Active Distribution Systems Research Articles

Spatial-Temporal Coordinated Volt/VAR Control for Active Distribution Systems

Spatial-Temporal Coordinated Volt/VAR Control for Active Distribution Systems

Electricity arbitrage for mobile energy storage in marginal pricing mechanism via bi-level programming

The rapid growth of renewable energy is integrated into the distribution system. The creation of both new market mechanisms and investment models has critical effects on the economics and security of the distribution market. Mobile energy storage has been used to increase the resilience of distribution grids due to their advantages in mobility and flexibility, which offer electricity arbitrage options for merchant investments. This paper presents a bi-level optimization framework based on location marginal pricing settlement of mobile energy storage financial rights revenue in active distribution systems. In the developed framework, the upper-level problem determines the optimal capacity, routing, and dispatching of mobile energy storage to maximize revenue in the liberalized electricity market. The lower-level problem performs the joint optimization of energy and reserve market clearing as well as renewable energy capacity optimization based on the alternating current optimal power flow model. The non-convexity of the line flow and location marginal pricing is linearized via second-order cone relaxation and Karush-Kuhn-Tucker. The bi-level programming is reformatted as mathematical programming with equilibrium constraints. Further, the revenue risk due to renewable energy uncertainty is measured via conditional value-at-risk. Finally, the effectiveness of the optimization framework and solution method is verified using the modified IEEE-33 test system. The results show that mobile energy storage promotes renewable energy and reduces distribution network costs by 2.3%.

Optimal siting and sizing of battery energy storage systems in unbalanced distribution systems: A multi objective problem under uncertainty

In this paper the siting and sizing problem of battery energy storage systems in unbalanced active distribution systems is formulated as a mixed-integer, non-linear, constrained multi objective (MO) optimization problem under uncertainties. The problem is cumbersome from the computational point of view due to the presence of intertemporal constraints, a great number of state variables and the presence of uncertainties in the problem input data. A new approach based on the trade-off/risk analysis is proposed to obtain with acceptable computational efforts a solution that may not be the optimal solution but represents a reasonable and robust compromise. We use the trade-off/risk analysis, because it was specifically developed for power system planning problems in which we deal with a wide range of options, with possible conflicting objectives, and with uncertainty and risk. The proposed approach includes new procedures to select an adequate set of planning alternatives to be considered in the trade-off/risk analysis framework and to assist the planning engineer when difficulties arise in setting probabilities of the input data. Numerical applications to an IEEE unbalanced test system demonstrate the effectiveness of the proposed procedure and indicate the best alternatives of storage systems in the range from 450 kW to 600 kW globally installed in a reduced set of nodes.

Optimal operation of three-phase unbalanced active distribution system based on semidefinite relaxation and convex-concave procedure

The three-phase distribution optimal power flow (D-OPF) problem has attracted much attention in recent years due to practical requirements for managing distributed energy resources and multi-energy customers in unbalanced active distribution systems. To deal with the strong nonconvexity of the three-phase OPF problem, this paper develops the semidefinite programming (SDP) formulation of three-phase OPF problems considering the mutual coupling in multi-phase networks. Instead of directly dropping the rank-one constraints, a sequential convex optimization algorithm based on the convex-concave procedure (CCP) is proposed for recovering a feasible and optimal solution of three-phase power flow. The proposed iterative algorithm not only has high computational efficiency but also guarantees the optimality when the semidefinite relaxation or second-order cone relaxation method is inexact. Case studies on various IEEE testing distribution systems verify that the proposed algorithm recovers the feasible and optimal power flow solution. It also exhibits a relatively low number of iterations and short solving time in large-scale IEEE testing systems.

Analytical approach for allocation of energy losses in active distribution system using the method of energy summation

Analytical approach for allocation of energy losses in active distribution system using the method of energy summation

An active protection method based on superimposed phase currents for active distribution systems

To enhance the fault current amplitude of active distribution systems during the weak fault period and decrease the influence of the measurement equipment noise, this paper proposes an active protection method based on superimposed phase currents for active distribution systems. This method is shown as follows. First, the reference value of the filter inductor current of each distributed generator (DG) is set as different values when the fault intensity of the active distribution system changes. Second, the superimposed phase current in each relay is obtained by subtracting each sample of phase current from its corresponding sample of phase current in the previous cycle. Third, the fundamental phase angle of the superimposed phase current is extracted. This is because the fundamental current component of the superimposed current cannot be affected by the active control strategy, which is proved in this paper in detail. Meanwhile, the feature direction is built by fundamental phase angles at both ends of the line to detect faulty zone. Finally, the proposed protection method has been validated in a modified IEEE 13-bus test system, and the test results prove the effectiveness of the proposed active protection method that can enhance the faulty feature to detect faults.

