Learnings

Outcomes

 
Development of a Market-Informed Battery Dispatch Model: The BATSEC project successfully delivered a new battery dispatch model tailored for direct and easy integration into NESO’s Construction Planning Assumptions (CPA) process. This model simulates how Battery Energy Storage Systems (BESS) behave in the Frequency Response (FR), Balancing Mechanism (BM) and Wholesale markets, using historical market data to generate realistic dispatch profiles. 
 
Accurate Power Flow Modelling: The project has successfully developed a model that generates timeseries power flow data for either a single battery or multiple batteries connected to a given network node.  This model provides valuable insights into battery behaviour and its impact on network capacity, supporting more informed decision-making and planning. 
 
Reduced Connection Timescales and Investments: Provided detailed guidance on the implementation of the model into connection assessments which is expected to reduce the connection timescales and network reinforcements.  This will eventually support faster and more cost-effective connection offers, especially under high volumes of BESS applications. As the Exploratory Data Analysis results have identified Battery activity within the Wholesale, Frequency Response and Balancing Mechanism markets, the reduced capacity will be utilized in the POUYA arbitrage for the despatch as per the historical distributions.  
 
Improved Battery Energy Storage System (BESS) Behavior Understanding: The project enhanced understanding of Battery Energy Storage System (BESS) operation from both the system operator and network operators' perspectives. This will lead to better policy decisions and more efficient network management. 
 
Data Strategy and Challenges: The project initially faced delays due to limited access to high-quality BESS datasets. To mitigate this, the team developed a custom dataset using open-source data from Elexon and verification from NESO portals. Also, the quality of the data from the BESS developers have been enhanced after identification of quality issues.   
 
Modular Architecture and Easy Integration: The model has been designed with a modular structure that allows for future updates and integration of new market services or battery technologies without overhauling the entire system. 
Further, the model has been designed to interface with the existing *POUYA model, NESO’s economic dispatch tool for ease of integration and reduced complexity. 
 
*POUYA – POwer Uncertainty Year-round Analyser is NESO’s internal economic despatch tool to evaluate the impact of generation on the NETS – through a detailed year round probabilistic modelling and statistical analysis of network flows. 

Lessons Learnt

Data Availability & Quality: One of the key challenges faced during the project was the unavailability of expected data. This highlighted the importance of securing reliable data sources and having contingency plans in place. Relying on open-source data required significant additional time for acquisition and processing, emphasising the need for thorough data validation and quality assurance processes. 
 
Integration Approaches: The integration of the model with existing systems required careful planning and testing. Ensuring compatibility and seamless data flow between systems was essential for successful integration. 
Addressing security and compliance requirements early in the integration process helped prevent delays and facilitated smoother collaboration. 
 
Stakeholder Engagement: Effective communication and collaboration with stakeholders were key to the project's success. Regular updates and workshops helped in presenting findings and capturing design requirements collaborating with different teams involving Markets, SCADA, Battery working groups, Analytical Tool Development and Network Development.  Establishing clear data sharing agreements and confidentiality protocols ensured that data was handled appropriately and securely. 
 
Modular and Phased Development: The modular design of the project allowed for future adjustments to individual components, making the model adaptable to evolving market conditions. Dividing the project into multiple phases, including exploratory data analysis, model development, and system integration, helped in managing the project effectively and meeting milestones. 
 
Flexibility and Adaptability:  The project demonstrated flexibility by adapting its model design approach in response to early insights from data analysis. This included developing alternative model configurations that aligned with the available data and reflected historical market participation patterns. To support seamless integration, multiple implementation options were explored from the outset, ensuring adaptability to different system environments and user needs.