Operation Of Distribution Grids Research Articles

Modeling local distributed solar energy potential: a case study from Virginia, USA

ABSTRACT In this paper, we use GIS analysis to estimate potential distributed solar PV capacity and solar electricity generation in a suburban neighborhood in Virginia, United States. Using a combination of LiDAR and solar insolation data, we find that 37% of rooftop space in the study area would receive sufficient solar insolation to support a distributed solar (DPV) system. Applying conservative assumptions, we estimate that nearly 19 MW (AC) of distributed rooftop PV could realistically be installed, providing 28% of the study area’s estimated annual electricity demand. These findings provide evidence of the significant untapped potential of DPV, and support the need for streamlined permitting processes and other incentives to reduce soft costs and facilitate DPV installation. We also discuss opportunities for merging planning and engineering research to support targeted utilization of DPV in locations where it can best support distribution grid operations, such as on commercial sector buildings in particular.

Simultaneous Impacts of Correlated Photovoltaic Systems and Fast Electric Vehicle Charging Stations on the Operation of Active Distribution Grids

This paper presents two novel probabilistic models developed to account for the uncertainties of aggregated fast electric vehicle charging stations (FEVCSs) demand and correlated photovoltaic (PV) injections in active distribution network (ADN) analysis. Both models are more precise than the available ones. A probabilistic model based on the Beta distribution is used for solar radiance, while the shared random variables technique is proposed considering correlated solar radiation random variables. Furthermore, a probabilistic negative exponential load model is extended for modeling the FEVCSs based on the Weibull probability density function. Moreover, the proposed probabilistic load flow (PLF) model is solved using the combined cumulants and saddle-point approximation method. Numerical tests are provided and discussed by applying the IEEE 69-bus distribution system for different PV correlation coefficients and FEVCS load models. The results demonstrate how the uncertainty of PLF outputs is increased by integrating FEVCSs and correlated PV resources into the distribution network. In addition, simulation results validate that the cumulants-based methodology provides satisfactory accuracy with a low computational cost.

Features of planning and managing power flows in distribution grids of megalopolises

The study examines the root causes of disruptions of normal operation of power distribution grids in large cities and metropolitan areas. The existing principles of scheduling and maintaining power flows in power distribution grids as well as ways of ensuring the reliability of their operation are analyzed. The integration of fuel-fired distributed generation facilities and renewable generation plants into distribution networks is shown to affect the ability to ensure transient stability during emergency disturbances. We establish evaluation criteria and provide upward margin values to determine the feasibility of the planned power flows. The study reveals general patterns in the calculation of the upward margin for power distribution grids of large cities and metropolitan areas. Test power flow analysis is performed for a real-world segment of a metropolitan area power distribution grid to identify possible overloads of power grid equipment and develop and implement a list of measures for the power flow to achieve the feasible region. The developed approach makes it possible to minimize load shedding or rule out load shedding altogether, ensuring reliable power supply to consumers in large cities and metropolitan areas in the event of an unexpected load growth.

The efficacy of a cellular approach to resilience-oriented operation of distribution grids based on eigenvectors of the Laplacian matrix

This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.

Virtual inertia control of distribution grids under automobile charging and discharging load uncertainty

Abstract Automobiles with unstable charging and discharging loads usually have unstable power, frequency, and other parameters of the distribution network. Therefore, a virtual inertia control strategy is proposed to enhance the distribution network’s stability and improve the grid’s voltage quality. This method can enhance the inertia control capability of the grid by incorporating the sag coefficient and SOC control, which in turn achieves the purpose of improving the grid’s reliability. The results show that changing the sag coefficient can enhance the voltage stability to reduce the impact of the battery while incorporating the SOC inertia coefficient control, the distribution grid operation will be more stable, and the power quality will be higher. The new method enhances the stability of voltage load to a certain extent and lays the foundation for the subsequent virtual inertia control technology.

Study on phase selection control characteristics of fast vacuum circuit breakers suitable for new power systems

