Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Distributed generation technologies offer electric utilities an alternative to large system capacity investments. This paper presents a simplified method to determine the value of deferring electric utility capacity investments using distributed generation. Consideration is given to both economic and technical factors, including uncertainty in the price of distributed generation. The technical evaluation is based on measured data from a 500 kW distributed generation PV plant in Kerman, California. Results indicate that the cost savings associated with deferring capacity investments can be accurately estimated using only seven economic parameters and one day's worth of distributed generation output and utility system data. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Important research effort has been devoted to the topic of optimal planning of distribution systems. However, in general it has been mostly referred to the design of the primary network, with very modest considerations to the effect of the secondary network in the planning and future operation of the complete grid. Relatively little attention has been paid to the optimization of the secondary grid and to its effect on the optimality of the design of the complete electrical system, although the investment and operation costs of the secondary grid represent an important portion of the total costs. Appropriate design procedures have been proposed separately for both the primary and the secondary grid; however, in general, both planning problems have been presented and treated as different-almost isolated-problems, setting aside with this approximation some important factors that couple both problems, such as the fact that they may share the right of way, use the same poles, etc., among other factors that strongly affect the calculation of the investment costs. The main purpose of this work is the development and initial testing of a model for the optimal planning of a distribution system that includes both the primary and the secondary grids, so that a single optimization problem is stated for the design of the integral primary-secondary distribution system that overcomes these simplifications. The mathematical model incorporates the variables that define both the primary as well as the secondary planning problems and consists of a mixed integer-linear programming problem that may be solved by means of any suitable algorithm. Results are presented of the application of the proposed integral design procedure using conventional mixed integer-linear programming techniques to a real case of a residential primary-secondary distribution system consisting of 75 electrical nodes. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <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>  <s> This paper proposes a methodology to achieve integrated planning and projects for secondary distribution circuits. The planning model is formulated as a mixed integer nonlinear programming problem (MINLP). In order to resolve this problem, a tabu search (TS) algorithm is used, with a neighborhood structure developed to explore the physical characteristics of specific geographies included in the planning and expansion of secondary networks, thus obtaining effective solutions as well as low operating costs and investments. The project stage of secondary circuits consists of calculating the mechanical efforts to determine the support structures of the primary and secondary distribution systems and determining the types of structures that should be used in the system according to topological and electrical parameters of the network and, therefore, accurately assessing the costs involved in the construction and/or reform of secondary systems. A constructive heuristic based on information of the electrical and topological conditions between the medium voltage and low voltage systems is used to connect the primary systems and secondary circuits. The results obtained from planning and design simulations of a real secondary system of electric energy distribution are presented. <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. In this scope, renewable technologies which are considered as a kind of distributed generations are developing due to their environmental benefits. However, only a few renewable energies have proven to be competitive so far, while their economic viability is also limited to certain regions of the world. In this paper, an encouraging mechanism is proposed in favor of clean technologies in the planning process. This mechanism is defined based on a grant function of emission not polluted which is paid to DG owners to promote renewable and clean technologies. In the planning process, a multi-objective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions (cost and grant functions). The best planning scheme among the Pareto set is chosen based on a composite utility which are obtained through a Monte Carlo simulation of uncertain situations. Distributed generation technologies which are considered in this paper are conventional and renewable technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE). To assess the ability of the proposed method, a typical distribution system is used for expansion planning under two environmental scenarios. <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> This paper presents a dynamic multi-objective model for distribution network expansion, considering the distributed generations as non-wire solutions. The proposed model simultaneously optimizes two objectives namely, total costs and technical constraint