Distribution Network Expansion 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.

Factors Influencing Digital Warehousing and AI Utilization in Modern Supply Chains: Implications for Warehouse Maintenance Costs and Product Pricing

In order to improve operational efficiency and competitiveness, supply chains currently we have to combine digital technology and artificial intelligence (AI). The adoption of digital warehousing and the application of artificial intelligence (AI) in supply chain management are examined in this study, with particular attention to how these developments may affect product pricing and warehouse maintenance expenses. Utilizing a comprehensive literature review and empirical analysis, this study identifies the primary factors driving the integration of artificial intelligence (AI) and digital warehousing into supply chain processes. The results illustrate the tactical implications for supply chain professionals in terms of refining maintenance procedures, streamlining warehouse management procedures, and product pricing strategies. For any given business, a smoothly running supply chain is essential to success. Possessing a highly accurate inventory estimate gives you a significant competitive edge. The operation of the entire supply chain is affected by an extensive variety of external as well as internal variables, including the environment, excessive volatility, shifts in customer perception, and media coverage. Examples of internal elements that impact supply chain performance are product releases and distribution network expansion. Artificial Intelligence (AI) has demonstrated in recent years to be a brain extension, allowing us to operate at levels above our wildest expectations. Contrary to common perception, artificial intelligence (AI) will not replace individuals; rather, it will enable us to achieve our full innovative and tactical potential. [Yang, M., Fu, M., & Zhang, Z. et al 2021]

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.

Dynamic Programming of Resilient Smart Distribution Network Expansion Planning against Cyber-Attacks on end-user customers

Dynamic Programming of Resilient Smart Distribution Network Expansion Planning against Cyber-Attacks on end-user customers

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.

Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach

Escalation of extreme weather events represents substantial threat to power system infrastructure. Mobile emergency generators (MEGs) can form part of a flexible restoration strategy against such destructive events. However, with continued expansion of distribution networks, quantification of the impact of MEGs has become increasingly challenging owing to extreme-weather-event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. First, we propose a distance measure to represent the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs under different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of the entire distribution network under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structural features, and power grid resilience. The proposed method enables analytic assessment of the impact of MEG deployment on the resilience of distribution networks, and provides beneficial insights to help formulate efficient measures for enhancing resilience. Case studies demonstrated that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive potential failure scenarios.

수산업을 통한 지역사회의 연대활동과 글로벌 수산식품 유통 네트워크

This paper examines the historical background and characteristics of the Guryongpo fishery industry, and analyzes the local community’s solidarity activities through the fishery industry, focusing on the big catch festival hosted by the Guryongpo Fisheries Cooperative and the Guamegi Festival hosted by the Pohang Guryongpo Guryongpo Catfish Business Cooperative. In addition, the global distribution network of Guryongpo’s representative seafood was first examined through newspaper articles, and the Gupaegi distribution network was analyzed through interviews with Gupyongpo overseas exporters to further understand the local situation.
 The Guryong Po Fisheries Industry can be traced back to the Bronze Age with the excavation of fishing nets (net weights) in the Bronze Age remains. After the opening of the port, many Japanese people migrated to the Korean Peninsula, and Guryongpo changed rapidly as the Japanese immigrants formed fishing villages and the settlement of immigrants began. In the residential complex of immigrants, which extended along the main road along the coast, important facilities were equipped, and various facilities such as common workshops, fish markets, fishery transport shops, shipyards, canneries, pioneer shops, fishing gear stores, and warehouses took their place. Guryongpo’s representative fish species were mackerel, herring, and sardines in the early days when the Japanese spoke the language. However, herring has not been caught since the 1950s, and in the middle of the year, Gwamegi was already being made with saury instead of herring. From the early 1960s to the 1980s, saury was caught so much that it was considered a representative fish species of the East Coast. However, fishermen always felt uneasy about human casualties and natural disasters caused by storms during fishing. In response, the Shipowners’ Association held the Guryongpo Big Fishing Festival in hopes of fulfilling the wishes of fishermen who wish for safe fishing and a large catch of fish and for income increase. However, as time passed, the shipowners’association ran into a difficult situation due to the cost of the festival and asked the Fisheries Cooperative, and the Guryongpo Fisheries Cooperative held the festival every four years from 2005. Even though the festival is being held under the sponsorship of the Fisheries Cooperatives, looking at the progress so far, it can be seen that there are various solidarity activities between the citizens as well as between the Fisheries Cooperatives and the citizens. Also, through the Guryongpo Gwamegi Festival, it was confirmed that solidarity activities with the Gwamegi business cooperative, merchants, and citizens were continuously conducted. Lastly, as for the analysis of the distribution network of global seafood, first, the characteristics of newspaper articles and press materials were reviewed, and interviews were conducted with Guppong overseas exporters to better understand the current situation. As a result, the current reality is that overseas exports are experiencing more difficulties than domestic sales in many aspects, such as procedures, tastings and promotion methods overseas, distribution network expansion to various countries, customer management, economic costs, and passive government support. was Therefore, Guryongpo, including Pohang City, should strive to expand the global seafood distribution network so that continuous research and improvement can be made inconsideration of the sustainability of seafood distribution to contribute to the revitalization of the local economy.

