Distribution System Planning Research Articles

A hosting capacity based approach toward distribution system planning for high PV penetration

This paper presents an approach to estimate the hosting capacity for distribution networks considering the impact of PV penetration at different voltage levels. The estimation and the method were selected such that the results were most suitable for distribution system planning. A time-series based method was used as it covers significant aspects needed for prioritising network reinforcement. The MV background voltage was modelled varying in time, assuming the same penetration level in the other LV networks supplied by the same MV system. The hosting capacity is defined as the maximum acceptable PV size per customer for a given PV penetration. Based on the different possible combinations of PV location, the probability of overvoltage and overloading is used as a performance index. The planning risk is used as a limit for the performance criterion. The method can be automated for a large number of networks due to using an IEC 61970-based input format. It also enables linking DSO network models to customer smart metre databases. The severity and risks of limit violations are analysed with different metrics from the time-series simulations. The change in background voltage with increasing penetration is shown to impact the results significantly. When considering it, the estimated hosting capacity was reduced by 32 %, on average.

Optimal renewable distributed generation planning in radial distribution systems: a probabilistic and multi-objective approach with enhanced Young’s double-slit experiment optimizer

Optimal renewable distributed generation planning in radial distribution systems: a probabilistic and multi-objective approach with enhanced Young’s double-slit experiment optimizer

A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

Distributionally robust CVaR optimization for resilient distribution system planning with consideration for long-term and short-term uncertainties

Climate change is exerting excessive strain on the environment, leading to an ever-increasing frequency of extreme weather events that negatively impact distribution systems. To mitigate these impacts, this paper presents a distributionally robust conditional value-at-risk (DR-CVaR) optimization approach for resilience-oriented distribution system planning. Firstly, a resilience-oriented distribution system planning model is developed, which incorporates flexible operational resources, such as mobile energy storage prepositioning and demand response program, in the emergency response phase. Then, the uncertainties associated with extreme weather events are addressed using a DR-CVaR optimization approach, where the short-term uncertainty in the occurrence of outages caused by specific extreme weather events is addressed through a distributionally robust optimization approach, while the long-term uncertainty regarding the risk of potential extreme weather events is addressed by a conditional value-at-risk optimization approach. The DR-CVaR model is eventually transformed into an equivalent three-level model and solved using a customized nested column-and-constraint generation algorithm. Finally, case studies are conducted based on the IEEE 33-bus distribution system under several extreme weather events. The numerical results show that the proposed model can reduce the load shedding cost as well as investment cost, which confirms the effectiveness and superiority of the proposed model.

Low and medium voltage distribution network planning with distributed energy resources: a survey

Abstract The penetration of distributed energy resources (DERs) such as photovoltaic systems, energy storage systems, and electric vehicles is increasing in the distribution system. The distinct characteristics of these resources, e.g., volatility and intermittency, introduce complexity in operation and planning of the distribution system. This paper first summarized the physical characteristics and morphological evaluation of the current and future distribution networks. Then, the impact of these changes on system operation and planning is outlined. Next, the tools, methods, and techniques for energy forecasting, optimal planning, and distribution system state estimation are reviewed and discussed, along with the challenges. As the main contributions, this research systematically organized the published works and assessed the relevant milestones regarding distribution system planning with DERs and emerging technologies. Finally, the key research directions in this domain are outlined. Graphical abstract

A novel approach for techno-economic reliability oriented planning and assessment of droop-control technique for DG allocation in islanded power distribution systems

An effective planning strategy for deploying distributed generators (DGs) provides economic advantages through optimal net present cost (NPC), improved technical performance, enhanced voltage profile, and minimized line losses. Nevertheless, technical performance varies based on the control techniques implemented for power regulation in DGs. Therefore, it is crucial to incorporate DG control strategies for efficient and robust power distribution system planning. This study discusses a novel approach for techno-economic reliability-oriented planning in an IEEE-33 bus droop-controlled islanded power distribution system. The presented study evaluates the influence of incorporating control techniques on the sizing and placement of DGs along with the technical, economic and reliability parameters. The problem formulated for optimal DG allocation aims to achieve the optimum NPC, minimize line losses, improve system reliability, and ensure voltage and frequency agreement with IEEE std 1547™-2018. The problem formulation is structured as a multi-objective problem and is solved using the particle swarm optimization (PSO) technique to achieve optimal bus location and size for DG installation. The study reveals that considering the control strategy helps in achieving efficient and reliable planning structure.