satisfaction by finding the optimal schemes of sizing, placement and specially the dynamics (i.e., timing) of investments on DG units and/or network reinforcements over the planning period. An efficient heuristic search method is proposed to find non-dominated solutions of the formulated problem and a fuzzy satisfying method is used to choose the final solution. The effectiveness of the proposed model and search method are assessed and demonstrated by various studies on an actual distribution network. <s> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Conventional distribution planning method based on peak load brings about large investment, high risk and low utilization efficiency. A distribution network planning method considering distributed generation (DG) for peak cutting is proposed in this paper. The new integrated distribution network planning method with DG implementation aims to minimize the sum of feeder investments, DG investments, energy loss cost and the additional cost of DG for peak cutting. Using the solution techniques combining genetic algorithm (GA) with the heuristic approach, the proposed model determines the optimal planning scheme including the feeder network and the siting and sizing of DG. The strategy for the site and size of DG, which is based on the radial structure characteristics of distribution network, reduces the complexity degree of solving the optimization model and eases the computational burden substantially. Furthermore, the operation schedule of DG at the different load level is also provided. <s> BIB007 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> It is widely accepted that renewable energy sources are the key to a sustainable energy supply infrastructure since they are both inexhaustible and nonpolluting. A number of renewable energy technologies are now commercially available, the most notable being wind power, photovoltaic, solar thermal systems, biomass, and various forms of hydraulic power. In this paper, a methodology has been proposed for optimally allocating different types of renewable distributed generation (DG) units in the distribution system so as to minimize annual energy loss. The methodology is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DG units with their probabilities, hence accommodating this model in a deterministic planning problem. The planning problem is formulated as mixed integer nonlinear programming (MINLP), with an objective function for minimizing the system's annual energy losses. The constraints include the voltage limits, the feeders' capacity, the maximum penetration limit, and the discrete size of the available DG units. This proposed technique has been applied to a typical rural distribution system with different scenarios, including all possible combinations of the renewable DG units. The results show that a significant reduction in annual energy losses is achieved for all the proposed scenarios. <s> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Summary form only given: This paper presents a model for active distribution systems expansion planning based on Genetic Algorithms, where Distributed Generation (DG) integration is considered together with conventional alternatives for expansion, such as, rewiring, network reconfiguration, installation of new protection devices, etc. The novel approach of planning DG integration together with network expansion is a requirement for the modern active distribution network. However, the uncertainties related to DG power generation and load response growth must be taken into account in order to plan a safe system at a minimum cost. Thus, two different methodologies for uncertainties incorporation through the use of multiple scenarios analysis are proposed and compared. The multiple objectives optimization algorithm applied in the model takes into account the costs of reliability, losses, power imported from transmission, and network investments. <s> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Abstract A multi-objective planning approach for electrical distribution systems using particle swarm optimization is presented in this paper. In this planning, the number of feeders and their routes, number and locations of sectionalizing switches, and number and locations of tie-lines of a distribution system are optimized. The multiple objectives to determine optimal values for these planning variables are: (i) minimization of total installation and operational cost and (ii) maximization of network reliability. The planning optimization is performed in two steps. In the first step, the distribution network structure, i.e., number of feeders, their routes, and number and locations of sectionalizing switches are determined. In the second step, the optimum number and locations of tie-lines are determined. Both the objectives are minimized simultaneously to obtain a set of non-dominated solutions in the first step of optimization. The solution strategy used for the first step optimization is the Strength Pareto Evolutionary Algorithm-2 (SPEA2) based multi-objective particle swarm optimization (SPEA2–MOPSO). In the second step, the solutions/networks obtained from the previous step are further optimized by placement of tie-lines. SPEA2-based binary MOPSO (SPEA2–BMOPSO) is used in the second step of optimization. The proposed planning algorithm is tested and evaluated on different practical distribution systems. