Distribution Network Expansion Planning Research Articles

Reinforcement Learning for Efficient Power Systems Planning: A Review of Operational and Expansion Strategies

The efficient planning of electric power systems is essential to meet both the current and future energy demands. In this context, reinforcement learning (RL) has emerged as a promising tool for control problems modeled as Markov decision processes (MDPs). Recently, its application has been extended to the planning and operation of power systems. This study provides a systematic review of advances in the application of RL and deep reinforcement learning (DRL) in this field. The problems are classified into two main categories: Operation planning including optimal power flow (OPF), economic dispatch (ED), and unit commitment (UC) and expansion planning, focusing on transmission network expansion planning (TNEP) and distribution network expansion planning (DNEP). The theoretical foundations of RL and DRL are explored, followed by a detailed analysis of their implementation in each planning area. This includes the identification of learning algorithms, function approximators, action policies, agent types, performance metrics, reward functions, and pertinent case studies. Our review reveals that RL and DRL algorithms outperform conventional methods, especially in terms of efficiency in computational time. These results highlight the transformative potential of RL and DRL in addressing complex challenges within power systems.

Active Distribution Network Expansion Planning With Dynamic Thermal Rating of Underground Cables and Transformers

The rapid development of renewable distributed generation in active distribution networks (ADNs) imposes an increasing burden on the transfer capability of the ADNs, bringing new challenges to the distribution network expansion planning (DNEP) problem. Dynamic thermal rating (DTR), which evaluates the equipment rating based on the actual weather conditions and equipment thermal states, can enhance the equipment transfer capability to support the integration of renewable distributed generation. In this paper, we propose a DNEP model of ADNs incorporating the DTR of cables and transformers, given that underground cable feeder is preferred in the rapid urbanization trends. Then, we derive a linear reformulation of the original DNEP model and propose a modified Benders decomposition (MBD) algorithm to solve the DNEP model. To select more effective representative day scenarios, we propose a cost-based clustering method for representative day selection applicable to solving the DNEP model. Case studies based on the IEEE 33-node system and the PG&E 69-node system show that the implementation of DTR saves 14.7% and 15.1% of the investment costs of the two systems respectively. The effectiveness of the MBD algorithm and the cost-based clustering method is also verified by the case studies.

Long-Term Renewable Energy Planning in the Indonesian Context: A Lombok Island Study Case

In recent years, community microgrids have expanded their power systems with many aims. One of the most important goals of microgrids is to increase resiliency. The main objective of this paper is to develop effective planning tools for community microgrids within electrical distribution networks, with a specific focus on ensuring the provision of critical loads during natural disasters. Additionally, this paper emphasized emphasises long-term planning considerations by using DIgSILENT 15.2 tools. The primary goal of this issue is to create the best planning tools for community microgrids in order to increase the network’s resilience to natural disasters, with a focus on important loads like hospitals and hotels. Also, the second goal is an optimization that seeks to reduce overall expenses. Finally, we’ll talk about how to get two results: one is to choose a few microgrid-based loads to link to the electrical distribution network, and the other is to show that each microgrid has the power needed to support both its local loads and the system’s vital loads in the event of a natural disaster. In this paper, the use of community microgrids for energy access against natural disasters in Indonesia is investigated by considering the case study of microgrids on Lombok Island. The study results, using the proposed framework, show that the presence of a microgrid structure in the distribution network expansion planning helps to improve operating conditions and supply critical loads in natural disasters.

Efficient expansion planning of modern multi-energy distribution networks with electric vehicle charging stations: A stochastic MILP model

