Distribution System Operators Research Articles

Disaggregation Model: A Novel Methodology to Estimate Customers’ Profiles in a Low-Voltage Distribution Grid Equipped with Smart Meters

In the context of increasingly necessary energy transition, the precise modeling of profiles for low-voltage (LV) network consumers is crucial to enhance hosting capacity. Typically, load curves for these consumers are estimated through measurement campaigns conducted by Distribution System Operators (DSOs) for a representative subset of customers or through the aggregation of load curves from household appliances within a residence. With the instrumentation of smart meters becoming more common, a new approach to modeling profiles for residential customers is proposed to make the most of the measurements from these meters. The disaggregation model estimates the load profile of customers on a low-voltage network by disaggregating the load curve measured at the secondary substation level. By utilizing only the maximum power measured by Linky smart meters, along with the load curve of the secondary substation, this model can estimate the daily profile of customers. For 48 secondary substations in our dataset, the model obtained an average symmetric mean average percentage error (SMAPE) error of 4.91% in reconstructing the load curve of the secondary substation from the curves disaggregated by the model. This methodology can allow for an estimation of the daily consumption behaviors of the low-voltage customers. In this way, we can safely envision solutions that enhance the grid hosting capacity.

The impact of energy transition on distribution network costs and effectiveness of yardstick competition: an empirical analysis for the Netherlands

We assess the impact of regional differences related to energy transition on average costs and allowed revenues for a panel of Dutch electricity distribution system operators (DSOs) subject to yardstick competition. Yardstick competition entails that the allowed revenues of DSOs are based on the average costs of the entire industry, which requires that these DSOs are comparable. This comparability requirement is challenged by the penetration of distributed generation and other distributed energy resources, which may cause regional differences among DSOs. Estimating an average-cost function for the entire population of Dutch DSOs for the period 2012–2020, we find that the installed capacity of solar PV, installed capacity of on-shore wind and number of public electric-vehicle charging points have a significant effect on unit costs of DSOs. If yardstick competition does not take these effects into account, the allowed revenues for some DSOs (having above-average shares of energy-transition variables) are too low, whereas allowed revenues for other DSOs (having below-average shares of energy-transition variables) are too high. We find that taking the impact into account can change the price caps of individual DSOs with a percentage up to around 20%.

False data injection in distribution system: Attacker's perspective

The distribution system is digitizing and occupying cyberspace with the help of information and communication technologies (ICTs). It is vulnerable to cyber-attacks like false data injection (FDI) and denial-of-services (DoS). However, limited research on cyber-attacks in the distribution system is reported in the literature, and these attacks are of serious concern to distribution system operators (DSOs). The DSO's primary challenge is to understand the attacker's perspective for FDI attack construction. Thus, the work presented in this paper aims to provide an in-depth insight for DSO to apprehend the attacker's perspective, attack flow, and the nature of the FDI attack vector. The prior knowledge of attack flow to DSO can help to protect critical infrastructures from cyber-attacks. Thus, this work comprehends the attacker's behaviour for deploying the optimal budget to disrupt the distribution system operation therein by injecting a stealthy FDI vector. The attacker is resource-constrained in terms of budget and network information. Therefore, the optimal budget for attack initiation is proposed and formulated as a multi-objective optimization problem to minimize the investment and maximize the economic loss for the DSO. Constructing the attack vectors for the attacker is challenging in the limited network information. It is complex because of network characteristics such as multi-phase configurations & an unbalanced nature, and higher resistance to reactance (r/x) ratio. Thus, the FDI attack vector construction is proposed based on non-linear programming optimization and sensitivity analysis considering partial information from the distribution system. The simulation results are presented and compared with available methods in the literature to validate the efficacy of the proposed methods.

DSO Market for Transactive Scheduling of Electric Vehicle Charging Stations in Constrained Hierarchical Power Distribution and Urban Transportation Networks

The trend toward the deployment of electric vehicle charging stations (EVCSs) equipped with on-site distributed energy resources indicates potential transactive energy market (TEM) operations among EVCSs. However, unlike regular prosumers, EVCS operations are influenced by both the power distribution network (PDN) and the urban transportation network. In this paper, we propose a three-stage approach for solving the hierarchical energy scheduling and trading problem of EVCSs. At the first stage, the day-ahead traffic assignment problem of the transportation system is solved to determine individual EVCSs’ charging load. At the second stage, EVCSs solve their profit maximization problem to determine their energy scheduling and market participation decisions. Lastly, at the third stage, TEM among EVCSs is cleared by the distribution system operator (DSO). With the DSO shouldering both grid operator and market operator functions, the DSO problem is formulated as an unbalanced three-phase optimal power flow (OPF) model integrating a double-sided auction mechanism. To further improve the PDN operation, grid features such as network reconfiguration and tap changers are introduced into the OPF model. The effectiveness of the proposed framework is proved through case studies.

