Distribution Network Expansion Planning Research Articles

An economic-environmental asset planning in electric distribution networks considering carbon emission trading and demand response

Initiatives such as government programs and investment in low-carbon technologies have been adopted to mitigate the carbon emissions in the electricity sector. These initiatives have resulted in new challenges in the power sector, and to address them adequately, innovative frameworks are required in the electric distribution network (EDN) expansion planning and operation problems. Therefore, this work proposes an environmentally committed asset planning approach to remedy the existing issues to some extent. The proposed strategy investigates the benefits of the simultaneous allocation of several assets such as capacitor banks (CBs), distributed generation (DG) units based on renewable energy sources, and energy storage systems (ESSs). Moreover, an innovative carbon emission trading scheme is formulated in the planning stage to mitigate the CO2 emissions while a demand response program is applied to modify the consumption behavior. The proposed approach is formulated as a two-stage robust mixed-integer programming model, which considers uncertainties associated with the electricity demand and renewable-based DG. To cope with the difficulties of this complex model, utilizing an efficient decomposition algorithm, such as the C&CG decomposition algorithm, is essential. The potential of the proposed approach is studied under different operating conditions and via several test cases on a 137-node EDN. In addition, to validate the performance of the proposed carbon emission scheme, a multi-region 54-node distribution network is adequately evaluated. Results show that by considering simultaneously multiple planning alternatives, carbon emission trading scheme, and the demand response program, the total CO2 emissions are reduced by up to 15%.

Overview of Electric Energy Distribution Networks Expansion Planning

Planning of the electric distribution networks is complex and about upgrading the system to satisfy the demand and constraints with the best economic plan. The planning alternatives include the expansion of substations, installing new distributed generation (DG) facilities, upgrading distribution feeders, etc. In the modern networks, distribution planners must gain the confidence of the reversibility of the investment where renewable energy resources (RERs) inject clean and cost-effective electrical power to respond to the rising demand and satisfy environmental standards. This paper is an exhaustive review on the distribution network expansion planning (DEP) including the modelling of DEP (possible objective functions, problem constraints, different horizon time, and problem variables), optimization model (single/multi-objective), the expansion of distributed energy resources (DERs), problem uncertainties, etc. We discuss the requirements of integrated energy district master planning to avoid conflicts between the goal of independence of district planning on energy, e.g. heat and electricity, and that of dependencies on the local electric utilities regarding instant power balance and stability services. Finally, we describe the primary future R&D trends in the field of distribution network planning.

Expansion Planning of Automated Sub-transmission and Distribution Networks integrated by Distributed Generations

This paper presents sub-transmission and distribution network expansion planning (S&DEP) including distributed generation (DG) and distribution automation (DA) considering reliability indexes. The objective function is to minimize investment, operation, maintenance and reliability costs subjected to AC power flow, system operation and generating units and DG limits, reliability, and distribution automation constraints (including the constraints of protection devices and volt/VAr control mechanism). The proposed model is a mixed integer non-linear programming (MINLP) model which is hard to solve. For this reason, a MINLP problem is transformed to mixed integer linear programming (MILP) model. The validity of the proposed method is investigated in the two synthetic test networks.

A Model for Stochastic Planning of Distribution Network and Autonomous DG Units

This article presents a mixed-integer linear stochastic model for the optimal expansion planning of electricity distribution networks and distributed generation (DG) units. In the proposed framework, autonomous DG units are aggregated and modeled using the well-known energy hub concept. In this model, the uncertainties of heat and electricity demand as well as renewable generation are represented using various scenarios. Although this is a standard technique to capture the uncertainties, it drastically increases the dimensions of this optimization problem and makes it practically intractable. In order to address this issue, a novel iterative method is developed in this article to enhance the efficiency of the optimization model. The proposed framework is further utilized to assess the benefits of the collaborative distribution network and autonomous distributed generation planning through various case studies performed on the 24-node distribution test grid. With 5.93% cost reduction, the obtained results indicate the importance of such collaborations in reaching an efficient network expansion solution. Moreover, the total planning cost for the stochastic model is 1.23% lower than the deterministic case. Various sensitivity analyses are also carried out to investigate the impacts of parameters of the proposed model on the optimal planning solution. The scalability of the model is also assessed by its implementation on the 54-node distribution test network.

