Competitive Electricity Market Research Articles

Electricity price forecasting using neural networks with an improved iterative training algorithm

ABSTRACTIn a competitive electricity market, the forecasting of energy prices is an important activity for all market participants either for developing bidding strategies or for making investment decisions. In this article, a new forecasting strategy is proposed for short-term prediction of the electricity price, which is a complex quantity with nonlinear, volatile and time-dependent behaviour. Our forecast strategy includes two novelties: a new two-stage feature selection algorithm and a new iterative training algorithm. The feature selection algorithm has two filtering stages to remove irrelevant and redundant candidate inputs, respectively. This algorithm is based on mutual information and correlation analysis. The improved iterative training algorithm is composed of two neural networks in which the output of the first neural network is one of the inputs to the second. The overall proposed strategy is applied to the Pennsylvania–New Jersey–Maryland (PJM) electricity markets and compared with some of the most recent price forecast methods.

Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm

In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crucial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by firefly algorithm (FA). The proposed model is unique in the sense that VMD is specifically applied to further decompose the high frequency intrinsic mode functions (IMFs) generated by FEEMD into a number of modes in order to improve the forecast accuracy. To validate the effectiveness and accuracy of the proposed model, three electricity price series respectively collected from the real-world electricity markets of Australia and France are adopted to conduct the empirical study. The results indicate that the proposed model outperforms the other considered models over horizons of one-step, two-step, four-step and six-step ahead forecasting, which shows that the proposed model has superior performances for both one-step and multi-step ahead forecasting of electricity price.

Estimating the Price Impact of Proposed Wind Farms in Competitive Electricity Markets

It has been proven extensively in the literature that higher availability of wind generation reduces electricity prices. However, the impact on electricity prices of an individual wind farm under different conditions has yet to be explored. In this paper, a data-driven approach is proposed for analyzing the effect on wholesale electricity prices of integrating a new wind farm into the grid. First, the price impact of previously integrated wind farms into the system is modeled. Then, an algorithm is proposed to estimate the future impact of proposed wind farms on electricity prices. Numerical results based on Alberta's electricity system are provided.

Optimal bidding strategy for price takers and customers in a competitive electricity market

AbstractBidding strategies are highly associated with the profit maximization and decreasing the risks for power utilities in a competitive market. For finding the optimal bidding strategies price takers need appropriate bidding structure. Thus, it is required to consider the model as a bi-level optimization problem. In the lower level price takers submit bid strategically to the ISO and in the upper level maximization of social welfare performed by solving the ISO Market clearing price (MCP). This paper aim to summarize the price taker’s bidding strategy modeling methods for competitive market models on the state-of-the art. A new genetic algorithm approach in a day-ahead electricity market in sealed auction with a pay-as-bid MCP has been employed to solve the problem from two different viewpoints i.e. with symmetrical and unsymmetrical information. The efficiency of the proposed method has been tested on the IEEE-30 bus system.

Analysis of competitiveness: energy sector and the electricity market in Russia

This study provides an overview and conducts a thorough analysis of the current state and development trends of the energy sector in a competitive electricity market in Russia. It scrutinises and outlines the most probable scenarios and outcomes of the innovative development on the electricity market and describes the possible changes. Moreover, a comprehensive study of technology foresight methods that allows one to draw parametric models of the development in the technological industry is undertaken. The results indicate that, in the medium- and long-run, one can identify economic inefficiency and uncontrollability of the existing Russian energy sector as well a clear disharmony with the selected energy strategy. Thence, it is necessary to rely on economic innovations in order to develop predictive models for managing productive development of the industry in a competitive energy market.

The Ideal Competitive Electricity Market. A Simulation for Italy

Liberalization in the electricity markets has been characterized by oligopoly conditions and exercise of market power, largely studied in the empirical literature on the supply side. This paper provides a new contribution to the literature on the electricity market presenting a theoretical and empirical model to construct a competitive equilibrium, estimating market power both on the supply and demand side of the day-ahead electricity market. This model provides a useful analysis tool for the policy-maker to implement pro-competitive regulation, explicitly measuring the welfare loss associated with non-competitive market conditions. Results show the effect of non-competitive equilibria for the hourly markets in the period 2013-2014. In an ideal competitive market, prices would be lower than historical prices by about 2-5% and quantities would be higher by about 0.5-1%.

