Modified IEEE 30-bus Test System Research Articles

Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

Versatile static synchronous machine and its reference machine following control strategy

In the construction of the new-type power system, inverters with the synchronous machine characteristics are required. Here, the versatile static synchronous machine (VSSM) and its reference machine following control strategy are proposed. By integrating the battery energy storage system (BESS) into the modular multilevel converter (MMC), the VSSM has more operational flexibility than a conventional synchronous generator. Then, the basic theory of the reference synchronous machine is discussed, indicating that the VSSM can acquire the synchronous machine characteristics by following the operation of the reference synchronous machine. Based on the above principle, the implementation of the reference machine following control strategy is presented, including the synchronous machine mode and the current limiting mode. The case studies are performed on a 2-area test system and a modified IEEE 39-bus test system by time-domain simulation in PSCAD/EMTDC. The simulation results verify the synchronous machine characteristics in the proposed VSSM with the machine following control, and sheds some light on the potential of the VSSM in the new-type power system.

A Novel Probabilistic Method for Under Frequency Load Shedding Setting Considering Wind Turbine Response

The Under Frequency Load Shedding (UFLS) is the last automated action to restore power system frequency stability when a severe contingency causes a significant drop in the frequency of interconnection or islanded areas. In this paper, a novel probabilistic algorithm that considers the wind turbine frequency response is proposed to design and optimize the UFLS scheme. The uncertainties of power systems such as wind speed, hourly load, inertia time constant, and generation losses are taken into account. The Monte-Carlo Simulation (MCS) method is used to model the power system uncertainties. Additionally, a novel method is developed to calculate the power system frequency response and accelerate the multistage UFLS scheme optimization processes. In this method, a time-independent formulation for power system frequency response is utilized to reduce the computational complexity. More than Twenty different UFLS plans are designed using the proposed probabilistic algorithm. Besides, some time-domain simulations are carried on the modified IEEE 39-bus test system to investigate the performance of the designed UFLS schemes. Results indicate that designed UFLS schemes provide stable frequency response and improve the power system frequency stability.

Blockchain-Based Trustworthy Energy Dispatching Approach for High Renewable Energy Penetrated Power Systems

Renewable energy sources (RES) and low-carbon technology users play a vital role in modern power systems. However, RES generation is easily affected by the environment. Meanwhile, the load, such as electric vehicles (EVs) and prosumers, accounts for most low-carbon technology users. Their power is usually superimposed on peak loads without dispatching, which also exacerbates the instability of the power system. Current optimal dispatching mechanisms mainly rely on centralized organizations, while their dispatching process is not open and transparent. In this article, we propose a blockchain-based trustworthy dispatching approach for the distribution network in high renewable energy penetrated power systems. We first develop an optimal dispatching model considering EVs’ charging behavior and the prosumers’ economic benefits. With the model, prosumers can be dispatched to balance power and consume renewable energy, reducing the impact of disorderly charging on the grid and the abandonment of RES generation. An orderly charging iteration optimization (OCIO) algorithm is proposed to implement orderly EV charging while considering the charging cost and the period. We also propose a modified particle swarm optimization (mPSO) algorithm to publish dispatching tasks based on real-time power balance. Furthermore, blockchain is applied as an open and transparent ledger to record each entity’s power generation and consumption information, ensuring that the dispatching process is trustworthy. Finally, the effectiveness of the dispatching approach is verified in the modified IEEE 33-bus test system and Ethereum-based smart contracts.

Social welfare improvement using market clearing mechanism of a wind farm‐CAES hybrid congested transmission system

The goal of any well-established deregulated power sector is to satisfy their customer by delivering the power with maintaining the best quality at a reasonable price. This paper presents the impression of the wind farm (WF) and compressed air energy storage (CAES) hybrid systems in the congested deregulated power system. The main motto of this work is to enhance social welfare by using a market-clearing mechanism of the WFCAES hybrid system. To determine the effect of the hybrid system, initially the market-clearing power (MCPO) problem is measured without considering the WF-CAES hybrid system, and then the same work has been carried out with considering the WF-CAES hybrid system. The bus sensitivity factor (BSF) decides the optimal position of the WF-CAES hybrid system in the congested day-ahead electrical system. The study is followed out through generator sensitivity factor (GSF) with the application of artificial bee colony algorithm (ABC) and slime mould algorithm (SMA) to calculate optimal market clearing price (MCP) and market clearing volume (MCV). The market-clearing power problem has been solved in a modified IEEE-30 bus test system. The SMA algorithm is been implemented in the market-clearing mechanism for the first time which is the novelty of this paper.

