Applications of Optimization Modeling in the Energy Industry

Optimization modeling helps energy industry stakeholders make efficient decisions and manage resources effectively. This article explains how these models are applied to economic analyses and simulations of electricity markets, resource planning, resource adequacy, risk management, and more.

Key Takeaways

• Optimization modeling is critical for market operations in the electric power industry, influencing generator scheduling, price formation, and resource allocation.
• Long-term resource planning and adequacy analyses rely on optimization to ensure cost-effective capacity management given reliability mandates and environmental regulations.
• Emerging applications of optimization, such as integrating hydrogen and assessing data center impacts, highlight its role in addressing the evolving challenges of the electric power sector.

Introduction

Optimization is a natural tool for analyzing electric power markets because these markets are built on optimization principles. Two of the core processes involved in the clearing of competitive wholesale electricity markets—unit commitment and economic dispatch—are formulated and solved as optimization problems (or co-optimization problems when reserves are solved for simultaneously).¹ Optimization models also play an essential role in price formation.² The solution to the economic dispatch problem produces shadow prices, or dual solutions, which form the basis for market-clearing prices.

Day-ahead and real-time markets use optimization to determine generator schedules, manage congestion, and ensure grid reliability. These models help create a seamless system where power generation and transmission are efficiently controlled to meet the demands of end-users.

Given all these use cases, it makes sense that optimization is prevalent as a tool for the analysis of electric power markets. From simulating the behavior of market participants to resource adequacy and other analyses, this article covers the various areas where optimization can be applied to electricity markets in order to provide valuable insights and guide decision-making.

Simulation of Market Behavior

Market simulation analyses can offer valuable insight into how the market will likely evolve or react in response to new regulations and specific market events and offer a best course of action to market participants making operating and investment decisions. Stakeholders use optimization models of electric markets to simulate the behavior of other market participants so they can make better-informed decisions themselves. By incorporating forecasts of future electric demand, natural gas prices, or solar and wind generation availability, optimization models can simulate expected generation schedules and energy imports into and exports out of the system. Such simulations help forecast the utilization of generation resources for different market conditions and provide a clear picture of the state of the market.

Simulating market conduct also allows market analysts to estimate strategic bidding behavior and identify potential anti-competitive actions.³ This capability is crucial for assessing market power and competition and for ensuring that the market operates efficiently and in a competitive manner. Depending on how the optimization model is formulated, it can simulate both a fair market behavior and potential price manipulations and offer valuable insights into the dynamics of the market and its participants.

In addition to evaluating market competitiveness, the ability to simulate market behavior is useful for stakeholders seeking to assess the performance of specific generation resources or portfolios of assets. Understanding operational profitability under different scenarios is essential for getting a fair valuation of physical assets and for understanding the risks of investing in those assets.

Resource Planning

Long-term capacity expansion planning, also known as resource planning, uses optimization to determine the generation resource additions and retirements that minimize costs and ensure an adequate level of capacity. This process is crucial for utilities that need to file integrated resource plans with state commissions. When done correctly and with appropriate assumptions, resource planning assists in minimizing the economic impact on customers while ensuring that the system is able to meet expected demand.

Optimization models play a key role in resource planning by optimizing the mix of resource types, including renewable sources, fossil fuels, nuclear, and energy storage. This helps satisfy expected demand and meet policy goals, such as renewable portfolio standards and emissions caps. By evaluating the trade-offs between different resource types, optimization can identify the most cost-effective and sustainable solutions for meeting environmental regulations and other policies.

Additionally, optimization can recommend transmission system improvements or expansions to resolve system congestion or as an alternative to generation expansion. This holistic approach ensures that the entire infrastructure is considered in the planning process and leads to more efficient and reliable electric power systems.

Resource Adequacy

As electric systems evolve and electric demand rises, ensuring there is enough capacity to meet demand during peak times is more important than ever. Systems now include a large and growing share of wind and solar generators, which are intermittent resources whose availability is highly dependent on weather. Additionally, as coal units retire, base load generation increasingly relies on natural gas, which needs to be delivered to plants through a system prone to congestion, especially during the winter months. Resource adequacy analyses help ensure the reliability of an electric system by testing its ability to meet demand under various conditions.

