Risks are inherent in any situation of practical interest. We tend to see them as something negative, in particular because they bring unexpected effects – and as we still have our evolutionary instinct of surprises, we don't really like surprised, particularly in corporate contexts. Uncertainty is troublesome because it limits the predictability we are accustomed to in moments of calm.
In fact, uncertainty is always present: in natural phenomena and the instruments we use to understand them; in the information we consume about our environment and in the effects of the decisions we make; in the behavior of markets and in the models we develop to analyze them.
Risk x Uncertainty
Risk and uncertainty are used almost as synonyms, but the represent somewhat different concepts:
Uncertainty represents any situation in which future events are apparently unknown to the decision maker – situations of uncertainty are neither quantifiable nor controllable.
Risks are quantifiable and therefore can be estimated and controlled – the effects of our actions, when in situations of risk, can be mapped according to probability functions. In fact, the risks would represent the probability of destroying or creating value for the decision maker.
Harry Markowitz, an American economist, applied these concepts clearly and pragmatically in his work on portfolio optimization, which won him the Nobel Prize in Economics in 1990. In his work the risk of financial assets is quantified as a function of the deviations of their returns from an historical average – in statistical terms, its variance.
The relationship between risk and return, long known intuitively, was thus formally defined and and the door was opened to increasingly sophisticated analytical models. Risks could be analyzed, quantified and controlled. More "risky" investments and investments having higher expected returns could be balanced with other initiatives that are less uncertain but that conferred limits – or reduced exposure – to potential losses, facilitating the valuation of portfolios, projects, and organizations.
Management of Risks in the Management of Energy and Utilities
The evolution risk assessment models, the development of new and more robust quantitative measures (e.g., Value at Risk), and the incorporation of analytical techniques (e.g. Monte Carlo simulation) in user-friendly computational tools have helped to popularize the discipline of Risk Management and to make its application essential in decision-making processes of high relevance.
In the management of energy and utilities, the management of risks is fertile ground with extensive possibilities for value creation (or reduction of the changes of loss of value). In the context of Industry 4.0, production units are fully used, generating large volumes of data that can provide information, beyond the levels of production and consumption of energy inputs, on the efficiency and resilience of processes, the condition of productive assets, and the opportunities for reducing costs and losses.
However, any physical instrument is of course uncertain, and consequently the information it produces is also uncertain. Knowing these levels of uncertainty and controlling them – by proper calibration, application of data quality heuristics, network measurement redundancy, and reconciliation of information on energy and mass balances – increases the chances of obtaining good results from operational and energy efficiency initiatives.
Similarly, in the functions of energy and utilities planning, risk management offers interesting opportunities, especially when uncertainties are observed in the information involved and in medium- and long-term effects.
For example, the future levels of energy demand of an industry depend on various factors, in particular the production levels required to meet the expectation of market demand and the energy consumption levels of the assets involved in the production of those goods.
Both the behavior of the market and the energy behavior of production equipment are uncertain, but can be modeled as random variables that represent the levels of risk and expected values.
For their part, future electricity rates can vary as a function of many other random variables: rainfall, reservoir levels, expectations of increases in economic activity and energy consumption, different perspectives on the evolution of production capacity and the transmission system, in addition to the current political situation.
Such elements also directly influence the risks associated with contracts in the free energy market. Such risks can be quantified financially as a function of the levels of demand and the respective rates contracted by an organization given the values of energy commanded in the spot market. The negotiation of parameters like price, flexibility, duration, and seasonality directly influence the conditions of risk and of the attractiveness of contracts.
Similarly the establishment of energy contracts and investments in production capacity and in the capacity of energy co-generation also have their value given by elements of risk associated with demand levels and future prices. And yet the use of these same assets at economically efficient levels depends on the characteristics of production programs, the availability of energy inputs, and short-term rates, among many other elements that are no less uncertain.
In all cases the different results arising from the realization of risk variables can be modeled as scenarios representing known states with estimated probabilities and impacts and that make it possible to determine the most appropriate strategies.
The analysis and determination of ideal policies for each scenario can be made both by qualitative techniques like "what if?" planning popularized by Shell and 4by quantitative techniques, both offering opportunities for understanding, prioritization, and mitigation actions for identified risks.
For example, if an organization's strategy is to diversify its energy matrix in the long term, it can be interesting to evaluate different future scenarios for availability and for the prices of different sources of energy, considering technical, environmental, and market uncertainties as well as uncertainties of competitive strategy and of politico-economic perspectives. For each scenario various options of investment in co-generation, in contracting in the free market, and in efficiency can be prioritized – and revisited when the triggers that characterize each scenario arise.
As we have seen, uncertainty is a characteristic inherent to any practical decision-making process, and it is fundamental that any decision maker have in hand the knowledge and tools needed to constructively handle the risks inherent in energy and utilities management processes.
The Viridis platform for energy and utilities management provides capabilities to directly support the management of risks associated with functions of energy and utilities management in industrial and administrative organizations, covering monitoring, planning, contracting, costing, measurement and verification, simulation, and optimization of the consumption and generation of energy. The monitoring functions permit the capture and analysis of energy performance indicators of assets at any organizational level, taking into account the uncertainties associated with measurements, the balancing of networks of measurement, and the reconciliation of mass and energy balance data. The integration of monitoring data with planning functions facilitates the development of robust models for predicting consumption, which permits the analysis of different scenarios of future consumption, and consequently the exposure of the organization to risks – operational and financial – associated with short- and long-term energy contracts. The analysis of scenarios also promotes the identification and quantitative assessment of risks related to the results of energy efficiency projects, contributing to the proper valuation of these investments. Click here to find out more about our solutions.