Decentralized Energy Market for Smart Neigbourhoods
Our vision is to build Smart Cities within which, participants share resources in a controlled way including monetary exchange. There, Smart Buildings organised in Smart Districts offer their flexibilities to the market. Decentralized Cross-commodity Energy Management (DECENT) implements a merit-order clearing mechanism for an energy market. Here participants can connect virtually and trade heat and electricity.
Peet-to-Peer energy trading makes the energy market more competitive. It gives the consumers the flexibility of deciding the energy provider besides the main grid. The consumer can have preferences such as buying energy locally, favoring renewabel energy sources, etc. Blockchain helps in achieving P2P energy trading through decentralization. It makes the system transparent by providing the audit trails. Moreover, smart contracts can be used to automate the transactions in a publicly verifiable manner. Trading agents can send offers/demands to the market.
A Model-View-Controller (MVC) design pattern is used for market implementation. The market contains models for the Offer and Organization. A controller is used to delegate requests for all relevant operations. The algorithm iterates through all the offers and demands. Consequently, the offers are filtered based on the start time and valid until properties of the offer. Only the offers that are valid for the current time step and still have unsettled energy are considered during the clearing.
Furthermore, market clearing price is calculated. The Market uses the merit order algorithm and market clearing price is calculated every 15 minutes. Grid operator is responsible for triggering the market clearing process. Only the grid operator is authorized to initiate the clearing process and after every round of clearing, the Grid operator also publishes the matching results the MQTT broker. All the peers in the network can subscribe to the channel to get the latest market clearing information. Using this environment we can investigate on smart trading agents learn and use market patterns, derive alternative clearing mechanisms and observe limits on local energy markets and define possible mitigation strategies.