An agent based framework of a blockchain enabled peer to peer energy market

The shift in the electric power generation market from utilities to end-users has been helped by distributed energy resources, improving the system?s efficiency. As a result, a system that can manage generation resources at the grid edge is now necessary. For the first time in distributed electricit

Project Title

An agent based framework of a blockchain enabled peer to peer energy market

Project Area of Specialization

Electrical/Electronic Engineering

Project Summary

The shift in the electric power generation market from utilities to end-users has been helped by distributed energy resources, improving the system’s efficiency. As a result, a system that can manage generation resources at the grid edge is now necessary. For the first time in distributed electricity generation, households can trade additional power with one another for a price they determine. Many questions remain unanswered because of the novelty of the concept, including the impact of high electricity availability, individual cost and profit plans on neighborhood exchanges, and market outcomes. Agent-based modeling (ABM) will be used to develop a platform to simulate peer-to-peer power trading. Energy analysts anticipate potential difficulties and devise management measures by simulating the existing system's supply and demand. A model and simulation of the various scenarios can help make decisions and strategies to lessen the supply and demand gap for a sustainable energy system. We'll conduct a case study in Lahore to see if this framework can meet its stated goals. Simulating supply, profit maximization, and cost reduction of units can assist stakeholders in better understanding the proposed simulation framework. Using blockchain technology, traders can benefit from a safe trading environment that records all transactions between themselves and other users.

Project Objectives

A rising number of P2P energy trading enterprises are using blockchain technology and changing their business model as a result. New ideas and a smart grid transition are being spurred by the blockchain revolution. Due to a lack of adoption and proof-of-concept processes for blockchain technology, finding the most value and optimizing blockchain technology in the P2P energy trade remains difficult. There is still discussion over the current limitations of blockchain technology in terms of performance, scalability, and interoperability. However, large-scale implementations are still difficult to accomplish.

Project Implementation Method

In this study, a simulation platform for peer-to-peer energy trading will be developed using agent-based modeling (ABM) framework to simulate peer-to-peer electricity exchanges. Energy supply and demand simulations can aid in the analysis of the current system as well as the prediction of problems in order to develop management strategies. Modeling and simulation of various scenarios can help inform decisions and strategies for managing energy resources in order to achieve a sustainable energy system and close the supply-demand imbalance. We'll look at a case study from Lahore to see if this approach meets its optimization goals. The suggested simulation framework will assist stakeholders in analyzing and dealing with high supply availability, profit maximization, and unit cost reduction.

In the first step we have collected the dataset of PRECON from Lahore Region. Dataset contains 42 houses with property area, building structure and appliances information. To make clustering findings easier to understand, we provide a new method for estimating the relevance of features in k-means clustering or variants thereof. To make even the most sophisticated models easier to understand, supervised machine learning makes extensive use of the concept of feature importance. To account for the complexities of scaling, K-Means makes use of the Euclidean distance metric. When conducting Principal Component Analysis, scaling is essential (PCA). Because high magnitude features have a high variance, PCA is biased toward finding the features with the most variance. K means clustering is applied to preprocessed datasets, and each attribute is ranked in terms of relevance before the entire dataset is clustered. k is the number of attacks on the datasets in multi-class classification, which signifies that the feature data point is clustering into two groups, normal and anomalous. It is used to rank the characteristics based on the clusters' homogeneity score, which indicates how similar the objects in each cluster are to each other. The higher the score, the more essential this trait is in the classification, whereas the lower the value, the less important it is.

Then we have calculated the energy consumption of each cluster according to the four seasons

Benefits of the Project

Individuals are more likely to use P2P energy trading systems if they have a compelling reason to do so. It gives customers the ability to create electricity utilizing renewable energy resources (RER) such as the sun, wind, water, and other natural resources. This study provides new insight into the possible market advantages that can be realized through alternative market designs and structures, depending on buyer and seller electricity prices. The model presented here can be used to assess alternative market designs and choose market governance structures for novel applications in different systems

Technical Details of Final Deliverable

nill

Final Deliverable of the Project

Software System

Core Industry

Energy

Other Industries

Core Technology

Blockchain

Other Technologies

Sustainable Development Goals

Responsible Consumption and Production

Required Resources

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