In today's world, the price of a kilowatt-hour is no longer determined in smoke-filled rooms by men in suits, but in digital clouds by algorithms executing thousands of operations per second. The transition of Greece and Europe to the so-called "Target Model" has transformed energy from a social good into a highly complex financial product. The pressing question is whether these algorithms work for the benefit of the consumer or if they represent a new tool for digital speculation threatening households and businesses.
The Digital Transformation of the Energy Market
The introduction of Artificial Intelligence (AI) into the energy market initially promised efficiency. These systems can predict demand with precision, optimize the use of Renewable Energy Sources (RES), and reduce grid losses. However, in practice, the complexity of the algorithms used in Energy Exchanges has created a "black hole" of transparency. These algorithms, based on machine learning models, analyze millions of data points—from weather conditions to geopolitical tensions—to determine the System Marginal Price.
The problem lies in the fact that the speed and nature of these decisions exceed human oversight capabilities in real-time. When electricity prices spike within minutes for no apparent reason, the answer often lies in an algorithm that "read" a trend and reacted preemptively. This creates a sense of injustice for citizens who see their bills rise even as sun and wind—the country's free resources—are in surplus.
The Greek Paradox and the Target Model
In Greece, the situation is further complicated by structural market weaknesses. Despite high RES penetration, prices remain stubbornly high. This is partly due to how Target Model management algorithms prioritize the last and most expensive production unit (usually natural gas) to meet demand. These algorithms are not "neutral"; they are programmed to maximize producer and trader surplus, according to market rules established at the EU level.
- The lack of depth in the Greek market makes algorithmic fluctuations more intense.
- Vertical players use advanced AI tools to predict competitor moves, often leading to synchronized practices that are difficult to prove.
- Energy poverty is exacerbated as algorithms do not consider social criteria, only economic data.
"Energy is the lifeblood of the economy, and when control of that blood is handed over to opaque algorithms, the risk of a social fabric thrombosis is real," market analysts state.
The Need for "Algorithmic Accountability"
To address this situation, simple bill subsidies are not enough. A new regulatory oversight framework is required, including "algorithmic accountability." Regulatory authorities, such as RAE in Greece, must be equipped with their own AI systems capable of monitoring and auditing energy company algorithms in real-time. Transparency in code and input data is essential to ensure the market operates competitively rather than predatorily.
Furthermore, the discussion must shift toward decoupling the price of electricity from natural gas, a move that would require reprogramming European market algorithms. Technology must return to its original purpose: serving societal needs in the most economical and sustainable way. The kilowatt-hour cannot remain a "bet" in a digital roulette; it must be treated as the foundation of modern living.
Conclusion: Toward Democratic Energy
The battle for electricity prices is now a battle of data and code. If we fail to democratize the technology managing our resources, we will remain hostage to a digital elite hiding behind "complexity." The energy of the future must be green, but above all, it must be fair. And justice in the 21st century is written in lines of code that must be open to public scrutiny.
