The smart Energy management system as a powerful tool is implemented to manage both demands and generation units. The Energy management problem in a Microgrid is usually formulated as a nonlinear optimization problem. According to nonlinear and discreet nature of the problem, solving it by a centralized method requires high computational capabilities. In this paper, two Distributed Energy management system called Alternating Direction Method of multiplier Predictor (ADMM) and Corrector Proximal Multiplier (PCPM) have been investigated in or(DER) to jointly schedule the central controller as well as local controllers. The algorithms consi(DER) optimal power flow equations within the Distributed Energy management problem. The proposed Distributed algorithms have been investigated on a typical MG and the efficiency of the algorithm has been evidenced through case studies. Simulation results show that the proposed method decreases the operational cost of MG. Also, the results evidenced that the ADMM has been converged faster and provided a lower operation cost if compared to the PCPM.

With the warming of the weather conditions and significant load growth in the distribution sector, peak management in power systems has become an essential issue in hot seasons, and demand response programs are one of the most suitable common methods for managing peak. In this article, the authors propose a new approach to network peak management in critical conditions by the distribution company. This approach is the optimal scheduling of Distributed Energy sources and various demand response Resources to reduce the network's peak consumption during critical peak hours and with the lowest cost. Demand response Resources in this article include the three critical peak pricing, incentive-based load curtailment, and emergency load response, which the distribution company implements. The problem is optimized using a robust complex number linear programming and consi(DER)ing the power uncertainty of renewable Resources. Providing a peak price 14% larger than the peak price of the normal tariff can bring a suitable load impact from the critical peak program customers in the network. The participation of customers in the load curtailment program in the beginning and end hours of peak management is equal to 30% and 20% of the customer's baseline. Consi(DER)ing the risk related to solar production, power contribution in this program increases by 117 and 74 kilowatts for the same hours. Adopting a risk-averse strategy increases the cost by 745 $ for the distribution company and the reason is the increase in curtailment of 120 kW through the emergency demand response program.

In this paper, a microgrid including a solar panel, a battery Energy storage system and a diesel generator as the backup source are optimally is designed. The microgrid is consi(DER)ed as a part of a distribution network, that could be consi(DER)ed as local area. As microgrids can operate in grid-connected mode, the proposed algorithm investigates the minimization of the distribution network power loss, the amount of imported power from the utility and the curtailed load in case of emergency. The main purpose of the proposed algorithm is to minimize the overall investment, replacement and operation and maintenance costs for a microgrid. This article suggests a Lightning Attachment Procedure Optimization Algorithm to optimally design the problem of sitting and scheduling of microgrids in distribution systems. Both problems are solved simultaneously. The proposed approach is applied on 33-bus test system, and the results are discussed. Fast convergence, best global answer finding, and robustness are the characteristics of the proposed method, which are concluded from the results and discussion.

In this paper, an attempt has been made to introduce a new control strategy including Plug-in Hybrid Electric Vehicle (PHEV) and Diesel engine generator to control the voltage and frequency of autonomous microgrids. The proposed control strategy has multiple advantages over the recent control methods in microgrids. The proposed method applies the primary and secondary frequency control strategy, simultaneously. However the secondary voltage control scheme is obligatory. In present study, Artificial Neural Networks (ANN) has been implemented for training and validating the advanced droop control (ADC). After ADC unit training, the inverter based DG can be installed in complex microgrids consist of several DGs and loads. Simulation results indicate the effectiveness and applicability of proposed method in controlling the voltage and frequency in autonomous microgrids.

Traditionally, conventional generation units were used to provide ancillary services e. g. reactive power support, and spinning reserve. Nonetheless, with the emergence of highly penetrated Distributed Energy Resources ((DER)s) systems and consi(DER)ing how beneficial they can be; it appears reasonable to use them as reactive power provi(DER)s. Therefore, this paper introduces a new procedure for (DER)s to participate in the reactive power market. To do that, an algorithm is proposed to calculate the deliverable reactive power capability of (DER)s from distribution networks to transmission systems. Furthermore, a reactive power procurement model is introduced to consi(DER) (DER)s participation in the reactive power market. Finally, to show the effectiveness, and validity of this method many case studies are carried out on 33-bus distribution and CIGRE 32-bus transmission test systems

The integration of Distributed Energy Resources ((DER)) in power systems can provide the opportunity to supply electricity to customers more efficiently and effectively. The (DER) includes Distributed Generation (DG) and Demand Side Management (DSM). In a smart grid incorporating automated control and Distributed Energy systems an effective DSM can alleviate the peak load and shift part of the demand to off-peak hours. The aim of this research is to assess the impact of the DG and the DSM on reliability of the smart distribution system by analyzing different case studies. Roy Billinton test system RBTS Bus2 is used to validate case studies which are performed as a part of this paper. For more practical consi(DER)ations, a modification of the RBTS Bus2 model is developed to assess the system reliability

This paper presents a multi-stage planning framework for analysis of stochastic Distributed Energy Resources ((DER)s) comprising of solar, wind, and battery storage. The existing models do not consi(DER) penetration level analysis in conjunction with sizing, placement, and economic assessment. The main objective of this research is to embed all these dimensions of system planning in one structure. The first stage involves reliability constrained component sizing. The second stage pertains to placement of (DER)s based on loss minimization and voltage profile. The third stage is the main thrust of this work which provides exhaustive economic evaluation and cost-benefit analysis. The novelty of this work lies in the consi(DER)ation of penetration level in backdrop of all three stages. The proposed formulation is implemented on a 33-Bus radial distribution fee(DER) located in Jaisalmer, Rajasthan, India. Four penetration levels viz. 10, 20, 40, and 60 percent have been investigated and analyzed un(DER) different planning scenarios. The results facilitate the determination of optimum penetration level.

In addition to the great benefits of Distributed generation (DG) Resources to power systems, there are some disadvantages. In spite of some benefits like decrease of received power from transmission grid, increment of DGs penetration can lead to voltage increase in distribution networks during off peak. Hence the recent efforts have been made to handle problems to increase the penetration of these Resources. The use of Energy storage systems (ESSs) is one of the ways to prevent defects may arise from use of DGs in power systems and can help to increase penetration of DGs in power systems. ESSs can save Energy during off peak and deliver it to the network in peak hours; hence, these equipment can reduce power losses and prevent voltage deviation during off peak by increasing load due to ESS charging.In this paper, first DG allocation and ESS placement are introduced then simultaneous placement of DG and ESS to reduce power losses in the distribution network are described. The proposed models are solved using genetic algorithm as optimization tool. The obtained results show that simultaneous placement could increase DG penetration compared to the separate allocation of these devices and provide further reduce of the losses in the distribution network.

In this paper, an innovative model for managing load demand based on the amount of power generated and market clearing predicted price, consi(DER)ing uncertainty parameters, is presented for non-dispatch able Resources, and load demand. Furthermore, a bidding strategy based on cooperative game theory is presented by taking into account the price uncertainties for determining the players’ pay-off function. The methodology presented for determining optimal supply and consumer resource bidding strategy is affected by the pricing behavior of other players, and is also based on maximizing their profit. The proposed framework for calculating the Nash equilibrium in a structure with several players in the constrained electricity market has been presented. The proposed framework is generally implementable over different game conditions in the electricity market and is based on cooperative game among participating players in the market, with discrete bidding strategies.