Distribution System Operators Research Articles

Nodal Operating Envelopes vs. Network-wide Constraints: What is better for network-safe coordination of DERs?

For network-safe coordination of distributed energy resources (DERs), many approaches impose some form of constraint set to guarantee the safe operation of a distribution network. This paper presents a comparative analysis of two distinct approaches that leverage a constraint set for network-safe coordination: nodal operating envelopes versus network-wide constraints on the action of a DER aggregator. We investigate their respective strengths and limitations by considering information and communication requirements and trade-offs in terms of flexibility and fairness. The availability of potentially private information to the aggregator or to the distribution system operator determines which of the approaches is feasible. The results of the case studies suggest that if the goal is to maximize DER flexibility, a nodal constraint approach should be used. However, if the aggregator does not have information on how the constraints map to its DERs and/or does not have the ability to send out node-specific control inputs, the nodal constraint approach is not feasible and a network-wide constraint approach is needed. We show that a network-wide constraint approach constraining the aggregator’s control input provides a good balance between flexibility and fairness.

An empirical analysis of electric vehicle charging behavior based on real Danish residential charging data

In the last two years there has been a significant increase in the global adoption of electric vehicles (EVs). Particularly in Denmark, 40% of new vehicle registrations in 2023 were electric, marking a notable shift towards e-mobility. As a result, EV loading begins to constitute a large part of residential electricity demand. The concurrent increase in the interest for reduction in charging costs via demand response leads to a growing need for better understanding of charging behavior and its impact on consumption profiles. This works addresses the lack of up-to-date large-scale empirical studies on residential EV charging behavior. It presents a thorough analysis of all important charging characteristics based on a large set of 5534 Danish residential chargers between 2021 and 2023, providing unique insight in the effect of user-induced controlled charging. Our analysis shows the difference between actual charging profiles and those commonly used by distribution system operators in Denmark, and reveals the significant change over the span of one year with the doubling of peak demand per charger from 1.25 kW to 2.5 kW.

Transformer-customer relationship identification based on deep Gaussian mixture model in low-voltage distribution system

Accurate transformer-customer relationship is critical for better operation and management of low-voltage distribution system. It is of high cost to establish and check the profiles of transformer-customer relationship by manual maintenance, which is often mistakenly archived too. This paper proposes a novel automatic identification method based on a deep Gaussian mixture model with voltage data. The improved model is built with feature extraction network, clustering network and splitting and merging network, which can adjust the number of transformers adaptively and dynamically. It can also identify outliers of the data and effectively solve the uncertainty and imbalance of samples. Finally, the proposed method is tested in a practical power supply district which has 2752 customers with 9 transformers. Five different test cases are set to verify its effectiveness. The method can dynamically adjust to the actual number of transformer under different initial clustering numbers. The experimental results demonstrate that our model achieves an accuracy of up to 98.8% in profiling unlabeled users, while achieving an error correction accuracy of 99% in the case of imbalanced users in the transformer area. To further validate the superiority of our model, we conducted robustness experiments under different user scales and data loss scenarios. The results consistently outperform current state-of-the-art algorithms.

Optimizing local electricity markets: A bi-level primal-dual approach for integrating price-based demand response and distribution locational marginal pricing

This paper addresses the conundrum of optimizing electricity consumption patterns in response to fluctuations in demand and price, a task managed by both load aggregators and the distribution system operator (DSO). The conventional approaches in the literature to integrate demand response (DR) into optimal power flow (OPF) problems typically overlook the price responsiveness of consumers or simplify power flow equations to account for price-elastic demand.In this paper, we strive to close this gap by introducing a bi-level primal-dual optimization framework that incorporates aggregators' objectives into the market-clearing process while preserving the precision of the OPF equations. At the upper level, the model seeks to minimize both the total payment and peak load. The lower level, structured as a second-order conic (SOC) problem, aims to reduce overall generation costs subject to constraints of the second-order conic (SOC) branch flow model (BFM). The two levels interact through the optimal demand response and distribution locational marginal prices (DLMPs). The principles of convex duality principles together with the strong duality constraint are leveraged to transform the market-clearing problem into a single primal-dual problem. We also circumvent the non-linearity that stems from the DR payment term by incorporating discretizing loads and the big-M method, thus converting the problem into a mixed-integer SOC (MI-SOC) formulation. The merits of the proposed MI-SOC framework are validated through case studies conducted on the IEEE 33-bus test system, showcasing its potential to enforce price-elasticity demand response in distribution system's electricity markets.

