A Novel Stochastic Framework Based on Cloud Theory and θ-Modified Bat Algorithm to Solve the Distribution Feeder Reconfiguration

Abstract

Distribution feeder reconfiguration (DFR) is a precious operation strategy that can improve the system from different aspects including total cost, reliability, and power quality. Nevertheless, the high complexity of the new smart grids has resulted in much uncertainty in the DFR problem that necessities the use of a sufficient stochastic framework to deal with them. In this way, this paper proposes a new stochastic framework based on cloud theory to account the uncertainties associated with multiobjective DFR problem from the reliability point of view. Cloud theory is constructed based on fuzzy theory and probability idea. In comparison with the Monte Carlo simulation method, cloud models can give more information on the uncertainties associated with the problem. This special aspect of cloud models makes it possible to integrate the fuzziness and randomness of qualitative concepts through the cloud drops and then transforms them to the quantitative model. In order to solve the proposed problem, a fast and powerful optimization technique is required. To deal with this issue, a new optimization algorithm designated as θ -bat algorithm is proposed in this paper. The feasibility and satisfying performance of the proposed method are examined on the 32-bus and 69-bus IEEE distribution test system.Index Terms-Cloud theory, distribution feeder reconfiguration (DFR), θ -modified bat algorithm (θ -MBA), uncertainty.

Efficient integration of plug-in electric vehicles via reconfigurable microgrids

“…Constraints (18) and (19) show the maximum/minimum charge and discharge power rates, respectively. Constraint (20) represents the hourly energy balance in PEV batteries and (21) shows the amount of power charged or discharged in each PEV fleet. Since the scheduling time resolution is assumed to be 1 h, Dt is considered to be 1 in this equation.…”

“…The positive effect of optimal reconfiguration on the network power losses is investigated using different methods such as brute-force approach [12], neural network [13], optimum flow pattern [14], graph theory [15], heuristic techniques [16], expert systems [11], ant colony optimization algorithm [17] and hybrid simulated annealing algorithm [18]. The benefit of reconfiguration on other objectives such as enhancing load balance [19], improving voltage profile [20], reducing total system cost [21] and enhancing system reliability [22] are further discussed in the literature. However, the important role of reconfiguration in improving microgrid's operational viability is still an untapped area of research.…”

Coupling Neighboring Microgrids for Overload Management Based on Dynamic Multicriteria Decision-Making

“…Calculate ALS 2 from (12) above-mentioned uncertainties can be compromised by the historical statistics, the standard deviation is another uncertain parameter [35], [36]. This issue can be solved by considering where E x is the expected (mean) value, E n is the entropy (variation range), and H e is the hyper entropy (divergence of variation range) [35], [36].…”

“…This issue can be solved by considering where E x is the expected (mean) value, E n is the entropy (variation range), and H e is the hyper entropy (divergence of variation range) [35], [36]. H e uses a normal distribution to model the entropy of E n and C L uses a normal distribution to model uncertainty x with the assumed mean of E x and standard deviation of E n .…”

A Novel Combined Approach for Daily Electric Load Forecasting Based on Artificial Neural Network and Modified Bat Algorithm

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