Abstract
Time of use (TOU) rate has been regarded as an effective strategy to associate utility companies to avoid peak time financial risks and make the most profit out of the market, while most programs are not effective as expected to reduce peak time demand of residents. Exploring the impact factors of peak demand reduction (PDR) can help policy makers find reasons that weaken effects of programs and corresponding measures can be carried out to maximize the benefits. However, averaging quantitative indicators for program assessment and incomplete impactor analysis method in existing research show limitations of revealing the complex reasons behind it. In this paper, an association rule mining based quantitative analysis framework is built to explore the impact of household characteristics on PDR under TOU price making up for the deficiencies in current research. Firstly, a probability distribution based customer PDR characterizing model is proposed, in which difference-indifference model is adopted to quantify the effect of PDR and probability distribution fitting method is used to characterize the feature of PDR for households. Then a comprehensive association rule mining analysis using Apriori algorithm is presented to explore the impacts factors of PDR covering four categories of household characteristics including dwelling characteristics, socio-demographic, appliances and heating and attitudes towards energy. Finally, analysis results of a case study based on 2993 household records containing smart metering data and survey data illustrate that PDR level cannot be obtained simply based on the appliance's ownership and its usage habits. Socio-demographic information of households should be taken into consideration together; Internet connection and good house insulation contribute to the increase of PDR level. Moreover, the percentage of renewable generation for households also show a certain relationship with PDR. The proposed analysis framework and findings will associate retailer to improve the benefits of TOU programs and guide policy makers to design more efficient energy saving policies for residents.

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.

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.

Abstract
New energy market players such as micro-grid aggregators (MGA), distributed energy resource aggregators (DERA), and load aggregators (LAs) have all emerged to facilitate the integration of DERs into power systems. These players can participate in wholesale markets either individually or through distribution companies (Discos). In both cases, several operational challenges emerge for transmission system operators (TSOs) and distribution system operators (DSOs). Meanwhile, a transition is occurring from centralized wholesale markets into local energy markets (LEMs). A literature review shows that these LEMs are mostly modeled focusing on the coordination between DSOs and TSOs to meet demand in real-time operation using ancillary service markets and balancing markets. The main contribution of this paper is to model a local day-ahead energy market (LDEM) for optimal operation of a distribution network. This LDEM is cleared by the DSO with the aim of maximizing the social welfare of market players while satisfying the technical constraints of the network. To investigate the effectiveness of the proposed model, it is applied on the IEEE 33-bus network. Moreover, the effect of technical constraints of the network on the distribution locational marginal price (DLMP) is studied.

Abstract
Microgrids are often designed as energy systems that supply electrical and thermal loads with local resources such as combined heat and power units, renewable energy sources, diesel generators, and others. However, increasing interaction between natural gas and electrical systems, in addition to increased penetration of natural gas fired units gives rise to both opportunities and challenges in microgrid operation scheduling. In this paper, the energy hub concept is used to construct a scenario-based model for the optimal scheduling of electrical and thermal resources in a microgrid with integrated electrical and natural gas infrastructures. The objective function of the proposed model minimizes the expected operation costs while considering all network constraints and uncertainties. The natural gas and electricity flow equations are linearized and formulated as a mixed-integer linear programming problem. Scenarios associated with stochastic variables such as renewable generation and electrical and thermal loads are generated using the corresponding probability distribution functions and reduced using a scenario reduction technique. The proposed model is applied to an energy hub-based microgrid and the simulation results demonstrate the effectiveness of the approach. Furthermore, the benefits of implementing electrical and thermal demand response schemes are quantified. Also, more in-depth analyses for this network-constrained model are performed, including natural gas flow rate variations, natural gas pressures, power flow, and nodal voltages.

Abstract
In this paper, a new method for optimal sizing of distributed generation (DG) is presented in order to minimize electricity costs in smart microgrids (MGs). This paper presents a study of the effect of wholesale electricity market on smart MGs. The study was performed for the Ekbatan residential complex which includes three smart MGs considering high penetration of renewable energy resources and a 63/20 kV substation in Tehran, Iran. The role of these smart MGs in the pool electricity market is a price maker, and a game-theoretical (GT) model is applied for their bidding strategies. The objective cost function considers different cost parameters in smart MGs, which are optimized using particle swarm optimization (PSO). The results show that applying this method is effective for economic sizing of DGs.