Distribution Network Expansion Planning Research Articles

Active Distribution Network Expansion Planning Integrating Practical Operation Constraints

This paper proposes a mathematics model of active distribution network expansion planning (DNEP) integrating the practical operation constraints, which are the OLTC (On-Load Tap Changer) tap adjusting frequency and substation voltage variation. Distributed Generation (DG) curtailment, load curtailment, OLTC tap adjustment, and reactive power compensation are considered as the active management schemes. The expansion model allows alternatives to be considered for new wiring, DG installation, new substation, and substation expansion. The DNEP problem is a mixed integer non-linear programming problem, active management, and uncertainties, especially with the DG integration make the DNEP problem much complex. In order to find the suitable algorithm, this paper converts the DNEP problem to a Second-Order Cone Programming (SOCP) model through distflow equations and constraints relaxation. A modified 33-bus application example is used to verify the proposed model with different active management scenarios.

Exploiting Linkage Information and Problem-Specific Knowledge in Evolutionary Distribution Network Expansion Planning.

This article tackles the Distribution Network Expansion Planning (DNEP) problem that has to be solved by distribution network operators to decide which, where, and/or when enhancements to electricity networks should be introduced to satisfy the future power demands. Because of many real-world details involved, the structure of the problem is not exploited easily using mathematical programming techniques, for which reason we consider solving this problem with evolutionary algorithms (EAs). We compare three types of EAs for optimizing expansion plans: the classic genetic algorithm (GA), the estimation-of-distribution algorithm (EDA), and the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA). Not fully knowing the structure of the problem, we study the effect of linkage learning through the use of three linkage models: univariate, marginal product, and linkage tree. We furthermore experiment with the impact of incorporating different levels of problem-specific knowledge in the variation operators. Experiments show that the use of problem-specific variation operators is far more important for the classic GA to find high-quality solutions. In all EAs, the marginal product model and its linkage learning procedure have difficulty in capturing and exploiting the DNEP problem structure. GOMEA, especially when combined with the linkage tree structure, is found to have the most robust performance by far, even when an out-of-the-box variant is used that does not exploit problem-specific knowledge. Based on experiments, we suggest that when selecting optimization algorithms for power system expansion planning problems, EAs that have the ability to effectively model and efficiently exploit problem structures, such as GOMEA, should be given priority, especially in the case of black-box or grey-box optimization.

An approach for simultaneous distribution, sub-transmission, and transmission networks expansion planning

Sub-transmission network, as an intermediate grid between the distribution and transmission systems, receives the electric energy from the transmission network at extra high voltage levels, and delivers it to the distribution network at medium or low voltage levels. The adequate design and operation of sub-transmission system will lead to an efficient design of transmission network from the technical and economic viewpoints on one hand and the adequacy of power delivery to the distribution loads on the other hand. Therefore, the design optimality of these three networks is highly dependent on each other. However, as the simultaneous design of distribution, sub-transmission, and transmission systems is highly complicated, very few researches have tried to model and solve such a difficult problem. In this paper, a new approach has been developed for simultaneous distribution, sub-transmission, and transmission networks expansion planning. The proposed approach has been formulated as an optimization problem where an efficient and improved genetic algorithm (GA) is employed to solve such a complex problem. The utilized GA has been equipped with different modifying operators in order to make sure of its appropriate performance in obtaining useful and optimal solutions for the coordinated planning problem. The conducted approach has been implemented on a real network of Zanjan Regional Electrical Company (ZREC), and the results are compared with those of conventional method, i.e. separate expansion planning of these networks. The simulation results demonstrate the effectiveness of the conducted approach.

多様性に注目した配電系統拡張のための多目的最適化手法の適用

This paper presents the application of multi-objective optimization methods for Network expansion planning. According to the electricity liberalization in fiscal year 2016, a suitable distribution method for both supply and demand selection is desired. Therefore, the distribution network expansion planning was studied in this research. In recent research, Controlled NSGA-Ⅱmethod regarding to the linkage distinction method using count box was being considered. This study proposes the same idea with an improvement to the method. It is also expected for an outcome which preserves the individual that supposed to be destroyed along the calculation process and also, necessary for wide solution.