Optimization supports resource adequacy analyses by simulating operational scenarios to identify potential shortfalls during peak load hours or periods with low renewable generation. These shortfalls are evaluated using standard reliability measures such as loss of load expectation (LOLE) or expected unserved energy (EUE). Based on the characteristics of the periods with deficits (e.g., length, magnitude, reoccurring frequency of shortfalls), system operators use optimization models to determine the capacity and type of resources (e.g., storage, peaker, demand-side resources, etc.) that alleviate the expected shortages while minimizing the economic impacts on customers.

Evaluating capacity accreditation is also an important analysis in electric power markets. Optimization assists in finding an equitable amount of capacity credit for various resources. This ensures that there is enough capacity to meet future demand and that the capacity is compensated fairly. Whether capacity accreditation is used in an organized capacity market or in a utility’s internal modeling, it can send a signal to the market as to the desirability of having specific resources on the system.

Through these analyses, stakeholders can make informed decisions that enhance the reliability and efficiency of the electric power system.

Environmental Analysis

Policymakers rely on optimization models to design programs that incentivize clean energy generation and demand reductions, such as subsidies or tax credits. These programs play a crucial role in promoting sustainable practices and reducing emissions. By leveraging optimization, policymakers can create effective and efficient strategies that support environmental goals while minimizing economic impact.

From the perspective of energy market participants, optimization models are also indispensable for achieving environmental compliance in a cost-effective manner. Whether at the level of an electric system or that of a corporation, these models evaluate the tradeoffs between sustainability and economic impact and help stakeholders achieve environmental goals.

Risk Management

A risk management component can be added to any application of optimization in energy markets, but the results must be interpreted carefully. Deterministic optimization models solve over a single set of expected future inputs, providing stakeholders with point estimates of expected outputs. However, these models do not account for the riskiness of decisions or the variability of outcomes. Analysts can address this by incorporating uncertainty into their analyses following two alternative methodologies.

One approach is scenario analysis, where the same optimization model is run iteratively over many different exogenously generated scenarios of future input variables. Usually, one to three input variables are chosen as stochastic variables, which means that their values vary based on the scenario. This method helps stakeholders understand the range of possible outcomes for the stochastic variables and the best strategies to follow given a particular modeled scenario occurs. This type of analysis is appropriate for situations where analysts wish to evaluate the performance and risk profile of an exogenously chosen decision. It is, however, not the best framework to endogenously produce a recommendation that maximizes expected value or minimizes risk given the uncertainty in the stochastic variables.

As an alternative to scenario analysis, analysts can formulate a stochastic optimization model. While they are more time-consuming to solve, the structure of stochastic optimization models mimics reality in that some decisions have to be made before the values of the stochastic variables are revealed or observed. These models assume that, at the time the main decision needs to be made, decision makers know the range of possible values of the stochastic variables and have at least a general sense of the likelihood of their occurrence, but not their exact values. Consequently, the stochastic optimization framework, unlike scenario analysis, can recommend an optimal course of action given the uncertainty in the stochastic variables.

Another advantage is that stochastic optimization can account for the risk preferences of decision makers. By solving over many scenarios simultaneously, instead of each scenario in isolation as in scenario analysis, stochastic optimization offers the possibility to optimize not only based on the expected value of the revenues or costs but also based on a specific risk measure (e.g., conditional value-at-risk), reflecting the risk aversion or tolerance of the decision maker. If the decision maker is risk-averse, the model will recommend a more conservative solution. If, on the other hand, the decision maker is risk-seeking, the model will recommend a more aggressive course of action. This feature of stochastic optimization models leads to superior market insights and more robust operation and investment decisions compared to scenario analysis.

Emerging Applications of Optimization Modeling in the Energy Industry

Optimization is increasingly applied to cutting-edge areas of the energy industry. From integrating storage and renewable generation to evaluating the impact of data centers on electric demand and exploring hydrogen as a decarbonization strategy, optimization models are at the forefront of innovation. These emerging applications highlight the versatility and potential of optimization modeling in addressing the evolving challenges of the electric power industry.

Integration of Storage and/or Renewable Generation

Storage is often used to complement intermittent generation, and optimization plays a crucial role in evaluating the best combinations of storage and renewables to enhance reliability and profitability. By analyzing various scenarios, stakeholders can determine the optimal mix of storage and renewable resources, ensuring that the system retains or improves its reliability.

