Deregulation Of Power Industry Research Articles

Performance Evaluation of Optimally Placed SVC for Dynamic Stability of the Power System

Given the complexity of power systems, particularly in the deregulated power industry found in Nigeria, a consistent, safe, controllable, and high-quality power supply is required. The loss of the system's overall damping torque, which reduces the system's susceptibility to fluctuations and problems with dynamic stability, is one of the key downsides of network expansion. Flexible AC transmission systems (FACTS) controllers have been used to overcome problems with power system stability control. In order to improve dynamic stability, this study investigates how the generator's rotor angle, speed, voltage magnitude profile, and real power affect how well SVC operates under the effect of a three-phase fault. With a fault introduced on Bus 33 (Geregu Substation) and SVC placed optimally on Bus 21 (Jos Transmission Station) using voltage stability sensitivity factor (VSSF) after the simulation of continuation power flow (CPF), the 48-bus power system network in Nigeria was modeled using commercial PSAT software in a MATLAB environment. The power system's oscillation was significantly reduced, and the voltage profile was enhanced for power system dynamic stability, per simulation results with and without SVC.

Reinforcement learning in deregulated energy market: A comprehensive review

The increasing penetration of renewable generations, along with the deregulation and marketization of power industry, promotes the transformation of energy market operation paradigms. The optimal bidding strategy and dispatching methodologies under these new paradigms are prioritized concerns for both market participants and power system operators. In contrast with conventional solution methodologies, the Reinforcement Learning (RL), as an emerging machine learning technique that exhibits a more favorable computational performance, is playing an increasingly significant role in both academia and industry. This paper presents a comprehensive review of RL applications in deregulated energy market operation including bidding and dispatching strategy optimization, based on more than 150 carefully selected papers. For each application, apart from a paradigmatic summary of generalized methodology, in-depth discussions of applicability and obstacles while deploying RL techniques are also provided. Finally, some RL techniques that have great potentiality to be deployed in bidding and dispatching problems are recommended and discussed.

Day-ahead Optimal Scheduling for Cluster Smart Buildings Considering Power Sharing

With the deregulation of power industry and increasing penetration of distributed energy resources, the study on the optimal schedule of demand-side resource under the electricty market environment has attracted wide attention from academia and industry. In this study, a market-based operational framework for cluster smart buildings is proposed. On this basis, a day-ahead optimal scheduling strategy and model for cluster smart buildings considering the overall power balance and consumer preferences and behaviors is proposed. The smart building energy management system comprising photovoltaic, electric vehicles and thermostatic controllable loads aims to minimize the total energy procurement cost. And then, the numerical results indicate that the proposed scheduling model can achieve local power sharing in the smart community. Finaly, the optimal scheduling results under consideration or no consideration of the transformer power limit are discussed.

Joint energy and reserve market design with explicit consideration on frequency quality

AbstractEnergy and reserve markets are two important market components in the deregulated power industry. Energy markets provide a platform for generating companies, retailers, and big energy consumers to centrally trade electricity. However, owing to the varying renewable power generation, load variation and unexpected contingencies, energy supply and consumption always embody high uncertainties. Furthermore, due to the lack of low‐cost, large‐capacity energy storage devices, an instant balance between the generation and load is required under these uncertainties. Therefore, the reserve market is set up to address the balance‐disruptive uncertainties intrinsic to the power grid. Although this combined market design reduces the risk of the possible imbalance in energy supply and consumption, it does not guarantee that the frequency of the grid is satisfactory, even in the normal operation. This study analyzes the insufficiency of the energy and reserve market design in addressing frequency quality problems by exploring the root causes for the deteriorating frequency quality in the Australian National Electricity Market. A novel framework that incorporates the frequency quality, control capacity, and control cost to achieve the desired frequency quality is then presented. Case studies through a simple power system and the IEEE 30‐bus system demonstrate the efficacy of the presented methods.