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

Multi-stage resilience scheduling of electricity-gas integrated energy system with multi-level decentralized reserve

Extreme events pose threats to secure and consistent supply of electricity-gas integrated energy system (EGIES), and adequate resilience is essential for improving risk defense abilities. Considering the constraints of power distribution network (PDN) and natural gas system (NGS), a multi-stage strategy integrating multi-level decentralized reserve is proposed for resilience enhancement in EGIES. Storages of multi-area community integrated energy systems (CIESs) serve as the secondary reserve while the primary reserve, i.e., independent electric power/natural gas system, fails to meet reserve requirements. Furthermore, the thermal storage of building air on the consumption side is taken as the tertiary reserve, so as to obtain a better emergency response effect. The scheduling framework consists of reserve calculation, economic scheduling, and fault restoration. On the basis of multi-area reserve calculation, day-ahead economic scheduling is conducted, maintaining adequate reserve with consideration of decentralized reserve constraints; thus, high-priority demands can be recovered in case of supply disruptions. In addition, the flexible topology of PDN is utilized to facilitate the restored loads by optimal islanding partition. Via conic relaxation conversion, the original nonconvex model is tracked into a unified mixed-integer second-order cone programming (MISOCP) formulation, which can be effectively and accurately solved. Finally, numerical study on a modified IEEE 33-bus test system and a modified 7-node gas system connecting multiple community integrated energy systems shows the effectiveness of the proposed resilience strategy.

Islanded AC/DC microgrids supervisory control: A novel stochastic optimization approach

Load demand has locally met the distributed energy resources (DERs) under an infrastructure that is called microgrid. In this paper, a new supervisory control approach is presented based on MPC method, using a stochastic optimization model. The objective function (OF) of the proposed central controller consists of the combined cost-based and system-based parts. Indeed, the setpoints of DERs are updated by the supervisory control method to regulate voltage and frequency to their nominal values as well as to decrease the generation costs. The stochastic optimization formulation is conducted based on MPC structure. Moreover, the uncertain nature of DERs is considered in the optimization formulation and MPC framework using several scenarios. In addition, the effect of reactive power on frequency is considered in this paper and the results show better voltage and frequency regulations. The algorithm is implemented on a modified IEEE 33-bus test system with AC and DC parts. Furthermore, several cases and the sensitivity analysis are accomplished to show the efficiency of the proposed model.

A Joint Scheduling Strategy for Wind and Solar Photovoltaic Systems to Grasp Imbalance Cost in Competitive Market

The integration of renewable energy sources with active thermal power plants contributes to the green environment all over the globe. To achieve maximum reliability and sustainability of the renewable-thermal hybrid system, plentiful constraints need to be considered for minimizing the situation, which creates due to the unpredictable nature of renewable energy. In wind integrated deregulated system, wind farms need to submit the power generation scenario for future days to Independent System Operator (ISO) before the date of operation. Based on their submitted bid, ISO scheduled the power generation from different generating stations, including thermal and renewable. Due to the uncertain nature of the wind flow, there is always a chance of not fulfilling the scheduling amount of power from the wind farm. This violation in the market can impose an economic burden (i.e., imbalance cost) on the generating companies. The solar photovoltaic cell can be used to decrease the adverse economic effects of unpredicted wind saturation in the deregulated system. This paper presents consistent, competent, and effective operating schemes for the hybrid operation of solar PV and wind farms to maximize the economic profit by minimizing the imbalance cost, which occurs due to the mismatch between the actual and predicted wind speed. Modified IEEE 14-bus and modified IEEE 30-bus test systems have been used to check the usefulness of the proposed approach. Three optimization techniques (i.e., Sequential Quadratic Programming (SQP), Smart Flower Optimization Algorithm (SFOA), Honey Badger Algorithm (HBA)) have been used in this work for the comparative study. Bus Loading Factor (BLF) has been proposed here to identify the most sensitive bus in the system, used to place wind farms. The SFOA and HBA optimization technique has been used first time in this type of economic assessment problem, which is the novelty of this paper. The Bus Loading Factor (BLF) has been introduced here to identify the most sensitive bus in the system. After implementing the work, it has been seen that the operation of the solar PV system has reduced the adverse effect of imbalance cost on the renewable integrated deregulated power system.