This paper presents a stochastic Mixed-Integer Linear Programming (MILP) model developed to address the collaborative expansion planning of modern multi-energy distribution networks, taking into account the integration of Electric Vehicle Charging Stations (EVCSs) in the presence of uncertainties. The model is comprehensively formulated to encompass Distributed Generation (DG) resources, Electric Vehicles (EVs), and Capacitor Banks (CBs), alongside traditional expansion options like substations and circuit construction/reinforcement. The essence of this Distribution System Expansion Planning (DSEP) problem formulation lies in its stochastic approach, featuring chance constraints to effectively manage and address uncertainties. The central objective of the model is to minimize the total investment and operational expenditures associated with the DSEP problem over a designated planning horizon. The rigorous application of optimization techniques ensures the convergence of the MILP model, thereby delivering an efficient and reliable solution. The model's effectiveness is initially assessed using an 18-bus system, followed by comprehensive testing on a larger 123- bus system to gauge its robustness. Thorough performance evaluations are conducted across various case studies, encompassing both the 18- bus and 123- bus systems, culminating in the validation of the model's resilience, scalability, and replicability. These case studies encompass deterministic and stochastic methodologies across six distinct scenarios. The numerical outcomes conclusively demonstrate that the DSEP solution, when utilizing the stochastic approach without considering EVCSs, is associated with elevated costs. However, with a strategic allocation strategy for EVCSs, noteworthy reductions in investment expenditures related to substations, lines, and overall costs are notably achieved. The incorporation of uncertainty management within the DSEP problem underscores the flexibility and adaptability intrinsic to the proposed stochastic MILP model. These attributes render it ideally suited to effectively orchestrating the expansion of multi-energy distribution networks in the presence of EVCSs, thereby enhancing its utility and relevance in addressing modern energy distribution challenges.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Optimal AC/DC Distribution Systems Expansion Planning From DSO's Perspective Considering Topological Constraints

As the integration of DC distributed resources increases in recent years, hybrid AC/DC topology emerges as a promising alternative in the planning of distribution systems. This paper presents an AC/DC optimal expansion planning of distribution networks from Distribution System Operator's perspective with a special focus on grid architecture through Integer Programming-based topological constraints. The proposed formulation aims to determine the optimal location and sizing of converters and to select lines type (AC or DC) and state (closed or open). The problem is formulated as a mixed integer second-order conic programming model that guarantees global optimality and verifiable exactness by using off-the-shelf solvers. A multi-scenario approach with a Wasserstein distance-based scenario reduction is applied to deal with resource uncertainty. Case studies based on a 13 bus and IEEE 33 bus distribution networks were used to verify the effectiveness of the model. The results show that a hybrid architecture can improve the efficiency of the grid through optimal active power flow and reduce the total network costs.

Developing an Integration of Smart-Inverter-Based Hosting-Capacity Enhancement in Dynamic Expansion Planning of PV-Penetrated LV Distribution Networks

With the penetration of distributed energy resources (DERs), new network challenges arise that limit the hosting capacity of the network, which consequently makes the current expansion-planning models inadequate. Smart inverters as a promising tool can be utilized to enhance the hosting capacity. Therefore, in response to technical, economic, and environmental challenges, as well as government support for renewable resources, especially domestic solar resources located at the point of consumption, this paper is an endeavor to propose a smart-inverter-based low-voltage (LV) distribution expansion-planning model. The proposed model is capable of dynamic planning, where multiple periods are considered over the planning horizon. In this model, a distribution company (DISCO), as the owner of the network, intends to minimize the planning and operational costs. Optimal loading of transformers is considered, which is utilized to operate the transformers efficiently. Here, to model the problem, a mixed-integer nonlinear programming (MINLP) model is utilized. Using the GAMS software, the decision variables of the problem, such as the site and size of the installation of distribution transformers, and their service areas specified by the LV lines over the planning years, and the reactive power generation/absorption of the smart inverters over the years, seasons, and hours are determined. To tackle the operational challenges such as voltage control in the points of common coupling (PCC) and the limitations in the hosting capacity of the network for the maximized penetration level of PV cells, a smart-inverter model with voltage control capability in PCC points is integrated into the expansion-planning problem. Then, a two-stage procedure is proposed to integrate the reactive power exchange capability of smart inverters in the distribution expansion planning. Based on the simulations of a residential district with PV penetration, results show that by a 14.7% share of PV energy generation, the loss cost of LV feeders is reduced by 28.3%. Also, it is observed that by optimally making use of the reactive power absorption capability of the smart inverters, the hosting capacity of the network is increased by 50%.