Navigating the energy transition: Identifying critical success factors for ancillary services provision and sustainable energy solutions in Germany

The provision of ancillary services (AS) is subject to changes associated with the energy transition. Due to new requirements, the power supply quality, reliability, and safety must be achieved by simultaneously complying with technological, economic, and environmental constraints. To mitigate these challenges, we derive factors responsible for a successful venture of all stakeholders, referred to as critical success factors (CSFs). In a Design Science Research (DSR)-based approach, twelve specific CSFs are deduced from expert interviews with transmission-, plant-, and distribution system operators. These CSFs are evaluated in a focus group discussion with academic experts afterward. We summarize practical results and findings from failed and successful projects concerning energy trading strategies, asset portfolios, grid expansion, and communication technologies. We contribute to AS knowledge and derive recommendations for further research and practice.

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

This paper presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

Strategies for Improving the Resiliency of Distribution Networks in Electric Power Systems during Typhoon and Water-Logging Disasters

Coastal cities often face typhoons and urban water logs, which can cause power outages and significant economic losses. Therefore, it is necessary to study the impact of these disasters on urban distribution networks and improve their flexibility. This paper presents a method for predicting power-grid failure rates in typhoons and water logs and suggests a strategy for improving network elasticity after the disaster. It is crucial for the operation and maintenance of power distribution systems during typhoon and water-logging disasters. By mapping the wind speed and water depth at the corresponding positions in the evolution of wind and water logging disasters to the vulnerability curve, the failure probability of the corresponding nodes is obtained, the fault scenario is generated randomly, and the proposed dynamic reconstruction method, which can react in real-time to the damage the distribution system received, has been tested on a modified 33-node and a 118-node distribution network, with 3 and 11 distribution generators loaded, respectively. The results proved that this method can effectively improve the resiliency of the distribution network after a disaster compared with the traditional static reconstruction method, especially in the case of long-lasting wind and flood disasters that have complex and significant impacts on the distribution system, with about 26% load supply for the 33-node system and nearly 95% for the 118-node system.

Optimal feeder routing and phase balancing for an unbalanced distribution system: a case study in Cambodia

This paper aims to minimize the distance of the feeder path from high-voltage/medium-voltage (HV/MV) substation to medium-voltage/low-voltage (MV/LV) transformers and minimize power loss in an unbalanced distribution system by the phase-swapping concept-based load balancing. The shortest path algorithm (SPA) and the genetic algorithm (GA) for optimal feeder routing and phase balancing separately in the MV unbalanced distribution network are proposed. First, the relevant data for the system is collected. These data include substation coordinates (X, Y), active and reactive power (P, Q), phase connections, and lines’ impedance (Z). secondly, the performance of the existing configuration of the test system with numerous indications is presented. Finally, the proposed method is performed to minimize the length and power losses. The real 47-bus test system in Cambodia is chosen to demonstrate the proposed method. In this study, overall power losses, the maximum voltage imbalance, and voltage regulation are computed by the backward/forward sweep load flow. The results based on the simulation indicate the importance of the proposed approach, especially for distribution system designers and operators.

A Transformerless Converter with Common-Mode Decoupling in Low-Voltage Hybrid Grids

