Podcast Summary
Collaborating with AI for real-world energy solutions: Through human-AI collaboration, real-world energy challenges can be tackled, leading to innovative solutions and advancements in the industry.
The collaboration between humans and AI through large language models can lead to real-world applications and transformative solutions, even in complex industries like energy and climate tech. Duncan Campbell of Scale Microgrids shared his experience of working with ChatGPT to write Python code for battery optimization, which was a real-world challenge for the energy sector. Although his initial attempt didn't fully work, it was a promising start. To take it further, they brought in Syed Madani, an expert in writing software for battery optimization, to create a more complex challenge for Duncan. The challenge they focused on was optimizing a battery in the wholesale electricity market of Kaiso. This scenario reflected real-world conditions and could potentially be assigned to a team of software engineers. By collaborating with AI, humans can unlock new possibilities and push the boundaries of what's possible in their respective fields. Kjell Khan, an investor in revolutionary climate technologies, expressed his initial skepticism about AI's potential impact on energy but was convinced after witnessing this collaboration firsthand. The combination of human expertise and AI's capabilities can lead to significant advancements and innovations in various industries.
Optimizing energy storage asset scheduling for maximum revenue: Effectively bidding energy storage assets into the wholesale market involves complex analysis of load patterns, historical data, weather data, and market prices to optimize charging and discharging schedules for maximum revenue.
Effectively bidding an energy storage asset into the wholesale market involves optimizing its schedule for charging and discharging based on day-ahead energy and ancillary service prices, battery parameters, and market conditions. This is a complex problem that requires the integration of various factors, including load patterns, historical data, weather data, and market prices. The goal is to maximize revenue by determining the optimal use of the energy storage asset for energy and ancillary services. In the absence of advanced tools like ChatGPT, this task would be time-consuming and would require a team of traders and software engineers to manually analyze data and optimize the battery's schedule. Traditional trading shops that rely on outdated tools like Excel spreadsheets may struggle to keep up with the demands of the evolving energy market.
Using Large Language Models for Complex Business Problems: Large language models can assist in solving complex business problems but require expertise, resources, and a systematic approach to yield optimal results.
While it's theoretically possible to use a large language model like ChatGPT to solve complex business problems, such as market bidding and trading, it's a challenging and time-consuming task that requires expertise and resources. The team at Fluence, as well as Tesla and other companies, are working on this systematically. A simple attempt might yield some results, but it would leave significant value on the table. During a discussion, it was mentioned that without a large language model, it would be difficult for an individual to tackle this problem. The team at Fluence, while capable, acknowledged that they would need significant time and resources to build the analysis from scratch and the tool to implement it. The first attempt by Duncan was to copy and paste the problem into ChatGPT and expect a straightforward solution. However, the result was a guide on how to approach the problem, suggesting regression models for forecasting prices and providing a Python package. While this was a helpful starting point, it didn't provide a complete solution. In conclusion, while large language models like ChatGPT can be useful tools in solving complex business problems, they require expertise, resources, and a systematic approach to yield optimal results. The process involves forecasting prices based on time series data, setting up the problem and constraints, applying an optimizer, and parsing the results. Without these elements, the results may be incomplete or ineffective.
Teaching ChatGPT with clear instructions and descriptions: Effective communication and guidance are crucial when working with AI models, as they lack domain expertise and require clear instructions to understand and solve complex problems.
Interacting with ChatGPT requires a guided approach, similar to mentoring a junior software engineer. The buckshot approach, which involves giving it a high-level problem, did not yield successful results. Instead, breaking down the problem into smaller parts and explaining concepts in detail led to better and more understandable results. This approach involved teaching ChatGPT with clear instructions and descriptions, rather than requesting code snippets directly. The process involved providing multiple paragraphs of explanations, which helped ChatGPT understand the context and constraints of the problem. This experience highlights the importance of clear communication and guidance when working with AI models, as they do not possess the same level of domain expertise as humans.