A multi‐objective interval optimization approach to expansion planning of active distribution system with distributed internet data centers and renewable energy resources

AbstractWith the development of the digital economy, the power demand for data centers (DCs) is rising rapidly, which presents a challenge to the economic and low‐carbon operation of the future distribution system. To this end, this paper fully considers the multiple flexibility of DC and its impact on the active distribution network, and establishes a collaborative planning model of DC and active distribution network. Differing from most existing studies that apply robust optimization or stochastic optimization for uncertainty characterization, this study employs a novel interval optimization approach to capture the inherent uncertainties within the system (including the renewable energy source (RES) generation, electricity price, electrical loads, emissions factor and workloads). Subsequently, the planning model is reformulated as the interval multi‐objective optimization problem (IMOP) to minimize economic cost and carbon emission. On this basis, instead of using a conventional deterministic‐conversion approach, an interval multi‐objective optimization evolutionary algorithm based on decomposition (IMOEA/D) is proposed to solve the proposed IMOP, which is able to fully preserve the uncertainty inherent in interval‐typed information and allow to obtain an interval‐formed Pareto front for risk‐averse decision‐making. Finally, an IEEE 33‐node active distribution network is utilized for simulation and analysis to confirm the efficacy of the proposed approach.

Power electronic converters in the unbalance compensation method for renewable energy-powered active distribution systems: AOA-RERNN approach

Power electronic converters in the unbalance compensation method for renewable energy-powered active distribution systems: AOA-RERNN approach

Simultaneous feeder reconfiguration and distributed generation planning in the presence of voltage-dependent and variable loads

In distribution systems, where distribution losses and the output of distributed generators (DGs) are significantly impacted by load power, an effective approach for reducing power loss is required, which the hybrid operation of DG units and grid reconfiguration can serve as a best alternative. Load power exhibits variability, altering alongside the voltage fluctuations which occur over time. Furthermore, the correlation between power demand and voltage relies on the type of load. Nonetheless, the incorporation of these important concerns into research on reconfiguration and distributed generation planning is rare. Only a limited number of papers have taken into account the voltage dependence and the type of time-varying loads in their respective models. Nevertheless, they proposed models with significant nonlinearity, requiring computation through nonlinear solvers or metaheuristic algorithms. Meanwhile, these models require the use of intensive linearization techniques to facilitate their implementation through linear solvers. High computational time is demanded by nonlinear solvers, while metaheuristic algorithms cannot guarantee the attainment of optimal solutions. Hence, the accurate modeling of load behavior holds importance in the active distribution system reconfiguration. In this paper, a proficient reconfiguration model is presented, which is both straightforward for implementation in conventional optimization tools and adept at identifying appropriate solutions for the reconfiguration and DG planning problem.

A multi-stage framework for coordinated scheduling of networked microgrids in active distribution systems with hydrogen refueling and charging stations

Due to the increase in electric energy consumption and the significant growth in the number of electric vehicles (EV) at the level of the distribution network, new networks have started using new fuels such as hydrogen to improve environmental indicators and at the same time better efficiency from the excess capacity of renewable resources. In this article, the services that can be provided by hydrogen refueling stations and charging electric vehicles in the optimal performance of microgrids have been investigated. The model proposed in this paper includes a two-stage stochastic framework for scheduling resources in microgrids, especially hydrogen refueling stations and electric vehicle charging. In this model, two main goals of cost minimization and greenhouse gas emissions are considered. In the proposed framework and in the first stage, the service range of microgrids is determined precisely according to the electrical limitations of distribution systems in emergency situations. Then, in the second stage, the problem of energy management in each microgrid will be solved centrally. In this situation, various indicators including the output energy of renewable sources, smart charging of hydrogen and electric vehicle charging stations (EV/FCV) and flexible loads (FL) are evaluated. The final mathematical model is implemented as a multivariate integer multiple linear problem (MILP) using the GUROBI solver in GAMS software. The simulation results on the modified IEEE 118-Bus network show the positive effect of the presence of flexible loads and smart charging strategies by charging stations. Also, the numerical derivation shows that the operating costs of the entire system can be reduced by 4.77% and the use of smart charging strategies can reduce greenhouse gas emissions by 49.13%.

