OPTIMIZATION TASKS OF THE COMBINED ENERGY SYSTEMS BY ECONOMIC INDICATORS
The purpose of the paper is to solve a multicriteria optimization problem for a local energy systems (LES) with renewable sources (RES). The primary objective of traditional energy is to minimize the cost of electricity, but in case of renewable energy systems the reliability of energy is at the forefront, given by changing nature of the RES generation, such as wind and solar power. The subject of the study is the proportions of wind, solar generation and energy storage systems, which provide the set requirements for reliability with minimal cost of electricity. A feature of this study is the consideration of uncertainty in the modes of consumption and use of renewable energy sources. Research methods include the application of a statistical approach and the simulation of random processes for stochastic optimization. Historical (statistical) data on energy consumption and climatic factors that influence generation are used as input. Not only average values but also variance indicators (variance, distribution density, marginal deviations) are significant for reliability indicators. Uncertainty related to renewables can lead to uncertain operational costs. An additional source of uncertainty is the variable nature of electricity consumption. The risks of the project are the danger of losing some of the energy or not meeting the needs of the consumer. Having energy accumulators reduces the risks, but adds to the cost of the project. The result of the study is a way to optimize such a system. Risk can be defined as the variance of a random component, then the target functions will be the cost of energy and the standard deviation of the power imbalance. To find the optimal solution under two criteria, a mathematical model of the current state of the system with different RES and accumulation system was applied. The envelope of the multiple states is the line of possible optimal values. The limitations of the task are the requirements for reliability, such as the confidence probability of an generation and consumption imbalance. Ref. 11, tab. 2, fig. 3.
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