Expansion Planning of Active Distribution Networks With Centralized and Distributed Energy Storage Systems

A multistage active distribution network (ADN) planning model that is integrated with the application of energy storage system (ESS) is presented in this paper. Both the long-term investment cost and short-term operation conditions of ADN are considered in the proposed model. The power supply reliability improvement brought by ESS is also analyzed. At each planning stage, the operation conditions are divided into several typical day scenarios and an extreme condition scenario which are based on the load forecast data. The long-term expansion planning decisions including those for replacing and adding circuits, introducing ESS, and considering ADN short-term operation strategies for the ESS charging and discharging are optimized together in the proposed model. The ESS benefits pertaining to peak load shaving and power reliability enhancement are demonstrated using numerical cases, in which the centralized and the distributed ESS are considered as options for the ADN implementation. The effectiveness of the proposed model is demonstrated through various discussions in the paper.

A Genetic Algorithm Approach for Optimal Distribution System Network Reconfiguration

Electrical energy is an essential ingredient for the industrial and all-round development of any country. Power distribution systems are radial in configuration and this makes the networks hard to manage, thus, the need for optimization. This paper presents the optimization of network reconfiguration of distribution system using genetic algorithm to get the optimal switching scheme for network reconfiguration with objective function to reduce power loss and improve active power of the system. Load flow for the network reconfiguration problem was formulated as single objective optimization problem. The optimization model was simulated using MATLAB/SIMULINK and validated on standard IEEE 13-bus and 25-bus distribution test feeders. The result shows that active power increases by 91.1% (1.6469p.u.) while the power loss reduced by 99.4% (1.6372p.u.) for 13-bus system. For 25-bus system, active power increased by 27% (0.9154p.u.) and power loss reduced by 96.2% (4.3074p.u.) after optimization. The results provide solutions to the power distribution system for the optimal switching scheme for network reconfiguration with improvement in active power of the system. Total real power loss was minimized according to the corresponding fitness values of the genetic algorithm solutions. The paper provides technical information that could help in the future expansion and operation planning of the power distribution network.

Stochastic expansion planning of interconnected distribution networks with renewable sources considering uncertainties and power transfer capability

This study proposes a stochastic optimisation model for interconnected distribution network planning (IDNP) in the presence of renewable sources (RSs) considering power transfer capability after an N − 1 contingency. Two types of scenarios, feeder contingency, and substation transformer contingency, are formulated in the IDNP problem. Uncertainties of the RS output and load fluctuation are also integrated. The planning method provides planners with the decisions of feeder reformation, new tie lines, new feeders, substation expansion, new substations and new load allocation. The IDNP model is a chance-constrained mixed integer non-linear programming problem, which is solved by dynamic niche differential evolution algorithm. A case study carried out on a modified 104-bus distribution network demonstrates the effectiveness of those techniques. Compared with the traditional planning approach, the IDNP method could exploit asset utilisation by optimising existing networks efficiently as well as improve system economy and security simultaneously.

Multi-Objective Distribution Network Expansion Incorporating Electric Vehicle Charging Stations

The paper develops a multi-objective planning framework for distribution network expansion with electric vehicle charging stations. Charging loads are modeled in the first place, and then integrated into the optimal distribution network expansion planning. The formulation is extended from the single objective of the economic cost minimization into three objectives with the additional maximization of the charging station utilization, and maximization of the reliability level. Compared with the existing models, it captures the interactive impacts between charging infrastructures planning and distribution network planning from the aspects of economy, utilization, and reliability. A multi-stage search strategy is designed to solve the multi-objective problem. The models and the strategy are demonstrated by the test case. The results show that the proposed planning framework can make a trade-off among the three objectives, and offer a perspective to effectively integrate the network constraints from both the transportation network and distribution network.