A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy

Abstract Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

Objective functions of distribution network expansion planning - a comprehensive and exhaustive review

Utmost of elucidation about the research accomplished in an article is cleared from the objective function of the planning problem. An objective function is a mathematical expression which describe the existing condition of a system with numerous variables, in which alteration of these variables result in optimized value largest or smallest, depending on problem or desired value. That value may be obtained by minimizing or maximizing the objective function. In this paper, a review has been carried out on objective functions of distribution network expansion planning (DNEP). These objective functions have been classified into five main categories: financial, income related, technical, optimal size & location and social & economic. The selection of objective function clearly shows increasing penetration of distributed generation (DG), distributed energy storing systems (DESS) and fuel cells with renewable technologies. Most of the reviewed articles highlight these objectives in details, however; not all fields have been covered in any single work on DNEP. This review article aims to address this gap so that widespread DNEP can be achieved with flawlessness. Substantial information has been offered of research work done in the field of DNEP through this review article which will mitigate the impending researchers from the difficulties of getting apposite supervision.

Expansion Planning of Active Distribution Networks With Multiple Distributed Energy Resources and EV Sharing System

The ever-increasing energy demand and high penetration rate of distributed renewable generation brings new challenges to the planning of power distribution networks. This paper proposes an expansion planning model for distribution networks by considering multiple types of energy resources in distribution side, including shared electric vehicle (SEV) charging stations, solar-based distributed generation sources, and battery energy storage systems. The operational characteristics of SEV are considered and modeled. The proposed planning model aims to minimize the weighted sum of network investment cost, energy losses, and queue waiting time of SEVs. A stochastic scenario generation method is introduced to address the stochastic feature of SEVs’ driving behaviors. Numerical studies are tested on the systems with 54-node distribution network and 25-node traffic network.

An optimal expansion planning of electric distribution network incorporating health index and non-network solutions

The health status of distribution equipment and networks is not considered directly in existing distribution network planning methods. In order to effectively consider the health status and deal with the risk associated with load and renewable generation uncertainties, this paper presents a new optimal expansion planning approach for distribution network (EPADN) incorporating equipment's health index (HI) and non-network solutions (NNSs). HI and relevant risk are used to help develop the optimal equipment replacement strategy and temporary NNSs are considered as promising options for handling the uncertainties of load growth, reliability requirements of power supply and output of distributed energy resources (DERs) at a lower cost than network alternatives. An EPADN model using network solutions (NSs) and NNSs is proposed. The planning objectives of the proposed model are safety, reliability, economy, and `greenness' that are also the meaning of distribution network HI. A method integrating an improved niche genetic algorithm (INGA) and a spanning tree algorithm (STA) is fitted to solve the model presented here for real sized networks with a manageable computational cost. Simulation results of an actual 22-node distribution network in China, illustrate the effectiveness of the proposed approach.

Optimal expansion planning of active distribution system considering coordinated bidding of downward active microgrids and demand response providers

This paper addresses a framework for expansion planning of an active distribution network (ADS) that supplies its downward active microgrids (AMGs) and it participates in the upward wholesale market to sell its surplus electricity. The proposed novel model considers the impact of coordinated and uncoordinated bidding of AMGs and demand response providers (DRPs) on the optimal expansion planning. The problem has six sources of uncertainty: upward electricity market prices, AMGs location and time of installation, AMGs power generation/consumption, ADS intermittent power generations, DRP biddings, and the ADS system contingencies. The model uses the conditional value at risk (CVaR) criterion in order to handle the trading risks of ADS with the wholesale market. The proposed formulation integrates the deterministic and stochastic parameters of the risk-based expansion planning of ADS that is rare in the literature on this field. The introduced method uses a four-stage optimisation algorithm that uses genetic algorithm, CPLEX and DICOPT solvers. The proposed method is applied to the 18-bus and 33-bus test systems to assess the proposed algorithm. The proposed method reduces the aggregated expansion planning costs for the 18-bus and 33-bus system about 44.04% and 11.82% with respect to the uncoordinated bidding of AMGs/DRPs costs, respectively.

Evaluation whether photovoltaic distributed generation postpones or anticipates reinforcements detected by distribution network expansion planning

There is a growth of distributed generation (DG) impacting the network. There are several studies which focus on defining the location and size of DG to optimise network performance. However, this approach is only possible in a market regulation where utilities can control the DG. Otherwise, this information depends on the consumer and not on the utilities. In this case, the optimisation studies do not make sense anymore. This work aims to identify if the distribution network expansion planning (DNEP) will be changed by the DG integration in a scenario where DG is not controlled by the utilities. The method consists of performing load flows in the feeder considering the load growth during the planning horizon to calculate voltage levels, currents and technical losses. Then, the procedures are repeated considering DG integration in many penetration levels, after that, the results are compared. The results showed that, for the feeder under study, the critical hours do not necessarily occur at the period of peak load, but in the moments with greater generation and smaller load. Therefore, the focus of the DNEP should be changed when considering DG integration.