AI can assist but not replace human involvement in software development: AI models can generate code and scripts, but they still need human oversight and guidance to ensure error-free implementation
While AI models like ChatGPT can generate code and scripts, they still require human oversight and guidance. The process of developing software involves defining parameters, importing packages, and creating scripts, which AI models can assist with but cannot fully replace human involvement. During the discussion, it was noted that while AI can produce a script with clear bones, there may still be errors that need to be addressed. For instance, if the AI is not fully informed of all the requirements, it may miss crucial details. Moreover, even when the code produced by the AI is relatively clear, it's essential to remember that someone prompted the model to generate the code first. This means that the foundation of the project has been laid out, making it easier for humans to build upon. However, the code generated by AI is not ready to roll straight out of the box. It may contain errors that need debugging, and some of these errors may be challenging to address. Nevertheless, the code is a step closer to being usable than a mere sketch of what should be done. In summary, while AI can assist in software development, it cannot replace human involvement entirely. Humans are still needed to provide clear objectives, define parameters, and ensure that the code generated by AI is error-free and ready for implementation.
AI models lack domain knowledge and common sense, leading to suboptimal results: Human oversight and QA are necessary to ensure AI-generated code aligns with intended goals, as models may overlook constraints and lack intuition
While AI models can generate code, they don't possess the same level of domain knowledge and common sense that human product people or software engineers have. This was highlighted during a discussion about optimizing battery usage for an AI model. The model failed to understand that a battery cannot charge and discharge at the same time, a constraint that seems obvious to humans but not to the model. Similarly, a software engineer without experience in energy problems might also overlook this constraint. The model also struggled to understand the definition of a cycle, leading to suboptimal results. These issues underscore the importance of human oversight in the development process. The model may produce code that works, but it may not be optimal or intuitive. Human QA is necessary to ensure that the results make sense and align with the intended goals. The model is not inherently smarter than a human software engineer, but it can generate code based on text prompts. Therefore, it's crucial to provide clear and precise instructions and to intervene when necessary to guide the model towards the desired outcome.
Effective communication and understanding of problem crucial for ChatGPT use in energy market analysis: Clear instructions and accurate, up-to-date data are essential for ChatGPT to provide useful long-term energy market forecasts, which can significantly impact decision-making.
Effective communication and understanding of the problem are crucial in any project, even when using advanced tools like ChatGPT. While the software may be capable of solving daily problems, the ability to forecast and analyze long-term trends is a significant challenge. The lack of clear instructions and the need for accurate, up-to-date data pose major hurdles. The speaker in this conversation acknowledged that they didn't fully achieve the desired outcome when trying to use ChatGPT for long-term energy market analysis, but they also recognized that with more time and specific instructions, the software could likely provide useful forecasts. The importance of accurate forecasting lies in its impact on decision-making, as it can significantly influence the quality of the results. The speaker emphasized that the quality of the forecast drives the quality of the decisions, and maximizing market position without considering forecast and optimization may not yield the best outcomes.
Using ChatGPT to build an optimizer for energy price forecasting: Despite limitations, ChatGPT helped build an optimizer for energy price forecasting, generating and printing the optimized schedule and associated forecast market settlements, and adding value to the project.
While large language models (LLMs) can be helpful in generating ideas and code snippets, they still have limitations when it comes to complex tasks like building a neural net from scratch for energy price forecasting. The speaker in this conversation opted to skip this part of the project due to unfamiliarity and the challenge of gathering necessary data. Instead, they focused on building an optimizer using ChatGPT's assistance, which they were able to extend for multiple days and integrate with Grid Status API. The optimizer's output was then used to create a schedule and associated forecast market settlements, as required by the project. However, the process of parsing and presenting the optimized schedule in useful charts proved to be a separate challenge due to the speaker's lack of knowledge in plotly and its functions. Overall, the partnership between the model and the speaker felt similar to working with a team member, and the optimizer's ability to generate and print the optimized schedule and associated forecast market settlements, along with the charts, added value to the project.
