Abstract
This paper optimizes the price-based demand response of a large customer in a power system with stochastic production and classical fuel-supplied power plants. The implemented method of optimization, under uncertainty, is helpful to model both the utility functions for the consumers and their technical limitations. The consumers exposed to price-based demand can reduce their cost for electricity procurement by modifying their behavior, possibly shifting their consumption during the day to periods with low electricity prices. The demand is considered elastic to electricity price if the consumer is willing and capable to buy various amounts of energy at different price levels, the demand function being represented as purchasing bidding blocks. The demand response is seen also by the scientific literature as a possible source of the needed flexibility of modern power systems, while the flexibility of conventional generation technologies is restricted by technical constraints, such as ramp rates. This paper shows how wind power generation affects short term operation of the electricity system. Fluctuations in the amount of wind power fed into the grid require, without storage capacities, compensating changes in the output of flexible generators or in the consumers’ behavior. In the presented case study, we show the minimization of the overall costs in presence of stochastic wind power production. For highlighting the variability degree of production from renewable sources, four scenarios of production were formulated, with different probabilities of occurrence. The contribution brought by the paper is represented by the optimization model for demand-response of a large customer in a power system with fossil fueled generators and intermittent renewable energy sources. The consumer can reduce the power system costs by modifying his demand. The demand function is represented as purchasing bidding blocks for the possible price forecasted realizations. The consumer benefit function is modelled as a piecewise linear function.
Power system flexibility improvement with a focus on demand response and wind power variability