Abstract With a high proportion of new energy access to the power system, the user side of the randomness and volatility of distributed photovoltaic large-capacity access to the 10 kV medium-voltage distribution grid operation results in more frequent fluctuations in the system voltage and more frequent switching of reactive compensation of the shunt capacitor bank on the 10 kV side of the conventional substation. Conventional reactive compensation switching circuit breakers adopt a spring mechanism whose mechanical stability is insufficient and the selection of the relevant fit capacitor bank accuracy is not enough. The 12 kV fast vacuum circuit breaker of small mechanical dispersion for selection related to the combination was proposed. First of all, based on the neutral grounded and ungrounded system of the capacitor bank, the strategy of capacitor bank phase selection closing was developed. For the action characteristics of the phase selection of the 12 kV fast vacuum circuit breaker, the variation laws of mechanical dispersion and the influence factors such as ambient temperature, capacitance voltage, capacitance degradation, mechanical wear, and conductive rod deformation were obtained by simulation and experiment. The results showed that the ambient temperature and capacitance voltage had a great influence on the switching time of the fast circuit breaker, while other factors had a small influence. Again, the pre-breakdown characteristics of the 12 kV fast vacuum circuit breaker were obtained by experiments. The results showed that the influence of contact opening distance and pre-breakdown voltage was a linear relationship, and the pre-breakdown slope was 30 kV/mm. According to the pre-breakdown closing characteristic curve, the pre-breakdown time of the fast circuit breaker was 0.45 ms at most. The feedforward compensation mechanism based on a neural network was proposed to compensate for the action characteristics of the fast circuit breaker and aimed to improve the phase selection accuracy. Finally, two rounds of capacitive current switching tests of 12 kV fast vacuum circuit breakers with C2 level were carried out in a third-party test station. The accuracy of phase closing is within the ±0.5 ms closing window, effectively preventing the occurrence of closing inrush current.

Dynamic operation of distribution grids with the integration of photovoltaic systems and distribution static compensators considering network reconfiguration

The optimal operation of Distribution Networks (DNs) using network reconfiguration has become more critical in the modern power system due to the widespread use of Renewable Energy Sources (RESs) and the imbalance between load demand and energy provided by RESs. However, attaining the most efficient functioning while integrating RESs is a challenging endeavor due to the unpredictability of the electrical system and the complexities associated with network reconfiguration. Integrating Distribution Static Compensators (D-STATCOMs) with network reconfiguration is powerful for improving voltage deviation reducing overall costs, and minimizing active power losses, while successfully accommodating the fluctuating characteristics of renewable energy sources. To tackle these difficulties, we offer the Horned Lizard Optimization Algorithm (HLOA), a new approach for optimizing the operation of DNs. The efficacy of HLOA is showcased on the IEEE 33-bus DN, to minimize costs, voltage deviations, real power losses, and emissions in the presence of unpredictable factors like photovoltaic (PV) uncertainties, price changes, and load demand. The analysis encompasses three case studies: one focused on optimizing operation solely with PV integration, another using both PV and D-STATCOM integration, and a third incorporating PV, D-STATCOMs, and network reconfiguration. The results indicate that the combination of PV, D-STATCOMs, and network reconfiguration significantly decreases overall cost by 45.6 %, real power losses are reduced by 66.3 %, and voltage variations are improved by 71.04 %. Emissions are mitigated by 36.72 % compared to the base case.

Distributionally robust chance-constrained operation of distribution grids considering voltage constraints

Distributionally robust chance-constrained operation of distribution grids considering voltage constraints

Collaborative Pricing Strategy of Distribution Network and Electric Vehicle Aggregator Based on Integral System

Scaled EVs as a new type of load have great potential to participate in the standby market. However, the uncertainty of EV users' participation in aggregator regulation affects the optimal strategy for aggregators to participate in the market. Aiming at the uncertainty of users' willingness and the conflict of interest between aggregators and the distribution network, this paper proposes an optimal scheduling strategy of master-slave game for EV aggregators to improve users' willingness with a point system. First, the second-order cone optimal current model of the distribution network is established, and then the distribution network node marginal tariff is obtained. Second, the aggregator revenue model with energy storage is established, and the price-guided demand response model is proposed, while the aggregator point system is established to guide the charging behavior of EV users, so that the cooperative operation optimization problem between the distribution grid and the aggregator can be solved, and the economic interests among EV users, EV aggregators and distribution grid operators can be effectively balanced.

An ADP framework for flexibility and cost aggregation: Guarantees and open problems

With the increasing amount of Distributed Energy Resources (DERs), coordination of Distribution Grid Operators (DSOs) and Transmission Grid Operators (TSOs) is of paramount importance. Managing a large number of DERs at the TSO level is, however, challenging. To address this problem, flexibility aggregation is a topic of frequent research activities. Aggregation means to describe the combined flexibility of the DERs at the vertical grid coupling between DSO and TSO. Existing works are often limited with respect to guaranteeing feasibility, with respect to efficient numerical implementation, and in terms of quantification of the cost of DER usage. In the present paper, we investigate aggregation based on Approximate Dynamic Programming (ADP). We propose efficient numerical aggregation schemes using tools from computational geometry thus avoiding the need to solve multiple OPF problems. We rely on different variants of the DistFlow model for radial grids, which are computationally efficient. This allows to model of current and voltage limits and enables the consideration of voltage dependencies in the aggregation. Furthermore, we propose a method for cost aggregation and identify open problems of flexibility aggregation.