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <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> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Abstract This paper presents a novel dynamic programming approach for multi-objective planning of electrical distribution systems. In this planning, the optimal feeder routes and branch conductor sizes of a distribution system are determined by simultaneous optimization of cost and reliability. The multiple planning objectives are minimization of: (i) installation and operational cost, and (ii) interruption cost. The first objective function consists of the installation cost of new feeder branches and substations, maintenance cost of the existing and new feeder branches, and the cost of energy losses. The second objective function measures the reliability of the distribution network in terms of the associated interruption costs for all the branches, which includes the cost of non-delivered energy, cost of repair, and the customer damage cost due to interruptions. A dynamic programming based planning algorithm for optimization of the feeder routes and branch conductor sizes is proposed. A set of Pareto solutions is obtained using a weighted aggregation of the two objectives with different weight settings. The proposed approach is evaluated on 21-, 54-, and 100-node distribution systems. The simulation test results are analyzed with various case studies and are compared with those of two existing planning approaches based on multi-objective evolutionary algorithm. <s> BIB012 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> The main goal of this paper is to present a Multistage Distribution network Expansion Planning (MDEP) problem in the presence of Distributed Generations (DGs) in a multi-objective optimization framework. The proposed model simultaneously optimizes two objectives: minimization of investment and operation costs and maximization of reliability index. The proposed optimization model is solved subject to AC power flow constraints to obtain the optimal configuration of feeders (adding and removing lines) including the optimal size of branch conductor, replacement of conductor for reserve feeders, and generated power of DGs. To include reliability concerns in the proposed MDEP problem, an analytical approach on the basis of graph theory is implemented to evaluate the Energy-Not-Supplied (ENS) index as an extra objective function. Also, in this paper, in order to identify Pareto optimal solutions of the multi-objective MDEP problem, a hybrid Particle Swarm Optimization (PSO) and Shuffled Frog Leaping (SFL) algorithm is implemented. A synthetic distribution test system is considered for the MDEP problem in a 4-year planning horizon. The results of the hybrid PSO and SFL algorithm are compared with those of the classical PSO and SFL methods. <s> BIB013 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Multistage distribution network expansion because of load growth is a complex problem in distribution planning. The problem includes minimizing cost of objective function subject to technical constraints. The objective function consists of investment, operation and reliability costs. In this paper, HV/MV substations, main and reserve MV feeders, dispatchable DG sources and storage units are considered as possible solutions for multistage distribution expansion planning. A three-load level is used for variable load and some strategies are proposed for DG and storage units operation. A modified PSO algorithm is applied to solve the complex optimization problem. Numerical results of the case studies show the ability of the modification. Moreover, the proposed strategies improve the distribution network from both economical and reliability points of view compared with the other methods. <s> BIB014 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <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> BIB015 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming. <s> BIB016 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle and demand response have indispensable impacts on future smart grid. Since integration of electric vehicles into the grid is a key element to achieve sustainable energy systems, this paper studies the behavior of plug-in electric vehicle parking lots in the electricity markets. Participation of these new market players, as responsive demands, is modeled in both price-based and incentive-based demand response programs. Moreover, impacts of different demand response programs on optimal operational behavior of the parking lots are investigated. In addition to uncertainties of activated reserve, uncertainties of electric vehicle class, arrival and departure times, and initial battery state-of-charge are also considered. Stochastic programming is used to address the mentioned uncertainties. Numerical studies indicate that the participation of parking lots in different demand response programs can increase their profit. <s> BIB017 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> The concept of electricity markets in the deregulated environment generally refers to energy market and reactive power market is not paid attention as much as it deserves to. However, reactive power plays an important role in distribution networks to improve network conditions such as voltage profile improvement and loss reduction. Plug-in electric vehicles (PEVs) are mobile sources of active and reactive power, capable of being participated in energy market, and also in reactive power market without battery degradation. Active and reactive powers are coupled through the ac power flow equations and branch loading limits, as well as PEVs and synchronous generators capability curves. This paper presents a coupled energy and reactive power market in the presence of PEVs. The objective function is threefold, namely