Modified IEEE 30-bus Test System Research Articles

A three-level robust scenario-based model for resilience-oriented placement of remote-controlled switches in distribution networks

This paper introduces an innovative strategy to improve the resilience of Distribution Networks (DN) following natural disasters. The approach involves a multi-stage model for the strategic placement of Remote-Controlled Switches (RCS) to expedite fault isolation and service restoration. The model integrates various resilience enhancement techniques, such as network reconfiguration and microgrid formation, throughout the pre-disaster, disaster progression, and post-disaster phases. To mitigate uncertainties related to line failures and ensure planning robustness, a novel three-level Robust Scenario-based Model (RSM) is introduced. The RSM optimizes the strategic RCS placement to enhance System Performance Criteria (SPC) in response to anticipated line failure scenarios, considering individual line vulnerabilities and disaster severity. Subsequently, the model identifies the most critical failures from each predicted scenario based on RCS placements and then recalibrates the placements to enhance DN resilience against both anticipated and critical failures. The RSM is formulated as a mixed-integer linear programming problem to maximize SPC by allocating resources optimally to serve maximum weighted loads while adhering to structural and operational constraints. The effectiveness and validity of the proposed model are demonstrated through case studies conducted on a modified IEEE 33-bus test system and a modified real DN.

Optimal location and operation of energy storage and transmission switching for minimizing wind power spillage

This paper proposes a bi-level multi-objective optimization model to improve the integration of wind power generators in electrical networks based on the optimal location and operation of the energy storage system and transmission switching strategies. The minimization of wind power spillage, load shedding, power losses, and the improvement of the voltage stability margin are the objectives of the upper-level subproblem. While the lower-level subproblem aims to maximize social welfare under different scenarios and overall system constraints. Under the uncertainty of the wind power and load demand, a collection of lower-level subproblems that represent the market clearing conditions is used to constrain the upper-level subproblem. The effectiveness of the proposed algorithm is examined on a modified IEEE 24-bus test system. The results show that the applicability of the proposed algorithm in aiding power system improvement planning for minimizing wind power spillage to integrate wind energy with maximizing the social welfare and improving the loadability and the voltage stability.

Differential evolutionary particle swarm optimization with orthogonal learning for wind integrated optimal power flow

This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential evolutionary evolution PSO with orthogonal learning (OL), i.e., DEEPSO-OL in short. Evolutionary computing can explore the solution space efficiently because of its self-evolving attribute as iteration continues. The OL enhances its exploitation by focusing on deeper search for promising solutions. It utilizes the concept of orthogonal experimental design (OED) which predicts the best combination of control variables without exhaustive evaluation of all possible combinations. In addition, to avoid premature convergence in a local optimum, a stochastic star topology for particles is proposed. Such topology ensures just enough communication among the best performing particles, while encouraging them to explore other spaces. The efficacy of the algorithm is evaluated through real-world scenarios such as optimal power flow (OPF) and wind integrated OPF, which are hard to solve with classical mathematical methods. The proposed algorithm is run on a modified IEEE 30-bus test system and compared to the state-of-the-art evolutionary computing algorithms for a variety of cost objective functions with high levels of non-linearity and non-convexity. The DEEPSO-OL demonstrates its performance to generate more accurate feasible solutions and construct promising and efficient search method for real-world complex optimization problems.

Risk-constrained bidding and offering strategy for sector-coupled electricity-hydrogen systems incorporating accessibility level of mobility sector

To enhance the economic viability of integrated energy systems, it is important to balance risk and reward while ensuring operational flexibility and compliance with regulatory constraints. Developing an integrated risk measurement method for energy trading in energy markets that complies with regulatory constraints adds another layer of complexity, especially when dealing with large-scale heterogeneous energy systems, such as those dominated by green hydrogen. Ensuring adherence to market regulations, grid codes, and flexible requirements requires a thorough understanding of legal frameworks and policy considerations, as well as integrating regulatory constraints into the bidding/offering strategies. To address these challenges and enable the profitable development of integrated electricity-hydrogen energy systems (IEHSs), this paper develops a risk-constrained bidding/offering self-scheduling strategy for IEHSs that allows for simultaneous trading in day-ahead energy and reserve electricity markets. The developed self-scheduling strategy regulates hydrogen and power flow in a centralized manner, taking into account the operational limits of the power sector, hydrogen fueling stations (HFSs), electric vehicle parking lots, fuel cell vehicle parking lots, and renewable energy sources. As a salient feature, the important human-related factors in the mobility sector are included in the proposed strategy to motivate private owners to collaborate with IEHSs and to accommodate the charging demand of vehicles with electricity market signals. To obtain the robust day-ahead schedule, economic risks associated with renewable power generation, calls for reserve deployment, and electricity market prices are taken into consideration. Towards this end, a stochastic adaptive robust optimization approach is proposed to control the risk of profit variability, which is solved by a nested column-and-constraint generation algorithm. The effectiveness and feasibility of the proposed strategy are verified using the modified IEEE 14-bus test system, as a standard scale of local IEHSs. The numerical results highlight the synergies impact between HFSs and the power sector, resulting in an increase in the profit of the IEHS from $101.654k to $275.078k. Furthermore, the IEHS can improve its profit margin and competitiveness by employing the tri-level stochastic adaptive robust optimization approach. This approach ensures that the IEHS is resistant to sudden changes in real-time interactions, even with an 8.14% increase in the imbalance cost.