Investigation of spatial correlation on optimal power flow with high penetration of wind power: A comparative study

Spatial correlation is a critical characteristic of adjacent wind farms, but its importance is always ignored in wind power modeling, further resulting in modeling bias. In order to investigate the necessity of involving spatial correlation and validate whether it has effects on power system, this paper proposes a method to construct the spatial correlation model and applies it into an IEEE test system. Specifically, a 7-dimensional t-copula method is used to capture the spatial dependence by forming a multi-variable distribution function, from which different scenarios are generated. Subsequently, a k-means clustering method is used for scenario reduction. As for the case study, a modified IEEE 39-bus test system with 7 wind farms deriving from the AEMO dataset is utilized to conduct optimal power flow under the circumstance with and without considering spatial dependence. Based on this, the cost of generation, including traditional fuel cost and levelized cost of wind power is obtained. Through comparing the cost results and analyzing the gap, it can be concluded that the spatial correlation has significant effects that when levelized cost of wind power exceeds a particular value, the generation cost can be greatly underestimated if ignoring spatial correlation with a high penetration of wind power.

A sustainable framework for multi-microgrids energy management in automated distribution network by considering smart homes and high penetration of renewable energy resources

This paper presents a new framework for the scheduling of microgrids and distribution feeder reconfiguration (DFR), taking into consideration the uncertainties due to the load demand, market price, and renewable power generation. The model is implemented on the modified IEEE 118-bus test system, including microgrids and smart homes. The problem has been formulated as a two-stage model, which at the first stage, the day-ahead self-scheduling of each microgrid is carried out as a two-objective optimization problem. The two objectives include the minimization of the total operating cost and maximization of the consumer's comfort index. Then, the solution, obtained from the first stage is delivered to the distribution system operator (DSO). Then, at the second stage, the DSO determines the optimal configuration of the system with the aim of minimizing operating costs of the main grid and the penalty of deviating from microgrid scheduling. Note that the penalty is due to the difference in power exchange requested by the microgrids from the power exchange finalized by the DSO. The presented two-stage optimization problem is modeled in a mixed-integer linear programing (MILP) framework with four case studies, and solved in GAMS by using the GURUBI solver. The simulation results show that in the cases the DSO is able to reconfigure the system, the deviation from the optimal scheduling of microgrids would be considerably lower than the cases with fixed system configuration. • Proposing a two-stage optimization framework for scheduling of microgrids and DFR. • Considering smart homes and traditional loads in the model. • Two-objective modeling of microgrids' scheduling problem by the fuzzy method. • Solving the DFR problem with regard to the optimal scheduling of microgrids. • Investigating the impact of different objective functions on smart homes' scheduling.

Multi-objective-based reactive power planning and voltage stability enhancement using FACTS and capacitor banks

Reactive power planning (RPP) and voltage stability improvement (VSI) consider two of the most important problems to meet a major challenge of the power system. In this work, a multi-objective genetic algorithm (MOGA) for RPP with objectives of cost minimization of the power losses, new reactive power (VAR) sources, maximization of the VSI, and enhancement of total transfer capacity (TTC) is introduced. Different optimization variables are considered including generator voltages, transformer tap changers besides load, and different operational constraints. The best compromise solution is determined through a fuzzy min–max approach. Comparison studies among capacitor banks, flexible ac transmission systems (FACTS) or both as new VAR support sources to achieve better performance are explored. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. Added to that many voltage stability indicators are presented, and their results are compared. The effectiveness of the proposed algorithm is examined on a modified IEEE 30-bus test system and South Egypt Electricity network where felicitous results have been acquired. The results expound on the effectiveness of the proposed approach compared with other optimization methods.

