Distribution Network Capacity Research Articles

Potential Assessment of Spatial Correlation to Improve Maximum Distributed PV Hosting Capacity of Distribution Networks

Successful distributed photovoltaic (PV) planning now requires a hosting capacity assessment process that accounts for an appropriate model of PV output and its uncertainty. This paper explores how the PV hosting capacity of distribution networks can be increased by means of spatial correlation among distributed PV outputs. To achieve this, a novel PV hosting capacity assessment method is proposed to account for arbitrary geographically dispersed distributed PVs. In this method, the empirical relation between the spatial correlation coefficient and distance is fitted by historical data in one place and then applied to model the joint probability distribution of PV outputs at a neighboring location. To derive the PV hosting capacity at candidate locations, a stochastic PV hosting capacity assessment model that aims to maximize the PV hosting capacity under thermal and voltage constraints is proposed. Benders decomposition algorithm is also employed to reduce the computational cost associated with the numerous sampling scenarios. Finally, a rural 59-bus distribution network in Suzhou, China, is used to demonstrate the effectiveness of the proposed PV hosting capacity assessment methodology and the significant benefits obtained by increasing geographical distance.

Distributed generation hosting capacity in electric distribution network in the presence of correlated uncertainties

High penetration of renewable distributed generations is one of the most current challenges of electric distribution networks due to resources uncertainties, and hosting capacity limitation of the networks. The challenges may be more critical when uncertainties are highly correlated. The effect of correlated uncertainties of wind speed and load on the distributed generation hosting capacity of distribution networks is evaluated. A combination of point estimation method, and inverse Nataf transformation is proposed for correlated uncertainties modelling. The efficiency of some ANM schemes on the hosting capacity improvement is also studied in the presence of correlated uncertainties. To do so, an optimisation framework with the objective of maximising the installed capacity of distributed generation subject to network operational constraints such as voltage deviation is proposed. The proposed optimisation problem is formulated in a mixed-integer quadratically constrained program form, and is solved via CPLEX solver. The proposed method is implemented on the 33-bus standard test system. The results demonstrate the significant effect of the correlation between uncertainties on the distributed generation hosting capacity of network.

Decarbonising heat with optimal PV and storage investments: A detailed sector coupling modelling framework with flexible heat pump operation

This paper analyses optimal electricity investments (PV and battery storage) to decarbonise heat supply in residential buildings under different heat pump and energy retrofitting scenarios in a detailed representation of the Swiss power and heating system. The sensitivity of PV and storage deployment, including lithium-ion (LiB) and vanadium redox flow batteries (VRFB), with respect to distribution network capacity is also investigated. We propose an open-source dispatch sector coupling model (GRIMSEL-AH) to minimise energy system costs (social planner perspective) for heating and electricity supply in Switzerland with hourly and daily time resolution for electricity and heating respectively. Moreover, our representation of the Swiss energy system includes various types of consumers and urban settings which are represented with monitored electricity demand data for each sector and simulated heat demand data at the building level for the residential sector. We find that under a “business as usual” heat pump deployment and retrofitting rate, the optimal electricity investments correspond to 27.8 GWp of PV combined with 16.9 GW (33.8 GWh) for LiB and 1.9 GW (7.6 GWh) for VRFB. For this case, 57% (13.3 TWth/year) of the residential heat demand is covered by heat pumps with a total installed capacity of 19.7 GWth by 2050 (capacity exogenously set with its operation optimised). With increasing heat pump deployment, retrofitting rates are found to have a large impact on the investment in storage and a 100% heat pump scenario for the residential sector appears to be feasible. Our results show that heat pumps do not only decarbonise heat but also provide extra flexibility to the power system, since they increase local PV self-consumption, resulting in higher PV deployment. The model and the methodology presented in this study can be applied to other countries.

Improving Hosting Capacity of Unbalanced Distribution Networks via Robust Allocation of Battery Energy Storage Systems

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method.

