Post-analysis is a crucial component of portfolio optimization, specifically within PCI’s energy trading and optimization sector.
Post-analysis has four modules: transaction costing, outage costing, operational efficiency, and custom analysis, which provide four perspectives to help users analyze their portfolio operations. This blog post will walk you through the four modules and explain how they can help you with portfolio optimization.
Post-analysis meaning
Post-analysis refers to the process of evaluating the performance of energy projects and portfolios after they have been implemented. This involves analyzing data related to energy consumption, generation, and other metrics to identify areas for improvement and opportunities for optimization. Post-analysis solutions are designed to help energy companies make data-driven decisions to improve their operations, reduce costs, and increase efficiency. These solutions may involve the use of advanced analytics tools, machine learning algorithms, and other technologies to process large volumes of data and generate actionable insights.
By leveraging post-analysis solutions, energy companies can gain a deeper understanding of their energy usage patterns, identify opportunities to reduce waste, and optimize their portfolios for maximum performance.
How does post-analysis work?
The post-analysis workflow begins after the fact, following the forecasting process, long-term and midterm planning, and short-term, day-ahead, and real-time operations. It helps users analyze the P&L of the transactions, the replacement cost and the lost opportunity of the unit events, and the benefits and operational efficiency of their portfolio operations.
Module 1: A closer look at transaction costing
Transaction costing is one of the most critical modules in post-analysis. It calculates the actual costs for serving the native load, the revenue, cost, P&L for serving sales, and the avoidance of the generation costs due to purchases. It uses actual fuel prices, heat rate curves, operations and maintenance (O&M) costs, and other factors to calculate the units’ actual fuel consumption, emissions, and production costs. For market participants, it also allocates market credits and costs to the corresponding demand bucket and provides a consolidated benefit analysis to users.
Transaction costing has many variations to suit clients’ needs in different markets and regions. Today, we support clients who have Joint Dispatch Agreements (JDAs) or operate in bilateral markets, Independent Service Operator (ISO) markets, and the Southeast Energy Exchange Market (SEEM), which launched last year. A transaction costing deployment involves meeting customers to define their business rules.
Given the business rules, transaction costing uses the stacking method. A typical transaction costing run starts with collecting data from different sources, including but not limited to Energy Trading & Risk Management (ETRM) for deal data, energy accounting for meter generation and load, settlements from ISO settlements and contract settlements, and outage management for unit events. It is also essential to use the asset characteristic data stored in GenBase.
After all position data is collected, the inputs are sent to the stacking engine, which categorizes and orders positions, stacks them, and allocates them based on business rules. Finally, the transaction costing module produces results that users can quickly look up. These include transaction revenue, generation cost to serve the transactions, corresponding ISO credits or charges if applied, and other financial positions associated with the transaction or the unit.
The results of transaction costing can be used to evaluate native load, off-system sales margin, and avoided generation cost of purchases. This helps accountants with month-end close and traders assess the effectiveness of trading strategies.
Module 2: Assessing replacement costs and lost market opportunities with outage costing
The second module in post-analysis is outage costing. This module calculates the replacement cost of unit outages and derates and evaluates the loss of market opportunities due to those unit events. It leverages the power of PCI GenTrader. First, the baseline GenTrader study is run, an optimal commitment case with all the constraints. Then, a simulation study is run with all the unit derates or outages removed from the targeted unit. The replacement cost of the targeted unit is determined by calculating the delta in the portfolio revenues and expenses between the two studies.
Additionally, it can identify the units used to replace the constrained unit. It repeats this process for each constrained unit, then consolidates the result for the user to view in an HTML report.
Module 3: Measuring the distance from optimal operation with operational efficiency
The third module in post-analysis is operational efficiency. This module assesses how far the actual operation is from the optimal operation. It can detect systematic uneconomic decisions such as over-committing or under-committing units or committing uneconomic units. It can also quantify and measure the benefits and costs of adjusting operational strategies.
The solution used is like that of outage costing. First, a baseline GenTrader study is run, where the units are forced to operate at the meter readings (actual operation). Then a second study is run where the unit commitments are forced at the meter readings. However, GenTrader can re-optimize the dispatch with unit constraints considered (optimal dispatch case). The difference in cost between the two studies is the cost of suboptimal dispatch.
Finally, a third study, the optimal commitment case, is run where GenTrader can freely commit and dispatch units. The total cost of a suboptimal operation can be determined by comparing the actual operation with the optimal commitment case and summing the suboptimal dispatch and suboptimal commitment cost.
Module 4: How custom analysis works with merger savings
The fourth module in post-analysis is custom analysis. It can be adapted to run any pair of GenTrader studies and compare the difference in the unit commitment and dispatch, fuel consumption, portfolio P&L, and more.
A use case of the custom analysis is merger savings. Merger savings quantifies and evaluates the benefit and reduction of costs due to increased efficiency following a merger of two companies.
The solution used is like that of operational efficiency. First, a baseline GenTrader study is run to simulate the merger operation, and then a second study is run to simulate the standalone operation of the two companies as if they were not merged. The difference in cost between the two studies is the merger savings.
The future of post-analysis in energy market decision-making
Post-analysis solutions are an essential tool for portfolio optimization in the energy market. The four modules provided by post-analysis — transaction costing, outage costing, operational efficiency, and custom analysis — provide a comprehensive analysis of portfolio operations.
With support for clients from different markets and the ability to customize business rules, post-analysis is a valuable tool for any market participant. As a result, post-analysis will play an increasingly important role in portfolio optimization and decision-making as the energy market evolves.
If you’re interested in post-analysis, find more information on our portfolio optimization page.
