. For example, the DSO might send a power setpoint to the VPP to avoid network congestion in high-demand hours or to smooth power, with the aim of better exploiting the grid capacity and managing grid losses. In Case 1, the ESS is well exploited for the optimization process; thus, low or even no power is available for ASs during some hours (Figure 9a). In Case 2, the potential ranges of ESS power increase/decrease (Figure 9b) suffer from high variability, as in Case 1, although they outperform those of Case 1 during the morning. The use of the ESS in Case 3 is not as strong as in other cases. As a result, the potential increase/decrease in ESS power for providing other ASs is generally higher in this case than in previous ones (Figure 9c). Therefore, this technique is the only one capable of providing peak shaving AS, and it is the best suited for other AS provisions among the techniques studied. Finally, the higher range of power change observed in Case 4 (Figure 9d) is due to the lower participation of the ESS in the optimization process.

## 5. Conclusions

- Optimally sizing energy resources (PV power plants, ESSs, and EV clusters) to maximize economic advantages
- Considering uncertainties regarding PV generation and EV users’ habits within the optimization model
- Updating the energy scheduling during the day as far as information and estimations are better known
- Adapting constraints to the AS market at the distribution level, as far as they are developed in the regulations of different countries
- Designing a framework of peer-to-peer or aggregator-to-stakeholder contracts to optimally allocate economic benefits and savings
- Proposing similar management procedures for industrial/commercial VPPs, adapting assumptions and constraints
- Coordinating the daily energy scheduling of different VPPs to increase the capability of the aggregated resources to participate in electricity markets
- Exploring other heuristic and meta-heuristic optimization tools

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

ACRONYMS | |

CVPP | Commercial virtual power plant |

DSM | Demand side management |

DSO | Distribution system operator |

EMS | Energy management system |

ESS | Energy storage system |

EV | Electric vehicle |

GA | Genetic algorithm |

GSA | Gravitational search algorithm |

GWO | Grey wolf optimization |

IPS | Inclined planes system |

MILP | Mixed integer linear programming |

MOPSO | Multi-objective particle swarm optimization |

MPC | Model predictive control |

NLP | Non-linear programming |

OPF | Optimal power flow |

PSO | Particle swarm optimization |

PV | Photovoltaic |

SC | Self-consumption rate |

SS | Self-sufficiency rate |

TVPP | Technical virtual power plant |

VPP | Virtual power plant |

V2G | Vehicle-to-grid |

VARIABLES | |

C_{nom} | Nominal capacity of ESS, kWh |

E_{EVtotal} | Energy required for full EV charge, kWh |

I_{ESSch}(h) | Binary index, 1 when P_{ESS}(h) > 0 |

I_{ESSdi}(h) | Binary index, 1 when P_{ESS}(h) ≤ 0 |

I_{pur}(h) | Binary index, 1 when P_{grid}(h) ≤ 0 |

I_{sel}(h) | Binary index, 1 when P_{grid}(h) > 0 |

h_{EV} | Hour at which any EV is grid-connected |

n_{EV}(h) | Number of EVs connected at hour h |

nh_{EV} | Number of hours with any EV connected |

P_{ESS}(h) | ESS power at hour h (positive when charging), kW |

P_{ESSmax} | Maximum ESS power, kW |

P_{EV}(h) | EV power at hour h (positive when charging), kW |

P_{EVmax} | Maximum power of an individual EV charger, kW |

P_{grid}(h) | Power interchanged with the grid at hour h (positive when injecting), kW |

P_{LD}(h) | Load demand power at hour h, kW |

P_{peak} | Peak value allowed for peak shaving, kW |

P_{PV}(h) | PV generation power at hour h, kW |

p_{pur}(h) | Price for purchased energy at hour h, EUR/kWh |

p_{sel}(h) | Price for sold energy at hour h, EUR/kWh |

SoC(h) | State of charge of ESS at hour h, % |

SoC_{in} | Initial value of SoC at hour h = 0, % |

SoC_{lo} | Lower value of SoC recommended by manufacturer, % |

SoC_{up} | Upper value of SoC recommended by manufacturer, % |

η_{ch} | Charging efficiency of ESS, pu |

η_{di} | Discharging efficiency of ESS, pu |

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**Figure 2.**(

**a**) Initial accumulated demand and generation, excluding the ESS; (

**b**) Power interchange with the distribution grid in the initial situation.

**Figure 3.**Prices of energy for prosumers according to the Spanish-regulated frame on 1 February (elaborated from [41]).