offers cost (for energy market), total payment function (for reactive power market), and lost opportunity cost, all to be minimized. The effectiveness of the proposed coupled energy and reactive power market is studied based on a 134-node microgrid with and without PEV participation. <s> BIB018 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Abstract The management of distributed energy resources (DER) in power distribution systems and the concept of demand side management (DSM) are becoming increasingly important in recent years, provided that emerging communication technologies contribute to the formation of smart distribution networks of the future. Several methods have been proposed in the literature for the optimal operation of smart distribution networks (OOSDN) with renewables and/or non-renewable DER, DSM and energy storage systems. The main scope of this paper is to review the most significant papers in the area of OOSDN and to introduce a taxonomy of models and optimization methods that are applied to the OOSDN problem. Moreover, the basic schemes for active network management are briefly presented. The article also discusses challenges and areas for future research in the field of OOSDN. <s> BIB019 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Distribution automation systems in terms of automatic and remote-controlled sectionalizing switches allows distribution utilities to implement flexible control of distribution networks, which is a successful strategy to enhance efficiency, reliability, and quality of service. The sectionalizing switches play a significant role in an automated distribution network, hence optimizing the allocation of switches can improve the quality of supply and reliability indices. This paper presents a mixed-integer nonlinear programming aiming to model the optimal placement of manual and automatic sectionalizing switches and protective devices in distribution networks. A value-based reliability optimization formulation is derived from the proposed model to take into consideration customer interruption cost and related costs of sectionalizing switches and protective devices. A probability distribution cost model is developed based on a cascade correlation neural network to have a more accurate reliability assessment. To ensure the effectiveness of the proposed formulation both technical and economic constraints are considered. Furthermore, introducing distributed generation into distribution networks is also considered subject to the island operation of DG units. The performance of the proposed approach is assessed and illustrated by studying on the bus 4 of the RBTS standard test system. The simulation results verify the capability and accuracy of the proposed approach. <s> BIB020 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> The proliferation of small-scale renewable generators and price-responsive loads makes it a challenge for distribution network operators (DNOs) to schedule the controllable loads of the load aggregators and the generation of the generators in real-time. Additionally, the high computational burden and violation of the entities' (i.e., load aggregators' and generators') privacy make a centralized framework impractical. In this paper, we propose a decentralized energy trading algorithm that can be executed by the entities in a real-time fashion. To address the privacy issues, the DNO provides the entities with proper control signals using the Lagrange relaxation technique to motivate them towards an operating point with maximum profit for entities. To deal with uncertainty issues, we propose a probabilistic load model and robust framework for renewable generation. The performance of the proposed algorithm is evaluated on an IEEE 123-node test feeder. When compared with a benchmark of not performing load management for the aggregators, the proposed algorithm benefits both the load aggregators and generators by increasing their profit by 17.8%and 10.3%, respectively. When compared with a centralized approach, our algorithm converges to the solution of the DNO's centralized problem with a significantly lower running time in 50 iterations per time slot. <s> BIB021 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Abstract Distributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network. The authors strongly recommend this review to researchers, scientists and engineers who are working in this field of research work. <s> BIB022 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> Smart cities are developed to enable modern functionalities (e.g., sustainable energy systems, smart power grids, and electrified transportation networks) and focus on the information and communication technologies (ICTs) in order to improve the operation and efficiency of the future cities. Further, the concept of smart cities is developed to integrate smart technologies and solutions with the foremost goal of improving the quality of life. The advanced metering, control, information, and communication technologies are the backbones of smart cities that are utilized to gather and analyze the data in order to provide energy-efficient, cost-efficient, reliable, and secure services. Smart cities focus on the coordination between different sectors including energy and transportation systems, water supply networks, and healthcare in order to enable more efficient applications. This chapter aims to provide a review on the smart cities and the needs of sustainability for the future cities, as well as the ongoing challenges for smart city implementation. Furthermore, an overview is provided to explain the interdependent