Distribution System Operators Research Articles

A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.

Hierarchical optimal dispatching method of transmission system operators‐distribution system operators cooperated integrated energy system considering gas‐thermal equivalence model

AbstractThe integrated energy system (IES) is moving towards the large‐scale cooperation between transmission system operators (TSO) and distribution system operators (DSO) with the coupling of electricity, heating, and gas networks. The conventional optimization method is not time‐efficient for solving such a large‐scale problem when taking account of the cooperation of TSO‐DSO of the IES. To solve such a problem, this paper proposed a hierarchical optimization framework for TSO‐DSO cooperated IES. To further improve the computation efficiency, the equivalent models of heating and gas system that retain the heating inertia characteristic and gas pipe storage characteristic are presented. A computation method for the TSO‐DSO cooperated IES is also proposed from the hierarchical distributed optimization perspective. The case study confirms the efficacy of the proposed gas‐thermal equivalent model in significantly enhancing computational speed. Furthermore, the proven effectiveness of the hierarchical distributed optimization approach in leveraging the full potential of flexible resources in transmission and distribution systems while safeguarding internal information privacy enables the development of robust dispatching strategies for each system.

Resilience-Oriented Planning of Urban Distribution System Source–Network–Load–Storage in the Context of High-Penetrated Building-Integrated Resources

Building-integrated flexible resources can offer economical availability to accommodate high-penetrated renewable energy sources (RESs), which can be potentially coordinated to achieve cost-effective supply. This paper proposes a resilience-oriented planning model of urban distribution system source–network–load–storage in the context of high-penetrated building-integrated resources. In this model, source–network–load–storage resources are cost-optimally planned, including the lines, soft open point (SOP), building-integrated photovoltaics (BIPVs), building-integrated wind turbine (BIWT), building-integrated energy storage system (ESS), etc. To enhance fault recovery capability during extreme faults, fault scenarios are incorporated into the distribution system operation via coupled multiple recovery stages. The resilience-oriented planning is a thorny problem due to its source–network–load–storage couplings, normal-fault couplings, etc. The original resilience-oriented planning is reformulated as a mixed-integer linear programming (MILP) problem, which can then be solved with a two-stage method and evaluated via a multi-dimensional evaluation metrics. The proposed planning methodology is benchmarked over a Portugal 54-node urban distribution system to verify the superiority and effectiveness on the system economy and resilience levels. Case studies show that the proposed methodology can exploit the optimal synergies of different source–network–load–storage components and enhance system dispatchability.

Machine learning for water demand forecasting: Case study in a Brazilian coastal city

ABSTRACT Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

On the operation and implications of grid-interactive renewable energy communities

The increasing installations of photovoltaic systems, electric vehicle chargers, heat pumps, and battery storage systems challenge distribution system operators to prevent critical grid operations. Nonetheless, flexibility potential from customer-owned assets remains untapped due to lacking federated coordination and incentives. In Europe, emerging renewable energy communities can address this challenge by facilitating collective optimal operation of flexible assets and providing grid services. This paper presents an approach for renewable energy communities to offer participants’ flexibility through grid-interactive operations. Distribution system operators then based on optimal power flow calculations request flexibility use to prevent voltage violations, transformer overloads, and line overloads. A case study with simulations for a future rural low-voltage grid is conducted. The results show that, compared to business-as-usual operations or renewable energy communities with fixed import and export limits, the grid-interactive approach ensures non-critical grid operations cost-efficiently and reduces curtailment by 60% to 90%, down to 1.1% of total generation. By providing both upward and downward flexibility and aligning with distribution system operators’ requirements, grid-interactive renewable energy communities can be key in enhancing the efficient use and stability of distribution grids.

Evaluating the impact of virtual energy storage under air conditioning and building coupling on the performance of a grid-connected distributed energy system