Fault Prognosis in Smart Distribution System with Distributed Generation Using Fast Fourier Transform Assisted Machine Learning

The affordability and accessibility of electrical data related to microgrids and active distribution networks (ADN) have significantly improved due to smart digital relays with advanced communication capabilities. These relays can communicate with each other and central protection systems. Due to data accessibility a machine learning (ML) based module is proposed. This module predicts both fault type and location within an IEEE 34 bus active distribution system featuring distributed generations. To design this module, current measurements are retrieved from five specified bus locations and further Fast Fourier Transform is applied for feature extraction. The feature creation method is much simpler without any complexity. These features are further used for ML module’s training. Practical ADN operating conditions are considered when curating the training dataset. Weighted K-nearest neighbors are used for fault type prediction and linear regression is applied for fault location estimation and they are proven to be superior to other ML approaches. The obtained results show high accuracy and reliability in predictions, making these modules suitable for ADN protection. They also exhibit robustness against input measurement noise, ensuring consistent protection in various operating scenarios. The ADN is simulated in SIMULINK 2020a, and ML modeling is done using MATLAB 2020a.

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.

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.

Reliable high impedance fault detection method based on the roughness of the neutral current in active distribution systems

Detecting High Impedance Faults (HIFs) in Distribution Systems (DSs) still requires effective solutions. HIFs can cause damage, such as shock hazards and bushfires. However, conventional protection cannot identify HIFs due to their low fault current, and most existing solutions neglect the multiple operating scenarios of the DSs. Thus, this paper proposes a new self-adaptive approach for HIF detection, considering the conductor fall period in a system with distributed generation, a condition usually overlooked in existing studies. The algorithm is based only on the fundamental component and third harmonic extracted from the neutral current using the Stockwell Transform. The approach was thoroughly evaluated, considering several scenarios, such as changes in the system loading, background noise, events that can cause false detection, and variations in the distributed generator power output. The tests considered HIFs signals from real field measurements and generated by a simulation model. The results showed that the algorithm is promising, with high detection rates for all the evaluated scenarios, which included over 400,000 HIF and 6,000 non-HIF cases. Additionally, a comparison proved the superiority of the proposed approach over existing methodologies.

A Novel High-Impedance Fault Detection Technique in Smart Active Distribution Systems

Identification of high impedance fault (HIF) is important in smart active distribution systems (SADSs). The existing protection methods fail to detect HIF, as HIFs have peculiar fault current characteristics. However, if HIFs are left undetected, they result in severe fire hazards and arcing. The paper proposes a novel fault detection indicator that utilizes the differential energy associated with the positive sequence current components during faults. IEEE-13 bus and IEEE-34 bus system is used for simulation in Real Time Digital Simulator (RTDS). The results show that proposed technique accurately identify HIFs and perfectly discriminates the transients due to HIFs, non-linear loads and voltage and reactive power compensation devices. Furthermore, it differentiates HIFs from other switching transients like capacitor or load switching events. Other factors, like transformer inrush currents, effect of noise and sampling rate are also considered to ensure the accuracy and reliability of the proposed method. Moreover, the practical feasibility of the proposed method is also experimentally validated.

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.

Intelligent protection scheme using combined Stockwell-Transform and deep learning-based fault diagnosis for the active distribution system

Intelligent protection scheme using combined Stockwell-Transform and deep learning-based fault diagnosis for the active distribution system

Active and reactive energy storage STATCOM distribution system power management

One of the critical factors influencing the overall development of modern power systems is the control of active and reactive power flows in distribution power systems. The static synchronous compensator (STATCOM) with storage energy is a powerful device that can control active and reactive power flow in a distribution system. A simulation model of power management using STATCOM with energy storage is presented in this paper. A fuzzy logic controller is proposed to manage the powers. The simulation results demonstrate STATCOM's ability to manage the active and reactive power flow in a controlled distribution line, and thus the powers regulated between feeders, by utilizing storage energy.

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.