Optimal expansion planning considering storage investment and seasonal effect of demand and renewable generation

A new era of cleaner distributed generators, like wind and solar, dispersed along the distribution network are gaining great importance and contributing to the environment and political goals. However, the variability and intermittency of those generators pose new complexities and challenges to the network planning. This research paper proposes an innovative stochastic methodology to deal with the expansion planning of large distribution networks in a smart grid context with high penetration of distributed renewable energy sources and considering the seasonal impact. Also, new power lines locations and types, the size and the location of energy storage systems are considered in the optimization. A distribution network with 180 buses located in Portugal considering high distributed generators penetration is used to illustrate the application of the proposed methodology. The results demonstrate the advantage of the stochastic model when compared with a deterministic formulation, avoiding the need for larger investments in new lines and energy storage systems.

Stochastic System of Systems Architecture for Adaptive Expansion of Smart Distribution Grids

The incorporation of the reconfiguration into the expansion planning of smart distribution networks is addressed in this paper, in which the potential of distributed energy resources and demand response (DR) are modeled. The system of systems (SoS) architecture is employed to model the strategy of a distribution company (DISCO), a private investor (PI), and a DR provider (DRP). The SoS is an efficient modeling architecture to model the behavior of independent and autonomous systems with distinct objective functions who are able to share some data and work together. The aim of the DISCO is to upgrade the system with the optimal cost and reliability, whereas the PI and DRP want to maximize their profit. The DISCO should try to persuade the PI to install DGs (Distributed generations) by offering the guaranteed purchasing prices. Furthermore, the DRP is a market player who can negotiate with the DISCO to sign a contract to sell the purchased DR capacities from the customers. The uncertainties of the DISCO problem is handled by using the chance-constraint method, but the PI and DRP use the conditional value at risk method to model their uncertainties. Finally, to solve the proposed model, the multiobjective optimization algorithm is employed.

Analysis of renewable generation’s integration using multi-objective fashion for multistage distribution network expansion planning

In this paper, the integration of intermittent distributed generation (wind and solar) is dealt with by proposing a multi-objective multistage distribution network expansion planning formulation based on ε-constraint method. This approach allows the planners to obtain pareto-optimal solutions based on the two objective functions: (1) installation costs of distributed generation, substations, feeders, maintenance costs of assets, costs of energy purchased from substations, cost of unserved energy and (2) utilization of renewable distributed generation. To represent the intermittent nature of load and renewable distributed generation, several time blocks are utilized, and its effectiveness is compared with an existing approach. The overall problem is formulated as a mixed-integer linear programming and a linear equation-based substation price is utilized within the optimization framework to reduce the size of the problem. The proposed method is applied to the modified 24-nodes test network for different cases to analyse the utilization of renewable distributed generation from different point of views and it is inferred from the results that the proposed model yields a variety of planning solutions which are better to increase the utilization of renewable distributed generation.

Stochastic integration of demand response and reconfiguration in distribution network expansion planning

Planning the distribution network of the future involves forecasting the most likely scenario to make appropriate investment decisions. Many uncertainties concerning, e.g. the evolution of conventional loads, renewable production and electric vehicles (EVs) make it difficult to predict the location of the distribution network's weaknesses (overvoltages, undervoltages and overcurrents) and their occurrence. In some cases, alternative solutions such as demand response (DR) and reconfiguration can remove the constraints and prevent expensive network investment. This study proposes a two-stage algorithm that is able to give the probability that no technical constraints will appear as a function of the reinforcement cost with and without using DR and/or reconfiguration. The first stage of the algorithm consists in running Monte Carlo simulations based on realistic scenarios for loads, EVs and renewable production development provided by French governmental roadmaps. The cost of reinforcement per line and per hour of constraints enables selection of the feeders, where DR (solved with linear programming) and/or reconfiguration (exhaustive research) will be implemented in the second stage of the algorithm to remove these constraints. The methodology is applied to a real part of a French distribution network.

