Deregulated Power Market Research Articles

Retraction Note: Performance comparison using firefly and PSO algorithms on congestion management of deregulated power market involving renewable energy sources

Retraction Note: Performance comparison using firefly and PSO algorithms on congestion management of deregulated power market involving renewable energy sources

An Effective Strategy for Achieving Economic Reliability by Optimal Coordination of Hybrid Thermal–Wind–EV System in a Deregulated System

This paper describes an effective operating strategy for electric vehicles (EVs) in a hybrid facility that leverages renewable energy sources. The method is to enhance the profit of the wind–thermal–EV hybrid plant while maintaining the grid frequency (fPG) and energy level of the EV battery storage system. In a renewable-associated power network, renewable energy producers must submit power supply proposals to the system operator at least one day before operations begin. The market managers then combine the power plans for the next several days based on bids from both power providers and distributors. However, due to the unpredictable nature of renewable resources, the electrical system cannot exactly adhere to the predefined power supply criteria. When true and estimated renewable power generation diverges, the electrical system may experience an excess or shortage of electricity. If there is a disparity between true and estimated wind power (TWP, EWP), the EV plant operates to minimize this variation. This lowers the costs associated with the discrepancy between actual and projected wind speeds (TWS, EWS). The proposed method effectively reduces the uncertainty associated with wind generation while being economically feasible, which is especially important in a deregulated power market. This study proposes four separate energy levels for an EV battery storage system (EEV,max, EEV,opt, EEV,low, and EEV,min) to increase system profit and revenue, which is unique to this work. The optimum operating of these EV battery energy levels is determined by the present electric grid frequency and the condition of TWP and EWP. The proposed approach is tested on a modified IEEE 30 bus system and compared to an existing strategy to demonstrate its effectiveness and superiority. The entire work was completed using the optimization technique called sequential quadratic programming (SQP).

Correction: Pourdaryaei et al. Recent Development in Electricity Price Forecasting Based on Computational Intelligence Techniques in Deregulated Power Market. Energies 2021, 14, 6104

There was an error in the original publication [...]

Improved frequency control performance of restructured multi-area power system through optimized controller design

This paper presents an optimal cascaded three degree of freedom proportional integral (3DOF PI) controller and compares its performance with conventional proportional integral (PI) controller for load frequency control of two area deregulated power system. The 3DOF PI controller enhances the degree of freedom and gives better dynamic response. Genetic algorithm (GA) is used to get the optimal gains of the proposed controllers. In the considered two area thermal-gas-fuel cell system, the thermal system consists of a reheat turbine with appropriate generation rate constraint non linearity. Battery storage and unified power flow controller (UPFC) are also assumed to be present. The performance of the proposed controllers is evaluated under different operating conditions that take place in deregulated power market. To tackle any contract violation this article emphasizes on the ancillary services provided by electric vehicles (EVs) and distributed generations. A robustness analysis is also presented by varying parameters from their nominal values.

An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market

It is widely acknowledged that electricity price forecasting become an essential factor in operational activities, planning, and scheduling for the participant in the price-setting market, nowadays. Nevertheless, electricity price became a complex signal due to its non-stationary, non-linearity, and time-variant behavior. Consequently, a variety of artificial intelligence techniques are proposed to provide an efficient method for short-term electricity price forecasting. BSA as the recent augmentation of optimization technique, yield the potential of searching a closed-form solution in mathematical modeling with a higher probability, obviating the necessity to comprehend the correlations between variables. Concurrently, this study also developed a feature selection technique, to select the input variables subsets that have a substantial implication on forecasting of electricity price, based on a combination of mutual information (MI) and SVM. For the verification of simulation results, actual data sets from the Ontario energy market in the year 2020 covering various weather seasons are acquired. Finally, the obtained results demonstrate the feasibility of the proposed strategy through improved preciseness in comparison with the distinctive methods.

