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Distribution network operators face technical and operational challenges in integrating the increasing number of distributed energy resources (DER) with the distribution network. The hosting capacity analysis quantifies the level of DER penetration on the network without violating power supply qualities, such as over voltage, under voltage, transformer and feeder overloading, and protection failures that may be caused due to the high penetration of the DER. Artificial Intelligence (AI) techniques could be a useful tool for quantifying the hosting capacity of the network, real-time monitoring of the DER and network parameters and ensuring quality power supply to the consumers.
- artificial intelligence
- machine learning
- deep learning
- hosting capacity
- impact factors
- optimisation
- distributed energy resources
1. Introduction
Renewable energy resources have become significant contributors to energy supply in electricity distribution networks [1][2][6,7]. The high penetration of distributed energy resources with the network poses technical and operational challenges for distribution system operators (DSOs) [1][3][4][6,8,9]. Sustainable transmission and distribution network operation maintaining the statutory power quality limits has become a significant technical and operational concern for DSOs. The electricity distribution system operators should supply electric power to the consumers without violating statutory power quality limits [5][10]. Traditionally, the electric power flows unidirectionally from the generating station to the consumers through the transmission and distribution networks [3][8]. The unidirectional power flow concept has changed due to integrating a large number of distributed energy resources. The variable nature of the power of the DER and customer load uncertainties requires real-time monitoring and high data visibility for distribution network stability. Other factors such as over voltage, under voltage, voltage unbalanced, transformer and feeder overloading, reverse power flow, network control scheme, and thermal limits of network components have emerged as significant concerns for maintaining statutory limits of the power supply [5][6][7][8][9][10,11,12,13,14]. The capability to inject power from the maximum number of DER without violating the power quality limits, known as the hosting capacity, should be determined and monitored in real time for the sustainability and reliability of the network.
The hosting capacity provides policy support and network information to the DSOs. The dynamic characteristics of the distribution network integrate additional power generated by the DER, maintaining the power quality within limits. Based on the network and DER characteristics, the distribution network hosting capacity estimates the network capability to accommodate DER while retaining the power quality limits without re-enforcing the network equipment. It also emphasises network reliability without further auguring the existing control configuration and infrastructure [10][15]. The National Renewable Energy Laboratory (NREL) has defined the hosting capacity of the distribution network as the capability of the network to accommodate additional DER without further enhancing the control mechanism, upgrading the system components, and maintaining the safety and reliability of power supply to the consumers [11][16]. The Energy and Power Research Institute (EPRI) considers the hosting capacity as the output of a systematic study of the distribution network and renewable energy resources’ input data based on carefully selecting a series of analytical parameters. EPRI has identified the hosting capacity as the estimation of additional DER to connect to any place on the existing network within power quality and control configuration [10][15]. It has emphasised the quality and granularity of data, careful selection of methods, tools, and parameters for analysis and appropriate application of the hosting capacity results. Researchers have illustrated the hosting capacity of the network as the capability to sustainably integrate the maximum amount of power from the DER with the distribution network without further augmentation of the existing resources and control systems [2][4][12][13][7,9,17,18]. The study of the hosting capacity is concerned with integrating distributed energy resources into the electric distribution networks in a technically feasible, operationally sustainable, and economically profitable manner. The hosting capacity analysis enables the network operators to integrate distributed renewable energy resources [12][17]. It also helps to maintain network stability and DER integration reliability without further investment in upgrading network components [3][9][13][14][8,14,18,19]. The hosting capacity of the distribution network is not any static value. It depends on factors like inputs collected from the network analysis, assumptions for the hosting capacity estimation, grid models, and impact factors [15][20].
The impact factors are the DER and Grid characteristics that influence the capability of the distribution network to integrate additional DER, maintaining the power quality limit and reliability of the network operation [15][20]. Impact factors are sometimes opposing each other. Sometimes they also complement each other to enhance the DER integration capability of the network. The impact factors such as over voltage, under voltage, power loss, thermal limits, power factor, location of the renewable energy resources, harmonic distortion, real power, reactive power, control mechanism, technology, and frequency impact the capability of the network to accommodate DER in different ways. All these factors may influence the approximate estimation of the distribution network capability for DER penetration [16][21]. Different hosting capacity analysis approaches consider the impact factors differently. Such consideration may affect the hosting capacity simulation results.
