Elsevier

Renewable Energy

Volume 36, Issue 1, January 2011, Pages 179-188
Renewable Energy

A practical eco-environmental distribution network planning model including fuel cells and non-renewable distributed energy resources

https://doi.org/10.1016/j.renene.2010.06.019Get rights and content

Abstract

This paper presents a long-term dynamic multi-objective planning model for distribution network expansion along with distributed energy options. The proposed model optimizes two objectives, namely costs and emissions and determines the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on distributed generation units and network reinforcements over the planning period. An efficient two-stage heuristic method is proposed to solve the formulated planning problem. The effectiveness of the proposed model is demonstrated by applying it to a distribution network and comparing the simulation results with other methods and models.

Introduction

Distributed Generation (DG) is an electric power source connected directly to the distribution network. Different factors like electricity market liberalization, system reliability enhancement [1] and efforts to lower the global warming have made them more interesting for electricity sector. DG units are owned either by distribution network operators (DNOs) [2] or by non-DNO entities [3]. In either case, DG may offer DNOs more diverse, flexible, and secure options for managing their electricity systems to meet the load growth as an alternative to traditional network reinforcement. In recent years, many approaches have been proposed addressing DG planning and integration of them into distribution systems. The literature suggests a wide range of objectives, such as investment deferral in network capacity [4] and active loss reduction [5], reactive loss reduction [6], [7], reliability improvement [8], reducing the cost of energy required for serving the customers [8], increasing the incentives received by distribution network owners for using DGs [6], reducing the cost of energy not supplied and emission reduction [9], [5]. These studies have considered a variety of technical issues including voltage profile [4], [7], capacity limits of conductors [4], [7], substation capacity [4], [10], three phase and single phase to ground short circuit, and load modeling [7]. The reported models for DG planning can generally be divided into two major categories: static and dynamic models. In static models, investment decisions are implemented in the first year of the planning horizon [8]. In this category, the models are single or multi-objectives. The DG planning can be formulated as a single or multi-objective optimization problem. If only a single objective is of interest for the planner, then it is formulated as a single-objective problem. When many objectives are of interest, the problem is either translated to a single-objective problem (usually adding objectives into a single measure of performance [4]), or formulated as “true” multi-objective problem using Pareto optimality concept [10], [9]. In static models, [11], [12] consider network reinforcement along with DG investment. The value of multi-objective problem formulation of DG planning is that the objectives are usually in conflict or they cannot be easily converted into a single-objective problem [13]. It should also be noted that using the multi-objective methods can provide a decision making support tool for the planner that is able to justify its choices clearly and consistently [14]. In this paper, a planning model for DG-planning problem is formulated which is not only multi-objective but also it is dynamic and a two-stage algorithm is proposed to solve the problem. In the first stage, the set of Pareto optimal solutions is found using a new hybrid Immune-GA method, and in the second stage, the best solution is chosen using a fuzzy satisfying technique. The model aims at all three aspects of placement, sizing and timing of DG investment simultaneously, while also considering distribution feeder and transformer reinforcements. The main contributions of this paper are:

  • 1.

    A multi-objective dynamic DG-planning model with the consideration of network reinforcements is proposed.

  • 2.

    The proposed model is solved using a new efficient heuristic method which dominates the other heuristic methods.

This paper is set out as follows: Section 2 presents problem formulation, Section 3 sets out the proposed solution method for solving the problem. The application of the proposed model and the simulation results are presented in Section 4 and finally, Section 5 summarizes the findings of this work.

Section snippets

Problem formulation

The multi-objective DG-planning formulation is presented in this section. The decision variables are the number of DG units from each specific technology, to be installed in each bus in each year, i.e., ξitdg; binary investment decision in feeder in the year t, i.e. γt which can be 0 or 1, and finally the number of new installed transformers in the year t, i.e. ψttr. The assumptions used in problem formulation, constraints and the objective functions are explained next.

The proposed solution method

The DG-planning problem formulated in Section 2 is a mixed integer non-linear multi-objective problem. In general, multi-objective optimization problem consists of more than one objective function which are needed to be simultaneously optimized. One available approach for solving such problems is the weighted sum approach in which the multi-objective problem is converted into a single-objective problem using pre-specified weights. Although the weighted sum approach is simple and easy to

Simulation results

The proposed methodology is applied to a 9-bus test system which is shown in Fig. 2. The technical data of this network are shown in Table 1 [10], [11]. This network consists of a 132/33 kV substation with 40 MVA capacity and 8 feeders with eight aggregated loads and their base values, i.e. Si, baseD, are given in Table 1. Total base load of the system in the first year is 28.12 MVA and at the end of the planning horizon this value reaches to 38.325 MVA. The peak power of the system will be 51.1 MVA

Conclusion

This paper presents a dynamic multi-objective formulation of DG-planning problem and an Immune-GA based method to solve the formulated problem. The proposed two-step algorithm finds the non-dominated solutions by simultaneous minimization of total costs and emissions in the first stage and uses a fuzzy satisfying method to select the best solution from the candidate set in the second stage. The new planning model is applied to a test system and its flexibility and effectiveness are demonstrated

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