Compared with isolated converters, transformerless converters are a preferred choice in low-voltage grids due to their efficiency and lower cost. However, leakage current and common mode (CM) voltage appear through the converter and ground in hybrid grids, which consist of AC and DC subgrids. The leakage current and CM voltage seriously influence operation and power quality in low-voltage distribution systems. This paper proposes a common-ground-type (CGT) converter equipped with a CM decoupling control strategy to eliminate the leakage current and CM voltage. A CM model is derived, and the leakage current and CM voltage are analyzed in detail. A CGT four-leg converter is constructed to eliminate the high frequency CM voltage. A dual DQ current control loop is developed to suppress the DC double-frequency ripple. Additionally, an active damping method is proposed, based on the neutral current feed-forward plus inductor current feedback, to attenuate the low frequency CM voltage. The proposed converter and control strategy guarantees excellent performance in suppressing leakage current and CM voltage. The DC voltage of the converter connected to the DC grid maintains stability and symmetry. The leakage current is significantly reduced, and the leakage current suppression performance is improved by 83%. The high frequency CM voltage is attenuated from 50%udc to 2%udc, and the low frequency CM voltage is suppressed from approximately 32%udc to 3%udc, which is a significant improvement compared with the traditional method. In addition, the proposed control strategy has good transient performance when the load changes abruptly. Finally, an experimental platform is established to validate the feasibility and performance. The experiment results showed that the proposed control strategy improves the system performance and power quality.

Risk-averse bi-level planning model for maximizing renewable energy hosting capacity via empowering seasonal hydrogen storage

Renewables (i.e., solar and wind power) in the Nordic area have highly seasonal characteristics, which severely restrict the wide development of renewables since they can be excessive in summer but insufficient in winter with intermittent outputs. The conventional battery energy storage system (BESS) with short-term adjustment functionality cannot eliminate the seasonal imbalance of renewables. In this regard, a risk-based bi-level planning model is presented to maximize the hosting capacity (HC) of renewables through configuring seasonal hydrogen storage (SHS) and BESS. Specifically, the upper level applies a multi-objective scheme to maximize HC and minimize the investment cost simultaneously. In turn, the lower level is driven by maximizing the profits of the distribution system operator (DSO) with the implementation of price-based demand response (PBDR). Due to forecasting errors of renewables and load resulting from intermittent output and random behaviors, a stochastic programming (SP) method is developed to address and adapt multiple uncertain fluctuations. Moreover, the conditional value-at-risk (CVaR) is introduced to measure the effect of the risks raised by multiple uncertainties on system operation. Finally, numerical studies are employed to verify the effectiveness of the proposed model. Compared to the cases without considering PBDR and SHS, the total renewable energy HC in the proposed model is increased by 2.30 MW and 0.37 MW, respectively, while the yearly cost benefit of DSO is enhanced by 28063.2 US$ and 17823.7 US$, respectively. The promising results demonstrate the empowerment of SHS can promote the cross-seasonal consumption of renewables, effectively maximizing the HC and improving the economic operation of distribution systems.

Optimal operational planning of distribution systems: A neighborhood search-based matheuristic approach

This study proposes a strategy for short-term operational planning of active distribution systems to minimize operating costs and greenhouse gas (GHG) emissions. The strategy incorporates network reconfiguration, switchable capacitor bank operation, dispatch of fossil fuel-based and renewable distributed energy resources, energy storage devices, and a demand response program. Uncertain operational conditions, such as energy costs, power demand, and solar irradiation, are addressed using stochastic scenarios derived from historical data through a k-means technique. The mathematical formulation adopts a stochastic scenario-based mixed-integer second-order conic programming (MISOCP) model. To handle the computational complexity of the model, a neighborhood-based matheuristic approach (NMA) is introduced, employing reduced MISOCP models and a memory strategy to guide the optimization process. Results from 69 and 118-node distribution systems demonstrate reduced operational costs and GHG emissions. Moreover, the proposed NMA outperforms two commercial solvers. This work provides insights into optimizing the operation of distribution systems, yielding economic and environmental benefits.

Customer-oriented scheduling of active distribution system considering integrated demand response programs and multi-carrier energy hubs

The increasing development of infrastructure and improvement of automation services have changed the role of household loads from fixed and unresponsive loads to responsive household loads. Meanwhile, the demand response program capability of smart residential consumers, considering their comfort level along with their power exchange effects on energy hubs, is an important part of active distribution system Scheduling, which should be noted comprehensively. In this regard, this paper presents a two-stage optimization framework based on iteration for customer-oriented scheduling of an active distribution system by incorporating the participation of smart residential consumers in energy hubs. In the first stage-first level, a flexible integrated demand response program process along with a storage system for smart residential consumers with real-time electricity prices is implemented to maximize the comfort level of smart residential consumers. In the first stage-second level, the energy hub operator presents day-ahead optimal self-scheduling to maximize their profit and send their bid strategies to the distribution system operator, considering the effects of smart residential consumer's power exchange on profit and cost. Finally, in the second stage of the optimization model, customer-oriented scheduling of an active distribution system is implemented by the distribution system operator to minimize operating costs. The comfort level of smart residential consumers for 20, 25, and 30 households of the first, second, and third energy hubs are increased by 112.92%, 161.64%, and 120.77%, respectively, compared to the base case. In addition, the total operating costs of active distribution system and energy hubs are reduced by $38,200, $4643, $3287, and $5523, respectively, compared to the base case.

