Local Energy Marketplace Agents-based Analysis

The concept of "energy prosumer"is becoming an increasingly important part of our energy landscape due to the emergence of distributed energy sources, including photovoltaic (PV) technology. This new phenomenon has blurred the line between energy producers and consumers, creating a new class of prosumers. Blockchain technology has been instrumental in facilitating secure, cost-effective energy transactions between prosumers, consumers, and utilities, automating the process and making it more efficient. In the present study, an agent-based model (ABM) simulation framework for energy exchange was developed, demonstrating the power profiles of households and the operation of blockchain-aware energy transactions. The study was conducted in the Education City Community Housing (ECCH) microgrid, using a multi-agent framework for a transactive energy (TE) distributed energy resource (DER) that requires blockchain technology. The current blockchain-based local energy market (LEM) works to balance supply and demand by using precise short-term energy generation forecasts and home consumption estimates. To evaluate the accuracy of the current state-of-the-art energy forecasting methods, the researchers conducted a simulation to evaluate the accuracy of energy forecasts in predicting household energy generation and consumption. They examined the impact of forecasting errors on market outcomes under different supply scenarios. The study found that while models using long short-term memory (LSTM) may provide low forecasting errors, the prediction process needs to be modified for a blockchain-based LEM. This study stands out from previous research because it attempts to forecast the timeline of smart meters in general, rather than simply focusing on short-term energy forecasting. This is an important step in the development of more secure and cost-effective energy transactions and will pave the way for a more efficient and seamless energy market.