Electric Distribution Network Planning

The widespread growth of distributed generation (DG), mainly due to its numerous operational and planning benefits and to the penetration of renewable energy, inevitably requires the inclusion of this kind of generation in distribution planning models. This chapter addresses the multistage expansion planning problem of a distribution system where investments in the distribution network and in DG are jointly considered. The optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets. The incorporation of DG in distribution system expansion planning drastically increases the complexity of the optimization process. In order to shed light on the modeling difficulties associated with the co-optimized planning problem, a deterministic model is presented first. The model is driven by the minimization of the net present value of the total cost including the costs related to investment, maintenance, production, losses, and unserved energy. As a relevant feature, radiality conditions are specifically tailored to accommodate the presence of DG in order to avoid the islanding of distributed generators and the issues associated with transfer nodes. Since a large portion of DG relies on non-dispatchable renewable-based technologies, the uncertainty associated with the high variability of the corresponding energy sources needs to be properly characterized in the planning models. Based on the previous deterministic model, uncertainty is incorporated using a stochastic programming framework. Within such a context, the uncertainty featured by renewable-based generation and demand is characterized through a set of scenarios that explicitly capture the correlation between uncertainty sources. The resulting stochastic program is driven by the minimization of the total expected cost. Both deterministic and stochastic optimization problems are formulated as mixed-integer linear programs for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Numerical results illustrate the effective performance of the approaches presented in this chapter.

This chapter presents static and dynamic optimization-based models for planning the electric distribution network. Based on a branch flow model, two Mixed-Integer Conic Quadratic Programming (MICQP) convex formulations are proposed to solve the network expansion planning models including high modeling fidelity of the intrinsic interaction of the manifold elements of the networks. The objective of the presented models is to minimize investment and operation costs by optimally deciding on installing new feeders and/or changing existing ones for others with larger capacities, installing new substations or expanding existing ones and, finally, installing capacitor banks and voltage regulators, modifying the network topology. In addition, discrete tap settings of voltage regulators are modeled as a set of mixed-integer linear equations, which are embedded in an ac optimal power flow. The presented MICQP models are convex optimization problems. Therefore globality and convergence are guaranteed. Computational results to verify the efficiency of the proposed methodology are obtained for a 24-node test system. Finally, conclusions are duly drawn.

Electric distribution networks should be prepared to provide an economic and reliable service to all customers, as well as to integrate technologies related to distributed generation, energy storage, and plug-in electric vehicles. A proper representation of the electric distribution network operation, taking into account smart grid technologies, is key to accomplish these goals. This chapter presents mathematical formulations for the steady-state operation of electric distribution networks, which consider the unbalance of three-phase grids. Mathematical models of the operation of smart grid related devices present in networks are discussed (e.g., volt-var control devices, energy storage systems, and plug-in electric vehicles). Furthermore, features related to the voltage dependency of loads, distributed generation, and voltage and thermal limits are also included. These formulations constitute a mathematical framework for optimization analysis of the network operation, which makes it possible to model decision-making processes. Different objectives related to technical and/or economic aspects can be pursued within the framework; in addition, the extension to multi-period and multi-scenario optimization is discussed. The presented models are built based on mixed integer linear programming formulations, avoiding the use of conventional mixed integer nonlinear formulations. The application of the presented framework is illustrated throughout control approaches for the voltage control and the plug-in electric vehicle charging coordination problems.

This chapter presents an approach for Integrated Distributed Generation and primary-secondary network Expansion Planning (IDGNEP) in the presence of wholesale and retail markets. The presented method uses a unified model to explore the impacts of retail market participants on the IDGNEP procedure. While the theory and practice of IDGNEP have advanced over the years, the Non-Utility Retail Market Participants (NURMPs) and Customers’ Active MicroGrids (CAMGs) introduce some other resources which can also be included in distribution network planning exercises. An electric distribution network may interchange energy with wholesale/retail market participants and downward CAMGs. When the volume of the energy interchanged between the network and NURMPs/CAMGs is comparable with the volume of electricity delivered to the end users, the IDGNEP results may considerably be different from the condition that no energy is interchanged. The presented model of IDGNEP is a Mixed Integer Non Linear Programming (MINLP) problem and the introduced algorithm decomposes the IDGNEP problem into multi sub-problems to achieve an optimal expansion planning of a network, in which the investment and operational costs are minimized, while the reliability of the network is maximized. Demand Side Management (DSM) programs, Distribution Automation (DA) investment alternatives and NURMP and CAMGs contribution scenarios which may significantly change the network’s resources are considered in IDGNEP formulation. The algorithm was successfully tested for an urban distribution network.

