Hosting Capacity Of Distribution Networks Research Articles

Renewable energy hosting capacity assessment in distribution networks based on multi‐strategy improved whale optimization algorithm

AbstractThe increasing penetration of distributed renewable energy poses significant challenges to the safety and stability of distribution system, rendering the renewable energy hosting capacity of distribution networks a major concern. To effectively evaluate the renewable energy hosting capacity in distribution networks, this paper introduces a comprehensive hosting capacity assessment model considering various safety and stability constraints. Given the numerous non‐linear constraints inherent in the hosting capacity assessment model for distribution networks, a multi‐strategy improved Whale Optimization Algorithm is proposed. Improvements are made to the population initialization and population position update strategies, and a mutation strategy is introduced to prevent premature convergence, thereby enhancing the algorithm's performance. Finally, the effectiveness of the proposed model and the method is validated through the utilization of three distribution systems. Moreover, the key factors that restrict the improvement of hosting capacity are revealed from the perspective of distribution network topology.

Assessment of renewable energy hosting capacity in distribution networks considering demand-side management and network reconfiguration

Abstract A renewable energy carrying capacity assessment method for distribution grids considering demand-side management (DSM) and network reconfiguration (NR) was proposed. First, a DSM and NR model applicable to the renewable energy carrying capacity (NECC) assessment of the distribution network (DN) was established; second, an NECC evaluation model for DN considering DSM and NR was established with the aim of maximizing the access capacity of distributed new energies; second, the second-order conical relaxation method was applied to convex solve the model; third, the simulation example adopts an improved IEEE 33-node DN to evaluate the NECC of system with various scenarios. Finally, the simulation experiment based on improved IEEE 33-node DN was conducted to assess the distributed NECC of the system in some scenarios.

Calculation of renewable energy hosting capacity of distribution network based on multi strategy improved adaptive manta ray foraging optimization

With the increasing penetration of renewable energy in the distribution network, however, due to the randomness and volatility of renewable energy output, the large-scale grid connection of renewable energy will bring challenges to the safe and stable operation of the distribution network. The calculation of renewable energy hosting capacity of distribution network is not only beneficial to the construction of renewable clean and low-carbon power system, but also of great significance for the planners of power grid to carry out renewable energy planning. Renewable energy hosting capacity calculation involves many constraints, including a large number of nonlinear constraints, and the hosting capacity calculation model is very complex, lacking efficient solution algorithm. Aiming at the existing problems, the calculation model of renewable energy hosting capacity of distribution network is established based on full consideration of power quality, relay protection and thermal stability test. A multi strategy improved adaptive manta ray foraging optimization algorithm (MSAMRFO) is proposed to solve the hosting capacity model. MSAMRFO algorithm adopts half uniform initialization strategy, adaptive variable step size strategy and local convergence mutation strategy to improve manta ray foraging optimization algorithm (MRFO) algorithm. Finally, the effectiveness of the proposed model and method is verified by an example. The example shows that the MRFO algorithm after multi strategy improvement has outstanding effect both in convergence speed and algorithm stability.

Analyzing the Impact of EV and BESS Deployment on PV Hosting Capacity of Distribution Networks

The current article analyzes the impact of charging electric vehicles and battery energy storage systems on the photovoltaic hosting capacity of low-voltage distribution networks. A Monte Carlo-based simulation is used to analyze predominantly rural, intermediate and predominantly urban residential regions facing different penetrations of electric vehicles utilizing uncontrolled and controlled charging, and evaluate their impact on photovoltaic hosting capacity. Subsequently, electric vehicles are replaced or supplemented by residential battery energy storage systems, and their combined impact on the hosting capacity is studied. The results revealed that electric vehicles solely do not improve the hosting capacity unless they are connected to the network during sunshine hours. However, controlled storage provides a remarkable increase to the hosting capacity and exceptional contribution in combination with electric vehicles and customers with high loads. Finally, a feasibility analysis showed that controlled charging of the storage has a lower marginal cost of increasing hosting capacity as compared to network reinforcement.

Increasing RES Hosting Capacity in Distribution Networks Through Closed-Loop Reconfiguration and Volt/VAr Control

This article presents a novel mixed-integer second-order cone programming model to increase the photovoltaic (PV) hosting capacity and optimize the operation of distribution networks. The problem considers voltage and reactive (Volt/VAr) control through the optimal operation of capacitors banks, substations’ on-load tap changers, voltage regulators, and network reconfiguration with radial and closed-loop operation topologies. The proposed formulation considers voltage-dependent models for loads and capacitor banks. The objective function maximizes the PV hosting capacity of the network. Numerical experiments are carried out using the 33-node and the 85-node networks. Results demonstrate the effectiveness of the proposed formulation to increase the penetration of PV sources, especially when the closed-loop operation is allowed, together with network reconfiguration and Volt/VAr control.