power and transportation networks, as one of the key elements of future smart cities. Regarding the applications of smart cities, increasing amount of information should be gathered from the cities and community via distributed sensors, as well as the control devices. Privacy and security issues will rise due to the concerns regarding the protection of the collected data and transmitted control signals against the malicious behaviors. Such issues are also covered in this chapter. Thus, although smart cities can benefit citizens in a variety of aspects, there are some privacy and security concerns regarding the possible data leakage and malicious attacks. To enhance the acceptability of the high penetration of smart cities and their real implementation potential, all the presented aspects are addressed. <s> BIB023 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s>  <s> High penetration of renewable energy sources, especially weather-dependent sources, has increased the power systems uncertainties. For any analysis in power systems such as planning and operation, it is essential to confront the stochastic nature of these sources in order to get much more precise results. Since operators need proper strategies and methods to decline negative effects of the stochastic behaviour of renewable power generators, such as total operation cost growth, this paper provides a review of different state-of-the-art approaches from the operator's viewpoint for handling the stochastic behaviour of renewable sources. Hence, in this paper, three different strategies are categorized for stochastic analysis of these sources. The first strategy is mathematical modelling including stochastic dependency and independency, multi-dimensional dependence, forecast and scenarios. Afterwards, demand side management, which is one of the other approaches for dealing with these uncertainties, is investigated and different demand response programs and some methods to model them are presented. Finally, the effect of different electricity market schemes and relevant optimization methods to mitigate the variations of renewable energy sources are discussed. The study demonstrates that an operator should choose one or a combination of these three approaches based on its requirements. <s> BIB024
environment  , BIB012 . Several researchers introduced Different mathematical and meta-heuristic optimization methods to find an optimal solution to DEP whose complexity can be increased due to problem dimensionally. The added complexity can also be caused by making complicated decisions on the allocation of transformers and substations, finding feeders' routing for the primary and secondary systems, the time of investment, choice of complex objective functions, and different uncertainty resources BIB013 , BIB010 , BIB002 . Feeders' reinforcement, the expansion of distribution substations, and also the installation of distribution alternatives such as new feeders, substations, transformers, and typically open switches, are conventional DEP decision options. In recent years, the researchers and stakeholders are focused on expansion planning of DGs, and purchasing their generated power BIB011 , BIB009 , due to the advantages including economic benefits, reliability improvement, ancillary services, emission reduction, postponing and reducing other expansion requirements in both the transmission and distribution levels, and socio-political acceptance BIB001 BIB003 BIB006 BIB014 . Moreover, in reliability and service quality of DEP, allocating protective devices and automatic/manual switches, costeffectively is a significant trend BIB020 . In recent years, many researchers have developed optimization algorithms to assign the sectionalizing switches optimally. Sectionalizing switches are generally utilized in mediumvoltage distribution systems with the aim of improving service reliability. Plug-in Electric Vehicles (PEVs) integration is one of the other subjects of interest due to their capabilities to incorporate in the ancillary service market, participate in demand response programs, integrate into microgrids, as well as their ability to generate reactive power BIB017 BIB018 BIB023 . With the rising penetration of PEVs, we can improve the general system performance along with the consumption of fuel-based resources BIB017 . At the same time, high penetration of DGs may cause some problems in the conventional distribution networks with HV/MV substations as the sole power sources BIB021 , BIB024 , which we must consider in establishing a reliable and cost-effective operation of distribution networks BIB019 . Traditional and non-traditional DGs (in different studies the ''distributed'' part is also called dispersed, decentralised, district or with some modifications embedded, local, and onsite generation) must provide economically viable solutions and reliable services to consumers BIB022 . We usually divide the DG units into traditional combustion generators (e.g. diesel reciprocating generators and natural gas turbines), and nontraditional generators (e.g. fuel cells, storage devices and RERs such as wind turbines and photovoltaic units) BIB004 . In a DEP programming problem, binary variables describe installing new feeders and distribution substations, changing the type of conductors, and upgrading the capacity of resources (RERs). Limited sources of fossil fuels and its environmental hazards, make the RERs as a dominant choice in the planning decisions BIB015 , BIB005 with the low investment risk and flexibility BIB005 BIB016 BIB007 BIB008 . Fig. 1 shows an illustration of different aspects of a DEP problem.