Impacts of Plug-in EVs and decentralized power generation on distribution system operation

Plug-in Electric Vehicles (PEVs) have gained popularity in recent years due to their environmentally friendly attributes. However, integrating PEVs into distribution systems poses novel challenges and opportunities. This research paper undertakes a comprehensive assessment of the repercussions of PEVs and distributed generation (DG) integration on the distribution system’s reliability, power loss, and voltage stability. In order to alleviate peak loads, select PEVs are deployed in Vehicle-to-Grid (V2G) mode. The integration of DG units, particularly solar Photovoltaic (PV) and wind turbine (WT) systems, is explored to mitigate the potential strain of PEV charging on the distribution system. The uncertainty of renewable DG and PEV charging-discharging, along with time-varying residential and commercial loads, are included in the analysis. Power interruption duration and frequency-based reliability index are considered in this work. The combined effects of PEVs’ Grid-to-Vehicle (G2V) and V2G mode and DG integration are comprehensively investigated, and the impact assessment is conducted on the modified IEEE 33 bus distribution system to ensure the practicality and applicability of the research findings.

Hierarchical robust Day-Ahead VPP and DSO coordination based on local market to enhance distribution network voltage stability

As distributed energy resources (DER) are increasing, a variety of business models and markets are appearing. Business models such as virtual power plants (VPPs) contribute to enhanced stability of the power system while improving the profits of participating resources. However, the uncertainty of the behavior of DERs and the resulting stability issues are a major barrier to DER deployment and require a coordination framework between grid operators and business models to overcome. Therefore, in this paper, a hierarchical robust day-ahead coordination of a VPP and distribution system operator (DSO) based on the local market is proposed to improve the voltage stability of the distribution network by considering the uncertainty of DERs. For fairness reasons, the contribution of DERs to the distribution system can be addressed by a market-based approach. Local markets are divided into local energy markets (LEM) and local flexibility markets (LFM), which can consider the uncertainty and profitability of DERs. First, the DER determines the seller and buyer status of the LEM based on its self- scheduling with expected prices and is matched based on a double-auction mechanism. At this time, the worst-case scenario of load and renewable energy generation predicted by the Gaussian process (GP) is considered. The mismatched amounts in the LEM are aggregated by the VPP and bid into the wholesale energy market (WEM) operated by the TSO (Transmission system operator). Subsequently, DSO operates a LFM that coordinates microgrid (MG) and VPP on the distribution grid to increase voltage stability. The LFM and WEM problems are considered as interval optimization in the form of mixed integer linear programming and are applied together. The flexibility supply resulting from the adjustment of VPP is compensated by a loss-sensitivity-based flexibility price. Since voltage stability exists in upper and lower bounds, the upper and lower bound of net load through GP-based forecasting is considered along with the linearized network constraints. The results are validated through Monte-Carlo simulation-based scenarios on IEEE benchmark system in MATLAB environment. As a result, it is proved that the proposed model improves the distribution network voltage stability and the profit of the participants.

An Optimal Upgrading Framework for Water Distribution Systems Operation

An Optimal Upgrading Framework for Water Distribution Systems Operation

Reactive power compensation during the convergence of grid system with FACTS devices

From the last few years power demanding is increasing as per load requirement. The condition of power demand is not accompanied by only generation and transmission of electricity. Many industrial power plants are running on higher capacity to fulfill load requirement. There are many issues such as voltage instability and power quality which led to many power plants. In distributed generation generators are distributed asynchronous type and mostly affected by the reactive power. They basically cannot generate reactive power but draws away from the power system. It causes the voltage drop to the consumer side which may be not acceptable. Then reactive power management is essential for reducing those problems and provides continuity of electricity to the consumers. Distributed generation systems and FACTS devices are converting conventional power grid into smart grid. Reactive power compensation is now challenging issue to preserve adequate power quality and improve the performance of distribution system. There are many FACTS devices such as we can control power factor of the circuit and reactive power compensation. Many researchers have focused on behavior of FACTS device in an unbalanced condition and majority work is done only with DSTATCOM. This paper deals with different FACTS devices in grid systems with analysis and overview of D-FACTS devices. Hence in this paper, applications of various D-FACTS devices have been described to find location of compensation devices for reliable operation.