Energy management system based on economic Flexi-reliable operation for the smart distribution network including integrated energy system of hydrogen storage and renewable sources

This paper presents the energy management of smart distribution network including integrated system of hydrogen storage and renewable sources. Objective is to assess economic, operation, flexibility, and reliability goals of the distribution system operator. Objective function minimizes costs of operation, reliability, energy losses, and network flexibility. Scheme is constrained by AC optimal power flow equations, network reliability limitation, and integrated system model. Scheme utilizes scenario-based stochastic optimization to model of uncertainties, such as load, parameters of renewable resources, energy prices, and the availability of network equipment. Novelty is modeling and performance evaluation of the proposed integrated system as a type of flexibility resource in the operation of distribution systems proportional to the economic, operation, flexibility, and reliability objectives of the network operator. Numerical results demonstrate energy management capabilities of the discussed integrated energy system, which contribute to enhancing economic and technical conditions of distribution network. The inclusion of hydrogen storage in the renewable integrated energy system has been found to enhance the economic viability, operational efficiency, and reliability of the distribution network by around 46.8%, 41%–53%, and 95%, respectively, when compared to network power flow. Aforementioned integrated system demonstrates 100% flexibility through the utilization of hydrogen storage.

Enhancement of distribution system performance with reconfiguration, distributed generation and capacitor bank deployment

This paper presents a comparative study of optimal reconfiguration, distributed generation, and shunt capacitor bank deployment for power loss minimization and voltage profile improvement in distribution systems. A metaheuristic approach based on the grey wolf optimizer (GWO) algorithm has been proposed for solving this high-dimensional, nonlinear, constrained, combinatorial optimization problem. Two standard IEEE 33- and 69-bus radial distribution systems (RDSs), and a practical 83-bus RDS of Taiwan Power Company have been considered for this study. The solutions obtained are compared with one another and those in the recent literature which includes classical and non-classical, metaheuristic-based methods. Going further, the little-studied problem of simultaneous reconfiguration, distributed generation, and capacitor bank deployment has been solved. The results suggest that the GWO has excellent potential for solving complicated optimization problems in distribution systems and elsewhere.

Визначення граничних величин споживання електричної енергії засобами оптимізаційного моделювання

In the heating season of 2022/2023, as a result of the military aggression of the Russian Federation and targeted attacks on electric power facilities, a significant shortage of electric energy occurred in the Integrated power system of Ukraine. Restriction schedules and emergency disconnection of consumers were forcibly applied. In the conditions of such an unprecedented violation of the security of electricity supply, the problem of determining the limit values of electric energy and power consumption has gained new relevance for the electricity market participants. In this study, the problem of determining the optimal limit values of electricity consumption based on the criterion of maximum income from its sale to three groups of consumers was set and solved: "Critical infrastructure", "Households" and "Non-household consumers". The formation of the three specified groups is related to price discrimination in the electricity market and the need to supply critical infrastructure facilities without limits. The decision variables in the optimization model are proposed to be specified in fractions of the provision of electrical energy needs (unit fractions), which allows for establishing equal conditions regarding restrictions for all distribution system operators. To take into account the social significance of electric energy as a commodity, an additional constraint was included to the basic optimization model: the lower limit of the decision variable for the "Households" group. Approbation of the proposed optimization model was carried out using spreadsheet modeling in MS Excel on reported data on the results of electrical load measurements on regime days. The results of computational experiments are presented for the conditions of different values of the deficit of electric energy in the Integrated power system of Ukraine and different values of the additional constraint for the «Households» group, in particular, quantitative estimates of the consequences of the included constraints for the functioning of the electricity market. The proposed method of determining the limit values of electric energy consumption allows to formalize the procedure for decision-making according to predetermined rules and thereby achieve a fair distribution of limited resources. Keywords: electric energy consumption, shortage, limit value, optimization modelling, spreadsheet model.

