Distribution Network Expansion Research Articles

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.

Multi-Stage Planning of Active Distribution Networks Considering the Co-Optimization of Operation Strategies

This paper presents a multi-stage and multi-load-scenario active distribution network (ADN) expansion planning model. The proposed model considers the applications of new distributed generation (DG) and construction of feeders at the planning level, and the utilization of DG supply and topology reconfiguration of distribution network including microgrid at the operation level. The proposed co-optimization model incorporates both investment and operation costs of ADN in the objective function. The ADN operation at each time stage is divided into several scenarios based on the load forecast data, in which the optimal reconfiguration of the ADN topology and the power output of DG units are calculated. The co-optimization considers investment decisions at each planning stage and operation strategies at each ADN loading scenario. The benefits of introducing DG and network construction as planning options, and DG supply and network reconfiguration as operation strategies, are discussed. The effectiveness of the proposed co-optimization of investment-operation is demonstrated using the numerical results.

Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part II: Numerical Results

The second part of this two-paper series analyzes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for the isolated system of La Graciosa, Canary Islands, Spain. Based on the stochastic programming model developed in its companion paper, the impact of DR and ESS on the location and size of new generation and storage units and the distribution assets to be installed, reinforced, or replaced are defined. Numerical results illustrate the effective performance of the proposed approach. Additionally, a set of metrics showing the welfare achieved by the different stakeholders has been implemented.

Joint Distribution Network and Renewable Energy Expansion Planning Considering Demand Response and Energy Storage—Part I: Stochastic Programming Model

The first part of this two-paper series describes the incorporation of demand response (DR) and energy storage systems (ESSs) in the joint distribution and generation expansion planning for isolated systems. The role of DR and ESS has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR and ESS on an equal footing. The model presented includes DR and ESS in the planning of insular distribution systems. Hence, this paper presents a novel model to decide the joint expansion planning of distributed generation and the distribution network considering the impact of ESS and price-dependent DR programs. The problem is formulated as a stochastic-programming-based model driven by the maximization of the net social benefit. The associated deterministic equivalent is formulated as a mixed-integer linear program suitable for commercially available software. The outcomes of the model are the location and size of new generation and storage units and the distribution assets to be installed, reinforced or replaced. In the second companion paper, an insular case study (La Graciosa, Canary Islands, Spain) is provided illustrating the effects of DR and ESS on social welfare.

Data‐driven partition control strategy for harmonic distortion and voltage unbalance

Local controls for harmonic distortion and voltage unbalance have become inapplicable due to the increasing penetration and decentralisation of disturbance sources. The complication and expansion of distribution networks cause difficulties in measuring certain system parameters, which reduce the practicability of global optimisation controls. Accordingly, this study introduces a partition control strategy driven by the disturbance data of nodes, which is used to allocate active power filters (APFs) to individually or simultaneously suppress harmonic distortion and voltage unbalance in distribution networks. A multidimensional piecewise linear representation based on local extreme points is proposed to extract the trend features of univariate disturbance time series (UDTS) and multivariate DTS (MDTS). Trend features, disturbance severities, and subsection lengths are used to establish a parameter matrix as the pattern representation of DTS. A feature distance (FD) method is developed to implement pattern matching on UDTS or MDTS to measure the coupling degrees of single or integrated disturbance among nodes. Area division and APF allocation are performed based on the coupling degrees. Case studies on a 14-bus distribution system are conducted to verify the rationality of the partition control strategy and the accuracy of the FD method.

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.

A flexible active distribution system expansion planning model: A risk-based approach

This paper presents an active distribution network expansion planning framework, which concurrently uses the renewable distributed generations and energy storage systems as capacity expansion options. In order to enhance the network reliability, the model takes into account the island mode operation of the renewable resources and energy storage systems. The proposed planning framework, which is modeled as a probabilistic bi-level optimization problem quantifies and controls the economic risk level associated with the stochastic nature of these resources. The master level is devoted to the here-and-now decisions in the planning phase, whereas the slave level, which is formulated as a two-stage model, is related to the wait-and-see decisions in the operational phase. At the first stage of the slave level, the network operational behaviour is determined by performing an optimal power flow modeled as a mixed-integer linear programming problem. At the second stage of the slave problem, the network reliability is optimized considering island mode operation and taking into account energy-limited nature of storage systems. The effectiveness of the proposed active distribution network planning model is demonstrated through several case studies. Simulation results demonstrate that the proposed approach can result in a flexible low-risk plan for the expansion of the network.

