Distribution System Operators Research Articles

Maximum load for medium voltage lines in N-1 conditions

This paper proposes a methodology able to assess the maximum value of the load that can be connected to a medium voltage (MV) feeder in addition to the existing one without impairing the N-1 security criterion. A simplified approach is pursued, the original problem is split into several separate subproblems, and the maximum additional load is calculated solving multiple optimization problems. The proposed method is simple and practically applicable by distribution system operators (DSOs) that, after a request by a customer to connect a load, must know if an MV feeder is fit to host the additional demand or if a new infrastructure is needed. The application of the methodology to the whole MV distribution grid of Rome, in Italy, is presented; execution times show that the DSO can apply the model daily, to follow the load growth day by day.

Investigations on solar PV integration and associated power quality challenges in distribution systems through the application of MCS and GA

In the forthcoming decades, significant advancements will shape the construction and operations of distribution systems. Particularly, the increasing prominence of photovoltaic (PV) systems in the power industry will impact the security of these systems. This study employs Monte Carlo Simulation (MCS) in conjunction with genetic algorithm (GA) and differential evolution (DE) to address uncertainties. The probability density functions (pdf) for total voltage harmonic distortion (UTHD), individual voltage harmonic distortion (UIHDh), and RMS voltage (URMS) have been determined for utilization in chance constrained framework. In addition, the uncertainty effects of PV systems on grid losses for various solar radiation conditions are also investigated. Specifically, the paper aims to evaluate these impacts within the context of stochastic limits. The PV system sizing problem has been addressed inside the distribution system using a chance-constrained framework. A key contribution is the integration of GA, DE, and MCS into a cohesive approach, and the study evaluates the benefits of this approach through an analysis of outcomes derived from the stochastic method. The simulation results illustrate the advantages of the proposed stochastic GA methodology.

Collaborative scheduling method of active-reactive power for rural distribution systems with a high proportion of renewable energy

Large-scale distributed renewable energy connected to the rural distribution network has given birth to a new rural distribution system with a high proportion of new energy typical characteristics, and the optimal scheduling of the new rural distribution system has become an important issue to ensure the safe and stable operation of the power grid. This paper proposes a method of active-reactive power collaborative optimization scheduling for rural power distribution system with a high proportion of renewable energy. Firstly, the active support capability evaluation model is established, and the active power support capability and reactive power support capability of rural power distribution system are quantitatively evaluated, which provides data basis and boundary conditions for the scheduling part. Then, considering power-loss cost, distribution network operation cost, and penalty cost, a method of active-reactive power collaborative optimization scheduling for rural power distribution systems with a high proportion of renewable energy is proposed. Finally, the active support capability evaluation and regulation platform of the rural power distribution system is built to provide technical support services for the safe and stable operation of the rural power distribution system. Given the problems of overload and overvoltage faced by rural power distribution systems with a high proportion of renewable energy, this paper aims to solve the key technical challenges of optimization and regulation of new rural power distribution systems. The results show that the optimized control method proposed in this paper has better security and economy, and is conducive to promoting the construction and operation of the new rural power distribution system.

Graph‐based solution for smart grid real‐time operation and control

AbstractUnder the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed.

Two‐stage stochastic‐robust planning of distributed energy storage systems with Archimedes optimisation algorithm

AbstractWith the advancement of energy storage technologies, energy storage systems (ESSs) have emerged as a promising solution for distribution networks to mitigate the impact of intermittent and violate renewable energy sources. The optimal planning of distributed ESS is studied to minimise the investment and operational costs for the distribution system operator. To address the various uncertainties associated with load demand and distributed generation, the authors formulate the problem as a two‐stage stochastic‐robust optimisation problem. The proposed formulation implements various representative scenarios of actual operating conditions and constructs the robust uncertainty set to ensure feasibility under worst‐case scenarios. In view of the computational complexity of the proposed model, a solution approach combining the Archimedes optimisation algorithm and the global optimisation method is presented. By decomposing the investment and operation stages, the subproblems are relaxed into mixed integer second‐order cone programming models, which can be solved in parallel based on scenarios. Numerical studies are carried out on a 17‐node test system to demonstrate the validity of the proposed model and algorithm. In addition, a comparison between the proposed method and the genetic algorithm is performed, to illustrate its superiority in solving speed and solution optimality.

