Optimizing Energy Exchange with Vehicle-to-Grid Technology

Ⅰ. Introduction of Vehicle-to-Grid Technology

The power system has evolved into the smart grid (SG), with numerous new communication capabilities added to it as a result of the adoption of modern information and communication technology. One such function is vehicle-to-grid (V2G), which makes electric vehicle (EV) transportation effective and efficient, along with making the appropriate choices. The vehicle-to-grid technology enables the interchange of energy, appropriate data, and extra power grid facilities. 

To transmit data and energy between battery electric vehicles and the power grid, a bidirectional technique is used. It provides the power system with additional services, including reactive power compensation, load balancing, peak load reduction, and spinning reserves. Additionally, the smart grid's design allows for features like vehicle-to-home (V2H), vehicle-to-vehicle (V2V), and grid-to-vehicle (G2V), all of which are crucial. 

To share power and useful data between electric vehicles, smart homes, devices, and batteries, vehicle-to-home technology will be used. Electricity from the nearby charging station, as well as local electricity, will be shared via vehicle-to-grid technology. Vehicle-to-grid technology is divided into unidirectional and bidirectional categories. Both types of vehicle-to-grid can offer a wide range of additional services, such as peak load shaving and load leveling, as part of improving energy efficiency for power grids. 

Table 1 provides an example comparison of the power flow profiles for unidirectional and bidirectional vehicle-to-grid technologies. This technology employs optimization strategies to optimally charge or discharge electricity from the grid and vice versa.

Table 1. Comparison between unidirectional and bidirectional vehicle-to-grid power flows Source: IET Renewable Power Generation

Optimization techniques

A complex vehicle-to-grid system is produced by the integration of a large number of electric vehicles into the power grid and several non-linear variables. The massive number of electric vehicles and grid integration make it difficult to provide the necessary services to all systems inside the integrated network. The previous issue will be impossible to solve when power consumption is at its highest. Therefore, these systems require a tool that can optimize the requirements of each system participant. 

Therefore, an optimization strategy is crucial in these kinds of circumstances. Traditionally, linear and quadratic programming have been used to accomplish this strategy. Numerous non-linear and uncertain variables, as well as a significant volume of real-time computational resources, are present in this programming. The genetic algorithm (GA) and particle swarm optimization (PSO), the most widely used and practical optimization approaches, are covered below.

Genetic Algorithm - 

It is a method of evolutionary analysis that relies on a genetic chromosome in a living thing. The objective function runs, assesses the score for the developing chromosomes (or output for the smart grids), and repeats until the intended output condition is either met or fulfilled. The process ends when the requirement is satisfied, and the best result is given. Fig. 1 depicts the genetic algorithm flowchart.

Fig. 1. Flowchart of a genetic algorithm. Source: IET Renewable Power Generation

For grid stability and optimal delivery, the genetic algorithm is most frequently utilized. It is founded on the core concept that the population's fittest member has the best chance of surviving. To find the best parameter setting, it uses the objective information function. In addition to several other factors, the genetic algorithms use the non-linear algebraic technique as the goal function for the load flow application. 

The minimal loss is represented by a power loss penalty. The genetic algorithm adaptive technique processes the data information that is available and keeps track of the power flow within the grid as described in the steps of operation below. Along with the factors listed above, minimizing fuel costs, minimal transmission losses, and economic dispatch are considered when determining the best power flow.

The following steps describe how the genetic algorithm works:

• Generate a population of binary strings.

• Calculating each string's fitness index after it has been formed.

• The process of mutation and crossover produces offspring.

• Evaluation of the freshly created strings and determination of fitness indices.

• If the search conditions are met, the process is finished; otherwise, go back to step 3.

It's been shown that the genetic algorithm is significantly more effective than the other optimizing algorithms. With the input parameters, it permits various types of recombination in the production of the newly generated solutions. The concept of mutation is generally less used in algorithms, which reduces the number of offspring solutions. The genetic algorithm solves this issue and, using additional mutation techniques, accelerates the convergence of the algorithm to more effective optimal solutions with fewer iterations.

Particle Search Optimization - 

It determines the best solution, particularly for random population solutions. It runs at a specific velocity in a multidimensional environment. Each individual is permitted to communicate with others throughout the computation. It helps individuals alter their speed and make appropriate movement plans. The movements are, therefore, convergent towards the minimum condition that must be satisfied in the end. The method produces an optimal output after the minimal condition is met, at which point iteration ceases. 

The flowchart of the particle swarm optimization algorithm is shown in Fig. 2. The particle swarm optimization algorithm's operational steps are described below as part of the process:

Specify the problem's dimension and objective function.

Create the input parameters.

The iteration has started.

Evaluate the goal function by updating the parameters continuously.

After each iteration, update the archive.

Verify the termination requirements.

If the condition is met, the algorithm ends; otherwise, it returns to step 4.

Fig. 2. Flowchart of the particle swarm optimization algorithm Source: IET Renewable Power Generation

The particle swarm algorithm and other evolutionary comparison methods, particularly the genetic algorithm, share numerous similarities. However, the primary distinction is that particle swarm optimization lacks crossover and mutation techniques. Particle swarm optimization uses internal velocities and data stored in memory to update itself. Particle swarm optimization is a one-way solution provider that produces superior results compared to the competition. In addition, particle swarm optimization is simpler to design and run than a genetic algorithm, and only a few parameters need to be changed.

Ⅱ. Summarizing the Key Points

Vehicle-to-grid can provide a wide range of additional services, including reactive power compensation, load balancing, peak load reduction, and spinning reserves.

Bidirectional techniques for transmitting data and energy between electric vehicles and the power grid are crucial for vehicle-to-grid technology. 

They allow features like vehicle-to-home, vehicle-to-vehicle, and grid-to-vehicle, all of which can help to share power and useful data between electric vehicles, smart homes, devices, and batteries.

Vehicle-to-home technology is particularly important for sharing power and useful data between electric vehicles and smart homes. It enables using electric vehicles as mobile storage units to provide backup power during outages or peak demand periods.

Despite some challenges associated with vehicle-to-grid implementation, it has great potential to revolutionize the way we think about transportation and energy management in the future smart grid era.

This blog post is part of a full research article from IET Renewable Power Generation.

The featured image is courtesy of Midjourney.