Optimal Asset Expansion in Distribution Networks Considering Battery Nonlinear Characteristics Expansion optimale des actifs dans les réseaux de distribution en tenant compte des caractéristiques non linéaires des batteries

Asset expansion planning in distribution systems is vital and should be extended to consider utility-scale energy storage systems such as batteries. Unlike other assets, usage parameters such as number of storage cycles and depth of discharge (DOD) have a dramatic nonlinear effect on the lifetime of battery energy storage systems (BESSs). Hence, it is imperative to include the relationship between lifetime, number of storage cycles, and DOD of BESS in the optimal asset planning formulation. This paper presents a new formulation and solution for the optimization problem of asset expansion planning in power distribution systems. The research considers adding new BESS units to existing distribution grids. The nonlinear life-cycling-usage relationship of BESS has been modeled for the first time in the context of asset expansion planning in power systems. The formulation aims at minimizing the annualized cost of the expansion plan while satisfying forecasted demand and other distribution system requirements. The methodology is used to optimally plan for the expansion of 6-bus and 33-bus distribution networks. The results show the effect of considering the life-cycling-usage relationship of BESS on optimal asset expansion plans including the optimal size and capacity of the assets. In addition, the impact of the ratio of off-peak load to peak load on total asset cost is analyzed and reported. It is shown that an annual cost saving of 51.79% is possible via the proposed approach. Findings of this paper will capture the attention of planning and asset management departments of electric distribution utilities.

Active Distribution Network Expansion Planning Considering Distributed Generation Integration and Network Reconfiguration

Active Distribution Network Expansion Planning Considering Distributed Generation Integration and Network Reconfiguration

The renewable energy sector in Afghanistan: Policy and potential

Afghanistan has one of the lowest rates of access to and usage of electricity in the world. Fuelwood, charcoal, agricultural, and animal waste still dominate in meeting energy needs for cooking and heating, with a large percentage of the population using kerosene, candles, and gas for lighting. Yet the situation has changed significantly since the U.S. and coalition combat operations that began at the end of 2001. The period through to the present day has seen a fivefold increase in the electrification rate; agreements with Central Asian countries and Iran for importing electricity; the implementation of thousands of small‐scale renewable energy projects in rural areas; the development and rehabilitation of several large‐scale energy projects; the (partial) development of the institutional, policy, and regulatory landscape; and the expansion of transmission lines and distribution networks. Although accurate information is scarce, Afghanistan has both substantial fossil fuel reserves and renewable energy potential. The country's wind power potential alone looks likely to exceed projected power demand for several decades ahead. Similarly, both the estimated hydropower and solar photovoltaic (PV) potential each exceed projected 2032 power demand. The institutional context of the Afghanistan energy sector is complex, comprising multiple ministries, government agencies, aid agencies, and intergovernmental organizations. Nonetheless, given suitable coordination, the technologies, natural resources, and capabilities are available for transforming the sector and the lives of many people. WIREs Energy Environ 2018, 7:e280. doi: 10.1002/wene.280This article is categorized under: Energy Infrastructure > Climate and Environment Energy Policy and Planning > Economics and Policy Energy Policy and Planning > Systems and Infrastructure

Review of Machine Vision Based Insulator Inspection Systems for Overhead Power Distribution System

The necessity to have reliable and quality power distribution is increasing, and hence there is great scope for research on automation of distribution system. There are signs of increased research in the work on condition monitoring of insulators during the last few decades. The possible failures can be predicted before they actually occur by using the condition monitoring of cables or any electrical equipment on-line. Those assets such as towers, conductors and insulators which are on the threshold of failure have to be replaced or repaired, so that forced outages reduce. Traditionally the workers who inspect these lines check them in close proximity by going for foot-patrolling and pole-climbing. With an incredible expansion of power distribution network even to remote areas, previously mentioned methods do not seem to be viable. In developed countries aerial patrolling has been adopted to monitor the insulators as an alternative. The development of an efficient method of condition monitoring by using image processing followed by machine learning techniques is found to be a suitable method and thus emerging as a feasible option for real-time implementation. This review paper covers overall aspects of automatic detection of defects of insulator systems of electric power lines and classification into different classes by using vision-based techniques.

