• Energy Research

AbstractThe increasing interactivity between energy carrier networks such as electrical, natural gas (NG), and heating can be appropriately used for improving the operation optimality along with overcoming some challenges of those systems. With such a view, this paper studies the practical issue of power transmission lines congestion management in integrated multicarrier energy systems (MCESs) and makes a comparison with the only electrical operation mode. The MCES consisting of electrical and NG network infrastructures with exchanged energy between them is modelled by considering the several local energy hubs and their electrical and heat loads. Rescheduling of energy sources (power plants and gas wells) and demand side management in both electrical and NG networks have been utilized as the two important tools in the developed congestion alleviation framework. The proposed method confirms that the provided flexibility in the integrated operation of the MCESs can lead to a remarkable reduction in the congestion management cost index. Implementing the proposed congestion management framework on an assumed test MCES with a combined 39‐bus New England power system and 20‐node Belgium NG system demonstrates its effectiveness and applicability for the system operators.

The main idea of this paper is to present a method for offering a power generation rate of wind power plants in the electricity market in the conditions, in which there is a lack or extreme shortage of information. In such a state, methods conventionally used in normal conditions are no more applicable. The methods that have been formerly presented for the conditions with severe uncertainty are mostly applied in local decision making, but in the method proposed in this paper, global decision making is considered. The proposed method is based on Liu's uncertainty theory, and the information related to uncertain variables is provided by experts. After developing a cumulative distribution function for expert data, Liu's distribution is calculated. Then, the values of uncertain variables are determined based on various belief degrees. After converting the uncertain model of the objective function into a certain equivalent model in the Liu space, the profit function should be maximized via the two-stage stochastic programming method. The data used are real and relevant to the Nord Pool market. Investigation of the simulation results for two different and specific days indicates the efficiency of the proposed strategy.

Abstract The development of renewable energy resources, distributed generation (DG), energy storage, and nonlinear controllable loads in distribution networks (DNs) have attracted attention towards state estimation (SE) in active distribution networks (ADNs). The energy management center (EMC) of DN is based on the results obtained from SE. ADN is intrinsically unbalanced. A two-step state estimation is applied to an unbalanced DN using a network reduction process (NRP) and a meter locating process (MLP) in this paper. Considering the shortage of measurements in DN, obtaining accurate primary information from network conditions enhances SE. Primary SE runs on the reduced network and its outputs are used as measurements to enhance total estimation (secondary SE). This method resolves the shortage of accurate measurements and elevates SE accuracy. The considered method is executed in a 37-bus unbalanced DN, and the results of the simulation are used to validate the proposed method.