Modified IEEE 30-bus System Research Articles

VSC-HVDC Incorporated Corrective Security-Constrained Optimal Power Flow

With the rapid development and diverse engineering applications of VSC-HVDC technology, development in power industry has put forward higher economic and security requirements for the security-constrained optimal power flow. Under these circumstances, this paper proposes a security-constrained optimal power flow model for hybrid AC/DC power grids that incorporates the VSC-HVDC corrective control. The linear relationship between the pre-contingency state power flow and post-contingency state power flow is established based on the shift factor and the reliability constraints are described as the linear inequality constraints of the post-contingency state active power flow which is expressed by the pre-contingency state branch power flow. With the inherent ability of the VSC-HVDC converter station (short as VSC station) to rapidly regulate active power taken into account, this paper utilizes the linear relationship between the active power adjustment of the VSC station and the power flows of transmission lines and electrical equipment and corrects the reliability constraints with the sensitivity coefficient. Hence the correction of the post-contingency state power flow is reflected in the constraints. By solving the proposed model with the primal-dual interior-point method, the pre-contingency state optimal security operating point that considers the corrective capability of the VSC stations is obtained. Finally, the correctness and validity of the proposed model is verified based on a VSC-HVDC modified IEEE 14-bus system.

Impact of Dynamic Thermal Rating on optimal siting and sizing of energy storage systems under Renewable Portfolio Standards requirements

Renewable Portfolio Standards (RPS), enforced as part of environmental regulations, are taking precedence worldwide to promote investments in renewable energy sources (RES). From the grid perspective, one of the bottle-neck factors that limits large-scale RES integration is the transmission network congestion. Dynamic Thermal Rating (DTR) is a smart grid technology that utilizes line condition and real-time weather conditions to alleviate network congestion by exploiting the transmission network inherent flexibility. Due to the intermittency of the RES, energy storage systems (ESS) have emerged as an essential component of power system that stores the excess generated energy by RES and pumps it back into the network at peak hours. However, despite the benefits of these technologies, the coordination of these two technologies with RPS regulations are not studied together. This paper presents a co-optimized model to evaluate the role of DTR in optimal siting and sizing of ESS units in connection with RPS requirements. The modeling framework co-optimizes bulk ESS expansion and DTR investment planning subject to constraints on RPS regulations and is formulated as a two-stage stochastic mixed integer linear programming (MILP) optimization problem. The model utilizes DTR to overcome the problem of network congestion and ESS to mitigate RES curtailment by saving the extra generated energy in non-peak hours. The efficacy of the proposed model in realizing impact of the DTR on ESS (location, energy capacity and power rating) in achieving high RPS targets is investigated on the modified IEEE 24-Bus system. Results demonstrate that DTR complements ESS under different RPS constraints by reducing the total system cost and ESS size significantly.

A cross-chain enabled day-ahead collaborative power-carbon-TGC market

The contradiction between the independent operation and inner connection of the power market, carbon market and TGC market have become the primary challenge towards developing renewable energy and low-carbon technologies. This paper proposes a day-ahead collaborative power-carbon-TCG market framework and its implementation methodology based on the cross-chain technology to cope with the challenge. The daily collaborative market is designed to operate sequentially to reflect the time attribute of carbon emission and TGC production. Penalty factors are introduced in the power market to indicate the inter-market influences from the carbon and TGC markets. Furthermore, cross-chain technologies are leveraged to form a unified framework for the coordination of the three markets, and the chain structures of each market are redesigned to fit into the unified framework. The conclusion is drawn from the case studies on the modified IEEE-30 bus system. The results indicate that the total carbon emission of the system is reduced under the collaborative market framework. The market shares and profits of low-emission and renewable units increase vastly, benefiting low-carbon and renewable energy companies in the long term.

An AGC Dynamic Optimization Method Based on Proximal Policy Optimization

The increasing penetration of renewable energy introduces more uncertainties and creates more fluctuations in power systems than ever before, which brings great challenges for automatic generation control (AGC). It is necessary for grid operators to develop an advanced AGC strategy to handle fluctuations and uncertainties. AGC dynamic optimization is a sequential decision problem that can be formulated as a discrete-time Markov decision process. Therefore, this article proposes a novel framework based on proximal policy optimization (PPO) reinforcement learning algorithm to optimize power regulation among each AGC generator in advance. Then, the detailed modeling process of reward functions and state and action space designing is presented. The application of the proposed PPO-based AGC dynamic optimization framework is simulated on a modified IEEE 39-bus system and compared with the classical proportional−integral (PI) control strategy and other reinforcement learning algorithms. The results of the case study show that the framework proposed in this article can make the frequency characteristic better satisfy the control performance standard (CPS) under the scenario of large fluctuations in power systems.