Revolutionizing energy management: A two-step optimal day-ahead scheduling approach for advanced distribution systems with enhanced transactional efficiency

This study makes a significant contribution to advancing smart energy management systems, crucial for effective control of energy supply and demand. Traditional methods for optimal energy management in power grids often encounter computational challenges due to the nonline ar and discrete nature of the optimization problem. In response, our research introduces a novel two-step Optimal Day-Ahead Scheduling approach for an Advanced Distribution System. This system incorporates Renewable Energy Sources, Distributed Generation units, Electric Vehicles, and Energy Storage Systems, while also facilitating surplus electricity sales to electricity markets. The proposed optimal day-ahead scheduling involves both central and local controllers collaborating to optimize a unified program. The Active Distribution System engages in electricity transactions with multiple descending energy hubs, including Combined Cooling, Heating, and Power plants. Each energy hub (EH) operator optimizes day-ahead scheduling problems, submitting bids to the distribution system operator. This unique approach integrates Demand Load Curtailment and Time-of-Use schemes as demand response programs in descending energy hubs. Addressing the inherent complexity of this optimization challenge, the study introduces a novel path-finding algorithm leveraging an improved local search operator. The application of this proposed method to a 33-bus system demonstrates significant advantages. Results showcase a notable increase of up to 193% in the benefit of selling electricity, coupled with an impressive 84% reduction in distribution system operating costs, considering base values. This contribution highlights the effectiveness of the proposed approach in optimizing energy transactions and enhancing the overall efficiency of distribution system operations.

Optimizing pump operations in water distribution networks: Balancing energy efficiency, water quality and operational constraints

Optimizing pump operation in urban water distribution systems represents a critical task for enhancing energy efficiency. Despite extensive research, achieving optimal pump operation remains a challenge due to the presence of conflicting objectives, such as minimizing energy costs and ensuring adequate pressure head availability. The recent emphasis on maintaining distributed water quality further increases the complexity of the problem. Mathematically, the optimization problem manifests as a non-convex and non-continuous search space, necessitating the application of non-derivative optimization techniques, which are highly susceptible to constraints. This work presents a comprehensive analysis of optimal pump operation, considering energy efficiency and water quality as primary objectives. A series of constraints are progressively incorporated into the model to evaluate their impact on the final operational strategy. Initially, single-objective optimization is employed using the Particle Swarm Optimization (PSO) algorithm, a robust and widely adopted approach in water resources research. Subsequently, a multi-objective algorithm, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), is utilized to identify the Pareto front, encompassing both energy consumption and water quality indicators. The results demonstrate that constraints exert a significant influence on the final energy consumption and the convergence of the optimization process. Moreover, by simultaneously considering both objectives, this study proves the feasibility of identifying an optimal operational scheme that minimizes energy consumption while maintaining acceptable water quality.

Active Asset Management

Robust and real‐time asset management is crucial for distribution utilities to operate their infrastructure efficiently. The importance and challenge of asset management has also grown significantly in response to new regulatory requirements, growing customer expectations, and rapid technological advancements. While the importance of actively managing assets is understood within the utility industry, the application of “best practices” in asset management varies based on the asset's criticality to the business. Issues surrounding the management of system critical assets have significant implications for distribution system operations. The strategies and technologies used to maintain critical infrastructure, and an examination of how other industries like commercial real estate manage assets, can shed light on how utilities could improve their approach to managing both critical and non‐critical assets.

Risk Assessment of Radial Distribution Systems using Modified Jelly Fish Search Algorithm to analyse the Performance Indices

One of the essential techniques for figuring out Power Distribution System performance is reliability evaluation. With time, the range of methods for assessing reliability has grown, and the distribution system's evolution has also become more intricate. The likelihood of a network failing grows with time once it begins to function, especially if it is used for an extended period. Reliability indices have been evaluated using different algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and various modified versions of algorithms. The Jelly Fish Search Algorithm has been used in various power system applications such as to determine the most cost-effective way to dispatch generating units' loads, integrate Distributed Generation (DG) units, track the maximum power of photovoltaic systems, and determine optimal power flow solutions, among other uses. The performance indices for the Radial Distribution System (RDS) have not been evaluated using JFSA.Variation of performance indices with respect to unavailability has not been discussed in the literature. Here an attempt is made to analyse the variation of the indices with the unavailability. The proposed algorithm is evaluated first on 3 load point radial distribution system and the computations are performed on the popular IEEE-RBTS Bus2 test system. Based on the output it is observed that for the both the systems the behaviour of the indices with respect to the unavailability is in similar fashion. The equations are derived for the performance indices using the Minitab. With the equations obtained the Performance indices values can be predicted for the given unavailability value. It is the better appropriate method for the assessing the performance indices of Radia Distribution System.