Autonomous topology planning for distribution network expansion: A learning-based decoupled optimization method

The distribution of urban electricity load is highly uneven in both space and time, posing significant challenges to the problem of distribution network expansion planning (DNEP). An autonomous topology planning method for distribution network expansion is proposed to address that, driven by a learning-based decoupled optimization approach. The planning problem is decoupled from the impact of distribution network operation and used as reinforcement cost prediction for autonomous planning after the generation of topology schemes. First, a reinforcement model of the original distribution network is built that considers the actual distance calculation method of load nodes. Then an effective method for generating historical operation samples is designed, followed by the development of a learning-based reinforcement cost prediction model for the original distribution network based on operation state learning using XGBoost, which enables the prediction of said costs under different scenarios. Utilizing a distribution network topology generating method driven by the adjustable weight minimum spanning tree (AWMST) algorithm, an autonomous topology expansion planning strategy generation method employing the learning-based decoupled optimization approach is established. The feasibility and superior analysis of the proposed method is verified and analyzed on an actual urban distribution network case with simulation scenarios. The results show that the proposed method is faster and more flexible to deal with the distribution network planning problem brought about by rapid load growth in urban distribution networks.

Optimal planning of distribution network with transactive energy: Review and framework

AbstractWith the emergence of distributed energy resources (DER) and prosumers in the distribution networks, the traditional centralised operation structure has transformed into the decentralised operation and transaction structure, which is regarded as the transactive energy market. Furthermore, the planning problem in the distribution network is deeply affected by the transactive energy market. First, the literature and studies on distribution network planning with DERs are reviewed. Moreover, the multi‐stakeholder operation and planning framework under the transactive energy market are analysed. The transactive energy market provides a peer‐to‐peer (P2P) transaction for third‐party prosumers. Several techniques for multi‐stakeholders planning and P2P transaction are introduced. Finally, a tri‐layer distribution network expansion planning (DNEP) framework integrated with virtual P2P transactions is proposed and analysed to accommodate uncertainties and postpone investment. The key technology and difficulties are also discussed.

Dynamic expansion planning in active distribution grid integrated with seasonally transferred battery swapping station and solar energy

This paper addresses a novel multi-stage dynamic distribution network expansion planning (DNEP) taking into account the electric vehicle (EV) charging station based on the battery swapping model. The battery swapping station (BSS) is transferred seasonally and integrated to different buses in various seasons in order to defer the investment on the new lines. The BSS is as well integrated with rooftop solar panels. The uncertainties of loads and renewable energies are taken into account and handled by stochastic programming. The charging scheduling is optimized for all the batteries inside the BSS. A comparative study is presented for 3 cases including expansion planning without BSS, planning with a fixed location of BSS, and the proposed method (i.e., planning with seasonally transferred BSS). The numerical simulations demonstrate that the proposed model transfers the BSS every six months, where, it is installed on bus 25 in seasons 1 and 2, and on bus 17 in seasons 3 and 4. The congested lines such as lines 2 to 19, 16 to 17, and 6 to 7 are expanded by installing new lines in years 2, 4, and 5, respectively. The BSS sends power to the external grid in on-peak loading times such as hours 12 to 15 and 18 to 21. The proposed model reduces the planning cost by 5% compared to the expansion planning with a fixed location for BSS.

Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.

Presenting a Stochastic Model of Simultaneous Planning Problem of Distribution and Subtransmission Network Development considering the Reliability and Security Indicators

Consumption growth demands power system expansion, and changes in a network lead to changes in operation indices of the upstream network. Hence, to establish a reliable planning of the distribution network, simultaneous planning of the subtransmission network should also be taken into account. It is predicted that this planning impacts different technical indices; therefore, formulation of various technical indices need to be included in the planning model. Therefore, in this study, the stochastic problem model of simultaneous planning of distribution and super distribution network development considering the reliability and security indicators is presented. The aforementioned problem is in the form of an optimization problem whose objective function is to minimize the costs of construction, replacement, operation, maintenance, reliability, and security. Furthermore, the mentioned problem is bound to the equations of power distribution, reliability, and security of voltage in addition to the limitation of network indicators. It is worth mentioning that, in the proposed problem, the active and reactive loads in addition to the operating price are in the form of uncertainty. Thus, the mentioned problem is in the form of a stochastic problem in which the point estimation method is used to evaluate the scenarios. Simultaneous expansion planning of distribution and subtransmission networks along with the placement of distributed generations and capacitors, as well as modeling security and reliability indices, is the novelty of the proposed scheme. In the end, the proposed problem is applied to the test network by GAMS optimization software, and then the capabilities of the proposed problem are extracted. Accordingly, based on numerical results, the expansion of distribution networks imposes considerable cost. Moreover, expansion planning of both distribution and subtransmission networks helps voltage to be in its permissible range, lower power loss, and improve reliability and security to 100%.