On grid-serving grid tariff design in Local Energy Markets

Local Energy Markets (LEMs) are virtual market places that allow for energy sharing of prosumers in proximity to each other. In LEMs, participants’ offers and bids are coordinated by a pricing mechanism, which sets price signals for generation and loads. This impacts distribution grid utilization. Dynamic grid fees could be used by the distribution system operator to add price signals for market participants that are beneficial to the distribution grid operation. To develop such a grid-serving LEM design, we propose a framework for the design of alternative grid tariffs as a trade-off between cost-reflective and cost-recovering grid tariffs. We analyse three alternative grid tariff components in the context of LEMs: time-varying energy fees, critical peak pricing (CPP) and capacity fees for power not traded on the LEM. A case study on a synthetically generated grid region shows that while the alternative tariff components can reduce the aggregated peak load of the region by up to 31% with the CPP, the fees can also lead to increased line utilization. Also, while total local added value can be increased with all alternative grid tariffs, especially capacity-based grid tariffs are connected to a strong redistribution of grid costs from flexible to inflexible prosumers.

Can we compute the worst voltage case under power flows uncertainty at TSO-DSO interfaces?

This paper proposes the new problem of calculating the worst-voltage-case (WVC) under power flows uncertainty, particularly at interfaces between transmission and distribution grid operators. The goal of the calculation is to verify if the realization of the worst uncertainty scenario leads to abnormal voltages, or assure where the voltage instability is not a risk, and inform the transmission system operator. The problem is formulated as a tailored non-convex, nonlinear optimal power flow (OPF) with complementarity or equilibrium constraints (ECs). The latter model the generator switch between under voltage control and under reactive power limit. The problem is converted into a mathematically equivalent nonlinear programming OPF problem in which ECs are handled as penalty terms in the objective function. The paper has conducted a series of experiments on the 60-bus Nordic system, which have pointed out the challenges associated to such computations to obtain meaningful results and unveiled new insights. The findings and scalability are confirmed using a real-world 1203-bus system.

Low-carbon operation of smart distribution grid based on life cycle assessment and ladder-type carbon trading

A large number of EVs are commonly added to peak loads without dispatch in the smart distribution grid system, and the grid system usually suffers from excessive carbon emission problems. This paper proposes an integrated approach to control the smart distribution grid system by utilizing life cycle assessment and the market mechanism. First, the system’s carbon emissions are estimated according to life cycle assessment and carbon flow theory. Then, a bi-level optimization strategy for a smart distribution grid is presented. The upper-layer operation object is the minimum comprehensive operation cost of the smart distribution grid, which is solved using an optimization algorithm. The lower-level optimization goal is to minimize the operating cost of electric vehicles, which is solved using the CPLEX toolkit. Our simulation results demonstrate that overall operating costs have been reduced by 71.11%, while SDG’s overall carbon emissions have been reduced by 49 kg. The low-carbon growth of the smart distribution grid may be successfully promoted by appropriate trading market scheduling and efficient low-carbon dispatching.

Simulation of a Line Voltage Regulator in a Low-Voltage Grid That Is Subject to Strong Voltage Surges Due to the Provision of Fast Frequency Reserve

The increasing adoption of battery storage units alongside private PV systems may prove to be a new challenge for distribution grid operators. This study explored the potential impact of marketing aggregated battery discharge power as a Fast Frequency Reserve (FFR) and its effect on the distribution grid stability. We investigated the efficacy of Line Voltage Regulators (LVRs) in mitigating voltage surges caused by simultaneous battery activation. For this purpose, a simulation was developed via Matlab (Version R2023a) to simulate the voltage at the nodes of an arbitrary distribution grid, using the feed-in and consumed power of the customers as the input. We applied the model to a distribution grid section in Sundsvall (Sweden). The results confirmed that LVRs can amplify voltage surges when their adjustments are not synchronized with the FFR activation. This study underscored the need for proactive measures to address the voltage maintenance challenges arising from the integration of battery storage units and renewable energy sources.