Social welfare maximization in deregulated power market incorporating wind power plants using metaheuristic algorithm

The increasing integration of renewable energy sources (RESs), particularly wind power plants (WPP), into deregulated power markets introduces complexities in optimizing social welfare (SW). This article proposes a recent metaheuristic algorithm to address this challenge and maximize SW while accounting for the presence of WPP and the inherent uncertainty associated with wind power forecasting. The proposed algorithm optimizes generation scheduling and demand-side bidding strategies in the deregulated power market to maximize SW while ensuring economic efficiency. To validate the effectiveness and robustness of the proposed algorithm, MATLAB simulations are conducted on IEEE 30 and IEEE 118-bus systems. The results demonstrate that the proposed algorithm provides promising solutions for maximizing SW, especially in the context of incorporating WPP. This research contributes to the advancement of power market optimization methods and promotes the seamless integration of RESs, fostering a more sustainable energy future.

How do uncertain renewable energy induced risks evolve in a two-stage deregulated wholesale power market

Integrating renewable power into power market transactions is significant in terms of achieving carbon peaking and carbon neutrality. However, the integration of intermittent renewable energy sources (RES) introduces instability into power generation and brings certain risks to deregulated power market transactions. This study aims to identify the risks arising from RES power integration and analyze how these risks transmit and evolve in deregulated power markets. A Cournot game model is constructed to analyze the generation decisions of a thermal power generation company (GENCO) and a wind power GENCO in a two-stage power market. According to the results, in the mid-to-long-term power market, an increase in the uncertainty of wind power generation (WPG) could reduce wind power supply, increase the clearing price and decrease the power demand. Then, in the spot power market, the increased uncertainty related to WPG might decrease the amount of wind power traded, increase the marginal generation cost of the thermal power GENCO, raise the clearing price, and reduce the market demand for power. Finally, the uncertainty related to wind power generation could result in diminished revenue for the wind power GENCO and a loss in overall social welfare in the two-stage power market transaction.

A Novel Adaptive Restarting Genetic Algorithm for Transmission Congestion Alleviation in the Deregulated Power Market

A key concern in a deregulated power market (DPM) scenario is congestion on electricity transmission lines, which is one of the most pressing problems. The independent system operator (ISO) manages congestion to ensure the power system framework operates reliably and securely. Considering the context of the restructured power system’s inherent uncertainties, congestion management (CM) is critical in the operation and security of DPM. CM aims to ease congestion on the transmission lines while meeting all system constraints at the lowest possible congestion cost. This study employs a novel adaptive restarting genetic algorithm (ARGA) for rescheduling the generation to alleviate transmission congestion in the energy market. This work uses the proposed ARGA CM technique to reduce congestion costs by rescheduling generators’ active power output most cost-effectively. The participation of generation units in the CM is made possible by considering the generator sensitivity factors (GSF). IEEE 57-bus and IEEE 118-bus test systems are employed to evaluate the suggested methodology’s accuracy. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The proposed algorithm was tested in Python, and power flow analysis was done using the PANDAPOWER tool.

A New Model to Calculate Contributions of the Distributed Power

Besides metering, a more transparent load with known power distribution is valuable to draft energy strategies, especially in a deregulated power market. Thus, the usage-of-transmission can be considered properly. There are many studies published on related subjects and every research tried to solve a particular part of the problem. There are three basic categories for discussing power distributions: (i) the real power distribution, (ii) the reactive power distribution, and (iii) the loss allocation. These categories were very often treated separately, with the mutual coupling terms, counter flows, and line charging largely neglected. However, we know that these entities are non-separable. These are inter-related entities; the change of one entity will cause the change to every other entity. A good method should consider these entities altogether, while satisfying all electrical theories. This study developed a method to solve the above problem, with all electrical entities solved, satisfying all electrical circuit theories. With several matrix formulations, this method is capable of solving and tracing all electrical entities, including the current flow, the real and reactive power, the counter flow, and the couplings between the active and reactive power. The algorithm can also allocate power distributions and loss among participants effectively. Besides, a line usage idea is formed to allocate the loss to each generator, where counter flows are not necessarily penalized. It can be awarded sometimes. The idea can integrate with the existent tariffs in a deregulated market.