Frequency stabilization in deregulated energy system using coordinated operation of fuzzy controller and redox flow battery

The increasing integration of regional power grids, rising penetration of multi-generation sources, and day-ahead power agreements have raised the operational challenges on deregulated modern electric energy systems. Among various challenges, frequency regulation becomes more prominent in the restructured power system (RPS) due to increased uncertainties and intricacies among other operational challenges. A minor deviation in system frequency affects the safety, quality, stability, and operation of the interconnected power system (IPS). An appropriate control mechanism and energy storage device are essential to regulate the system's dynamic responses during continuously varying loading conditions. This study proposed a novel Salp swarm algorithm (SSA) optimized fuzzy-based proportional-integral-derivative filter (FPIDF) controller with redox flow battery (RFB) to regulate the frequency of a realistic multi-area multi-source (thermal-hydro-gas) interconnected power system. All probable contract transaction scenarios are simulated that can be possible in a deregulated power industry. Various nonlinearities such as time delay (TD), governor dead band (GBD), and generation rate constraint (GRC) are incorporated to resemble the realistic operational conditions of the IPS. The effectiveness of the suggested controller has been validated by comparing the system performances with a recently published sine-cosine algorithm (SCA) based proportional-integral (PI) controller and SSA-PID controller. The investigation has been further extended by incorporating RFB in the proposed system. The study reveals that the suggested controller provided superior system control under various system uncertainties in all contact scenarios of the competitive electricity market. Additionally, the system performances have been significantly enhanced due to quick response and precise control offered by RFB.

Assessing the impacts of large‐scale offshore wind power in Southern China

As an important renewable energy resource, wind power has been greatly developed in Southern China to meet the increasing demands. In the context of power industry deregulation and hence electricity market development, wind power introduces new uncertainties for market participants and affects the long-term planning of the power system concerned. In this paper, we use a simplified Guangdong power system model to simulate the electricity market in Southern China from 2019 to 2028. To study the economic and reliability impacts of installing large-scale offshore wind farms, case studies are conducted to simulate two targeted scenarios, that is, with and without offshore wind power penetration. Based on the presented model, the well-established Monte Carol simulation method is applied to assess the reliability of the Guangdong power grid by calculating the expected unsupplied energy (EUE). The experimental results show that the integration of offshore wind power farms will reduce the locational marginal prices (LMPs) as well as the weighted average spot price. The power flow on transmission lines with nodes connected to offshore wind farms will increase and the increase rate is higher in cases with higher demand. In addition, the increased EUE indicates that the integration of large-scale offshore wind power farms will impair the reliable operation of the power grid. The proposed system model delivers practical references to renewable energy evaluation and regional electricity market operations.

SVC device optimal location for voltage stability enhancement based on a combined particle swarm optimization-continuation power flow technique

The increased power system loading combined with the worldwide power industry deregulation requires more reliable and efficient control of the power flow and network stability. Flexible AC transmission systems (FACTS) devices give new opportunities for controlling power and enhancing the usable capacity of the existing transmission lines. This paper presents a combined application of the particle swarm optimization (PSO) and the continuation power flow (CPF) technique to determine the optimal placement of static var compensator (SVC) in order to achieve the static voltage stability margin. The PSO objective function to be maximized is the loading factor to modify the load powers. In this scope, two SVC constraints are considered: the reference voltage in the first case and the total reactance and SVC reactive power in the second case. To test the performance of the proposed method, several simulations were performed on IEEE 30-Bus test systems. The results obtained show the effectiveness of the proposed method to find the optimal placement of the static var compensator and the improvement of the voltage stability.

Modelling and Analysis of Deregulated Electricity Market Operations

Deregulation in power industry is restructuring of the rules and economic incentives that government set up to control and drive the electrical power industry. Restructuring in electricity industry will create new business opportunities where new firms selling new products and services will appear, consumers will have alternatives in buying electricity services, and new technologies such as metering and telecommunication devices will develop. Bidding by participants is one of the major challenges. To overcome this issue, strategic bidding models are being developed based on market structure, auction rules and bidding protocols. This paper proposes to formulate an optimal bidding strategy algorithm for DisCos and analyze its impact in the market operations. The bidding optimization is aimed at developing bids that get selected in the market operations and simultaneously maximizing the total profit of DisCos and minimizing power imbalances. Power Exchange market, which is simple yet enables competition, will be considered. This is proposed to be done using MATLAB coding and Genetic Algorithm tool box. Multiple simple bids are modeled. Power imbalance for each of the possible cases is computed. Decision variables (L1,L2,L3) and (C1,C2,C3) are used in order to compute objective functions i.e. Minimization of Power imbalances and maximization of profit, using MATLAB coding and GA tool box.