Novel cyber-physical architecture for optimal operation of renewable-based smart city considering false data injection attacks: Digital twin technologies for smart city infrastructure management

Smart grid is considered a cyber-physical system, which is a combination of physical devices and computational processes. Since there are lots of interactions between the cyber layer and the physical layer, the operation, management, and security of the entire system are crucial topics that must be taken into account. In this regard, this paper tackles two problems in AC MGs, as a special case of smart cities. Firstly, a framework is proposed to solve the optimal scheduling problem in these systems. In this stage, the optimal management and scheduling of the system are structured and modeled as an optimization problem. The dragonfly is utilized as a powerful optimization technique to solve this optimization problem. It is worth noting that in this paper different renewable energy sources, as well as batteries are considered. Since cyber-attacks are among the greatest threats to the system and can cause disruption and outage in smart grids, in this paper a deep-learning-based method called long short-term memory along with the concept of prediction interval is utilized to develop a cyber-attack detection model for false data injection attacks on smart meters. The proposed cyber-attack detection model is first trained using historical data and then is used in real-time for detection. In order to investigate the effectiveness of the proposed optimal scheduling scheme, the modified IEEE 33-bus test system is utilized. Also, for testing the proposed cyber-attack detection model a real-world dataset is used. The simulation results show the great performance and effectiveness of both proposed methodologies. It should be noted that all model are simulated in digital twin simulator. The novel LSTM-based cyber-attack detection model enhances security in electric grids, demonstrating remarkable accuracy.

A Linked Swing Contract Market Design With High Renewable Penetration and Battery Firming

As renewable generation and energy storage systems become increasingly cost-effective, modern power systems are increasingly reliant on variable energy resources (VERs) to meet demand. However, the variability and uncertainty of VERs' generation require additional flexibility to mitigate their impacts. Unfortunately, the current power market designs rigidly define different power and reserve products, making it difficult to fully recognize and compensate for this flexibility. Furthermore, since the marginal operating costs of the system quickly approach zero, current marginal cost-based market mechanisms struggle to provide price signals that allow independent power producers to make informed decisions about constructing, operating, and retiring plants. To address these issues, this paper proposes a new linked electricity market design based on swing contracts for operating power systems with high penetration of VERs and energy storage resources. The feasibility of the proposed approach is demonstrated through study results on the modified IEEE 118-bus test system.

Value of Resilience for Self-Sustained Highway Transportation Systems

Nowadays, self-sustained highway transportation systems (HTSs) are calling for the inter-operation of road networks, distribution networks (DNs), and electric vehicles (EVs) towards the unexpected impacts inducted by extreme weather events (EWEs). A novel joint resilience assessment scheme is proposed to quantify the resilience of self-sustained HTSs under tropical cyclone (TC) events. The impacts of TCs on transmission lines and highways are formulated using a two-step ambiguity set. In the first step, a multitask learning (MTL) based model with a novel training strategy is proposed to simulate the future TC tracks; the impacts of TCs are then calculated in the second step based on the simulation results. Using the ambiguity set, the self-sustained HTSs resilience assessment problem is formulated based on the dynamic optimal traffic assignment problem, where EVs are integrated as third-party emergency resources before the advent of TCs. This scheme is then formulated as a two-stage distributionally robust optimization (DRO) problem and reformulated as a mixed-integer linear programming (MILP) problem. Case studies have been conducted on a self-sustained HTS consisting of a modified IEEE-14 bus test system with 2 wind farms (WFs) and a 14-node transportation network with 4 charging stations (CSs). Numerical results indicate that the proposed scheme can quantify the resilience of self-sustained highway transportation systems under the joint operation of EVs regarding the unmet travel demand and load-shedding costs. In comparison to the statistical TC forecasting models, the proposed MTL-based model is more accurate and can reduce the system cost in most cases.