Optimal Placement and Capacity of BESS and PV in EV Integrated Distribution Systems: The Tenth Feeder of Phitsanulok Substation Case Study

Installing a battery energy storage system (BESS) and renewable energy sources can significantly improve distribution network performance in several aspects, especially in electric vehicle (EV)-integrated systems because of high load demands. With the high costs of the BESS and PV, optimal placement and capacity of them must be carefully considered. This work proposes a solution for determining the optimal placement and capacity of a BESS and photovoltaic (PV) in a distribution system by considering EV penetrations. The objective function is to reduce system costs, comprising installation, replacement, and operation and maintenance costs of the BESS and PV. The replacement cost is considered over 20 years, and the maintenance and operation costs incurred in the distribution system include transmission line loss, voltage regulation, and peak demand costs. To solve the problem, two metaheuristic algorithms consisting of particle swarm optimization (PSO) and the African vulture optimization algorithm (AVOA) are utilized. The tenth feeder of Phitsanulok substation 1 (PLA10), Thailand, which is a 91-bus distribution network, is tested to evaluate the performance of the proposed approach. The results obtained from the considered algorithms are compared based on distribution system performance enhancement, payback period, and statistical analysis. It is found from the simulation results that the installation of the BESS and PV could significantly minimize system cost, improve the voltage profile, reduce transmission line loss, and decrease peak demand. The voltage deviation could be reduced by 86%, line loss was reduced by 0.78 MW, and peak demand could be decreased by 5.706 MW compared to the case without BESS and PV installations.

Incorporating energy storage system into grid connected photovoltaic system with the application of wireless sensor network

The growing use of residential photovoltaics (PV) poses several challenges for distribution system operators. Technical challenges arise when excess PV energy is integrated into the low-voltage utility grid. This leads to current and voltage increase. On the other hand, economic challenges show up when the energy storage system (ESS) is fully charged without a mechanism in place to store excess energy. Residential ESS not only mitigates these challenges but also assists in mitigating irregular PV power issues. Different methodologies have been developed to construct models assessing the techno-economic benefits of integrating ESS with PV systems. Nevertheless, these approaches do not account for PV-ESS operation with feed-in-tariff (FiT) incentives, different electricity rate structures, and the lifetime model of ESS. This paper proposes an energy management strategy (EMS) suitable for rooftop PV installations. The EMS incorporates a temperature model and optimizes scheduling to regulate the power output of the PV/ESS in response to changes/needs in electricity demand. In addition, tactical information exchange between the ESS and the utility grid is facilitated by Link 16 wireless sensor networks. Furthermore, hybrid gated recurrent unit (GRU) and graph convolutional network (GCN) model is introduced for predicting PV power and electricity load. Consumer electricity data, PV data, and tariffs are utilized in the proposed study to examine the impact of MESS on electricity charges. The impact of multi-ESS (MESS) on the objective function is evaluated using the optimization model. Simulation results on a typical system show that the proposed approach significantly enhances battery performance. By the end of the year, the SOC reaches 84.78 % for the proposed MESS and 84.64 % for the DESS. The results reveal that the proposed approach yields 22 % cost savings compared to a dual ESS system and 24 % cost reduction compared to a single ESS. Moreover, the elevated packet delivery ratio values corresponding to higher signal-to-noise ratio levels signify enhanced reliability and superior communication performance. The impact of jamming attacks on battery consumption employing additive white Gaussian noise (AWGN) is compared with AWGN and single-tone, and AWGN and multi-tone. The results show that when dealing solely with AWGN, less power is needed to filter out noise.

Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Statistical characterization of reliability indices in medium voltage networks using a Monte Carlo-based method

Distribution companies are compelled to provide high-quality services to their customers in compliance with existing regulations. Key performance indicators such as system average interruption duration index and system average interruption frequency index are commonly employed to assess the quality of service provided by these utilities. In certain countries, distribution system operators may face penalties or receive bonuses based on their performance within specified thresholds in their reliability indices. This paper introduces a novel, integrated algorithm, based on Monte Carlo simulations, for conducting a risk assessment in real MV networks using local data from the distribution system operator. The proposed technique is tested in a real MV network, and the obtained results are analyzed both at feeder level and for the entire network. The feeder-level results might be used to identify the sections of the network with the worst quality of power supply, based on the estimated KPIs. The overall results of the study offer valuable insights into the likelihood of specific KPI values using probability density function. The Weibull distribution and log-logistic distribution are found to be those with the best-fitting probability density functions for the two indices evaluated in this work. Furthermore, a penalty-based rate approach is presented as an application of the proposed method to assess the economic risk of the MV network. The findings reveal that the test system is expected to incur penalties amounting to 7.55 million dollars per year.