Risk-based optimal bidding patterns in the deregulated power market using extended Markowitz model

Deregulation of power industry has entailed important changes in the energy market. With the power industry being restructured, a generation company (GenCo) sells energy through auctions in a daily market, and submission of the appropriate amount of electricity with the right bidding price is important for a GenCo to maximize their profits and minimize the acceptance risk. The objective of this paper is to propose a novel approach for determination of the optimal biding patterns among GenCos in the deregulated power market using a hybrid of Markowitz Model and Genetic Algorithm (GA). While Markowitz Model as an optimization model considers the risk premium for biding patterns and GA as a search engine, considering the acceptance risk in deregulated market. A case study is used to examine the findings of the proposed approach. Also, to compare the proposed model, neural network by back propagation learning algorithm and real proposed pattern were considered. The numerical results indicate that the proposed model is statistically efficient and offers effective curves and biding patterns by lesser risk and equal profitability in day-ahead market as it is able to achieve better results compared to the neural network.

Profit based unit commitment using memetic binary differential evolution algorithm

Profit-based unit-commitment problem (PBUCP) is a notable combinatorial optimizing problem faced in the deregulated power industry. The PBUCP finds the best profitable solution by committing and scheduling the thermal generating units efficiently. To solve the PBUCP, a new memetic binary differential evolution algorithm is proposed which considers binary differential evolution (BDE) algorithm as global search operator to improve the exploration aspect and binary hill-climbing (BHC) algorithm as local search operator to improve the exploitation aspect. A binary differential evolution algorithm is introduced whereby a new mutation strategy is implemented. A novel BHC algorithm makes priority-based perturbations on unit’s status to improve the global best solution searched by the BDE algorithm alone. A new excessive unit de-commitment strategy based on priority and total profit is also proposed. The power to committed units is allocated based on priority of units. The efficacy of algorithms has been researched on the PBUCP test systems comprising of 10-, 40- and 100-units over a time horizon. The outcomes of the proposed algorithms are compared with previously known best solutions. Simulated outcomes achieved by the proposed algorithms compete with the already reported algorithms to solve the PBUCP. Wilcoxon signed-rank test proves the predominance of the proposed algorithms statistically.

Market Clearing Mechanism Considering Congestion under Deregulated Power System

Deregulation in power industry involves restructuring of rules and regulations, set up by the government. The new scenario is brought into practice mainly as a result of privatization, together with the encouragement of innovation of new technology. These factors are expected to reduce the cost of electrical energy. Deregulation creates a platform called as power market where electricity is traded as a commodity. The power market can be single buyer, bilateral or poolco model. Under single buyer model, the market is cleared in a pool by conducting single sided auction among suppliers, for each time slot, for a predicted demand. Bilateral model allows suppliers and buyers to enter into contract for an amount of power during a time period. Under poolco model, the market is cleared by performing double sided auction that involves both suppliers and buyers. Once the market is cleared, market clearing price, in-merit suppliers and buyers with their allotted power, power flow through transmission line between areas are determined. However, the scheduling of power is not coordinated with transmission planning. This results in transmission congestion. One of the methods to negate congestion is to split the market at the predicted congestion bottleneck. This paper presents a real time market clearing hardware model that accepts auction data and clears the market considering transmission congestion. The proposed model uses raspberry pi board that performs market clearing based on offers and bids accepted from a created webpage. The transmission congestion is managed by splitting the market at the predicted congestion bottleneck. The effectiveness of the proposed model is tested on three area deregulated power system.

The Next-Generation U.S. Retail Electricity Market with Customers and Prosumers—A Bibliographical Survey

Due to the rapidly-changing technologies in the power industry, many new references addressing the frameworks and business models of the next-generation retail electricity market are entering the research community. In particular, considering new customers with considerable demand response awareness and so-called prosumers with localized power generation based on distributed energy resources (DERs), the next-generation retail electricity market infrastructure will be a level playing field for local energy transactions, strategic pricing scheme design, new business model design and building an innovative energy ecosystem. Consequently, there is an urgent need to keep track of international experiences and activities taking place in the field of the market mechanism design problem at the distribution level. This paper provides a comprehensive survey of recent technology developments and aims to inspire awareness of the further deregulation of the electricity market, especially in areas close to customers. We mainly bring attention to the more than 90 articles published during the past five years. The collected literature has been divided into different sections to discuss different aspects of the next-generation retail electricity market under the deregulated power industry.