Bidding strategy in south region day-ahead demand model by improved artificial bee colony algorithm

In this paper, an improved artificial bee colony (ABC) technique-based modelling of the optimal bidding strategies for a competitive electricity market is proposed. The novelty of the proposed method is improved searching ability by utilising the bat-inspired algorithm in the place of scout bee phase. In the proposed method optimum bidding parameters are determined by using the two phases of the ABC. From the optimised parameters the exact solution is predicted by using Bat-inspired algorithm. Here, the bee's population velocity and the position vector are improved to find the exact bidding parameters. The Indian Energy Exchange (IEX) hourly-based load demand dataset is utilised for the learning and testing the artificial neural network (ANN). This action makes the ABC as an improved technique. Finally, the proposed method is implemented in the MATLAB/Simulink platform and effectiveness is analysed by using the comparison of different techniques like ABC algorithm, particle swarm optimisation (PSO) algorithm, ABC-PSO and ABC-Cuckoo Search (CS). The comparison results demonstrated the superiority of the proposed approach and confirm its potential to solve the problem.

A New Model for Short-Term Hydrothermal Scheduling of a GENCO in the Competitive Electricity Market

A new mathematical model has been proposed for a hydrothermal Generating Company (GENCO) considering profit maximization as objective function is presented initially. Followed by this, bi-objective problem of profit maximization with simultaneous emission minimization has been formulated. Further, the different bidding strategies adopted for maximizing the social profit has been discussed. Methods/Statistical Analysis: IEEE 30-bus system has been chosen as the test case here. All the above mentioned problem formulations are simulated using a hybrid algorithm of LR-PSO (Lagrangian Relaxation - Particle Swarm Optimization). Findings: In Short-Term HydroThermal Scheduling (STHTS), the problem of fuel cost minimization has been attempted for several decades. In the competitive electricity market there is a need for maximization of profit rather than thermal plants fuel cost minimization subjected to hydro-thermal constraints. This necessitates a suitable mathematical model. In this paper the required model has been arrived at for the IEEE test case. The findings obtained prove effectiveness of the LR-PSO algorithm in arriving at the optimal scheduling much suitable for the GENCO in the deregulated environment. Applications: The various strategies followed will definitely enable the GENCO in the optimal allocation of selling the power that is generated as well as the reserve power generated in the deregulated market. This will also provide an opportunity for the ISO to maximize the social profit. Keywords: Competitive Electricity Market, GENCO, LR-PSO STHTS

Co-evolutionary approach for strategic bidding in competitive electricity markets

Determining optimal bidding strategies in a competitive electricity market to maximize the profit of each bidder is a challenging economic game problem. In this paper, it is formulated as a bi-level optimization problem in which, in the lower level, the community’s social welfare is maximized by solving a power flow problem while, in the upper level, the profits of individual bidders are maximized. In this bidders’ game, instead of using a set of discrete strategies as is usual, we consider continuous functions as strategies. To solve the upper-level problem, two co-evolutionary approaches are proposed and, for the lower level, an interior point algorithm is applied. Three IEEE benchmark problems in four different scenarios are solved and their results compared with those obtained from two conventional approaches and the literature which indicate that the proposed approaches have some merit regarding quality and efficiency.