Joint Optimal Scheduling of Renewable Energy Regional Power Grid With Energy Storage System and Concentrated Solar Power Plant

Under the path of global low-carbon development, increasing the proportion of renewable energy in the power grid will become the main goal in the future. But, it will also aggravate the problem of wind and solar curtailment. A joint optimal scheduling model of a renewable energy regional power grid with an energy storage system and concentrated solar power plant is proposed in this study. The proposed model takes the lowest comprehensive operation cost of the power grid as the optimization goal and considers various constraints of concentrated solar power plants, energy storage systems, thermal power units, wind power, and photovoltaic power generation. Finally, a modified IEEE 9-bus system is used to verify the validity. The results show that the model can effectively improve the system node voltage, promote the accommodation of wind and solar power, and alleviate the peak shaving of thermal power units under the premise of optimal economy pressure.

Chance-Constrained OPF in Droop-Controlled Microgrids With Power Flow Routers

High penetration of renewable generation poses challenges to power system operation due to its uncertain nature. In droop-controlled microgrids, the voltage volatility induced by renewable uncertainties is aggravated by the high droop gains. This paper proposes a chance-constrained optimal power flow (CC-OPF) problem with power flow routers (PFRs) to better regulate the voltage profile in microgrids. PFR refers to a general type of network-side controller that brings more flexibility to the power network. Comparing with the normal CC-OPF that relies on node power flexibility only, the proposed model introduces a new dimension of control from power network to enhance system performance under renewable uncertainties. Adopting a partial linearization method and an iterative algorithm allows us to address the CC-OPF problem by iteratively solving a subproblem. Since the inclusion of PFRs complicates the subproblem and makes common solvers no longer applicable directly, a semidefinite programming relaxation is used to transform each subproblem into a convex form. The proposed method is verified on a modified IEEE 33-bus system and the results show that PFRs significantly reduce the standard deviations of voltage magnitudes and contribute to mitigating the voltage volatility, which makes the system operate in a more economic and secure way.

A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System

Electricity demand is sharply increasing with the growing population of human beings. Due to financial, social, and political barriers, there are lots of difficulties when building new thermal power plants and transmission lines. To solve this problem, renewable energy sources and flexible AC transmission systems (FACTS) can operate together in a power network. Renewable energy sources can provide additional power to the grid, whereas FACTS devices can increase the thermal limit of existing transmission lines. It is always desirable for an electrical network to operate under stable and secure conditions. The system runs at risk if any abnormality occurs in the generation, transmission, or distribution sections. This paper outlines a strategy for reducing system risks via the optimal operation of wind farms and FACTS devices. Here, a thyristor-controlled series compensator (TCSC) and a unified power flow controller (UPFC) have both been considered for differing the thermal limit of transmission lines. The impact of the wind farm, as well as the combined effect of the wind farm and FACTS devices on system economy, were investigated in this work. Both regulated and deregulated environments have been chosen to verify the proposed approach. Value at risk (VaR) and cumulative value at risk (CVaR) calculations were used to evaluate the system risk. The work was performed on modified IEEE 14 bus and modified IEEE 30-bus systems. A comparative study was carried out using different optimization techniques, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Honey Badger Algorithm (HBA), and Sequential Quadratic Programming (SQP) to check the effect of renewable integration in the regulated and deregulated power systems in terms of system risk and operating cost.

Optimal Power Flow in AC/DC Microgrids With Enhanced Interlinking Converter Modeling

In this article, we propose an optimal power flow (OPF) paradigm for hybrid ac/dc microgrids. A meticulous model of the interlinking converter (IC) is developed and integrated into the OPF problem formulation. The resulting formulation is capable of solving the OPF of ac and dc subgrids in their standalone operations. A computationally efficient parabolic relaxation method transforms the nonconvex OPF model into a convex quadratic-constrained quadratic programming form. A sequential penalization method is applied to the relaxed OPF to achieve feasible solutions for the original formulation. The developed OPF formulation is tested on multiple modified 5-, 14-, 30-, 24-, 118-bus test systems, and a significant reduction in computational time is achieved in comparison with semidefinite programming relaxation. For a sequential penalized ac/dc OPF, the optimal values of both relaxations have a maximum difference of 0.025% for a 5-bus system. Finally, the modified IEEE 14-bus system is emulated in a real-time controller/hardware-in-the-loop setup to validate the proposed framework with continuously varying loads and IC switching status.

