Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> The paper addresses the problem of distribution network expansion planning under uncertainty. The deterministic problem is itself a large-scale, complex, difficult-to-define one; the combinatorial nature of the decision making can be efficiently handled by a specific evolutionary approach, We develop, in the evolutionary optimization context, a hedging algorithm to deal with scenario representation of uncertainty; decision making results in a convergence process for the first stage naturally conflicting decisions. The process robustness is discussed and illustration is provided for a comprehensive example. <s> BIB001 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> Abstract In this paper a novel approach for optimal location, sizing and determining the service areas of HV/MV substations in a long-range planning period using pseudodynamic methodology is presented. Costs of equipment, areas, cables in MV side and power loss are considered in cost function. Uncertainty in loads is modeled using LR fuzzy numbers in load points. For optimization procedure, genetic algorithm with a new coding is used. Load points splitting in each area between substations and infeasibility solutions are considered in search procedure. Based on this, a software was developed and integrated into an optimization tool for substation allocation. The software was applied in many cases with satisfactory results. <s> BIB002 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> This paper presents a new possibilistic (fuzzy) model for the multiobjective optimal planning of power distribution networks that finds out the nondominated multiobjective solutions corresponding to the simultaneous optimization of the fuzzy economic cost, level of fuzzy reliability, and exposure (optimization of robustness) of such networks, using an original and powerful meta-heuristic algorithm based on Tabu Search. This model determines the optimal location and size of the future feeders and substations in distribution networks with dimensions significantly larger than the ones usually presented in papers on the matter. The model also allows to determine the optimal reserve feeders (location and size) that provide the best distribution network reliability at the lowest cost for a given level of robustness (exposure). The model and the algorithm have been intensively tested in real distribution networks, which proves their practical application to large power distribution systems. <s> BIB003 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties <s> BIB004 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> This paper is the first of a two-part paper on optimal distribution system planning. The horizon distribution planning problem and optimal distribution system model formulation are described. The horizon planning mission is to minimize future costs by determining optimal design parameters given assumptions about the future. Prior work addressed short-range and expansion planning of subsets or combinations of design parameters. The many distribution requirements and associated constraints inhibited an all-inclusive evaluation of total horizon design requirements for the 20+ year period. The proposed model and optimization formulation provides a generalized horizon planning approach and introduce a fully functioning comprehensive horizon planning model using a perspective that encompasses all necessary parameters and constraints. Parameters include: substation and distribution transformer capacities; number, size, and lengths of distribution feeders and secondary conductors; and primary voltage class. Optimal design voltage drops and reliability indices are determined. The horizon planning optimization application is described and solved in the second companion paper using continuous constrained nonlinear programming methods. The application is demonstrated with Snohomish PUD case studies <s> BIB005 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <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> BIB006 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <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> BIB007 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <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> BIB008 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <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> BIB009 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> Distributed generation (DG) systems are considered an integral part in future distribution system planning. The active and reactive power injections from DG units, typically installed close to the load centers, are seen as a cost-effective solution for distribution system voltage support, energy saving, and reliability improvement. This paper proposes a novel distribution system expansion planning strategy encompassing renewable DG systems with schedulable and intermittent power generation patterns. The reactive capability limits of different renewable DG systems covering wind, solar photovoltaic, and biomass-based generation units are included in the planning model and the system uncertainties such as load demand, wind speed, and solar radiation are also accounted using probabilistic models. The problem of distribution system planning with renewable DG is formulated as constrained mixed integer nonlinear programming, wherein the total cost will be minimized with optimal allocation of various renewable DG systems. A solution algorithm integrating TRIBE particle swarm optimization (TRIBE PSO) and ordinal optimization (OO) is developed to effectively obtain optimal and near-optimal solutions for system planners. TRIBE PSO, OO, and the proposed algorithm are applied to a practical test system and results