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This paper expresses economic flexible–securable operation (EFSO) in smart distribution networks (SDNs), including distributed generation and storage systems to produce green energy. This is structured as an optimization formulation to obtain a minimum expected operation cost, constrained by optimal power flow, environmental, security, and flexibility limits. Also, mathematical expressions of power sources and storage are presented. The scheme considers uncertainties related to load, price of energy, and generation power of renewable sources. The work adopts the point estimation method (PEM)-based stochastic optimization to model the uncertainties. It also uses to evaluate the flexibility index accurately while requiring short computation time (among the objectives of power system operation). Then, the results demonstrate its capabilities in enhancing various aspects of the network, including economic, environmental, operational, security, and flexibility indicators. The design helps derive flexibility conditions of almost 100%. It improves the operating cost, energy loss, voltage deviation, amount of pollution emission, and voltage security index by approximately 23%, 29%, 47%, 51%, and 13% compared with power flow studies.

In this paper, we proposed the distributed quantization and sparsity aware zero attracting least mean square (DQA-ZA-LMS) and its reweighted version (DQA-RZA-LMS) algorithms that can perform sparse spectrum sensing with the lowest power possible. The usage of the quantization aware diffusion adaptive networks has recently been proposed and they can be used in many possible mobile communicative applications. The sparsity aware feature of the proposed algorithm can help the network to track and estimate sparse random vectors that are shown to be the case with the spectrum of the new generation wireless communication systems such as 4G, 5G, 6G, and beyond. The spectrum sensing is considered in this paper to be performed by small cell eNode Bs (SC-eNBs) for the 4th generation long term evolution (LTE) and the next generation eNB (ng-eNB) networks for the 5th and 6th generation mobile communication systems that are scattered in an area collecting distributed quantized data from the environment and working collaboratively to estimate the sparse spectrum vectors. Our findings show that in comparison with the nonquantized version of the distributed ZA-LMS (DZA-LMS) and distributed regularized ZA-LMS (DRZA-LMS) algorithms, our proposed schemes perform considerably well using the quantized data and also reduce power consumption.

Point of common coupling (PCC) arrays are the most prominent and widely-used intermittent distributed generations (DGs). Due to the right-of-way, environmental, economical and other restrictions, the connection of these types of DGs to the preferred point of the distribution network is very difficult or impossible in some cases. Therefore, because of non-optimal locations, they may cause a voltage rise at the PCC. In this paper, a coordinated design of switchable capacitor banks (SCBs) with dynamic reconfiguration of the distribution network is proposed to avoid low- and high-voltage violations. The distribution network reconfiguration is implemented to mitigate the voltage rise at PCCs and capacitor banks (CBs) to solve the low-voltage problem. A novel method is presented for determining the optimal size of CBs. The proposed capacitor sizing method (CSM) effectively determines the optimal values of reactive power for the given nodes. The optimal locations of SCB are determined using particle swarm optimization algorithm. The 24-hour reactive power curve optimized by the proposed method plays a pivotal role in designing SCBs. The simulation results show that the implementation of the dynamic network reconfiguration and the placement of SCB is required to maintain a standard voltage profile for better employment of DG embedded distribution networks.