Abstract

Abstract

Abstract
Congestion in transmission lines is an important topic in power systems and it continues to be an area of active research. Various approaches have been proposed to mitigate congestion especially immediate ready ones such as Congestion Management (CM) and Transmission Switching (TS). Using either of the two or their combination (CMTS) may have undesirable consequences like increasing operational costs or increasing the number of switching of transmission lines. More switching aggravates system reliability and imposes extra costs on the operator. In this paper, a multi-objective model is introduced which reduces overall operation costs, the number of switching in transmission lines, and the congestion of lines, compared to available approaches which employ congestion management and TS simultaneously. To verify the performance of the proposed model, it is implemented using GAMS and tested on 6- and 118- bus IEEE test systems. A benders' decomposition approach was employed. © 2019 IEEE.

Abstract
This paper presents an operation strategy of energy hubs in the presence of electrical, heating, and cooling demand as well as renewable power generation uncertainties. The proposed strategy can be used for optimal decision making of energy providers companies, as well as, other private participants of hub operators. The presence of electrical energy storage devise in the assumed energy hub can handle the fluctuations in the operating points raised by such uncertainties. In order to modeling of hourly demands and renewable power generation uncertainties a scenario generation model is adopted in this paper. The considered energy hub in this study follows a centralized framework and the energy hub operator is responsible for optimal operation of the hub assets based on the day-ahead scheduling. The simulation result illustrates that in the presence of electrical energy storage devices the optimal operation of hub assets can be attained. © 2019 IEEE.

Abstract
This paper demonstrates day-ahead operation of power systems in the presence of a Demand Response Program (DRP) for serving exact amounts of demanded energy over the operational horizon. The proposed two-stage model features a here-and-now framework for shaping the aggregated demands during operation. First, the day-ahead scheduling problem is solved by adopting Unit Commitment (UC) to determine the generation level of power generation units as well as the Locational Marginal Prices (LMPs). Afterwards, the obtained LMPs are considered as the Time of Use (ToU) for the second step of the scheduling and reshaping the demanded loads of each aggregator. A new methodology is provided in this paper to estimate the reaction of consumers behavior in terms of encouraging their participation in DRPs. Unlike classical models which adopt load reduction over the operational horizon, this model ensures that the total demanded loads will be served. Therefore, the total supplied energy for the operational period before and after DRP implementation remains unchanged. Meanwhile, the total payment of consumers will be considerably reduced by adopting this strategy. The simulation results on the 6-bus test system clarify that the proposed model can reduce the total operational cost as well as smoothen the load profile and nodal prices over the operational horizon. © 2019 IEEE.

Abstract
We present a novel fully distributed strategy for joint scheduling of consumption and trading within transactive energy networks. The aim is maximizing social welfare, which itself is redefined and adapted for peer-to-peer prosumer-based markets. In the proposed scheme, hourly energy values are calculated to coordinate the joint scheduling of consumption and trading, taking into consideration both preferences and needs of all network participants. Electricity market prices are scaled locally based on hourly energy values of each prosumer. This creates a system where energy consumption and trading are coordinated based on the value of energy use throughout the day, rather than only the market price. For each prosumer, scheduling is done by allocating load (consumption) and supply (trading) blocks, maximizing the energy value globally and locally within the network. The proposed strategy was tested using a case study of typical residential prosumers. It was shown that the proposed model could provide potential benefits for both prosumers and the grid, albeit with a user-centered, fully distributed management model which relies solely on local scheduling in transactive energy networks. © 2019 IEEE.