The selection of the hosting capacity analysis methods depends on present and future needs [15][20]. The simulation result depends upon the characteristics of the electricity network, DER, control mechanisms, transformers, conductors, and inverter technologies. The effect relies on harmonic injection, real and reactive power flow, power losses, consumer demand, and energy usage patterns. Electric Vehicles (EVs) have added new dimensions for the hosting capacity analysis [17][22]. The complexity of the distribution grid and DER uncertainties have made the analysis more challenging. The high volume of network data, power quality indicators, and variability of DER output made the conventional hosting capacity estimation approaches insufficient to tackle the operational and reliability issues of DER integration [18][23]. The artificial intelligence approach for hosting capacity analysis could encompass the real-time monitoring and estimation of network variables and impact factors for calculating the hosting capacity of the network.
Both conventional and artificial intelligence approaches for hosting capacity analysis depend on power flow analysis tools for the network input. The power flow analysis tools also assist planning, design, and operation of the network [19][24]. Some power flow analysis tools are commercially available, such as PowerFactory, PSS/Sincal, PSCAD, and PSS/E. On the other hand, PandaPower, OpenDSS, PowerModelsDistribution, and OpenDSOPF are open-sourced power flow analysis tools. Different tools have their strong points and focused areas. The selection of power flow analysis tools depends on the purpose and need of the analysis. Standard, efficient, and industry-proven tools could help to overcome network analysis challenges. Although several literature reviews on the hosting capacity analysis methods have summarised various aspects, the utilisation and impacts of artificial intelligence on hosting capacity analyses still need to be explored. They need further studies to capture its usage for system stability and network sustainability.
2. Artificial Intelligence Approach of Hosting Capacity Analysis
The electricity power network is a complex system comprising generation, transmission, distribution, and control equipment. The modern power network consists of sophisticated communication systems, power conversion equipment, renewable energy resources, power storage networks, and information processing systems. The growing trend of digitisation, decentralisation, and real-time monitoring of power networks requires a high volume of data collection, analysis, and decision-making capabilities for sustainable operation, quick decision, and economic viability. Artificial Intelligence has great potential for managing energy supply, reliable operation, demand forecasting, and distributed energy resources integration through intelligent decision-making tools [20][87]. The growing demand for green energy has triggered the integration of a large number of distributed energy resources and energy storage devices with the power network that requires real-time network data analysis integration sustainability, network stability, operation reliability, and economic value [21][88]. The operational uncertainties of DER, EVs, energy storage devices, power conversation systems, and uncertain load consumption patterns involve analysing a large number of data [22][89]. Traditional analysis tools are unable to fulfil the requirement of the modern electricity network [23][90]. Artificial Intelligence algorithms could be very useful for analysing dynamic behaviour, forecasting, integration, disturbance events, and cyber security of the electricity network with a high penetration of DER and energy storage devices [21][88].
The network operators have to face significant complexity in the power network design, operation, and integration for the increasing number of distributed energy resources [24][35]. Artificial Intelligence could overcome the ever-increasing technical and operational complexity of integrating distributed renewable energy resources with the distribution network [25][91]. It could be used for power output prediction from renewable energy resources, photovoltaic power optimisation, solar irradiance, wind speed prediction, risk and tariff optimisation, system stability, and integration. Artificial intelligence algorithms could be static or dynamic based on the input data and objective functions. Depending on the system and problem to be analysed, different artificial intelligence methods have been proposed, such as the meta-heuristic methods genetic algorithm (GA), particle swarm optimisation (PSO), simulated annealing (SA), random forest (RF), k-nearest neighbours (kNN), support vector machine (SVM), and artificial neural network (ANN) [26][92]. Power output forecasting has gained much attention for the sustainable integration of renewable energy resources with the power network. The artificial intelligence algorithms could increase the efficiency, effectiveness, and potentiality of real-time DER monitoring and achieve maximum potential power from these resources [22][89]. The stack auto-encoder (SAE), deep belief network (DBN), deep recurrent neural network (DRNN), stacked extreme learning machine (SELM), deep reinforcement learning (DEL), and deep convolution neural network (DCNN) are widely used deep learning algorithm for forecasting renewable energy [20][22][27][86,87,89].