Energy storage optimization is particularly important for energy arbitrage, grid balancing, and meeting 24/7 carbon-free energy goals. Optimization models help stakeholders determine the most appropriate storage technology, such as lithium-ion, flow, or iron-air, as well as the optimal size, duration, and co-location with renewable resources. This ensures that utility-scale storage supports renewables effectively and manages peak demand efficiently.

Evaluating the Effect of Data Centers on Electric Demand

Data centers have become one of the fastest-growing sources of electricity demand, driven by the rapid expansion of cloud computing and artificial intelligence. These facilities require massive amounts of power to operate the servers they house, reshaping electric demand patterns and prompting power providers to rethink how to generate electricity and implement reliability strategies.

Adding to the complexity is the uncertainty that any data center demand will materialize or be able to connect to the grid. Optimization models can estimate the system improvements needed to accommodate the expected demand increase, such as transmission and generation upgrades. Stakeholders can thus make informed decisions that ensure the electric power system can accommodate the growing demand from data centers. Additionally, examining the costs of the system improvements helps stakeholders assess the likelihood of demand materializing and its impact on the system.

Integration of Hydrogen as a Decarbonization Strategy

Optimization modeling is essential for evaluating the role, impact, and integration of hydrogen within energy systems. Optimization helps determine the most economic and sustainable hydrogen production mix by balancing capital investments, operating costs, and regulatory constraints. This ensures that hydrogen production is both cost-effective and aligned with environmental goals.

Optimization models also play a crucial role in determining the best storage and distribution strategies for hydrogen. By minimizing costs and balancing trade-offs between energy efficiency, infrastructure costs, and geographic feasibility, these models help stakeholders develop robust and sustainable hydrogen infrastructure.

Finally, optimization models explore hydrogen’s interaction with electricity markets, particularly its role in seasonal energy storage and as a source of electric demand. Integrating hydrogen into multi-energy system optimization frameworks allows analysts to evaluate its economic competitiveness, role in deep decarbonization, and large-scale deployment feasibility.

Summary

Throughout this article, we have explored the various applications of optimization modeling in the electric power industry. From simulating market behavior and resource planning to ensuring resource adequacy and achieving environmental compliance, optimization models provide valuable insights and guide decision-making.

As the energy industry continues to evolve, optimization modeling will play an increasingly important role in shaping its future. By leveraging these powerful tools, stakeholders will be able to make superior business decisions that enhance the reliability, economics, and sustainability of the electric power system.

Frequently Asked Questions

What is optimization and why is it important in the energy industry?

Optimization is a mathematical approach used to identify the best possible decisions under given constraints, such as minimizing costs, maximizing efficiency, or ensuring reliability. In the electric power sector, it helps stakeholders make data-driven decisions on resource planning, market operations, investment strategies and policy evaluations, which leads to a more efficient and resilient energy system.

How does optimization improve power generation and dispatch?

Optimization helps electricity system operators determine the most cost-effective way to produce electricity to serve demand while meeting transmission and regulatory constraints. It is used in economic dispatch to minimize fuel and operating costs and in unit commitment models to schedule power plants optimally, balancing startup costs, ramping constraints, and reserve requirements.

Can optimization help address energy market inefficiencies and market power?

Yes, optimization is widely used to analyze market behavior, detect inefficiencies, and assess the potential for market power abuse. Game-theoretic models simulate bidding strategies, while equilibrium-based approaches help regulators design market rules that promote competition and fair pricing.

How does optimization support energy storage and demand-side management?

Optimization models help determine the best strategies for energy storage integration and operation, such as recommending capacity and duration of storage systems and when to charge and discharge batteries to maximize economic and grid benefits. It also assists in demand-side management, optimizing electricity consumption patterns for large industrial consumers and demand response programs.

What role does optimization play in hydrogen and other emerging energy technologies?

Optimization is critical for evaluating the role of hydrogen in future energy systems, including decisions around production methods, storage, transportation, and end-use applications. It also helps assess the competitiveness of emerging technologies such as small modular nuclear reactors, advanced battery systems, and virtual power plants.

What future trends in optimization modeling will impact the energy industry?

Advancements in AI and machine learning are increasingly being integrated with traditional optimization techniques to improve forecasting and decision-making. Additionally, as energy systems become more decentralized, real-time optimization and distributed energy resource coordination will play a larger role in grid management. The growing need for stochastic and robust optimization will also help address the increasing uncertainty in energy markets.