Stochastic two-stage resilient scheduling of hydrogen system in power distribution grids with high penetration of solar PV systems: A convex worst case analysis

The renewable distribution network holds tremendous potential for economic advantages and sustainability improvements within the power industry. However, resilient operations in post-disaster scenarios present significant challenges for the system. Operational risks arise from uncertain resources, making day-ahead scheduling complex for the distribution grid's operator. Therefore, real-time modeling is considered more suitable to effectively manage these uncertainties. The hydrogen system (HSYS) is also an emerging technology that improves the technical and operational characteristics of distribution networks, particularly in challenging and unpredictable situations. The study presents a comprehensive framework for evaluating HSYSs within a photovoltaic-focused power distribution network, focusing on day-ahead resiliency-oriented operations (RESOP) while considering risk factors associated with uncertain incidents. The effectiveness and feasibility of implementing the proposed HSYS in day-ahead risk-based resilient scheduling of the distribution network are analyzed using a specific distribution system. The model is formulated as a MILP problem, and the impacting uncertainties are modeled using stochastic programming. The primary risk associated with uncertainties is captured using the conditional value-at-risk (CVaR) approach, which involves the possible worst realizations of uncertainties in the model. The study's findings demonstrate that the integration of distributed HSYSs leads to significant cost savings of up to 16 % under normal conditions and 1.25 % when considering risk modeling.

Optimal operation of flexible interconnected distribution grids based on improved virtual synchronous control techniques

With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.

Dynamic Management of Flexibility in Distribution Networks through Sensitivity Coefficients

Due to a rising share of renewable energy sources on the production side and electrification of transport and heating on the consumption side, the efficient management of flexibility in distribution networks is crucial for ensuring optimal operation and utilization of resources. Nowadays, the sensitivity-based approach is mainly used in medium-voltage (MV) networks for regulating voltage profiles with reactive power of distributed energy resources (DER). The main disadvantage of the simplified sensitivity-based method is its inaccuracy in case of a high deviation of the network voltage from the nominal values. Furthermore, it was also noted that despite the fact that the method is well described in the literature, there is a lack of systematic approach to its implementation in real-life applications. Thus, the main objective of this paper is to address this disadvantage and to propose an algorithm designed to calculate required consumer flexibility in near real-time to ensure distribution grid operation within operational criteria. In the first part of the paper, network state, including line loading and node voltages, is assessed to determine distribution network node capacity. By analyzing the sensitivity of network busbars to changes in consumption and production, our algorithm effectively identifies the most efficient nodes and facilitates strategic decision-making for resource allocation. We demonstrate the effectiveness of our approach through simulations of real-world distribution network data, highlighting its ability to enhance network flexibility and improve resource utilization. Leveraging sensitivity coefficients, the algorithm enables flexible consumption and production management across various scenarios, supporting the transition to a more dynamic and efficient power system.

Resilience-oriented planning for active distribution systems: A probabilistic approach considering regional weather profiles

Recent experiences with increasing climatic variability highlighted the importance of enhanced power distribution system resilience. Conventional methods of power distribution system planning usually focus on persistentinvestments associated with predicted faulty events, aiming to enhance the overall system reliability. Improving distribution system resilience in response to extreme weather events involves reduction of impact of the events having varying probability of occurrence. The probability of an extreme event varies significantly by location due to a combination of geographical, climatological, and meteorological factors. Thus, instead of requiring persistent investments, the solutions for resilience-oriented system upgrades should be guided by the potential effects and the probability of occurrence of extreme weather events in a specific area. To achieve this objective, the present paper proposes a two-stage stochastic mixed-integer linear programming (MILP) model based on regional weather profiles for the long-term resilience planning of distribution systems against extreme weather events. In the planning stage, investment decisions are made regarding overhead distribution line hardening and the distributed generation (DG) installation. During restoration process in the operational stage, dynamic microgrid formation is carried out for advanced distribution grid operations to minimize the cost corresponding to loss of load. The inclusion of regional weather profiles into planning objectives provide added flexibility to the network operators for analysing the trade-off between investment cost and load loss cost in resilient distribution system planning strategies.

Impact and Optimization Analysis of High Penetration Distributed Power Supplies on Distribution Grid Operation Characteristics

Impact and Optimization Analysis of High Penetration Distributed Power Supplies on Distribution Grid Operation Characteristics

Congestion forecast framework based on probabilistic power flow and machine learning for smart distribution grids

The increase in renewable energy sources and new technologies such as electric vehicles and storage can generate uncertainties in distribution grid operations, increasing the likelihood of congestions in power lines. Distribution system operators (DSOs) face several challenges while operating their grids in such conditions. These congestions deteriorate the electrical equipment in the long term, reducing its life span. This work proposes a framework to predict grid asset congestions on a daily basis. A congestion forecast framework is proposed by combining probabilistic power flows and machine learning algorithms to support DSOs in their daily decision-making. The framework is tested on a modified IEEE-33 bus system and CINELDI MV Reference system with hourly synthetic data. The results showed that the framework is able to closely predict the congestions on the lines. Computational capabilities are reported and discussed. The study indicates that the framework is a suitable tool for day-to-day congestion predictions in smart distribution grids yielding low error in expected values.