Reconfiguration of distribution network for improving power quality indexes with flexible lexicography method

Reconfiguration is a low-cost method to reduce power loss in distribution network (DN). Harmonics and unbalancing cause additional losses in DNs. Therefore, it is necessary to consider these two factors in distribution network reconfiguration (DNR). In this paper, the effect of reconfiguration in two harmonic polluted medium and large scale DN is evaluated. In addition, the effect of harmonic injection of distributed generation (DG) is studied. Moreover, the effect of reconfiguration in unbalanced DN in the presence of single-phase DGs is investigated, and it is concluded that distribution system operator (DSO) should consider network unbalance in DNR to more reduction of power losses. Furthermore, a new flexible lexicography method is introduced to solve multi-objective problems, especially in DNR. In the proposed method, the DSO determines the priority of each goal in comparison to the other goals, instead of using weighing factors of them. This method is effective for power quality goals which cannot be illustrated as cost function. In this paper, validation of the proposed method in IEEE 69 bus and 95 bus practical harmonic polluted DN and 25 bus unbalanced DN in presence of three-phase and single-phase PVs are investigated. Results show the effectiveness of the method in increasing the flexibility of DSO in DNR to improve power quality goals such as harmonics and unbalance, as well as reduce power losses.

Plug-in electric vehicle dynamic pricing strategies for bidirectional power flow in decentralized and centralized environment

With the increasing number of plug-in electric vehicles (PEV) power demand management without increasing existing facilities is becoming challenging. To address the issue two novel pricing strategies one for centralized and other for decentralized environments considering bidirectional flow of power in a dynamic power pricing market are proposed in this paper. The two proposed strategies aim to fulfil the requirement of PEV owners and the power system operated by the Distribution System Operator (DSO). In the proposed approach load demand management with peak saving and valley filling for charging/discharging of PEV is done, while the base load connected to the system is not affected. While availing Vehicle to Grid (V2G) services battery degradation cost is considered for increasing service effectiveness and encouraging PEV owners to participate. The randomness of PEV in terms of time, SOC, battery capacity and level of charging/discharging is considered to be in accordance with the real-time scenario. The two proposed strategies are compared based on objective achievement, loading on the distribution transformer and finding a real-time suitable environment for a 24-hour duration. The numerical case study of the proposed strategies are carried out by considering Indian environment.

Privacy-Preserving Electricity Trading for Connected Microgrids

The electricity market is evolving from the traditional unidirectional model into a bidirectional one in which households also generate and sell energy. This new scenario requires technology able to manage the available energy and guarantee that all the participants pay or are paid appropriately. Unfortunately, fine-grained monitoring of energy production and consumption makes it possible to infer sensitive information about confidential aspects of people’s private life. In this paper, we propose a system designed for privacy-preserving electricity trading in a connected microgrid. The system guarantees that at the end of a billing period, the distribution system operator can compute the quantity to be charged or paid to each household while being unable to trace its consumption details.

Deep Q-Learning-Based Smart Scheduling of EVs for Demand Response in Smart Grids

Economic and policy factors are driving the continuous increase in the adoption and usage of electrical vehicles (EVs). However, despite being a cleaner alternative to combustion engine vehicles, EVs have negative impacts on the lifespan of microgrid equipment and energy balance due to increased power demands and the timing of their usage. In our view, grid management should leverage on EV scheduling flexibility to support local network balancing through active participation in demand response programs. In this paper, we propose a model-free solution, leveraging deep Q-learning to schedule the charging and discharging activities of EVs within a microgrid to align with a target energy profile provided by the distribution system operator. We adapted the Bellman equation to assess the value of a state based on specific rewards for EV scheduling actions and used a neural network to estimate Q-values for available actions and the epsilon-greedy algorithm to balance exploitation and exploration to meet the target energy profile. The results are promising, showing the effectiveness of the proposed solution in scheduling the charging and discharging actions for a fleet of 30 EVs to align with the target energy profile in demand response programs, achieving a Pearson coefficient of 0.99. This solution also demonstrates a high degree of adaptability in effectively managing scheduling situations for EVs that involve dynamicity, influenced by various state-of-charge distributions and e-mobility features. Adaptability is achieved solely through learning from data without requiring prior knowledge, configurations, or fine-tuning.

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.