Monitoring the power flow of a low voltage distribution network using public lighting infrastructure

The production of renewable energy sources can change rapidly in a short period of time, making monitoring the power quality of Low Voltage Distribution Networks (LVDNs) one of the biggest challenges for Distribution System Operators (DSOs). An LVDN can overload some equipment if it does not monitors voltage profiles, voltage unbalance and reverse apparent power flow. Therefore, we propose a monitoring system that uses the Public Lighting Infrastructure (PLI), which is fed directly by the LVDN, to monitor the LVDN. It is based on remote management relays installed in the PLI, and replaces the installation of a power quality analyzer (PQ). The system has been empirically validated and the results show that the proposed system can identify reverse apparent power flow, profiles, and voltage unbalance with an average absolute error of 0.096% compared to a professional PQ analyzer. The proposed system can be a relevant contribution to the challenge of power quality monitoring, with cost savings; for example, DSOs can use the proposed system to access real-time information in the LVDN through a low-cost data acquisition model, without the need to send a team to install a PQ analyzer in the LVDN and then obtain the data.

Employing Tank Constraints to Present Total Cost and Water Age Trade-Offs in Optimal Operation of Water Distribution Systems

Employing Tank Constraints to Present Total Cost and Water Age Trade-Offs in Optimal Operation of Water Distribution Systems

Unlocking Flexibility from Third-Party Resources: Decoding the Interaction between Mechanisms for Acquiring Distribution System Operator Services

Unlocking Flexibility from Third-Party Resources: Decoding the Interaction between Mechanisms for Acquiring Distribution System Operator Services

Integration of wavelet packet transform, residual convolution neural network, and support vector machine for series arc fault detection

Series arc fault (SAF) poses a great challenge to the safe and stable operation of civil low-voltage distribution systems. For the accurate and rapid detection of SAF, this article proposes an SAF detection method using wavelet packet transform (WPT), residual convolution neural network (RCNN), and support vector machine (SVM). First, the raw current signal is decomposed into four wavelet components based on WPT. Then, the 1-D wavelet components are converted into 2-D matrices. Afterward, the matrices are input into RCNN through different channels. Finally, the detection results can be yielded by SVM. The effectiveness of the proposed method is verified based on offline experiments. The average detection accuracy of the proposed method is 99.72%, which is higher than that of the eight comparison methods. Moreover, the results of online experiments indicate that the detection time of the proposed method is less than 100 ms and can satisfy the requirement of standard 1699.

Robust economic-resilient operational planning of distribution networks considering multi-microgrids black-start service

The increasing growth of natural disasters and cyber-physical attacks has made the development of resilience enhancement strategies for distribution networks a significant challenge for operators. Hence, in this paper, a black-start strategy based on multi-microgrids capability is developed as a two-layer preventive-corrective measure within a cost-effective energy management framework. The preventive layer includes regulating bilateral contracts between the active distribution network operator and the multi-microgrids owner pre-event. The corrective layer includes islanding microgrids during the event, calling the responsive loads, and economic-resilient operational planning of the active distribution network post-event. In this regard, a hybrid robust multi-objective optimization approach is applied to model the proposed black-start strategy as a risk-averse portfolio. The effectiveness of the developed model is tested on the modified IEEE 33-bus distribution network. Results such as an 83.38 % load recovery of the active distribution network, a 65.78 % reduction in distribution system operator costs, and a 131.28 % increase in multi-microgrid owner profit clearly illustrate the enhanced resilience of the active distribution network within an economically robust framework, which indicates the effectiveness of the proposed strategy.

Techno-economic modeling of a preventive-corrective strategy based on black-start services to resilience enhancement of distribution networks

High-impact with low-probability (HILP) events such as natural disasters or cyber-physical attacks severely affect power systems. The distribution networks experience significant challenges during HILP events. So, strategies need to be developed to enhance distribution networks' resilience. Hence, in this paper, a resilience enhancement strategy based on the potential of multi-microgrids is developed. The proposed strategy has a two-layered structure consisting of preventive and corrective measures within the hierarchical centralized energy management framework. The preventive measures are based on the black-start bilateral contract between the active distribution network operator and the multi-microgrids owner. The corrective measures are based on islanding microgrids during the event. The LP-Metric-based multi-objective optimization approach is employed for economic-resilient operational planning of the active distribution network. Moreover, a scenario-based stochastic approach is utilized to deal with renewable resources and load uncertainties. On the modified IEEE 33-bus active distribution network, the effectiveness of the proposed strategy is evaluated using the supplied load as a resilience index. The proposed black-start strategy has resulted in an increase of 181.57 % in the supplied load of the distribution network, while a reduction of 83.12 % in its curtailed load has been achieved. Consequently, the resilience index increased by 84 %. Additionally, the distribution system operation cost was reduced by 87.44 %, while the multi-microgrid owner's profit increased by 1004.68 %. The obtained results demonstrate that distribution system resilience has been enhanced within an economic framework, which validates the effectiveness of the proposed strategy.