Optimization of location and rating of unified power flow controllers for congestion management

Large number of contracted power exchanges among sellers and purchasers, in a deregulated power market causes transmission line congestion. Congestion of lines undermines the security and economy of power networks. Flexible AC transmission system (FACTS) devices are introduced at appropriate spots to change the power flow in order to keep the flow of the lines within their capability. In this work, the most commonly used unified power flow controller (UPFC) is proposed for achievement of changing the line flow and for easing congestion from the over loaded lines. The size and site of UPFC controllers significantly impact their performance in congestion management task. The location and size of the UPFC devices are to be optimized by using an optimization algorithm. Whale optimization algorithm (WOA) is exploited in this effort for realizing the aim of determination of site and size of the FACTS converters. The present method has lesser quantity of variables which are tuned to get best outcomes. The suggested WOA based technique is executed on the IEEE 57 bus system. The outcomes comparison is made with particle swarm optimization algorithm (PSO) and firefly algorithm (FFA) and the better improved outcomes are found.

System Economy Improvement and Risk Shortening by Fuel Cell-UPFC Placement in a Wind-Combined System

It is important to understand the features of an integrated renewable energy power system, especially for deregulated systems. The greatest obstacle to assimilating renewable energy generators with the existing electrical system is their unpredictability. Because wind energy is inconsistent, incorporating it into an established power system necessitates more planning. The effects of wind farm (WF) incorporation with fuel cells and a unified power flow controller (UPFC) on electric losses, voltage profile, generating price, and the economics of the system in a deregulated power market are examined in this paper. An impact analysis of integrating wind farms into controlled and uncontrolled situations is conducted. At two randomly selected locations in India, the real-time statistics of the actual wind speed (AWS) and forecasted wind speed (FWS) were merged for this study. The surplus charge rate and deficit charge rate are intended to evaluate the imbalance cost which is arising from the difference between anticipated and true wind speeds to determine the economics of the system. Customers are always trying to find electricity that is reliable, inexpensive, and efficient due to the reconfiguration of the power system. As a consequence, the security limitations of the system may be surpassed or might function beyond the safety limit, which is undesirable. In the last section, heuristic algorithms, such as sequential quadratic programming (SQP), artificial bee colony algorithms (ABC), and moth-flame optimization algorithms (MFO), are employed to analyze economic risk. In the real-time energy market, it also covers how the fuel cells and UPFC are utilized to rectify the WF integration’s deviation. Economic risk evaluation approaches include value-at-risk (VaR) and conditional value-at-risk (CVaR). A modified IEEE 30-bus test system is used throughout the whole project.

Risk Mitigation & Profit Improvement of a Wind-Fuel Cell Hybrid System With TCSC Placement

The incorporation of renewable energy into the existing electrical system is vital in a competitive electrical system. The unpredictable nature of renewable sources is the main obstacle to energy source integration. Since wind energy is unpredictable, integrating it into an existing thermal system requires some additional operating procedures to maintain the economic and functioning sustainability of the system. In a competitive power network, renewable energy uncertainty creates an imbalance cost (IC) which directly affects the system economy. This study investigates system generation costs, voltage profiles, and electric losses in a deregulated power market incorporating wind farms (WF) & fuel cells (FC). The fuel cell has been used here as a reserve generating unit to mitigate the deficit of power in the renewable incorporated system. To check the efficacy of the presented method, two locations in India are chosen at random. To assess the imbalance cost caused by the discrepancy between forecasted (FWS) and actual wind speeds (AWS), several charge rates (i.e. surplus and deficit) were established. The electrical system has been restructured, so consumers are continually looking for efficient and stable economic power which is only possible by reducing the system risk. This paper outlines a strategy for the optimal operation of a Thyristor-Controlled Series Compensator (TCSC) and fuel cell in a wind-integrated system to maximize system profit and minimize the system risk. In this work, different algorithms like Sequential Quadratic Programming (SQP), Artificial Bee Colony Algorithms (ABC), and Moth Flame Optimization Algorithms (MFO) are used to analyze the economic and functional risk of the system. Additionally, it explains how the fuel cell system is employed to offset the wind farm integration’s deviation in the real-time power market. Value-at-Risk (VaR) and conditional Value-at-Risk (cVaR) have been used for risk analysis. A modified IEEE 14-bus test system is considered to validate the entire study whereas any small, large as well as hybrid systems can be considered to perform this methodology.