Overview of Electricity Market Monitoring

IntroductionThe monitoring of the electricity market and preventing incorrect usage behavior are well-known issues in the competitive electricity market (see for example Marulanda et al., 2006; Nagypal et al., 2015; Strielkowski and Lisin, 2016; Nagaj, 2016) that are well-embedded into the strategy of sustainable energy development (Streimikiene et al., 2016). The monitoring of the electricity market plays a key role in the free electric energy market (Kasperowicz, 2011; Lisin et al., 2016). Supervisory actions are important for the System Operator and the Regulatory Commissions in order to ensure the efficient market performance. For Federal Energy Regulatory Commission in USA (Haymes, 2000), the market monitoring has been identified as a standard market design component and a basic function in the deregulated electricity market. According to Rahimi et al. (2003) and London Economics et al. (2007), the electricity market monitoring could be organized as an internal system or an institution in the Independent System Operators (ISO) structures, as well as could function also outside of the ISO structures and can provide service for ISO or other recipients (Goldman and Lesieutre, 2004).Furthermore, the availability of a well-developed monitoring system would also help agents to optimize their bidding strategies (David and Wen, 2000; Dun-nan et al., 2006; Pires Manso et al., 2015). Currently agents are concentrating on increasing their efficiency in order to maximize profits through a complicated system of product and sales management. Due to the fact that the process of decision making is very complex, whereas complete information is often unavailable, the electricity market monitoring appears to be difficult (London Economics, 2007). Therefore, if the market monitoring system functions properly, the transparency and reliability of the information concerning the electricity market dynamics may be ensured.The main activities of market monitoring as described in Rahimi et al. (2003), London Economics et al. (2007), (Dehdashti, 2005) and (Guler et al., 2005) include: 1) Detection of attempts to exercise market power and fraudulent behavioral, 2) Monitoring of market performance, 3) Identification of market design imperfection, 4) Transmission and generation blackout controlling, 5) Market participants' behavioral monitoring (activities and transactions).After an in-depth analysis of the existing literature devoted to exploring the issue of electricity market monitoring, it is possible to state that the information regarding the general methods of monitoring are available. However, there is a deficit of information related to the monitoring techniques focused on economical parameters of Spanish electricity market. Furthermore, the conducted analyses of the market structure and of the available source data related to Spanish power system have proven that that there is a need for a modification or a creation of new methods and indices, which shall be applied in order to correctly monitor the economical parameters of Spanish electricity market.This paper firstly introduces the concept and functions of the electricity market monitoring system (section I). Subsequently, the main methods and indices applied to the monitoring (market power, market performance and behavioral monitoring) are outlined (section II). Section III outlines the particularities of Spanish market. Subsequently, section IV presents conclusions.1. The global overview of the monitoring systemThe result of the extensive analysis of the existing literature on market monitoring is represented graphically in Figure 1.The Figure 1 presents the market monitoring framework: the elements and the correlation between them. This extensive analysis is based inter alia on the following sources: London Economics et al. (2007), Goldman and Lesieutre (2004), Dun-nan et al. (2006), Chakrabarti et al. (2008), Newbery (2004), Asgari et al. …

Fuel cost uncertainty, capacity investment and price in a competitive electricity market

This paper studies the effect of natural-gas fuel cost uncertainty on capacity investment and price in a competitive electricity market. Our model has a two-stage decision process. In the first stage, an independent power producer (IPP) builds its optimal capacity, conditional on its perceived uncertainties in fuel cost and electricity demand. In the second stage, equilibrium prices and quantities are determined by IPPs competing in a Cournot market. Under the empirically reasonable assumption that per MWh fuel costs are log-normally distributed, we find that a profit-maximizing IPP increases its capacity in response to rising fuel cost volatility. Consequently, the expected profit of the IPP and expected consumer surplus increase with volatility, rejecting the hypothesis that rising fuel cost uncertainty tends to adversely affect producers and consumers. Expected consumer surplus further increases if the IPP hedges the fuel cost risk. However, the IPP's optimal strategy is not to do so. The policy implication of these results is that the government should not intervene to reduce the price volatility of a well-functioning spot market for natural gas, chiefly because such intervention can have the unintended consequence of discouraging generation investment, raising electricity prices, and harming consumers.