Refined modeling and approximated aggregation method for alkaline water electrolyzers in power system optimal scheduling

Large-scale hydrogen production through water electrolysis is recognized as a promising way to promote the utilization of renewable energy. However, the integration of massive electrolyzers will lead to low computational efficiency in their scheduling problem, thus an aggregation modeling method for electrolyzer clusters is urgently needed. In this paper, a refined operation model of alkaline water electrolyzers is established to accurately describe their operation characteristics. Moreover, to solve ‘the curse of dimensionality’ in their large-scale applications, an approximated aggregated model for electrolyzer clusters which can improve the computational efficiency is proposed. The proposed model is incorporated into the optimal scheduling of power systems, and its effectiveness is validated via numerical experiments on the modified IEEE 118-bus test system. The results show that the proposed method can improve the computational efficiency by at least 1–2 orders of magnitude while ensuring high modeling accuracy.

Coordinated Planning of Power Systems under Uncertain Characteristics Based on the Multilinear Monte Carlo Method

The randomness of the power supply side and the load side of comprehensive energy systems is increasingly prominent. It is very difficult to meet demand through traditional planning methods. To solve this problem, this paper explores the coordinated planning of a power system under uncertain characteristics using the multilinear Monte Carlo method. The uncertain characteristic model and probability density function of the system’s power supply side and load side are established. Taking the optimal operating cost and the maximum wind power consumption as the system planning objectives, a system coordination planning scheme is established, and it is solved by multilinear Monte Carlo simulation. The superiority of this method is verified by taking the modified IEEE 39-bus test system as an example. This method can provide a reference for system planning.

Flexibility enhancement of multi-district DISCOs considering a trade-off between congestion and extractable reserve capacity from virtual energy storage systems

One of the fundamental concerns of distribution companies (DISCOs) is how to satisfy security requirements under uncertain conditions and real-time contingencies. To address this concern, it is vital to employ operational flexibility levers aimed at mitigating congestion and retaining optimal reserve capacity, particularly in critical feeders. Increased interaction between DISCOs and virtual energy storage systems (VESSs) offers an unparalleled opportunity to unlock the energy flexibility of distribution grids at the local level without increasing operational complexity. According to the state-of-the-art, models that tap the economic performance of VESSs while respecting the flexibility and security setpoints adopted by DISCOs in a distributed manner have remained scarce. This study goes beyond by developing a flexibility-oriented decision-making strategy as a single-leader multi-followers stochastic model; in which a multi-district DISCO involves the flexibility setpoints and congestion management strategy in the market participation plan of each VESS locally to enhance the energy flexibility of each district. In the upper level, DISCO aims to deal with stressful situations in real-time sessions by (i) determining the minimum reserve capacity requirements in each district to be provided by VESSs with respect to the existing uncertainty sources and the importance of each district, and (ii) alleviating the congestion in critical feeders to release the feeder capacity for deployment of the available reserve power promptly. In the lower level, each VESS makes optimal decisions on power and reserve bidding in day-ahead, reserve, and real-time markets that are affected by the flexibility services required by the DISCO. Inspired by the activity schedule of different users, it also examines how demand response programs can affect DISCO’s ability to unlock consumer-side flexibility in realizing congestion management strategies. The developed bi-level scheduling model is first reformulated as single-level mathematical program with equilibrium constraints (MPEC) by employing Karush–Kuhn–Tucker (KKT) optimality conditions and then linearized into a mixed-integer linear program (MILP). The proposed strategy is validated through extensive simulations conducted on the modified IEEE 33-bus test system consisting of three districts with different users. Furthermore, DIgSILENT PowerFactory is used to verify the feasibility of the proposed model under contingency cases and to provide insights for DISCOs to control multiple VESSs and capacity-correlated uncertainties.

Dynamic reconfiguration of distribution network considering the uncertainty of distributed generation and loads

This study presents a new methodology to perform the distribution network dynamic reconfiguration (DNDR), taking into consideration the stochastic variations of loads and distributed generation (DG) of power. To solve the heavy computational burden that exists in traditional algorithms of the DNDR, this study first establishes the nodal sensitivity models to calculate the nodal variations caused by nodal power variations. Then, the DNDR is executed utilizing a co-evolutionary algorithm with the goal of loss minimization. The stochastic power flow calculations (PFCs) based on the nodal sensitivity are performed in the DNDR to handle the power fluctuations of the DGs and loads. Finally, the modified IEEE 33-bus test system and a practical distribution system are used for simulations. The simulation results validate the quickness and effectiveness of the proposed DNDR method.