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Accurate and reliable forecasting models for electricity demand (G) are critical in engineering applications. They assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for the National Electricity Market (NEM) in Australia, including the expansion of distribution networks, energy pricing, and policy development. In this study, data-driven techniques for forecasting short-term (24-h) G-data are adopted using 0.5 h, 1.0 h, and 24 h forecasting horizons. These techniques are based on the Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. This study is focused in Queensland, Australia’s second largest state, where end-user demand for energy continues to increase. To determine the MARS and SVR model inputs, the partial autocorrelation function is applied to historical (area aggregated) G data in the training period to discriminate the significant (lagged) inputs. On the other hand, single input G data is used to develop the univariate ARIMA model. The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models. The accuracy of the G forecasts, with respect to the measured G data, is assessed using statistical metrics such as the Pearson Product-Moment Correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Normalized model assessment metrics based on RMSE and MAE relative to observed means (RMSEG¯andMAEG¯), Willmott’s Index (WI), Legates and McCabe Index (ELM), and Nash–Sutcliffe coefficients (ENS) are also utilised to assess the models’ preciseness. For the 0.5 h and 1.0 h short-term forecasting horizons, the MARS model outperforms the SVR and ARIMA models displaying the largest WI (0.993 and 0.990) and lowest MAE (45.363 and 86.502 MW), respectively. In contrast, the SVR model is superior to the MARS and ARIMA models for the daily (24 h) forecasting horizon demonstrating a greater WI (0.890) and MAE (162.363 MW). Therefore, the MARS and SVR models can be considered more suitable for short-term G forecasting in Queensland, Australia, when compared to the ARIMA model. Accordingly, they are useful scientific tools for further exploration of real-time electricity demand data forecasting.

Bi-Level Approach to Distribution Network and Renewable Energy Expansion Planning Considering Demand Response

This paper presents a bi-level model for distribution network and renewable energy expansion planning under a demand response (DR) framework. The role of DR has recently attracted an increasing interest in power systems. However, previous models have not been completely adapted in order to treat DR on an equal footing. The target of the distribution network and generation planner, modeled through the upper-level problem, is to minimize generation and network investment cost while meeting the demand. This upper-level problem is constrained by the lower-level problem, stressing the importance of integrating DR to time-varying prices into those investment models. The objective function considered for the lower level is the minimization of overall payment faced by the consumers. Using the Karush-Kuhn-Tucker complementarity constraints, the proposed bi-level model is recast as a mixed-integer linear programming problem, which is solvable using efficient off-the-shelf branch-and-cut solvers. Detailed results from an insular case study (La Graciosa, Canary Islands, Spain) are presented.

Active Distribution Network Expansion Planning Integrating Practical Operation Constraints

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

Smart Planning: an innovative tool for the investment planning of smart distribution networks

An innovative distribution network expansion planning tool for smart distribution systems is proposed in this study. The tool supports the smart grid concept by evaluating the value of flexibility of generation and load in the optimal expansion planning of the distribution system in addition to the traditional infrastructure and operating costs. Additional to the classical decision variables in planning like the feeders/lines to be replaced, the tool also considers the flexibility of load and generation as the decision variables in the expansion planning of the network. The study illustrates the various features of the tool with the help of a real-world distribution network simulation results.

Approach to large distribution network optimisation using modern implementation of benders decomposition

Long-term electricity distribution network expansion planning problems (EDNEPPs) are complex combinatorial problems due to the large number of variables involved. Therefore, exact mathematical methods can only be applied to solve EDNEPPs for small-scale systems. Due to this reason, in the literature, to solve EDNEPPs for real-size networks, a variety of solution techniques have been utilised, with varying results. Early research on solving EDNEPPs focused on simplification of the model, while later research used decomposition techniques to solve EDNEPPs for real-sized networks. However, with the rapid changes and increased complexities of today's EDNEPPs, it has been difficult or impossible to solve EDNEPPs for real-sized networks using traditional decomposition approaches. Therefore, many authors have recently applied heuristics or local optimisation techniques, but these methods do not guarantee exact, globally optimal solutions, and provide little indication of solution quality. Recently, many new problem reformulation and decomposition techniques have been reported to cope with large-scale optimisation problems. However, there is a major void in the literature surrounding applications of modern problem decomposition and reformulation techniques from operations research to solve EDNEPPs. This study presents a modern approach to convex outer approximation using lazy constraints and capabilities of modern solvers for solving EDNEPPs.

Modeling and optimization of an electric power distribution network planning system using mixed binary integer programming

In this paper, the electric distribution network expansion planning problem (EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using piecewise linearization of the non-linear expressions, so as to ensure the model computational compatibility with existing commercial optimization solvers. The linearized formulation is verified to ensure its solution optimality and degree of error deviation. The proposed network model formulation considers the alternatives of installation of new transformers of various capacities to reinforce already existing ones at substations of the network, choosing and construction of new substations given feasible locations, re-conductoring of existing feeders in the network, construction of new feeders given various conductor types alternatives, cost lost as a result of power interruption, and changes in the overall network topology. The cost of interruption would contain a cost term called ‘cost of goodwill’, which was brought into the model formulation, to measure the loss in confidence of consumers to distributors of power as a result of interrupted power supply, which is prevalent in developing nations. Two test systems of 23 and 54 nodes was used in showing the efficiency of the proposed network model formulation. http://dx.doi.org/10.4314/njt.v36i2.31

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

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

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

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