Delay dependent wide area damping controller with actuator saturation and communication failure for wind integrated power system

Integration of wind farm with power system introduces nonlinearity and complexity of the power system, which deteriorates the performance of a wide area damping controller (WADC). A WADC is designed in this paper for wind energy conversion system (WECS) integrated power system taking the communication latency and communication failure of the wide area measurement system (WAMS) into consideration. Apart from communication delay and communication failure, the effect of saturation is also considered in this paper. A state feedback stabilizing controller is designed using linear matrix inequality (LMI) platform. The control signal chosen here is combination of local and global signal to ensure stability during communication failure. The inter-area oscillation is reduced by controlling the reactive power of the wind farm. The effectiveness of the controller developed is verified using a WECS integrated modified 11-bus test system and modified IEEE 39-bus test system. The simulation results show that the designed controller guarantees stability and also can eliminate inter-area oscillations effectively of a WECS integrated power system.

Probabilistic Load Shedding Scheme for Power System Voltage Stability Enhancement Using Discrete Group Search Optimization Algorithm

This paper presents a discrete heuristic algorithm to improve voltage stability margins of the power system, during under frequency conditions. Discrete group search optimization algorithm is proposed for the first time in this work and applied to a load shedding problem. Sometimes, after initial frequency drop following severe disturbances, the system frequency might return to its acceptable value, however, the system might become unstable due to voltage problems. This paper proposes discrete group search optimization algorithm to solve the load shedding problem as well as returning system frequency to the acceptable range. The objective of proposed method is to enhance voltage stability margins of the power system. Furthermore, in the paper the load uncertainty as the most critical source of uncertainty in power system is handled using point estimation approach. The proposed method is implemented on the modified IEEE 14-Bus test system as well as 39 bus New England Network to demonstrate its applicability and effectiveness. The performance of discrete group search optimization method is also compared with several methods in terms of enhancing voltage stability margins. The results confirm that the proposed method provides a considerable enhancement in the power system voltage stability margin.

A fully decentralized machine learning algorithm for optimal power flow with cooperative information exchange

Traditional power grids, being highly centralized in terms of generation, economy, and operation, continually employed probabilistic methods to account for load uncertainties. In modern smart grids (SG), rapid proliferation of non-dispatchable generation (physical decentralization) and liberal markets (market decentralization) leads to dismantling of the centralized paradigm, with operation being performed by several decentralized agents. Handling uncertainty in this new paradigm is aggravated due to 1) a vastly increased number of uncertainty sources, and 2) decentralized agents only having access to local data and limited information on other parts of the grid. A major problem identified in modern and future SGs is the need for fully decentralized optimal operation techniques that are computationally efficient, highly accurate, and do not jeopardize data privacy and security of individual agents. Machine learning (ML) techniques, being successors to traditional probabilistic methods are identified as a solution to this problem. In this paper, a conceptual model is constructed for the transition from a fully centralized operation of a SG to a decentralized one, proposing the transition scheme between the two paradigms. A novel ML algorithm for fully decentralized operation is proposed, formulated, implemented, and tested. The proposed algorithm relies solely on local historical data for local agents to accurately predict their optimal control actions without knowledge of the physical system model or access to historical data of other agents. The capability of cloud-based cooperative information exchange was augmented through a new concept of s-index activation codes, being encoded vectors shared between agents to improve their operation without sharing raw information. The algorithm is tested on a modified IEEE 24-bus test system and synthetically generating historical data based on typical load profiles. A week-ahead high-resolution (15-minute) fully decentralized operation case is tested. The algorithm is shown to guarantee less than 0.1% error compared to a centralized solution and to outperform a neural network (NN). The algorithm is exceptionally accurate while being highly computationally efficient and has great potential as a versatile model for fully decentralized operation of SGs.