Developing an optimizer for a battery system: Created an optimizer for a battery system to schedule and print a dispatch algorithm for a day ahead based on historical energy prices, with plans to address ancillary markets in the future.
The discussion centered around developing an optimizer for a battery system to schedule and print a dispatch algorithm for a day ahead based on historical energy prices, excluding ancillary markets. The speaker encountered challenges in creating an aesthetically pleasing and functional chart for debugging purposes and dealing with varying time frames for analysis. Despite these hurdles, a working optimizer was created. Energy was found to be much simpler to handle compared to ancillary services, which were not addressed due to a lack of understanding and time constraints. The final code is now ready for review, and the team is excited to discuss its performance and potential time savings and capabilities.
Python Notebook for Optimizing Battery Parameters and Energy Prices: The notebook efficiently optimizes battery parameters and energy prices using clear code, a user-friendly interface, and automated processes to save time and reduce errors.
The Python notebook presented in this discussion is an efficient solution for optimizing battery parameters and energy prices for renewable energy storage systems. The notebook is divided into four parts: defining battery parameters, retrieving day-ahead energy prices, dispatch optimization, and parsing results. The first section is simple but crucial, as it sets up the battery parameters as variables. The form-like interface makes it easy to input battery specifications and eliminates the need for manual data entry and debugging. The use of clear and descriptive variable names adds to the code's readability. The second part involves retrieving day-ahead energy prices, which can be a complex process. Instead of manually downloading and uploading data, the code uses a simpler method by pulling data from a structured format provided by Grid Status. This saves time and effort. The third part, dispatch optimization, uses all the battery parameters and prices to define the problem and run the optimizer against the constraints. The code's ability to automate this process is impressive, as it eliminates the need for manual calculations and potential errors. Lastly, the code parses the results and displays them in a user-friendly format. Overall, the Python notebook is a well-designed and effective tool for optimizing battery parameters and energy prices, earning a high score for its simplicity, efficiency, and ease of use.
Impressive coding ability but missing ancillary services: The code demonstrates strong optimization capabilities, but its value is limited without ancillary services for a complete energy management solution. Clear naming conventions would enhance the optimization model's readability.
The code under review showcases a high level of coding ability, with a clear and well-structured style, despite some minor issues. However, its value is diminished due to the absence of ancillary services, which are crucial for a complete energy management solution. The code's optimization capabilities are impressive, as it's not common for a single engineer to handle both back-end data engineering and optimization tasks. However, the optimization model could benefit from improved parameter and variable naming conventions for better clarity. Overall, the code is a strong foundation, but its full potential can only be realized when ancillary services are integrated.
Considering binary decisions and integer programming in a linear program: Linear programs can be complex, requiring careful handling of binary decisions and integer programming when dealing with battery states. Despite the challenges, an LP model for battery optimization received high marks, but processing the data frame output was time-consuming and required an FTE.
The linear programming problem discussed in the conversation, while being a linear program, requires careful consideration of binary decisions and integer programming when dealing with battery states. The objective function and constraints were defined correctly as linear, but there were instances where the meaning of charge and discharge efficiency needed clarification. Ignoring ancillary services, the LP model received a high rating. However, the most challenging part was processing the data frame output, which required calculating metrics and generating visualizations for the user. This process was time-consuming and required an FTE, but the speaker was impressed with the results. The entire process, from prompt to the current stage, was estimated to take around 4 hours of continuous work, a significant reduction compared to the potential team of software engineers and optimization experts who might have taken a year to complete the task.
Significantly reducing time and resources for software engineering projects: Duncan's optimization method, completed in 4 hours, could save up to $150,000 in engineering costs and reduce project timelines from 2 weeks to a month and a half.