In [24][35], the authors studied the deep-learning-based Spatial-Temporal Long Short-term memory (ST-LSTM) algorithm for calculating the real-time hosting capacity of the distribution network by correlating the spatial and temporal network and DER data. In the proposed algorithm, they have introduced a cloud-based end-to-end solar energy optimisation platform (e-SEOP) for accumulating and analysing data gathered from a dynamically calculated hosting capacity and real-time DER control system. The study introduced a sensitivity gate for increasing output accuracy. Power consumption forecasting is essential for the integration of DER with the network. In [28][38], the authors investigated the deep learning algorithm-based k-means clustering-based convolutional neural networks and a long short-term memory (k-CNNLSTM) model for reliable energy consumption forecasting. Using a case study and comparing results using various tools and techniques, the authors concluded that the proposed k-CNNLSTM model provides more accurate demand forecasting of energy consumption. The improved load forecasting results may help the network operator to manage the power demand more efficiently and effectively [28][38]. In [29][39], the low-voltage grid has been classified based on the network, simulation, and graph information. They proposed the support vector machine (SVM) for analysing low-voltage grid characteristic parameters. The researchers in [30][45] studied the supervised deep learning algorithm for forecasting the energy demand at the district level so that the designers, planners, and administrators could utilise the predicted result. They examined the one-step secant back propagation neural networks (OSSB-NN) and the BFGS quasi-newton back propagation (BFGS-QNB) using-real time consumption and climate data. In [31][93], the authors proposed swarm intelligence optimisation and data processing for estimating the potentiality of wind energy and forecasting the wind speed that would help reduce the operating cost of wind power generating stations. In [32][94], the researchers introduced the deep-learning-based framework (D-FED) for calculating the future energy demand, forecasting the electricity demand in real time, and estimating the dependencies of the load demand. They used the short long-term memory network moving window for their proposed framework. The artificial intelligence methods for distributed energy resources could be categorised as data-driven and optimisation-based.
2.1. Data-Driven Methods
The hosting capacity is not a static value of the integration capability of the power network. Instead, it is the estimation of the coordinated effects of different impact factors that express the capability of the power network to accommodate the maximum power from the distributed energy resources without violating the power quality limits. The model-based methods depend on the network’s worst-case scenarios, considering different DER penetration levels, network characteristics, and consumer load demands [33][95]. The scenarios representing the minimum or maximum allowable limits of the power indicators, such as voltage level, current injection, thermal overloading, load demand, and DER penetration, could not represent realistic scenarios of the network. Such approaches tend to overestimate or underestimate the hosting capacity of the network. Moreover, changing any parameter requires different scenarios that may generate millions of scenarios’ simulation burdens.
Model-based approaches become more complex to handle the time-based data for the hosting capacity analysis. Therefore, they could be more efficient, time-consuming, and error-prone hosting capacity analysis processes for time-based analysis. The data-driven hosting capacity analysis methods collect time-series input data of the network components, consumer load variation, and DER penetration for estimating the hosting capacity of the network. It considers uncertainties of the DER integration based on real-time data. Different artificial intelligence algorithms could be utilised to encompass the probabilistic nature of the network, load, and DER inputs. It could train the learning model using offline or online data to calculate the real-time hosting capacity [24][35]. The data-driven methods could enhance the computational capability and output accuracy of the hosting capacity of the network.
[37][47]. Researchers presented different artificial intelligence techniques for the hosting capacity analysis (Table 1).
Table 1.
Hosting Capacity Analysis Using AI Techniques.
| Area of Study | Algorithm Used | Objective Function | Reference |
|---|---|---|---|
| Photovoltaic hosting capacity analysis | Multi-parametric programming (MPP) OPF | Fast and scalable solutions for PHCA studies | [35][28] |
| Distribution grid hosting capacity in real-time | Deep-learning-based LSTM | Real-time prediction of Hosting Capacity for each feeder | [24][35] |
| Converter-interfaced generators (CIGs) integration | Static multi-agent reinforcement learning (MARL) algorithm | Maximising hosting capacity and voltage flexibility | [34][37] |
| Energy consumption forecasting | Deep-learning-based kCNN-LSTM | Energy consumption forecast | [28][38] |
| Low voltage grid | Support vector machines (SVM) | Low-voltage grid classification for hosting capacity | [29][39] |
| Energy demand predictions | Improved sine cosine optimisation algorithm-based LSTM (ISCOA-LSTM) | energy consumption forecasting | [38][44] |
| Energy requirement forecasting | OSSB-NN and BFGS-QNB | Load demand forecasting | [30][45] |
| Home PV system | Policy function approximation (PFA) | Impact of PV-battery systems on distribution networks | [39][46] |
| Optimal locations for new DERs | TLBO and HBMO algorithms | Cost, losses, and voltage deviation | [37][47] |
| PV System | Multiple swarm intelligence optimisation algorithm | Wind energy potential analysis, wind speed forecasting | [31][93] |
| Electricity consumption forecasting | Long short-term memory network | Timestamp prediction of future electricity demand | [32][94] |
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Machine learning:The high penetration of distributed energy resources in the high and medium voltage distribution network may affect the voltage profile and power quality. The active power control and reactive power generation capability of the network could positively impact the stability and reliability of the network [34][37]. Machine-learning-based approaches have been studied to mitigate the adverse impacts of the high penetration of renewable energy resources. In [34][37], the static multi-agent reinforcement learning (MARL) algorithm was studied to enhance the distribution network’s hosting capacity. The voltage flexibility of the network was analysed using the primary voltage, line, and transformer loading as input parameters. The method was tested using the Monte-Carlo-based power flow simulation on the modified IEEE 34 bus system with the converter-interfaced generation (CIG). The authors achieved about 7.53% voltage flexibility using their proposed machine-learning-based method. The feasible and infeasible nature of the optimal power flow (OPF) analysis was incorporated in [35][28] to achieve rapid and scalable solutions for probabilistic hosting capacity analysis. The proposed method solved a fraction of OPF to achieve speedy results compared to the traditional methods. The support vector machine (SVM) approach was studied in [29][39]. In this study, the authors have classified the low voltage distribution grid for hosting a capacity analysis based on the grid, simulation, and graph features. The study found that the reinforcement of the grid, utilisation of innovative technologies, and control of the reactive power could enhance the hosting capacity of the distribution network for integrating distributed energy resources. In [36][96], the network reconfiguration and distributed generators’ distribution were studied using the location-improved sine-cosine algorithm (LSCA). The voltage stability and active power loss were analysed to estimate the hosting capacity of the distribution network by applying the integrated forward-backward-based load flow analysis.
