Sharif Digital Repository

With the advent of small-scale heat and electricity producers in distribution energy systems, the interdependencies between energy carriers have been increased. Moreover, the rapid deployment of micro CHP, electric heat pumps, electricity-to-heat appliances etc., calls for new local market frameworks to be employed in distribution energy systems. In response, this paper presents a new energy market framework based on the concept of peer-to-peer negotiations to facilitate energy transactions between agents at the distribution level while addressing the interdependencies between different energy carriers. Moreover, linear optimization problems are proposed to investigate the optimal strategies... 

Space constraint is a limiting factor for the widespread commercial adaptation of switched reluctance generators (SRGs). Maximizing the output power and minimizing the dc bus filter size are thus highly desired. The output power profiles of a typical SRG are determined by applying various excitation angles. Excitation for maximum output power is obtained while limiting the rms value of the phase currents. The dc bus current of an SRG drive, operating in single pulse mode, is highly distorted by low-frequency ripples. Introducing a freewheeling angle in the excitation pattern can lower the current ripple factor. A novel transform is proposed to establish a relationship between the... 

The rapid deployment of district heating systems in local energy markets and increasing the number of small-scale heat producers along with the expansion of local electricity markets increase the need for a transaction framework to manage the transactions between local participants in both heat and electricity markets. This paper presents a peer-to-peer thermal energy transaction framework to manage the transactions between small-scale heat prosumers. This framework enables small-scale thermal energy producers and consumers to participate in the market as price maker agents. Moreover, the optimal strategy of heat market participants is determined by proposing a linear profit function for... 

Proliferation of Plug-in Hybrid Electric Vehicles (PHEVs) and integration of various distributed generation (DG) technologies have been recognized to play an undeniable role in modern power systems of the future. In order to effectively model the interactions of these two technologies, this paper develops a multi-criteria framework to coordinate the charging behaviors of PHEVs within an energy hub platform. In this regard, the desirable charging profiles from the viewpoint of both PHEV owners and hub manager are first captured and reported to the PHEVs Coordinator Entity (PCE). The PCE, then, runs an optimization framework in which several criteria including the PHEV owners’ convenience,... 

Reinforcement learning (RL) is a subset of artificial intelligence in which a decision-making agent tries to act optimally in an environment by controlling different parameters. There is no need to identify and mathematically formulate the environmental constraints in such a method. Moreover, the RL agent does not need prior information about future outcomes to act optimally in the current situation. However, its performance is adversely affected by the environmental complexity, which increases the agent's effort to choose the optimal action in a particular condition. Integrating RL and linear programming (LP) methods is beneficial to tackle this problem as it reduces the state-action space... 

Demand forecasting plays an important role as a decision support tool in power system management, especially in smart grid and liberalized power market. In this paper, a demand forecasting method is presented by using support vector regression (SVR). The proposed method is applied to practical hourly data of the Greater Tehran Electricity Distribution Company. The SVR parameters are selected by using a grid optimization process and an investigation on different kernel functions. Moreover, correlation analysis is used to find exogenous variables. Acceptable accuracy of load prediction is shown by comparing the result of SVR model to that of the artificial neural networks and the actual data,... 

This paper proposes a new experimental procedure for magnetic characterization of switched reluctance machines. In the existing methods, phase voltage and current data are captured and further processed to find the flux linkage. Conventionally, assuming zero initial flux value, the flux linkage can be found by integrating the corresponding voltage term. However, the initial flux value is usually unknown, e.g., it can be nonzero when the current is zero due to the residual flux effect, and, thus, imposes error in magnetic characterization. The proposed method addresses this issue by considering an additional equation in steady state. This method injects a low-frequency sinusoidal current to...