Analysis of Power Network for Line Reactance Variation to Improve Total Transmission Capacity

The increasing growth in power demand and the penetration of renewable distributed generations in competitive electricity market demands large and flexible capacity from the transmission grid to reduce transmission bottlenecks. The bottlenecks cause transmission congestion, reliability problems, restrict competition, and limit the maximum dispatch of low cost generations in the network. The electricity system requires efficient utilization of the current transmission capability to improve the Available Transfer Capability (ATC). To improve the ATC, power flow among the lines can be managed by using Flexible AC Transmission System (FACTS) devices as power flow controllers, which alter the parameters of power lines. It is important to place FACTS devices on suitable lines to vary the reactance for improving Total Transmission Capacity (TTC) of the network and provide flexibility in the power flow. In this paper a transmission network is analyzed based on line parameters variation to improve TTC of the interconnected system. Lines are selected for placing FACTS devices based on real power flow Performance Index (PI) sensitivity factors. TTC is computed using the Repeated Power Flow (RPF) method using the constraints of lines thermal limits, bus voltage limits and generator limits. The reactance of suitable lines, selected on the basis of PI sensitivity factors are changed to divert the power flow to other lines with enough transfer capacity available. The improvement of TTC using line reactance variation is demonstrated with three IEEE test systems with multi-area networks. The results show the variation of the selected lines’ reactance in improving TTC for all the test networks with defined contingency cases.

Generator scheduling under competitive environment using genetic algorithm

In this paper, genetic algorithm (GA) is used to solve the GENCOs profit based unit commitment problem (PBUCP) in a dayahead competitive electricity markets considering power and reserve generations simultaneously, whereas enhanced lambda iteration (ELI) method is used to solve the economic dispatch (ED) sub-problem. The proposed algorithm helps GENCO to take decision regarding how much power and reserve must be put up for sale in the markets to receive the maximum profit. Moreover, two types of market strategies based on the demand constraint are discussed and implemented. Performance of GA is tested on 3-unit, 12-h and 10-unit, 24-h test systems. Results demonstrate that the proposed method is superior to the other methods reported in the literature.Keywords: Competitive electricity market, Economic dispatch, Genetic algorithm, Unit commitment

Markov game approach for multi‐agent competitive bidding strategies in electricity market

In a competitive electricity market, suppliers seek the optimal bidding strategy, maximising their individual profit. Due to the uncertainties, unknown parameters and dynamics of electricity market, the optimal bidding strategies cannot be attained through straightforward optimisation methods. In this study, the supply function equilibrium model is considered together with uniform price market clearing mechanism. The interaction among suppliers is modelled as an incomplete information game problem and multi-agent reinforcement learning (MARL) is utilised to find the optimal bidding strategy of the suppliers in a non-cooperative game. The proposed framework is an extension, from single agent Markov decision process to Markov game process, which is suitable for studying multi-agent decision making problems in stochastic environments. In this study, a Markov game model of the electricity market is proposed and a new state-action and Markov model of electricity market is proposed to built up the MARL environment. In addition, a learning-based non-cooperative MARL method is utilised for learning the optimal bidding strategies of the suppliers in a day-ahead electricity market. The proposed method is successfully applied to IEEE-30-bus power system. To examine the statistical significance of the results, the T-test method is applied to compare the competitive behaviour of the players in both multi- and single-agent frameworks.

Network Losses-based Economic Redispatch for Optimal Energy Pricing in a Congested Power System

An efficient, low-cost and reliable operation of a power system by adjusting the available electricity generation resources to supply demand of the system is required to ensure satisfied economic plant dispatching. This paper proposes a scheme for economic redispatch model considering the transmission issues such as transmission congestion and network losses, in order to obtain an optimal energy price in supporting competitive electricity market under deregulated environment of a power system.