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Deep learning:The deep learning algorithms could enhance the performance of the hosting capacity analysis through training neural networks. In [24][35], the spatial-temporal LSTM (ST-LSTM) learning model was studied for predicting the real-time hosting capacity of each distribution network feeder. The deep learning algorithm kCNN-LSTM was studied in [28][38] to forecast energy consumption. The model was tested at the four-storied building in the Indian Institute of Technology (IIT), Bombay, India. In [31][93], the authors studied the multiple swarm intelligence optimisation (MSIO) algorithms for forecasting and estimating the potential of the power generated from wind energy sources. In [32][94], the authors considered the long-term historical data for electricity demand forecasting using the long short-term memory (LSTM) algorithm.
2.2. AI in Hosting Capacity Analysis
The real-time hosting capacity analysis requires a time-series data analysis for the reliable operation and sustainable integration of the DER with the distribution network. Different artificial intelligence techniques were proposed to estimate the hosting capacity using the non-linear behaviour of different uncertain parameters
2.3. Optimisation
Integrating distributed energy resources with the power network would be economically profitable and technically sustainable by optimising distribution network parameters, the network and DER control model, the DER output, the demand uncertainty model, and the DER output forecasting model. The sustainable integration of distributed renewable energy resources and energy storage devices within the electricity network requires analysing the microscopic information for network stability, reliability, and economic operation [21][88]. The varying nature of the power generated from the distributed energy resources, energy storage devices, power conversation electronic equipment, and uncertain load consumption pattern involves analysing a considerable amount of real-time data [22][89]. Traditional analysis tools are unable to fulfil the requirement of the modern electricity network [23][90]. Artificial intelligence algorithms could be very useful for analysing dynamic behaviour, forecasting, integration, disturbance events, and cyber security of the electricity network with a high penetration of DER and energy storage devices [21][88].
In [28][38], the authors investigated the deep learning algorithm-based k-means clustering-based convolutional neural networks and long short-term memory (k-CNNLSTM) model for reliable energy consumption forecasting. Using a case study and comparing the results using various tools and techniques, the authors concluded that the proposed k-CNNLSTM model could provide more accurate demand forecasting of energy consumption. The improved load forecasting results may help the network operator to manage the power demand more efficiently and effectively [28][38]. In [29][39], the low-voltage grid was classified based on the grid information, such as the network, simulation, and graph features. They proposed the support vector machine (SVM) for analysing low-voltage grid characteristic parameters. The researchers in [30][45] studied the supervised deep learning algorithm for forecasting the energy demand at the district level so that the designers, planners, and administrators could utilise the predicted result. They used real-time consumption and climate data to examine the one-step secant back propagation neural networks (OSSB-NN) and BFGS quasi-newton back propagation (BFGS-QNB). In [22][89], the authors reviewed the efficiency, effectiveness, and potentiality of artificial intelligence algorithms using deep learning techniques to forecast renewable energy output. The sparse autoencoder (SAE), deep belief network (DBN), deep recurrent neural network (DRNN), stacked extreme learning machine (SELM), deep reinforcement learning (DEL), and deep convolutional neural network (DCNN) are widely used deep learning algorithm for forecasting the renewable energy. Power consumption forecasting is essential for the integration of DER with the network. In [31][93], the authors proposed swarm intelligence optimisation and data processing for estimating the potentiality of wind energy and forecasting the wind speed that would help reduce the operating cost of wind power generating stations. In [32][94], the authors introduced the deep-learning-based framework (D-FED) for calculating the future energy demand, forecasting the electricity demand in real-time and estimating the dependencies of the load demand. They used the short long-term memory network moving window for the proposed framework.