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.

Distribution network forecasting and expansion planning with optimal location and sizing of solar photovoltaic-based distributed generation

Load forecasting is the prediction of demand for a future period, and it is a critical necessity for distribution system design and growth while also meeting all operational and technical constraints. In this paper, an analytical least squares extrapolation technique is applied to determine the optimal size and location of solar photovoltaic (SPV)-based distributed generation (DG) in the distribution system. The integration of SPV DG changes the operating characteristics of modern power systems, resulting in significant economic and technical outcomes such as minimizing losses and improving the voltage profile. The proposed work was performed on a practical Yirgalem-I distribution network in Ethiopia. The modeling and simulation of this feeder have been carried out using ETAP and MATLAB software. Simulation results show that the injection of active and reactive powers at the optimal buses reduces the active and reactive power losses by 94.03% and 96.07%, respectively, with an improved voltage profile.

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.

Methodology for integrating flexibility into realistic large-scale distribution network planning using Tabu search

A successful energy transition will be firmly based on the effective integration of distributed energy resources and on the integration of new flexibility providers into the energy system. To make this possible, a deep transformation in the design, operation and planning of power distribution systems is required. Currently, a lack of comprehensive planning tools capable of supporting operators in their investment plan options exists. As a result, reinforcements of conventional grid assets are common solutions put in place. This paper proposes a methodology to obtain cost-optimal distribution network expansion plans, by modelling a single-stage distribution network planning tool, using conventional assets as well as flexibility contracting from demand response. A Tabu Search metaheuristic has been implemented in order to solve the optimization problem. A case study based on a realistic large-scale city network model is presented, for a planning horizon of ten years with significant load growth due to electromobility penetration. Results show that, in the case study analysed, the use of load flexibility in combination with conventional reinforcements can reduce the total expansion network cost by about 7.5 %. Furthermore, a sensitivity analysis on the cost of flexibility contracting is undertaken. Remarkably, the methodology presented generalises to further alternative solutions by providing a straightforward financial benchmark between the latter and conventional grid expansion.

Distribution network expansion planning considering a distributed hydrogen-thermal storage system based on photovoltaic development of the Whole County of China

The large-scale access of a substantial proportion of the distributed photovoltaic (PV) power sources has introduced considerable source-side randomness and volatility to the distribution network in the development of PV power generation in the whole county of China. This paper proposes a cooling-heat-electric multi-energy coupled power distribution network expansion bi-level planning model to reduce the influence of uncertainty and improve the PV consumption rate (PVCR). It is based on the distributed hydrogen-thermal storage system (DHTSS) in a high-proportion PV scenario. First, an irradiation-load-temperature multi-grid joint scenario generation model is constructed based on each meteorological cluster to effectively describe the uncertainty of PV power generation and multi-energy consumption. Subsequently, a refined distributed PV system physical model chain is constructed to address the low accuracy of the traditional PV output model in the high PV output (HPVO) scenarios. Lastly, considering the power supply reliability objective of the low-probability with high-load (LPHL) scenarios, a multi-objective bi-level expansion planning model of the distribution network is constructed to accurately determine the capacity of each equipment in DHTSS, the distribution lines, and low-voltage transformers to be expanded in the distribution network.