Online condition monitoring system for substation and service transformers

Deregulation in power industry accelerates competition among various power companies. These companies are trying to gain maximum profit with minimum maintenance cost. This could be achieved by developing a condition-based monitoring product for the assessment of health of substation and service transformers. This study presents online condition monitoring system (OCMS) for transformers which is useful to replace reactive and preventive maintenance of transformers by predictive maintenance. Proposed OCMS is cost effective, online and accurate tool and it has several features like proposing corrective actions for the benefit of power utilities. Proposed system results are evaluated by comparing with the results obtained from utility model. Proposed OCMS applies only to transformers without abnormal faults such as detected by dissolved gas analysis. It is therefore very different from the other health index algorithms present in the market for condition monitoring of transformers, including in case of abnormal faults.

Modified meta heuristics and improved backpropagation neural network‐based electrical load demand prediction technique for smart grid

For the assessment of 1‐h‐ahead electrical energy demand, this paper presents an improved backpropagation neural network that has been integrated with a simulated annealing algorithm and a chaos search genetic algorithm. A self‐adaptive learning rate and modified momentum factor are suggested to enhance the performance of the traditionally used backpropagation algorithm. For the combination scheme, the initial parameters of the improved backpropagation neural network have been modified through the utilization of the global search capability of the genetic algorithm. This has been enhanced by cat chaotic mapping to increase the genetic algorithm's optimization ability. The strong local search feature of the simulated annealing algorithm has been used to further enhance the solution set that was created by the optimized genetic algorithm. The model is tested for small and large‐sized grid data, integrated with renewable sources. The performance of the model is also verified for fluctuating load demand conditions. Furthermore, the proposed model performance is tested for all the four seasons of the year to validate its efficacy during seasonal variations. The results of the proposed technique in all these scenarios show higher prediction accuracy and fast convergence than the existing methods. The acceptable precision of 1‐h‐ahead load forecast and its adaptation in different load demand conditions determine the usefulness of the proposed model in the modern deregulated power industry. In particular, the model can be effectively implemented for the enhancement of demand response and other dynamic features of the smart grid. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Nash equilibrium strategy in the deregulated power industry and comparing its lost welfare with Iran wholesale electricity market

With the increasing use of different types of auctions in market designing, modeling of participants’ behaviors to evaluate the market structure is one of the main discussions in the studies related to the deregulated power industries. In this article, we apply an approach of the optimal bidding behavior to the Iran wholesale electricity market as a restructured electric power industry and model how the participants of the market bid in the spot electricity market. The problem is formulated analytically using the Nash equilibrium concept composed of large numbers of players having discrete and very large strategy spaces. Then, we compute and draw supply curve of the competitive market in which all generators’ proposed prices are equal to their marginal costs and supply curve of the real market in which the pricing mechanism is pay-as-bid. We finally calculate the lost welfare or inefficiency of the Nash equilibrium and the real market by comparing their supply curves with the competitive curve. We examine 3 cases on November 24 (2 cases) and July 24 (1 case), 2012. It is observed that in the Nash equilibrium on November 24 and demand of 23,487 MW, there are 212 allowed plants for the first case (plants are allowed to choose any quantity of generation except one of them that should be equal to maximum Power) and the economic efficiency or social welfare of Nash equilibrium is 2.77 times as much as the real market. In addition, there are 184 allowed plants for the second case (plants should offer their maximum power with different prices) and the efficiency or social welfare of Nash equilibrium is 3.6 times as much as the real market. On July 24 and demand of 42,421 MW, all 370 plants should generate maximum energy due to the high electricity demand that the economic efficiency or social welfare of the Nash equilibrium is about 2 times as much as the real market.