Energy storage technologies are vital in improving the operation performance of grid-connected distributed energy systems. The adjustability of indoor temperature and the thermal inertia of buildings can form an excellent virtual energy storage. However, there are few studies on the impact of this virtual energy storage on the operation performance of grid-connected distributed energy systems. Therefore, this study first calculates the equivalent thermal resistance and thermal capacitance of a building by using EnergyPlus and establishes the first-order thermodynamic load calculation model. Subsequently, the system dynamic optimal scheduling model, considering the virtual energy storage, was developed based on the first-order thermodynamic load calculation model. The Gurobi nonconvex solver is used to optimize the model, and the results are compared with the optimal scheduling results without considering the virtual energy storage. The results indicate that, guided by time-of-use electricity pricing, the virtual energy storage effectively reduces the air conditioning load during high and peak tariff periods while increasing it during valley tariff periods. This change in air conditioning load leads to an increase in grid power consumption during valley tariff periods. However, it also improves the system's operation efficiency and ultimately reduces the total grid power and gas consumption. The lower energy consumption makes the primary energy saving rate and carbon dioxide emission reduction rate of Scenario 2 greater than zero compared to Scenario 1 and increases with the increase of renewable energy penetration of the integrated power system. Furthermore, affected by the difference in the proportion of grid power consumption under time-of-use electricity pricing and the total energy consumption, the system's operating cost in Scenario 2 is less than in Scenario 1. The reduction rates in summer and winter typical days are 1.95 % and 6.48 %, respectively. Therefore, fully utilizing the virtual energy storage under air conditioning and building coupling can reduce the operating cost, primary energy consumption, and carbon dioxide emissions of grid-connected distributed energy systems.

Coupling energy management of power systems with energy hubs through TSO-DSO coordination: a review

Abstract The transition towards a sustainable energy system requires the integration of high-penetration renewable energy sources (RESs) and the use of new energy management frameworks to handle RES variability and uncertainty. The evolution of novel energy management technologies has made it possible to design and operate integrated energy systems (IESs) that couple various energy carriers such as electricity, heat, and gas. The emergence of energy hubs (EHs) offers the potential for greater efficiency, reliability, and flexibility in energy supply and demand. However, integrating EHs into the power system operation remains a significant challenge due to the complex interaction between the transmission system operator (TSO), distribution system operator (DSO), and EHs. Enhanced coordination between TSOs and DSOs is of the utmost importance within this context. This paper first aims to review and highlight the most practical features of EHs including the EH concept, topology, elements, classifications, flexibility resources, and optimization approaches. Subsequently, it describes TSO-DSO roles and all concepts that are in line with the aims of this subject, such as conceptual framework, TSO-DSO coordination schemes, formulation, and solution techniques. Meanwhile, it will identify any remaining research gaps and prospective trends in this respect.

A MILP model for optimal switch placement to enhance power distribution system performance in normal and hurricane conditions

Severe hurricanes can inflict significant damages in the order of several times the common network failures on both the distribution companies and consumers. Network reconfiguration and condition-based switching are the common actions for enhancing distribution network performance in both normal and emergency conditions. In this paper, a novel approach is presented for simultaneous improvement in resiliency, reliability, and power losses. A practical strategy is proposed by considering the coordination between load restoration and repair of damaged lines in optimal switch placement problems. The use of both remote-controlled switches (RCS) and manual switches (MS) as well as considering practical constraints related to line repair and load restoration leads to the increased number of decision variables and consequently the degraded performance of meta-heuristic methods to obtain the global optimum solutions, especially for larger scale networks. Therefore, the proposed approach uses the exact method of MILP to solve the optimization problem. In this context, the uncertainty related to damage status and repair time of lines during High-Impact Low-Probability (HILP) events is the main challenge in linear modeling of the problem for which new methods are provided in the study. The programming is coded in MATLAB and tested on a modified IEEE 33-bus network.

Optimizing the Return Vent Height for Improved Performance in Stratified Air Distribution Systems

One of the factors that strongly impacts the efficacy of stratified air distribution (STRAD) systems is the return vent height (H), for which different studies have yielded different suggested values. This theoretical research uses a displacement ventilation (DV) system as an example to examine how the H affects the efficacy of STRAD systems through analysis of the trade-offs between the cost of the vertical temperature gradient and the benefits of energy reduction. The key results are as follows: (a) The energy savings due to a lower H are smaller than the cost of the vertical temperature gradient for all STRAD systems. (b) With a supply temperature (Ts) set at 18 °C, elevated return vent positions can result in excessively cooled areas, while extremely low vent positions create a temperature gradient exceeding 3 °C between the head and ankles. (c) The TOPSIS methodology reveals that the optimal H value lies in the range of 1.5–2.3 m when Ts is 18 °C. (d) When adjusting the Ts value to achieve thermal neutrality, 2.3 m is identified as the optimal H value, demonstrating superior performance over the 1.5 m to 2.3 m range at 18 °C Ts. These findings highlight the benefit of a higher H for STRAD systems and the significance of configuring ventilation systems for thermal neutrality.