Duncan's optimization method presented during the discussion has the potential to significantly reduce the time and resources required for complex software engineering projects. Although the example discussed was simplified and could be accomplished in Excel, adding a software foundation would typically require a team of two engineers: a back-end software engineer and an optimization engineer. The estimated timeframe for such a project ranges from two weeks to a month and a half. This is a remarkable improvement considering Duncan does not have a coding background and completed the task in just 4 hours. However, it's important to note that this optimized solution might not be suitable for production environments right away, especially when dealing with ancillary services and real-time decision making. Instead, it could be an effective tool for analysts making informative decisions or for project analysis before actual development begins. The cost savings are substantial, with Duncan's work costing $20 a month compared to a potential $150,000 salary for a software engineer. Overall, this optimization method represents a significant leap forward in technological capabilities.
Optimizing software for specific user needs: Understand users, optimize software for efficient work, provide accurate data, and visualize data for informed decisions.
Understanding your software users and their requirements is crucial in software development. For instance, if the user is an analyst, the software can be optimized for maintaining a continuous state, allowing for efficient work in shorter segments without losing focus. This is a trait humans lack, and software like Chat GPT in a state of PermaFlow can greatly benefit from this. Additionally, the software should provide accurate and detailed information about the system's performance, including charging and discharging revenue, costs, and net revenue. It should also allow for easy data visualization, such as a chart showing LMPs (Local Market Prices) and state of charges. A successful implementation of such a software would follow the imposed limitations, maximize cycle usage, and provide valuable insights into the average value of a megawatt hour through the battery. By focusing on throughput and visualizing the data in an understandable way, users can make informed decisions based on the system's performance. However, it's important to note that there might be parsing issues with the data, and errors should be carefully examined to avoid misunderstanding the results. Overall, understanding your users' needs and developing software that caters to those needs while providing accurate and actionable data is key to a successful implementation.
Large language models as a valuable resource for non-programmers in quantitative industries: Large language models can help non-programmers in quantitative industries, like energy, solve complex problems and describe concepts and math behind them, acting as an effective multiplier for teams and potentially disrupting industries.
Large language models, like ChatGPT, can serve as a valuable resource for non-programmers in quantitative and systems-thinking industries, such as energy, by providing quick solutions to complex problems and enabling the description of concepts and math behind them. While it may not introduce fundamentally new capabilities, it can act as an effective multiplier for teams lacking programming expertise. Additionally, the potential of these models to disrupt industries and change daily life is evident, as seen in their rapid adoption and integration into various applications. The energy industry, specifically, could greatly benefit from this technology as it is filled with individuals who excel in Excel and systems thinking but lack programming skills. However, it's important to remember that these models are not meant to replace human expertise but rather complement and enhance it.
Exploring the capabilities of large language models in industries: Large language models like Chachi BTA can revolutionize industries, especially software development and problem-solving, by bringing massive efficiency improvements. They have already shown potential in code generation and could further impact the electricity industry by parsing complex tariffs.
Large language models, such as Chachi BTA, have the potential to bring massive efficiency improvements to various industries, particularly in software development and problem-solving. During a recent experiment, two individuals successfully used Chachi BTA to write code for a mathematical problem, demonstrating its capabilities. However, it's important to note that while it can accomplish a lot today, there's still room for growth and innovation. One intriguing application of large language models lies in the electricity industry, where they could be used to parse and understand complex utility tariffs, making them more accessible and machine-ratable. Overall, this technology is an exciting development that has already proven its worth but still holds the potential for even more groundbreaking advancements in the future.
Unlocking valuable insights in energy industry through utility tariff documents analysis: By centralizing and standardizing utility tariff documents and other public utility commission data, we can make energy procurement and other applications more efficient and agile, leading to better decision-making and innovation in the distributed energy sector.
There is a significant opportunity to aggregate and analyze utility tariff documents and other public utility commission documents for the purpose of energy procurement and other applications. These documents are currently inaccessible in a centralized and standardized format, making it a challenge for companies and individuals to effectively utilize this data. However, by addressing this issue, we can make the process more efficient and agile, ultimately leading to better decision-making and innovation in the distributed energy sector. Duncan Campbell, Vice President at Scale Microgrid Solutions, has already started working on a solution for this problem, and it's encouraged that anyone interested in contributing to this effort should reach out. This is an important step towards unlocking valuable insights and streamlining processes in the energy industry.