Enhanced Combined Protection Scheme Using Dual Setting Directional Overcurrent and Distance Relays Considering Severe Line Contingencies

An electrical power system must have an optimal protection system for enhanced reliability. Numerous studies have attempted to address issues with protection schemes, such as coordination constraint violations due to change in network configuration. Furthermore, in a few articles, the N-1 line contingency is considered without considering the line contingency selection criterion, resulting in a large number of constraints to deal with in the optimization problem. This paper proposes a new relay coordination scheme for dual setting directional overcurrent relay (DOCR) and distance relay based on the severity index of line outages, which is calculated by using an active and reactive performance index. The proposed protection strategy is improved with the help of an intelligent selection of the relay characteristics coefficient for dual setting DOCR along with the other relay settings. Another significant contribution is to provide a comparative analysis of standard and user-defined relay characteristics using genetic algorithm and grey wolf optimization algorithms. The modified IEEE-14 bus test system and IEEE-39 bus test system is used to validate the efficacy of the proposed protection scheme.

Incorporating catastrophe insurance in power distribution systems investment and planning for resilience enhancement

Severe damage and high economic losses caused by frequent extreme disasters are becoming unbearable for distribution system operators (DSOs) under climate change. In order to cope with extreme disasters, this paper proposes an investment portfolio and planning framework of multi-resource for resilience enhancement. In the investment portfolio and planning framework, insurance is incorporated to mitigate high economic losses as a risk sharing and resilience enhancement supplementary strategy, and co-optimized with security investment from the perspective of DSOs. Firstly, the insurance investment is modelled based on Gordon-Loeb model according to insurance industry principles and formulated as constraints by piecewise linearization techniques. Secondly, the investment portfolio and planning framework proposed in this paper is modeled as a scenario-based two-stage stochastic mixed integer linear optimization program (MILP). In the first stage, DSO makes investment and planning decisions including energy storage (ESS) allocation, line hardening strategy and insurance investment. Both operation strategies of normal operation scenarios and extreme disaster scenarios under climate change are optimized in the second stage. Finally, progressive hedging (PH) algorithm is applied to solve the two-stage stochastic MILP model. The proposed model and algorithm are tested on the modified IEEE 33-bus test system. The results verify the validity of investment portfolio and planning framework, and the results show that portfolio investment considering insurance can realize resilience enhancement in both load shedding and economic losses.

Hierarchical Transactive Energy Scheduling of Electric Vehicle Charging Stations in Constrained Power Distribution and Transportation Networks

There is a growing opportunity to explore transactive energy potentials among electric vehicle charging station (EVCS) systems as the ongoing trend is toward the deployment of more distributed energy resources such as solar PV and energy storage units at EVCS premises. In this paper, we propose a multi-agent hierarchical framework for energy scheduling and trading of EVCSs considering the mobility constraints in the transportation network (TN) and the operational constraints in the power distribution network (PDN). Modeled as independent profit-driven entities, each EVCS optimally schedules its operation based on a multi-period traffic assignment problem (TAP) solved by the traffic operator (TO) agent. A modified single-sided auction mechanism with limited shared information is used to clear the electricity market based on submitted EVCS bids and offers. The resulting trading operations are shared with the PDN operator to guarantee a reliable network operation. A trading adjustment signal is sent back to market participants (i.e., EVCSs) in case of any PDN violations. We use a realistic three-phase unbalanced representation formulated as a MISOCP problem to model the PDN operation. A four-stage solution method is proposed to solve the proposed EVCS energy scheduling and trading problem. Numerical simulations performed on a modified IEEE 33-bus test system and 12-node benchmark TN prove the effectiveness of the proposed multi-agent-based hierarchical transactive market model and its solution approach for EVCSs.