Exploring electric vehicle's potential as capacity reservation through V2G operation to compensate load deviation in distribution systems

This paper addresses the significant challenges faced by distribution system operators (DSOs) in competitive electricity markets due to uncertainties arising from electricity markets, electric vehicles (EVs), and conventional loads. To tackle these challenges, this paper proposes a pseudo-local reserve market between the DSO and EVs, which enables the DSO to integrate EV flexibility into the overall market framework while compensating for deviations in the conventional load across the wholesale electricity market. Furthermore, a two-stage framework is developed to facilitate the participation of EVs in electricity markets. In the first stage, the reserve capacity of EVs is integrated into the decision-making process for day-ahead bidding, taking into account uncertainties of the load and electricity. In the second stage, the day-ahead results are coordinated to enable the efficient dispatch of EVs for real-time market participation. To assess the efficacy of the proposed framework, a case study is conducted within a 31-node distribution system situated on a university campus located in Guangdong, China. This paper investigates the economic impact of load deviation constraints on EV reserves, revealing a penalty reduction of 3100 CNY for a daily reservation of 1000 EVs and a net profit increase of 1300 CNY for DSOs utilizing reserves. It further introduces a method for quantifying reserve capacity, enhancing DSO decision-making accuracy.

Unified Framework for the Analysis of the Effect of Control Strategies on On-Load Tap-Changer’s Automatic Voltage Controller

With the advent of new loads and generation on the low voltage grid, voltage fluctuation has increased, especially in active distribution grids with a high penetration of distributed resources and a large deployment of electric vehicles. The coordination of different technologies has emerged as the best way for voltage regulation, among others, smart inverters, open soft points or transformers with on-load regulation capability. This paper proposes a novel way to model the control strategies for the automatic voltage controller of On-Load Tap-Changer transformers. The purpose is to standardize and simplify the way these strategies are represented, in order to facilitate (i) their selection by Distribution System Operators, (ii) their future integration with other systems, and (iii) to increase the ability to anticipate On-Load Tap-Changer behavior. The proposal has been validated using real data, obtaining an accuracy of 99.15% in the tap changer positions. A unified framework is also introduced, which allows the proposed functional representation to be combined with the On-Load Tap-Changer controller behavior estimation. A experimental validation has been carried out where more than 150 000 strategies have been simulated, finally determining the one that best fits the objectives. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Historically, on-load tap-changing transformers have been used in high-voltage substations. However, with the integration of electric vehicles and the penetration of distributed energy resources, the need to implement this type of solution in low-voltage substations has grown. However, the characteristics and requirements are not the same, for example, given their nature, low voltage grids are more unbalanced and are often regulated to ensure the quality of supply. Therefore, inheriting the control strategies of traditional on-load tap changers may pose a serious risk. This paper proposes a novel unified framework that facilitates the modeling and simulation of almost any control strategy for distribution transformers with on-load tap changers. This will allow the choice of control parameters that minimize voltage deviation from the voltage setpoint and maximize device lifetime. Our proposal can be considered as a solution to the uncertainty of which control parameters to use. It can be performed before commissioning, based on historical data, or a posteriori, based on the collected data.

Reliability analysis in distribution system by deep belief neural network

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

Effects of end-user participation under a TSO-DSO coordination scheme for Norway

This paper analyses the Norwegian Power System through a sequence of models that represents dynamics of the wholesale power market, distribution power network, end-users, and a coordination scheme between Transmission System Operators (TSOs) and Distribution System Operators (DSOs). The models assess the impact of end-user flexibility and TSO-DSO coordination on the power system’s operation, with an emphasis on two research questions: the extent to which flexibility from end-users affects the power system’s operation under the TSO-DSO coordination scheme, and the potential of the TSO-DSO coordination scheme in incentivizing end-users’ flexibility. The paper presents three scenarios, involving varying degrees of coordination and end-user participation that are modeled and applied to the entire Norwegian power system. The results indicate that active end-user participation in the power market with a TSO-DSO coordination scheme could enhance the flexibility of the power system and support redispatch, while attaining a 14.5% redispatch cost reduction and 0.33% total system cost reduction. These findings offer insights for power system stakeholders concerning the potential advantages and challenges associated with fostering end-user flexibility and further developing TSO-DSO coordination schemes.