Transmission Congestion Management in Deregulated Power System Using Adaptive Restarting Genetic Algorithm

Power systems in a deregulated environment have more intense and recurrent transmission line congestion than conventionally regulated power systems. With the help of generation rescheduling, this article shows how to effectively manage congestion in the day-ahead energy market by taking corrective measures to reduce congestion. The research employs an adaptive restarting genetic algorithm (ARGA) to provide an effective congestion management strategy in a deregulated power market (DPM). The study makes two significant contributions. First, the generator sensitivity factors (GSF) are calculated to choose re-dispatched generators. Second, the least congestion cost is calculated using the adaptive restarting genetic algorithm. Several different line outage contingency cases on IEEE 30 bus systems are used to examine the suggested algorithm’s implementation efficacy. The simulation results demonstrate a significant reduction in net congestion costs, resulting in a more reliable and secure power system operation. The proposed algorithm was tested in a python environment, and power flow analysis was done using the PANDAPOWER tool. The acquired results are contrasted using several contemporary optimization approaches to validate the suggested technique’s validity. The ARGA technique gives a lower congestion cost solution than the particle swarm optimization (PSO), real coded genetic algorithm (RCGA), and differential evolution (DE) algorithm.

Economic Enhancement of Wind–Thermal–Hydro System Considering Imbalance Cost in Deregulated Power Market

Studying the property of the combination of renewable energy sources in the existing power systems is of great importance, and especially in the case of deregulated systems. The uncertainty of renewable sources is the largest barrier to integrating renewable-energy-producing units into the existing electrical infrastructure. Due to its uncertainty, integrating wind power into an existing power system requires extra consideration. In this work, the impacts of wind farm (WF) integration and a pumped hydroelectric storage system (PHES) on the electric losses, voltage profiles, generation costs, and system economy in a deregulated power market were studied. A comparative study was performed to determine the impact of wind farm integration on regulated and deregulated environments. Four locations in India were chosen at random for this work, and we used the real-time statistics for the actual wind speeds (AWSs) and forecasted wind speeds (FWSs) for each chosen location. To determine the system economy, surplus charge rates and deficit charge rates were developed to evaluate the imbalance cost resulting from the mismatch between the predicted and actual wind speeds. Considering the effect of the imbalance cost, the system profit/day varies by an average of 1.6% for the locations studied. Because of the reorganization of the power system, consumers constantly look for reliable and affordable power that is also efficient. As a result, the system security limit may be breached, or the system may run in a dangerous state. Lastly, in this paper, an economic risk analysis is presented with the help of heuristic algorithms (i.e., artificial bee colony algorithm (ABC) and moth–flame optimization algorithm (MFO)), along with sequential quadratic programming (SQP), and the way in which the PHES is used to compensate for the deviation in the WF integration in the real-time electricity market is also presented. The value at risk (VaR) and conditional value at risk (CVaR) were used as the economic risk analysis tools. According to the work, with the increase in the wind generation, the system risk improves. The results show that, as the wind generation increases by three times, there is an improvement in the risk coefficient values by 1%. A modified IEEE 14-bus test system was used for the validation of the entire work.

Profit Maximization with Imbalance Cost Improvement by Solar PV-Battery Hybrid System in Deregulated Power Market

The changeable nature of renewable sources creates difficulties in system security and stability. Therefore, it is necessary to study system risk in several power system scenarios. In a wind-integrated deregulated power network, the wind farm needs to submit the bid for its power-generating quantities a minimum of one day ahead of the operation. The wind farm submits the data based on the expected wind speed (EWS). If any mismatch occurs between real wind speed (RWS) and expected wind speed, ISO enforces the penalty/rewards to the wind farm. In a single word, this is called the power market imbalance cost, which directly distresses the system profit. Here, solar PV and battery energy storage systems are used along by the wind farm to exploit system profit by grasping the negative outcome of imbalance cost. Along with system profit, the focus has also been on system risk. The system risk has been calculated using the risk assessment factors, i.e., Value-at-Risk (VaR) and Cumulative Value-at-risk (CVaR). The work is performed on a modified IEEE 14 and modified IEEE 30 bus test system. The solar PV-battery storage system can supply the demand locally first, and then the remaining power is given to the electrical grid. By using this concept, the system risk can be minimized by the incorporation of solar PV and battery storage systems, which have been studied in this work. A comparative study has been performed using three dissimilar optimization methods, i.e., Artificial Gorilla Troops Optimizer Algorithm (AGTO), Artificial Bee Colony Algorithm (ABC), and Sequential Quadratic Programming (SQP) to examine the consequence of the presented technique. The AGTO has been used for the first time in the risk assessment and alleviation problem, which is the distinctiveness of this work.