Local photovoltaic reactive power controller for increasing active distribution networks hosting capacity

The photovoltaic (PV) hosting capacity of active distribution networks is usually restricted by network overvoltage at peak PV generation hours. In the literature, several methodologies are presented to mitigate this problem caused by PVs reactive power regulation. However, the feeder transformer's thermal limit and branch lines loading confine the PVs reactive power capability. This study proposes a fully local reactive power regulation strategy enabling the PV systems to increase the feeder hosting capacity without using communication infrastructure. In the suggested framework, the distribution feeder is partitioned into several zones based on the feeder's electrical bottlenecks from the perspective of lines loading condition. The PV units placed in the vicinity of the electrical bottlenecks are equipped with the proposed local controller to moderate burden on the corresponding bottlenecks. Other PV units support the network voltage using a conventional local voltage-dependent reactive power controller. Consequently, the feeder hosting capacity can be enhanced while equipment loadings remain in their permissible limits. Moreover, the network power loss is decreased due to the local reactive power management. Furthermore, the proposed approach provides superior coordination between the PV systems controller and the transformer feeder tap changing mechanism. The advantages of the proposed approach are demonstrated by simulation results on 33- and 69-node radial and meshed distribution feeders.

Review on the PV Hosting Capacity in Distribution Networks

The increasing penetration of Photovoltaic (PV) generation results in challenges regarding network operation, management and planning. Correspondingly, Distribution Network Operators (DNOs) are in the need of totally new understanding. The establishment of comprehensive standards for maximum PV integration into the network, without adversely impacting the normal operating conditions, is also needed. This review article provides an extensive review of the Hosting Capacity (HC) definitions based on different references and estimated HC with actual figures in different geographical areas and network conditions. Moreover, a comprehensive review of limiting factors and improvement methods for HC is presented along with voltage rise limits of different countries under PV integration. Peak load is the major reference used for HC definition and the prime limiting constraint for PV HC is the voltage violations. However, the varying definitions in different references lead to the conclusion that, neither the reference values nor the limiting factors are unique values and HC can alter depending on the reference, network conditions, topology, location, and PV deployment scenario.

Mitigation of overvoltage due to high penetration of solar photovoltaics using smart inverters volt/var control

The modern photovoltaic (PV) inverters are embedded with smart control capabilities such as Volt/Var and Volt/Watt functions to mitigate overvoltage issues. The Volt/Var control has gained a significant attention in regulating grid voltage through reactive power compensation. However, the reactive power capability of a PV inverter is limited during peak irradiance and could be improved by curtailing the active power generation and by oversizing the PV inverter. This paper analyzes the performance of Volt/Var function of smart PV inverters in mitigating overvoltage issues due to high PV integration and thus increasing the hosting capacity of low voltage distribution networks (LVDNs). The study is conducted on a real Malaysian LVDN considering two different Volt/Var set points under different PV penetration levels. Results demonstrate that the oversized smart PV inverter could enhance the Volt/Var functionality by increasing its reactive power capability than a typical smart PV inverter. Further it reveals that adaptation of sensitive Volt/Var set points with shorter deadbands increase the PV hosting capacity of LVDNs.

Demonstrating a generic four-step approach for applying flexibility for congestion management in daily operation

Abstract New energy technologies like photovoltaics, electric vehicles, and heat pumps increasingly find their way to distribution networks. At the time the existing distribution networks were designed, only conventional loads were considered. The capacity of the (existing) distribution networks is therefore insufficient to handle the additional (bidirectional) peak load caused by these new technologies. The distribution system operator (DSO) is facing network congestion. Flexibility to shift and/or change power and energy in time and/or amount is considered as an option to mitigate network congestion with various implicit and explicit mechanisms. This leaves DSOs with the question on how to deploy such mechanisms seamlessly and effectively in daily business with uncertain congestion scenarios and complex integration processes. To tackle these operational challenges, this paper introduces a generic four-step approach to operationalize the flexibility need of a DSO, for any chosen implementation of an implicit or explicit flexibility mechanism (e.g. price-based schemes, flexibility markets, direct control). To this end, this paper addresses the steps: 1. Data acquisition, 2. Forecasting, 3. Decision-making, and 4. Flexibility mechanism interfacing. Furthermore, a particular implementation is described in relation to the Dutch’ H2020 InterFlex demonstrator, showing the field application of the proposed steps. In this demonstrator, a large amount of flexibility (26 electric vehicle charge points of 22kW, a 250kW/315kWh battery energy storage system, and a 260 kWp photovoltaic installation) is connected to two 630kVA transformers in a residential area with approximately 350 apartments. The results of the implementation show that the proposed steps enable the DSO to predict congestion, put a monetary value on flexibility, and use this value to evaluate flexibility offered through – in this case study – a flexibility market.