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%.

Distribution Network Expansion Planning Approach for Large Scale Electric Vehicles Accommodation

The increased power demand incurred by electric vehicles (EVs) has imposed new challenges on the distribution network. One significant responsibility of the distribution network operator (DNO) is to design and expand the distribution network periodically to accommodate the ever-increasing demand. The previous researches mainly use relaxation techniques to enhance the problem tractability, which would involve additional binary variables and incur computation burden to the optimization problem, or even worse, may lead to an intractable problem. This paper develops a mixed-integer non-linear programming (MINLP) model considering various network constraints for the distribution network expansion planning under a high penetration level of EVs. The stochastic characteristics of demands are addressed through set of scenarios. A distributed biased min-consensus algorithm based approach is proposed to solve the MINLP model. Finally, comparison tests are conducted on three different scales of distribution networks to validate the effectiveness of the proposed approach. Simulation results demonstrate that the computation time of the proposed approach is reduced by 42.46% on average for different systems compared with that of the traditional shortest path algorithm based approach.

Water distribution network expansion: an evaluation from the perspective of complex networks and hydraulic criteria

Abstract The expansion of water distribution networks is nowadays essential to meet the pressing population growth in many cities worldwide. With the subsequent increase in water demand, the hydraulic behaviour of a water supply system can change dramatically due to the significant head losses in the pipes caused by the larger flows released by pumping systems and reservoirs to supply the higher water consumption of the network. Strategical studies are necessary to delimit risk regions where a demand increase may affect the system too negatively. To analyse expansion scenarios with the lowest risk of failure and damage for the supply network operations, this research studies hydraulic performance and connectivity under various demand increase scenarios using calculations of complex network metrics together with relevant hydraulic criteria. For these calculations, this research, developed in Python, uses, respectively, the NetworkX 2.5 and WNTR 0.3.0 packages. The C-Town network is employed as a case study, and demand increasing scenarios are implemented on 30 nodes along the peripheral regions of the network to simulate the growth of the cities. Then, these scenarios are evaluated using the TOPSIS methodology, thus determining the best and worst sectors to expand the capacity of the network.

Maintenance Scheduling Algorithm for Transformers in Tanzania Electrical Secondary Distribution Networks

The drive by the government of Tanzania to electrify every village has resulted into expansion of the electrical secondary distribution networks (ESDNs). Therefore, maintenance management is of the highest priority for the smooth operation of the ESDNs to reduce unscheduled downtime and unexpected mechanical failures. Studies show that condition-based predictive maintenance (CBPdM) method allows the utility company to monitor, analyze and process the information obtained from ESDNs transformers. Thus, this study adopts the CBPdM method to develop a maintenance scheduling algorithm that can predict the transformer state, forecast maintenance time based on transformer load profile and schedule its maintenance using a knowledge-based system (KBS). Applying the challenge driven education approach, the requirements for developing an algorithm were established through an extensive literature survey and engagement of the key stakeholders from the Tanzania utility company. Our study uses the Dissolved Gas Analysis tool to collect the transformer parameters used in algorithm design. The parameter analysis was performed using Statistical Package for Social Sciences software. Results show that the designed KBS algorithm minimizes human-related maintenance errors and lowers labour costs as the system makes all the maintenance decisions. Specifically, the proposed maintenance scheduling algorithm reduces downtime maintenance costs by 1.45 times relative to the classical inspection-based maintenance model while significantly saving the maintenance costs.
 Keywords: Electrical power network, Forecasted load consumption, Knowledge-Based System, Maintenance Scheduling, Predictive Maintenance, Secondary Distribution