A multiyear DG-incorporated framework for expansion planning of distribution networks using binary chaotic shark smell optimization algorithm

In this paper, a new model for MEPDN (multiyear expansion planning of distribution networks) is proposed. By solving this model, the optimal expansion scheme of primary (i.e. medium voltage) distribution network including the reinforcement pattern of primary feeders as well as location and size of DG (distributed generators) during an ascertained planning period is determined. Furthermore, the time-based feature of proposed model allows it to specify the investments/reinforcements time (i.e. year). Moreover, a minimum load shedding-based analytical approach for optimizing the network's reliability is introduced. The associated objective function of proposed model is minimizing the total investment and operation costs. To solve the formulated MEPDN model as a complex multi-dimensional optimization problem, a new evolutionary algorithm-based solution method called BCSSO (Binary Chaotic Shark Smell Optimization) is presented. The effectiveness of the proposed MEPDN model and solution approach is illustrated by applying them on two widely-used test cases including 12-bus and 33-bus distribution network and comparing the acquired results with the results of other solution methods.

Active distribution network expansion planning integrating dispersed energy storage systems

This study proposes the convex model for active distribution network expansion planning integrating dispersed energy storage systems (DESS). Four active management schemes, distributed generation (DG) curtailment, demand side management, on-load tap changer tap adjustment and reactive power compensation are considered. The optimisation of DESS for peak shaving and operation cost decreasing is also integrated. The expansion model allows alternatives to be considered for new wiring, new substation, substation expansion and DG installation. The distribution network expansion planning (DNEP) problem is a mixed integer non-linear programming problem. Active management and uncertainties especially with the DG integration make the DNEP problem much complex. To find the suitable algorithm, this study converts the DNEP problem to a second-order cone programming model through distflow equations and constraints relaxation. A modified 50-bus application example is used to verify the proposed model.

Greenfield distribution network expansion strategy with hierarchical GA and MCDEA under uncertainty

Abstract Distribution network expansion planning (DNEP) is becoming more complex in nature. Addition of new load centers, due to increasing conversion of greenfield areas into habitats, have generated need of more intense and highly structured planning strategies. Micro level work on distribution expansion planning has been ignored by most of the researchers mainly in Indian scenario. Since practical distribution networks are quite large, number of candidates (load centers) will be more and, hence, number of variables (electrical parameters and new load center feasible connections with the existing system) are remarkable. Optimizing a large system may result in significant decrease of accuracy and increase of computation time. For deciphering this issue, segmentation procedure has been applicable. For this purpose, a sensitivity analysis has been applied to find dependent variables. It is obvious that a correct segmentation can decrease computation time (as a single task is operated in segments simultaneously) while accuracy decreases negligibly. In present work, a scheme has been introduced to connect three greenfield load centers with existing primary distribution system by using hierarchical genetic algorithm (HGA). HGA is an integrated approach of analytical hierarchical process and genetic algorithm. The paper reports best selection of investment with finest voltage profile and least losses while maintaining radiality of the system. DNEP has been done at micro level and proposed methodology has been tested on a small dimension practical distribution system. The novelty of this paper is to optimize the best possible selection of connection of new load centers with existing system with the help of AHP and GA and results have been verified with advance optimal tool multiple criteria data envelopment analysis (MCDEA). HGA and MCDEA are applied to practical nine bus distribution system and the results are presented and compared.

Probabilistic optimal robust multistage feeder routing under load forecasting uncertainty

One of the main uncertainties in long-term distribution network planning is load forecasting error. This paper presents a probabilistic multistage expansion planning method to consider the load forecasting error as well as pseudo dynamic behavior of network parameters and geographical constraints. The optimal routes of MV feeders as backbone of distribution networks are obtained for both mid and long-term case with deterministic and probabilistic modeling. The Imperialist Competitive Algorithm (ICA) is adapted for the optimal expansion planning of MV distribution network. To check and maintain the radial configuration of the MV distribution network during ICA random operators, a novel algorithm is employed. Comparative tabular and graphical results are given for deterministic and probabilistic planning. In probabilistic case the results is also represented by histogram of statistics data, mean and standard deviation of input and output variables using Monte Carlo Simulation (MCS). Based on the results it is possible to design the MV network at the presence of uncertainties more robust and flexible with respect to deterministic planning without reasonable increase in total cost. The efficiency and capability of the method is tested on a relatively large-scale distribution network.