Ray-Tracing modeling for urban photovoltaic energy planning and management

The traditional Radiative Transfer Modelling solutions for Solar Energy monitoring and forecasting often provide outputs for a single point location or an area location. However, for high resolution representation of areas these solutions suffer due to low simulation speeds. This approach makes it difficult for decision-makers to accurately estimate the solar potential of the administrative area and plan installations accordingly. In this direction, the study introduces three-dimensional Ray-Tracing based radiative modeling which is a high-speed area-based solution for solar energy monitoring. The three-dimensional ray-tracing was simulated by using advanced graphic creation platforms and cloud computing in conjunction with satellite data of the clouds, aerosols, building shadows effects and three-dimensional representations of the city using Cesium 3D tiles and Unreal Engine ®. The entire system was developed in a hybrid model to be exploited by urban planners for solar PV installations and by electricity distribution system operators for energy management and efficient incorporation of the produced energy into the regular and smart grids. This study implements and analyses this Ray-Tracing model for solar photovoltaic energy potential estimation at a rooftop level for the city of Athens, Greece. The total rooftop exploitable area in Athens was found to be close to 34 km2, which is able to massively host distributed PVs followed by almost 4.3 TWh of annually produced energy, whilst Penteli (a Municipality in Athens) possessed a potential of 96.8 GWh with an exploitable area of just 0.8 km2. This amount of energy, in a hypothetical full coverage scenario, is able to provide for 48.7% of Athens’s total energy requirement. Similar year-long simulations were conducted using the EU’s largest rooftop solar installation at Stavros Niarchos Foundation Cultural Center and randomly selected rooftops having solar installations in different municipalities in Athens. With these estimated solar potential values, the gross savings in natural gas consumption and hence the CO2 equivalent emissions can be computed. With the current estimated solar potential of Athens, the analyzed savings accounted of nearly 2.43 billion euros and 18 MT CO2 equivalent emissions. These computed annual savings are capable of covering installation costs for nearly 100,000 new solar installations. The end-product of this study is the development of a solar cadastre web tool which will support the decision-makers in the energy transition policies and the solar PV penetration into the urban environment and eventually drive the effort into renewable energy transition across the globe.

ФОРМУВАННЯ ЛОКАЛЬНИХ ЕЛЕКТРОЕНЕРГЕТИЧНИХ СИСТЕМ В СКЛАДІ ОБ’ЄДНАНОЇ ЕЛЕКТРОЕНЕРГЕТИЧНОЇ СИСТЕМИ

At present, renewable sources of energy (RSE) are geographically diffused in energy supply systems, which complicates the formation of local electric energy systems (LEES), which operating in normal modes in parallel with the energy system as balancing groups, in extreme cases can operate in isolation in autonomous mode. However, the diffuse distribution of RSE does not allow for the effective formation of LEES in such a way that they ensure the required level of reliability of electricity supply to electricity consumers. The article proposes to integrate RSE into distribution power grids in the form of separate microgrid (MG), which is a key part of the transition to a power grid operating on the principles of SMART Grid. Local MGs, in addition to generation sources and consumers, also have the means to accumulate a certain amount of energy. To ensure the technical and economic efficiency, MGs are combined into an intelligent control system, which allows for more rational use of MG resources, efficient interaction with the distribution grid, and the use of active electricity consumers in the process of balancing the LEES mode. The paper proposes a hierarchical structure of the intellectual system of LEES. Thus structured, LEES with intelligent power grids can not lose RSE during the limitation of centralized power supply, but fully use their advantages together with energy storage systems for reliable power supply to consumers. Thanks to the intelligent system proposed in this paper, the LEES implements the principles of SMART Grid and functions as an informative energy system. The LEES formed in this way can operate as a balancing group within the ESS, performing the tasks of the distribution system operator depending on the voltage and power of its MG components. Since LEES are a part of the ESS, they operate under the conditions and according to the rules, without disrupting the functioning of the ESS, which currently provides a stable power supply. This applies to all modes of operation of LEES.