Resilience-oriented planning for active distribution systems: A probabilistic approach considering regional weather profiles

Recent experiences with increasing climatic variability highlighted the importance of enhanced power distribution system resilience. Conventional methods of power distribution system planning usually focus on persistentinvestments associated with predicted faulty events, aiming to enhance the overall system reliability. Improving distribution system resilience in response to extreme weather events involves reduction of impact of the events having varying probability of occurrence. The probability of an extreme event varies significantly by location due to a combination of geographical, climatological, and meteorological factors. Thus, instead of requiring persistent investments, the solutions for resilience-oriented system upgrades should be guided by the potential effects and the probability of occurrence of extreme weather events in a specific area. To achieve this objective, the present paper proposes a two-stage stochastic mixed-integer linear programming (MILP) model based on regional weather profiles for the long-term resilience planning of distribution systems against extreme weather events. In the planning stage, investment decisions are made regarding overhead distribution line hardening and the distributed generation (DG) installation. During restoration process in the operational stage, dynamic microgrid formation is carried out for advanced distribution grid operations to minimize the cost corresponding to loss of load. The inclusion of regional weather profiles into planning objectives provide added flexibility to the network operators for analysing the trade-off between investment cost and load loss cost in resilient distribution system planning strategies.

Comparative assessment of generative models for transformer- and consumer-level load profiles generation

Residential load profiles (RLPs) play an increasingly important role in the optimal operation and planning of distribution systems, particularly with the rising integration of low-carbon energy resources such as PV systems, electric vehicles, small-scale batteries, etc. Despite the prevalence of various data-driven models for generating consumption profiles, there is a lack of clear conclusions about their relative strengths and weaknesses. This study undertakes a comprehensive comparison of frequently used data-driven models in recent research, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAE), Wasserstein GANs (WGAN), WGANs with Gradient Penalty (WGANGP), Gaussian Mixture Models (GMMs), and Gaussian Mixture Copulas (GMC). The presented comparison explores the effectiveness of the above-mentioned models on transformer- and consumer-level consumption profiles, as well as for different time resolutions (15-min, 30-min, and 60-min). The objective of this research is to elucidate the respective advantages and drawbacks of these models, thereby providing valuable insights for subsequent research in this field.

Resilience Oriented Planning of Distribution System With Electric Vehicle Charging Station

Resilience Oriented Planning of Distribution System With Electric Vehicle Charging Station

Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach

This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm

Optimal planning of power distribution system employing electric vehicle charging stations and distributed generators using metaheuristic algorithm

Cooperative operational planning of multi-microgrid distribution systems with a case study

Clustering historical electricity consumption data is very important for creating representative demand profiles for the planning and operation of the power grids. This paper investigates a multi-dimensional framework for data clustering, which takes scattering and separation metrics, as well as the number of clusters into account. A combination of wavelet mutation with the Invasive Weed Optimization (IWO) method for clustering features is proposed. One notable advantage of the IWO method over other metaheuristic optimization algorithms is its ability to dynamically adapt the number of weed colonies during the search process, resulting in improved exploration and exploitation of the search space. The proposed strategy is applied to cluster the electricity consumption data from a large municipal government center in Perth, Western Australia. The suggested method is then evaluated by comparing it with the well-known method in the literature, namely, the k-means technique. After the data clustering, the obtained results are implemented in the design of a multi-microgrid system under two different scenarios of cooperative and noncooperative modes. To evaluate the performance of the proposed method, the proposed method is implemented on the operational planning of a real multi-microgrid distribution system in Western Australia using linear programming to take the advantage of the mathematical-based solvers. After performing some investigations, the cooperative mechanism, where the microgrids have participated in supplying the demand of microgrids was found to yield to greater operational and investment cost minimimzation. In terms of numerical comparison, the total cost in the cooperative model is 6.5% lower than that in a non-cooperative situation.

Modeling of high uncertainty photovoltaic generation in quasi dynamic power flow on distribution systems: A case study in Java Island, Indonesia

Integrating uncertainties associated with photovoltaic (PV) generation is an important aspect used to ensure the planning and operation of power distribution systems. Therefore, this research proposed an uncertainty model for PV generation by combining the methods of change point detection, cyclic k-means clustering (KMC), Monte Carlo simulation (MCS) with freedman diaconis estimator (FDE), and KMC with soft-dynamic time warping (DTW). Firstly, a seasonal split was performed using change point detection techniques and cyclic KMC to identify shifts in global horizontal irradiance (GHI) points. Secondly, the uncertainty of the GHI was generated using MCS for each season and the FDE method to optimize the number of bins of the data distribution. Finally, the PV generation curve from MCS was simplified through KMC with the soft-DTW metric, which facilitated a more straightforward representation of a PV profile. The impact of PV profile integration on quasi-dynamic power flow was tested on an IEEE 33 Bus system. The voltage profile on the feeder significantly impacted the PV integration, specifically during hours when high PV power is produced. For instance, at 11:00 a.m., voltage values on buses 18, 17, and 33 increased from 0.933, 0.934, and 0.935, respectively, to 0.982, 0.980, and 0.972. Similarly, with the value of power losses, the greater the PV power profile produced at a certain hour, the smaller the losses generated. The experimental results of the uncertainties PV model proposed in this research indicated that changes in electrical parameters change over time according to the input PV power profile produced.