A Conceptual Model to Measure Digital Maturity Level in Electricity Distribution Companies

Digitalization, while being one of the primary objectives in today's business world, increases its significance with technological advancements. Recently, there has been a need for tools to determine the levels of digitalization within certain standards and metrics for companies seeking to understand and accelerate their digitalization process, and to determine how these companies should proceed to address their shortcomings. When specifically looking at electricity distribution companies, the technological advancements such as distributed production, storage, and electricity vehicles make the digitalization process not just a choice but a necessity to operate distribution systems reliably and effectively. This study proposes a conceptual model specifically for electricity distribution companies for the first time, by researching digital maturity models developed for other sectors in the literature.

Establishing a hierarchical local market structure using multi-cut Benders decomposition

Local electricity markets (LEMs) such as peer-to-peer (P2P) and community-based markets allow prosumers and consumers to exchange electricity products and services locally. In order to coordinate electricity trading and flexibility services, this paper proposes a hierarchical prosumer-centric market framework with a hybrid LEM and a local flexibility market (LFM). Multi-cut Benders decomposition (MCBD) is employed to decompose the integrated hybrid LEM into a centralized P2P market and multiple community-based markets. The aggregators coordinate energy sources and demands of households in low voltage (LV) distribution networks (DN) as virtual power plants (VPPs) and engage in trading through a P2P market over the medium voltage (MV) DN. In addition, a modified MCBD (M-MCBD) approach is proposed to accelerate the convergence process. The LFM is operated by the distribution system operator (DSO) and is formulated as a mixed-integer nonlinear programming (MINLP) problem which is further relaxed to a mixed-integer second-order cone programming (MI-SOCP) problem. The case study demonstrates that aggregators were able to collaborate on trading within the hybrid LEM to minimize the costs incurred by prosumers within the network. Furthermore, the proposed M-MCBD method improves the scalability of the MCBD by enhancing its convergence speed and accuracy, as demonstrated by testing on problems of varying scales.

Individual emission assessment of harmonics in DSO environment

The paper presents the propagation assessment and power quality parameters improvement system in power distribution grid. In this work main functionalities of the system are described focusing on the individual assessment module. The module is using CIRED/CIGRE C4.109 method which is based on the 10-minutes aggregated data. Three cases of individual emission assessment using real measurement data in the distribution system operator environment ware analyzed. The obtained results confirmed the legitimacy of using 10-minute data to assess the emissions of harmonics.

A comparison on power flow models for optimal power flow studies in integrated medium-low voltage unbalanced distribution systems

The uptake of distributed energy resources has led to new challenges in distribution system operation. These challenges include voltage and congestion issues along with a higher degree of unbalance due to the installation of mostly single-phase resources. This calls for more accurate distribution system modelling to perform three-phase power flow and optimal power flow analysis to ensure a safe operation. This paper provides a comparison of four unbalanced three-phase power flow formulations to be utilised in optimal power flow-based studies. To adapt these formulations to realistic-sized integrated medium and low voltage unbalanced distribution networks, we propose a linear model for low voltage transformers that is suitable to apply in convex power flow models. The accuracy of these models is compared to the non-linear model considering voltage and current magnitude errors, as well as voltage unbalance factor as performance indices. Our results show that out of the convex formulations, the linear approximated model, which takes the couplings between phases into account, combined with our transformer linearisation, is the most accurate at capturing voltage and current magnitudes. The voltage error of this model in our 5336-node and 3148-node medium-low voltage test networks remains less than 0.38% under a variety of loading and generation scenarios.

Optimizing Peer-to-Peer Energy Transactions: Determining the Allowable Maximum Trading Power for Participants

This paper presents a comprehensive study on the impacts of peer-to-peer (P2P) energy markets on distribution systems, specifically focusing on voltage, power loss, and congestion. While P2P energy markets create opportunities for direct trading between prosumers and consumers, ensuring compliance with distribution system constraints remains a challenge. This paper proposes an iterative method and graphical interpretation in order to assess complex interactions, addressing the persistent issue of network constraints. Additionally, this paper proposes a method to determine distribution locational marginal prices (DLMPs) for real-time traditional energy markets. This ensures effective coordination among sellers, buyers, and the distribution system operator. The proposed method aims to prevent negative impacts on distribution system operation via the determination of the allowable maximum trading power (MTP), ensuring empirical validity and practical implementation via operating conditions and forecast errors, thus distinguishing it from prior studies. This paper also establishes a model for P2P energy market interactions, utilizing linear estimations for efficient DLMP updates. The contributions of this paper enhance the understanding and operation of P2P energy markets, and is supported by simulation results validating the proposed method.

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

A cluster-based appliance-level-of-use demand response program design

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers’ financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid’s low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers’ distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms.