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This article presents a novel linear programming (LP) based two-stage stochastic approach for microgrids (MGs) under uncertainties. In this regard, the day-ahead programming of dispatchable resources in MG was modeled, considering the uncertainties in demand loads, and upstream network electricity price. Moreover, the inherent stochastic nature of wind and solar resources as well as the environmental aspects were modeled to find a realistic solution. Also, real-time pricing (RTP), demand response (DR) program considering the energy storage system (ESS) as a DR option was implemented on an MG as a smart customer. The extensive form of the two-stage stochastic recourse model was properly implemented for dispatchable and non-dispatchable resources. Furthermore, scenario generation and reduction procedures were realized with autoregressive and moving average (ARMA) model-based time-series (TS), and backward reduction (BR) method by the Kantorovich distance (KD), respectively. The simulations on a grid-connected MG, including micro-turbine (MT), fuel-cell (FC), wind-turbine (WT), photovoltaic module (PV), and ESS were reported in operational cases based on one month of real recorded data for wind speed, solar irradiance, demand load, and upstream network electricity price. The results for the next day in real-time confirmed the accuracy of the developed optimization methodology. [ABSTRACT FROM AUTHOR]

• Presenting a complete model of an EH contains RES to investigate the OPGF problem. • Modeling the operation limits of CHP, and ramp rate of generators. • Considering demand response programs for both electrical and thermal loads. • Analyzing several EHs, allocated in various buses to minimize the operation costs. • Applying the GAMS software to modified integrated power and gas IEEE14-bus system. Energy hubs (EHs) can help connect different types of energy and create a multi-carrier energy network (MCEN), thereby promoting energy network operation flexibility. Due to the connection of different types of energy in these networks, reduction of operating costs has become a controversial challenge. Herein, the optimal power and gas flow (OPGF) problem in an MCEN was investigated in the presence of wind and solar power sources, electrical and thermal energy storage (EES and TES), and electrical and thermal demand response programs (EDRP and TDRP). In the mentioned EHs, the operating costs of electric and gas networks were minimized, and the operation of EHs was simultaneously investigated. All constraints of the electric and gas networks and different equipment, including combined heat and power (CHP) units, were also considered. The simulation results in GAMS showed that the simultaneous optimization of the operation cost of the case study network, including 14 electric buses and 9 gas buses, during a 24-hour period was reduced up to 4%. Also, due to the lower price of gas, its consumption has increased by about 5%, and this ultimately decreases the total cost of optimal energy distribution of the network compared to the case in which the mentioned strategies and programs were not implemented. [ABSTRACT FROM AUTHOR]

• Improving the voltage in a wind farm by a self-corrective static VAR compensator. • Considering the steady-state and transient modes under harsh faults in analysis. • Enhancing the system performance by employing an optimized Battery Energy Storage. • Reporting the results in different cases, with or without the mentioned equipment. Maintaining synchronism and voltage stability, especially in the presence of wind farms, has a crucial role in confirming the reliability requirements of the power grid, as the natural fluctuations of wind speed result in intermittency and uncontrollability of power generation. To this end, an optimized Battery Energy Storage System (BESS) was suggested to make an equilibrium between the generation and consumption in wind farms under harsh conditions. Additionally, the instability of wind farms is very likely after transmission line faults occur. To overcome this problem, in this paper, the impact of a constant capacitor bank, connected in parallel with the wind generators, is investigated. Also, a novel self-corrective Static Volt-ampere reactive Compensator (SVC) is proposed to overcome the variations of voltage and damp the reactive power oscillations after occurring the faults. The proposed SVC compares the network voltage profile as the reference voltage with the voltage deviations in a control block diagram. For this purpose, three similar wind farms of Permanent Magnet Synchronous Generator (PMSG) type are considered in a wind park. The simulation case studies are performed in Matlab/Simulink under different conditions of transient and steady-state voltage stabilities in the presence of proposed BESS including, only capacitor bank (without self-corrective SVC), and with self-corrective SVC. The results authenticate the ability of the proposed SVC strategy to correct the voltage deviations, and voltage stability improvement of the under study system. [ABSTRACT FROM AUTHOR]

In this work; we suggest a new intelligent optimization method to solve the optimal energy management issue in the renewable micro-grids (MGs). The proposed method is constructed based on the harmony search (HS) algorithm as well as a novel adaptive modification method to solve the problem effectively. The proposed modification method consists of three sub-modifications to provide the algorithm with powerful tool to escape from the local optima of the problem with fast convergence. The feasibility and satisfying performance of the proposed method is examined on a typical MG including different types of renewable energy sources such as photovoltaic, fuel-cell, micro-turbine, wind turbine and battery as the storage device. Comparative studies demonstrate the consistent superiority of the proposed method over the other well-known algorithms in the area. [ABSTRACT FROM AUTHOR]

• Applying the GA-PLB strategy to limit the vast search space of the UCP by focusing on only some feasible solutions. • Initial checking conditions of MDT, MUT, and SR which reduce the computing time effectively. • Arranging the unit combinations and output power of thermal power plants, stored (injected) power in (by) ESSs applying GA. • Applying TOAT to overcome the hardness of uncertainty modeling in UUCP. The main purpose of unit commitment problem (UCP) is to find the optimal operation cost (OOC) by considering power system constraints. In this paper, an uncertain UCP (UUCP) is proposed in the presence of energy storage systems (ESSs) via applying genetic algorithm-priority list-based (GA-PLB) strategy and considering operational constraints such as power balance, minimum down time (MDT), minimum up time (MUT), and spinning reserve (SR). To find the robust optimal scheduling due to modeling the uncertainty of renewable energy sources (RESs), the Taguchi orthogonal arrays technique (TOAT) is applied. Furthermore, to consider the environmental concerns, the emission produced by thermal power plants is modeled using the emission cost function as a term of the objective function. The proposed model is tested on the standard cases of IEEE 10, and 38 units and the results are reported. The obtained results confirm that the proposed approach can effectively reduce the OOC. One of the main advantages of the proposed method is the reduction in computational burden due to the modeled uncertainties in solving the UUCP compared to the other proposed methods based on iterations while improving the final solutions. [ABSTRACT FROM AUTHOR]