A Multi-Timescale Allocation Algorithm of Energy and Power for Demand Response in Smart Grids: A Stackelberg Game Approach

The proliferation of renewable generation brings challenges to the power supply-demand balance. To relieve the power fluctuations caused by photovoltaic (PV) generation variability, a multi-timescale allocation algorithm that considers the allocation of available energy and power is proposed. Contracted demand response energy (CDRE) refers to the regulation energy, which is specified in contracts in advance, that can be used to relieve power fluctuations. First, the capacity of CDRE and available regulation power (RP) provided by load aggregators (LAs) that aggregate different demand response resources (DRRs) are estimated. In hour-timescale, CDRE is allocated to maximize the control economy of all participants, where a sample average approximation-based Stackelberg game is proposed to optimize the behavior of each participant based on considering PV generation uncertainty. In minute-timescale, RP is allocated to smooth the power fluctuations and minimize the power deviations based on the allocated CDRE results. Simulation on a modified IEEE-24 bus system verifies the effectiveness of the proposed algorithm in terms of reducing the power supply-demand imbalance with maximum revenue.

Optimal placement and sizing of FACTS devices for optimal power flow using metaheuristic optimizers

This paper proposes the implementation of various metaheuristic algorithms in solving the optimal power flow (OPF) with the presence of Flexible AC Transmission System (FACTS) devices in the power system. OPF is one of the well-known problems in power system operations and with the inclusion of the FACTS devices allocation problems into OPF will make the solution more complex. Thus, seven metaheuristic algorithms: Barnacles Mating Optimizer (BMO), Marine Predators Algorithm (MPA), Moth–Flame Optimization (MFO), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching–Learning-Based Optimization (TLBO) and Heap-Based Optimizer (HBO) are used to solve two objective functions: power loss and cost minimizations. These algorithms are selected from the different metaheuristics classification groups, where the implementation of these algorithms into the said problems will be tested on the modified IEEE 14-bus system. From the simulation results, it is suggested that TLBO and HBO perform better compared to the rest of algorithms.

A Modified Long Short-Term Memory-Deep Deterministic Policy Gradient-Based Scheduling Method for Active Distribution Networks

To improve the decision-making level of active distribution networks (ADNs), this paper proposes a novel framework for coordinated scheduling based on the long short-term memory network (LSTM) with deep reinforcement learning (DRL). Considering the interaction characteristics of ADNs with distributed energy resources (DERs), the scheduling objective is constructed to reduce the operation cost and optimize the voltage distribution. To tackle this problem, a LSTM module is employed to perform feature extraction on the ADN environment, which can realize the recognition and learning of massive temporal structure data. The concerned ADN real-time scheduling model is duly formulated as a finite Markov decision process (FMDP). Moreover, a modified deep deterministic policy gradient (DDPG) algorithm is proposed to solve the complex decision-making problem. Numerous experimental results within a modified IEEE 33-bus system demonstrate the validity and superiority of the proposed method.

Preallocation of Electric Buses for Resilient Restoration of Distribution Network: A Data-Driven Robust Stochastic Optimization Method

In recent years, severe hurricanes have occurred frequently, posing a huge challenge to the distribution network (DN) operation. Electric buses (EBs) possess large-capacity batteries and are widely used in public transit under normal circumstances. Before a hurricane occurs, some idle EBs can be preallocated to different charging stations equipped with vehicle-to-grid and served as sources for emergency power supply. This article proposes a prehurricane EB preallocation method to assist the resilience enhancement of a fragile DN. A two-stage data-driven robust stochastic programming technique is applied to build this model. Different scenarios are generated, and the corresponding posthurricane restoration processes are considered in the determination of the preallocation strategy. Also, the uncertainties of hurricane-induced physical damages are considered in modeling, which is described as a strengthened confidence set. The established model aims at exploring the optimal preallocation strategy of EBs with minimum load losses under the worst-case distribution. A column-and-constraint generation algorithm is then used to solve this proposed model. The proposed method is tested on a modified IEEE 33-bus system with 50 EBs. The results indicate that preallocating EBs to charging stations can improve the resilience of the posthurricane DN effectively.