An intelligent hybrid technique for optimal generator rescheduling for congestion management in a deregulated power market

Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches.

A novel optimization model for biding in the deregulated power market with pay as a bid settlement mechanism, based on the stochastic market clearing price

In this paper, a novel optimization-based bidding strategy will propose for an electricity generation company in the day-ahead market with a pay-as-bid settlement system. In this proposed model, Market Clearing Price is considered as a stochastic parameter with different distribution functions per hour. Moreover, days are also clustered in three different clusters based on the historical data by using the K-Means Algorithm. In addition, three types of linear, quadratic, and cubic cost functions are taken into account in the proposed model, where the results indicated that the cubic function is the best estimation model for the generation cost. Finally, the League Championship Algorithm is applied in order to solve our proposed model. The proposed model has been applied to real case studies where the obtained results indicated that the proposed model is able to improve the expected revenue by 17% in comparison with the basic model.

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.

A Risk Curtailment Strategy for Solar PV-Battery Integrated Competitive Power System

Power system networks are becoming more complex and decentralized with the foreword of deregulation in the global power sector. In this scenario, an independent system operator (ISO) is responsible for determining the appropriate actions to deliver stable and quality power to the customers connected to the network at the lowest cost without violating the system security limits. Violations of any security limit may result in system risk. The unstable and non-reliable system always has some drawbacks and is not desirable from the consumer’s point of view. A deregulated power market always keeps the consumer on the advantage side by giving stable, reliable, and less costly power. By using risk assessment tools, we identify the fault conditions and we try to minimize the risk by various uses of sequential programming methods. In this paper, a novel power system risk analysis and congestion management approach are introduced with considering meta-heuristic algorithms i.e., Slime Mould Algorithm (SMA) and Artificial Bee Colony Algorithm (ABC) in renewable energy integrated electricity market. The proposed power system risk analysis is constructed with the help of two risk valuation tools named Conditional-Value-at-risk (CVaR) and Value-at-risk (VaR). The higher negative value of VaR and CVaR represents the higher risk system and lower negative value or towards a positive value of VaR and CVaR denotes the less risk or stable system. The projected method has been experienced on the IEEE 14-bus test system and IEEE 30-bus test system to examine the usefulness of the meta-heuristic algorithm in system risk analysis under the deregulated environment. The importance of renewable energy integration in system risk curtailment has also been depicted in this work: basically, to measure the system’s risk, hence enhancing the system’s reliability and societal welfare. As a result, it will benefit both supply and demand-side participants.

Impact of Wind Farm Integration on LMP in Deregulated Energy Markets

The integration of renewable energy generation affects the operating characteristics of a power system, such as electric losses, voltage profile, generation cost, system stability, and reliability of the system. The installation of renewable energy generation units in non-optimal locations may increase system losses, costs, voltage fluctuations, etc. The main hurdle in integrating renewable energy generation units with an existing electrical grid is the uncertainty of renewable sources. This paper presents the impact of wind farm integration on the system economy in a wind-integrated deregulated power market. The importance of deregulation in terms of the system generation cost, bus voltage profile, and locational marginal pricing (LMP) are also studied in this work. LMP is the main parameter responsible for handling the system economy (i.e., profit of generating units and profit of customers). Considering the variable nature of wind flow, three different real-time wind speed datasets are used to validate this work. Bus sensitivity factor (BSF) is considered for equating the optimal position of the wind farm in the integrated system. Five different optimization techniques, i.e., sequential quadratic programming (SQP), artificial bee colony (ABC) algorithms, particle swarm optimization (PSO), ant colony optimization (ACO) algorithm, and slime mold algorithm (SMA), are introduced to solve the optimal power flow problem. The SMA and ACO are used for the first time in this type of economic assessment (i.e., impact valuation of LMP) in a deregulated power system, which is the novelty of this work. The entire work is performed in a modified IEEE 30 bus test system.