Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> In this paper, a new attempt has been introduced with the implementation of multiobjective optimization (MO) using GAM. The two objectives are considered here. One is to minimize the substation and their associated feeder costs, and the other is to minimize the interruption cost for those substation and feeders. The radiality, load, and power flow and interruption duration constraints of the systems are taken into consideration. Hence, the formulation is based on the multiobjective constraint optimization with both equality and inequality constraints. The objectives are constructed and based on a linear programming (LP) approach. The GAM is applied here for the evaluation of the MO, which is available in the optimization package MATLAB. The solution methodology is a global, noninferior solution for the multiobjective problem. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> In this paper, a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) optimization problem is presented. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem with both equality and inequality constraints. A new nondominated sorting genetic algorithm (NSGA) based approach is proposed to handle the problem as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving technique to overcome the premature convergence and search bias problems and produce a well-distributed Pareto-optimal set of nondominated solutions. A hierarchical clustering technique is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Several optimization runs of the proposed approach are carried out on a standard IEEE test system. The results demonstrate the capabilities of the proposed NSGA based approach to generate the true Pareto-optimal set of nondominated solutions of the multiobjective EED problem in one single run. Simulation results with the proposed approach have been compared to those reported in the literature. The comparison shows the superiority of the proposed NSGA based approach and confirms its potential to solve the multiobjective EED problem. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> In the restructured electricity industry, the engineering aspects of planning need to be reformulated even though the goal to attain remains substantially the same, requiring various objectives to be simultaneously accomplished to achieve the optimality of the power system development and operation. In many cases, these objectives contradict each other and cannot be handled by conventional single optimization techniques. In this paper, a multiobjective formulation for the siting and sizing of DG resources into existing distribution networks is proposed. The methodology adopted permits the planner to decide the best compromise between cost of network upgrading, cost of power losses, cost of energy not supplied, and cost of energy required by the served customers. The implemented technique is based on a genetic algorithm and an /spl epsiv/-constrained method that allows obtaining a set of noninferior solutions. Application examples are presented to demonstrate the effectiveness of the proposed procedure. <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> Collaborative production-distribution planning problem in supply chain systems is dealt with in this paper. A multi-objective linear programming model is developed in this concern. In order to reflect the collaborative planning issues to the model and to provide a more realistic model structure, decision makers' imprecise aspiration levels for the goals are incorporated into the model using fuzzy goal programming approach. To explore the viability of different fuzzy goal programming approaches for the collaborative production-distribution problem in different supply chain structures, i.e. centralized and decentralized, computational experiments are performed on a hypothetically constructed case problem. Computational results support our assertion that fuzzy goal programming approaches can effectively be used in handling the collaborative production-distribution planning problems in different supply chain structures. <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> This paper presents a multiobjective performance index-based size and location determination of distributed generation in distribution systems with different load models. Normally, a constant power (real and reactive) load model is assumed in most of the studies made in the literature. It is shown that load models can significantly affect the optimal location and sizing of distributed generation (DG) resources in distribution systems. The simulation technique based on genetic algorithms is studied. The studies have been carried out on 16-bus and 37-bus distribution systems. <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> In this paper, the remuneration of fixed costs of distribution networks with distributed generation is evaluated by means of an efficient planning strategy that includes the concept of fuzzy robustness of a given solution plan. As a key contribution, it is included the possibility of choice among different conductor sizes in order to assess the deep costs by overcapacity introduced by distributed generation. A single-stage multicriteria nonlinear problem is stated using three different criteria: investment cost in lines and transformers or distributed generators, power losses cost and unserved energy cost. Pareto or efficient plans are identified using the epsiv-Constraint/Weighting method and solved by Benders decomposition algorithm. Uncertainty associated to load demand and power injections of distributed resources are integrated using a fuzzy power flow in order to obtain the robustness indexes of each Pareto solution. The annualized fixed charge rate (AFCR) associated to new and existing distribution lines and transformers or distributed generators is assessed through a with-without analysis permitting to compute the annual avoided charge rate (AACR) in order to send incentives or charges to distributed generation promoters by the avoided or added investment costs on the system. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through a real 201-node distribution network. <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> The operational cost of an in-house distribution system (IDS) has been formulated as a multi-objective function to minimise the overall cost of MW, Mvar and MVA intakes. The reconfiguration investigations have been carried out considering different load types and a comparison is made with the commonly assumed constant load type used in such studies. Different tariff situations are simulated on 33-node test systems. The results obtained establish that load type is a major deciding factor in reconfiguration and cannot be represented by constant load models as used in such studies in case of IDS. <s> BIB007 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> Fuel cells are environmentally clean, have low emission of oxides of nitrogen and sulfur and, at the same time, they can operate with a very low level of noise. In addition, they can provide energy in a controlled way with higher efficiency compared to conventional power plants. This study presents an efficient multi-objective new fuzzy self adaptive particle swarm optimisation evolutionary algorithm to solve the multi-objective optimal operation management considering fuel