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A Bilevel Stochastic Programming Problem (BSPP) model of the decision-making of an energy hub manager is presented. Hub managers seek ways to maximize their profit by selling electricity and heat. They have to make decisions about: i) the level of involvement in forward contracts, electricity pool markets and natural gas networks and ii) the electricity and heat offering prices to the clients. These decisions are made under uncertainty of pool prices, demands as well as the prices offered by rival hub managers. On the other hand, the clients try to minimize the total cost of energy procurement. This two-agent relationship is presented as a BSPP in which the hub manager is placed in the upper level and the clients in the lower one. The bilevel scheme is converted to its equivalent single-level scheme using the Karush-Kuhn-Tucker (KKT) optimality conditions although there are two bilinear products related to electricity and heat. The heat bilinear product is replaced by a heat pricequota curve and the electricity bilinear product is linearized using the strong duality theorem. In addition, Conditional Value at Risk (CVaR) is used to reduce the unfavorable effects of the uncertainties. The effectiveness of the proposed model is evaluated in various simulations of a realistic case study. Index Terms -Bilevel stochastic programming, energy hub, hub manager, electricity pool, forward contract, Conditional Value at Risk. NOMENCLATURE Indices  Scenario index f Forward contract index t Time index k Forward contract block A. Najafi, H. Falaghi and M. Ramezani are with the

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Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

Summary Voltage and current imbalance have adverse impacts on power systems such as power loss increase, communication interference, and component lifetime reduction. This paper attempts to look at unbalance impacts from distribution system operator's (DSO) viewpoint in order to understand them and then mitigate the impacts. Since solving the unbalance conditions is influenced by the way the imbalance is defined, standard unbalance indexes are studied, and new indexes are proposed in this paper. Re‐phasing of the customers is selected to reduce the level of unbalance in distribution feeders. Due to the huge number of customers, a wide variety of choices can be selected for re‐phasing of customers, which makes the solution questionable. Therefore, discrete genetic algorithm (DGA) as a metaheuristic method has been utilized in order to distribute customers among the network phases optimally considering the fact that DSO has a limitation for the re‐phasing practice. The aim is to reduce the unbalance indexes and power losses throughout the network. Simulations have been carried out on a real test case network, which shows the importance of load balancing and its effects on the power losses, voltage profile, and current flow in that network. The effectiveness of the proposed indexes has also been demonstrated for the four‐wire multigrounded distribution system. Since re‐phasing of the majority of customers in distribution networks seems impractical, a re‐phasing limitation is also investigated in this paper, and some practical suggestions of optimal load balancing in a real‐world low‐voltage distribution system have been presented here. Results show the importance of load balancing in power loss reduction and voltage unbalance improvement in the low‐voltage four‐wire multigrounded distribution system. They also illustrate that by changing phases of a few customers, power losses and unbalance indexes will be improved significantly.

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The energy management of virtual power plants faces some fundamental challenges that make it complicated compared to conventional power plants, such as uncertainty in production, consumption, energy price, and availability of network components. Continuous monitoring and scaling of network gain status, using smart grids provides valuable instantaneous information about network conditions such as production, consumption, power lines, and network availability. Therefore, by creating a bidirectional communication between the energy management system and the grid users such as producers or energy applicants, it will afford a suitable platform to develop more efficient vector of the virtual power plant. The paper is treated with optimal sizing of DG units and the price of their electricity sales to achieve security issues and other technical considerations in the system. The ultimate goal in this study to determine the active demand power required to increase system loading capability and to withstand disturbances. The effect of different types of DG units in simulations is considered and then the efficiency of each equipment such as converters, wind turbines, electrolyzers, etc., is achieved to minimize the total operation cost and losses, improve voltage profiles, and address other security issues and reliability. The simulations are done in three cases and compared with HOMER software to validate the ability of proposed model. Energy management is a common and widely spread concept, including all measures that are planned and implemented to ensure the minimum amount of energy consumed in different activities. Trading, industries, and organizations have found themselves under high economic and environmental pressure in the last two decades to minimize their consumptions. Economic competition in the world market (especially the electricity market) and increasing the state of environmental regulations and standards in order to reduce climate pollutants are the most important factors in investment costs and exploitation of all organizations [3]. Actually, energy management is an important instrument in assisting various institutions to reduce their costs in order to meet these essential goals to survive and succeed in long term. The energy management of the VPPs faces challenges that make it complicated. These challenges include uncertainty in production, consumption, energy prices, and availability of network components. The smart grid increases the ability of the energy management system in the fields of overcoming uncertainties, aggregation of renewable sources, load responsiveness, monitoring, and network control [4,5].In [6], a pricing model for the electricity market of the previous day and the regulated market are proposed to maximize the expected profits of the VPP utilization, while the pricing problem is modeled as a two-stage stochastic program. In [7], a two-stage refinement optimization strategy has been proposed for pricing the VPP in day ahead and real time. The practicality of the decisions made and the...