Multistage distribution network expansion planning considering the emerging energy storage systems

This paper addresses multistage distribution network expansion planning (DNEP) incorporating energy storage systems (ESSs). The ESSs are utilized to shave the peak demand and to reduce the planning cost. Annual and daily load duration curves are considered to evaluate the impacts of the ESSs on the planning. The proposed planning is carried out based on the AC power flow including active power, reactive power, and network loss. The problem is formulated as a constrained, mixed-integer, and nonlinear programming (MINLP) and solved by using particle swarm optimization (PSO) algorithm. A 11kV and 30-bus radial distribution network is considered as case study and the typical ESSs are also regarded to install on the network. Simulation results demonstrate the effectiveness and viability of the proposed method to consider the ESSs in DNEP. The results indicate that integrating the ESSs in DNEP reduces the planning cost significantly, as well improves the technical parameters of the network such as bus voltages and line loading.

Renewable power generation employed in an integrated dynamic distribution network expansion planning

The recent developments in green energy technologies and the importance of using clean energies have made the renewable resources more attractive for distribution network operators, specifically due to their inexhaustible and non-polluting features. In this way, the present study initiates an integrated dynamic distribution network expansion planning (DDNEP) in which most of the planning alternatives along with renewable and non-renewable distributed generation (DG) are taken into account. The proposed framework considers the important cost terms, including both the investment and operational ones. The uncertainties regarding the intermittent nature of renewable DGs, load demand, and energy price have been well-regarded in calculating the cost components. With the aim of a precise calculation of operation and interruption costs, the load duration curve (LDC) has been established for modeling of the network loads. Moreover, both the possibility of operating the DG units in islanding mode, and, the load transferring through the reserve feeders have been interrogated in the problem in order to improve the network reliability. In the present research, to provide an accurate evaluation of reliability indices, and to cover all of the uncertainty states, the required conditions for the successful and safe operation of island have been suitably studied in the problem. In numerical evaluations, a combination of improved genetic algorithm (IGA) and optimal power flow (OPF) is employed to solve the integrated problem in the 54-bus test system. The obtained results are discussed in details.

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

Abstract Distribution network expansion planning (DNEP) is one of the most important tools to deal with the demand growth in a system. DNEP is usually carried out through reinforcement or installation of new components. In this paper, a new and combined methodology is used to consider several practical aspects in DNEP such as uncertainty, distributed generation (DG), load growth, electricity market and multi stage dynamic expansion are included in the planning. So that DNEP is addressed in the presence of distributed generation (DG), considering load and price uncertainties under electricity market environment. The proposed planning aims at minimizing investment and operational costs simultaneously. Since DNEP in coordination with DG planning leads to reduce planning cost; therefore, the coordinated DNEP and DG planning are presented in this paper. The proposed planning is implemented by the particle swarm optimization (PSO) technique. Besides, the uncertainties are modeled as the probability distribution function (PDF) and Monte-Carlo simulation (MCS) is used to insert the uncertainties into the programming. The proposed planning is carried out based on the 9-bus as well as Kianpars–Ahvaz test systems (Kianpars–Ahvaz is a practical network in Ahvaz province, Iran). The simulation results demonstrate the ability and effectiveness of the proposed planning to deal with uncertainties under electricity market environment.