Robust Resilience Enhancement by EV Charging Infrastructure Planning in Coupled Power Distribution and Transportation Systems

Robust Resilience Enhancement by EV Charging Infrastructure Planning in Coupled Power Distribution and Transportation Systems

Optimal Planning of Distribution Systems and Charging Stations Considering PV-Grid-EV Transactions

Optimal Planning of Distribution Systems and Charging Stations Considering PV-Grid-EV Transactions

Pemilihan Lokasi Prioritas Pelayanan Penyaluran Air Limbah di Kabupaten Cianjur Dengan Metode Skoring dan Pembobotan

The increase in population in Cianjur Regency has begun to be significant. Inadequate urban sanitation systems make people dispose of their domestic waste carelessly into the nearest river or water body resulting in environmental pollution. One way to reduce these problems is to build an adequate domestic wastewater distribution system. The planning of a domestic wastewater distribution system in an area must be accompanied by the selection of the best location to increase the effectiveness of environmental pollution prevention. Priority service areas are selected based on 5 available criteria, namely: Population density, Topography, Clean water distribution network, Sanitation condition, and Slum area. The method of determining priority areas used is the scoring and weighting method. The results obtained are 3 priority zones so that it can be determined which sub-district is the planning area, namely Cianjur District.

A linear Distflow model considering line shunts for fast calculation and voltage control of power distribution systems

The line shunts are usually ignored by various linear power flow (PF) models in power distribution system analysis, planning and optimization. However, “charging effects” from line shunts of underground/submarine power cables would cause non-negligible model errors for these commonly used PF models. In this paper, we propose a modified Linear Distflow model (LinDist) with line shunts, i.e., LinDistS. We also further propose its extensions considering the ZIP load, weakly-meshed topology and unbalanced three-phase systems. Moreover, the linearization error of voltage component is theoretically analyzed. Case studies show that compared with other models, the proposed LinDistS achieves the descent calculation accuracy and efficiency. We also show the application scope of LinDistS by a Volt/VAr control (VVC) framework with distributed generators, mainly photovoltaic (PV), and the non-negligible “charging effect”. Simulation exhibits that with LinDistS, the VVC can optimally dispatch the shunt capacitors and also optimize the real-time reactive power output of PV. Moreover, with LinDistS, the VVC shows better solutions’ consistency and higher computing efficiency compared to traditional VVC methods.

Bi-level stackelberg game-based distribution system expansion planning model considering long-term renewable energy contracts

With the deregulation of electricity market in distribution systems, renewable distributed generations (RDG) are being invested in by third-party social capital, such as distributed generations operators (DGOs) and load aggregators (LAs). However, their arbitrary RDG investment and electricity trading behavior can bring great challenges to distribution system planning. In this paper, to reduce distribution system investment, a distribution system expansion planning model based on a bi-level Stackelberg game is proposed for the distribution system operator (DSO) to guide this social capital to make suitable RDG investment. In the proposed model, DSO is the leader, while DGOs and LAs are the followers. In the upper level, the DSO determines the expansion planning scheme including investments in substations and lines, and optimizes the variables provided for followers, such as RDG locations and contract prices. In the lower level, DGOs determine the RDG capacity and electricity trading strategy based on the RDG locations and contract prices, while LAs determine the RDG capacity, demand response and electricity trading strategy based on contract prices. The capacity information of the DRG is sent to the DSO for decision-making on expansion planning. To reduce the cost and risk of multiple agents, two long-term renewable energy contracts are introduced for the electricity trading. Conditional value-at-risk method is used to quantify the RDG investment risk of DGOs and LAs with different risk preferences. The effectiveness of the proposed model and method is verified by studies using the Portugal 54-bus system.

A Review of Demand-Side Resources in Active Distribution Systems: Communication Protocols, Smart Metering, Control, Automation, and Optimization

Power systems have been going through a barrage of transformations due to the recent developments in the field, such as deregulation and restructuring of the electric power supply chain, the proliferation of distributed generation (DG), and advancements in information and communications technologies. These have significantly impacted the approach to the planning, design, and operation of active distribution networks or systems. Due to this constant change, the system has become more complex to plan, maintain, and control. In this paper, the benefits and challenges of active distribution systems relative to traditional passive and active distribution systems are evaluated and investigated while the management and operational characteristics of demand-side resources in active distribution systems (ADS) are studied. In a typical ADS, there exist several vulnerabilities and threats that eventually pose a challenge in the control and automation of substations. These vulnerabilities and threats are reviewed, and potential mitigation measures are suggested. Also in this paper, the communication technologies and their implementation in terms of control and automation capabilities in active distribution networks are also studied. From this work, it is concluded that communication technologies play an integral role in the realization of more active distribution networks and that the Internet of Energy (IoE) is a major player in ADS in the reduction of faults due to human error, fast responses, and improving the stability of power supply. Cyber threats are also and will still be a continuous challenge in smart metering technologies and in substation automation systems (SAS), which will require frequent evaluation and mitigation measures so as not to prevent the power supply system from collapsing.

Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.