Development of chaotically improved meta-heuristics and modified BP neural network-based model for electrical energy demand prediction in smart grid

In this paper, a modified backpropagation neural network is combined with a chaos-search genetic algorithm and simulated annealing algorithm for very short term electrical energy demand prediction in deregulated power industry. Multiple modifications are carried out on the conventional backpropagation algorithm such as improvements in the momentum factor and adaptive learning rate. In the hybrid scheme, the initial parameters of the modified neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. The solution set provided by the optimized genetic algorithm is further improved by using the strong local search ability of simulated annealing algorithm. The real data of New South Wales, Australian grid, is used in the experimentation for 1-h-ahead forecast with an emphasis on data analysis and preprocessing framework. The correlation analysis is used for the identification and selection of the most influential input variables. The simulation results reveal that the proposed combination technique effectively enhanced the prediction accuracy as compared to the available conventional methods. The prediction of 1-h-ahead load demand is critically important for decision-making response of the modern smart grid system. The acceptable precision of the proposed model concludes that it can be applied in the smart grid to enhance its demand responsiveness and other intelligent features.

Analysis of Investment Issues and Transmission Schemes for Grid Integration of Remote Renewable Energy Sources

Investment in the transmission sector faces a set of technical, economic and regulatory challenges. Ever rising energy demands, increased renewable energy penetration into remote grid and deregulation of power industry necessitates the need for evaluation of these challenges prior to investment in the transmission sector. The present study aims at investigating these challenges and recommends possible solutions for the development of transmission sector. A review of challenges faced by renewable energy penetration into remote grid and an analysis of case studies involving principal transmission schemes employed in various countries for evaluating these challenges are presented.

Hybrid multiple attribute group decision-making for power system restoration

Due to deregulated power industry, distributed power generation, aging infrastructure and many other factors, the modern society is exposed to higher blackout risks. Decision-making optimization is indispensable for ensuring fast and secure power supply restoration to end users. As an important stage of power system restoration, backbone-network reconfiguration is necessary to re-establish the skeleton network and restore loads. Backbone-network reconfiguration after a large-scale outage is influenced by many factors about system safety and restoration speed. In order to evaluate candidate restoration schemes, multiple types of attributes including crisp data, fuzzy numbers, interval numbers and linguistic terms are employed. An extended VIKOR method is proposed to provide compromise solutions considering hybrid attributes. The method can reflect the vagueness and uncertainties in practical restoration problems, and avoid too much fuzzification. Different forms of preference relations and maximum deviation model are integrated by the minimum relative entropy to determine combined weights of attributes. Sensitivity analysis on the weights provides efficient guidelines for decision makers. Finally, an actual power system demonstrates the basic features of the developed method. It is more reasonable and creditable to consider multiple types of information and unsatisfactory attributes in decision-making.

Analysis of Investment Issues and Transmission Schemes for Grid Integration of Remote Renewable Energy Sources

Investment in the transmission sector faces a set of technical, economic and regulatory challenges. Ever rising energy demands, increased renewable energy penetration into remote grid and deregulation of power industry necessitates the need for evaluation of these challenges prior to investment in the transmission sector. The present study aims at investigating these challenges and recommends possible solutions for the development of transmission sector. A review of challenges faced by renewable energy penetration into remote grid and an analysis of case studies involving principal transmission schemes employed in various countries for evaluating these challenges are presented.

Measuring efficiency and scope economies of cogeneration enterprises in Northeast China under the background of energy saving and emission reduction

Cogeneration is internationally recognized as an important way to save energy and improve environment. At present, the cogeneration scale in China has been ranked at the second place all round the world. China formulated relevant laws, regulations and technology policies to actively encourage and support the development of cogeneration. This article selects 62 Northeast regional Chinese cogeneration enterprises' financial data from 2003 to 2007, and analyzes the impact of the electric power industry deregulation to cogeneration enterprises' performance by using a partial frontier nonparametric. Further more, we study whether the cogeneration enterprises actualize horizontal scope economy by comparing with the thermal power alone or heat production alone enterprises. Results show that the factory network separation and regional market trial reform can promote the cost efficiency of both five major power group cogeneration enterprises and independent cogeneration enterprises. Compared to five group enterprises, the scope economies index of the independent cogeneration enterprises is lower, the unbundling reform and northeast regional market trial reform have a negative impact on cogeneration enterprises to achieve scope economies and improve cost efficiency.