A Static State Estimation Scheme in Microgrid Utilizing μPMU Measurements

The operation of power systems needs uninterrupted and precise wide-area real-time monitoring for secure and automated control of power networks, therefore, there is a requirement of robust state estimation (SE) by the smart control centre. An iterative free static state estimation (IFSSE) scheme is developed using the linear measurement model (LMM) in this paper for microgrids (MGs) integrating distributed generation energy resources (DGERs). The developed scheme of IFSSE utilizes M-estimator (ME) concept, which is combined with the linear static state estimation (SSE) approach in order to provide robust estimation. The developed IFSSE scheme is iteration-free due to the implementation LMM that is achieved from placing the microphasor measurement units (μPMUs) and separate bad data processing (BDP) is not required for this proposed scheme. The proposed scheme's performance has been verified by employing it on modified IEEE-14 bus test systems (BTS) integrating DGERs and modified IEEE 30 BTS integrating DGERs then comparison has been done by taking two cases, i.e., linear SSE without BDP and linear SSE with BDP.

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. The closely-intertwined ecosystem of water and power infrastructures is commonly known as water-energy nexus. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a Boolean solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

Reliability-Based Optimal Integrated Microgrid Scheduling in Distribution Systems

This paper proposes a reliability-based optimal scheduling model of integrated microgrids. The proposed model is capable of delivering optimal scheduling of each individual microgrid and aggregated system. In addition, it is compatible with both grid-connected and islanded operation modes. The developed problem formulation of the proposed model takes into account the reliability indices SAIDI, SAIFI, and CAIDI to evaluate the reliability of the integrated system. Numerical simulations on a modified IEEE-33 bus test system, involving four integrated microgrids, were performed to investigate the advantages and the robustness of the developed model. Furthermore, the impact of merging Battery Energy Storage Systems (BESSs) on the optimization outcome is examined, which demonstrates its significant effectiveness in improving the power systems' reliability.

Optimal resilient microgrids formation based on darts game theory approach and emergency demand response program for cyber-physical distribution networks considering natural disasters

In this paper, novel multi-objective microgrids (MGs) formation method-based darts game theory optimization algorithm is proposed. The main objectives are to minimize the total losses, amount of load-shedding, and total restoration cost while ensuring a variety of topological and electrical constraints. The network graph theory represents the formed MG, and the non-linear equations of power flow and loss calculations are adopted in the MG formation model. In the proposed model, the DRERs include diesel generators, fuel cells, micro-turbines, and renewables wind and PV units with energy storage systems. The demand response program includes curtailable loads to improve load profile reduction during peaks. To assess the system's resiliency under extremely inevitable incidents, resiliency metrics are applied. To validate the effectiveness of the proposed approach, the modified IEEE 33-bus test system is utilized. Single-fault and multiple faults disaster case studies are performed with and without considering the emergency demand response program (EDRP). The results show the effect of EDRP in minimizing system losses, load-shedding, restoration cost, and improving the resiliency metrics after natural disasters. Finally, the analytic hierarchy process (AHP) model indicates the most efficient operation scenario while achieving the system objectives considering the problem alternatives.

Generic security-constrained inertia emulation scheme for VSI-based DC system using supercapacitor

The large-scale grid integration of renewable energy source via a voltage source inverter (VSI)-based DC system results in a continuous reduction in system kinetic inertia, deteriorating grid frequency stability. To mitigate this problem, this paper proposes a generic security-constrained inertia emulation (SIE) scheme, which integrates an appropriately-sized supercapacitor (SC) bank with a VSI-based DC system via a bidirectional DC/DC converter. Inertial response provision is enabled by an SIE control loop developed for the DC/DC converter controller to adjust the SC voltage along with the grid frequency. It is further underpinned by adding an inertia adjustment algorithm to cope with potential security risks of VSI overcurrent and DC system overvoltage. The SIE scheme features a plug-and-play advantage without modifying the original system. The SIE controller parameter optimization is also carried out through the small-signal stability analysis. The effectiveness of the SIE scheme is evaluated using a modified IEEE 39-bus test system in the presence of generator outage and grid fault.

Impact of implementing emergency demand response program and tie-line on cyber-physical distribution network resiliency

Recently, due to the complex nature of cyber-physical distribution networks (DNs) and the severity of power outages caused by natural disasters, microgrid (MG) formation, distributed renewable energy resources (DRERs), and demand response programs (DRP) have been employed to enhance the resiliency of these networks. This paper proposes a novel multi-objective MGs formation method-based darts game theory optimization algorithm. The microgrid formation is obtained by controlling the sectionalizing and tie-line switches. The network graph theory is used to represent the constructed microgrid, and the non-linear equations of power flow and loss calculations are adopted in the microgrid formation model. To measure the system's resiliency under extreme disaster events, metrics are utilized to prove the system's flexibility and resiliency. The modified IEEE 33-bus test system is designed to validate the proposed approach's effectiveness. Three case studies are performed with and without considering the emergency demand response program (EDRP) and tie-lines.