January 30, 2024

Exploring the role of blockchain choices and forecasting algorithm in the Local Energy Markets

Let’s explore how the choice of blockchain matters in LEM and its comparison with the business-as-usual (BAU) approach and the efficiency of an AI-powered forecasting algorithm. 

As the global appetite for energy continues to surge, the twin issues of grid congestion and escalating energy expenses loom large. Local Energy Markets (LEMs) emerge as a solution to the shortcomings of centralised energy distribution systems by promoting decentralised Peer-to-Peer (P2P) trading. While blockchain enhances scalability and transparency in Local Energy Markets (LEMs), it's crucial to grasp that the specific type of blockchain used significantly influences the overall outcome. In this study, we have integrated second-generation blockchain and forecasting algorithms. 

Let’s explore how the choice of blockchain matters in LEM and its comparison with the business-as-usual (BAU) approach, and the efficiency of an AI-powered forecasting algorithm.

The Local Energy Market (LEM) platform:

In this study, the proposed LEM trading platform introduces a transformative model, encompassing various critical steps for efficient P2P energy trading. These include the creation of forecasting profiles, pricing bids facilitated by a trading agent service, and matchmaking through a forward-facing market. The trading engine processes data and bid prices, providing dispatch signals for prices and energy values, all stored securely on the blockchain. This ensures transparency and traceability throughout the trading process.

Key pillars of the LEM framework:

  • Blockchain Integration: In this study, the LEM platform is trialled with second-generation blockchain, ensuring secure P2P trading and transparent recording of bidding and transaction information. This layer-2 solution enhances transaction times and reduces energy trading costs.
  • Forecasting for Real-Time Trading: A forecasting service generates typical profiles for load consumption, solar PV generation, and Battery Energy Storage System (BESS) state-of-charge (SOC), enabling participants to trade ahead of real-time.
  • Trading Agent and Engine Services: The integration of trading agent and trading engine services facilitates the efficient collection of bid prices and matchmaking, optimising forward-facing trading in the market.
  • P2P Trading-Based LEM Model: The proposed model guarantees tangible benefits, providing monetary gains, confirming margins and network integrity, and reducing CO2 emissions. The LEM smart contract acts as a secure ledger, managing energy trading data.

Key findings from the study:

Local Energy Markets (LEMs):

  • Electric vehicle (EV) owners experience a reduction of 9.2%, while general consumers, prosumers with solar PV installations, and prosumers with solar PV and Battery Energy Storage Systems (BESS) witness reductions of 3.6%, 29.5%, and 45%, respectively. On average, all participants enjoy a substantial reduction of 21.6%.
  • Designed a Local Energy Market (LEM) platform utilising Gen2 blockchains, fostering secure peer-to-peer (P2P) trading among participants and ensuring transparent storage of all bidding and transaction information.
  • Created a forecasting service to generate standard profiles for load consumption, solar PV generation, and BESS SOC, facilitating energy trading in advance of
  • Incorporated trading agent and trading engine services to gather bid prices and facilitate forward-facing matchmaking.
  • Proposed a Local Energy Market (LEM) based on peer-to-peer (P2P) trading to ensure tangible advantages for participants, stakeholders, and the environment.
    This includes financial gains, validated margins, network integrity, and reduced CO2 emissions.


  • The integration of blockchain technology guarantees secure data storage and faster data retrieval, enhancing participant trust and mitigating the risk of fraudulent activities. However, second-generation blockchain could have costs and other technical limitations.

Forecasting algorithm:

  • Integrating a forecasting solution improved the effectiveness of the proposed LEM framework. The forecasting algorithm learns from historical profiles, ensuring that the forecasted profiles closely align with the actual profiles received on-site and are optimised accordingly.

Environmental impact:

  • The study highlights the positive environmental impact of the LEM model. It reduces CO2 emissions by 984 kg, increases self-sufficiency by 2.2%, and improves financial benefits for all participants by 21.6%. 
  • By embracing sustainable practices, LEM becomes a key player in mitigating grid congestion and promoting green energy solutions.

Challenges and future considerations:

The implementation of blockchain technology in Local Energy Markets (LEMs) is essential; however, second-generation blockchains have shortcomings, particularly in areas such as throughput, transaction costs, complexity, and environmental impact. For enhanced sustainability in LEMs, we need third-generation blockchains like Solana and Powerledger blockchain (PLBC), which improve throughput, have minimal gas fees, and consume less energy.

Read the full paper for more insights

Authors: Dr. Vivek Bhandari, Dr. Jemma Green, Dr. Liaqat Ali, M.Imran Azim, Anand Menon, Jan Peters, Nabin Babu Ojha, S.M. Muyeen

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