The volatile feed-in of distributed generation based on renewable energy sources as well as new and intelligent loads and storages require an appropriate consideration in the distribution grid planning process. With the conventional planning method being dependent on extreme scenarios, the consideration is very limited. Therefore, a new planning tool based on the concept of a multi-agent system is presented. In this system, every network user is represented by an agent, allowing not only the consideration of the volatile feed-in characteristics of renewable energy sources but also of the dependencies between the network users and their environment. Every network user is modeled as an agent of its own, guaranteeing the preservation of its individual character. Within this chapter, a system overview is given and the agent design process demonstrated on the example of the household load agent and the storage agent, including negotiations. This multi-agent system generates time series for all relevant system variables, defining detailed input parameters in the distribution grid planning process. The probabilities of occurrence of loading situations can be derived from the time series. For the first time, this allows for a detailed determination of the conditions in the up to now rarely measured medium and low voltage grids. As a consequence, new assumptions for the planning process are derivable, permitting a demand- and future-oriented grid planning and avoiding over-dimensioning of the grids.

Recently, the penetration of distributed generations (DG) has been obviously increased in electric distribution networks throughout the world. DGs are small scale generators connected near load centers in networks, thereby avoiding losses in transmission systems and releasing system capacity. At present, there are many types of DG, such as wind power, solar power, fuel cell, biomass, micro-turbines, and diesel engines. DG can play an important role in improving the performance of the networks; therefore, allocating DG optimally is one of the most crucial subjects in DG planning. In this chapter, the DG allocation problem is studied, and an efficient method is presented for accurately solving this optimization problem. The proposed method combines between analytical expressions and an optimal power flow (OPF) algorithm to determine the optimal locations, sizes and the best mix of various DG types for minimizing the total real power loss in electric distribution networks. The proposed analytical expressions are general for directly calculating the optimal sizes of any combination of multi-type DG technologies. The optimal power factors of the various units can be analytically computed, thereby contributing positively to loss reduction. The 69-bus test system is used to test the proposed method. The effectiveness of the proposed method is demonstrated for determining the optimal mix of various combinations of different DG types.

Rechargeable grid-scale batteries are suitable and mature technology for energy storage in active distribution networks. Battery energy storage (BES) units have many advantages and are used for several purposes in electric systems and distribution grids. They are used not only for peak shaving and voltage regulation, but also for reliability enhancement and dispatching the renewable-based distributed generation (DG) sources. However, BES technologies are still expensive and need to be employed optimally to prevent excess investment cost. Optimal planning of BES is a complex approach that determines the type, location, capacity and power rating of energy storage units. The optimization should handle the uncertain conditions and it requires to develop the appropriate models and methods. There are many effective components that should be addressed. These components influence the results of the optimal planning and make it more complicated. In this chapter the optimal BES planning methodologies are presented. Firstly the optimization problem is formulated considering different economic perspectives. Then the approaches and strategies for solving the combinatorial problem are described. In this way, both the probabilistic and possibilistic methods and models are displayed. In addition, the most important components and factors that affect the optimal planning are characterized and analyzed, including conventional DGs, renewable-based DGs, capacitor banks, plug-in electric vehicles, etc.

This chapter introduces the Optimal Distributed Generation Placement problem towards power and energy loss minimization. Several solving methods are applied in order for the most suitable to emerge. Apart from technical and DG constraints, recent raised issues due to high Distributed Generation penetration like the reverse power flow effect is considered as well. The load and generation variability and their impact in integrating Renewable Energy Sources are examined, aided by the use of Capacity Factors implementation. In addition, the impact of Optimal Distributed Generation Placement problem in conjunction with Network Reconfiguration and Optimal Energy Storage Systems Placement is introduced aiming to examine how joined management schemes could be efficiently combined in order to maximize the potential loss and energy reduction.

This chapter presents a hybrid methodology based on a local search algorithm and a genetic algorithm, used to address the multi-objective and multistage optimal distribution expansion planning problem. The methodology is conceived to solve optimal network investment problems under the new possibilities enabled by the smart grid, namely the new observability and controllability investments that will be available to enable demand response in the future. The multi-objective methodology is applied to an existing low-voltage electric distribution network under a congestion scenario to yield a Pareto-optimal set of solutions. The solutions are then projected onto the two investment possibilities considered: demand control investments and traditional network asset investments. The projected surface is then analyzed to discuss the merit of demand control with respect to postponing traditional asset investments.