Power Distribution Network Expansion Planning Considering Distribution Automation

This paper presents a novel profit-based model for multistage distribution network expansion planning. In the proposed model, some capabilities of the distribution automation system are considered to achieve a plan that is more compatible with the strategic plan towards a smart distribution grid. Moreover, a method for reliability evaluation of a distribution network in the planning studies is proposed. The objective function of the planning problem is the net present value of the company's profit. The problem is solved by employing the genetic algorithm approach as a promising technique in solving the mixed-integer nonlinear programming problem. The proposed method is evaluated on an illustrative test network and the obtained results are presented and discussed.

Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions

In this paper, an integrated methodology is proposed for distribution network expansion planning which considers most of the planning alternatives. The planning aims to determine the optimal reinforcement of existing medium voltage lines and high voltage/medium voltage substations, or installation of new ones to meet the load growth in the planning horizon subject to the technical and operational constraints. Also, to take the advantages of new technologies, the renewable and non-renewable distributed generations have been included in the problem as another alternative. The uncertainties related to renewable DGs, load demand, and energy price have been considered in the calculation of cost components. The load duration curve has been utilized for loads such that the results be more precise. The possibility of islanding and load transferring through the reserve feeders have been regarded in the problem to improve the reliability of the network. Also, the required condition for successful and safe operation of island considering all of uncertainty states have been checked out to accurately calculate the reliability. The genetic algorithm is employed to solve this integrated problem. Finally, the proposed method is applied to the 54-bus system and also a real large-scale distribution network, and the results are discussed. The results verify the effectiveness of the presented method.

Multistage distribution network expansion planning under smart grids environment

Abstract This paper presents a novel model for multistage smart distribution network expansion planning (MSDNEP) in the presence of vehicle to grid (V2G) and fault passage indicator (FPI) in a multi-objective optimization framework. Distribution networks should be expanded in order to serve load growth in the best manner possible. The two objectives considered are: minimization of total cost during the planning horizon and maximization of reliability index. The proposed model determines the sitting and sizing of parking lot as well as conventional alternatives for expansion, such as, optimal size, location and time in which the new lines must be added, replaced or removed. Moreover, the impacts of using FPIs in a distribution network on reliability and the investment cost are in the scope of this study. Due to its good handling of mixed integer nonlinear problem, a non-dominated sorting genetic algorithm (NSGA-II) is utilized for optimization such a complex problem. The effectiveness of the proposed model is demonstrated by applying to a distribution test system and results show a significant improvement in the area of distribution network planning.

Corrigendum to “On optimal multistage electric power distribution networks expansion planning” [Int. J. Electr. Power Energy Syst. 54 (2014) 487–497

Corrigendum to “On optimal multistage electric power distribution networks expansion planning” [Int. J. Electr. Power Energy Syst. 54 (2014) 487–497

A new evolutionary solution method for dynamic expansion planning of DG-integrated primary distribution networks

Reconstruction in the power system and appearing of new technologies for generation capacity of electrical energy has led to significant innovation in Distribution Network Expansion Planning (DNEP). Distributed Generation (DG) includes the application of small/medium generation units located in power distribution networks and/or near the load centers. Appropriate utilization of DG can affect the various technical and operational indices of the distribution network such as the feeder loading, energy losses and voltage profile. In addition, application of DG in proper size is an essential tool to achieve the DG maximum potential benefits. In this paper, a time-based (dynamic) model for DNEP is proposed to determine the optimal size, location and installation year of DG in distribution system. Also, in this model, the Optimal Power Flow (OPF) is exerted to determine the optimal generation of DGs for every potential solution in order to minimize the investment and operation costs following the load growth in a specified planning period. Besides, the reinforcement requirements of existing distribution feeders are considered, simultaneously. The proposed optimization problem is solved by the combination of evolutionary methods of a new Binary Enhanced Particle Swarm Optimization (BEPSO) and Modified Differential Evolution (MDE) to find the optimal expansion strategy and solve OPF, respectively. The proposed planning approach is applied to two typical primary distribution networks and compared with several other methods. These comparisons illustrate the effectiveness of the proposed DNEP approach.