Distribution network expansion planning and DG placement in the presence of uncertainties

Hemmati, Reza; Hooshmand, Rahmat‐Allah; Taheri, N.

doi:10.1016/j.ijepes.2015.05.024

Cited by 84 publications

(51 citation statements)

References 20 publications

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“…The average annual investment cost coefficient of the DG is 0.1, and the power factor is 0.85. Considering the proportion of the DC load in each load node, the candidate nodes for the DG are 7,10,12,13,14,16,17,18,19,20,22,23,24,25,26,29,30,31, and 32. The minimum unit capacity of the grid-connected DG is 60 kVA, and if the grid-connected cardinal number is m (m = 0, 1, 2, .…”

Section: Node Proportion Of DC Load Node Proportion Of Dc Loadmentioning

confidence: 99%

Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics

et al. 2017

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With increased direct current (DC) load density and the penetration of a large number of distributed generation (DG) units in alternating current (AC) distribution networks (DNs); a planning approach that considers transforming some of the AC lines into DC lines and building the DC network is proposed. Considering the DG output uncertainty and the load fluctuation, a planning model for an AC/DC hybrid distribution network (DN) with the DG based on the construction of multi-scenario technology with timing characteristics is built. In the DG configuration planning model, the lines to be transformed into DC form the access location and the decision regarding the DC or AC form of the newly built lines are considered optimizing variables. The DG investment, the network and converters of the DG and load, the active power loss and the voltage stability are considered in the objective functions. An improved adaptive niche genetic algorithm based on the fuzzy degree of membership and variance weighting is used to solve the nested model. Finally, considering the improved electrical and electronic engineers 33 (IEEE33) node system as an example, the correctness and effectiveness of the proposed planning method are verified. Compared to the plan without transforming some of the AC lines into DC lines and building a DC network, more DG can be admitted, and the economic cost of the AC/DC hybrid DN is notably decreased when planning to transform some of the AC lines into DC lines and build a DC network. The active power network loss and the voltage stability index are similarly further optimized.

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Section: Node Proportion Of DC Load Node Proportion Of Dc Loadmentioning

confidence: 99%

Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics

et al. 2017

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“…It is based on the exclusion mechanism to exclude similar individuals in the process of evolution and to maintain the diversity of feasible solutions; therefore more solutions can be searched. The fitness of antibody to antigen can be represented by the reciprocal of the objective function shown in Equation (15). The smaller the objective function value of the antibody is, the closer it is to the optimal solution, and the higher the fitness is.…”

Section: Model Solutionmentioning

confidence: 99%

Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

et al. 2017

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Abstract:To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.

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“…The authors in [20], [21] proposed an evolutionary PSO to find the optimal investment in new equipment for the network while the uncertainties in demand and energy costs are treated with Monte Carlo simulation (MCS). A two-stage PSO algorithm was proposed in [22] for solving the DSEP problem where at the first stage, the reinforcement of the existing circuits and the allocation of DG were determined, and at the second stage, the demand growth was determined through probability curves and MCS. In [23], a genetic algorithm (GA) was proposed to determine the network reinforcement and the allocation of dispatchableand wind-based DGs, allowing the system to operate isolated.…”

Section: Introductionmentioning

confidence: 99%

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

Home-Ortiz

Pourakbari‐Kasmaei

Lehtonen

et al. 2020

IEEE Trans. Smart Grid

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This paper presents a mixed-integer conic programming model (MICP) and a hybrid solution approach based on classical and heuristic optimization techniques, namely matheuristic, to handle long-term distribution systems expansion planning (DSEP) problems. The model considers conventional planning actions as well as sizing and allocation of dispatchable/renewable distributed generation (DG) and energy storage devices (ESD). The existing uncertainties in the behavior of renewable sources and demands are characterized by grouping the historical data via the k-means. Since the resulting stochastic MICP is a convex-based formulation, finding the global solution of the problem using a commercial solver is guaranteed while the computational efficiency in simulating the planning problem of medium-or large-scale systems might not be satisfactory. To tackle this issue, the subproblems of the proposed mathematical model are solved iteratively via a specialized optimization technique based on variable neighborhood descent (VND) algorithm. To show the effectiveness of the proposed model and solution technique, the 24-node distribution system is profoundly analyzed, while the applicability of the model is tested on a 182node distribution system. The results reveal the essential requirement of developing specialized solution techniques for large-scale systems where classical optimization techniques are no longer an alternative to solve such planning problems.Index Terms-Distribution systems expansion planning, energy storage devices, VND-based matheuristic algorithm, mixed-integer conic programming, stochastic programming.

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Distribution network expansion planning and DG placement in the presence of uncertainties

Cited by 84 publications

References 20 publications

Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics

Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics

Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

A Mixed Integer Conic Model for Distribution Expansion Planning: Matheuristic Approach

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