Research on Multi-Scenario Distributed Wind and Photovoltaic Power Accommodation Capacity of Distribution Network Based on Time Series Production Simulation

With the large-scale access of distributed new energy into the distribution network, the evaluation on the accommodation capacity of distributed new energy in the distribution network has important guiding significance for the scientific planning of distributed generation and grid connection. Considering the parked and regionalized development trend of distributed power generation mode, this paper proposes a method for evaluating the distributed wind and photovoltaic power accommodation capacity of regional distribution networks based on time series production simulation from the perspective of practical applications. Based on the timing difference and complementarity of the load characteristics of substations on the distribution network side, we conducted an evaluation on the distributed wind and photovoltaic power accommodation capacity of the regional distribution network, and through taking the flexible operation mode of the distribution network side into consideration, realized the mutual assistance from time domain and space dimension of distributed new energy consumption and carrying capacity of various substations. Through establishing a mathematical model for the consumption and carrying capacity analysis in the multi-scenario modes such as local node consumption, local station consumption and allowing certain percentage of abandoned electricity, we came out the scale of the maximum consumption of distributed new energy in different scenarios as well as the overall grid connection layout scheme. In the end, we used such evaluation method to conduct an empirical analysis on the distribution network in certain region, verified the correctness and effectiveness of the model, and provided theoretical supports for guiding the regional distributed new energy overall planning and scientific grid connection.

Coordinated deliverable energy flexibility and regulation capacity of distribution networks

The increasing capacity of distributed flexibility resources (DFRs) in power distribution systems provides an unprecedented opportunity for distribution system operators (DSOs) to offer the available distributed flexibility as services in electricity markets. This paper proposes a novel model to define and co-optimize the deliverable energy flexibility and frequency regulation capacity of power distribution systems. The distributed flexibility is provided in distribution buses by flexible loads, modeled by a novel queuing system, energy storage (ES) devices, and distributed solar resources with controllable inverters. The proposed model co-optimizes the DFRs schedule in three operating points that model the operation of flexible loads, distributed solar generation units, and ES devices in distribution networks for providing energy flexibility, as well as regulation up and down capacity in electricity markets. The proposed model takes into account the interdependence between DFRs schedule in the three operating points, which enables the DSO to provide non-conflicting capacity offers to different services in the market. In addition, the proposed model ensures the deliverability of energy flexibility and regulation capacity in the markets by satisfying the power flow constraints of distribution networks in all three operating points. The numerical studies, conducted on the 33-bus test distribution system, demonstrate the effectiveness of the proposed model for scheduling realistic energy flexibility and regulation capacity offers that are deliverable in the markets.

Analysis on Demand and Development Direction of Smart Grid under the Background of Two-network Convergence

With regard to development strategy of “two networks integration”, based on improvement of power supply capacity, power supply security and reliability, fault response, information collection capability as well as improvement, intelligence, integration and automation upgrade on real-time monitoring capability, “two networks integration” planning strategy and implementation plan, such as planning for distribution network, distribution itself, ubiquitous power Internet of Things, are made to greatly improve overall capacity of distribution network, which further provides reference for similar projects, such as high-quality, stable, efficient and green power supply.

The enabling impact of digital technologies on distributed energy resources integration

The future of energy systems is currently driven by three key trends: electrification, decarbonization, and digitalization. A growing share of renewable generation is without doubt part of the solution to accommodate the increasing electricity demand with a low-carbon emission constraint. The question addressed in this review paper is to what extent digitalization will enable the decentralization of renewable energy generation close to the point of consumption. Digital technologies, namely, Big Data, Artificial Intelligence, IoT, and Distributed Ledgers, will have a positive impact on renewable Distributed Energy Resources adoption by contributing to achieve a better balance between supply and demand at the edge of the grid and by improving the hosting capacity of distribution networks. However, decentralized energy production is still limited by structural factors (e.g., low power density of renewable resources, economies of scale in renewable generation and storage), regulatory factors (e.g., access to the grid, pricing policy), and digital technology constraints (e.g., limited number of transaction per second). As a result, the likely scenario of development is a reasonable mix between decentralized and (also digital enabled) centralized renewable-based energy generation interconnected together with an engaged customer and highly developed and smarter distribution and transmission networks.