A bi‐level multistage distribution network expansion planning framework with the cooperation of residential private investors (A case study in Iran)

AbstractIn recent years, supporting schemes have been legislated by several governments to encourage private investors in installing renewable energy resources (RER). In such cases, the supportive policies are mainly enacted based on the either investor or distribution companies’ standpoint. In this paper, a distribution network expansion planning (DNEP) framework with the cooperation of residential private investors (RPI) is proposed. Due to the presence of a couple of main participants, the proposed framework is arranged in a bi‐level framework, where the RPI participation is optimized at the upper level, and the system structure is determined at the lower level. In order to assess the profitability of the project from the investors’ attitude, payback period years (PBY) is utilized. Meanwhile, due to the presence of uncertainty resources, fuzzy clustering method (FCM) is developed to catch the intermittency of the problem. The proposed framework is implemented on a real 81 bus distribution test system in Iran. Moreover, the existing scheme in Iran and also a modified plan are investigated to make cost‐effective decisions. Finally, sensitivity analysis is performed to reach a more beneficial result. Obtained results demonstrated how distribution companies can utilize the potential of residential customers in long‐term planning.

Power distribution network expansion planning to improve resilience

AbstractHigh‐impact, low‐probability events that cause significant annual damages seriously threaten the health of distribution networks. The effects of these events have made the expansion planning for distribution systems something beyond the traditional reliability criteria, so there is an ever‐increasing need for modifications in current planning approaches and focusing on the resilience in the expansion planning of distribution networks. The new attitude dealing with resilience and distributed generation sources in distribution networks necessitates a fundamental reconsidering of traditional distribution network planning methods. Here, by modelling common natural disasters such as floods and storms, an appropriate index is introduced to evaluate the distribution network resilience in the presence of distributed generation (DG) sources, including conventional gas‐fired and photovoltaic sources. Then, by presenting an appropriate model for load and photovoltaic production, the problem of comprehensive distribution network planning, including substations, feeders, and DG sources, is mathematically formulated as a multi‐objective optimization problem to improve resilience and optimize costs. Furthermore, a non‐dominated sorting genetic algorithm is used to solve the problem of comprehensively planning a resilient distribution network. Implementation of the proposed model on the IEEE 54‐bus sample network shows that network resilience can be improved with minimum cost by optimal network planning.

Risk-based joint flexible distribution network expansion planning and allocation of EV parking lots

Risk-based joint flexible distribution network expansion planning and allocation of EV parking lots

A novel methodology for the planning of charging infrastructure in the scenario of high EV penetration

This article presents a novel methodology for distribution network expansion planning (DNEP) considering the inclusion of electric vehicles (EVs), especially, electric bus (EB) charging loads. The proposed methodology addresses network congestion through an optimum time of charging, cost optimization, new charging infrastructure, and minimization of losses under a set of technical and physical constraints, which represents practical uncertainties. Along with load flow analysis, selection of the number of ports and technology at the host charging station is obtained through the application of response surface methodology. The proposed methodology provides coordinated planning for the development of EB charging station infrastructure that takes into account the effects of both the power dispersion framework and transportation framework. The effectiveness of the proposed methodology is investigated by applying it to the 69-node IEEE modified distribution test system considering three charging technologies, viz. fast charging, ultra-fast charging, and battery swapping. The results of the proposed model are compared with the direct statistical method, and it revealed that the right selection of technology for EB charging and the right planning of the charging infrastructure can effectively optimize the cost of EV charging infrastructure and thereby catalyze the decarbonization of the transportation sector.