Aggregated model of an active distribution system for coordinated TSO-DSO fast frequency control

The growing integration of distributed energy sources (DERs) challenges the traditional independent operation of transmission system operators (TSOs) and distribution system operators (DSOs). The coordination between TSOs and DSOs becomes essential, as DERs not only offer local ancillary services but also enable the provision of these services at the distribution network level following disturbances in the transmission system. To enhance power system analyses in TSO-DSO coordination, the availability of reduced-order or aggregated models proves crucial, avoiding detailed modeling and reducing computational burden. Addressing the lack of reduced-order models capturing the dynamics of diverse distributed technologies and their control systems within a unified single-device model, this paper introduces a single equivalent device based on virtual synchronous machine (VSM) technology. This device effectively subsumes the frequency response of various DERs and loads. The proposed aggregation methodology undergoes validation on systems of different sizes and compositions of DERs. Results from simulated scenarios consistently demonstrate the accuracy and generalizability of the proposed aggregated model, irrespective of system size and included technologies.

Experimental implementation of an emission-aware prosumer with online flexibility quantification and provision

Active building energy management can facilitate the development of low-carbon buildings and support flexible operations of future smart cities, thanks to advancements in digitalization To fully leverage these benefits, it is essential to integrate diverse objectives and engage multiple stakeholders. However, a gap remains in comprehensive field insights into emission reduction, flexibility provision, and user impacts. This study examined how a real occupied building, with all its energy assets, could function as an emission-aware prosumer with flexible energy consumption. An existing building energy management system was enhanced by integrating a model predictive control strategy. The setup reduced equivalent carbon emissions from electricity imports and provided flexibility to the energy system. The experimental results indicated an emission reduction of 12.5% compared to a rule-based controller that maximized PV self-consumption. In addition, a minimal flexibility provision experiment was demonstrated with a locally emulated distribution system operator. The results suggested that flexibility was provided without the risk of rebound effects, as flexibility was quantified and communicated to the system operator in advance. This study demonstrates the feasibility of low-carbon buildings and their support for flexible energy systems, while also identifying and discussing practical scalability challenges.

Smart Vehicle-to-Grid Integration Strategy for Enhancing Distribution System Performance and Electric Vehicle Profitability

Smart Vehicle-to-Grid Integration Strategy for Enhancing Distribution System Performance and Electric Vehicle Profitability

Measuring the digitalisation of electricity distribution systems in Europe: Towards the smart grid

This paper proposes a set of digitalisation indicators focused on measuring the different digital capabilities and infrastructure of electricity distribution systems, as opposed to previous indicators which have mainly focused on performance and quality of service aspects.The indicators are classified according to the pillars of digitalisation: sensor and actuator, connectivity, data processing, and digital culture. They are use-case-agnostic and do not require a huge amount of information. In addition to this, three possible new applications of these indicators for distribution system operators and regulatory authorities are identified and discussed.The extensive use of these indicators in Europe could allow the development of fruitful collaborations between distribution system operators, allow the identification of cause–effect relations between grid performance and digital infrastructure, and improve the replicability of innovative smart grid solutions. However, this will only be possible if regulators promote the adoption of the proposed indicators and the dissemination of their results.

Common TSO-DSO market framework with no upfront priority to utilize DER flexibility

Grid integration of distributed energy resources (DERs) is vital to provide flexibility services, required to ensure efficient system operations at both transmission and distribution levels. To prevent negative interactions between transmission system operators (TSO) and distribution system operators (DSOs) while procuring flexibility services from DERs, it is imperative to prioritize them. However, for system's cost-effectiveness allocating DER flexibility to the operator with the highest need is preferable rather than giving upfront priority. To address this, a novel coordinated decision-making approach for a common TSO-DSO market is proposed. This approach considers global balancing for the TSO and distribution congestion management for the DSO as objectives, using them as deciding factor to allocate DER flexibility to the system operator with the greatest need to fulfil its objective. Results show proposed method enhances DSO's flexibility transfer to TSO and maintains reactive power at T-D interface. Gap between TSO's DER flexibility needs and availability, influenced by congestion and line losses, is critical for prioritization. Specifically, network capacity and line losses constitute 18.79 % and 3.42 % of offered DER flexibility, respectively. Reactive power deployment improves Q/P ratio, reducing standard deviation from 0.19 to 0.018. Framework reduces conflicts, provides clear flexibility allocation rules without upfront priority.