A proposed algorithm to detemine hosting capacity of distribution network

Today, solar energy has great potential and is, therefore, being promoted for use not only in Vietnam but also worldwide. In particular, households with rooftop solar energy can sell excess energy to the power company, which is being noted for strong development. However, the development of rooftop solar will have some negative effects on the distribution grid if the selling capacity from households is too large. PV rooftop is a part of distributed resources. Thus the paper will discuss distributed resources in general. In this paper, an iterative algorithm that determines the maximum number of distributed resources that can penetrate into the grid is developed in the Matlab environment. The proposed model is tested with a simple distribution grid to determine the hosting capacity of the distribution grid.

Lightening the load: quantifying the potential for energy-efficient lighting to reduce peaks in electricity demand

One of the key challenges to greater renewable electricity supplies is the temporal mismatch between non-dispatchable renewable sources and peaks in electricity demand. In addition, increased electrification coupled with the de-carbonisation of electricity generation is likely to increase the scale of demand peaks. This could force investment in carbon-intensive peaker generation or capital intensive storage capacity as well as additional transmission and distribution network capacity which may then be substantially underutilised. Whilst considerable effort has been devoted to testing a range of demand response interventions to reduce or shift consumption, less attention has been given to the ability of certain appliances to permanently reduce demand at peak through energy efficiency. In this paper, we use a published model of future energy-efficient lighting uptake together with multi-year measured lighting demand data from a sample of residential households to model the potential power (MW) and energy (MWh) reductions of a ‘business as usual’ rate of efficient lighting adoption. Our estimates suggest that whilst lighting comprises ~ 4% of overall New Zealand annual electricity consumption, it comprises up to 12% of evening peak electricity consumption in winter. As a result, we estimate that by 2029, more efficient residential lighting could reduce New Zealand’s total annual demand by 1 TWh and reduce the highest winter evening peaks (at 17:00) by at least 500 MW (9%). The winter evening demand reduction would be roughly equivalent to avoiding the need for additional generation capacity of the scale of New Zealand’s Huntly Power Stations 1–4 (coal/gas) plus the Stratford peaker plant (gas open-cycle) and has clear implications for any electricity system that is intending to transition towards ~ 100% renewable generation at least cost.

Low-Capacity Exploitation of Distribution Networks and Its Effect on the Planning of Distribution Networks

The continuous variation and dispersion of the load demand during a 24-h day are uncontrolled aspects that affect the efficiency, operational conditions, and total cost of the power distribution network. The cost of the network is strongly related to the peak of demand, but the available capacity of the network is not used efficiently during the day because feeders and branches usually work under 70% of their full capacity. In this way, it is necessary to measure how efficiently the distribution network capacity is used and to identify the aspects that can be modified to improve it. This article proposes a new exploitation capacity index to measure the efficiency of a/the whole distribution network throughout the day in relation to the total available capacity of the branches that compose the network. The paper presents the mathematical formulation and the validation process of the index, and then it provides a planning case study in which the index and the total cost of the planning problem are calculated and compared in four different scenarios in which the peak of the load demand changes. The results show a direct relation between the exploitation capacity and the peak of demand, so lower exploitation capacities are strongly related to higher peaks of demand. As for the capital investments for the network planning, it is found that higher peaks of demand involve more upgrade necessities and higher capital investments compared to the other cases.

PV hosting capacity of LV distribution networks using smart inverters and storage systems: a practical margin

This study proposes an approach to evaluate a practical margin for photovoltaic (PV) generation hosting capacity (HC) of low voltage distribution networks. This HC is determined considering the randomness of the connection points and is supposed to be the maximum value of PV penetration up to which the utility can authorise any interconnection without performing additional case studies. Smart inverter control strategies and battery storage systems are used to avoid costly network expansion solutions. The simulations are performed using actual solar radiation data and residential demand profiles. The results show an increase in the network HC, bringing benefits by deferring network investments such as conductors and asset upgrades.