Expansion planning of active distribution networks achieving their dispatchability via energy storage systems

This paper presents a combined framework for power distribution network expansion planning (DNEP) and energy storage systems (ESSs) allocation in active distribution networks (ADNs) hosting large amount of photovoltaic (PV) generations and loads. The proposed DNEP ensures the reliable operation of the targeted ADN with the objective of achieving its dispatchability while minimizing grid losses by determining the optimal grid expansion to connect new nodes, the reinforcement of existing lines, and the ESS allocation. The allocated ESSs compensate for the stochastic power flows caused by the stochastic loads and generation, allowing ADNs to follow a pre-defined power schedule at the grid connection point. The grid constraints are modeled by using a modified augmented relaxed optimal power flow (AR-OPF) model that convexifies the classical AC-OPF providing the global optimal and the exact solution of the OPF problem for radial networks. The DNEP problem’s complexity is handled by employing a sequential algorithm where new nodes are added one by one, following the priorities determined by the user. In each stage of the sequential planning, the Benders decomposition algorithm determines the optimal solution for investments and ADN operation iteratively. Moreover, the siting and sizing problems associated with the ESSs and line investment are solved separately to enhance the convergence speed. Simulations are conducted on a real 55-node Swiss ADN hosting sizeable stochastic photovoltaic generation.

Simultaneous Planning of the Medium and Low Voltage Distribution Networks under Uncertainty: A Bi-Level Optimization Approach

Distribution network expansion planning (DNEP) is one of the important matters in the field of planning and operation of electrical power systems. Since many costs and losses have occurred in the distribution networks, it has increased attention towards this network. The electrical energy distribution network is divided into two parts: the medium voltage (MV) network and the low voltage (LV) network. The main problem in this field is that planning is done either only on the MV network or only on the LV network. While planning in each of these networks has a significant effect on the other networks, this important case has not been considered in most research studies. Therefore, this study has tried to do integrated planning in the form of a bi-level model in the presence of different types of distributed generations (DGs) and consider the uncertainties of renewable sources and load demand in both MV and LV networks so that the planning and operation costs are minimized. In the proposed bi-level model, the upper-level section aims to minimize the investment and the operation cost of the MV network, and the lower-level problem minimizes the investment and the operation cost of the LV network considering the DGs and pollution emission. The obtained results show the effectiveness of the proposed model.

Research on Data-Driven Distribution Network Planning Method

With the development of the intelligent and interactive power system, the elements of distribution network planning continue to increase. The distribution network connects the transmission system and individuals, directly affects the individual’s power consumption experience, and is a key link in the power system. A reasonable planning scheme can not only improve the power supply capacity and reliability of the distribution network but also fully apply the data of each system in the distribution network to realize the optimal planning of the medium-voltage distribution network driven by data. Firstly, this paper constructs the CIM model and the distribution network topology model and establishes the wiring pattern recognition feature library. The network reconstruction and planning method research was carried out for the target line, and a typical operation scenario of the distribution network was generated. At the same time, based on the time period network loss index, the distribution network reconfiguration optimization model and distribution network expansion planning model are established, and the solution method of the distribution network reconstruction and expansion planning model is expounded. A reconstruction optimization scheme with the best overall network loss performance is in the network operation scenario. The experimental results of the final example show that based on the proposed time period network loss index, the overall operation loss of the distribution network in a period can be calculated more accurately, and the optimized planning scheme is more suitable for the load power consumption characteristics of the region, and the method has certain feasibility.

Towards a new effective strategy to obtain optimal radial structure in power distribution networks: Graph theory-based topology assessment

Power distribution networks (PDNs) are principally operated and planned in a radial—open-loop—structure to satisfy several techno-economic goals. It is, therefore, indispensable to develop new practical strategies to rigorously investigate the radial structure-related limitation in the operation and planning of the PDNs. In this study, after a thorough and critical review of existing strategies in the literature, a new effective graph theory-based topology assessment strategy (GT-ToST) is outlined to examine the radial structure-related limitation. Then, both the distribution branch reconfiguration (DBR) problem and the multi-stage joint distributed generation and distribution network expansion planning (DG&DNEP) problem (most appropriate for the short and long-term horizons, respectively) are employed to explore the feasibility and practicality of the newly proposed strategy compared to the existing ones. The proficiency of the proposed strategy is investigated for the DBR problem with a modified 33-bus PDN and a modified real-world 177-bus PDN in Rio de Janeiro, as well as for the DG&DNEP problem with a modified 27-bus PDN and a modified real-world 104-bus PDN in the northeast part of Brazil. The obtained results from the illustrative examples and case studies prove the effectiveness of the proposed GT-ToST compared to the existing strategies.