A Two-Stage Hybrid Optimization Approach to Improve Transmission System Performance using FACTS Controllers under Open Access Environment

The Flexible AC Transmission System (FACTS) controllers have been resolved many technical issues which emerged with deregulated environment. One of such issues is transmission system performance improvement under open access. In this paper, two FACTS controllers, Thyristor-Switched Series Capacitor (TSSC) and Unified Power Flow Controller (UPFC) are selected to improve the system performance. At the first stage, the generation schedule under competitive electricity market dispatch model is obtained and at the second stage, the impact of the selected FACTS controllers is analyzed while executing various power transactions. A two-stage hybrid optimization approach using Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) algorithm is adopted to find out the optimal location and parameters of FACTS controller to achieve the required objectives like voltage stability index maximization as well as transmission loss minimization. For case studies, standard IEEE 30 bus test system is used and the simulation results obtained have been validating for real-time adaptability of this proposed method.

An Analysis of Time-Based Pricing in Electricity Supply Chains

This paper empirically evaluates the impact of time-based tariffs on the electricity supply chain. We investigate the residential electricity demand and supply equilibrium, using data from a field experiment conducted by the Ireland Commission for Energy Regulation: We first estimate individual households’ demand as a function of retail electricity prices. Then, we characterize the optimal retail prices under the flat rate, time-of-use (TOU) and real-time pricing (RTP) schemes for the social planner, the monopolist retailer and in a perfectly competitive retail electricity market. We find that time-based pricing schemes can be effective in reducing the peak load. Specifically, a simple TOU tariff with predetermined rates for different hours of the day can reduce the peak load as much as RTP. However, time-based tariffs do not reduce consumers’ electricity bills or total consumption significantly. Finally, time-based prices only slightly increase the carbon dioxide emissions in Ireland. Nonetheless, the environmental impact of these tariffs can be significant in other electricity markets with different generation mix and dispatch decisions.

Модель конкурентного равновесия на рынке электроэнергии с улучшенной адекватностью математического описания генерирующих компаний, системного оператора и электрической сети

Модель конкурентного равновесия на рынке электроэнергии с улучшенной адекватностью математического описания генерирующих компаний, системного оператора и электрической сети

Multi area AGC scheme using imperialist competition algorithm in restructured power system

This paper is focused on optimization based design methodology and application of PID controller in restructured, competitive electricity market environment, for AGC problem. The paper compares two search algorithms for designing of PID controller used for AGC in multiarea power system. The optimal parameters of PID controller have been determined with the use of Imperialist Competitive Algorithm (ICA). A deregulated scenario has been considered to develop the model of the multiarea AGC scheme. This paper presents that the ICA tuned PID (ICA-PID) controller can optimally regulate the generators output and can provide the best dynamic response of frequency and tie-line power on a load perturbation. The performance of proposed controller has been checked on 2-area thermal power system and 3-area thermal-hydro power system with the consideration of generation rate constraint (GRC). The results obtained by ICA-PID controller and genetic algorithm tuned (GA-PID) controller have been compared on the basis of performance parameters (settling time and oscillations). It is seen that ICA-PID controller shows the better performance as compared to GA-PID controller.

Comparison of Incentive Policies for Renewable Energy in an Oligopolistic Market with Price-Responsive Demand

This article compares different incentive policies to encourage the development of renewable energy (RE). These incentive policies (carbon tax, feed-in tariff, premium payment and quota system) are modeled in a simplified radial power network, using price-responsive demand. Most results are derived assuming an oligopolistic Cournot competitive framework and that the costs of subsidies are covered by the government (i.e., customers do not directly pay back for the subsidies). We compare the different RE incentive schemes at different congestion levels in terms of energy prices, RE generation, CO2 emissions, and social welfare. We find that the effectiveness of the different incentive schemes varies significantly depending on the market structure assumed, the costs of renewable energy, and the subsidy recovery method considered. Subsidy policies (FIT and premium payments) are more cost effective in reducing CO2 emissions than those policies that apply penalties or taxes, when assuming oligopoly competition and that customers do not directly pay back for the subsidies. Quota and carbon tax policies are more cost effective when assuming that either a perfectly competitive electricity market takes place or customers directly pay back for the subsidies. Additionally, we show that, in the feed-in tariff system, there is an interaction among incentive levels for renewable energy technologies. Given a certain feed-in tariff price to be set for a particular renewable technology, this price influences the optimal feed-in tariff price to be set for another technology.