Optimised allocation of PV and storage capacity among different consumer types and urban settings: A prospective analysis for Switzerland

This paper discusses the optimal configuration of PV and electricity storage in a detailed representation of the Swiss power system. The sensitivity of storage deployment with respect to distribution network capacity is also investigated. We propose an open-source dispatch model to optimise the electricity supply of Switzerland and its four neighbouring countries with hourly time resolution. Moreover, our representation of the Swiss power system includes various types of consumers, namely, the residential (single-family house and multi-family house), service and industrial sectors, with a differentiation by rural, suburban and urban settings, resulting in 12 sub-national nodes. We then use monitored electricity demand load data for each sector. We find that the achievement of the Swiss federal target of 13.8 GWp PV capacity by 2050, which corresponds to 30% of the annual production, requires an additional storage capacity of 11 GWh (corresponding to 1.6 h of average consumption) in order to minimise total costs. However, when letting the optimisation model choose both the PV capacity and the size of energy storage, then minimal costs are achieved by a system with 24.6 GWp of PV (corresponding to 53% of the annual production) combined with 29.2 GWh (4.1 h of average consumption) of storage capacity. Our results show that storage plays an important role in minimising the total cost for energy systems with large PV capacity as well as satisfying the distribution capacity constraints. The detailed results and mechanisms at the level of sub-national nodes are discussed. The model and the methodology presented in this study with a focus the Swiss case can be applied to any country/region.

Research the Impact of Battery Energy Storage Systems on the Transmitting Capacity Electric Power Distribution Networks

The increasing peaks of load profile and increasing penetration of distributed generation (DG) based on renewable energy sources (RES) determine the critical importance of developing the use of battery energy storage systems (BESS) in order to eliminate the necessity of modernization of power grid facilities and increase the transmission capacity of electric power distribution network with a voltage 6-20 kV (DN). However, there are currently no factors to evaluate the effectiveness of the BESS using to increase the transmission capacity of the DN. Defining these factors is the purpose of this article. In solving the problems used, the method of regression analysis of the theory of experimental design. In the process of performing the practical part, the research method was mathematical modeling. To increase the available power of technological connection and reduce voltage deviations on the 0,4 kV buses of transformer substations 6-20 / 0,4 kV (TS) in the area adjacent to the DG based on RES of DN, the independent factorial signs and the boundaries of their changes were determined, and linear multiple regression mathematical models are developed. The most significant factors for increasing the transmission capacity of the DN according to the specified criteria are determined. The influence of the loading of the TS transformer and the complex effect of the power flow and voltage levels in the network must be taken into account as the main factors in determining the best possible option for the parameters of the BESS and their locations, used to increase the transmission capacity of the DN.

Enhancing Hosting Capacity of Uncertain and Correlated Wind Power in Distribution Network With ANM Strategies

To settle the large-scale integration of wind power, it is necessary to enhance the wind power hosting capacity of distribution network. To this end, this paper proposes a stochastic mixed-integer linear programming (MILP) model to enhance the hosting capacity of distribution network with active network management (ANM) strategies. The considered ANM strategies include power factor control, reactive power compensation and network reconfiguration. The coordination of these strategies enables the distribution network to accommodate more wind power without violating its constraints. The proposed model relaxes the original nonlinear power flow equations into linear format, so that a near optimal solution can be obtained with moderate computational burden. The wind power and load demand are inherently variable due to the influence of various factors. Moreover, the output powers of multiple wind turbines (WTs) located at adjacent sites of a wind farm are spatially correlated. To represent the uncertain loads, the roulette wheel mechanism is used to generate the load samples. In addition, a combined method of inverse transformation and Nataf transformation is established to deal with the uncertain and correlated wind power. Then, scenario combination and reduction are conducted to generate representative scenarios. Finally, the feasibility and accuracy of the proposed method are verified via numerical tests on the modified IEEE 33-bus test system.

Hierarchical Evaluation of Distribution Network Access Capacity Taking Into Various Prosumer Group Load Forecasts

Now the measurement means of access capacity at all levels of distribution network are insufficient and it is difficult to calculate effectively based on limited data. This paper proposes a hierarchical evaluation method of access capacity of distribution network with multiple prosumer group load forecasting. Firstly, a load dynamic prediction model is constructed. According to the typical load model of historical data mining, a unit prediction model of typical users is established by using the conjugate gradient RBF neural network learning algorithm. Then, according to the charge-discharge dynamic model of the energy storage power station and the load dynamic prediction results, an assessment model of access capacity at the 10kV side was constructed. By analyzing the known load type, load type ratio and transformer capacity on the low-voltage side, the conversion coefficient of 10kV side load to 0.4kV side transformer capacity is obtained by weighting different types of loads proportionally. Then, access distribution capacity of different prosumer group can be obtained based on conversion coefficient. It can ensure the effective measurement of access capacity at all levels of distribution network under the premise of meeting N-1 safety criteria. Finally, an example is given to demonstrate the effectiveness of the proposed method.