A techno-economic scheduling of distribution system including multi-microgrids considering system reconfiguration and bus selector switches

The Microgrid (MG) as an alternative energy system, would reduce power loss, enhance energy consumption efficiency, and improve the system reliability for a sustainable city. Energy scheduling of distribution systems (DSs) including multi-microgrids (MMGs), would result in a multi-objective problem from DS operators’ point of view for a techno-economic operation. In this regard, a multi-objective model for operation scheduling of DS and connected MGs, is proposed in this paper, which also considers the system management solutions such as reconfiguration and determination of the energy trading points for MGs connection. The proposed model is applied to a modified IEEE 33-bus distribution system including 4 MGs. Simulation results and analysis based on the fuzzy satisfying method indicate that the simultaneous consideration of distribution system reconfiguration and Bus Selector Switches (BSSs) for MGs can improve the social welfare function by 4.58 % and reduces the energy losses by 15.82 %.

Hybrid data-driven operation method for demand response of community integrated energy systems utilizing virtual and physical energy storage

Community integrated energy systems (CIES) have become flexible contributors to DR within distribution networks. Buildings’ thermal capacities can serve as virtual energy storage (VES) to augment CIES flexibility. However, uncertainties in environmental factors and building parameters hinder the accurate use of virtual energy storage without compromising user comfort. In addition, a single type of flexible resource is usually insufficient to meet the complex DR requirements of distribution system operators, underscoring the need for coordinating physical and virtual energy storage. This study proposes a hybrid data-driven operational approach to enhance the DR of a CIES. Real-time measurements were employed instead of exact system models and parameters to effectively dispatch VES during DR events. Additionally, rolling robust optimization was implemented to coordinate physical and virtual energy storage for DR, accommodating various uncertainties and multi-timescale characteristics within the CIES. Through its application to a case study, the proposed method demonstrated its efficacy in leveraging VES in a CIES for DR without compromising users’ thermal comfort. In a typical power reduction scenario, the DR revenue increased by 39.6%, while the overall operational cost decreased by 17.0%. In the power increase scenario, the DR revenue increased by 122.0%, and the overall operational cost decreased by 20.0%.

Performance Evaluation of Grounding Systems of Medium-Voltage Concrete Poles: A Comprehensive Analysis

Designing and installing efficient grounding systems in power distribution networks is considered a complex and crucial task to ensure the reliable operation of power-protective schemes while mitigating hazardous potentials arising from faults, thereby safeguarding both personnel and equipment. This paper aims to offer guidance on designing effective grounding systems in distribution networks by assessing the influence of parameters such as soil structure, fault current magnitude, and fault clearing time. This involves proposing a more precise methodology for calculating hazardous potentials, leveraging software tools like PowerFactory, to accurately determine short-circuit (SC) currents and fault clearing times at specific locations where grounding grids are to be installed. Consequently, Distribution System Operators (DSOs) can design tailored grounding systems that optimize techno-economic considerations without unnecessary over-dimensioning, accounting for the unique characteristics of the Medium-Voltage (MV) Line and soil structure.

Distribution and transmission coordinated dispatch under joint electricity and carbon day-ahead markets

Worldwide efforts to attain a more renewable-based energy matrix have led to the creation of carbon emission-related markets. Simultaneously, the increasing penetration, mostly of renewable-based sources, in distribution networks may allow distribution system operators (DSOs) to change their traditional participation in wholesale markets as passive buyers. Over the last few years, many authors have investigated and proposed optimization models for the strategic participation of DSOs in electricity markets. Nonetheless, proposals regarding the DSO as a strategic agent in both electricity and carbon-related markets are rare. In this paper, we propose a stochastic bilevel formulation to address this gap. The economic and operational constraints of each stakeholder are considered in the model. Additionally, the problem is written in a convex formulation for which finite convergence to optimality is guaranteed. The formulation was tested for a 14-node transmission system and a 34-node distribution system in which dispatchable and non-dispatchable energy sources, energy storage systems, and demand response units are installed. The results show the DSO’s capacity to place strategic bids in both market environments to maximize its profit, which features intentionally taking financial losses in the carbon allowance market to maximize the overall profit due to the income maximization in the electricity market. Finally, an additional case study with a 123-node distribution system shows the proposed method’s scalability.

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.