are compared and presented. <s> BIB010 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> The planning and development of distribution networks with a substantial penetration of microgrids connected to the medium voltage (MV) network form the main themes of this paper. The impact of microgrids is assessed in terms of their effect on optimal network topology, losses, reliability, reserve connections, network upgrade and expansion savings. The earning base of the distribution system operator also comes under scrutiny. A suburban MV cable network is planned using a network planning algorithm developed by the authors, first with optimal routing for demand-only nodes and then with a 33% penetration of randomly located microgrids. The network is then expanded to meet the requirements of a future planning horizon, in order to compare the expanded and upgraded optimum MV network topology with and without microgrids. Apart from visually depicting the topological differences, the savings such microgrids can give to the long term distribution network investment and running costs are quantified in terms of the investment costs, loss costs and interruption costs. When networks are planned with optimal rather than full backup, the introduction of microgrids is shown to have a considerable saving impact on all cost components except the cost per unit power transfer in the distribution network. <s> BIB011 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <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> G. DEALING WITH UNCERTAINTIES <s> Abstract The optimal expansion planning of electric power distribution network to meet system load growth and overcome to pseudo dynamic behavior of network parameters considering a large number of constraints is a hard satisfactory multiobjective optimization problem. This paper implements new developed Imperialist Competitive Algorithm (ICA) for the optimal expansion planning of distribution network. The topology of medium voltage (MV) distribution network as backbone of electric power distribution systems is designed by optimal sizing, siting and timing of medium voltage network components such as HV substation and MV feeders’ routes. A multistage expansion planning is proposed to consider dynamic behavior of the system parameters asset management and geographical constraints. In order to reach the global solution an efficient coding is developed for ICA parameters. The Greedy algorithm is used to solve the minimum spanning tree problem to construct a radial configuration of the mesh network. At each stage of the problem the results are fully illustrated either by figures or by tables. A sensitivity analysis is used to show the robustness of the results with respect to ICA parameters variation. The obtained results are compared with GA as well known heuristic optimization tool. The efficiency and capability of the methodology has been tested on an under developed relatively large-scale distribution network. <s> BIB013 </s> Overview of Electric Energy Distribution Networks Expansion Planning <s> G. DEALING WITH UNCERTAINTIES <s> Abstract This paper coordinates the reconfiguration of distribution systems with the expansion problem while the potential of demand response (DR) programs and distributed generation (DG) units are modeled in the active distribution expansion planning. The concept of system of systems (SoS) is proposed to model the expansion of DGs that are owned by private investors. SoS is an efficient system consisting of some autonomous and heterogeneous systems with distinct objective functions. According to the concept of SoS, a decision-making paradigm is developed to determine the location, size, and time of DG investment made by a commercial agent, as well as the price of generated power. From the distribution company (DISCO) viewpoint, the proposed model is a multi-objective (MO) optimization problem. The first objective function is the net present value of the total investment and operation costs related to the network. The second objective function is a reliability index, i.e. the expected energy not-supplied (EENS). The uncertainty of load growth in future years is handled by using a scenario-based approach. The introduced problem is solved by using multi-objective particle swarm optimization (MOPSO) algorithm empowered with an innovative three-layer procedure that is provided to better manage the space of the decision variables. The first layer is based on PSO particles while the second and third layers are based on a sensitivity analysis. Finally, a standard 33-bus distribution system is utilized to obtain the simulation results that show the performance and advantages of the proposed method. <s> BIB014
Due to the different kinds of uncertain parameters (like economic or technical; controllable or uncontrollable; non-stochastic or stochastic; and measurable or unmeasurable), the way of modelling the uncertainties is a difficult task  . The main uncertainties in the DEP problem are: load level  , BIB009 , BIB008 , BIB007 , BIB010 , BIB003 , BIB004 , BIB013 , BIB001 , the location of load points BIB002 , BIB001 , tax for demand BIB004 , energy price BIB006 , BIB013 , price of purchasing electricity from DG BIB005 , investment cost BIB001 , and power supplied by DG  , BIB009 , BIB008 , BIB012 , BIB007 , BIB011 , BIB013 . The unavoidable impacts of uncertainty resources in real-world problems force planners to consider the effects of such resources in the decision making using probabilistic tools BIB014 . Fig. 9 lists the uncertainty parameters and Fig. 10 illustrates the frequency percentile graph of papers considering different types of uncertainty parameters. In the following, we briefly discuss several methods in the literature to deal with uncertain parameters.