Solar PV stochastic hosting capacity in distribution networks considering aleatory and epistemic uncertainties

This paper proposes a stochastic method, ''mixed aleatory-epistemic“, for estimating solar PV hosting capacity (HC) of low-voltage (LV) distribution networks. The approach treats the aleatory and epistemic uncertainties in a different way. The HC is estimated by applying the transfer impedance matrix, 'which is only calculated once', and the superposition principle to determine the voltage magnitude rise due to solar PV. By distinguishing between aleatory and epistemic uncertainties, the calculations are limited to the relevant hours (time-of-day or time-of-year) during which high solar PV production is expected. In this way, the random aleatory uncertainties (background voltage, solar PV production, local consumption) are modelled by their probability distributions during the selected time period. The distributions for the epistemic uncertainties (installed capacity per customer, number of customers with solar PV, phase to which single-phase units are connected) are created with simple models involving the interval value and possible occurrence. The stochastic approach proposed is applied to three LV distribution networks to illustrate the method. The results show that both types of uncertainties affect the HC. The need for distribution network planners to identify and distinguish between the types of uncertainties is emphasised.

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.

Enhancing photovoltaic hosting capacity—A stochastic approach to optimal planning of static var compensator devices in distribution networks

To improve photovoltaic hosting capacity of distribution networks, this paper proposes a novel optimal static var compensator planning model which is formulated as a two-stage stochastic programming problem. Specifically, the first stage of our model determines the static var compensator planning decisions and the corresponding photovoltaic hosting capacity. In the second stage, the feasibility of the first stage results is evaluated under different uncertainty scenarios of load demand and photovoltaic output to ensure no constraint violations, especially no voltage constraint violations. In addition, we simultaneously consider the minimization of static var compensator planning cost and the maximization of photovoltaic hosting capacity by formulating a multi-objective function. To improve the computational efficiency, a solution method based on Benders decomposition is developed by decomposing the two-stage problem into a master problem and multiple subproblems. The effectiveness of the proposed model and solution method is validated on modified IEEE 37-node and 123-node distribution systems. Last, our results show that the proposed model can significantly improve the photovoltaic hosting capacity. The case studies also demonstrate that the PV hosting capacity becomes insensitive to the additional SVC planning cost when the total cost exceeds about $175,000.

Impact of Smart Inverters on Feeder Hosting Capacity of Distribution Networks

Penetration of renewable energy based distributed generators into power networks is limited. This is due to several reasons such as intermittency of the generation, low-inertia during disturbances and high variation of local voltage and frequency. Traditionally, distribution networks are designed for a finite voltage drop with distance from feeder connection. When PV panels are deployed at households, their impact on the local voltage profile becomes very substantial. Especially, when there is high solar radiation, generated power increases and the local voltage exceeds the permittable limits. Smart inverters are introduced to tackle this problem. Unlike other inverters, they can follow the local network measurements and provide auxiliary voltage and frequency support. With these features, smart inverters have the ability to push the renewable energy penetration level further. To investigate this phenomenon, custom smart inverter models are developed. Power flow calculation steps are modified to include smart inverter's active inputs. Then, feeder hosting capacity studies were run with conventional and smart inverters to investigate the improvement. Developed models, modified power flow algorithm and results of simulations are reported. Findings show renewable energy share can be increased in networks with smart inverter deployments only, without making substantial changes to rest of the network.

Optimal accommodation and management of high renewable penetration in distribution systems

The paper presents a new bi-level optimisation framework for optimal accommodation and operational management of wind power generation and battery energy storage system (BESS) simultaneously, aiming to maximise the renewable hosting capacity of distribution networks. A new objective function is suggested comprising of annual energy loss in feeders, reverse power flow into the grid, non-utilised BESS capacities, round-trip conversion losses of BESSs and node voltage deviation subjected to various system security constraints. An artificial-intelligence-based optimal management of BESS is proposed for effective control of high-renewable power generation. Due to the high investment and running costs of BESS, minimum storage capacity has been ensured in planning stage. In order to show the effectiveness of the proposed model, it is implemented on a benchmark test distribution system of 33-bus. Besides, various test cases are investigated and compared, which shows that the proposed optimisation model is promising.