A data-driven approach for microgrid distributed generation planning under uncertainties

The increasing demand for power system decarbonization and resilience raises the necessity of incorporating the renewable distributed generation (DG) into the microgrid planning. The complexity of the microgrid renewable DG planning largely roots from the intermittent wind and solar energy and load variations throughout the planning period. This paper proposes a novel two-stage data-driven adaptive robust distributed generation planning (DDARDGP) framework considering both grid-connected and islanded modes of microgrids, wherein the overall system cost is minimized. By leveraging the spatio-temporal property of historical weather and grid information, a compact uncertainty set is developed based on a data-driven Bayesian nonparametric approach. The problem is further solved by a modified column and constraint generation (CC&G) algorithm. In the study, the effectiveness of the proposed framework is demonstrated using a modified IEEE 33-bus test system. The case study considers the optimal generation sizing, allocation and mixtures. The simulation results confirm that the proposed data-driven uncertainty set adapts well to the increase of data dimensions and solves the over-conservatism issue, leading to 34.14% reduction in uncertainty estimation compared with the traditional budget uncertainty set. Accordingly, the total cost can achieve a $23,185 reduction under the proposed DDARDGP framework.

A two-stage joint operation and planning model for sizing and siting of electrical energy storage devices considering demand response programs

This study describes a computationally efficient model for the optimal sizing and siting of Electrical Energy Storage Devices (EESDs) in Smart Grids (SG), accounting for the presence of time-varying electricity tariffs due to Demand Response Program (DRP) participation. The joint planning and operation problem for optimal siting and sizing of the EESD is proposed in a two-stage optimization problem. In this regard, the long-term decision variables deal were the size and location of the EESDs and have been considered at the master level while the operating point of the generation units and EESDs is determined by the slave stage of the model utilizing a standard mixed-integer linear programming model. To examine the effectiveness of the model in the slave sub-problem, the operation model is solved for different working days of different seasons. Binary Particle Swarm Optimization (BPSO) and Binary Genetic Algorithm (BGA) have been used at the master level to propose different scenarios for investment in the planning stage. The slave problem optimizes the model in terms of the short-term horizon (day-ahead). Additionally, the slave problem determines the optimal schedule for an SG considering the presence of EESD (with sizes and locations provided by the upper level). The electricity price fluctuates throughout the day, according to a Time-of-Use (ToU) DRP pricing scheme. Moreover, the impacts of DRPs have been addressed in the slave stage. The proposed model is examined on a modified IEEE 24-Bus test system.

Optimal Bidding Strategy for Social Welfare Maximization in Wind Farm Integrated Deregulated Power System Using Artificial Gorilla Troops Optimizer Algorithm

PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier’s bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms.

Hybrid renewable energy resources incorporated optimal power flow using single phase multi-group teaching learning-based optimiser

The latest scenario of electrical system consists of conventional generating units along with the renewable energy resources. The proposed article recommends a method for the solution of optimal power flow, integrating with wind generating units, solar photovoltaic system and hybrid solar with small hydro power that is run-of-river with traditional coal-based generating stations. The irregularity of renewable source's output intensifies the complications of the optimal power flow issue. In the proposed work, lognormal, Weibull and Gumble probability density functions are also utilised for predicting power outputs of the renewables, respectively. The modified IEEE-30 bus test system is used to validate the results, which is incorporated with wind-solar-small hydro generating plants. The single phase multi-group teaching learning-based optimiser is used as the optimisation tool and the simulation results are compared with the newly developed algorithm.

Modeling a Local Electricity Market for Transactive Energy Trading of Multi-Aggregators

The present article aims at modeling a day-ahead local electricity market (DA LEM) for transactive energy trading at the distribution level. In this regard, a wide range of distributed energy resources (DERs) in the form of multiple aggregators (AGs) participates in the DA LEM in order to trade energy with the distribution system operator (DSO), the operator of the market. On the other hand, the DSO, as the owner of the system, has the responsibility to procure the required energy of its customers with respect to the technical constraints of the distribution network. To settle the designed local market, a Stackelberg game-based approach is exploited in this research work. In the raised Stackelberg scheme, the leader of the game, the DSO, seeks to maximize its expected profit, while followers of the game, DER AGs, tend to minimize their operating costs. Ultimately, to evaluate the proposed framework, a typical case study is implemented on a modified IEEE-33 bus test system.