Voltage Regulation With High Penetration of Low-Carbon Energy in Distribution Networks: A Source–Grid–Load-Collaboration-Based Perspective

In this article, a source–grid–load-collabora tion-based control framework is proposed to improve the power quality of active distribution networks (ADNs) with high penetration of low-carbon energy. First, hybrid dynamics of ADNs are characterized by addressing the voltage regulation and operation economics in each operation mode, and the mode switching control is designed in line with the operation principle of the on-load tap changer, where voltage security events are used to build the event-triggered functions. Second, multiobjective optimization is formulated with consideration of the system-wide operation cost and distribution circuit loss of the ADN in a relatively slow time scale, while in the fast time scale, all the inverter-based distributed generators, energy storages, and static var compensator devices are coordinated at the source–load side, through which multiple voltage issues, including voltage profile issue and voltage increment issue, can be addressed in a fully distributed manner. Finally, simulation results validate the effectiveness and robustness of the proposed method based on the modified IEEE 33-bus system.

Incorporating Complex Inter-Inverter Interactions Into Strength Assessment for Emerging Hierarchical-Infeed LCC-UHVDC Systems

The strength concept is an important tool for voltage stability evaluation of emerging hierarchical-infeed LCC-UHVDC (HIDC) systems. However, the complex inter-inverter interactions in such systems have caused significant difficulties for the strength assessment. This makes the accuracy of earlier strength assessment indices limited due to empirical analysis rather than rigorous theoretical reasoning. Thus in this paper, the hierarchical-infeed voltage interaction factor (HVIF) index is firstly utilized to quantitatively depict the complex inter-inverter interactions in HIDC systems. The use of the HVIF enables these interactions to be effectively incorporated into the subsequent strength assessment by proposing the hierarchical-infeed interactive effective short-circuit ratio (HIESCR) strength index. The critical HIESCR (CHIESCR) defined to delineate the voltage stability limit also evaluates to consistently 1.0 such that the distance between the HIESCR and CHIESCR is naturally an accurate stability margin. Since the HIESCR is developed based on the rigorous theoretical reasoning, it is more accurate than earlier indices for voltage stability evaluation. Moreover, the parametric dependence analysis of the HIESCR versus various system parameters and rated operation variables is conducted. This offers a comprehensive perspective for better understanding of the strength variation characteristics. Finally, case study on the modified IEEE 118-bus system validates the HIESCR.

Low-carbon economic dispatch strategy for renewable integrated power system incorporating carbon capture and storage technology

With the global warming and climate change, the low-carbon power system has attracted increasing attention. A low-carbon optimal dispatch model incorporating carbon capture and storage technology and the uncertainty of wind power is proposed. An accurate calculation of carbon dioxide emission is established, and the carbon tax mechanism is incorporated into the traditional economic dispatch taking into account the economy and low-carbon of power production. The effectiveness of the proposed method is verified by simulation results of modified IEEE 24-bus system. The case study shows that the proposed model can significantly reduce carbon emissions of coal-fired units and encourage coal-fired units to improve their emission performance.

Optimal Scheduling of Movable Electric Vehicle Loads Using Generation of Charging Event Matrices, Queuing Management, and Genetic Algorithm

The extensive adoption of electric vehicles (EVs) can introduce negative impacts on electric infrastructure in the form of sporadic and excessive charging demands, line overload, and voltage quality. Because EV loads can be movable around the system and time-dependent due to human daily activities, it is therefore proposed in this research to investigate the spatial effects of EV loads and their impacts on a power system. We developed a behavior-based charging profile simulation for daily load profiles of uncontrolled and controlled charging simulations. To mitigate the impact of increased peak demand, we proposed an optimal scheduling method by genetic algorithm (GA) using charging event matrices and EV queuing management. The charging event matrices are generated by capturing charging events and serve as an input of the GA-based scheduling, which optimally defines available charging slots while maximizing the system load factor while maintaining user satisfaction, depending on the weight coefficients prioritized by the system operator. The EV queuing management strategically selects EVs to be filled in the available slots based on two qualification indicators: previous charging duration and remaining state of charge (SoC). The proposed methodology was tested on a modified IEEE-14 bus system with 3 generators and 20 transmission lines. The simulation results show that the developed methodology can efficiently manage the peak demand while respecting the system’s operational constraints and the user satisfaction level.