cell power plants in the distribution network. The objective functions of the problem are to decrease the total electrical energy losses, the total electrical energy cost, the total pollutant emission and deviation of bus voltages. The proposed algorithm is tested on a real distribution test feeder and the results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal non-dominated solutions. <s> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. The obtained results show that the improved hybrid genetic algorithm gives us the best solutions in a reasonable computational time. <s> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> This paper presents a methodology for active distribution networks dynamic expansion planning based on Genetic Algorithms, where Distributed Generation integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. All aspects related to the expansion planning problem, such as multiple objective analysis, reliability constraints, modeling under uncertainties of demand and power supplied by Distributed Generation units and multistage planning, which are usually dealt with separately, are considered in an integrated model. Uncertainties are represented through the use of multiple scenario analysis. Multiple stages are incorporated by an algorithm based on the pseudo-dynamic programming theory. Results obtained with a test system and with an actual large scale system are presented and demonstrate the flexibility of applying the model for different purposes active network planning. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> This paper presents a multi-objective optimization methodology to solve the Optimal Reactive Power Flow (ORPF) problem. The e-constraint approach is implemented for the Multi-objective Mathematical Programming (MMP) formulation. The solution procedure uses Mixed Integer Non-Linear Programming (MINLP) model due to discrete variables, such as the tap settings of transformers and the reactive power output of capacitor banks. The optimum tap settings of transformers are directly determined in terms of the admittance matrix of the network since the admittance matrix is constructed in the optimization framework as additional equality constraints. The optimization problem is modeled in General Algebraic Modeling System (GAMS) software and solved using DICOPT solver. Simulation results are implemented on the IEEE 14-, 30-, and 118-bus test systems to simultaneously optimize the total fuel cost, power losses and the system loadability as objective functions. The simulation results show that the proposed algorithm is suitable and effective for the reactive power planning. <s> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> Abstract This paper proposes a long-term planning method to maximize the benefits of network reconfiguration and distributed generation (DG) allocation in distribution networks. The proposed method handles long-term yearly load increase and seeks to define the reinforcements (i.e., line upgrades, network reconfiguration, and DG integration) and when and where they are required to meet the load rise with minimal cost and acceptable quality standards. The objectives considered in this paper are: economic (costs of line upgrades, energy losses, switching operations, and DG capital, operation and maintenance costs) and environmental (emissions from grid and DG units). The proposed method takes into consideration the uncertainty related to renewable DG output power and the load variability. The long-term planning problem is defined as multi-objective nonlinear mixed integer programming. The outcomes of the planning problem are the Pareto front, which represents different optimum operating system points. Finally, the local distribution company (LDC) can choose the system operating point based on its preferences. <s> BIB012 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> E. MULTI-OBJECTIVE OPTIMIZATION <s> This paper presents new analytical expressions to efficiently capture the optimal power factor of each Distributed Generation (DG) unit for reducing energy losses and enhancing voltage stability over a given planning horizon. These expressions are based on the derivation of a multi-objective index (IMO), which is formulated as a combination of active and reactive power loss indices. The decision for the optimal location, size and number of DG units is then obtained through a benefit–cost analysis. Here, the total benefit includes energy sales and additional benefits, namely energy loss reduction, network upgrade deferral and emission reduction. The total cost is a sum of capital, operation and maintenance costs. The methodology was applied to a 69-bus industrial distribution system. The results showed that the additional benefits are imperative. Inclusion of these in the analysis would yield faster DG investment recovery. <s> BIB013
In many applications, there are different or conflicting objective functions to be satisfied by the optimization process. As mentioned, a DEP problem is a multi-objective (MO) optimization problem with different goals, including minimizing investment cost, power losses, and pollution emission as well as maximizing reliability level. The MO problems will result in a set of solutions instead of one solution where each solution has some advantages compared to others BIB008 . Hence, the decision-maker must pick a solution as the best solution. The classical methods are the approaches to convert all the objective functions into one function to be optimized as a Single-Objective (SO) problem. In this type of methods, different objective functions may be aggregated to construct one objective function, or one of the functions is the primary objective function, and the remaining objectives are the problem's constraints. In this way, we can optimize a single objective function while all the constraints are fulfilled. Fig. 8 shows the frequency of single and multi-objective DEP programming in different references. As noticed, SO optimization methods appeared more than MO methods, in DEP problems. Furthermore, heuristic algorithms and hybrid methods are the most and the least implemented approaches, respectively. The weighted aggregation technique is one of the approaches to convert the MO problems into the SO problems using a function operator to the objective vector  , BIB010 , BIB012 , BIB013 , BIB005 , BIB007 . A linear summation of the objective functions is a simple method among others. However, the main concern is the values of the weighting coefficients. Dynamic Weighted Aggregation (DWA)  , is an augmented weighted MO method in which the weights are altered incrementally. In addition, the goal programming (or goal attainment) is a technique with the aim of minimizing the deviations from the specified goals BIB004 BIB009 BIB001 . The ε-constraint is another approach that optimizes one of the objectives, while other objectives are considered as problem restrictions bound by some allowable range ε  , BIB003 , BIB006 , BIB011 . Such methods generate a Pareto front by running the optimization problem iteratively and renewing weights. Generally, the optimization results are local nadir points. Note that the overall efficiency of the solution highly depends on efficiency of the SO solver on the efficiency BIB002 . Despite their ease of implementation, their efficiency depends on the characteristics of the Pareto curve.