This chapter introduces simultaneous optimization concept of Network Reconfiguration and Distributed Generation sizing. The main objective of the introduced concept is to reduce the real power loss and improve the overall voltage profile in the electric distribution network through optimal network reconfiguration and Distributed Generation sizing, while at the same time satisfy the system operating constraints. The meta-heuristic methods have been applied in the optimization process due to its excellent capability for searching optimal solution in a complex problem. The applied meta-heuristics methods are Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization, Artificial Bee Colony and their respective modified types. A detail performance analysis is carried out on IEEE 33-bus systems to demonstrate the effectiveness of the proposed concept. Through simultaneous optimization, it was found that power loss reduction is more as compared to conducting reconfiguration or DG sizing approach alone. The test result also indicated that Evolutionary Particle Swarm Optimization produced better result in terms of power loss and voltage profile than other methods.

This chapter investigates implementation of some parking lots for a plug-in electric vehicle (PEV) aggregator to participate in energy market. Herein, behaviors of the PEVs’ drivers regarding their cooperation with the aggregator with respect to the introduced incentive (value of discount on charging fee of PEVs) are modeled. The considered incentive includes the value of discount on the charging fee of PEVs’ batteries. In addition, the capability of parking lots for transacting electrical energy is modeled based on the hourly arrival/departure time of PEVs to/from the parking lots and the hourly state of charge (SOC) of PEVs’ batteries. Also, the degradation of PEVs’ batteries is modeled based on the effective ampere-hours throughput of the PEVs’ batteries due to vehicle-to-grid (V2G). Moreover, the economic factors such as inflation and interest rates and the technical factors including the PEVs’ batteries power limit, the depth of discharge (DOD) constraint of PEVs’ batteries, the yearly maintenance of parking lot, and the yearly replacement rate of the conventional vehicles with the PEVs are taken into consideration in the problem over the definite planning horizon. Furthermore, due to variability and uncertainties involved with the energy market prices and the PEVs’ drivers’ behavior, the planning problem is solved stochastically.

Electric distribution networks mainly deliver the electric power from the high-voltage transmission system to the consumers. In these networks, the R/X ratio is significantly high compared to transmission systems hence power loss is high (about 10–13% of the generated power). Moreover, poor quality of power including the voltage profile and voltage stability issues may arise. The inclusion of shunt capacitors and distributed Flexible ac transmission system (D-FACTS) devices can significantly enhance the performance of distribution networks by providing the required reactive power. D-FACTS include different members such as; distributed static compensator (DSTATCOM), Distribution Static Var Compensator (D-SVC) and unified power quality conditioner (UPQC). Optimal allocation of these controllers in the distribution networks is an important task for researchers for power loss minimizing, voltage profile improvement, voltage stability enhancement, reducing the overall system costs and maximizing the system load ability and reliability. Several analytical and optimization methods have been presented to find the optimal siting and sizing of capacitors and shunt compensators in electric distribution networks. This chapter presents a survey of new optimization techniques which are used to find the optimal sizes and locations of such devices. This chapter also presents an application of new optimization technique called Grasshopper Optimization Algorithm (GOA) to determine the optimal locations and sizes of capacitor banks and DSTATCOMs. The obtained results are compared with different algorithms such as; Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA).

This chapter presents a methodology for the allocation of Automatic Reclosers (AR) in medium voltage electric distribution networks. The methodology defines strategic positions for installing Normally Closed (NC) and Normally Opened (NO) reclosers to improve the system’s performance in terms of quality of power supply. The restriction relies on the budget available for investing in purchasing and installing AR. The methodology supports power distribution planning activities, as it focusses on defining the optimal positions for installing reclosers in a large network. Due to the size of the electric distribution networks considered during planning activities, hundreds different positions for installing Normally-Opened Automatic Reclosers (NO-AR) and Normally-Closed Automatic Reclosers (NC-AR) must be assessed. To deal with the size of the problem, covering all states the network may assume and assuring the positions for installing AR were optimum ones, the proposed methodology divides this problem into three states. Through this approach, the planning engineer need to carry out several simulations in just a few minutes, evaluating the technical benefits achieved from different investment levels. Similar approaches could not be found in the current literature. The methodology was assessed considering two substations of a Brazilian electric distribution company, corresponding to twenty-five medium voltage feeders. Two analyses were carried out: the brown field analysis, where the positions of thirty new automatic reclosers were determined; and the green field analysis, where forty-five existing automatic reclosers were reallocated. The results indicate significant improvements in quality of service indices, which may reach over 30% reduction level.