Springer Science and Business Media LLC

This paper presents a stochastic scenario-based approach to finding an efficient plan for the electrical power distribution systems. In this paper the stochasticity for the distribution system expansion planning (DSEP) problem refers to the loads and wind speed behavior. The proposed DSEP model consist the expansion and/or construction of new substations, installation of new primary feeders and/or reinforcement the existing, installation of wind-distributed generation based, reconfiguration of existing network, and the proposed DSEP is solved considering uncertainty in electric demand and distributed generation. In this regard, a two-stage stochastic programming model is used, wherein the first stage the investment decision is made and the second stage calculates the expected operating value which depends on the stochastic scenarios. The mathematical approach is based on a mixed integer conic programming (MICP) model. By using this MICP model and a commercial optimization solver, finding the optimal global solution is guaranteed. Moreover, in this paper by using the Tabu Search algorithm and take the advantages of a stochastic conic optimal power flow model, an efficient hybrid algorithm is developed. With the aim of comparing the performance of the optimization techniques based on solution of MICP model directly and using a hybrid proposed methodology, they are tested in a 24-node distribution system and the results are compared in detail.

Solar energy has been considered as one of the leading renewable energy sources for electric power generation. Therefore, intending to deal with a low energy conversion efficiency of photovoltaic (PV) materials problems, artificial intelligence (AI) techniques are playing an essential role in enhancing the performance and reliability of photovoltaic systems. Consequently, many researchers have focused their studies on using AI applied to photovoltaic solar energy. Adaptative neural fuzzy inference system (ANFIS) has shown excellent performance and potential use among AI methods. Therefore, ANFIS architecture has been widely applied in PV systems, and many papers were found. However, a survey with classifications or comparisons was not detected. In this regard, this paper surveys the literature about ANFIS architecture applied to photovoltaic systems. And, to help the readers, the authors propose new categorization based on applicability. The six different categorizations are Solar irradiance forecasting; Photovoltaic output power estimation; Parameter identification for photovoltaic system sizing; Maximum power point tracking (MPPT); Inverter control; and Fault diagnosis photovoltaic systems. Furthermore, in each categorization, a comparison is made among the papers approached. Finally, a comparison among ANFIS architecture and other techniques also are presented in each categorization.

Despite the many benefits, the remote wind energy conversion systems (WECSs) that operate using self-excited squirrel cage induction generators (SCIGs) suffer from poor voltage and frequency regulation. The current study establishes an efficient and feasible grid forming control structure to enhance the self-excited SCIG-based WECS’s voltage and frequency regulation. Apart from a fixed parallel excitation capacitor, the presented framework adopts a static compensator (STATCOM) as a reactive power (RP) compensator. The STATCOM’s operation frequency is forced in an open loop control to be constant, which makes the synchronous frequency of the SCIG match with the STATCOM in a steady state. In addition, the output voltage magnitude of the STATCOM is adjusted to balance the inductive and the capacitive RPs and improve the system voltage regulation by driving the needed RP. Moreover, this paper presents an active power (AP) control approach in which the speed of the SCIG’s prime mover is regulated to make the STATCOM’s AP zero. The designed power controller enhances the SCIG-based WECS’s frequency regulation by balancing the produced and consumed active powers. Simulation and experimental results reveal the presented framework’s accuracy and efficiency.

A first order vector autoregression topology was used to model and predict Turkey’s net electricity consumption in the future. Input variables for the model were the annual values of electricity consumption along with four demographic and economic indicators such as, population, gross domestic product, imports and exports. Output variables were the one-step-ahead values of the same variables. First, polynomial regressions were used to determine and remove the trend components of all these five variables. Then, principal components regression method was applied to evaluate the coefficients of the vector autoregression model. Electricity consumption of Turkey was modeled using annual data from 1970 to 2016 and the model was used to predict future consumption values until year 2030. Singular value decomposition was used to determine the number of important dimensions in the data. This approach yielded a significant reduction in the dimensionality of the problem and thus provided robustness to the predictions. The results showed the feasibility of applying principal components regression method to vector autoregression model for electricity consumption prediction.

The Adrar site is among sites of the highest solar radiation potential in the world. This region is characterized by high ambient temperature in the summer, which in some days is exceed 45 °C, where the high ambient temperature is affects the solar cells.The aim of this paper is to assessments the PV cells types mono crystalline, poly crystalline, micro crystalline amorphous and tripple junction for meteorological variables of Adrar environment.These models were tested by using input data from meteorological station of (URERMS-Adrar),and these inputs are used in software for PV system (PVsyst) to identify the characteristics of these types of PV module. The comparison between PV cells type mono-crystalline and poly-crystalline of same maximum power (250 W) it show that the cells type poly crystalline it more affected by the temperature of than mono crystalline. Finally, have reached that the assessments the performance of PV module is linked to weather (clear sky, overcast sky, shading, cold and hot).

The objective of this paper is to propose a proportional-fair energy exchange framework in a prosumer microgrid system taking into consideration the trading preferences of buyer microgrids. In fact, in a multi-microgrid system, there are potential seller microgrids with energy surplus and buyer microgrids with energy demand. The buyer microgrids may have different and,sometimes, competing trading preferences. The proposed framework ensures that each buyer gets a part of its energy requirement fulfilled from its preferred suppliers. Simultaneously, it incentivizes local energy trading over the central high-cost transactions with the main central grid. To this end, a Knapsack problem inspired approach is first suggested to unravel and fix the optimal market preferences of buyer microgrids. Afterwards, a self-organizing, transparent and anti-greed by design Last Diminisher protocol, which is a procedure of fair Cake-Cutting problem, is used to fairly meet the conflicting energy demand of buyers. The proposed approach is evaluated and validated through simulations. It has been shown that the suggested protocol can achieve an efficient and fair energy allocation among interconnected microgrids while reducing the total energy buying cost compared to other methods used in the literature.