Predrag Marić

Electricity spot market simulation involving bilateral contracts hedging

The traditional electric companies operated as a regulated monopoly are separated in those for generation (GenCo), transmission (TransCo) and distribution (DisCo). Demand companies (DemCo), ISO (Independent system operator), and regulator are new companies which enable function of restructured electricity market. Regarding the time dimension, wholesale electricity markets are divided into the two groups: spot market and bilateral electricity market. A bilateral contract is an agreement between two parties to exchange electric power under a set of the specified conditions such as MW amount, time of delivery, duration, and price. Forward contract price predictability is the great advantage to spot price volatility, contracted parties are in that way hedged against such a risk, but the forward contracted price may be disadvantageous compared to the spot price. Generator and load can conclude a mutually beneficial and risk tolerable forward contract. In order to investigate GenCo strategies and possible benefit of bilateral agreement for two agents, model of electricity market with 6 GenCos and 5 DemCos is made in EMCAS. Three scenarios are simulated: first one is the base case where all GenCos are applying production cost strategy; in second case one GenCo is applying price-probing strategy; third scenario is case where bilateral forward agreement is conclude between one GenCo and one DemCo. Comparisons of the results are presented.

VOLTAGE AND POWER LOSSES CONTROL USING DISTRIBUTED GENERATION AND COMPUTATIONAL INTELLIGENCE

The paper analyzes the possibility of reducing active power losses in power system, constrained by regulated voltage levels, by implementing appropriate distributed generation capacity. The objectives of this paper were achieved by developing hybrid methods based on artificial neural network and genetic algorithm. Methods have been developed to determine the impact of different distributed generation power on all terminals in the observed system. The method that uses artificial neural network and genetic algorithm is applicable for radial distribution networks, and method using load flow and genetic algorithm is applicable to doubly-fed distribution network. For comparison purposes, additional method was developed that uses neural networks for the decision-making process. Data for training the neural network was obtained by power flow calculation in the DIgSILENT PowerFactory software on a part of Croatian distribution network. The same software was used as an analytical tool for checking the correctness of solutions obtained by optimization.

Applying an evolutionary strategy for multiobjective optimization of capacitor banks allocation in distribution feeders

This paper aims at applying the Evolutionary Strategy (ES) to capacitor banks optimal allocation problem in the case of multiobjective optimization. The multiobjective optimization includes several objectives such as power losses, capacitor costs and voltage deviation. The multiobjective optimization problem consists of conflicting objectives which need to be simultaneously optimized. Some other constraints are also considered. In the paper the following is proposed: the ES mutation operator adapted to the optimization problem, individual coding and generating of the initial population and finally, the mutation operator adapted to the optimization problem with discrete variables. The proposed approach has been evaluated on the test radial feeder and simulation results have been compared with those existing in the literature.