**Figure 7.**Accumulated demand and generation after scheduling: (

**a**) Case 1; (

**b**) Case 2; (

**c**) Case 3; (

**d**) Case 4.

**Figure 8.**Power interchange with the distribution grid: (

**a**) Case 1; (

**b**) Case 2; (

**c**) Case 3; (

**d**) Case 4.

**Figure 9.**Potential increase/decrease in ESS power for ASs: (

**a**) Case 1; (

**b**) Case 2; (

**c**) Case 3; (

**d**) Case 4.

Paper | ESS | EV | Role of EV within EMS | Optimization Method | AS or Network Support |
---|---|---|---|---|---|

[7] | ✓ | ✕ | - | MILP | ✕ |

[8] | ✓ | ✕ | - | OPF | ✓ |

[9] | ✓ | ✕ | - | MILP | ✓ |

[10] | ✓ | ✕ | - | MILP/OPF | ✓ |

[11] | ✕ | ✓ | EMS resource | MILP | ✓ |

[12] | ✓ | ✓ | Backup storage | Several heuristic | ✓ |

[13] | ✕ | ✓ | EMS resource | OPF | ✓ |

[14] | ✓ | ✓ | EMS resource | MILP | ✕ |

[15] | ✓ | ✓ | EMS resource | MPC | ✕ |

[16] | ✓ | ✓ | Charging—V2G if possible | None | ✕ |

[17] | ✓ | ✓ | States: charge/discharge/idle, no power modulation | GWO | ✕ |

[28] | ✓ | ✕ | - | GA | ✕ |

[29] | ✓ | ✓ | EMS resource | GA/MILP | ✕ |

[30] | ✓ | ✕ | - | GA/MOPSO | ✕ |

[31] | ✓ | ✕ | - | GA | ✕ |

[32] | ✕ | ✓ | Charging—V2G only in overloaded hours | GA | ✓ |

[33] | ✓ | ✕ | - | GA | ✕ |

[34] | ✓ | ✕ | - | GA | ✕ |

Current Paper | ✓ | ✓ | EMS resource | GA | ✓ |

Case | Base | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|

Description | Without ESS | Min. power interchange (2) | Min. cost (3) | Min. cost (3) + peak shaving (11) | Multi-objective (2) and (3) |

SC (%) | 59.87 | 79.10 | 58.16 | 62.88 | 57.75 |

SS (%) | 33.89 | 53.30 | 39.19 | 42.36 | 38.91 |

Electricity bill (EUR/day) | 23.47 | 20.58 | 19.95 | 21.94 | 22.53 |

Cost saving (%) | 0 | 12.3 | 15.0 | 6.5 | 4.0 |

Extreme power peak (kW) | −36.44 | −11.09 | −23.67 | −10 | −18.32 |

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## Share and Cite

**MDPI and ACS Style**

González-Romera, E.; Romero-Cadaval, E.; Roncero-Clemente, C.; Milanés-Montero, M.-I.; Barrero-González, F.; Alvi, A.-A.
A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services. *Electronics* **2023**, *12*, 3717.
https://doi.org/10.3390/electronics12173717

**AMA Style**

González-Romera E, Romero-Cadaval E, Roncero-Clemente C, Milanés-Montero M-I, Barrero-González F, Alvi A-A.
A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services. *Electronics*. 2023; 12(17):3717.
https://doi.org/10.3390/electronics12173717

**Chicago/Turabian Style**

González-Romera, Eva, Enrique Romero-Cadaval, Carlos Roncero-Clemente, María-Isabel Milanés-Montero, Fermín Barrero-González, and Anas-Abdullah Alvi.
2023. "A Genetic Algorithm for Residential Virtual Power Plants with Electric Vehicle Management Providing Ancillary Services" *Electronics* 12, no. 17: 3717.
https://doi.org/10.3390/electronics12173717