Flexible and robust distribution network expansion planning in the presence of distributed generators

Flexible and robust distribution network expansion planning in the presence of distributed generators

Probabilistic–possibilistic model for a parking lot in the smart distribution network expansion planning

Conventional distribution network departs to the smart grid. The parking lot will have an important role in the smart grid as a distributed generation. Due to the output power of parking lots is uncertain, More accurate modeling of parking lot output power is necessary for the future of distribution network studies such as Distribution Network Expansion Planning (DNEP). In this paper, a systematic method based on the Z -number concept is utilized to represent the uncertainty of Vehicle to Grid's (V2G's) presence. In order to investigate the impact of V2Gs uncertainty on the DNEP, we proposed a Probabilistic–Possibilistic DNEP in the presence of V2Gs referred to as P-PDNEPV2G. If the V2Gs historical data is incomplete, the proposed structure can significantly consider the effects of V2G on the DNEP. In P-PDNEPV2G, parking lots output power is described as a probabilistic–possibilistic variable by Z -number method. The optimization of P-PDNEPV2G is executed by the Non-Dominated Sorting Genetic Algorithm (NSGA-II). A 24-bus test system and the real 20 kV distribution network of Ghale-Ganj city of Kerman province in Iran are used to demonstrate the effectiveness of the proposed methodology. Eventually, several analyses are conducted to investigate the impact of probabilistic–possibilistic V2G model on the DNEP problem.

Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network

This paper presents a novel multi-objective model of active distribution network planning based on stochastic programming and uncertain random network (URN) theory. The planning model is proposed to find the final scheme with optimal alternative, location, size and operational strategy for the candidate distribution lines, transformer substations (TSs), distribution generations (DGs), static var compensators (SVCs) and on-load tap changers (OLTCs). Firstly, a scenario-based approach is developed to analyse the uncertainties in network system, such as the demand and intermittency of renewable sources. Since the impact of multiple uncertain factors on network cannot be ignored, a network frame is then modelled by uncertain and random weights of spanning tree (ST) instead of fixed value. In order to achieve the minimization of total cost, and further the selection of a minimum spanning tree (MST) with the uncertain and random weight, a 3-dimensional uncertain space is constructed based on the combination of the previous two targets. In addition, a second-order cone programming (SOCP) is applied to cope with the multi-objective, mixed-integer nonlinear nature of the proposed planning model. Simulation is performed on a modified Pacific Gas and Electric Company (PG&E) 69-bus distribution system, and the results demonstrate the effectiveness of the proposed model.

Improved harmony search algorithm for electrical distribution network expansion planning in the presence of distributed generators

Distribution network expansion planning problem is carried out to supply the forecasted demand of distribution network in a certain time in which optimal size and location of distribution substations and feeders should be determined. In this paper, this problem in the presence of different types of distributed generators is addressed. For this purpose, a new approach is applied to model several practical aspects such as pollution, investment and operation costs of distributed generators, purchased power form the main grid, dynamic planning, and uncertainties of load demand and electricity prices. The uncertainties are modeled using the probability distribution function and Monte-Carlo simulation is applied to insert them into the planning problem. Because the problem involves many variables and constraints and is a non-convex and large-scale one, improved harmony search algorithm is used to solve it. To show the effectiveness of the proposed model and solving approach, it is applied to the 9-node and 69-node standard radial distribution networks and a real system of western part of Iranian national 20 kV distribution grid. The results show that the harmony search algorithm can solve the problem in a better manner in comparison with other methods such as genetic algorithm and particle swarm optimization.

Chance constrained simultaneous optimization of substations, feeders, renewable and non-renewable distributed generations in distribution network

The penetration of distributed generators (DGs) is continually increasing in the power sector due to its ability in enhancing technical specifications as well as providing a promising future for power generation in electric networks. The aforementioned objectives will be realized if DG units are allocated optimally and coordinately simultaneous with distribution network expansion planning. On the other hand, given the stochastic nature of renewable generation and severe fluctuations of load consumption and electricity price, the DGs planning problem should be accomplished under uncertainties. To address these issues, this paper proposes a novel joint chance constrained programming (JCCP) method to fulfill an acceptable level of constraint feasibility for optimal simultaneous expansion planning of HV/MV substations and multiple-DG units along with robust MV feeder routing problem. Our design objective is to determine the optimal site and size of sub-transmission substations and various DG units associated with optimally construction of network by implementing the feeder routing problem with aim to minimize the investment costs, energy not supplied (ENS) cost and energy purchasing cost from upstream network. The diverse objectives are mathematically formulated as an MINLP model and converted into a single-objective function through weighted sum method and subsequently has been minimized by adaptive genetic algorithm. Furthermore, the Taguchi method is utilized in order to furnish an efficient algorithm that can find a satisfactory solution. Finally, the effectiveness of the proposed method is investigated by applying it on the 54-bus distribution network and the obtained results are duly drawn and discussed.