Exploring the trade-off between competing objectives for electricity energy retailers through a novel multi-objective framework

Energy retailer is the intermediary between Generation Companies and consumers. In the medium time horizon, in order to gain market share, he has to minimize his selling price while looking at the profit, which is dependent on the revenues from selling and the costs to buy energy from forward contracts and participation in the market pool. In this paper, the two competing objectives are engaged proposing a new multi-objective framework in which a ε-constraint mathematical technique is used to produce the Pareto front (set of optimal solutions). The stochasticity of energy prices in the market and customer load demand are coped with the Lattice Monte Carlo Simulation (LMCS) and the method of the roulette wheel, which allow the stochastic multi-objective problem to be turned into a set of deterministic equivalents. The method performance is tested into some case studies

Clustering of electrical load patterns and time periods using uncertainty-based multi-level amplitude thresholding

This paper proposes a novel model to cluster similar load consumption patterns and identify time periods with similar consumption levels. The model represents the customer's load pattern as an image and takes into account the load variation and uncertainty by using exponential intuitionistic fuzzy entropy. The advantage is that the proposed method can handle the uncertain nature of customer's load, by adding a hesitation index to the membership and non-membership functions. A multi-level representation of the load patterns is then provided by creating specific bands for the load pattern amplitudes using intuitionistic fuzzy divergence-based thresholding. The typical load pattern is then determined for each customer. In order to reduce the number of features to represent each load pattern with respect to the time-domain data, the discrete wavelet transform is used to extract some spectral features. To cope with the data representation with fuzzy rules, the fuzzy c-means is implemented as the clustering algorithm. The proposed approach also identifies the time periods associated to different load pattern levels, providing useful hints for demand side management policies. The proposed method has been tested on ninety low voltage distribution grid customers, and its superior effectiveness with respect to the classical k-means algorithm has been represented by showing the better values obtained for a set of clustering validity indicators. The combination of load pattern clusters and time periods associated with the segmented load pattern amplitudes provides exploitable information for the efficient design and implementation of innovative energy services such as demand response for different customer categories

A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company

In order to supply the demands of the end users in a competitive market, a distribution company purchases energy from the wholesale market while other options would be in access in the case of possessing distributed generation units and interruptible loads. In this regard, this study presents a two-stage stochastic programming model for a distribution company energy acquisition market model to manage the involvement of different electric energy resources characterized by uncertainties with the minimum cost. In particular, the distribution company operations planning over a day-ahead horizon is modeled as a stochastic mathematical optimization, with the objective of minimizing costs. By this, distribution company decisions on grid purchase, owned distributed generation units and interruptible load scheduling are determined. Then, these decisions are considered as boundary constraints to a second step, which deals with distribution company's operations in the hour-ahead market with the objective of minimizing the short-term cost. The uncertainties in spot market prices and wind speed are modeled by means of probability distribution functions of their forecast errors and the roulette wheel mechanism and lattice Monte Carlo simulation are used to generate scenarios. Numerical results show the capability of the proposed method