Economic Loss Utilized Probabilistic Defense against Load Redistribution Attacks by Selecting Optimal Critical Measuring Units

State Estimation (SE) reflects the real time operation of power system network in the present cyber-physical power world. However, prior research works depict that bad/false undetectable data can be injected into the system on compromising measuring devices like Remote Terminal Units or Phasor Measurement Units. If an attacker intrudes into the cyber network and injects successful undetectable bad data, then that attack is popular as False Data Injection Attack (FDIA). One practical FDIA is Load Redistribution Attack (LRA), which target bus active power injections and line active power flows. LRA harms SE and subsequently disturbs Security Constrained Economic Dispatch (SCED) which result in severe rise of power generation and load shedding costs. Hence to maintain grid’s security, defense is one of the optimistic options. Attacker or defender won’t have access or control over all units. So, certain critical measuring units must be considered to attack or defend the system. In this research article, a procedure is developed to select optimal critical units subjected to all possible attack resources and load variations. The developed procedure is analyzed on three loading scenarios of modified IEEE-14 bus test system. These critical units are set as basis to find an optimal attack-defense strategy among possible strategies, which is accomplished by static zero-sum game theory in which economic loss is utility. This study provides an in-sight of consequences due to LRA, critical units’ selection under load variations and probabilistic optimal attack-defense strategy of the modified IEEE-14 bus system at three loading conditions.Graphical Abstract

Robust optimization approach with acceleration strategies to aggregate an active distribution system as a virtual power plant

An effective way to make full use of the flexibility of distributed generations in an active distribution system (ADS) is to aggregate ADS as a virtual power plant (VPP) to participate in the day-ahead energy market. This paper proposes a robust optimization model to obtain parameters of the VPP that do not depend on the information of day-ahead energy markets, such as time-varying power bounds and ramp rates. The robust model considers the uncertainty of dispatch orders from the transmission system operator (TSO) and outputs of renewable distributed generations. Furthermore, the column-and-constraint generation algorithm is applied to solve the robust model. To accelerate its solution, the period decomposition and connection method is first proposed to change the max–min sub-problem into small-scale independent linear programming problems. Second, the directional per-unit method is proposed to establish the linear relationship between uncertain dispatch orders from the TSO and the parameters of the VPP, and this relationship is substituted into the master problem to reduce iterations. Finally, the proposed method is evaluated on a modified IEEE 33-bus system and a practical 185-bus distribution system. The results verify the correctness and effectiveness of the proposed method.

Dynamic economic dispatch of transactive energy market using dynamic programming with state-restructuring feature

Nowadays, transactive energy markets (TEMs) are emerging as interesting frameworks in deregulated power markets (DPMs) to control the balance of supply and demand in the entire electrical infrastructure. TEMs can facilitate power delivery with high efficiency and more economic benefits without any security issues and also encourage interaction between market participants in the DPMs. Due to wide deployment of renewable energy resources, open access transmission systems, and improper dispatch of transactions, the operation of TEMs becomes complex. So, an appropriate dispatch model for TEMs should be designed to achieve the TEMs objectives. This paper presents a novel dynamic programming (DP) based dispatch model for optimal dispatch of transactions without any security and economic issues. New techniques, i.e., variable step size (VSS) and state-restructuring (SR) features are proposed to combine with DP for solving the proposed dynamic dispatch problem of TEMs with minimization of the operational cost of transactions. The VSS and SR features are integrated with DP to reduce the computational burden and operational complexity of the proposed problem remarkably. Also, the proposed approach determines the power contribution of generation units to transactions in order to accurately assess the cost of transactions. An index, relative load-contribution factor is proposed in this paper to analyse the behaviour of generation units with respect to load variations of the transactions. Numerical simulation results on the modified IEEE 14-bus system and modified IEEE 118-bus system illustrate the efficacy of the proposed DP based model in solving the proposed dispatch problem of TEMs with optimal operational costs of the system. The results highlight the proposed approach benefits and provide a strong validation of its applicability to large-scale TEMs.

Day-Ahead Economic Dispatch Strategy for Distribution Network Considering Total Cost Price-Based Demand Response

The guidance of EV via a price-based demand response is of great significance to the security and economy of the distribution network. However, the current price-based demand response mechanisms fail to consider the spatial-temporal distribution of large-scale EVs connected to the distribution network. For tackling this challenge, this paper proposes a day-ahead economic dispatch strategy for distribution networks considering total cost price-based demand response. A two-layer model of the day-ahead economic dispatch for the distribution network is utilized to obtain the interactive calculation framework for the total cost price. The total cost price iterates between the economic dispatch model of the distribution network and the total cost price-based demand response model until no significant changes in total cost price are observed. Among them, the price-responsive load considers spatial-temporal shift of the EV charging load. Finally, simulation study case based on a modified IEEE 33-bus system is employed to demonstrate the effectiveness of this strategy for the economic operation of the distribution network.